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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 17 Dec 2011 06:37:01 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/17/t13241218374eoqlqr99md352c.htm/, Retrieved Sat, 20 Apr 2024 02:25:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156209, Retrieved Sat, 20 Apr 2024 02:25:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [PAPER] [2011-12-17 11:37:01] [9c3f7eb531442757fa35fbfef7e48a65] [Current]
- RMP     [Recursive Partitioning (Regression Trees)] [PAPER] [2011-12-18 12:10:11] [27e29806e0b1d1351a97bc4ee4116294]
- RMP     [Recursive Partitioning (Regression Trees)] [PAPER] [2011-12-18 12:10:11] [27e29806e0b1d1351a97bc4ee4116294]
-    D    [Multiple Regression] [PAPER] [2011-12-20 16:19:25] [27e29806e0b1d1351a97bc4ee4116294]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
30	210907	56	79	112285	1418	396	81	24.188	144
28	120982	56	58	84786	869	297	55	18.273	103
38	176508	54	60	83123	1530	559	50	14.130	98
30	179321	89	108	101193	2172	967	125	32287	135
22	123185	40	49	38361	901	270	40	8654	61
26	52746	25	0	68504	463	143	37	9245	39
25	385534	92	121	119182	3201	1562	63	33251	150
18	33170	18	1	22807	371	109	44	1271	5
11	101645	63	20	17140	1192	371	88	5279	28
26	149061	44	43	116174	1583	656	66	27.101	84
25	165446	33	69	57635	1439	511	57	16.373	80
38	237213	84	78	66198	1764	655	74	19.716	130
44	173326	88	86	71701	1495	465	49	17753	82
30	133131	55	44	57793	1373	525	52	9.028	60
40	258873	60	104	80444	2187	885	88	18653	131
34	180083	66	63	53855	1491	497	36	8828	84
47	324799	154	158	97668	4041	1436	108	29498	140
30	230964	53	102	133824	1706	612	43	27563	151
31	236785	119	77	101481	2152	865	75	18.293	91
23	135473	41	82	99645	1036	385	32	22.530	138
36	202925	61	115	114789	1882	567	44	15977	150
36	215147	58	101	99052	1929	639	85	35.082	124
30	344297	75	80	67654	2242	963	86	16.116	119
25	153935	33	50	65553	1220	398	56	15.849	73
39	132943	40	83	97500	1289	410	50	16026	110
34	174724	92	123	69112	2515	966	135	26569	123
31	174415	100	73	82753	2147	801	63	24.785	90
31	225548	112	81	85323	2352	892	81	17.569	116
33	223632	73	105	72654	1638	513	52	23.825	113
25	124817	40	47	30727	1222	469	44	7869	56
33	221698	45	105	77873	1812	683	113	14975	115
35	210767	60	94	117478	1677	643	39	37.791	119
42	170266	62	44	74007	1579	535	73	9.605	129
43	260561	75	114	90183	1731	625	48	27295	127
30	84853	31	38	61542	807	264	33	2746	27
33	294424	77	107	101494	2452	992	59	34.461	175
13	101011	34	30	27570	829	238	41	8098	35
32	215641	46	71	55813	1940	818	69	4787	64
36	325107	99	84	79215	2662	937	64	24.919	96
0	7176	17	0	1423	186	70	1	603	0
28	167542	66	59	55461	1499	507	59	16329	84
14	106408	30	33	31081	865	260	32	12558	41
17	96560	76	42	22996	1793	503	129	7.784	47
32	265769	146	96	83122	2527	927	37	28.522	126
30	269651	67	106	70106	2747	1269	31	22265	105
35	149112	56	56	60578	1324	537	65	14.459	80
20	175824	107	57	39992	2702	910	107	14.526	70
28	152871	58	59	79892	1383	532	74	22.240	73
28	111665	34	39	49810	1179	345	54	11.802	57
39	116408	61	34	71570	2099	918	76	7623	40
34	362301	119	76	100708	4308	1635	715	11.912	68
26	78800	42	20	33032	918	330	57	7935	21
39	183167	66	91	82875	1831	557	66	18.220	127
39	277965	89	115	139077	3373	1178	106	19199	154
33	150629	44	85	71595	1713	740	54	19918	116
28	168809	66	76	72260	1438	452	32	21.884	102
4	24188	24	8	5950	496	218	20	2.694	7
39	329267	259	79	115762	2253	764	71	15.808	148
18	65029	17	21	32551	744	255	21	3597	21
14	101097	64	30	31701	1161	454	70	5296	35
29	218946	41	76	80670	2352	866	112	25.239	112
44	244052	68	101	143558	2144	574	66	29.801	137
21	341570	168	94	117105	4691	1276	190	18.450	135
16	103597	43	27	23789	1112	379	66	7.132	26
28	233328	132	92	120733	2694	825	165	34861	230
35	256462	105	123	105195	1973	798	56	35.940	181
28	206161	71	75	73107	1769	663	61	16688	71
38	311473	112	128	132068	3148	1069	53	24683	147
23	235800	94	105	149193	2474	921	127	46.230	190
36	177939	82	55	46821	2084	858	63	10387	64
32	207176	70	56	87011	1954	711	38	21436	105
29	196553	57	41	95260	1226	503	50	30.546	107
25	174184	53	72	55183	1389	382	52	19.746	94
27	143246	103	67	106671	1496	464	42	15.977	116
36	187559	121	75	73511	2269	717	76	22.583	106
28	187681	62	114	92945	1833	690	67	17.274	143
23	119016	52	118	78664	1268	462	50	16469	81
40	182192	52	77	70054	1943	657	53	14251	89
23	73566	32	22	22618	893	385	39	3.007	26
40	194979	62	66	74011	1762	577	50	16851	84
28	167488	45	69	83737	1403	619	77	21.113	113
34	143756	46	105	69094	1425	479	57	17401	120
33	275541	63	116	93133	1857	817	73	23958	110
28	243199	75	88	95536	1840	752	34	23567	134
34	182999	88	73	225920	1502	430	39	13.065	54
30	135649	46	99	62133	1441	451	46	15358	96
33	152299	53	62	61370	1420	537	63	14.587	78
22	120221	37	53	43836	1416	519	35	12770	51
38	346485	90	118	106117	2970	1000	106	24.021	121
26	145790	63	30	38692	1317	637	43	9648	38
35	193339	78	100	84651	1644	465	47	20.537	145
8	80953	25	49	56622	870	437	31	7905	59
24	122774	45	24	15986	1654	711	162	4.527	27
29	130585	46	67	95364	1054	299	57	30.495	91
20	112611	41	46	26706	937	248	36	7.117	48
29	286468	144	57	89691	3004	1162	263	17719	68
45	241066	82	75	67267	2008	714	78	27056	58
37	148446	91	135	126846	2547	905	63	33.473	150
33	204713	71	68	41140	1885	649	54	9758	74
33	182079	63	124	102860	1626	512	63	21.115	181
25	140344	53	33	51715	1468	472	77	7.236	65
32	220516	62	98	55801	2445	905	79	13.790	97
29	243060	63	58	111813	1964	786	110	32.902	121
28	162765	32	68	120293	1381	489	56	25.131	99
28	182613	39	81	138599	1369	479	56	30910	152
31	232138	62	131	161647	1659	617	43	35.947	188
52	265318	117	110	115929	2888	925	111	29.848	138
21	85574	34	37	24266	1290	351	71	6943	40
24	310839	92	130	162901	2845	1144	62	42.705	254
41	225060	93	93	109825	1982	669	56	31.808	87
33	232317	54	118	129838	1904	707	74	26.675	178
32	144966	144	39	37510	1391	458	60	8.435	51
19	43287	14	13	43750	602	214	43	7409	49
20	155754	61	74	40652	1743	599	68	14993	73
31	164709	109	81	87771	1559	572	53	36.867	176
31	201940	38	109	85872	2014	897	87	33835	94
32	235454	73	151	89275	2143	819	46	24164	120
18	220801	75	51	44418	2146	720	105	12.607	66
23	99466	50	28	192565	874	273	32	22609	56
17	92661	61	40	35232	1590	508	133	5.892	39
20	133328	55	56	40909	1590	506	79	17.014	66
12	61361	77	27	13294	1210	451	51	5.394	27
17	125930	75	37	32387	2072	699	207	9178	65
30	100750	72	83	140867	1281	407	67	6.440	58
31	224549	50	54	120662	1401	465	47	21916	98
10	82316	32	27	21233	834	245	34	4011	25
13	102010	53	28	44332	1105	370	66	5.818	26
22	101523	42	59	61056	1272	316	76	18.647	77
42	243511	71	133	101338	1944	603	65	20.556	130
1	22938	10	12	1168	391	154	9	238	11
9	41566	35	0	13497	761	229	42	70	2
32	152474	65	106	65567	1605	577	45	22.392	101
11	61857	25	23	25162	530	192	25	3913	31
25	99923	66	44	32334	1988	617	115	12.237	36
36	132487	41	71	40735	1386	411	97	8.388	120
31	317394	86	116	91413	2395	975	53	22.120	195
0	21054	16	4	855	387	146	2	338	4
24	209641	42	62	97068	1742	705	52	11727	89
13	22648	19	12	44339	620	184	44	3.704	24
8	31414	19	18	14116	449	200	22	3.988	39
13	46698	45	14	10288	800	274	35	3.030	14
19	131698	65	60	65622	1684	502	74	13.520	78
18	91735	35	7	16563	1050	382	103	1421	15
33	244749	95	98	76643	2699	964	144	20.923	106
40	184510	49	64	110681	1606	537	60	20237	83
22	79863	37	29	29011	1502	438	134	3219	24
38	128423	64	32	92696	1204	369	89	3.769	37
24	97839	38	25	94785	1138	417	42	12.252	77
8	38214	34	16	8773	568	276	52	1888	16
35	151101	32	48	83209	1459	514	98	14497	56
43	272458	65	100	93815	2158	822	99	28864	132
43	172494	52	46	86687	1111	389	52	21.721	144
14	108043	62	45	34553	1421	466	29	4821	40
41	328107	65	129	105547	2833	1255	125	33.644	153
38	250579	83	130	103487	1955	694	106	15923	143
45	351067	95	136	213688	2922	1024	95	42935	220
31	158015	29	59	71220	1002	400	40	18.864	79
13	98866	18	25	23517	1060	397	140	4977	50
28	85439	33	32	56926	956	350	43	7785	39
31	229242	247	63	91721	2186	719	128	17.939	95
40	351619	139	95	115168	3604	1277	142	23436	169
30	84207	29	14	111194	1035	356	73	325	12
16	120445	118	36	51009	1417	457	72	13.539	63
37	324598	110	113	135777	3261	1402	128	34.538	134
30	131069	67	47	51513	1587	600	61	12.198	69
35	204271	42	92	74163	1424	480	73	26924	119
32	165543	65	70	51633	1701	595	148	12716	119
27	141722	94	19	75345	1249	436	64	8172	75
20	116048	64	50	33416	946	230	45	10.855	63
18	250047	81	41	83305	1926	651	58	11.932	55
31	299775	95	91	98952	3352	1367	97	14.300	103
31	195838	67	111	102372	1641	564	50	25.515	197
21	173260	63	41	37238	2035	716	37	2.805	16
39	254488	83	120	103772	2312	747	50	29.402	140
41	104389	45	135	123969	1369	467	105	16440	89
13	136084	30	27	27142	1577	671	69	11221	40
32	199476	70	87	135400	2201	861	46	28732	125
18	92499	32	25	21399	961	319	57	5.250	21
39	224330	83	131	130115	1900	612	52	28.608	167
14	135781	31	45	24874	1254	433	98	8.092	32
7	74408	67	29	34988	1335	434	61	4.473	36
17	81240	66	58	45549	1597	503	89	1.572	13
0	14688	10	4	6023	207	85	0	2065	5
30	181633	70	47	64466	1645	564	48	14.817	96
37	271856	103	109	54990	2429	824	91	16714	151
0	7199	5	7	1644	151	74	0	556	6
5	46660	20	12	6179	474	259	7	2089	13
1	17547	5	0	3926	141	69	3	2658	3
16	133368	36	37	32755	1639	535	54	10695	57
32	95227	34	37	34777	872	239	70	1.669	23
24	152601	48	46	73224	1318	438	36	16267	61
17	98146	40	15	27114	1018	459	37	7.768	21
11	79619	43	42	20760	1383	426	123	7252	43
24	59194	31	7	37636	1314	288	247	6.387	20
22	139942	42	54	65461	1335	498	46	18.715	82
12	118612	46	54	30080	1403	454	72	7.936	90
19	72880	33	14	24094	910	376	41	8.643	25
13	65475	18	16	69008	616	225	24	7.294	60
17	99643	55	33	54968	1407	555	45	4570	61
15	71965	35	32	46090	771	252	33	7.185	85
16	77272	59	21	27507	766	208	27	10058	43
24	49289	19	15	10672	473	130	36	2342	25
15	135131	66	38	34029	1376	481	87	8.509	41
17	108446	60	22	46300	1232	389	90	13.275	26
18	89746	36	28	24760	1521	565	114	6816	38
20	44296	25	10	18779	572	173	31	1930	12
16	77648	47	31	21280	1059	278	45	8086	29
16	181528	54	32	40662	1544	609	69	10.737	49
18	134019	53	32	28987	1230	422	51	8.033	46
22	124064	40	43	22827	1206	445	34	7058	41
8	92630	40	27	18513	1205	387	60	6782	31
17	121848	39	37	30594	1255	339	45	5.401	41
18	52915	14	20	24006	613	181	54	6521	26
16	81872	45	32	27913	721	245	25	10.856	23
23	58981	36	0	42744	1109	384	38	2.154	14
22	53515	28	5	12934	740	212	52	6.117	16
13	60812	44	26	22574	1126	399	67	5238	25
13	56375	30	10	41385	728	229	74	4.820	21
16	65490	22	27	18653	689	224	38	5.615	32
16	80949	17	11	18472	592	203	30	4272	9
20	76302	31	29	30976	995	333	26	8.702	35
22	104011	55	25	63339	1613	384	67	15.340	42
17	98104	54	55	25568	2048	636	132	8.030	68
18	67989	21	23	33747	705	185	42	9526	32
17	30989	14	5	4154	301	93	35	1278	6
12	135458	81	43	19474	1803	581	118	4.236	68
7	73504	35	23	35130	799	248	68	3023	33
17	63123	43	34	39067	861	304	43	7.196	84
14	61254	46	36	13310	1186	344	76	3394	46
23	74914	30	35	65892	1451	407	64	6.371	30
17	31774	23	0	4143	628	170	48	1.574	0
14	81437	38	37	28579	1161	312	64	9.620	36
15	87186	54	28	51776	1463	507	56	6978	47
17	50090	20	16	21152	742	224	71	4911	20
21	65745	53	26	38084	979	340	75	8.645	50
18	56653	45	38	27717	675	168	39	8.987	30
18	158399	39	23	32928	1241	443	42	5.544	30
17	46455	20	22	11342	676	204	39	3083	34
17	73624	24	30	19499	1049	367	93	6.909	33
16	38395	31	16	16380	620	210	38	3189	34
15	91899	35	18	36874	1081	335	60	6.745	37
21	139526	151	28	48259	1688	364	71	16.724	83
16	52164	52	32	16734	736	178	52	4850	32
14	51567	30	21	28207	617	206	27	7.025	30
15	70551	31	23	30143	812	279	59	6047	43
17	84856	29	29	41369	1051	387	40	7377	41
15	102538	57	50	45833	1656	490	79	9.078	51
15	86678	40	12	29156	705	238	44	4.605	19
10	85709	44	21	35944	945	343	65	3238	37
6	34662	25	18	36278	554	232	10	8100	33
22	150580	77	27	45588	1597	530	124	9.653	41
21	99611	35	41	45097	982	291	81	8.914	54
1	19349	11	13	3895	222	67	15	786	14
18	99373	63	12	28394	1212	397	92	6.700	25
17	86230	44	21	18632	1143	467	42	5.788	25
4	30837	19	8	2325	435	178	10	593	8
10	31706	13	26	25139	532	175	24	4.506	26
16	89806	42	27	27975	882	299	64	6.382	20
16	62088	38	13	14483	608	154	45	5621	11
9	40151	29	16	13127	459	106	22	3997	14
16	27634	20	2	5839	578	189	56	520	3
17	76990	27	42	24069	826	194	94	8891	40
7	37460	20	5	3738	509	135	19	999	5
15	54157	19	37	18625	717	201	35	7067	38
14	49862	37	17	36341	637	207	32	4639	32
14	84337	26	38	24548	857	280	35	5654	41
18	64175	42	37	21792	830	260	48	6.928	46
12	59382	49	29	26263	652	227	49	1.514	47
16	119308	30	32	23686	707	239	48	9.238	37
21	76702	49	35	49303	954	333	62	8.204	51
19	103425	67	17	25659	1461	428	96	5926	49
16	70344	28	20	28904	672	230	45	5785	21
1	43410	19	7	2781	778	292	63	4	1
16	104838	49	46	29236	1141	350	71	5930	44
10	62215	27	24	19546	680	186	26	3710	26
19	69304	30	40	22818	1090	326	48	705	21
12	53117	22	3	32689	616	155	29	443	4
2	19764	12	10	5752	285	75	19	2.416	10
14	86680	31	37	22197	1145	361	45	7747	43
17	84105	20	17	20055	733	261	45	5.432	34
19	77945	20	28	25272	888	299	67	4913	32
14	89113	39	19	82206	849	300	30	2650	20
11	91005	29	29	32073	1182	450	36	2370	34
4	40248	16	8	5444	528	183	34	775	6
16	64187	27	10	20154	642	238	36	5576	12
20	50857	21	15	36944	947	165	34	1352	24
12	56613	19	15	8019	819	234	37	3080	16
15	62792	35	28	30884	757	176	46	10205	72
16	72535	14	17	19540	894	329	44	6095	27

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156209&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156209&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156209&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
compendiums_reviewed[t] = + 8.97056514854926 + 5.69493069724503e-05time_in_rfc[t] + 0.011661267483928logins[t] + 0.106872929649124blogged_computations[t] + 7.52212260060416e-05totsize[t] + 0.00164087634094966pageviews[t] -0.011822255470435compendium_views_info[t] + 0.0103494515800751compendium_views_pr[t] + 4.81934828252209e-05totrevisions[t] -0.0217206052886413tothyperlinks[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
compendiums_reviewed[t] =  +  8.97056514854926 +  5.69493069724503e-05time_in_rfc[t] +  0.011661267483928logins[t] +  0.106872929649124blogged_computations[t] +  7.52212260060416e-05totsize[t] +  0.00164087634094966pageviews[t] -0.011822255470435compendium_views_info[t] +  0.0103494515800751compendium_views_pr[t] +  4.81934828252209e-05totrevisions[t] -0.0217206052886413tothyperlinks[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156209&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]compendiums_reviewed[t] =  +  8.97056514854926 +  5.69493069724503e-05time_in_rfc[t] +  0.011661267483928logins[t] +  0.106872929649124blogged_computations[t] +  7.52212260060416e-05totsize[t] +  0.00164087634094966pageviews[t] -0.011822255470435compendium_views_info[t] +  0.0103494515800751compendium_views_pr[t] +  4.81934828252209e-05totrevisions[t] -0.0217206052886413tothyperlinks[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156209&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156209&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
compendiums_reviewed[t] = + 8.97056514854926 + 5.69493069724503e-05time_in_rfc[t] + 0.011661267483928logins[t] + 0.106872929649124blogged_computations[t] + 7.52212260060416e-05totsize[t] + 0.00164087634094966pageviews[t] -0.011822255470435compendium_views_info[t] + 0.0103494515800751compendium_views_pr[t] + 4.81934828252209e-05totrevisions[t] -0.0217206052886413tothyperlinks[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.970565148549260.81685810.981800
time_in_rfc5.69493069724503e-051.3e-054.31842.2e-051.1e-05
logins0.0116612674839280.0176190.66190.5086110.254306
blogged_computations0.1068729296491240.0245514.35321.9e-059e-06
totsize7.52212260060416e-051.6e-054.80872e-061e-06
pageviews0.001640876340949660.0022770.72070.4717240.235862
compendium_views_info-0.0118222554704350.005115-2.31110.0215570.010778
compendium_views_pr0.01034945158007510.0091111.1360.2569410.12847
totrevisions4.81934828252209e-054.8e-050.99940.3184780.159239
tothyperlinks-0.02172060528864130.01777-1.22230.2226130.111307

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 8.97056514854926 & 0.816858 & 10.9818 & 0 & 0 \tabularnewline
time_in_rfc & 5.69493069724503e-05 & 1.3e-05 & 4.3184 & 2.2e-05 & 1.1e-05 \tabularnewline
logins & 0.011661267483928 & 0.017619 & 0.6619 & 0.508611 & 0.254306 \tabularnewline
blogged_computations & 0.106872929649124 & 0.024551 & 4.3532 & 1.9e-05 & 9e-06 \tabularnewline
totsize & 7.52212260060416e-05 & 1.6e-05 & 4.8087 & 2e-06 & 1e-06 \tabularnewline
pageviews & 0.00164087634094966 & 0.002277 & 0.7207 & 0.471724 & 0.235862 \tabularnewline
compendium_views_info & -0.011822255470435 & 0.005115 & -2.3111 & 0.021557 & 0.010778 \tabularnewline
compendium_views_pr & 0.0103494515800751 & 0.009111 & 1.136 & 0.256941 & 0.12847 \tabularnewline
totrevisions & 4.81934828252209e-05 & 4.8e-05 & 0.9994 & 0.318478 & 0.159239 \tabularnewline
tothyperlinks & -0.0217206052886413 & 0.01777 & -1.2223 & 0.222613 & 0.111307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156209&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]8.97056514854926[/C][C]0.816858[/C][C]10.9818[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]5.69493069724503e-05[/C][C]1.3e-05[/C][C]4.3184[/C][C]2.2e-05[/C][C]1.1e-05[/C][/ROW]
[ROW][C]logins[/C][C]0.011661267483928[/C][C]0.017619[/C][C]0.6619[/C][C]0.508611[/C][C]0.254306[/C][/ROW]
[ROW][C]blogged_computations[/C][C]0.106872929649124[/C][C]0.024551[/C][C]4.3532[/C][C]1.9e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]totsize[/C][C]7.52212260060416e-05[/C][C]1.6e-05[/C][C]4.8087[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]pageviews[/C][C]0.00164087634094966[/C][C]0.002277[/C][C]0.7207[/C][C]0.471724[/C][C]0.235862[/C][/ROW]
[ROW][C]compendium_views_info[/C][C]-0.011822255470435[/C][C]0.005115[/C][C]-2.3111[/C][C]0.021557[/C][C]0.010778[/C][/ROW]
[ROW][C]compendium_views_pr[/C][C]0.0103494515800751[/C][C]0.009111[/C][C]1.136[/C][C]0.256941[/C][C]0.12847[/C][/ROW]
[ROW][C]totrevisions[/C][C]4.81934828252209e-05[/C][C]4.8e-05[/C][C]0.9994[/C][C]0.318478[/C][C]0.159239[/C][/ROW]
