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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 computationFri, 23 Dec 2011 07:45:24 -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/23/t132464442819zpozkwxlo57zs.htm/, Retrieved Mon, 29 Apr 2024 23:54:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160355, Retrieved Mon, 29 Apr 2024 23:54:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-08 19:22:39] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMPD  [(Partial) Autocorrelation Function] [] [2011-12-01 17:46:48] [86f7284edee3dbb8ea5c7e2dec87d892]
- R P     [(Partial) Autocorrelation Function] [] [2011-12-05 14:45:05] [86f7284edee3dbb8ea5c7e2dec87d892]
- RMP       [ARIMA Forecasting] [] [2011-12-05 16:06:32] [86f7284edee3dbb8ea5c7e2dec87d892]
- R P         [ARIMA Forecasting] [] [2011-12-16 07:39:11] [86f7284edee3dbb8ea5c7e2dec87d892]
- RMPD          [Multiple Regression] [] [2011-12-23 12:25:40] [ad2d4c5ace9fa07b356a7b5098237581]
- R                 [Multiple Regression] [] [2011-12-23 12:45:24] [daf26cf00f2f7a7ee0a1368c8ac8117e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1418	210907	56	396	81	3	79	30	115	94	112285	24188	146283	144	145
869	120982	56	297	55	4	58	28	109	103	84786	18273	98364	103	101
1530	176508	54	559	50	12	60	38	146	93	83123	14130	86146	98	98
2172	179321	89	967	125	2	108	30	116	103	101193	32287	96933	135	132
901	123185	40	270	40	1	49	22	68	51	38361	8654	79234	61	60
463	52746	25	143	37	3	0	26	101	70	68504	9245	42551	39	38
3201	385534	92	1562	63	0	121	25	96	91	119182	33251	195663	150	144
371	33170	18	109	44	0	1	18	67	22	22807	1271	6853	5	5
1192	101645	63	371	88	0	20	11	44	38	17140	5279	21529	28	28
1583	149061	44	656	66	5	43	26	100	93	116174	27101	95757	84	84
1439	165446	33	511	57	0	69	25	93	60	57635	16373	85584	80	79
1764	237213	84	655	74	0	78	38	140	123	66198	19716	143983	130	127
1495	173326	88	465	49	7	86	44	166	148	71701	17753	75851	82	78
1373	133131	55	525	52	7	44	30	99	90	57793	9028	59238	60	60
2187	258873	60	885	88	3	104	40	139	124	80444	18653	93163	131	131
1491	180083	66	497	36	9	63	34	130	70	53855	8828	96037	84	84
4041	324799	154	1436	108	0	158	47	181	168	97668	29498	151511	140	133
1706	230964	53	612	43	4	102	30	116	115	133824	27563	136368	151	150
2152	236785	119	865	75	3	77	31	116	71	101481	18293	112642	91	91
1036	135473	41	385	32	0	82	23	88	66	99645	22530	94728	138	132
1882	202925	61	567	44	7	115	36	139	134	114789	15977	105499	150	136
1929	215147	58	639	85	0	101	36	135	117	99052	35082	121527	124	124
2242	344297	75	963	86	1	80	30	108	108	67654	16116	127766	119	118
1220	153935	33	398	56	5	50	25	89	84	65553	15849	98958	73	70
1289	132943	40	410	50	7	83	39	156	156	97500	16026	77900	110	107
2515	174724	92	966	135	0	123	34	129	120	69112	26569	85646	123	119
2147	174415	100	801	63	0	73	31	118	114	82753	24785	98579	90	89
2352	225548	112	892	81	5	81	31	118	94	85323	17569	130767	116	112
1638	223632	73	513	52	0	105	33	125	120	72654	23825	131741	113	108
1222	124817	40	469	44	0	47	25	95	81	30727	7869	53907	56	52
1812	221698	45	683	113	0	105	33	126	110	77873	14975	178812	115	112
1677	210767	60	643	39	3	94	35	135	133	117478	37791	146761	119	116
1579	170266	62	535	73	4	44	42	154	122	74007	9605	82036	129	123
1731	260561	75	625	48	1	114	43	165	158	90183	27295	163253	127	125
807	84853	31	264	33	4	38	30	113	109	61542	2746	27032	27	27
2452	294424	77	992	59	2	107	33	127	124	101494	34461	171975	175	162
829	101011	34	238	41	0	30	13	52	39	27570	8098	65990	35	32
1940	215641	46	818	69	0	71	32	121	92	55813	4787	86572	64	64
2662	325107	99	937	64	0	84	36	136	126	79215	24919	159676	96	92
186	7176	17	70	1	0	0	0	0	0	1423	603	1929	0	0
1499	167542	66	507	59	2	59	28	108	70	55461	16329	85371	84	83
865	106408	30	260	32	1	33	14	46	37	31081	12558	58391	41	41
1793	96560	76	503	129	0	42	17	54	38	22996	7784	31580	47	47
2527	265769	146	927	37	2	96	32	124	120	83122	28522	136815	126	120
2747	269651	67	1269	31	10	106	30	115	93	70106	22265	120642	105	105
1324	149112	56	537	65	6	56	35	128	95	60578	14459	69107	80	79
2702	175824	107	910	107	0	57	20	80	77	39992	14526	50495	70	65
1383	152871	58	532	74	5	59	28	97	90	79892	22240	108016	73	70
1179	111665	34	345	54	4	39	28	104	80	49810	11802	46341	57	55
2099	116408	61	918	76	1	34	39	59	31	71570	7623	78348	40	39
4308	362301	119	1635	715	2	76	34	125	110	100708	11912	79336	68	67
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
1831	183167	66	557	66	0	91	39	149	138	82875	18220	93176	127	127
3373	277965	89	1178	106	8	115	39	149	133	139077	19199	161632	154	152
1713	150629	44	740	54	3	85	33	122	113	71595	19918	87850	116	113
1438	168809	66	452	32	0	76	28	118	100	72260	21884	127969	102	99
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2253	329267	259	764	71	8	79	39	144	140	115762	15808	155135	148	141
744	65029	17	255	21	5	21	18	67	61	32551	3597	25109	21	21
1161	101097	64	454	70	3	30	14	52	41	31701	5296	45824	35	35
2352	218946	41	866	112	1	76	29	108	96	80670	25239	102996	112	109
2144	244052	68	574	66	5	101	44	166	164	143558	29801	160604	137	133
4691	341570	168	1276	190	1	94	21	80	78	117105	18450	158051	135	123
1112	103597	43	379	66	1	27	16	60	49	23789	7132	44547	26	26
2694	233328	132	825	165	5	92	28	107	102	120733	34861	162647	230	230
1973	256462	105	798	56	0	123	35	127	124	105195	35940	174141	181	166
1769	206161	71	663	61	12	75	28	107	99	73107	16688	60622	71	68
3148	311473	112	1069	53	8	128	38	146	129	132068	24683	179566	147	147
2474	235800	94	921	127	8	105	23	84	62	149193	46230	184301	190	179
2084	177939	82	858	63	8	55	36	141	73	46821	10387	75661	64	61
1954	207176	70	711	38	8	56	32	123	114	87011	21436	96144	105	101
1226	196553	57	503	50	2	41	29	111	99	95260	30546	129847	107	108
1389	174184	53	382	52	0	72	25	98	70	55183	19746	117286	94	90
1496	143246	103	464	42	5	67	27	105	104	106671	15977	71180	116	114
2269	187559	121	717	76	8	75	36	135	116	73511	22583	109377	106	103
1833	187681	62	690	67	2	114	28	107	91	92945	17274	85298	143	142
1268	119016	52	462	50	5	118	23	85	74	78664	16469	73631	81	79
1943	182192	52	657	53	12	77	40	155	138	70054	14251	86767	89	88
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
1762	194979	62	577	50	7	66	40	155	151	74011	16851	93487	84	83
1403	167488	45	619	77	2	69	28	104	72	83737	21113	82981	113	113
1425	143756	46	479	57	0	105	34	132	120	69094	17401	73815	120	118
1857	275541	63	817	73	4	116	33	127	115	93133	23958	94552	110	110
1840	243199	75	752	34	3	88	28	108	105	95536	23567	132190	134	129
1502	182999	88	430	39	6	73	34	129	104	225920	13065	128754	54	51
1441	135649	46	451	46	2	99	30	116	108	62133	15358	66363	96	93
1420	152299	53	537	63	0	62	33	122	98	61370	14587	67808	78	76
1416	120221	37	519	35	1	53	22	85	69	43836	12770	61724	51	49
2970	346485	90	1000	106	0	118	38	147	111	106117	24021	131722	121	118
1317	145790	63	637	43	5	30	26	99	99	38692	9648	68580	38	38
1644	193339	78	465	47	2	100	35	87	71	84651	20537	106175	145	141
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
1654	122774	45	711	162	0	24	24	90	69	15986	4527	25157	27	27
1054	130585	46	299	57	5	67	29	109	107	95364	30495	76669	91	91
937	112611	41	248	36	0	46	20	78	73	26706	7117	57283	48	48
3004	286468	144	1162	263	1	57	29	111	107	89691	17719	105805	68	63
2008	241066	82	714	78	0	75	45	158	93	67267	27056	129484	58	56
2547	148446	91	905	63	1	135	37	141	129	126846	33473	72413	150	144
1885	204713	71	649	54	1	68	33	122	69	41140	9758	87831	74	73