[ROW][C]tothyperlinks[/C][C]-0.0217206052886413[/C][C]0.01777[/C][C]-1.2223[/C][C]0.222613[/C][C]0.111307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156209&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156209&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.970565148549260.81685810.981800
time_in_rfc5.69493069724503e-051.3e-054.31842.2e-051.1e-05
logins0.0116612674839280.0176190.66190.5086110.254306
blogged_computations0.1068729296491240.0245514.35321.9e-059e-06
totsize7.52212260060416e-051.6e-054.80872e-061e-06
pageviews0.001640876340949660.0022770.72070.4717240.235862
compendium_views_info-0.0118222554704350.005115-2.31110.0215570.010778
compendium_views_pr0.01034945158007510.0091111.1360.2569410.12847
totrevisions4.81934828252209e-054.8e-050.99940.3184780.159239
tothyperlinks-0.02172060528864130.01777-1.22230.2226130.111307






Multiple Linear Regression - Regression Statistics
Multiple R0.821183194667321
R-squared0.674341839204027
Adjusted R-squared0.663836737242867
F-TEST (value)64.1918414211695
F-TEST (DF numerator)9
F-TEST (DF denominator)279
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.14309415581992
Sum Squared Residuals10528.792020228

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.821183194667321 \tabularnewline
R-squared & 0.674341839204027 \tabularnewline
Adjusted R-squared & 0.663836737242867 \tabularnewline
F-TEST (value) & 64.1918414211695 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 279 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.14309415581992 \tabularnewline
Sum Squared Residuals & 10528.792020228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156209&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.821183194667321[/C][/ROW]
[ROW][C]R-squared[/C][C]0.674341839204027[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.663836737242867[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]64.1918414211695[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]279[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.14309415581992[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10528.792020228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156209&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156209&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.821183194667321
R-squared0.674341839204027
Adjusted R-squared0.663836737242867
F-TEST (value)64.1918414211695
F-TEST (DF numerator)9
F-TEST (DF denominator)279
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.14309415581992
Sum Squared Residuals10528.792020228






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13033.8806340232156-3.88063402321564
22825.33736376883942.66263623116056
33826.608705844331311.3912941556687
43031.4240476417131-1.42404764171311
52222.367159378076-0.367159378076043
62616.46941791204669.53058208795337
72539.6784247930137-14.6784247930137
81812.62008594267095.37991405732907
91117.0474437196571-6.04744371965708
102625.00881496013580.991185039864188
112525.6601374216322-0.660137421632212
123829.86887024824458.13112975175547
134430.989435073444413.0105649265556
143021.52493161559718.47506838440291
154033.66896607749846.33103392250165
163426.32423167902617.6757683209739
174742.64859770738834.35140229261166
183037.7669829102495-7.76698291024947
193131.811168744328-0.811168744327991
202327.9059741284736-4.90597412847358
213636.5154795773664-0.515479577366392
223633.94323825109412.05676174890593
233033.6915771797678-3.69157717976784
242524.68784243480140.312157565198587
253929.38106987482719.61893012517285
263431.05037155813562.94962844186442
273126.84770157384534.15229842615468
283129.8296887139531.17031128604701
293333.9521353435581-0.952135343558111
302519.95837736400075.04162263599929
313333.4922130575869-0.492213057586901
323533.52690757446971.47309242553028
334223.879452458304718.1205475416953
344338.1562635838584.84373641614198
353020.94534662875369.05465337124637
363334.8125559602428-1.81255596024277
371319.0005586183403-6.00055861834029
383226.64127999928215.3587200007179
393635.44457165056970.555428349430293
4009.20156996542681-9.20156996542681
412825.79778514769082.20221485230924
421419.6364456369621-5.63644563696206
431718.8843796381135-1.88437963811353
443233.1555822694517-1.15558226945166
453030.3285399129726-0.328539912972572
463523.416788525245711.5832114747543
472022.5944243814322-2.59442438143215
482825.82911232935032.17088767064973
492820.81838586179367.18161413820637
503918.205002090351220.7949979096488
513440.3517071100557-6.3517071100557
522616.69129139368669.30870860631345
533930.47571151716348.52428848283659
543938.87565570975690.124344290243091
553325.59309086055277.40690913944732
562828.044248890679-0.044248890678992
57410.2221727375221-6.22217273752205
583940.0786964317668-1.07869643176677
591815.70571541178982.29428458821017
601417.822292888416-3.82229288841596
612928.45685409149170.543145908508278
624439.69573958892284.30426041107719
632140.8838261180098-19.8838261180098
641617.5094753013081-1.50947530130813
652835.7708675593069-7.77086755930691
663536.3117595759775-1.31175957597747
672830.0118703904833-2.01187039048334
683842.7014897849361-4.70148978493614
692336.3004329705972-13.3004329705972
703622.49880948721613.501190512784
713228.06168148342453.93831851657547
722926.63613845705982.36386154294021
732527.6145161217314-2.61451612173145
742728.398927019686-1.39892701968595
753628.339844607397.66015539260995
762831.9953866977531-3.99538669775314
772331.0535891897381-8.05358918973811
784028.174615816434511.8253841835655
792314.33858857512738.6614114248727
804028.993126991667511.0068730083325
812826.03434991259061.96565008740944
823429.61023034434494.38976965565514
833337.7091197643714-4.70911976437141
842832.9922999814398-4.99229998143976
853441.8264981431661-7.82649814316608
863028.64995822935841.35004177064158
873324.44438755792078.55561244207928
882221.26783863448290.732161365517092
893841.8665699325308-3.86656993253077
902618.83938357528597.16061642471406
913532.48368915832832.51631084167167
92819.0497874394414-11.0497874394414
932415.65341204360038.34658795639974
942928.08703458389640.912965416103616
952020.7227243192891-0.722724319289102
962933.0929423074063-4.09294230740629
974532.435880924844912.5641190751551
983733.33070312536743.66929687463259
993326.66096477393936.33903522606074
1003334.4006700749582-1.40067007495825
1012521.21209728667723.78790271332284
1023228.94693784970113.05306215029894
1032930.598884845541-1.59888484554103
1042829.8444336306254-1.84443363062539
1052834.258529558533-6.25852955853295
1063140.8644685179402-9.86446851794018
1075237.877002079684914.1229979203151
1082118.1877579167412.81224208325896
1092440.1628844682344-16.1628844682344
1104135.10696521997165.89303478002839
1113336.8749161536415-3.87491615364154
1123222.27658206651889.72341793348123
1131914.47489142707414.52510857292588
1142025.1377147417462-5.1377147417462
1153127.40392347430423.59607652569581
1163132.2120364897185-1.21203648971848
1173238.9520873528784-6.95208735287839
1181825.8743566985699-7.87435669856986
1192331.1066508922514-8.10665089225144
1201719.0169386320757-2.01693863207567
1212022.2787800589825-2.2787800589825
1221213.8437580478187-1.84375804781869
1231719.7162308038572-2.71623080385717
1243031.7386938209795-1.7386938209795
1253133.4045524629885-2.40455246298852
1261016.9885148123057-6.98851481230573
1271319.2827028400842-6.2827028400842
1282223.6065452527266-1.60654525272658
1294239.41423596295852.58576403704148
130110.4504583363095-9.45045833630951
131911.6971449838239-2.69714498382388
1323228.75758529086853.24241470913152
1331115.5093740219651-4.50937402196508
1342518.94193185485396.05806814514611
1353623.45897062046512.541029379535
1363136.039528956769-5.03952895676896
13709.70703772858626-9.70703772858626
1382429.0208627139297-5.02086271392966
1391313.8759344068124-0.875934406812368
140811.7197492270755-3.71974922707551
1411312.55652934193860.443470658061445
1421924.4779677288784-5.47796772887844
1431814.61243980029013.38756019970986
1443332.47641521201760.523584787982354
1454031.295256481598.70474351841
1462217.53884766975424.46115233024581
1473825.153950913568212.8460490864318
1482420.48744127654553.51255872345448
149811.863898094724-3.86389809472403
1503526.15175910761658.84824089238352
1514336.36066374043946.6393362595606
1524325.472829365300717.5271706346993
1531419.7411034584183-5.74110345841826
1544137.92371878220893.07628121779109
1553839.6482986999512-1.64829869995119
1564551.6425026737159-6.64250267371589
1573125.58456048500145.41543951499858
1581316.9002643800539-3.90026438005394
1592819.32705351710628.67294648289385
1603132.8873227252432-1.88732272524318
1614039.17694656161110.823053438388909
1623021.96475186662388.03524813337615
1631621.190004871942-5.19000487194204
1643738.2208179485666-1.22081794856656
1653020.75795761070059.24204238929947
1663532.6346161307362.36538386926397
1673225.83779308010846.16220691989159
1682722.15793072240754.84206927759245
1692022.113979002247-2.11397900224703
1701829.6734749901571-11.6734749901571
1713132.4256493031291-1.42564930312908
1723132.7328199726152-1.73281997261517
1732121.6651236992755-0.665123699275478
1743937.50246029275071.49753970724929
1754135.86437486918025.13562513081979
1761317.0385118671654-4.03851186716543
1773233.2080738372757-1.20807383727575
1781816.83258362358251.16741637641747
1793939.296305806672-0.29630580667201
1801421.0032320805885-7.00323208058854
181716.6298106849437-9.62981068494369
1821721.3043461408259-4.30434614082591
183010.1298846102113-10.1298846102113
1843024.44676574147155.55323425852849
1853734.15089573719412.84910426280593
18609.58002111735156-9.58002111735156
187511.2148779695407-6.21487796954073
18819.83309226155184-8.83309226155184
1891619.6044704339389-3.60447043393892
1903220.190720107661511.8092798923385
1912425.4610948387214-1.46109483872137
1921714.84017301522222.15982698477784
1931117.9780490228606-6.97804902286056
1942417.15577046978866.84422953021139
1952223.1241069467842-1.12410694678418
1961220.0211813547829-8.02118135478293
1971913.74421203436815.25578796563189
1981317.1063314649362-4.10633146493622
1991718.0564755249746-1.05647552497463
2001517.1454804452542-2.14548044525419
2011617.0006699196795-1.00066991967947
2022413.586634050338310.4133659496617
2031520.6383085422541-5.6383085422541
2041719.4701699051171-2.47016990511711
2051815.85540269949082.14459730050918
2062013.31256226496716.68743773503292
2071617.5310343107644-1.53103431076439
2081621.4008260716442-5.40082607164419
2091819.3796203397457-1.37962033974574
2102219.33446489059912.66553510940091
211816.6668894655039-8.66688946550393
2121720.2471263225322-3.24712632253219
2131815.2649481224312.73505187756897
2141617.7237644839339-1.72376448393389
2152313.3338341931459.66616580685497
2162212.75086571578839.24913428421169
2171314.9569847140816-1.95698471408162
2181315.5099005603233-2.50990056032331
2191615.42626274678780.573737253212235
2201615.23624645059060.763753549409396
2212016.31192294100783.68807705899221
2222220.8604242619751.13957573802499
2231718.7195695508086-1.71956955080857
2241817.25235556627610.747644433723863
2251711.4333910526655.56660894733496
2261219.5239690575996-7.5239690575996
227717.1771228929633-10.1771228929633
2281716.07882923937360.921170760626416
2291415.674178903663-1.67417890366297
2302319.86404049470623.13595950529376
2311710.87745956429336.12254043570671
2321418.2529202970035-4.25292029700355
2331517.754265624617-2.75426562461698
2341714.4638497015182.53615029848199
2352116.25361216876694.74638783123308
2361817.74164550001420.258354499985803
2371819.9634241254969-1.96342412549693
2381713.56493498114683.43506501885323
2391715.744761435271.25523856473005
2401612.80386091767933.19613908232067
2411516.9406755995163-1.94067559951626
2422122.6991738025811-1.6991738025811
2431616.4065144692127-0.406514469212711
2441414.8283963989754-0.828396398975437
2451516.0774057132511-1.07740571325107
2461717.3806788929561-0.380678892956067
2471520.9006657131072-5.90066571310718
2481516.2349204173196-1.23492041731958
2491016.8335093095881-6.83350930958805
250613.832026060563-7.83202606056302
2512221.50660024836390.493399751636145
2522120.66241538614940.33758461385063
253111.344302800547-10.344302800547
2541816.48751914176921.51248085823083
2551714.28672219469072.71327780530931
256410.545875940893-6.54587594089304
2571014.0853986904618-4.08539869046177
2581617.7052696561044-1.70526965610438
2591615.10305868866740.896941311332567
260913.9089414102042-4.90894141020419
2611610.68397856989615.31602143010387
2621719.5634680375674-2.56346803756736
263711.5280365404273-4.52803654042729
2641516.3092927141946-1.30929271419457
2651415.2498167629926-1.24981676299256
2661417.8245652207641-3.82456522076415
2671816.49468016966271.50531983033728
2681215.871108046622-3.87110804662202
2691619.3446718044629-3.34467180446293
2702118.5221731073462.477826892654
2711916.94102065026962.05897934973043
2721616.2867176483343-0.286717648334337
273111.0765901975638-10.0765901975638
2741620.4270835000218-4.4270835000218
2751015.6637491182031-5.66374911820309
2761917.26765036290391.73234963709614
2771214.4445464331769-2.44454643317694
278211.2979788093154-9.29797880931545
2791417.4084777102958-3.40847771029584
2801715.16355366115291.83644633884712
2811916.69350935684772.30649064315233
2821420.5647114844965-6.56471148449646
2831116.3710980327988-5.37109803279881
284411.6755465550012-7.67554655500124
2851614.14596808525161.85403191474841
2862015.99277174654614.00722825345387
2871213.3837991965567-1.38379919655672
2881516.8356768794819-1.8356768794819
2891614.29138363834971.7086163616503

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 30 & 33.8806340232156 & -3.88063402321564 \tabularnewline
2 & 28 & 25.3373637688394 & 2.66263623116056 \tabularnewline
3 & 38 & 26.6087058443313 & 11.3912941556687 \tabularnewline
4 & 30 & 31.4240476417131 & -1.42404764171311 \tabularnewline
5 & 22 & 22.367159378076 & -0.367159378076043 \tabularnewline
6 & 26 & 16.4694179120466 & 9.53058208795337 \tabularnewline
7 & 25 & 39.6784247930137 & -14.6784247930137 \tabularnewline
8 & 18 & 12.6200859426709 & 5.37991405732907 \tabularnewline
9 & 11 & 17.0474437196571 & -6.04744371965708 \tabularnewline
10 & 26 & 25.0088149601358 & 0.991185039864188 \tabularnewline
11 & 25 & 25.6601374216322 & -0.660137421632212 \tabularnewline
12 & 38 & 29.8688702482445 & 8.13112975175547 \tabularnewline
13 & 44 & 30.9894350734444 & 13.0105649265556 \tabularnewline
14 & 30 & 21.5249316155971 & 8.47506838440291 \tabularnewline
15 & 40 & 33.6689660774984 & 6.33103392250165 \tabularnewline
16 & 34 & 26.3242316790261 & 7.6757683209739 \tabularnewline
17 & 47 & 42.6485977073883 & 4.35140229261166 \tabularnewline
18 & 30 & 37.7669829102495 & -7.76698291024947 \tabularnewline
19 & 31 & 31.811168744328 & -0.811168744327991 \tabularnewline
20 & 23 & 27.9059741284736 & -4.90597412847358 \tabularnewline
21 & 36 & 36.5154795773664 & -0.515479577366392 \tabularnewline
22 & 36 & 33.9432382510941 & 2.05676174890593 \tabularnewline
23 & 30 & 33.6915771797678 & -3.69157717976784 \tabularnewline
24 & 25 & 24.6878424348014 & 0.312157565198587 \tabularnewline
25 & 39 & 29.3810698748271 & 9.61893012517285 \tabularnewline
26 & 34 & 31.0503715581356 & 2.94962844186442 \tabularnewline
27 & 31 & 26.8477015738453 & 4.15229842615468 \tabularnewline
28 & 31 & 29.829688713953 & 1.17031128604701 \tabularnewline
29 & 33 & 33.9521353435581 & -0.952135343558111 \tabularnewline
30 & 25 & 19.9583773640007 & 5.04162263599929 \tabularnewline
31 & 33 & 33.4922130575869 & -0.492213057586901 \tabularnewline
32 & 35 & 33.5269075744697 & 1.47309242553028 \tabularnewline
33 & 42 & 23.8794524583047 & 18.1205475416953 \tabularnewline
34 & 43 & 38.156263583858 & 4.84373641614198 \tabularnewline
35 & 30 & 20.9453466287536 & 9.05465337124637 \tabularnewline
36 & 33 & 34.8125559602428 & -1.81255596024277 \tabularnewline
37 & 13 & 19.0005586183403 & -6.00055861834029 \tabularnewline
38 & 32 & 26.6412799992821 & 5.3587200007179 \tabularnewline
39 & 36 & 35.4445716505697 & 0.555428349430293 \tabularnewline
40 & 0 & 9.20156996542681 & -9.20156996542681 \tabularnewline
41 & 28 & 25.7977851476908 & 2.20221485230924 \tabularnewline
42 & 14 & 19.6364456369621 & -5.63644563696206 \tabularnewline
43 & 17 & 18.8843796381135 & -1.88437963811353 \tabularnewline
44 & 32 & 33.1555822694517 & -1.15558226945166 \tabularnewline
45 & 30 & 30.3285399129726 & -0.328539912972572 \tabularnewline
46 & 35 & 23.4167885252457 & 11.5832114747543 \tabularnewline
47 & 20 & 22.5944243814322 & -2.59442438143215 \tabularnewline
48 & 28 & 25.8291123293503 & 2.17088767064973 \tabularnewline
49 & 28 & 20.8183858617936 & 7.18161413820637 \tabularnewline
50 & 39 & 18.2050020903512 & 20.7949979096488 \tabularnewline
51 & 34 & 40.3517071100557 & -6.3517071100557 \tabularnewline
52 & 26 & 16.6912913936866 & 9.30870860631345 \tabularnewline
53 & 39 & 30.4757115171634 & 8.52428848283659 \tabularnewline
54 & 39 & 38.8756557097569 & 0.124344290243091 \tabularnewline
55 & 33 & 25.5930908605527 & 7.40690913944732 \tabularnewline
56 & 28 & 28.044248890679 & -0.044248890678992 \tabularnewline
57 & 4 & 10.2221727375221 & -6.22217273752205 \tabularnewline
58 & 39 & 40.0786964317668 & -1.07869643176677 \tabularnewline
59 & 18 & 15.7057154117898 & 2.29428458821017 \tabularnewline
60 & 14 & 17.822292888416 & -3.82229288841596 \tabularnewline
61 & 29 & 28.4568540914917 & 0.543145908508278 \tabularnewline
62 & 44 & 39.6957395889228 & 4.30426041107719 \tabularnewline
63 & 21 & 40.8838261180098 & -19.8838261180098 \tabularnewline
64 & 16 & 17.5094753013081 & -1.50947530130813 \tabularnewline
65 & 28 & 35.7708675593069 & -7.77086755930691 \tabularnewline
66 & 35 & 36.3117595759775 & -1.31175957597747 \tabularnewline
67 & 28 & 30.0118703904833 & -2.01187039048334 \tabularnewline
68 & 38 & 42.7014897849361 & -4.70148978493614 \tabularnewline
69 & 23 & 36.3004329705972 & -13.3004329705972 \tabularnewline
70 & 36 & 22.498809487216 & 13.501190512784 \tabularnewline
71 & 32 & 28.0616814834245 & 3.93831851657547 \tabularnewline
72 & 29 & 26.6361384570598 & 2.36386154294021 \tabularnewline
73 & 25 & 27.6145161217314 & -2.61451612173145 \tabularnewline
74 & 27 & 28.398927019686 & -1.39892701968595 \tabularnewline
75 & 36 & 28.33984460739 & 7.66015539260995 \tabularnewline
76 & 28 & 31.9953866977531 & -3.99538669775314 \tabularnewline
77 & 23 & 31.0535891897381 & -8.05358918973811 \tabularnewline
78 & 40 & 28.1746158164345 & 11.8253841835655 \tabularnewline
79 & 23 & 14.3385885751273 & 8.6614114248727 \tabularnewline
80 & 40 & 28.9931269916675 & 11.0068730083325 \tabularnewline
81 & 28 & 26.0343499125906 & 1.96565008740944 \tabularnewline
82 & 34 & 29.6102303443449 & 4.38976965565514 \tabularnewline
83 & 33 & 37.7091197643714 & -4.70911976437141 \tabularnewline
84 & 28 & 32.9922999814398 & -4.99229998143976 \tabularnewline
85 & 34 & 41.8264981431661 & -7.82649814316608 \tabularnewline
86 & 30 & 28.6499582293584 & 1.35004177064158 \tabularnewline
87 & 33 & 24.4443875579207 & 8.55561244207928 \tabularnewline
88 & 22 & 21.2678386344829 & 0.732161365517092 \tabularnewline
89 & 38 & 41.8665699325308 & -3.86656993253077 \tabularnewline
90 & 26 & 18.8393835752859 & 7.16061642471406 \tabularnewline
91 & 35 & 32.4836891583283 & 2.51631084167167 \tabularnewline
92 & 8 & 19.0497874394414 & -11.0497874394414 \tabularnewline
93 & 24 & 15.6534120436003 & 8.34658795639974 \tabularnewline
94 & 29 & 28.0870345838964 & 0.912965416103616 \tabularnewline
95 & 20 & 20.7227243192891 & -0.722724319289102 \tabularnewline
96 & 29 & 33.0929423074063 & -4.09294230740629 \tabularnewline
97 & 45 & 32.4358809248449 & 12.5641190751551 \tabularnewline
98 & 37 & 33.3307031253674 & 3.66929687463259 \tabularnewline
99 & 33 & 26.6609647739393 & 6.33903522606074 \tabularnewline
100 & 33 & 34.4006700749582 & -1.40067007495825 \tabularnewline
101 & 25 & 21.2120972866772 & 3.78790271332284 \tabularnewline
102 & 32 & 28.9469378497011 & 3.05306215029894 \tabularnewline
103 & 29 & 30.598884845541 & -1.59888484554103 \tabularnewline
104 & 28 & 29.8444336306254 & -1.84443363062539 \tabularnewline
105 & 28 & 34.258529558533 & -6.25852955853295 \tabularnewline
106 & 31 & 40.8644685179402 & -9.86446851794018 \tabularnewline
107 & 52 & 37.8770020796849 & 14.1229979203151 \tabularnewline
108 & 21 & 18.187757916741 & 2.81224208325896 \tabularnewline
109 & 24 & 40.1628844682344 & -16.1628844682344 \tabularnewline
110 & 41 & 35.1069652199716 & 5.89303478002839 \tabularnewline
111 & 33 & 36.8749161536415 & -3.87491615364154 \tabularnewline
112 & 32 & 22.2765820665188 & 9.72341793348123 \tabularnewline
113 & 19 & 14.4748914270741 & 4.52510857292588 \tabularnewline
114 & 20 & 25.1377147417462 & -5.1377147417462 \tabularnewline
115 & 31 & 27.4039234743042 & 3.59607652569581 \tabularnewline
116 & 31 & 32.2120364897185 & -1.21203648971848 \tabularnewline
117 & 32 & 38.9520873528784 & -6.95208735287839 \tabularnewline
118 & 18 & 25.8743566985699 & -7.87435669856986 \tabularnewline
119 & 23 & 31.1066508922514 & -8.10665089225144 \tabularnewline
120 & 17 & 19.0169386320757 & -2.01693863207567 \tabularnewline
121 & 20 & 22.2787800589825 & -2.2787800589825 \tabularnewline
122 & 12 & 13.8437580478187 & -1.84375804781869 \tabularnewline
123 & 17 & 19.7162308038572 & -2.71623080385717 \tabularnewline
124 & 30 & 31.7386938209795 & -1.7386938209795 \tabularnewline
125 & 31 & 33.4045524629885 & -2.40455246298852 \tabularnewline
126 & 10 & 16.9885148123057 & -6.98851481230573 \tabularnewline
127 & 13 & 19.2827028400842 & -6.2827028400842 \tabularnewline
128 & 22 & 23.6065452527266 & -1.60654525272658 \tabularnewline
129 & 42 & 39.4142359629585 & 2.58576403704148 \tabularnewline
130 & 1 & 10.4504583363095 & -9.45045833630951 \tabularnewline
131 & 9 & 11.6971449838239 & -2.69714498382388 \tabularnewline
132 & 32 & 28.7575852908685 & 3.24241470913152 \tabularnewline
133 & 11 & 15.5093740219651 & -4.50937402196508 \tabularnewline
134 & 25 & 18.9419318548539 & 6.05806814514611 \tabularnewline
135 & 36 & 23.458970620465 & 12.541029379535 \tabularnewline
136 & 31 & 36.039528956769 & -5.03952895676896 \tabularnewline
137 & 0 & 9.70703772858626 & -9.70703772858626 \tabularnewline
138 & 24 & 29.0208627139297 & -5.02086271392966 \tabularnewline
139 & 13 & 13.8759344068124 & -0.875934406812368 \tabularnewline
140 & 8 & 11.7197492270755 & -3.71974922707551 \tabularnewline
141 & 13 & 12.5565293419386 & 0.443470658061445 \tabularnewline
142 & 19 & 24.4779677288784 & -5.47796772887844 \tabularnewline
143 & 18 & 14.6124398002901 & 3.38756019970986 \tabularnewline
144 & 33 & 32.4764152120176 & 0.523584787982354 \tabularnewline
145 & 40 & 31.29525648159 & 8.70474351841 \tabularnewline
146 & 22 & 17.5388476697542 & 4.46115233024581 \tabularnewline
147 & 38 & 25.1539509135682 & 12.8460490864318 \tabularnewline
148 & 24 & 20.4874412765455 & 3.51255872345448 \tabularnewline
149 & 8 & 11.863898094724 & -3.86389809472403 \tabularnewline
150 & 35 & 26.1517591076165 & 8.84824089238352 \tabularnewline
151 & 43 & 36.3606637404394 & 6.6393362595606 \tabularnewline
152 & 43 & 25.4728293653007 & 17.5271706346993 \tabularnewline
153 & 14 & 19.7411034584183 & -5.74110345841826 \tabularnewline
154 & 41 & 37.9237187822089 & 3.07628121779109 \tabularnewline
155 & 38 & 39.6482986999512 & -1.64829869995119 \tabularnewline
156 & 45 & 51.6425026737159 & -6.64250267371589 \tabularnewline
157 & 31 & 25.5845604850014 & 5.41543951499858 \tabularnewline
158 & 13 & 16.9002643800539 & -3.90026438005394 \tabularnewline
159 & 28 & 19.3270535171062 & 8.67294648289385 \tabularnewline
160 & 31 & 32.8873227252432 & -1.88732272524318 \tabularnewline
161 & 40 & 39.1769465616111 & 0.823053438388909 \tabularnewline
162 & 30 & 21.9647518666238 & 8.03524813337615 \tabularnewline
163 & 16 & 21.190004871942 & -5.19000487194204 \tabularnewline
164 & 37 & 38.2208179485666 & -1.22081794856656 \tabularnewline
165 & 30 & 20.7579576107005 & 9.24204238929947 \tabularnewline
166 & 35 & 32.634616130736 & 2.36538386926397 \tabularnewline
167 & 32 & 25.8377930801084 & 6.16220691989159 \tabularnewline
168 & 27 & 22.1579307224075 & 4.84206927759245 \tabularnewline
169 & 20 & 22.113979002247 & -2.11397900224703 \tabularnewline
170 & 18 & 29.6734749901571 & -11.6734749901571 \tabularnewline
171 & 31 & 32.4256493031291 & -1.42564930312908 \tabularnewline
172 & 31 & 32.7328199726152 & -1.73281997261517 \tabularnewline
173 & 21 & 21.6651236992755 & -0.665123699275478 \tabularnewline
174 & 39 & 37.5024602927507 & 1.49753970724929 \tabularnewline
175 & 41 & 35.8643748691802 & 5.13562513081979 \tabularnewline
176 & 13 & 17.0385118671654 & -4.03851186716543 \tabularnewline
177 & 32 & 33.2080738372757 & -1.20807383727575 \tabularnewline
178 & 18 & 16.8325836235825 & 1.16741637641747 \tabularnewline
179 & 39 & 39.296305806672 & -0.29630580667201 \tabularnewline
180 & 14 & 21.0032320805885 & -7.00323208058854 \tabularnewline
181 & 7 & 16.6298106849437 & -9.62981068494369 \tabularnewline
182 & 17 & 21.3043461408259 & -4.30434614082591 \tabularnewline
183 & 0 & 10.1298846102113 & -10.1298846102113 \tabularnewline
184 & 30 & 24.4467657414715 & 5.55323425852849 \tabularnewline
185 & 37 & 34.1508957371941 & 2.84910426280593 \tabularnewline
186 & 0 & 9.58002111735156 & -9.58002111735156 \tabularnewline
187 & 5 & 11.2148779695407 & -6.21487796954073 \tabularnewline
188 & 1 & 9.83309226155184 & -8.83309226155184 \tabularnewline
189 & 16 & 19.6044704339389 & -3.60447043393892 \tabularnewline
190 & 32 & 20.1907201076615 & 11.8092798923385 \tabularnewline
191 & 24 & 25.4610948387214 & -1.46109483872137 \tabularnewline
192 & 17 & 14.8401730152222 & 2.15982698477784 \tabularnewline
193 & 11 & 17.9780490228606 & -6.97804902286056 \tabularnewline
194 & 24 & 17.1557704697886 & 6.84422953021139 \tabularnewline
195 & 22 & 23.1241069467842 & -1.12410694678418 \tabularnewline
196 & 12 & 20.0211813547829 & -8.02118135478293 \tabularnewline
197 & 19 & 13.7442120343681 & 5.25578796563189 \tabularnewline
198 & 13 & 17.1063314649362 & -4.10633146493622 \tabularnewline
199 & 17 & 18.0564755249746 & -1.05647552497463 \tabularnewline
200 & 15 & 17.1454804452542 & -2.14548044525419 \tabularnewline
201 & 16 & 17.0006699196795 & -1.00066991967947 \tabularnewline
202 & 24 & 13.5866340503383 & 10.4133659496617 \tabularnewline
203 & 15 & 20.6383085422541 & -5.6383085422541 \tabularnewline
204 & 17 & 19.4701699051171 & -2.47016990511711 \tabularnewline
205 & 18 & 15.8554026994908 & 2.14459730050918 \tabularnewline
206 & 20 & 13.3125622649671 & 6.68743773503292 \tabularnewline
207 & 16 & 17.5310343107644 & -1.53103431076439 \tabularnewline
208 & 16 & 21.4008260716442 & -5.40082607164419 \tabularnewline
209 & 18 & 19.3796203397457 & -1.37962033974574 \tabularnewline
210 & 22 & 19.3344648905991 & 2.66553510940091 \tabularnewline
211 & 8 & 16.6668894655039 & -8.66688946550393 \tabularnewline
212 & 17 & 20.2471263225322 & -3.24712632253219 \tabularnewline
213 & 18 & 15.264948122431 & 2.73505187756897 \tabularnewline
214 & 16 & 17.7237644839339 & -1.72376448393389 \tabularnewline
215 & 23 & 13.333834193145 & 9.66616580685497 \tabularnewline
216 & 22 & 12.7508657157883 & 9.24913428421169 \tabularnewline
217 & 13 & 14.9569847140816 & -1.95698471408162 \tabularnewline
218 & 13 & 15.5099005603233 & -2.50990056032331 \tabularnewline
219 & 16 & 15.4262627467878 & 0.573737253212235 \tabularnewline
220 & 16 & 15.2362464505906 & 0.763753549409396 \tabularnewline
221 & 20 & 16.3119229410078 & 3.68807705899221 \tabularnewline
222 & 22 & 20.860424261975 & 1.13957573802499 \tabularnewline
223 & 17 & 18.7195695508086 & -1.71956955080857 \tabularnewline
224 & 18 & 17.2523555662761 & 0.747644433723863 \tabularnewline
225 & 17 & 11.433391052665 & 5.56660894733496 \tabularnewline
226 & 12 & 19.5239690575996 & -7.5239690575996 \tabularnewline
227 & 7 & 17.1771228929633 & -10.1771228929633 \tabularnewline
228 & 17 & 16.0788292393736 & 0.921170760626416 \tabularnewline
229 & 14 & 15.674178903663 & -1.67417890366297 \tabularnewline
230 & 23 & 19.8640404947062 & 3.13595950529376 \tabularnewline
231 & 17 & 10.8774595642933 & 6.12254043570671 \tabularnewline
232 & 14 & 18.2529202970035 & -4.25292029700355 \tabularnewline
233 & 15 & 17.754265624617 & -2.75426562461698 \tabularnewline
234 & 17 & 14.463849701518 & 2.53615029848199 \tabularnewline
235 & 21 & 16.2536121687669 & 4.74638783123308 \tabularnewline
236 & 18 & 17.7416455000142 & 0.258354499985803 \tabularnewline
237 & 18 & 19.9634241254969 & -1.96342412549693 \tabularnewline
238 & 17 & 13.5649349811468 & 3.43506501885323 \tabularnewline
239 & 17 & 15.74476143527 & 1.25523856473005 \tabularnewline
240 & 16 & 12.8038609176793 & 3.19613908232067 \tabularnewline
241 & 15 & 16.9406755995163 & -1.94067559951626 \tabularnewline
242 & 21 & 22.6991738025811 & -1.6991738025811 \tabularnewline
243 & 16 & 16.4065144692127 & -0.406514469212711 \tabularnewline
244 & 14 & 14.8283963989754 & -0.828396398975437 \tabularnewline
245 & 15 & 16.0774057132511 & -1.07740571325107 \tabularnewline
246 & 17 & 17.3806788929561 & -0.380678892956067 \tabularnewline
247 & 15 & 20.9006657131072 & -5.90066571310718 \tabularnewline
248 & 15 & 16.2349204173196 & -1.23492041731958 \tabularnewline
249 & 10 & 16.8335093095881 & -6.83350930958805 \tabularnewline
250 & 6 & 13.832026060563 & -7.83202606056302 \tabularnewline
251 & 22 & 21.5066002483639 & 0.493399751636145 \tabularnewline
252 & 21 & 20.6624153861494 & 0.33758461385063 \tabularnewline
253 & 1 & 11.344302800547 & -10.344302800547 \tabularnewline
254 & 18 & 16.4875191417692 & 1.51248085823083 \tabularnewline
255 & 17 & 14.2867221946907 & 2.71327780530931 \tabularnewline
256 & 4 & 10.545875940893 & -6.54587594089304 \tabularnewline
257 & 10 & 14.0853986904618 & -4.08539869046177 \tabularnewline
258 & 16 & 17.7052696561044 & -1.70526965610438 \tabularnewline
259 & 16 & 15.1030586886674 & 0.896941311332567 \tabularnewline
260 & 9 & 13.9089414102042 & -4.90894141020419 \tabularnewline
261 & 16 & 10.6839785698961 & 5.31602143010387 \tabularnewline
262 & 17 & 19.5634680375674 & -2.56346803756736 \tabularnewline
263 & 7 & 11.5280365404273 & -4.52803654042729 \tabularnewline
264 & 15 & 16.3092927141946 & -1.30929271419457 \tabularnewline
265 & 14 & 15.2498167629926 & -1.24981676299256 \tabularnewline
266 & 14 & 17.8245652207641 & -3.82456522076415 \tabularnewline
267 & 18 & 16.4946801696627 & 1.50531983033728 \tabularnewline
268 & 12 & 15.871108046622 & -3.87110804662202 \tabularnewline
269 & 16 & 19.3446718044629 & -3.34467180446293 \tabularnewline
270 & 21 & 18.522173107346 & 2.477826892654 \tabularnewline
271 & 19 & 16.9410206502696 & 2.05897934973043 \tabularnewline
272 & 16 & 16.2867176483343 & -0.286717648334337 \tabularnewline
273 & 1 & 11.0765901975638 & -10.0765901975638 \tabularnewline
274 & 16 & 20.4270835000218 & -4.4270835000218 \tabularnewline
275 & 10 & 15.6637491182031 & -5.66374911820309 \tabularnewline
276 & 19 & 17.2676503629039 & 1.73234963709614 \tabularnewline
277 & 12 & 14.4445464331769 & -2.44454643317694 \tabularnewline
278 & 2 & 11.2979788093154 & -9.29797880931545 \tabularnewline
279 & 14 & 17.4084777102958 & -3.40847771029584 \tabularnewline
280 & 17 & 15.1635536611529 & 1.83644633884712 \tabularnewline
281 & 19 & 16.6935093568477 & 2.30649064315233 \tabularnewline
282 & 14 & 20.5647114844965 & -6.56471148449646 \tabularnewline
283 & 11 & 16.3710980327988 & -5.37109803279881 \tabularnewline
284 & 4 & 11.6755465550012 & -7.67554655500124 \tabularnewline
285 & 16 & 14.1459680852516 & 1.85403191474841 \tabularnewline
286 & 20 & 15.9927717465461 & 4.00722825345387 \tabularnewline
287 & 12 & 13.3837991965567 & -1.38379919655672 \tabularnewline
288 & 15 & 16.8356768794819 & -1.8356768794819 \tabularnewline
289 & 16 & 14.2913836383497 & 1.7086163616503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156209&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]30[/C][C]33.8806340232156[/C][C]-3.88063402321564[/C][/ROW]
[ROW][C]2[/C][C]28[/C][C]25.3373637688394[/C][C]2.66263623116056[/C][/ROW]
[ROW][C]3[/C][C]38[/C][C]26.6087058443313[/C][C]11.3912941556687[/C][/ROW]
[ROW][C]4[/C][C]30[/C][C]31.4240476417131[/C][C]-1.42404764171311[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]22.367159378076[/C][C]-0.367159378076043[/C][/ROW]
[ROW][C]6[/C][C]26[/C][C]16.4694179120466[/C][C]9.53058208795337[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]39.6784247930137[/C][C]-14.6784247930137[/C][/ROW]
[ROW][C]8[/C][C]18[/C][C]12.6200859426709[/C][C]5.37991405732907[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]17.0474437196571[/C][C]-6.04744371965708[/C][/ROW]
[ROW][C]10[/C][C]26[/C][C]25.0088149601358[/C][C]0.991185039864188[/C][/ROW]
[ROW][C]11[/C][C]25[/C][C]25.6601374216322[/C][C]-0.660137421632212[/C][/ROW]
[ROW][C]12[/C][C]38[/C][C]29.8688702482445[/C][C]8.13112975175547[/C][/ROW]
[ROW][C]13[/C][C]44[/C][C]30.9894350734444[/C][C]13.0105649265556[/C][/ROW]
[ROW][C]14[/C][C]30[/C][C]21.5249316155971[/C][C]8.47506838440291[/C][/ROW]
[ROW][C]15[/C][C]40[/C][C]33.6689660774984[/C][C]6.33103392250165[/C][/ROW]
[ROW][C]16[/C][C]34[/C][C]26.3242316790261[/C][C]7.6757683209739[/C][/ROW]
[ROW][C]17[/C][C]47[/C][C]42.6485977073883[/C][C]4.35140229261166[/C][/ROW]
[ROW][C]18[/C][C]30[/C][C]37.7669829102495[/C][C]-7.76698291024947[/C][/ROW]
[ROW][C]19[/C][C]31[/C][C]31.811168744328[/C][C]-0.811168744327991[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]27.9059741284736[/C][C]-4.90597412847358[/C][/ROW]
[ROW][C]21[/C][C]36[/C][C]36.5154795773664[/C][C]-0.515479577366392[/C][/ROW]
[ROW][C]22[/C][C]36[/C][C]33.9432382510941[/C][C]2.05676174890593[/C][/ROW]
[ROW][C]23[/C][C]30[/C][C]33.6915771797678[/C][C]-3.69157717976784[/C][/ROW]
[ROW][C]24[/C][C]25[/C][C]24.6878424348014[/C][C]0.312157565198587[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]29.3810698748271[/C][C]9.61893012517285[/C][/ROW]
[ROW][C]26[/C][C]34[/C][C]31.0503715581356[/C][C]2.94962844186442[/C][/ROW]
[ROW][C]27[/C][C]31[/C][C]26.8477015738453[/C][C]4.15229842615468[/C][/ROW]
[ROW][C]28[/C][C]31[/C][C]29.829688713953[/C][C]1.17031128604701[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]33.9521353435581[/C][C]-0.952135343558111[/C][/ROW]
[ROW][C]30[/C][C]25[/C][C]19.9583773640007[/C][C]5.04162263599929[/C][/ROW]
[ROW][C]31[/C][C]33[/C][C]33.4922130575869[/C][C]-0.492213057586901[/C][/ROW]
[ROW][C]32[/C][C]35[/C][C]33.5269075744697[/C][C]1.47309242553028[/C][/ROW]
[ROW][C]33[/C][C]42[/C][C]23.8794524583047[/C][C]18.1205475416953[/C][/ROW]
[ROW][C]34[/C][C]43[/C][C]38.156263583858[/C][C]4.84373641614198[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]20.9453466287536[/C][C]9.05465337124637[/C][/ROW]
[ROW][C]36[/C][C]33[/C][C]34.8125559602428[/C][C]-1.81255596024277[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]19.0005586183403[/C][C]-6.00055861834029[/C][/ROW]
[ROW][C]38[/C][C]32[/C][C]26.6412799992821[/C][C]5.3587200007179[/C][/ROW]
[ROW][C]39[/C][C]36[/C][C]35.4445716505697[/C][C]0.555428349430293[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]9.20156996542681[/C][C]-9.20156996542681[/C][/ROW]
[ROW][C]41[/C][C]28[/C][C]25.7977851476908[/C][C]2.20221485230924[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]19.6364456369621[/C][C]-5.63644563696206[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]18.8843796381135[/C][C]-1.88437963811353[/C][/ROW]
[ROW][C]44[/C][C]32[/C][C]33.1555822694517[/C][C]-1.15558226945166[/C][/ROW]
[ROW][C]45[/C][C]30[/C][C]30.3285399129726[/C][C]-0.328539912972572[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]23.4167885252457[/C][C]11.5832114747543[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]22.5944243814322[/C][C]-2.59442438143215[/C][/ROW]
[ROW][C]48[/C][C]28[/C][C]25.8291123293503[/C][C]2.17088767064973[/C][/ROW]
[ROW][C]49[/C][C]28[/C][C]20.8183858617936[/C][C]7.18161413820637[/C][/ROW]
[ROW][C]50[/C][C]39[/C][C]18.2050020903512[/C][C]20.7949979096488[/C][/ROW]
[ROW][C]51[/C][C]34[/C][C]40.3517071100557[/C][C]-6.3517071100557[/C][/ROW]
[ROW][C]52[/C][C]26[/C][C]16.6912913936866[/C][C]9.30870860631345[/C][/ROW]
[ROW][C]53[/C][C]39[/C][C]30.4757115171634[/C][C]8.52428848283659[/C][/ROW]