1626	182079	63	512	63	2	124	33	124	118	102860	21115	96971	181	168
1468	140344	53	472	77	6	33	25	93	73	51715	7236	71299	65	64
2445	220516	62	905	79	1	98	32	124	119	55801	13790	77494	97	97
1964	243060	63	786	110	4	58	29	112	104	111813	32902	120336	121	117
1381	162765	32	489	56	2	68	28	108	107	120293	25131	93913	99	100
1369	182613	39	479	56	3	81	28	99	99	138599	30910	136048	152	149
1659	232138	62	617	43	0	131	31	117	90	161647	35947	181248	188	187
2888	265318	117	925	111	10	110	52	199	197	115929	29848	146123	138	127
1290	85574	34	351	71	0	37	21	78	36	24266	6943	32036	40	37
2845	310839	92	1144	62	9	130	24	91	85	162901	42705	186646	254	245
1982	225060	93	669	56	7	93	41	158	139	109825	31808	102255	87	87
1904	232317	54	707	74	0	118	33	126	106	129838	26675	168237	178	177
1391	144966	144	458	60	0	39	32	122	50	37510	8435	64219	51	49
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
1743	155754	61	599	68	4	74	20	75	31	40652	14993	76825	73	73
1559	164709	109	572	53	0	81	31	115	63	87771	36867	115338	176	177
2014	201940	38	897	87	0	109	31	119	92	85872	33835	109427	94	94
2143	235454	73	819	46	0	151	32	124	106	89275	24164	118168	120	117
2146	220801	75	720	105	1	51	18	72	63	44418	12607	84845	66	60
874	99466	50	273	32	0	28	23	91	69	192565	22609	153197	56	55
1590	92661	61	508	133	1	40	17	45	41	35232	5892	29877	39	39
1590	133328	55	506	79	0	56	20	78	56	40909	17014	63506	66	64
1210	61361	77	451	51	0	27	12	39	25	13294	5394	22445	27	26
2072	125930	75	699	207	4	37	17	68	65	32387	9178	47695	65	64
1281	100750	72	407	67	0	83	30	119	93	140867	6440	68370	58	58
1401	224549	50	465	47	4	54	31	117	114	120662	21916	146304	98	95
834	82316	32	245	34	4	27	10	39	38	21233	4011	38233	25	25
1105	102010	53	370	66	3	28	13	50	44	44332	5818	42071	26	26
1272	101523	42	316	76	0	59	22	88	87	61056	18647	50517	77	76
1944	243511	71	603	65	0	133	42	155	110	101338	20556	103950	130	129
391	22938	10	154	9	0	12	1	0	0	1168	238	5841	11	11
761	41566	35	229	42	5	0	9	36	27	13497	70	2341	2	2
1605	152474	65	577	45	0	106	32	123	83	65567	22392	84396	101	101
530	61857	25	192	25	4	23	11	32	30	25162	3913	24610	31	28
1988	99923	66	617	115	0	44	25	99	80	32334	12237	35753	36	36
1386	132487	41	411	97	0	71	36	136	98	40735	8388	55515	120	89
2395	317394	86	975	53	1	116	31	117	82	91413	22120	209056	195	193
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4
1742	209641	42	705	52	5	62	24	88	60	97068	11727	115814	89	84
620	22648	19	184	44	0	12	13	39	28	44339	3704	11609	24	23
449	31414	19	200	22	0	18	8	25	9	14116	3988	13155	39	39
800	46698	45	274	35	0	14	13	52	33	10288	3030	18274	14	14
1684	131698	65	502	74	0	60	19	75	59	65622	13520	72875	78	78
1050	91735	35	382	103	0	7	18	71	49	16563	1421	10112	15	14
2699	244749	95	964	144	2	98	33	124	115	76643	20923	142775	106	101
1606	184510	49	537	60	7	64	40	151	140	110681	20237	68847	83	82
1502	79863	37	438	134	1	29	22	71	49	29011	3219	17659	24	24
1204	128423	64	369	89	8	32	38	145	120	92696	3769	20112	37	36
1138	97839	38	417	42	2	25	24	87	66	94785	12252	61023	77	75
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
1459	151101	32	514	98	2	48	35	131	124	83209	14497	65176	56	55
2158	272458	65	822	99	0	100	43	162	152	93815	28864	132432	132	131
1111	172494	52	389	52	0	46	43	165	139	86687	21721	112494	144	131
1421	108043	62	466	29	1	45	14	54	38	34553	4821	45109	40	39
2833	328107	65	1255	125	3	129	41	159	144	105547	33644	170875	153	144
1955	250579	83	694	106	0	130	38	147	120	103487	15923	180759	143	139
2922	351067	95	1024	95	3	136	45	170	160	213688	42935	214921	220	211
1002	158015	29	400	40	0	59	31	119	114	71220	18864	100226	79	78
1060	98866	18	397	140	0	25	13	49	39	23517	4977	32043	50	50