[ROW][C]54[/C][C]39[/C][C]38.8756557097569[/C][C]0.124344290243091[/C][/ROW]
[ROW][C]55[/C][C]33[/C][C]25.5930908605527[/C][C]7.40690913944732[/C][/ROW]
[ROW][C]56[/C][C]28[/C][C]28.044248890679[/C][C]-0.044248890678992[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]10.2221727375221[/C][C]-6.22217273752205[/C][/ROW]
[ROW][C]58[/C][C]39[/C][C]40.0786964317668[/C][C]-1.07869643176677[/C][/ROW]
[ROW][C]59[/C][C]18[/C][C]15.7057154117898[/C][C]2.29428458821017[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]17.822292888416[/C][C]-3.82229288841596[/C][/ROW]
[ROW][C]61[/C][C]29[/C][C]28.4568540914917[/C][C]0.543145908508278[/C][/ROW]
[ROW][C]62[/C][C]44[/C][C]39.6957395889228[/C][C]4.30426041107719[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]40.8838261180098[/C][C]-19.8838261180098[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]17.5094753013081[/C][C]-1.50947530130813[/C][/ROW]
[ROW][C]65[/C][C]28[/C][C]35.7708675593069[/C][C]-7.77086755930691[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]36.3117595759775[/C][C]-1.31175957597747[/C][/ROW]
[ROW][C]67[/C][C]28[/C][C]30.0118703904833[/C][C]-2.01187039048334[/C][/ROW]
[ROW][C]68[/C][C]38[/C][C]42.7014897849361[/C][C]-4.70148978493614[/C][/ROW]
[ROW][C]69[/C][C]23[/C][C]36.3004329705972[/C][C]-13.3004329705972[/C][/ROW]
[ROW][C]70[/C][C]36[/C][C]22.498809487216[/C][C]13.501190512784[/C][/ROW]
[ROW][C]71[/C][C]32[/C][C]28.0616814834245[/C][C]3.93831851657547[/C][/ROW]
[ROW][C]72[/C][C]29[/C][C]26.6361384570598[/C][C]2.36386154294021[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]27.6145161217314[/C][C]-2.61451612173145[/C][/ROW]
[ROW][C]74[/C][C]27[/C][C]28.398927019686[/C][C]-1.39892701968595[/C][/ROW]
[ROW][C]75[/C][C]36[/C][C]28.33984460739[/C][C]7.66015539260995[/C][/ROW]
[ROW][C]76[/C][C]28[/C][C]31.9953866977531[/C][C]-3.99538669775314[/C][/ROW]
[ROW][C]77[/C][C]23[/C][C]31.0535891897381[/C][C]-8.05358918973811[/C][/ROW]
[ROW][C]78[/C][C]40[/C][C]28.1746158164345[/C][C]11.8253841835655[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]14.3385885751273[/C][C]8.6614114248727[/C][/ROW]
[ROW][C]80[/C][C]40[/C][C]28.9931269916675[/C][C]11.0068730083325[/C][/ROW]
[ROW][C]81[/C][C]28[/C][C]26.0343499125906[/C][C]1.96565008740944[/C][/ROW]
[ROW][C]82[/C][C]34[/C][C]29.6102303443449[/C][C]4.38976965565514[/C][/ROW]
[ROW][C]83[/C][C]33[/C][C]37.7091197643714[/C][C]-4.70911976437141[/C][/ROW]
[ROW][C]84[/C][C]28[/C][C]32.9922999814398[/C][C]-4.99229998143976[/C][/ROW]
[ROW][C]85[/C][C]34[/C][C]41.8264981431661[/C][C]-7.82649814316608[/C][/ROW]
[ROW][C]86[/C][C]30[/C][C]28.6499582293584[/C][C]1.35004177064158[/C][/ROW]
[ROW][C]87[/C][C]33[/C][C]24.4443875579207[/C][C]8.55561244207928[/C][/ROW]
[ROW][C]88[/C][C]22[/C][C]21.2678386344829[/C][C]0.732161365517092[/C][/ROW]
[ROW][C]89[/C][C]38[/C][C]41.8665699325308[/C][C]-3.86656993253077[/C][/ROW]
[ROW][C]90[/C][C]26[/C][C]18.8393835752859[/C][C]7.16061642471406[/C][/ROW]
[ROW][C]91[/C][C]35[/C][C]32.4836891583283[/C][C]2.51631084167167[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]19.0497874394414[/C][C]-11.0497874394414[/C][/ROW]
[ROW][C]93[/C][C]24[/C][C]15.6534120436003[/C][C]8.34658795639974[/C][/ROW]
[ROW][C]94[/C][C]29[/C][C]28.0870345838964[/C][C]0.912965416103616[/C][/ROW]
[ROW][C]95[/C][C]20[/C][C]20.7227243192891[/C][C]-0.722724319289102[/C][/ROW]
[ROW][C]96[/C][C]29[/C][C]33.0929423074063[/C][C]-4.09294230740629[/C][/ROW]
[ROW][C]97[/C][C]45[/C][C]32.4358809248449[/C][C]12.5641190751551[/C][/ROW]
[ROW][C]98[/C][C]37[/C][C]33.3307031253674[/C][C]3.66929687463259[/C][/ROW]
[ROW][C]99[/C][C]33[/C][C]26.6609647739393[/C][C]6.33903522606074[/C][/ROW]
[ROW][C]100[/C][C]33[/C][C]34.4006700749582[/C][C]-1.40067007495825[/C][/ROW]
[ROW][C]101[/C][C]25[/C][C]21.2120972866772[/C][C]3.78790271332284[/C][/ROW]
[ROW][C]102[/C][C]32[/C][C]28.9469378497011[/C][C]3.05306215029894[/C][/ROW]
[ROW][C]103[/C][C]29[/C][C]30.598884845541[/C][C]-1.59888484554103[/C][/ROW]
[ROW][C]104[/C][C]28[/C][C]29.8444336306254[/C][C]-1.84443363062539[/C][/ROW]
[ROW][C]105[/C][C]28[/C][C]34.258529558533[/C][C]-6.25852955853295[/C][/ROW]
[ROW][C]106[/C][C]31[/C][C]40.8644685179402[/C][C]-9.86446851794018[/C][/ROW]
[ROW][C]107[/C][C]52[/C][C]37.8770020796849[/C][C]14.1229979203151[/C][/ROW]
[ROW][C]108[/C][C]21[/C][C]18.187757916741[/C][C]2.81224208325896[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]40.1628844682344[/C][C]-16.1628844682344[/C][/ROW]
[ROW][C]110[/C][C]41[/C][C]35.1069652199716[/C][C]5.89303478002839[/C][/ROW]
[ROW][C]111[/C][C]33[/C][C]36.8749161536415[/C][C]-3.87491615364154[/C][/ROW]
[ROW][C]112[/C][C]32[/C][C]22.2765820665188[/C][C]9.72341793348123[/C][/ROW]
[ROW][C]113[/C][C]19[/C][C]14.4748914270741[/C][C]4.52510857292588[/C][/ROW]
[ROW][C]114[/C][C]20[/C][C]25.1377147417462[/C][C]-5.1377147417462[/C][/ROW]
[ROW][C]115[/C][C]31[/C][C]27.4039234743042[/C][C]3.59607652569581[/C][/ROW]
[ROW][C]116[/C][C]31[/C][C]32.2120364897185[/C][C]-1.21203648971848[/C][/ROW]
[ROW][C]117[/C][C]32[/C][C]38.9520873528784[/C][C]-6.95208735287839[/C][/ROW]
[ROW][C]118[/C][C]18[/C][C]25.8743566985699[/C][C]-7.87435669856986[/C][/ROW]
[ROW][C]119[/C][C]23[/C][C]31.1066508922514[/C][C]-8.10665089225144[/C][/ROW]
[ROW][C]120[/C][C]17[/C][C]19.0169386320757[/C][C]-2.01693863207567[/C][/ROW]
[ROW][C]121[/C][C]20[/C][C]22.2787800589825[/C][C]-2.2787800589825[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]13.8437580478187[/C][C]-1.84375804781869[/C][/ROW]
[ROW][C]123[/C][C]17[/C][C]19.7162308038572[/C][C]-2.71623080385717[/C][/ROW]
[ROW][C]124[/C][C]30[/C][C]31.7386938209795[/C][C]-1.7386938209795[/C][/ROW]
[ROW][C]125[/C][C]31[/C][C]33.4045524629885[/C][C]-2.40455246298852[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]16.9885148123057[/C][C]-6.98851481230573[/C][/ROW]
[ROW][C]127[/C][C]13[/C][C]19.2827028400842[/C][C]-6.2827028400842[/C][/ROW]
[ROW][C]128[/C][C]22[/C][C]23.6065452527266[/C][C]-1.60654525272658[/C][/ROW]
[ROW][C]129[/C][C]42[/C][C]39.4142359629585[/C][C]2.58576403704148[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]10.4504583363095[/C][C]-9.45045833630951[/C][/ROW]
[ROW][C]131[/C][C]9[/C][C]11.6971449838239[/C][C]-2.69714498382388[/C][/ROW]
[ROW][C]132[/C][C]32[/C][C]28.7575852908685[/C][C]3.24241470913152[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]15.5093740219651[/C][C]-4.50937402196508[/C][/ROW]
[ROW][C]134[/C][C]25[/C][C]18.9419318548539[/C][C]6.05806814514611[/C][/ROW]
[ROW][C]135[/C][C]36[/C][C]23.458970620465[/C][C]12.541029379535[/C][/ROW]
[ROW][C]136[/C][C]31[/C][C]36.039528956769[/C][C]-5.03952895676896[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]9.70703772858626[/C][C]-9.70703772858626[/C][/ROW]
[ROW][C]138[/C][C]24[/C][C]29.0208627139297[/C][C]-5.02086271392966[/C][/ROW]
[ROW][C]139[/C][C]13[/C][C]13.8759344068124[/C][C]-0.875934406812368[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]11.7197492270755[/C][C]-3.71974922707551[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]12.5565293419386[/C][C]0.443470658061445[/C][/ROW]
[ROW][C]142[/C][C]19[/C][C]24.4779677288784[/C][C]-5.47796772887844[/C][/ROW]
[ROW][C]143[/C][C]18[/C][C]14.6124398002901[/C][C]3.38756019970986[/C][/ROW]
[ROW][C]144[/C][C]33[/C][C]32.4764152120176[/C][C]0.523584787982354[/C][/ROW]
[ROW][C]145[/C][C]40[/C][C]31.29525648159[/C][C]8.70474351841[/C][/ROW]
[ROW][C]146[/C][C]22[/C][C]17.5388476697542[/C][C]4.46115233024581[/C][/ROW]
[ROW][C]147[/C][C]38[/C][C]25.1539509135682[/C][C]12.8460490864318[/C][/ROW]
[ROW][C]148[/C][C]24[/C][C]20.4874412765455[/C][C]3.51255872345448[/C][/ROW]
[ROW][C]149[/C][C]8[/C][C]11.863898094724[/C][C]-3.86389809472403[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]26.1517591076165[/C][C]8.84824089238352[/C][/ROW]
[ROW][C]151[/C][C]43[/C][C]36.3606637404394[/C][C]6.6393362595606[/C][/ROW]
[ROW][C]152[/C][C]43[/C][C]25.4728293653007[/C][C]17.5271706346993[/C][/ROW]
[ROW][C]153[/C][C]14[/C][C]19.7411034584183[/C][C]-5.74110345841826[/C][/ROW]
[ROW][C]154[/C][C]41[/C][C]37.9237187822089[/C][C]3.07628121779109[/C][/ROW]
[ROW][C]155[/C][C]38[/C][C]39.6482986999512[/C][C]-1.64829869995119[/C][/ROW]
[ROW][C]156[/C][C]45[/C][C]51.6425026737159[/C][C]-6.64250267371589[/C][/ROW]
[ROW][C]157[/C][C]31[/C][C]25.5845604850014[/C][C]5.41543951499858[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]16.9002643800539[/C][C]-3.90026438005394[/C][/ROW]
[ROW][C]159[/C][C]28[/C][C]19.3270535171062[/C][C]8.67294648289385[/C][/ROW]
[ROW][C]160[/C][C]31[/C][C]32.8873227252432[/C][C]-1.88732272524318[/C][/ROW]
[ROW][C]161[/C][C]40[/C][C]39.1769465616111[/C][C]0.823053438388909[/C][/ROW]
[ROW][C]162[/C][C]30[/C][C]21.9647518666238[/C][C]8.03524813337615[/C][/ROW]
[ROW][C]163[/C][C]16[/C][C]21.190004871942[/C][C]-5.19000487194204[/C][/ROW]
[ROW][C]164[/C][C]37[/C][C]38.2208179485666[/C][C]-1.22081794856656[/C][/ROW]
[ROW][C]165[/C][C]30[/C][C]20.7579576107005[/C][C]9.24204238929947[/C][/ROW]
[ROW][C]166[/C][C]35[/C][C]32.634616130736[/C][C]2.36538386926397[/C][/ROW]
[ROW][C]167[/C][C]32[/C][C]25.8377930801084[/C][C]6.16220691989159[/C][/ROW]
[ROW][C]168[/C][C]27[/C][C]22.1579307224075[/C][C]4.84206927759245[/C][/ROW]
[ROW][C]169[/C][C]20[/C][C]22.113979002247[/C][C]-2.11397900224703[/C][/ROW]
[ROW][C]170[/C][C]18[/C][C]29.6734749901571[/C][C]-11.6734749901571[/C][/ROW]
[ROW][C]171[/C][C]31[/C][C]32.4256493031291[/C][C]-1.42564930312908[/C][/ROW]
[ROW][C]172[/C][C]31[/C][C]32.7328199726152[/C][C]-1.73281997261517[/C][/ROW]
[ROW][C]173[/C][C]21[/C][C]21.6651236992755[/C][C]-0.665123699275478[/C][/ROW]
[ROW][C]174[/C][C]39[/C][C]37.5024602927507[/C][C]1.49753970724929[/C][/ROW]
[ROW][C]175[/C][C]41[/C][C]35.8643748691802[/C][C]5.13562513081979[/C][/ROW]
[ROW][C]176[/C][C]13[/C][C]17.0385118671654[/C][C]-4.03851186716543[/C][/ROW]
[ROW][C]177[/C][C]32[/C][C]33.2080738372757[/C][C]-1.20807383727575[/C][/ROW]
[ROW][C]178[/C][C]18[/C][C]16.8325836235825[/C][C]1.16741637641747[/C][/ROW]
[ROW][C]179[/C][C]39[/C][C]39.296305806672[/C][C]-0.29630580667201[/C][/ROW]
[ROW][C]180[/C][C]14[/C][C]21.0032320805885[/C][C]-7.00323208058854[/C][/ROW]
[ROW][C]181[/C][C]7[/C][C]16.6298106849437[/C][C]-9.62981068494369[/C][/ROW]
[ROW][C]182[/C][C]17[/C][C]21.3043461408259[/C][C]-4.30434614082591[/C][/ROW]
[ROW][C]183[/C][C]0[/C][C]10.1298846102113[/C][C]-10.1298846102113[/C][/ROW]
[ROW][C]184[/C][C]30[/C][C]24.4467657414715[/C][C]5.55323425852849[/C][/ROW]
[ROW][C]185[/C][C]37[/C][C]34.1508957371941[/C][C]2.84910426280593[/C][/ROW]
[ROW][C]186[/C][C]0[/C][C]9.58002111735156[/C][C]-9.58002111735156[/C][/ROW]
[ROW][C]187[/C][C]5[/C][C]11.2148779695407[/C][C]-6.21487796954073[/C][/ROW]
[ROW][C]188[/C][C]1[/C][C]9.83309226155184[/C][C]-8.83309226155184[/C][/ROW]
[ROW][C]189[/C][C]16[/C][C]19.6044704339389[/C][C]-3.60447043393892[/C][/ROW]
[ROW][C]190[/C][C]32[/C][C]20.1907201076615[/C][C]11.8092798923385[/C][/ROW]
[ROW][C]191[/C][C]24[/C][C]25.4610948387214[/C][C]-1.46109483872137[/C][/ROW]
[ROW][C]192[/C][C]17[/C][C]14.8401730152222[/C][C]2.15982698477784[/C][/ROW]
[ROW][C]193[/C][C]11[/C][C]17.9780490228606[/C][C]-6.97804902286056[/C][/ROW]
[ROW][C]194[/C][C]24[/C][C]17.1557704697886[/C][C]6.84422953021139[/C][/ROW]
[ROW][C]195[/C][C]22[/C][C]23.1241069467842[/C][C]-1.12410694678418[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]20.0211813547829[/C][C]-8.02118135478293[/C][/ROW]
[ROW][C]197[/C][C]19[/C][C]13.7442120343681[/C][C]5.25578796563189[/C][/ROW]
[ROW][C]198[/C][C]13[/C][C]17.1063314649362[/C][C]-4.10633146493622[/C][/ROW]
[ROW][C]199[/C][C]17[/C][C]18.0564755249746[/C][C]-1.05647552497463[/C][/ROW]
[ROW][C]200[/C][C]15[/C][C]17.1454804452542[/C][C]-2.14548044525419[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]17.0006699196795[/C][C]-1.00066991967947[/C][/ROW]
[ROW][C]202[/C][C]24[/C][C]13.5866340503383[/C][C]10.4133659496617[/C][/ROW]
[ROW][C]203[/C][C]15[/C][C]20.6383085422541[/C][C]-5.6383085422541[/C][/ROW]
[ROW][C]204[/C][C]17[/C][C]19.4701699051171[/C][C]-2.47016990511711[/C][/ROW]
[ROW][C]205[/C][C]18[/C][C]15.8554026994908[/C][C]2.14459730050918[/C][/ROW]
[ROW][C]206[/C][C]20[/C][C]13.3125622649671[/C][C]6.68743773503292[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]17.5310343107644[/C][C]-1.53103431076439[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]21.4008260716442[/C][C]-5.40082607164419[/C][/ROW]
[ROW][C]209[/C][C]18[/C][C]19.3796203397457[/C][C]-1.37962033974574[/C][/ROW]
[ROW][C]210[/C][C]22[/C][C]19.3344648905991[/C][C]2.66553510940091[/C][/ROW]
[ROW][C]211[/C][C]8[/C][C]16.6668894655039[/C][C]-8.66688946550393[/C][/ROW]
[ROW][C]212[/C][C]17[/C][C]20.2471263225322[/C][C]-3.24712632253219[/C][/ROW]
[ROW][C]213[/C][C]18[/C][C]15.264948122431[/C][C]2.73505187756897[/C][/ROW]
[ROW][C]214[/C][C]16[/C][C]17.7237644839339[/C][C]-1.72376448393389[/C][/ROW]
[ROW][C]215[/C][C]23[/C][C]13.333834193145[/C][C]9.66616580685497[/C][/ROW]
[ROW][C]216[/C][C]22[/C][C]12.7508657157883[/C][C]9.24913428421169[/C][/ROW]
[ROW][C]217[/C][C]13[/C][C]14.9569847140816[/C][C]-1.95698471408162[/C][/ROW]
[ROW][C]218[/C][C]13[/C][C]15.5099005603233[/C][C]-2.50990056032331[/C][/ROW]
[ROW][C]219[/C][C]16[/C][C]15.4262627467878[/C][C]0.573737253212235[/C][/ROW]
[ROW][C]220[/C][C]16[/C][C]15.2362464505906[/C][C]0.763753549409396[/C][/ROW]
[ROW][C]221[/C][C]20[/C][C]16.3119229410078[/C][C]3.68807705899221[/C][/ROW]
[ROW][C]222[/C][C]22[/C][C]20.860424261975[/C][C]1.13957573802499[/C][/ROW]
[ROW][C]223[/C][C]17[/C][C]18.7195695508086[/C][C]-1.71956955080857[/C][/ROW]
[ROW][C]224[/C][C]18[/C][C]17.2523555662761[/C][C]0.747644433723863[/C][/ROW]
[ROW][C]225[/C][C]17[/C][C]11.433391052665[/C][C]5.56660894733496[/C][/ROW]
[ROW][C]226[/C][C]12[/C][C]19.5239690575996[/C][C]-7.5239690575996[/C][/ROW]
[ROW][C]227[/C][C]7[/C][C]17.1771228929633[/C][C]-10.1771228929633[/C][/ROW]
[ROW][C]228[/C][C]17[/C][C]16.0788292393736[/C][C]0.921170760626416[/C][/ROW]
[ROW][C]229[/C][C]14[/C][C]15.674178903663[/C][C]-1.67417890366297[/C][/ROW]
[ROW][C]230[/C][C]23[/C][C]19.8640404947062[/C][C]3.13595950529376[/C][/ROW]
[ROW][C]231[/C][C]17[/C][C]10.8774595642933[/C][C]6.12254043570671[/C][/ROW]
[ROW][C]232[/C][C]14[/C][C]18.2529202970035[/C][C]-4.25292029700355[/C][/ROW]
[ROW][C]233[/C][C]15[/C][C]17.754265624617[/C][C]-2.75426562461698[/C][/ROW]
[ROW][C]234[/C][C]17[/C][C]14.463849701518[/C][C]2.53615029848199[/C][/ROW]
[ROW][C]235[/C][C]21[/C][C]16.2536121687669[/C][C]4.74638783123308[/C][/ROW]
[ROW][C]236[/C][C]18[/C][C]17.7416455000142[/C][C]0.258354499985803[/C][/ROW]
[ROW][C]237[/C][C]18[/C][C]19.9634241254969[/C][C]-1.96342412549693[/C][/ROW]
[ROW][C]238[/C][C]17[/C][C]13.5649349811468[/C][C]3.43506501885323[/C][/ROW]
[ROW][C]239[/C][C]17[/C][C]15.74476143527[/C][C]1.25523856473005[/C][/ROW]
[ROW][C]240[/C][C]16[/C][C]12.8038609176793[/C][C]3.19613908232067[/C][/ROW]
[ROW][C]241[/C][C]15[/C][C]16.9406755995163[/C][C]-1.94067559951626[/C][/ROW]
[ROW][C]242[/C][C]21[/C][C]22.6991738025811[/C][C]-1.6991738025811[/C][/ROW]
[ROW][C]243[/C][C]16[/C][C]16.4065144692127[/C][C]-0.406514469212711[/C][/ROW]
[ROW][C]244[/C][C]14[/C][C]14.8283963989754[/C][C]-0.828396398975437[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]16.0774057132511[/C][C]-1.07740571325107[/C][/ROW]
[ROW][C]246[/C][C]17[/C][C]17.3806788929561[/C][C]-0.380678892956067[/C][/ROW]
[ROW][C]247[/C][C]15[/C][C]20.9006657131072[/C][C]-5.90066571310718[/C][/ROW]
[ROW][C]248[/C][C]15[/C][C]16.2349204173196[/C][C]-1.23492041731958[/C][/ROW]
[ROW][C]249[/C][C]10[/C][C]16.8335093095881[/C][C]-6.83350930958805[/C][/ROW]
[ROW][C]250[/C][C]6[/C][C]13.832026060563[/C][C]-7.83202606056302[/C][/ROW]
[ROW][C]251[/C][C]22[/C][C]21.5066002483639[/C][C]0.493399751636145[/C][/ROW]
[ROW][C]252[/C][C]21[/C][C]20.6624153861494[/C][C]0.33758461385063[/C][/ROW]
[ROW][C]253[/C][C]1[/C][C]11.344302800547[/C][C]-10.344302800547[/C][/ROW]
[ROW][C]254[/C][C]18[/C][C]16.4875191417692[/C][C]1.51248085823083[/C][/ROW]
[ROW][C]255[/C][C]17[/C][C]14.2867221946907[/C][C]2.71327780530931[/C][/ROW]
[ROW][C]256[/C][C]4[/C][C]10.545875940893[/C][C]-6.54587594089304[/C][/ROW]
[ROW][C]257[/C][C]10[/C][C]14.0853986904618[/C][C]-4.08539869046177[/C][/ROW]
[ROW][C]258[/C][C]16[/C][C]17.7052696561044[/C][C]-1.70526965610438[/C][/ROW]
[ROW][C]259[/C][C]16[/C][C]15.1030586886674[/C][C]0.896941311332567[/C][/ROW]
[ROW][C]260[/C][C]9[/C][C]13.9089414102042[/C][C]-4.90894141020419[/C][/ROW]
[ROW][C]261[/C][C]16[/C][C]10.6839785698961[/C][C]5.31602143010387[/C][/ROW]
[ROW][C]262[/C][C]17[/C][C]19.5634680375674[/C][C]-2.56346803756736[/C][/ROW]
[ROW][C]263[/C][C]7[/C][C]11.5280365404273[/C][C]-4.52803654042729[/C][/ROW]
[ROW][C]264[/C][C]15[/C][C]16.3092927141946[/C][C]-1.30929271419457[/C][/ROW]
[ROW][C]265[/C][C]14[/C][C]15.2498167629926[/C][C]-1.24981676299256[/C][/ROW]
[ROW][C]266[/C][C]14[/C][C]17.8245652207641[/C][C]-3.82456522076415[/C][/ROW]
[ROW][C]267[/C][C]18[/C][C]16.4946801696627[/C][C]1.50531983033728[/C][/ROW]
[ROW][C]268[/C][C]12[/C][C]15.871108046622[/C][C]-3.87110804662202[/C][/ROW]
[ROW][C]269[/C][C]16[/C][C]19.3446718044629[/C][C]-3.34467180446293[/C][/ROW]
[ROW][C]270[/C][C]21[/C][C]18.522173107346[/C][C]2.477826892654[/C][/ROW]
[ROW][C]271[/C][C]19[/C][C]16.9410206502696[/C][C]2.05897934973043[/C][/ROW]
[ROW][C]272[/C][C]16[/C][C]16.2867176483343[/C][C]-0.286717648334337[/C][/ROW]
[ROW][C]273[/C][C]1[/C][C]11.0765901975638[/C][C]-10.0765901975638[/C][/ROW]
[ROW][C]274[/C][C]16[/C][C]20.4270835000218[/C][C]-4.4270835000218[/C][/ROW]
[ROW][C]275[/C][C]10[/C][C]15.6637491182031[/C][C]-5.66374911820309[/C][/ROW]
[ROW][C]276[/C][C]19[/C][C]17.2676503629039[/C][C]1.73234963709614[/C][/ROW]
[ROW][C]277[/C][C]12[/C][C]14.4445464331769[/C][C]-2.44454643317694[/C][/ROW]
[ROW][C]278[/C][C]2[/C][C]11.2979788093154[/C][C]-9.29797880931545[/C][/ROW]
[ROW][C]279[/C][C]14[/C][C]17.4084777102958[/C][C]-3.40847771029584[/C][/ROW]
[ROW][C]280[/C][C]17[/C][C]15.1635536611529[/C][C]1.83644633884712[/C][/ROW]
[ROW][C]281[/C][C]19[/C][C]16.6935093568477[/C][C]2.30649064315233[/C][/ROW]
[ROW][C]282[/C][C]14[/C][C]20.5647114844965[/C][C]-6.56471148449646[/C][/ROW]