956	85439	33	350	43	0	32	28	104	78	56926	7785	54454	39	39
2186	229242	247	719	128	4	63	31	120	119	91721	17939	78876	95	90
3604	351619	139	1277	142	4	95	40	150	141	115168	23436	170745	169	166
1035	84207	29	356	73	11	14	30	112	101	111194	325	6940	12	12
1417	120445	118	457	72	0	36	16	59	56	51009	13539	49025	63	57
3261	324598	110	1402	128	0	113	37	136	133	135777	34538	122037	134	133
1587	131069	67	600	61	4	47	30	107	83	51513	12198	53782	69	69
1424	204271	42	480	73	0	92	35	130	116	74163	26924	127748	119	119
1701	165543	65	595	148	1	70	32	115	90	51633	12716	86839	119	119
1249	141722	94	436	64	0	19	27	107	36	75345	8172	44830	75	65
946	116048	64	230	45	0	50	20	75	50	33416	10855	77395	63	61
1926	250047	81	651	58	0	41	18	71	61	83305	11932	89324	55	49
3352	299775	95	1367	97	9	91	31	120	97	98952	14300	103300	103	101
1641	195838	67	564	50	1	111	31	116	98	102372	25515	112283	197	196
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
2312	254488	83	747	50	10	120	39	150	117	103772	29402	120691	140	136
1369	104389	45	467	105	5	135	41	156	148	123969	16440	58106	89	89
1577	136084	30	671	69	0	27	13	51	41	27142	11221	57140	40	40
2201	199476	70	861	46	2	87	32	118	105	135400	28732	122422	125	123
961	92499	32	319	57	0	25	18	71	55	21399	5250	25899	21	21
1900	224330	83	612	52	1	131	39	144	132	130115	28608	139296	167	163
1254	135781	31	433	98	2	45	14	47	44	24874	8092	52678	32	29
1335	74408	67	434	61	4	29	7	28	21	34988	4473	23853	36	35
1597	81240	66	503	89	0	58	17	68	50	45549	1572	17306	13	13
207	14688	10	85	0	0	4	0	0	0	6023	2065	7953	5	5
1645	181633	70	564	48	2	47	30	110	73	64466	14817	89455	96	96
2429	271856	103	824	91	1	109	37	147	86	54990	16714	147866	151	151
151	7199	5	74	0	0	7	0	0	0	1644	556	4245	6	6
474	46660	20	259	7	0	12	5	15	13	6179	2089	21509	13	13
141	17547	5	69	3	0	0	1	4	4	3926	2658	7670	3	3
1639	133368	36	535	54	1	37	16	64	57	32755	10695	66675	57	56
872	95227	34	239	70	0	37	32	111	48	34777	1669	14336	23	23
1318	152601	48	438	36	2	46	24	85	46	73224	16267	53608	61	57
1018	98146	40	459	37	0	15	17	68	48	27114	7768	30059	21	14
1383	79619	43	426	123	3	42	11	40	32	20760	7252	29668	43	43
1314	59194	31	288	247	6	7	24	80	68	37636	6387	22097	20	20
1335	139942	42	498	46	0	54	22	88	87	65461	18715	96841	82	72
1403	118612	46	454	72	2	54	12	48	43	30080	7936	41907	90	87
910	72880	33	376	41	0	14	19	76	67	24094	8643	27080	25	21

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160355&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160355&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160355&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 time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
totsize[t] = + 2099.94582004143 + 3.2961823571038pageviews[t] -0.105535933643301time_in_rfc[t] + 46.764551412342logins[t] -16.7635951343183compendium_views_info[t] + 55.7978295051499compendium_views_pr[t] + 2262.1160856706shared_compendiums[t] + 68.7547200919634blogged_computations[t] + 597.315222323714compendiums_reviewed[t] -130.303136882258feedback_messages_p1[t] + 197.385030861271feedback_messages_p120[t] + 1.11723721551766totrevisions[t] + 0.406814703222625totseconds[t] -116.16580667081tothyperlinks[t] + 144.149022987652`totblogs `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totsize[t] =  +  2099.94582004143 +  3.2961823571038pageviews[t] -0.105535933643301time_in_rfc[t] +  46.764551412342logins[t] -16.7635951343183compendium_views_info[t] +  55.7978295051499compendium_views_pr[t] +  2262.1160856706shared_compendiums[t] +  68.7547200919634blogged_computations[t] +  597.315222323714compendiums_reviewed[t] -130.303136882258feedback_messages_p1[t] +  197.385030861271feedback_messages_p120[t] +  1.11723721551766totrevisions[t] +  0.406814703222625totseconds[t] -116.16580667081tothyperlinks[t] +  144.149022987652`totblogs