[ROW][C]283[/C][C]11[/C][C]16.3710980327988[/C][C]-5.37109803279881[/C][/ROW]
[ROW][C]284[/C][C]4[/C][C]11.6755465550012[/C][C]-7.67554655500124[/C][/ROW]
[ROW][C]285[/C][C]16[/C][C]14.1459680852516[/C][C]1.85403191474841[/C][/ROW]
[ROW][C]286[/C][C]20[/C][C]15.9927717465461[/C][C]4.00722825345387[/C][/ROW]
[ROW][C]287[/C][C]12[/C][C]13.3837991965567[/C][C]-1.38379919655672[/C][/ROW]
[ROW][C]288[/C][C]15[/C][C]16.8356768794819[/C][C]-1.8356768794819[/C][/ROW]
[ROW][C]289[/C][C]16[/C][C]14.2913836383497[/C][C]1.7086163616503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156209&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156209&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13033.8806340232156-3.88063402321564
22825.33736376883942.66263623116056
33826.608705844331311.3912941556687
43031.4240476417131-1.42404764171311
52222.367159378076-0.367159378076043
62616.46941791204669.53058208795337
72539.6784247930137-14.6784247930137
81812.62008594267095.37991405732907
91117.0474437196571-6.04744371965708
102625.00881496013580.991185039864188
112525.6601374216322-0.660137421632212
123829.86887024824458.13112975175547
134430.989435073444413.0105649265556
143021.52493161559718.47506838440291
154033.66896607749846.33103392250165
163426.32423167902617.6757683209739
174742.64859770738834.35140229261166
183037.7669829102495-7.76698291024947
193131.811168744328-0.811168744327991
202327.9059741284736-4.90597412847358
213636.5154795773664-0.515479577366392
223633.94323825109412.05676174890593
233033.6915771797678-3.69157717976784
242524.68784243480140.312157565198587
253929.38106987482719.61893012517285
263431.05037155813562.94962844186442
273126.84770157384534.15229842615468
283129.8296887139531.17031128604701
293333.9521353435581-0.952135343558111
302519.95837736400075.04162263599929
313333.4922130575869-0.492213057586901
323533.52690757446971.47309242553028
334223.879452458304718.1205475416953
344338.1562635838584.84373641614198
353020.94534662875369.05465337124637
363334.8125559602428-1.81255596024277
371319.0005586183403-6.00055861834029
383226.64127999928215.3587200007179
393635.44457165056970.555428349430293
4009.20156996542681-9.20156996542681
412825.79778514769082.20221485230924
421419.6364456369621-5.63644563696206
431718.8843796381135-1.88437963811353
443233.1555822694517-1.15558226945166
453030.3285399129726-0.328539912972572
463523.416788525245711.5832114747543
472022.5944243814322-2.59442438143215
482825.82911232935032.17088767064973
492820.81838586179367.18161413820637
503918.205002090351220.7949979096488
513440.3517071100557-6.3517071100557
522616.69129139368669.30870860631345
533930.47571151716348.52428848283659
543938.87565570975690.124344290243091
553325.59309086055277.40690913944732
562828.044248890679-0.044248890678992
57410.2221727375221-6.22217273752205
583940.0786964317668-1.07869643176677
591815.70571541178982.29428458821017
601417.822292888416-3.82229288841596
612928.45685409149170.543145908508278
624439.69573958892284.30426041107719
632140.8838261180098-19.8838261180098
641617.5094753013081-1.50947530130813
652835.7708675593069-7.77086755930691
663536.3117595759775-1.31175957597747
672830.0118703904833-2.01187039048334
683842.7014897849361-4.70148978493614
692336.3004329705972-13.3004329705972
703622.49880948721613.501190512784
713228.06168148342453.93831851657547
722926.63613845705982.36386154294021
732527.6145161217314-2.61451612173145
742728.398927019686-1.39892701968595
753628.339844607397.66015539260995
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812826.03434991259061.96565008740944
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842832.9922999814398-4.99229998143976
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863028.64995822935841.35004177064158
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983733.33070312536743.66929687463259
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1003334.4006700749582-1.40067007495825
1012521.21209728667723.78790271332284
1023228.94693784970113.05306215029894
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1042829.8444336306254-1.84443363062539
1052834.258529558533-6.25852955853295
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1092440.1628844682344-16.1628844682344
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1123222.27658206651889.72341793348123
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1253133.4045524629885-2.40455246298852
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1271319.2827028400842-6.2827028400842
1282223.6065452527266-1.60654525272658
1294239.41423596295852.58576403704148
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131911.6971449838239-2.69714498382388
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1331115.5093740219651-4.50937402196508
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1353623.45897062046512.541029379535
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1382429.0208627139297-5.02086271392966
1391313.8759344068124-0.875934406812368
140811.7197492270755-3.71974922707551
1411312.55652934193860.443470658061445
1421924.4779677288784-5.47796772887844
1431814.61243980029013.38756019970986
1443332.47641521201760.523584787982354
1454031.295256481598.70474351841
1462217.53884766975424.46115233024581
1473825.153950913568212.8460490864318
1482420.48744127654553.51255872345448
149811.863898094724-3.86389809472403
1503526.15175910761658.84824089238352
1514336.36066374043946.6393362595606
1524325.472829365300717.5271706346993
1531419.7411034584183-5.74110345841826
1544137.92371878220893.07628121779109
1553839.6482986999512-1.64829869995119
1564551.6425026737159-6.64250267371589
1573125.58456048500145.41543951499858
1581316.9002643800539-3.90026438005394
1592819.32705351710628.67294648289385
1603132.8873227252432-1.88732272524318
1614039.17694656161110.823053438388909
1623021.96475186662388.03524813337615
1631621.190004871942-5.19000487194204
1643738.2208179485666-1.22081794856656
1653020.75795761070059.24204238929947
1663532.6346161307362.36538386926397
1673225.83779308010846.16220691989159
1682722.15793072240754.84206927759245
1692022.113979002247-2.11397900224703
1701829.6734749901571-11.6734749901571
1713132.4256493031291-1.42564930312908
1723132.7328199726152-1.73281997261517
1732121.6651236992755-0.665123699275478
1743937.50246029275071.49753970724929
1754135.86437486918025.13562513081979
1761317.0385118671654-4.03851186716543
1773233.2080738372757-1.20807383727575
1781816.83258362358251.16741637641747
1793939.296305806672-0.29630580667201
1801421.0032320805885-7.00323208058854
181716.6298106849437-9.62981068494369
1821721.3043461408259-4.30434614082591
183010.1298846102113-10.1298846102113
1843024.44676574147155.55323425852849
1853734.15089573719412.84910426280593
18609.58002111735156-9.58002111735156
187511.2148779695407-6.21487796954073
18819.83309226155184-8.83309226155184
1891619.6044704339389-3.60447043393892
1903220.190720107661511.8092798923385
1912425.4610948387214-1.46109483872137
1921714.84017301522222.15982698477784
1931117.9780490228606-6.97804902286056
1942417.15577046978866.84422953021139
1952223.1241069467842-1.12410694678418
1961220.0211813547829-8.02118135478293
1971913.74421203436815.25578796563189
1981317.1063314649362-4.10633146493622
1991718.0564755249746-1.05647552497463
2001517.1454804452542-2.14548044525419
2011617.0006699196795-1.00066991967947
2022413.586634050338310.4133659496617
2031520.6383085422541-5.6383085422541
2041719.4701699051171-2.47016990511711
2051815.85540269949082.14459730050918
2062013.31256226496716.68743773503292
2071617.5310343107644-1.53103431076439
2081621.4008260716442-5.40082607164419
2091819.3796203397457-1.37962033974574
2102219.33446489059912.66553510940091
211816.6668894655039-8.66688946550393
2121720.2471263225322-3.24712632253219
2131815.2649481224312.73505187756897
2141617.7237644839339-1.72376448393389
2152313.3338341931459.66616580685497
2162212.75086571578839.24913428421169
2171314.9569847140816-1.95698471408162
2181315.5099005603233-2.50990056032331
2191615.42626274678780.573737253212235
2201615.23624645059060.763753549409396
2212016.31192294100783.68807705899221
2222220.8604242619751.13957573802499
2231718.7195695508086-1.71956955080857
2241817.25235556627610.747644433723863
2251711.4333910526655.56660894733496
2261219.5239690575996-7.5239690575996
227717.1771228929633-10.1771228929633
2281716.07882923937360.921170760626416
2291415.674178903663-1.67417890366297
2302319.86404049470623.13595950529376
2311710.87745956429336.12254043570671
2321418.2529202970035-4.25292029700355
2331517.754265624617-2.75426562461698
2341714.4638497015182.53615029848199
2352116.25361216876694.74638783123308
2361817.74164550001420.258354499985803
2371819.9634241254969-1.96342412549693
2381713.56493498114683.43506501885323
2391715.744761435271.25523856473005
2401612.80386091767933.19613908232067
2411516.9406755995163-1.94067559951626
2422122.6991738025811-1.6991738025811
2431616.4065144692127-0.406514469212711
2441414.8283963989754-0.828396398975437
2451516.0774057132511-1.07740571325107
2461717.3806788929561-0.380678892956067
2471520.9006657131072-5.90066571310718
2481516.2349204173196-1.23492041731958
2491016.8335093095881-6.83350930958805
250613.832026060563-7.83202606056302
2512221.50660024836390.493399751636145
2522120.66241538614940.33758461385063
253111.344302800547-10.344302800547
2541816.48751914176921.51248085823083
2551714.28672219469072.71327780530931
256410.545875940893-6.54587594089304
2571014.0853986904618-4.08539869046177
2581617.7052696561044-1.70526965610438
2591615.10305868866740.896941311332567
260913.9089414102042-4.90894141020419
2611610.68397856989615.31602143010387
2621719.5634680375674-2.56346803756736
263711.5280365404273-4.52803654042729
2641516.3092927141946-1.30929271419457
2651415.2498167629926-1.24981676299256
2661417.8245652207641-3.82456522076415
2671816.49468016966271.50531983033728
2681215.871108046622-3.87110804662202
2691619.3446718044629-3.34467180446293
2702118.5221731073462.477826892654
2711916.94102065026962.05897934973043
2721616.2867176483343-0.286717648334337
273111.0765901975638-10.0765901975638
2741620.4270835000218-4.4270835000218
2751015.6637491182031-5.66374911820309
2761917.26765036290391.73234963709614
2771214.4445464331769-2.44454643317694
278211.2979788093154-9.29797880931545
2791417.4084777102958-3.40847771029584
2801715.16355366115291.83644633884712
2811916.69350935684772.30649064315233
2821420.5647114844965-6.56471148449646
2831116.3710980327988-5.37109803279881
284411.6755465550012-7.67554655500124
2851614.14596808525161.85403191474841
2862015.99277174654614.00722825345387
2871213.3837991965567-1.38379919655672
2881516.8356768794819-1.8356768794819
2891614.29138363834971.7086163616503






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7567848715763310.4864302568473380.243215128423669
140.6236468735609590.7527062528780830.376353126439041
150.8757902830505810.2484194338988380.124209716949419
160.8686232620960760.2627534758078490.131376737903924
170.8471134891757370.3057730216485260.152886510824263
180.8811165513115130.2377668973769750.118883448688487
190.8345328362881910.3309343274236180.165467163711809
200.9343493380790030.1313013238419950.0656506619209973
210.9096921938520950.180615612295810.0903078061479052
220.8745464995604950.2509070008790090.125453500439505
230.8302614807808760.3394770384382480.169738519219124
240.7810447830213610.4379104339572780.218955216978639
250.8032665657285650.393466868542870.196733434271435
260.7502415910584360.4995168178831290.249758408941564
270.6975852340965980.6048295318068050.302414765903402
280.6400333330203340.7199333339593310.359966666979666
290.6061713429528240.7876573140943510.393828657047176
300.5439982943969290.9120034112061430.456001705603071
310.4827555280576360.9655110561152730.517244471942363
320.4212285579115370.8424571158230740.578771442088463
330.5991431217910580.8017137564178840.400856878208942
340.6016177188015850.7967645623968290.398382281198415
350.6138978012182930.7722043975634130.386102198781707
360.5631547253634570.8736905492730850.436845274636543
370.6951343434705890.6097313130588210.304865656529411
380.700365214379860.5992695712402810.299634785620141
390.6516896645139770.6966206709720450.348310335486023
400.8031390488296950.3937219023406110.196860951170305
410.766421524030970.4671569519380610.23357847596903
420.7809010594541210.4381978810917580.219098940545879
430.8173295407096370.3653409185807270.182670459290363
440.7979824492238120.4040351015523760.202017550776188
450.7709605889622820.4580788220754370.229039411037718
460.829011148842570.341977702314860.17098885115743
470.827275178202630.3454496435947410.17272482179737
480.7951911401988430.4096177196023150.204808859801158
490.7807032898693190.4385934202613620.219296710130681
500.9366156213429730.1267687573140530.0633843786570267
510.9322721450673410.1354557098653170.0677278549326587
520.934043501548080.1319129969038390.0659564984519196
530.9321829790819980.1356340418360040.0678170209180018
540.9240415811155750.151916837768850.0759584188844252
550.9210702870207820.1578594259584370.0789297129792184
560.9073633231490470.1852733537019060.0926366768509528
570.9303925577444920.1392148845110160.0696074422555082
580.9147625038143320.1704749923713360.0852374961856681
590.8983931834502320.2032136330995370.101606816549768
600.8939329815268190.2121340369463620.106067018473181
610.8756030856061310.2487938287877380.124396914393869
620.8569929453825550.286014109234890.143007054617445
630.9720073189085510.05598536218289820.0279926810914491
640.9663022973076020.06739540538479520.0336977026923976
650.9651295341703960.06974093165920770.0348704658296038
660.9607399204875350.078520159024930.039260079512465
670.9527487848679160.09450243026416770.0472512151320839
680.945684785320850.10863042935830.05431521467915
690.9814114482732460.03717710345350790.0185885517267539
700.9917350947828920.01652981043421530.00826490521710767
710.9906469428853220.01870611422935510.00935305711467757
720.9881735211284430.02365295774311420.0118264788715571
730.985680926668280.02863814666343970.0143190733317199
740.9830022111411340.03399557771773240.0169977888588662
750.9843318309664560.03133633806708740.0156681690335437
760.9836341807866680.03273163842666310.0163658192133315
770.9893023611644080.02139527767118410.0106976388355921
780.994121102297440.01175779540512020.00587889770256008
790.9944874175918810.01102516481623780.00551258240811892
800.9967104156278450.006579168744310590.00328958437215529
810.9957382331038420.008523533792315670.00426176689615783
820.9950279683521430.009944063295714490.00497203164785725
830.9943374468762230.01132510624755430.00566255312377717
840.9936866472083560.01262670558328740.00631335279164371
850.9945544118815560.01089117623688760.00544558811844381
860.9930292314427860.01394153711442710.00697076855721355
870.9938103874785210.01237922504295850.00618961252147925
880.9924170939631040.01516581207379160.00758290603689581
890.9911321576235730.0177356847528540.00886784237642698
900.9917188152011270.01656236959774680.00828118479887338
910.9897314900402610.02053701991947730.0102685099597386
920.995888753382740.008222493234519820.00411124661725991
930.9958943154941270.008211369011745760.00410568450587288
940.9946469735059560.01070605298808830.00535302649404414
950.9934712104211050.01305757915779050.00652878957889523
960.9931838172542340.01363236549153130.00681618274576567
970.9972129109556310.005574178088737590.00278708904436879
980.9968604589122070.006279082175585780.00313954108779289
990.9968766604018120.006246679196375970.00312333959818798
1000.9959520677693030.008095864461393740.00404793223069687
1010.9949466758371890.01010664832562110.00505332416281053
1020.9939630503422720.01207389931545670.00603694965772835
1030.992547739425640.01490452114871930.00745226057435967
1040.9908397407123090.0183205185753820.00916025928769101
1050.9904785332583460.01904293348330720.00952146674165359
1060.9928564298553650.01428714028926950.00714357014463475
1070.9979522295054670.004095540989064970.00204777049453248
1080.9974490114204320.005101977159136870.00255098857956844
1090.9994544831772980.001091033645403550.000545516822701774
1100.999463019723260.001073960553480030.000536980276740015
1110.999423353769580.001153292460840830.000576646230420415
1120.9996968715682960.0006062568634084070.000303128431704204
1130.9996143551912170.0007712896175653130.000385644808782656
1140.9996209273177390.0007581453645225560.000379072682261278
1150.9995229110861140.0009541778277725020.000477088913886251
1160.9993739180368260.001252163926348480.000626081963174241
1170.9993212958578610.001357408284278890.000678704142139447
1180.9995620642092780.0008758715814450320.000437935790722516
1190.9997321699442420.0005356601115154930.000267830055757746
1200.9996925017236980.0006149965526038230.000307498276301911
1210.9996307205029080.0007385589941840340.000369279497092017
1220.9996212025205960.000757594958806970.000378797479403485
1230.9996042970478660.0007914059042669890.000395702952133494
1240.9995124300572850.0009751398854291390.00048756994271457
1250.9994153398708510.001169320258298640.000584660129149321
1260.999566429480940.0008671410381201370.000433570519060069
1270.9996512485454720.0006975029090568620.000348751454528431
1280.9995728882721370.0008542234557261520.000427111727863076
1290.9994529503833050.001094099233389330.000547049616694664
1300.9997208578329950.0005582843340100990.00027914216700505
1310.9996733717607030.000653256478593340.00032662823929667
1320.9996387946732440.0007224106535121310.000361205326756065
1330.9996126678903110.0007746642193783080.000387332109689154
1340.9996177947153620.000764410569275620.00038220528463781
1350.9998416608228680.0003166783542649970.000158339177132498
1360.9998163920926950.0003672158146090720.000183607907304536
1370.9999023011713210.0001953976573575459.76988286787723e-05
1380.9999011030745060.0001977938509871579.88969254935787e-05
1390.999867548459930.0002649030801409060.000132451540070453
1400.9998453247747660.0003093504504687580.000154675225234379
1410.9998054259645670.0003891480708663350.000194574035433167
1420.9998066469692930.0003867060614146850.000193353030707342
1430.9997440469238770.0005119061522469290.000255953076123465
1440.9996477942458780.0007044115082445280.000352205754122264
1450.9997320373594950.000535925281009810.000267962640504905
1460.999665010908710.0006699781825794410.00033498909128972
1470.9998491840553250.0003016318893499070.000150815944674953
1480.9997976281981280.0004047436037442760.000202371801872138
1490.999762784024680.0004744319506394260.000237215975319713
1500.9998127519114730.0003744961770544410.000187248088527221
1510.9998496281403790.0003007437192409950.000150371859620497
1520.9999829926772243.4014645551914e-051.7007322775957e-05
1530.9999811744103023.76511793957875e-051.88255896978937e-05
1540.9999761216324834.77567350346735e-052.38783675173368e-05
1550.9999655443208356.8911358329629e-053.44556791648145e-05
1560.9999809209208223.81581583558423e-051.90790791779211e-05
1570.9999798894462314.02211075382895e-052.01105537691447e-05
1580.9999796214865144.07570269715487e-052.03785134857743e-05
1590.999987033548492.59329030194266e-051.29664515097133e-05
1600.9999813819863773.72360272454671e-051.86180136227335e-05
1610.9999729921806665.4015638668725e-052.70078193343625e-05
1620.9999727038359695.45923280621995e-052.72961640310998e-05
1630.9999687999268346.24001463317759e-053.12000731658879e-05
1640.9999548189343219.03621313583484e-054.51810656791742e-05
1650.9999808804269663.82391460686513e-051.91195730343256e-05
1660.9999727043727255.45912545506493e-052.72956272753246e-05
1670.9999704315708765.91368582483441e-052.9568429124172e-05
1680.9999672563941026.54872117957888e-053.27436058978944e-05
1690.9999545196670169.0960665968839e-054.54803329844195e-05
1700.9999888865125542.22269748914243e-051.11134874457121e-05
1710.9999835488443413.29023113177626e-051.64511556588813e-05
1720.9999761874881614.76250236781955e-052.38125118390978e-05
1730.9999669646997896.60706004216324e-053.30353002108162e-05
1740.9999540762358389.18475283239208e-054.59237641619604e-05
1750.9999618583026417.6283394718376e-053.8141697359188e-05
1760.9999522832432189.54335135633553e-054.77167567816776e-05
1770.9999307307703870.000138538459226256.92692296131252e-05
1780.9999051663702070.0001896672595851259.48336297925623e-05
1790.9998708367301670.0002583265396650930.000129163269832547
1800.9998929141558040.0002141716883916420.000107085844195821
1810.9999336728303870.0001326543392266756.63271696133375e-05
1820.9999115714095990.0001768571808030488.84285904015239e-05
1830.9999512826240859.74347518294739e-054.87173759147369e-05
1840.9999533219392269.33561215479259e-054.6678060773963e-05
1850.999969134888866.17302222793378e-053.08651111396689e-05
1860.9999839974737373.20050525262888e-051.60025262631444e-05
1870.9999824812308973.50375382058275e-051.75187691029137e-05
1880.9999914546579011.70906841983274e-058.54534209916369e-06
1890.9999879283017182.41433965648064e-051.20716982824032e-05
1900.9999987951328742.40973425147617e-061.20486712573808e-06
1910.9999982536945633.49261087496834e-061.74630543748417e-06
1920.9999976132731034.77345379457889e-062.38672689728944e-06
1930.9999979304533194.13909336158988e-062.06954668079494e-06
1940.9999969920814616.01583707898605e-063.00791853949303e-06
1950.999995911689258.17662149919978e-064.08831074959989e-06
1960.9999963851215047.22975699219249e-063.61487849609624e-06
1970.9999966539268746.69214625134389e-063.34607312567195e-06
1980.9999963136734097.37265318143208e-063.68632659071604e-06
1990.9999942416237251.15167525494674e-055.7583762747337e-06
2000.9999916871729961.66256540072256e-058.31282700361278e-06
2010.9999872727879682.54544240646483e-051.27272120323241e-05
2020.9999967704779176.45904416618809e-063.22952208309405e-06
2030.9999956600532788.67989344389898e-064.33994672194949e-06
2040.9999935153358061.29693283883626e-056.48466419418131e-06
2050.9999902094093631.9581181273283e-059.79059063664149e-06
2060.9999932791424821.34417150360065e-056.72085751800325e-06
2070.9999895249801682.09500396645265e-051.04750198322633e-05
2080.9999869641413452.60717173106756e-051.30358586553378e-05
2090.9999795723971044.08552057912006e-052.04276028956003e-05
2100.9999870827493742.5834501251098e-051.2917250625549e-05
2110.9999912566647491.74866705014802e-058.74333525074012e-06
2120.9999867711864872.64576270258745e-051.32288135129373e-05
2130.9999821140806563.57718386878796e-051.78859193439398e-05
2140.9999753879859884.92240280235389e-052.46120140117695e-05
2150.9999885204629952.29590740100148e-051.14795370050074e-05
2160.9999954229058839.15418823407444e-064.57709411703722e-06
2170.9999924752350971.50495298063533e-057.52476490317664e-06
2180.9999900078958771.9984208245834e-059.99210412291701e-06
2190.9999850113293872.99773412265254e-051.49886706132627e-05
2200.9999783785483764.32429032481563e-052.16214516240781e-05
2210.9999830896644043.38206711920138e-051.69103355960069e-05
2220.999972141397855.57172042993763e-052.78586021496882e-05
2230.9999606659769337.86680461336773e-053.93340230668386e-05
2240.9999402611420410.0001194777159172055.97388579586023e-05
2250.9999589163133888.21673732241896e-054.10836866120948e-05
2260.9999839420143313.21159713378011e-051.60579856689005e-05
2270.9999969073356726.18532865635873e-063.09266432817937e-06
2280.9999945968809751.08062380497388e-055.40311902486942e-06
2290.999991449521981.71009560404035e-058.55047802020173e-06
2300.9999874368343412.51263313188574e-051.25631656594287e-05
2310.9999920509865361.58980269284004e-057.94901346420021e-06
2320.9999896894752172.06210495651166e-051.03105247825583e-05
2330.9999834075094083.31849811842017e-051.65924905921008e-05
2340.9999743060610725.13878778567157e-052.56939389283578e-05
2350.9999722607306435.54785387130038e-052.77392693565019e-05
2360.9999683323050946.33353898110802e-053.16676949055401e-05
2370.9999451213492420.0001097573015153775.48786507576887e-05
2380.9999462559022110.0001074881955779385.37440977889689e-05
2390.9999071972192360.0001856055615279929.28027807639959e-05
2400.9999220791233010.0001558417533988057.79208766994027e-05
2410.9998775784350250.0002448431299492490.000122421564974624
2420.9998207748625870.0003584502748251940.000179225137412597
2430.9997473974847660.0005052050304675690.000252602515233785
2440.9996395477851280.0007209044297433850.000360452214871693
2450.9993942989138230.001211402172353610.000605701086176804
2460.9990547789904640.001890442019071410.000945221009535707
2470.9994718941909040.001056211618193010.000528105809096505
2480.9991513029946650.001697394010669820.000848697005334912
2490.9992746498868530.001450700226293530.000725350113146764
2500.9990685049863810.001862990027237850.000931495013618924
2510.9988330135254590.002333972949081850.00116698647454093
2520.9980275238397630.003944952320473950.00197247616023698
2530.998109286692380.003781426615239240.00189071330761962
2540.9968619778697090.006276044260582390.00313802213029119
2550.9969683909520840.006063218095831070.00303160904791553
2560.9951696027690580.009660794461884670.00483039723094233
2570.9922050529710280.01558989405794350.00779494702897173
2580.987455953872770.02508809225445930.0125440461272296
2590.9865265915001080.02694681699978450.0134734084998923
2600.9789572047593320.04208559048133640.0210427952406682
2610.9896734764859360.02065304702812720.0103265235140636
2620.9860814491059920.02783710178801580.0139185508940079
2630.9777470979482370.04450580410352520.0222529020517626
2640.9642090782360490.0715818435279020.035790921763951
2650.9509246057331590.09815078853368270.0490753942668414
2660.9262695232534660.1474609534930670.0737304767465335
2670.9172386939363040.1655226121273920.082761306063696
2680.8824979958778910.2350040082442190.117502004122109
2690.8518388781408440.2963222437183120.148161121859156
2700.939596313769270.120807372461460.0604036862307298
2710.9302323152710260.1395353694579480.0697676847289741
2720.8861444419858990.2277111160282010.113855558014101
2730.8708654103441840.2582691793116320.129134589655816
2740.8784428793790570.2431142412418870.121557120620943
2750.781433832386260.4371323352274810.21856616761374
2760.8258849100994450.3482301798011110.174115089900555

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.756784871576331 & 0.486430256847338 & 0.243215128423669 \tabularnewline
14 & 0.623646873560959 & 0.752706252878083 & 0.376353126439041 \tabularnewline
15 & 0.875790283050581 & 0.248419433898838 & 0.124209716949419 \tabularnewline
16 & 0.868623262096076 & 0.262753475807849 & 0.131376737903924 \tabularnewline
17 & 0.847113489175737 & 0.305773021648526 & 0.152886510824263 \tabularnewline
18 & 0.881116551311513 & 0.237766897376975 & 0.118883448688487 \tabularnewline
19 & 0.834532836288191 & 0.330934327423618 & 0.165467163711809 \tabularnewline
20 & 0.934349338079003 & 0.131301323841995 & 0.0656506619209973 \tabularnewline
21 & 0.909692193852095 & 0.18061561229581 & 0.0903078061479052 \tabularnewline
22 & 0.874546499560495 & 0.250907000879009 & 0.125453500439505 \tabularnewline
23 & 0.830261480780876 & 0.339477038438248 & 0.169738519219124 \tabularnewline
24 & 0.781044783021361 & 0.437910433957278 & 0.218955216978639 \tabularnewline
25 & 0.803266565728565 & 0.39346686854287 & 0.196733434271435 \tabularnewline
26 & 0.750241591058436 & 0.499516817883129 & 0.249758408941564 \tabularnewline
27 & 0.697585234096598 & 0.604829531806805 & 0.302414765903402 \tabularnewline
28 & 0.640033333020334 & 0.719933333959331 & 0.359966666979666 \tabularnewline
29 & 0.606171342952824 & 0.787657314094351 & 0.393828657047176 \tabularnewline
30 & 0.543998294396929 & 0.912003411206143 & 0.456001705603071 \tabularnewline
31 & 0.482755528057636 & 0.965511056115273 & 0.517244471942363 \tabularnewline
32 & 0.421228557911537 & 0.842457115823074 & 0.578771442088463 \tabularnewline
33 & 0.599143121791058 & 0.801713756417884 & 0.400856878208942 \tabularnewline
34 & 0.601617718801585 & 0.796764562396829 & 0.398382281198415 \tabularnewline
35 & 0.613897801218293 & 0.772204397563413 & 0.386102198781707 \tabularnewline
36 & 0.563154725363457 & 0.873690549273085 & 0.436845274636543 \tabularnewline
37 & 0.695134343470589 & 0.609731313058821 & 0.304865656529411 \tabularnewline
38 & 0.70036521437986 & 0.599269571240281 & 0.299634785620141 \tabularnewline
39 & 0.651689664513977 & 0.696620670972045 & 0.348310335486023 \tabularnewline
40 & 0.803139048829695 & 0.393721902340611 & 0.196860951170305 \tabularnewline
41 & 0.76642152403097 & 0.467156951938061 & 0.23357847596903 \tabularnewline
42 & 0.780901059454121 & 0.438197881091758 & 0.219098940545879 \tabularnewline
43 & 0.817329540709637 & 0.365340918580727 & 0.182670459290363 \tabularnewline
44 & 0.797982449223812 & 0.404035101552376 & 0.202017550776188 \tabularnewline
45 & 0.770960588962282 & 0.458078822075437 & 0.229039411037718 \tabularnewline
46 & 0.82901114884257 & 0.34197770231486 & 0.17098885115743 \tabularnewline
47 & 0.82727517820263 & 0.345449643594741 & 0.17272482179737 \tabularnewline
48 & 0.795191140198843 & 0.409617719602315 & 0.204808859801158 \tabularnewline
49 & 0.780703289869319 & 0.438593420261362 & 0.219296710130681 \tabularnewline
50 & 0.936615621342973 & 0.126768757314053 & 0.0633843786570267 \tabularnewline
51 & 0.932272145067341 & 0.135455709865317 & 0.0677278549326587 \tabularnewline
52 & 0.93404350154808 & 0.131912996903839 & 0.0659564984519196 \tabularnewline
53 & 0.932182979081998 & 0.135634041836004 & 0.0678170209180018 \tabularnewline
54 & 0.924041581115575 & 0.15191683776885 & 0.0759584188844252 \tabularnewline
55 & 0.921070287020782 & 0.157859425958437 & 0.0789297129792184 \tabularnewline
56 & 0.907363323149047 & 0.185273353701906 & 0.0926366768509528 \tabularnewline
57 & 0.930392557744492 & 0.139214884511016 & 0.0696074422555082 \tabularnewline
58 & 0.914762503814332 & 0.170474992371336 & 0.0852374961856681 \tabularnewline
59 & 0.898393183450232 & 0.203213633099537 & 0.101606816549768 \tabularnewline
60 & 0.893932981526819 & 0.212134036946362 & 0.106067018473181 \tabularnewline
61 & 0.875603085606131 & 0.248793828787738 & 0.124396914393869 \tabularnewline
62 & 0.856992945382555 & 0.28601410923489 & 0.143007054617445 \tabularnewline
63 & 0.972007318908551 & 0.0559853621828982 & 0.0279926810914491 \tabularnewline
64 & 0.966302297307602 & 0.0673954053847952 & 0.0336977026923976 \tabularnewline
65 & 0.965129534170396 & 0.0697409316592077 & 0.0348704658296038 \tabularnewline
66 & 0.960739920487535 & 0.07852015902493 & 0.039260079512465 \tabularnewline
67 & 0.952748784867916 & 0.0945024302641677 & 0.0472512151320839 \tabularnewline
68 & 0.94568478532085 & 0.1086304293583 & 0.05431521467915 \tabularnewline
69 & 0.981411448273246 & 0.0371771034535079 & 0.0185885517267539 \tabularnewline
70 & 0.991735094782892 & 0.0165298104342153 & 0.00826490521710767 \tabularnewline
71 & 0.990646942885322 & 0.0187061142293551 & 0.00935305711467757 \tabularnewline
72 & 0.988173521128443 & 0.0236529577431142 & 0.0118264788715571 \tabularnewline
73 & 0.98568092666828 & 0.0286381466634397 & 0.0143190733317199 \tabularnewline
74 & 0.983002211141134 & 0.0339955777177324 & 0.0169977888588662 \tabularnewline
75 & 0.984331830966456 & 0.0313363380670874 & 0.0156681690335437 \tabularnewline
76 & 0.983634180786668 & 0.0327316384266631 & 0.0163658192133315 \tabularnewline
77 & 0.989302361164408 & 0.0213952776711841 & 0.0106976388355921 \tabularnewline
78 & 0.99412110229744 & 0.0117577954051202 & 0.00587889770256008 \tabularnewline
79 & 0.994487417591881 & 0.0110251648162378 & 0.00551258240811892 \tabularnewline
80 & 0.996710415627845 & 0.00657916874431059 & 0.00328958437215529 \tabularnewline
81 & 0.995738233103842 & 0.00852353379231567 & 0.00426176689615783 \tabularnewline
82 & 0.995027968352143 & 0.00994406329571449 & 0.00497203164785725 \tabularnewline
83 & 0.994337446876223 & 0.0113251062475543 & 0.00566255312377717 \tabularnewline
84 & 0.993686647208356 & 0.0126267055832874 & 0.00631335279164371 \tabularnewline
85 & 0.994554411881556 & 0.0108911762368876 & 0.00544558811844381 \tabularnewline
86 & 0.993029231442786 & 0.0139415371144271 & 0.00697076855721355 \tabularnewline
87 & 0.993810387478521 & 0.0123792250429585 & 0.00618961252147925 \tabularnewline
88 & 0.992417093963104 & 0.0151658120737916 & 0.00758290603689581 \tabularnewline
89 & 0.991132157623573 & 0.017735684752854 & 0.00886784237642698 \tabularnewline
90 & 0.991718815201127 & 0.0165623695977468 & 0.00828118479887338 \tabularnewline
91 & 0.989731490040261 & 0.0205370199194773 & 0.0102685099597386 \tabularnewline
92 & 0.99588875338274 & 0.00822249323451982 & 0.00411124661725991 \tabularnewline
93 & 0.995894315494127 & 0.00821136901174576 & 0.00410568450587288 \tabularnewline
94 & 0.994646973505956 & 0.0107060529880883 & 0.00535302649404414 \tabularnewline
95 & 0.993471210421105 & 0.0130575791577905 & 0.00652878957889523 \tabularnewline
96 & 0.993183817254234 & 0.0136323654915313 & 0.00681618274576567 \tabularnewline
97 & 0.997212910955631 & 0.00557417808873759 & 0.00278708904436879 \tabularnewline
98 & 0.996860458912207 & 0.00627908217558578 & 0.00313954108779289 \tabularnewline
99 & 0.996876660401812 & 0.00624667919637597 & 0.00312333959818798 \tabularnewline
100 & 0.995952067769303 & 0.00809586446139374 & 0.00404793223069687 \tabularnewline
101 & 0.994946675837189 & 0.0101066483256211 & 0.00505332416281053 \tabularnewline
102 & 0.993963050342272 & 0.0120738993154567 & 0.00603694965772835 \tabularnewline
103 & 0.99254773942564 & 0.0149045211487193 & 0.00745226057435967 \tabularnewline
104 & 0.990839740712309 & 0.018320518575382 & 0.00916025928769101 \tabularnewline
105 & 0.990478533258346 & 0.0190429334833072 & 0.00952146674165359 \tabularnewline
106 & 0.992856429855365 & 0.0142871402892695 & 0.00714357014463475 \tabularnewline
107 & 0.997952229505467 & 0.00409554098906497 & 0.00204777049453248 \tabularnewline
108 & 0.997449011420432 & 0.00510197715913687 & 0.00255098857956844 \tabularnewline
109 & 0.999454483177298 & 0.00109103364540355 & 0.000545516822701774 \tabularnewline
110 & 0.99946301972326 & 0.00107396055348003 & 0.000536980276740015 \tabularnewline
111 & 0.99942335376958 & 0.00115329246084083 & 0.000576646230420415 \tabularnewline
112 & 0.999696871568296 & 0.000606256863408407 & 0.000303128431704204 \tabularnewline
113 & 0.999614355191217 & 0.000771289617565313 & 0.000385644808782656 \tabularnewline
114 & 0.999620927317739 & 0.000758145364522556 & 0.000379072682261278 \tabularnewline
115 & 0.999522911086114 & 0.000954177827772502 & 0.000477088913886251 \tabularnewline
116 & 0.999373918036826 & 0.00125216392634848 & 0.000626081963174241 \tabularnewline
117 & 0.999321295857861 & 0.00135740828427889 & 0.000678704142139447 \tabularnewline
118 & 0.999562064209278 & 0.000875871581445032 & 0.000437935790722516 \tabularnewline
119 & 0.999732169944242 & 0.000535660111515493 & 0.000267830055757746 \tabularnewline
120 & 0.999692501723698 & 0.000614996552603823 & 0.000307498276301911 \tabularnewline
121 & 0.999630720502908 & 0.000738558994184034 & 0.000369279497092017 \tabularnewline
122 & 0.999621202520596 & 0.00075759495880697 & 0.000378797479403485 \tabularnewline
123 & 0.999604297047866 & 0.000791405904266989 & 0.000395702952133494 \tabularnewline
124 & 0.999512430057285 & 0.000975139885429139 & 0.00048756994271457 \tabularnewline
125 & 0.999415339870851 & 0.00116932025829864 & 0.000584660129149321 \tabularnewline
126 & 0.99956642948094 & 0.000867141038120137 & 0.000433570519060069 \tabularnewline
127 & 0.999651248545472 & 0.000697502909056862 & 0.000348751454528431 \tabularnewline
128 & 0.999572888272137 & 0.000854223455726152 & 0.000427111727863076 \tabularnewline
129 & 0.999452950383305 & 0.00109409923338933 & 0.000547049616694664 \tabularnewline
130 & 0.999720857832995 & 0.000558284334010099 & 0.00027914216700505 \tabularnewline
131 & 0.999673371760703 & 0.00065325647859334 & 0.00032662823929667 \tabularnewline
132 & 0.999638794673244 & 0.000722410653512131 & 0.000361205326756065 \tabularnewline
133 & 0.999612667890311 & 0.000774664219378308 & 0.000387332109689154 \tabularnewline
134 & 0.999617794715362 & 0.00076441056927562 & 0.00038220528463781 \tabularnewline