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160355&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totsize[t] =  +  2099.94582004143 +  3.2961823571038pageviews[t] -0.105535933643301time_in_rfc[t] +  46.764551412342logins[t] -16.7635951343183compendium_views_info[t] +  55.7978295051499compendium_views_pr[t] +  2262.1160856706shared_compendiums[t] +  68.7547200919634blogged_computations[t] +  597.315222323714compendiums_reviewed[t] -130.303136882258feedback_messages_p1[t] +  197.385030861271feedback_messages_p120[t] +  1.11723721551766totrevisions[t] +  0.406814703222625totseconds[t] -116.16580667081tothyperlinks[t] +  144.149022987652`totblogs
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160355&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160355&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
totsize[t] = + 2099.94582004143 + 3.2961823571038pageviews[t] -0.105535933643301time_in_rfc[t] + 46.764551412342logins[t] -16.7635951343183compendium_views_info[t] + 55.7978295051499compendium_views_pr[t] + 2262.1160856706shared_compendiums[t] + 68.7547200919634blogged_computations[t] + 597.315222323714compendiums_reviewed[t] -130.303136882258feedback_messages_p1[t] + 197.385030861271feedback_messages_p120[t] + 1.11723721551766totrevisions[t] + 0.406814703222625totseconds[t] -116.16580667081tothyperlinks[t] + 144.149022987652`totblogs `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2099.945820041434904.7679920.42810.6690530.334526
pageviews3.29618235710389.3528410.35240.7249270.362463
time_in_rfc-0.1055359336433010.06991-1.50960.132880.06644
logins46.76455141234269.4735660.67310.501720.25086
compendium_views_info-16.763595134318321.192322-0.7910.4299610.21498
compendium_views_pr55.797829505149936.3094941.53670.1260970.063048
shared_compendiums2262.1160856706611.2862373.70060.0002850.000142
blogged_computations68.7547200919634104.5528520.65760.5116210.255811
compendiums_reviewed597.315222323714769.6104720.77610.4386820.219341
feedback_messages_p1-130.303136882258235.705478-0.55280.5810640.290532
feedback_messages_p120197.385030861271120.6954071.63540.1036940.051847
totrevisions1.117237215517660.3470973.21880.0015240.000762
totseconds0.4068147032226250.0949174.2862.9e-051.5e-05
tothyperlinks-116.16580667081480.400944-0.24180.80920.4046
`totblogs `144.149022987652497.5463970.28970.772360.38618

\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) & 2099.94582004143 & 4904.767992 & 0.4281 & 0.669053 & 0.334526 \tabularnewline
pageviews & 3.2961823571038 & 9.352841 & 0.3524 & 0.724927 & 0.362463 \tabularnewline
time_in_rfc & -0.105535933643301 & 0.06991 & -1.5096 & 0.13288 & 0.06644 \tabularnewline
logins & 46.764551412342 & 69.473566 & 0.6731 & 0.50172 & 0.25086 \tabularnewline
compendium_views_info & -16.7635951343183 & 21.192322 & -0.791 & 0.429961 & 0.21498 \tabularnewline
compendium_views_pr & 55.7978295051499 & 36.309494 & 1.5367 & 0.126097 & 0.063048 \tabularnewline
shared_compendiums & 2262.1160856706 & 611.286237 & 3.7006 & 0.000285 & 0.000142 \tabularnewline
blogged_computations & 68.7547200919634 & 104.552852 & 0.6576 & 0.511621 & 0.255811 \tabularnewline
compendiums_reviewed & 597.315222323714 & 769.610472 & 0.7761 & 0.438682 & 0.219341 \tabularnewline
feedback_messages_p1 & -130.303136882258 & 235.705478 & -0.5528 & 0.581064 & 0.290532 \tabularnewline
feedback_messages_p120 & 197.385030861271 & 120.695407 & 1.6354 & 0.103694 & 0.051847 \tabularnewline
totrevisions & 1.11723721551766 & 0.347097 & 3.2188 & 0.001524 & 0.000762 \tabularnewline
totseconds & 0.406814703222625 & 0.094917 & 4.286 & 2.9e-05 & 1.5e-05 \tabularnewline
tothyperlinks & -116.16580667081 & 480.400944 & -0.2418 & 0.8092 & 0.4046 \tabularnewline
`totblogs
` & 144.149022987652 & 497.546397 & 0.2897 & 0.77236 & 0.38618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160355&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]2099.94582004143[/C][C]4904.767992[/C][C]0.4281[/C][C]0.669053[/C][C]0.334526[/C][/ROW]
[ROW][C]pageviews[/C][C]3.2961823571038[/C][C]9.352841[/C][C]0.3524[/C][C]0.724927[/C][C]0.362463[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]-0.105535933643301[/C][C]0.06991[/C][C]-1.5096[/C][C]0.13288[/C][C]0.06644[/C][/ROW]
[ROW][C]logins[/C][C]46.764551412342[/C][C]69.473566[/C][C]0.6731[/C][C]0.50172[/C][C]0.25086[/C][/ROW]
[ROW][C]compendium_views_info[/C][C]-16.7635951343183[/C][C]21.192322[/C][C]-0.791[/C][C]0.429961[/C][C]0.21498[/C][/ROW]
[ROW][C]compendium_views_pr[/C][C]55.7978295051499[/C][C]36.309494[/C][C]1.5367[/C][C]0.126097[/C][C]0.063048[/C][/ROW]
[ROW][C]shared_compendiums[/C][C]2262.1160856706[/C][C]611.286237[/C][C]3.7006[/C][C]0.000285[/C][C]0.000142[/C][/ROW]
[ROW][C]blogged_computations[/C][C]68.7547200919634[/C][C]104.552852[/C][C]0.6576[/C][C]0.511621[/C][C]0.255811[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]597.315222323714[/C][C]769.610472[/C][C]0.7761[/C][C]0.438682[/C][C]0.219341[/C][/ROW]
[ROW][C]feedback_messages_p1[/C][C]-130.303136882258[/C][C]235.705478[/C][C]-0.5528[/C][C]0.581064[/C][C]0.290532[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]197.385030861271[/C][C]120.695407[/C][C]1.6354[/C][C]0.103694[/C][C]0.051847[/C][/ROW]
[ROW][C]totrevisions[/C][C]1.11723721551766[/C][C]0.347097[/C][C]3.2188[/C][C]0.001524[/C][C]0.000762[/C][/ROW]
[ROW][C]totseconds[/C][C]0.406814703222625[/C][C]0.094917[/C][C]4.286[/C][C]2.9e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]tothyperlinks[/C][C]-116.16580667081[/C][C]480.400944[/C][C]-0.2418[/C][C]0.8092[/C][C]0.4046[/C][/ROW]
[ROW][C]`totblogs
`[/C][C]144.149022987652[/C][C]497.546397[/C][C]0.2897[/C][C]0.77236[/C][C]0.38618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160355&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160355&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)2099.945820041434904.7679920.42810.6690530.334526
pageviews3.29618235710389.3528410.35240.7249270.362463
time_in_rfc-0.1055359336433010.06991-1.50960.132880.06644
logins46.76455141234269.4735660.67310.501720.25086
compendium_views_info-16.763595134318321.192322-0.7910.4299610.21498
compendium_views_pr55.797829505149936.3094941.53670.1260970.063048
shared_compendiums2262.1160856706611.2862373.70060.0002850.000142
blogged_computations68.7547200919634104.5528520.65760.5116210.255811
compendiums_reviewed597.315222323714769.6104720.77610.4386820.219341
feedback_messages_p1-130.303136882258235.705478-0.55280.5810640.290532
feedback_messages_p120197.385030861271120.6954071.63540.1036940.051847
totrevisions1.117237215517660.3470973.21880.0015240.000762
totseconds0.4068147032226250.0949174.2862.9e-051.5e-05
tothyperlinks-116.16580667081480.400944-0.24180.80920.4046
`totblogs `144.149022987652497.5463970.28970.772360.38618






Multiple Linear Regression - Regression Statistics
Multiple R0.845424606895746
R-squared0.714742765944826
Adjusted R-squared0.692799901786736
F-TEST (value)32.5729020967986
F-TEST (DF numerator)14
F-TEST (DF denominator)182
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22731.7130355937
Sum Squared Residuals94045001510.9293

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.845424606895746 \tabularnewline