135 & 0.999841660822868 & 0.000316678354264997 & 0.000158339177132498 \tabularnewline
136 & 0.999816392092695 & 0.000367215814609072 & 0.000183607907304536 \tabularnewline
137 & 0.999902301171321 & 0.000195397657357545 & 9.76988286787723e-05 \tabularnewline
138 & 0.999901103074506 & 0.000197793850987157 & 9.88969254935787e-05 \tabularnewline
139 & 0.99986754845993 & 0.000264903080140906 & 0.000132451540070453 \tabularnewline
140 & 0.999845324774766 & 0.000309350450468758 & 0.000154675225234379 \tabularnewline
141 & 0.999805425964567 & 0.000389148070866335 & 0.000194574035433167 \tabularnewline
142 & 0.999806646969293 & 0.000386706061414685 & 0.000193353030707342 \tabularnewline
143 & 0.999744046923877 & 0.000511906152246929 & 0.000255953076123465 \tabularnewline
144 & 0.999647794245878 & 0.000704411508244528 & 0.000352205754122264 \tabularnewline
145 & 0.999732037359495 & 0.00053592528100981 & 0.000267962640504905 \tabularnewline
146 & 0.99966501090871 & 0.000669978182579441 & 0.00033498909128972 \tabularnewline
147 & 0.999849184055325 & 0.000301631889349907 & 0.000150815944674953 \tabularnewline
148 & 0.999797628198128 & 0.000404743603744276 & 0.000202371801872138 \tabularnewline
149 & 0.99976278402468 & 0.000474431950639426 & 0.000237215975319713 \tabularnewline
150 & 0.999812751911473 & 0.000374496177054441 & 0.000187248088527221 \tabularnewline
151 & 0.999849628140379 & 0.000300743719240995 & 0.000150371859620497 \tabularnewline
152 & 0.999982992677224 & 3.4014645551914e-05 & 1.7007322775957e-05 \tabularnewline
153 & 0.999981174410302 & 3.76511793957875e-05 & 1.88255896978937e-05 \tabularnewline
154 & 0.999976121632483 & 4.77567350346735e-05 & 2.38783675173368e-05 \tabularnewline
155 & 0.999965544320835 & 6.8911358329629e-05 & 3.44556791648145e-05 \tabularnewline
156 & 0.999980920920822 & 3.81581583558423e-05 & 1.90790791779211e-05 \tabularnewline
157 & 0.999979889446231 & 4.02211075382895e-05 & 2.01105537691447e-05 \tabularnewline
158 & 0.999979621486514 & 4.07570269715487e-05 & 2.03785134857743e-05 \tabularnewline
159 & 0.99998703354849 & 2.59329030194266e-05 & 1.29664515097133e-05 \tabularnewline
160 & 0.999981381986377 & 3.72360272454671e-05 & 1.86180136227335e-05 \tabularnewline
161 & 0.999972992180666 & 5.4015638668725e-05 & 2.70078193343625e-05 \tabularnewline
162 & 0.999972703835969 & 5.45923280621995e-05 & 2.72961640310998e-05 \tabularnewline
163 & 0.999968799926834 & 6.24001463317759e-05 & 3.12000731658879e-05 \tabularnewline
164 & 0.999954818934321 & 9.03621313583484e-05 & 4.51810656791742e-05 \tabularnewline
165 & 0.999980880426966 & 3.82391460686513e-05 & 1.91195730343256e-05 \tabularnewline
166 & 0.999972704372725 & 5.45912545506493e-05 & 2.72956272753246e-05 \tabularnewline
167 & 0.999970431570876 & 5.91368582483441e-05 & 2.9568429124172e-05 \tabularnewline
168 & 0.999967256394102 & 6.54872117957888e-05 & 3.27436058978944e-05 \tabularnewline
169 & 0.999954519667016 & 9.0960665968839e-05 & 4.54803329844195e-05 \tabularnewline
170 & 0.999988886512554 & 2.22269748914243e-05 & 1.11134874457121e-05 \tabularnewline
171 & 0.999983548844341 & 3.29023113177626e-05 & 1.64511556588813e-05 \tabularnewline
172 & 0.999976187488161 & 4.76250236781955e-05 & 2.38125118390978e-05 \tabularnewline
173 & 0.999966964699789 & 6.60706004216324e-05 & 3.30353002108162e-05 \tabularnewline
174 & 0.999954076235838 & 9.18475283239208e-05 & 4.59237641619604e-05 \tabularnewline
175 & 0.999961858302641 & 7.6283394718376e-05 & 3.8141697359188e-05 \tabularnewline
176 & 0.999952283243218 & 9.54335135633553e-05 & 4.77167567816776e-05 \tabularnewline
177 & 0.999930730770387 & 0.00013853845922625 & 6.92692296131252e-05 \tabularnewline
178 & 0.999905166370207 & 0.000189667259585125 & 9.48336297925623e-05 \tabularnewline
179 & 0.999870836730167 & 0.000258326539665093 & 0.000129163269832547 \tabularnewline
180 & 0.999892914155804 & 0.000214171688391642 & 0.000107085844195821 \tabularnewline
181 & 0.999933672830387 & 0.000132654339226675 & 6.63271696133375e-05 \tabularnewline
182 & 0.999911571409599 & 0.000176857180803048 & 8.84285904015239e-05 \tabularnewline
183 & 0.999951282624085 & 9.74347518294739e-05 & 4.87173759147369e-05 \tabularnewline
184 & 0.999953321939226 & 9.33561215479259e-05 & 4.6678060773963e-05 \tabularnewline
185 & 0.99996913488886 & 6.17302222793378e-05 & 3.08651111396689e-05 \tabularnewline
186 & 0.999983997473737 & 3.20050525262888e-05 & 1.60025262631444e-05 \tabularnewline
187 & 0.999982481230897 & 3.50375382058275e-05 & 1.75187691029137e-05 \tabularnewline
188 & 0.999991454657901 & 1.70906841983274e-05 & 8.54534209916369e-06 \tabularnewline
189 & 0.999987928301718 & 2.41433965648064e-05 & 1.20716982824032e-05 \tabularnewline
190 & 0.999998795132874 & 2.40973425147617e-06 & 1.20486712573808e-06 \tabularnewline
191 & 0.999998253694563 & 3.49261087496834e-06 & 1.74630543748417e-06 \tabularnewline
192 & 0.999997613273103 & 4.77345379457889e-06 & 2.38672689728944e-06 \tabularnewline
193 & 0.999997930453319 & 4.13909336158988e-06 & 2.06954668079494e-06 \tabularnewline
194 & 0.999996992081461 & 6.01583707898605e-06 & 3.00791853949303e-06 \tabularnewline
195 & 0.99999591168925 & 8.17662149919978e-06 & 4.08831074959989e-06 \tabularnewline
196 & 0.999996385121504 & 7.22975699219249e-06 & 3.61487849609624e-06 \tabularnewline
197 & 0.999996653926874 & 6.69214625134389e-06 & 3.34607312567195e-06 \tabularnewline
198 & 0.999996313673409 & 7.37265318143208e-06 & 3.68632659071604e-06 \tabularnewline
199 & 0.999994241623725 & 1.15167525494674e-05 & 5.7583762747337e-06 \tabularnewline
200 & 0.999991687172996 & 1.66256540072256e-05 & 8.31282700361278e-06 \tabularnewline
201 & 0.999987272787968 & 2.54544240646483e-05 & 1.27272120323241e-05 \tabularnewline
202 & 0.999996770477917 & 6.45904416618809e-06 & 3.22952208309405e-06 \tabularnewline
203 & 0.999995660053278 & 8.67989344389898e-06 & 4.33994672194949e-06 \tabularnewline
204 & 0.999993515335806 & 1.29693283883626e-05 & 6.48466419418131e-06 \tabularnewline
205 & 0.999990209409363 & 1.9581181273283e-05 & 9.79059063664149e-06 \tabularnewline
206 & 0.999993279142482 & 1.34417150360065e-05 & 6.72085751800325e-06 \tabularnewline
207 & 0.999989524980168 & 2.09500396645265e-05 & 1.04750198322633e-05 \tabularnewline
208 & 0.999986964141345 & 2.60717173106756e-05 & 1.30358586553378e-05 \tabularnewline
209 & 0.999979572397104 & 4.08552057912006e-05 & 2.04276028956003e-05 \tabularnewline
210 & 0.999987082749374 & 2.5834501251098e-05 & 1.2917250625549e-05 \tabularnewline
211 & 0.999991256664749 & 1.74866705014802e-05 & 8.74333525074012e-06 \tabularnewline
212 & 0.999986771186487 & 2.64576270258745e-05 & 1.32288135129373e-05 \tabularnewline
213 & 0.999982114080656 & 3.57718386878796e-05 & 1.78859193439398e-05 \tabularnewline
214 & 0.999975387985988 & 4.92240280235389e-05 & 2.46120140117695e-05 \tabularnewline
215 & 0.999988520462995 & 2.29590740100148e-05 & 1.14795370050074e-05 \tabularnewline
216 & 0.999995422905883 & 9.15418823407444e-06 & 4.57709411703722e-06 \tabularnewline
217 & 0.999992475235097 & 1.50495298063533e-05 & 7.52476490317664e-06 \tabularnewline
218 & 0.999990007895877 & 1.9984208245834e-05 & 9.99210412291701e-06 \tabularnewline
219 & 0.999985011329387 & 2.99773412265254e-05 & 1.49886706132627e-05 \tabularnewline
220 & 0.999978378548376 & 4.32429032481563e-05 & 2.16214516240781e-05 \tabularnewline
221 & 0.999983089664404 & 3.38206711920138e-05 & 1.69103355960069e-05 \tabularnewline
222 & 0.99997214139785 & 5.57172042993763e-05 & 2.78586021496882e-05 \tabularnewline
223 & 0.999960665976933 & 7.86680461336773e-05 & 3.93340230668386e-05 \tabularnewline
224 & 0.999940261142041 & 0.000119477715917205 & 5.97388579586023e-05 \tabularnewline
225 & 0.999958916313388 & 8.21673732241896e-05 & 4.10836866120948e-05 \tabularnewline
226 & 0.999983942014331 & 3.21159713378011e-05 & 1.60579856689005e-05 \tabularnewline
227 & 0.999996907335672 & 6.18532865635873e-06 & 3.09266432817937e-06 \tabularnewline
228 & 0.999994596880975 & 1.08062380497388e-05 & 5.40311902486942e-06 \tabularnewline
229 & 0.99999144952198 & 1.71009560404035e-05 & 8.55047802020173e-06 \tabularnewline
230 & 0.999987436834341 & 2.51263313188574e-05 & 1.25631656594287e-05 \tabularnewline
231 & 0.999992050986536 & 1.58980269284004e-05 & 7.94901346420021e-06 \tabularnewline
232 & 0.999989689475217 & 2.06210495651166e-05 & 1.03105247825583e-05 \tabularnewline
233 & 0.999983407509408 & 3.31849811842017e-05 & 1.65924905921008e-05 \tabularnewline
234 & 0.999974306061072 & 5.13878778567157e-05 & 2.56939389283578e-05 \tabularnewline
235 & 0.999972260730643 & 5.54785387130038e-05 & 2.77392693565019e-05 \tabularnewline
236 & 0.999968332305094 & 6.33353898110802e-05 & 3.16676949055401e-05 \tabularnewline
237 & 0.999945121349242 & 0.000109757301515377 & 5.48786507576887e-05 \tabularnewline
238 & 0.999946255902211 & 0.000107488195577938 & 5.37440977889689e-05 \tabularnewline
239 & 0.999907197219236 & 0.000185605561527992 & 9.28027807639959e-05 \tabularnewline
240 & 0.999922079123301 & 0.000155841753398805 & 7.79208766994027e-05 \tabularnewline
241 & 0.999877578435025 & 0.000244843129949249 & 0.000122421564974624 \tabularnewline
242 & 0.999820774862587 & 0.000358450274825194 & 0.000179225137412597 \tabularnewline
243 & 0.999747397484766 & 0.000505205030467569 & 0.000252602515233785 \tabularnewline
244 & 0.999639547785128 & 0.000720904429743385 & 0.000360452214871693 \tabularnewline
245 & 0.999394298913823 & 0.00121140217235361 & 0.000605701086176804 \tabularnewline
246 & 0.999054778990464 & 0.00189044201907141 & 0.000945221009535707 \tabularnewline
247 & 0.999471894190904 & 0.00105621161819301 & 0.000528105809096505 \tabularnewline
248 & 0.999151302994665 & 0.00169739401066982 & 0.000848697005334912 \tabularnewline
249 & 0.999274649886853 & 0.00145070022629353 & 0.000725350113146764 \tabularnewline
250 & 0.999068504986381 & 0.00186299002723785 & 0.000931495013618924 \tabularnewline
251 & 0.998833013525459 & 0.00233397294908185 & 0.00116698647454093 \tabularnewline
252 & 0.998027523839763 & 0.00394495232047395 & 0.00197247616023698 \tabularnewline
253 & 0.99810928669238 & 0.00378142661523924 & 0.00189071330761962 \tabularnewline
254 & 0.996861977869709 & 0.00627604426058239 & 0.00313802213029119 \tabularnewline
255 & 0.996968390952084 & 0.00606321809583107 & 0.00303160904791553 \tabularnewline
256 & 0.995169602769058 & 0.00966079446188467 & 0.00483039723094233 \tabularnewline
257 & 0.992205052971028 & 0.0155898940579435 & 0.00779494702897173 \tabularnewline
258 & 0.98745595387277 & 0.0250880922544593 & 0.0125440461272296 \tabularnewline
259 & 0.986526591500108 & 0.0269468169997845 & 0.0134734084998923 \tabularnewline
260 & 0.978957204759332 & 0.0420855904813364 & 0.0210427952406682 \tabularnewline
261 & 0.989673476485936 & 0.0206530470281272 & 0.0103265235140636 \tabularnewline
262 & 0.986081449105992 & 0.0278371017880158 & 0.0139185508940079 \tabularnewline
263 & 0.977747097948237 & 0.0445058041035252 & 0.0222529020517626 \tabularnewline
264 & 0.964209078236049 & 0.071581843527902 & 0.035790921763951 \tabularnewline
265 & 0.950924605733159 & 0.0981507885336827 & 0.0490753942668414 \tabularnewline
266 & 0.926269523253466 & 0.147460953493067 & 0.0737304767465335 \tabularnewline
267 & 0.917238693936304 & 0.165522612127392 & 0.082761306063696 \tabularnewline
268 & 0.882497995877891 & 0.235004008244219 & 0.117502004122109 \tabularnewline
269 & 0.851838878140844 & 0.296322243718312 & 0.148161121859156 \tabularnewline
270 & 0.93959631376927 & 0.12080737246146 & 0.0604036862307298 \tabularnewline
271 & 0.930232315271026 & 0.139535369457948 & 0.0697676847289741 \tabularnewline
272 & 0.886144441985899 & 0.227711116028201 & 0.113855558014101 \tabularnewline
273 & 0.870865410344184 & 0.258269179311632 & 0.129134589655816 \tabularnewline
274 & 0.878442879379057 & 0.243114241241887 & 0.121557120620943 \tabularnewline
275 & 0.78143383238626 & 0.437132335227481 & 0.21856616761374 \tabularnewline
276 & 0.825884910099445 & 0.348230179801111 & 0.174115089900555 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156209&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]13[/C][C]0.756784871576331[/C][C]0.486430256847338[/C][C]0.243215128423669[/C][/ROW]
[ROW][C]14[/C][C]0.623646873560959[/C][C]0.752706252878083[/C][C]0.376353126439041[/C][/ROW]
[ROW][C]15[/C][C]0.875790283050581[/C][C]0.248419433898838[/C][C]0.124209716949419[/C][/ROW]
[ROW][C]16[/C][C]0.868623262096076[/C][C]0.262753475807849[/C][C]0.131376737903924[/C][/ROW]
[ROW][C]17[/C][C]0.847113489175737[/C][C]0.305773021648526[/C][C]0.152886510824263[/C][/ROW]
[ROW][C]18[/C][C]0.881116551311513[/C][C]0.237766897376975[/C][C]0.118883448688487[/C][/ROW]
[ROW][C]19[/C][C]0.834532836288191[/C][C]0.330934327423618[/C][C]0.165467163711809[/C][/ROW]
[ROW][C]20[/C][C]0.934349338079003[/C][C]0.131301323841995[/C][C]0.0656506619209973[/C][/ROW]
[ROW][C]21[/C][C]0.909692193852095[/C][C]0.18061561229581[/C][C]0.0903078061479052[/C][/ROW]
[ROW][C]22[/C][C]0.874546499560495[/C][C]0.250907000879009[/C][C]0.125453500439505[/C][/ROW]
[ROW][C]23[/C][C]0.830261480780876[/C][C]0.339477038438248[/C][C]0.169738519219124[/C][/ROW]
[ROW][C]24[/C][C]0.781044783021361[/C][C]0.437910433957278[/C][C]0.218955216978639[/C][/ROW]
[ROW][C]25[/C][C]0.803266565728565[/C][C]0.39346686854287[/C][C]0.196733434271435[/C][/ROW]
[ROW][C]26[/C][C]0.750241591058436[/C][C]0.499516817883129[/C][C]0.249758408941564[/C][/ROW]
[ROW][C]27[/C][C]0.697585234096598[/C][C]0.604829531806805[/C][C]0.302414765903402[/C][/ROW]
[ROW][C]28[/C][C]0.640033333020334[/C][C]0.719933333959331[/C][C]0.359966666979666[/C][/ROW]
[ROW][C]29[/C][C]0.606171342952824[/C][C]0.787657314094351[/C][C]0.393828657047176[/C][/ROW]
[ROW][C]30[/C][C]0.543998294396929[/C][C]0.912003411206143[/C][C]0.456001705603071[/C][/ROW]
[ROW][C]31[/C][C]0.482755528057636[/C][C]0.965511056115273[/C][C]0.517244471942363[/C][/ROW]
[ROW][C]32[/C][C]0.421228557911537[/C][C]0.842457115823074[/C][C]0.578771442088463[/C][/ROW]
[ROW][C]33[/C][C]0.599143121791058[/C][C]0.801713756417884[/C][C]0.400856878208942[/C][/ROW]
[ROW][C]34[/C][C]0.601617718801585[/C][C]0.796764562396829[/C][C]0.398382281198415[/C][/ROW]
[ROW][C]35[/C][C]0.613897801218293[/C][C]0.772204397563413[/C][C]0.386102198781707[/C][/ROW]
[ROW][C]36[/C][C]0.563154725363457[/C][C]0.873690549273085[/C][C]0.436845274636543[/C][/ROW]
[ROW][C]37[/C][C]0.695134343470589[/C][C]0.609731313058821[/C][C]0.304865656529411[/C][/ROW]
[ROW][C]38[/C][C]0.70036521437986[/C][C]0.599269571240281[/C][C]0.299634785620141[/C][/ROW]
[ROW][C]39[/C][C]0.651689664513977[/C][C]0.696620670972045[/C][C]0.348310335486023[/C][/ROW]
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[ROW][C]166[/C][C]0.999972704372725[/C][C]5.45912545506493e-05[/C][C]2.72956272753246e-05[/C][/ROW]
[ROW][C]167[/C][C]0.999970431570876[/C][C]5.91368582483441e-05[/C][C]2.9568429124172e-05[/C][/ROW]
[ROW][C]168[/C][C]0.999967256394102[/C][C]6.54872117957888e-05[/C][C]3.27436058978944e-05[/C][/ROW]
[ROW][C]169[/C][C]0.999954519667016[/C][C]9.0960665968839e-05[/C][C]4.54803329844195e-05[/C][/ROW]
[ROW][C]170[/C][C]0.999988886512554[/C][C]2.22269748914243e-05[/C][C]1.11134874457121e-05[/C][/ROW]
[ROW][C]171[/C][C]0.999983548844341[/C][C]3.29023113177626e-05[/C][C]1.64511556588813e-05[/C][/ROW]
[ROW][C]172[/C][C]0.999976187488161[/C][C]4.76250236781955e-05[/C][C]2.38125118390978e-05[/C][/ROW]
[ROW][C]173[/C][C]0.999966964699789[/C][C]6.60706004216324e-05[/C][C]3.30353002108162e-05[/C][/ROW]
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[ROW][C]175[/C][C]0.999961858302641[/C][C]7.6283394718376e-05[/C][C]3.8141697359188e-05[/C][/ROW]
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[ROW][C]177[/C][C]0.999930730770387[/C][C]0.00013853845922625[/C][C]6.92692296131252e-05[/C][/ROW]
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[ROW][C]180[/C][C]0.999892914155804[/C][C]0.000214171688391642[/C][C]0.000107085844195821[/C][/ROW]
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[ROW][C]182[/C][C]0.999911571409599[/C][C]0.000176857180803048[/C][C]8.84285904015239e-05[/C][/ROW]
[ROW][C]183[/C][C]0.999951282624085[/C][C]9.74347518294739e-05[/C][C]4.87173759147369e-05[/C][/ROW]
[ROW][C]184[/C][C]0.999953321939226[/C][C]9.33561215479259e-05[/C][C]4.6678060773963e-05[/C][/ROW]
[ROW][C]185[/C][C]0.99996913488886[/C][C]6.17302222793378e-05[/C][C]3.08651111396689e-05[/C][/ROW]
[ROW][C]186[/C][C]0.999983997473737[/C][C]3.20050525262888e-05[/C][C]1.60025262631444e-05[/C][/ROW]
[ROW][C]187[/C][C]0.999982481230897[/C][C]3.50375382058275e-05[/C][C]1.75187691029137e-05[/C][/ROW]
[ROW][C]188[/C][C]0.999991454657901[/C][C]1.70906841983274e-05[/C][C]8.54534209916369e-06[/C][/ROW]
[ROW][C]189[/C][C]0.999987928301718[/C][C]2.41433965648064e-05[/C][C]1.20716982824032e-05[/C][/ROW]
[ROW][C]190[/C][C]0.999998795132874[/C][C]2.40973425147617e-06[/C][C]1.20486712573808e-06[/C][/ROW]
[ROW][C]191[/C][C]0.999998253694563[/C][C]3.49261087496834e-06[/C][C]1.74630543748417e-06[/C][/ROW]
[ROW][C]192[/C][C]0.999997613273103[/C][C]4.77345379457889e-06[/C][C]2.38672689728944e-06[/C][/ROW]
[ROW][C]193[/C][C]0.999997930453319[/C][C]4.13909336158988e-06[/C][C]2.06954668079494e-06[/C][/ROW]
[ROW][C]194[/C][C]0.999996992081461[/C][C]6.01583707898605e-06[/C][C]3.00791853949303e-06[/C][/ROW]
[ROW][C]195[/C][C]0.99999591168925[/C][C]8.17662149919978e-06[/C][C]4.08831074959989e-06[/C][/ROW]
[ROW][C]196[/C][C]0.999996385121504[/C][C]7.22975699219249e-06[/C][C]3.61487849609624e-06[/C][/ROW]
[ROW][C]197[/C][C]0.999996653926874[/C][C]6.69214625134389e-06[/C][C]3.34607312567195e-06[/C][/ROW]
[ROW][C]198[/C][C]0.999996313673409[/C][C]7.37265318143208e-06[/C][C]3.68632659071604e-06[/C][/ROW]
[ROW][C]199[/C][C]0.999994241623725[/C][C]1.15167525494674e-05[/C][C]5.7583762747337e-06[/C][/ROW]
[ROW][C]200[/C][C]0.999991687172996[/C][C]1.66256540072256e-05[/C][C]8.31282700361278e-06[/C][/ROW]
[ROW][C]201[/C][C]0.999987272787968[/C][C]2.54544240646483e-05[/C][C]1.27272120323241e-05[/C][/ROW]
[ROW][C]202[/C][C]0.999996770477917[/C][C]6.45904416618809e-06[/C][C]3.22952208309405e-06[/C][/ROW]