R-squared & 0.714742765944826 \tabularnewline
Adjusted R-squared & 0.692799901786736 \tabularnewline
F-TEST (value) & 32.5729020967986 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 182 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 22731.7130355937 \tabularnewline
Sum Squared Residuals & 94045001510.9293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160355&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.845424606895746[/C][/ROW]
[ROW][C]R-squared[/C][C]0.714742765944826[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.692799901786736[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]32.5729020967986[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]182[/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]22731.7130355937[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]94045001510.9293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160355&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160355&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 R0.845424606895746
R-squared0.714742765944826
Adjusted R-squared0.692799901786736
F-TEST (value)32.5729020967986
F-TEST (DF numerator)14
F-TEST (DF denominator)182
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22731.7130355937
Sum Squared Residuals94045001510.9293






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112285109430.0217878832854.97821211731
28478691819.1386445416-7033.13864454164
38312391335.0768676245-8212.07686762447
410119399196.80966183871996.19033816127
53836155088.6964461765-16727.6964461765
66850450455.225548487718048.7744515123
7119182102381.76788917516800.2321108251
82280712072.25208303410734.747916966
91714022091.4712157128-4951.47121571275
1011617493037.804127346923136.1958726531
115763560145.8926578285-2510.89265782853
126619897861.0106150539-31663.0106150539
137170195811.8137839478-24110.8137839478
145779366756.5689607368-8963.5689607368
158044481467.3544268822-1023.35442688215
165385577932.679673867-24077.679673867
1797668116311.88854436-18643.8885443602
18133824109883.98980835223940.010191648
1910148177758.235022205923722.7649777941
209964576108.17476737623536.825232624
2111478999220.716390773615568.2836092264
2299052108550.269391584-9498.26939158443
236765471412.0248665141-3758.02486651412
246555382111.1622890438-16558.1622890438
259750097648.8000001838-148.800000183766
266911290625.5111338183-21513.5111338183
278275386364.3194577601-3611.31945776007
288532394924.3183840939-9601.31838409389
297265498596.1260371563-25942.1260371563
303072742904.668243278-12177.668243278
3177873106119.953821568-28246.9538215678
32117478127227.358567237-9749.35856723699
337400775364.2140938761-1357.21409387613
3490183121661.286282984-31478.2862829836
356154245862.174320733715679.8256792663
36101494120379.160086863-18885.1600868627
372757041250.2082343289-13680.2082343289
385581346771.58250672049041.41749327958
397921598393.9786766126-19178.9786766126
4014233091.49302622469-1668.49302622469
415546167468.397307263-12007.397307263
423108145685.8392472863-14604.8392472863
432299636504.7013793752-13508.7013793752
4483122103686.082341251-20564.0823412511
457010694379.6728737496-24273.6728737496
466057874735.1952417547-14157.1952417547
473999246818.6799145082-6826.67991450824
487989296065.6970791231-16173.697079123
494981057059.6919568891-7249.69195688912
507157056128.816381024815441.1836189752
5110070878941.137223651921766.8627763481
523303252822.632726626-19790.6327266261
538287585427.5873978297-2552.58739782968
54139077121573.05860488917503.941395111
557159584070.0328548076-12475.0328548076
567226091563.3354263456-19303.3354263456
57595011851.2214560107-5901.22145601069