[ROW][C]203[/C][C]0.999995660053278[/C][C]8.67989344389898e-06[/C][C]4.33994672194949e-06[/C][/ROW]
[ROW][C]204[/C][C]0.999993515335806[/C][C]1.29693283883626e-05[/C][C]6.48466419418131e-06[/C][/ROW]
[ROW][C]205[/C][C]0.999990209409363[/C][C]1.9581181273283e-05[/C][C]9.79059063664149e-06[/C][/ROW]
[ROW][C]206[/C][C]0.999993279142482[/C][C]1.34417150360065e-05[/C][C]6.72085751800325e-06[/C][/ROW]
[ROW][C]207[/C][C]0.999989524980168[/C][C]2.09500396645265e-05[/C][C]1.04750198322633e-05[/C][/ROW]
[ROW][C]208[/C][C]0.999986964141345[/C][C]2.60717173106756e-05[/C][C]1.30358586553378e-05[/C][/ROW]
[ROW][C]209[/C][C]0.999979572397104[/C][C]4.08552057912006e-05[/C][C]2.04276028956003e-05[/C][/ROW]
[ROW][C]210[/C][C]0.999987082749374[/C][C]2.5834501251098e-05[/C][C]1.2917250625549e-05[/C][/ROW]
[ROW][C]211[/C][C]0.999991256664749[/C][C]1.74866705014802e-05[/C][C]8.74333525074012e-06[/C][/ROW]
[ROW][C]212[/C][C]0.999986771186487[/C][C]2.64576270258745e-05[/C][C]1.32288135129373e-05[/C][/ROW]
[ROW][C]213[/C][C]0.999982114080656[/C][C]3.57718386878796e-05[/C][C]1.78859193439398e-05[/C][/ROW]
[ROW][C]214[/C][C]0.999975387985988[/C][C]4.92240280235389e-05[/C][C]2.46120140117695e-05[/C][/ROW]
[ROW][C]215[/C][C]0.999988520462995[/C][C]2.29590740100148e-05[/C][C]1.14795370050074e-05[/C][/ROW]
[ROW][C]216[/C][C]0.999995422905883[/C][C]9.15418823407444e-06[/C][C]4.57709411703722e-06[/C][/ROW]
[ROW][C]217[/C][C]0.999992475235097[/C][C]1.50495298063533e-05[/C][C]7.52476490317664e-06[/C][/ROW]
[ROW][C]218[/C][C]0.999990007895877[/C][C]1.9984208245834e-05[/C][C]9.99210412291701e-06[/C][/ROW]
[ROW][C]219[/C][C]0.999985011329387[/C][C]2.99773412265254e-05[/C][C]1.49886706132627e-05[/C][/ROW]
[ROW][C]220[/C][C]0.999978378548376[/C][C]4.32429032481563e-05[/C][C]2.16214516240781e-05[/C][/ROW]
[ROW][C]221[/C][C]0.999983089664404[/C][C]3.38206711920138e-05[/C][C]1.69103355960069e-05[/C][/ROW]
[ROW][C]222[/C][C]0.99997214139785[/C][C]5.57172042993763e-05[/C][C]2.78586021496882e-05[/C][/ROW]
[ROW][C]223[/C][C]0.999960665976933[/C][C]7.86680461336773e-05[/C][C]3.93340230668386e-05[/C][/ROW]
[ROW][C]224[/C][C]0.999940261142041[/C][C]0.000119477715917205[/C][C]5.97388579586023e-05[/C][/ROW]
[ROW][C]225[/C][C]0.999958916313388[/C][C]8.21673732241896e-05[/C][C]4.10836866120948e-05[/C][/ROW]
[ROW][C]226[/C][C]0.999983942014331[/C][C]3.21159713378011e-05[/C][C]1.60579856689005e-05[/C][/ROW]
[ROW][C]227[/C][C]0.999996907335672[/C][C]6.18532865635873e-06[/C][C]3.09266432817937e-06[/C][/ROW]
[ROW][C]228[/C][C]0.999994596880975[/C][C]1.08062380497388e-05[/C][C]5.40311902486942e-06[/C][/ROW]
[ROW][C]229[/C][C]0.99999144952198[/C][C]1.71009560404035e-05[/C][C]8.55047802020173e-06[/C][/ROW]
[ROW][C]230[/C][C]0.999987436834341[/C][C]2.51263313188574e-05[/C][C]1.25631656594287e-05[/C][/ROW]
[ROW][C]231[/C][C]0.999992050986536[/C][C]1.58980269284004e-05[/C][C]7.94901346420021e-06[/C][/ROW]
[ROW][C]232[/C][C]0.999989689475217[/C][C]2.06210495651166e-05[/C][C]1.03105247825583e-05[/C][/ROW]
[ROW][C]233[/C][C]0.999983407509408[/C][C]3.31849811842017e-05[/C][C]1.65924905921008e-05[/C][/ROW]
[ROW][C]234[/C][C]0.999974306061072[/C][C]5.13878778567157e-05[/C][C]2.56939389283578e-05[/C][/ROW]
[ROW][C]235[/C][C]0.999972260730643[/C][C]5.54785387130038e-05[/C][C]2.77392693565019e-05[/C][/ROW]
[ROW][C]236[/C][C]0.999968332305094[/C][C]6.33353898110802e-05[/C][C]3.16676949055401e-05[/C][/ROW]
[ROW][C]237[/C][C]0.999945121349242[/C][C]0.000109757301515377[/C][C]5.48786507576887e-05[/C][/ROW]
[ROW][C]238[/C][C]0.999946255902211[/C][C]0.000107488195577938[/C][C]5.37440977889689e-05[/C][/ROW]
[ROW][C]239[/C][C]0.999907197219236[/C][C]0.000185605561527992[/C][C]9.28027807639959e-05[/C][/ROW]
[ROW][C]240[/C][C]0.999922079123301[/C][C]0.000155841753398805[/C][C]7.79208766994027e-05[/C][/ROW]
[ROW][C]241[/C][C]0.999877578435025[/C][C]0.000244843129949249[/C][C]0.000122421564974624[/C][/ROW]
[ROW][C]242[/C][C]0.999820774862587[/C][C]0.000358450274825194[/C][C]0.000179225137412597[/C][/ROW]
[ROW][C]243[/C][C]0.999747397484766[/C][C]0.000505205030467569[/C][C]0.000252602515233785[/C][/ROW]
[ROW][C]244[/C][C]0.999639547785128[/C][C]0.000720904429743385[/C][C]0.000360452214871693[/C][/ROW]
[ROW][C]245[/C][C]0.999394298913823[/C][C]0.00121140217235361[/C][C]0.000605701086176804[/C][/ROW]
[ROW][C]246[/C][C]0.999054778990464[/C][C]0.00189044201907141[/C][C]0.000945221009535707[/C][/ROW]
[ROW][C]247[/C][C]0.999471894190904[/C][C]0.00105621161819301[/C][C]0.000528105809096505[/C][/ROW]
[ROW][C]248[/C][C]0.999151302994665[/C][C]0.00169739401066982[/C][C]0.000848697005334912[/C][/ROW]
[ROW][C]249[/C][C]0.999274649886853[/C][C]0.00145070022629353[/C][C]0.000725350113146764[/C][/ROW]
[ROW][C]250[/C][C]0.999068504986381[/C][C]0.00186299002723785[/C][C]0.000931495013618924[/C][/ROW]
[ROW][C]251[/C][C]0.998833013525459[/C][C]0.00233397294908185[/C][C]0.00116698647454093[/C][/ROW]
[ROW][C]252[/C][C]0.998027523839763[/C][C]0.00394495232047395[/C][C]0.00197247616023698[/C][/ROW]
[ROW][C]253[/C][C]0.99810928669238[/C][C]0.00378142661523924[/C][C]0.00189071330761962[/C][/ROW]
[ROW][C]254[/C][C]0.996861977869709[/C][C]0.00627604426058239[/C][C]0.00313802213029119[/C][/ROW]
[ROW][C]255[/C][C]0.996968390952084[/C][C]0.00606321809583107[/C][C]0.00303160904791553[/C][/ROW]
[ROW][C]256[/C][C]0.995169602769058[/C][C]0.00966079446188467[/C][C]0.00483039723094233[/C][/ROW]
[ROW][C]257[/C][C]0.992205052971028[/C][C]0.0155898940579435[/C][C]0.00779494702897173[/C][/ROW]
[ROW][C]258[/C][C]0.98745595387277[/C][C]0.0250880922544593[/C][C]0.0125440461272296[/C][/ROW]
[ROW][C]259[/C][C]0.986526591500108[/C][C]0.0269468169997845[/C][C]0.0134734084998923[/C][/ROW]
[ROW][C]260[/C][C]0.978957204759332[/C][C]0.0420855904813364[/C][C]0.0210427952406682[/C][/ROW]
[ROW][C]261[/C][C]0.989673476485936[/C][C]0.0206530470281272[/C][C]0.0103265235140636[/C][/ROW]
[ROW][C]262[/C][C]0.986081449105992[/C][C]0.0278371017880158[/C][C]0.0139185508940079[/C][/ROW]
[ROW][C]263[/C][C]0.977747097948237[/C][C]0.0445058041035252[/C][C]0.0222529020517626[/C][/ROW]
[ROW][C]264[/C][C]0.964209078236049[/C][C]0.071581843527902[/C][C]0.035790921763951[/C][/ROW]
[ROW][C]265[/C][C]0.950924605733159[/C][C]0.0981507885336827[/C][C]0.0490753942668414[/C][/ROW]
[ROW][C]266[/C][C]0.926269523253466[/C][C]0.147460953493067[/C][C]0.0737304767465335[/C][/ROW]
[ROW][C]267[/C][C]0.917238693936304[/C][C]0.165522612127392[/C][C]0.082761306063696[/C][/ROW]
[ROW][C]268[/C][C]0.882497995877891[/C][C]0.235004008244219[/C][C]0.117502004122109[/C][/ROW]
[ROW][C]269[/C][C]0.851838878140844[/C][C]0.296322243718312[/C][C]0.148161121859156[/C][/ROW]
[ROW][C]270[/C][C]0.93959631376927[/C][C]0.12080737246146[/C][C]0.0604036862307298[/C][/ROW]
[ROW][C]271[/C][C]0.930232315271026[/C][C]0.139535369457948[/C][C]0.0697676847289741[/C][/ROW]
[ROW][C]272[/C][C]0.886144441985899[/C][C]0.227711116028201[/C][C]0.113855558014101[/C][/ROW]
[ROW][C]273[/C][C]0.870865410344184[/C][C]0.258269179311632[/C][C]0.129134589655816[/C][/ROW]
[ROW][C]274[/C][C]0.878442879379057[/C][C]0.243114241241887[/C][C]0.121557120620943[/C][/ROW]
[ROW][C]275[/C][C]0.78143383238626[/C][C]0.437132335227481[/C][C]0.21856616761374[/C][/ROW]
[ROW][C]276[/C][C]0.825884910099445[/C][C]0.348230179801111[/C][C]0.174115089900555[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156209&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156209&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7567848715763310.4864302568473380.243215128423669
140.6236468735609590.7527062528780830.376353126439041
150.8757902830505810.2484194338988380.124209716949419
160.8686232620960760.2627534758078490.131376737903924
170.8471134891757370.3057730216485260.152886510824263
180.8811165513115130.2377668973769750.118883448688487
190.8345328362881910.3309343274236180.165467163711809
200.9343493380790030.1313013238419950.0656506619209973
210.9096921938520950.180615612295810.0903078061479052
220.8745464995604950.2509070008790090.125453500439505
230.8302614807808760.3394770384382480.169738519219124
240.7810447830213610.4379104339572780.218955216978639
250.8032665657285650.393466868542870.196733434271435
260.7502415910584360.4995168178831290.249758408941564
270.6975852340965980.6048295318068050.302414765903402
280.6400333330203340.7199333339593310.359966666979666
290.6061713429528240.7876573140943510.393828657047176
300.5439982943969290.9120034112061430.456001705603071
310.4827555280576360.9655110561152730.517244471942363
320.4212285579115370.8424571158230740.578771442088463
330.5991431217910580.8017137564178840.400856878208942
340.6016177188015850.7967645623968290.398382281198415
350.6138978012182930.7722043975634130.386102198781707
360.5631547253634570.8736905492730850.436845274636543
370.6951343434705890.6097313130588210.304865656529411
380.700365214379860.5992695712402810.299634785620141
390.6516896645139770.6966206709720450.348310335486023
400.8031390488296950.3937219023406110.196860951170305
410.766421524030970.4671569519380610.23357847596903
420.7809010594541210.4381978810917580.219098940545879
430.8173295407096370.3653409185807270.182670459290363
440.7979824492238120.4040351015523760.202017550776188
450.7709605889622820.4580788220754370.229039411037718
460.829011148842570.341977702314860.17098885115743
470.827275178202630.3454496435947410.17272482179737
480.7951911401988430.4096177196023150.204808859801158
490.7807032898693190.4385934202613620.219296710130681
500.9366156213429730.1267687573140530.0633843786570267
510.9322721450673410.1354557098653170.0677278549326587
520.934043501548080.1319129969038390.0659564984519196
530.9321829790819980.1356340418360040.0678170209180018
540.9240415811155750.151916837768850.0759584188844252
550.9210702870207820.1578594259584370.0789297129792184
560.9073633231490470.1852733537019060.0926366768509528
570.9303925577444920.1392148845110160.0696074422555082
580.9147625038143320.1704749923713360.0852374961856681
590.8983931834502320.2032136330995370.101606816549768
600.8939329815268190.2121340369463620.106067018473181
610.8756030856061310.2487938287877380.124396914393869
620.8569929453825550.286014109234890.143007054617445
630.9720073189085510.05598536218289820.0279926810914491
640.9663022973076020.06739540538479520.0336977026923976
650.9651295341703960.06974093165920770.0348704658296038
660.9607399204875350.078520159024930.039260079512465
670.9527487848679160.09450243026416770.0472512151320839
680.945684785320850.10863042935830.05431521467915
690.9814114482732460.03717710345350790.0185885517267539
700.9917350947828920.01652981043421530.00826490521710767
710.9906469428853220.01870611422935510.00935305711467757
720.9881735211284430.02365295774311420.0118264788715571
730.985680926668280.02863814666343970.0143190733317199
740.9830022111411340.03399557771773240.0169977888588662
750.9843318309664560.03133633806708740.0156681690335437
760.9836341807866680.03273163842666310.0163658192133315
770.9893023611644080.02139527767118410.0106976388355921
780.994121102297440.01175779540512020.00587889770256008
790.9944874175918810.01102516481623780.00551258240811892
800.9967104156278450.006579168744310590.00328958437215529
810.9957382331038420.008523533792315670.00426176689615783
820.9950279683521430.009944063295714490.00497203164785725
830.9943374468762230.01132510624755430.00566255312377717
840.9936866472083560.01262670558328740.00631335279164371
850.9945544118815560.01089117623688760.00544558811844381
860.9930292314427860.01394153711442710.00697076855721355
870.9938103874785210.01237922504295850.00618961252147925
880.9924170939631040.01516581207379160.00758290603689581
890.9911321576235730.0177356847528540.00886784237642698
900.9917188152011270.01656236959774680.00828118479887338
910.9897314900402610.02053701991947730.0102685099597386
920.995888753382740.008222493234519820.00411124661725991
930.9958943154941270.008211369011745760.00410568450587288
940.9946469735059560.01070605298808830.00535302649404414
950.9934712104211050.01305757915779050.00652878957889523
960.9931838172542340.01363236549153130.00681618274576567
970.9972129109556310.005574178088737590.00278708904436879
980.9968604589122070.006279082175585780.00313954108779289
990.9968766604018120.006246679196375970.00312333959818798
1000.9959520677693030.008095864461393740.00404793223069687
1010.9949466758371890.01010664832562110.00505332416281053
1020.9939630503422720.01207389931545670.00603694965772835
1030.992547739425640.01490452114871930.00745226057435967
1040.9908397407123090.0183205185753820.00916025928769101
1050.9904785332583460.01904293348330720.00952146674165359
1060.9928564298553650.01428714028926950.00714357014463475
1070.9979522295054670.004095540989064970.00204777049453248
1080.9974490114204320.005101977159136870.00255098857956844
1090.9994544831772980.001091033645403550.000545516822701774
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1820.9999115714095990.0001768571808030488.84285904015239e-05
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1890.9999879283017182.41433965648064e-051.20716982824032e-05
1900.9999987951328742.40973425147617e-061.20486712573808e-06
1910.9999982536945633.49261087496834e-061.74630543748417e-06
1920.9999976132731034.77345379457889e-062.38672689728944e-06
1930.9999979304533194.13909336158988e-062.06954668079494e-06
1940.9999969920814616.01583707898605e-063.00791853949303e-06
1950.999995911689258.17662149919978e-064.08831074959989e-06
1960.9999963851215047.22975699219249e-063.61487849609624e-06
1970.9999966539268746.69214625134389e-063.34607312567195e-06
1980.9999963136734097.37265318143208e-063.68632659071604e-06
1990.9999942416237251.15167525494674e-055.7583762747337e-06
2000.9999916871729961.66256540072256e-058.31282700361278e-06
2010.9999872727879682.54544240646483e-051.27272120323241e-05
2020.9999967704779176.45904416618809e-063.22952208309405e-06
2030.9999956600532788.67989344389898e-064.33994672194949e-06
2040.9999935153358061.29693283883626e-056.48466419418131e-06
2050.9999902094093631.9581181273283e-059.79059063664149e-06
2060.9999932791424821.34417150360065e-056.72085751800325e-06
2070.9999895249801682.09500396645265e-051.04750198322633e-05
2080.9999869641413452.60717173106756e-051.30358586553378e-05
2090.9999795723971044.08552057912006e-052.04276028956003e-05
2100.9999870827493742.5834501251098e-051.2917250625549e-05
2110.9999912566647491.74866705014802e-058.74333525074012e-06
2120.9999867711864872.64576270258745e-051.32288135129373e-05
2130.9999821140806563.57718386878796e-051.78859193439398e-05
2140.9999753879859884.92240280235389e-052.46120140117695e-05
2150.9999885204629952.29590740100148e-051.14795370050074e-05
2160.9999954229058839.15418823407444e-064.57709411703722e-06
2170.9999924752350971.50495298063533e-057.52476490317664e-06
2180.9999900078958771.9984208245834e-059.99210412291701e-06
2190.9999850113293872.99773412265254e-051.49886706132627e-05
2200.9999783785483764.32429032481563e-052.16214516240781e-05
2210.9999830896644043.38206711920138e-051.69103355960069e-05
2220.999972141397855.57172042993763e-052.78586021496882e-05
2230.9999606659769337.86680461336773e-053.93340230668386e-05
2240.9999402611420410.0001194777159172055.97388579586023e-05
2250.9999589163133888.21673732241896e-054.10836866120948e-05
2260.9999839420143313.21159713378011e-051.60579856689005e-05
2270.9999969073356726.18532865635873e-063.09266432817937e-06
2280.9999945968809751.08062380497388e-055.40311902486942e-06
2290.999991449521981.71009560404035e-058.55047802020173e-06
2300.9999874368343412.51263313188574e-051.25631656594287e-05
2310.9999920509865361.58980269284004e-057.94901346420021e-06
2320.9999896894752172.06210495651166e-051.03105247825583e-05
2330.9999834075094083.31849811842017e-051.65924905921008e-05
2340.9999743060610725.13878778567157e-052.56939389283578e-05
2350.9999722607306435.54785387130038e-052.77392693565019e-05
2360.9999683323050946.33353898110802e-053.16676949055401e-05
2370.9999451213492420.0001097573015153775.48786507576887e-05
2380.9999462559022110.0001074881955779385.37440977889689e-05
2390.9999071972192360.0001856055615279929.28027807639959e-05
2400.9999220791233010.0001558417533988057.79208766994027e-05
2410.9998775784350250.0002448431299492490.000122421564974624
2420.9998207748625870.0003584502748251940.000179225137412597
2430.9997473974847660.0005052050304675690.000252602515233785
2440.9996395477851280.0007209044297433850.000360452214871693
2450.9993942989138230.001211402172353610.000605701086176804
2460.9990547789904640.001890442019071410.000945221009535707
2470.9994718941909040.001056211618193010.000528105809096505
2480.9991513029946650.001697394010669820.000848697005334912
2490.9992746498868530.001450700226293530.000725350113146764
2500.9990685049863810.001862990027237850.000931495013618924
2510.9988330135254590.002333972949081850.00116698647454093
2520.9980275238397630.003944952320473950.00197247616023698
2530.998109286692380.003781426615239240.00189071330761962
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2760.8258849100994450.3482301798011110.174115089900555






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1590.602272727272727NOK
5% type I error level1950.738636363636364NOK
10% type I error level2020.765151515151515NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 159 & 0.602272727272727 & NOK \tabularnewline
5% type I error level & 195 & 0.738636363636364 & NOK \tabularnewline
10% type I error level & 202 & 0.765151515151515 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156209&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]159[/C][C]0.602272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]195[/C][C]0.738636363636364[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]202[/C][C]0.765151515151515[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156209&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156209&T=6

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1590.602272727272727NOK
5% type I error level1950.738636363636364NOK
10% type I error level2020.765151515151515NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}