58115762117642.078029822-1880.07802982221
593255137018.7847528281-4467.78475282814
603170138612.158362554-6911.15836255397
618067082881.0771761833-2211.07717618333
62143558137816.5084305815741.49156941911
6311710591781.017366508325323.9826334917
642378936519.5138000277-12730.5138000277
65120733140007.985195332-19274.985195332
66105195127384.424793499-22189.4247934991
677310781269.428519685-8162.42851968502
68132068130653.6758336971414.32416630312
69149193152116.486642739-2923.48664273871
704682166318.4034450566-19497.4034450566
718701193110.5127432282-6099.51274322824
7295260102252.980504933-6992.98050493286
735518380030.9137983012-24847.9137983012
7410667179951.179896644726719.8201033553
7573511109987.551951842-36476.5519518417
769294574369.520415754418575.4795842456
777866478220.8163194287443.183680571251
7870054100592.444503775-30538.4445037747
792261838714.7489291616-16096.7489291616
807401196285.5056862452-22274.5056862452
818373772234.213242469311502.7867575307
826909475481.3784007143-6387.37840071434
839313383661.29203165239471.70796834768
849553694646.9821050289889.017894971095
8522592097494.7096899657128425.290310034
866213371554.1294386445-9421.12943864448
876137060897.010298505472.989701494981
884383651169.6587255544-7333.65872555444
8910611785626.110192856220490.8898071438
903869261008.3420634413-22316.3420634413
918465190192.6423279059-5541.64232790587
925662234861.367374983621760.6326250164
931598627744.9804066333-11758.9804066333
9495364100075.18538853-4711.18538852975
952670645025.2809794329-18319.2809794329
968969177879.932976365711811.0670236343
976726793538.6664944519-26271.6664944519
9812684698351.854852612628494.1451473874
994114055093.4404312681-13953.4404312681
10010286092247.912073907410612.0879260926
1015171562819.9120545236-11104.9120545236
1025580164115.2941261775-8314.2941261775
103111813103645.4636555338167.53634446716
10412029388066.967499358232226.0325006418
105138599113681.4651048724917.5348951296
106161647127080.18159165934566.8184083408
107115929149044.848070442-33115.848070442
1082426630494.8439757332-6228.84397573319
109162901145262.36588348917638.6341165105
110109825114278.006609457-4453.0066094566
111129838114070.07769451415767.9223054861
1123751046245.8172911089-8735.81729110889
1134375041289.45960721572460.54039278428
1144065260488.915121797-19836.915121797
11587771103037.694853017-15266.6948530172
1168587292634.0919151266-6762.09191512661
1178927588822.1478479743452.852152025669
1184441852324.3515421344-7906.35154213439
119192565100461.55200265292103.447997348
1203523236543.9720254507-1311.9720254507
1214090954856.6091093121-13947.6091093121
1221329423144.4093211783-9850.40932117833
1233238756027.1865402713-23640.1865402713
12414086759081.117655890381785.8823441097
125120662105033.87766687515628.1223331248
1262123335479.5453029419-14246.5453029419
1274433237924.2684531616407.73154683899
1286105662783.8396959776-1727.8396959776
12910133884142.227775146117195.7722248539
13011683728.49658647283-2560.49658647283
1311349718774.4654215929-5277.46542159286
1326556776111.6097740206-10544.6097740206
1332516230434.8680825338-5272.86808253378
1343233447340.1156746685-15006.1156746685
1354073551978.7235932586-11243.7235932586
13691413111755.673535539-20342.6735355393
1378553024.46332653226-2169.46332653226
1389706871036.304763615526031.6952363845
1394433920436.050288263623902.9497117364
1401411614461.5140466695-345.514046669475
1411028818949.4110060669-8661.41100606688
1426562256786.86949637968835.13050362041
1431656314490.04523942032072.95476057967
14476643102703.627882247-26060.6278822472
14511068189440.275335804121240.7246641959
1462901129756.3446245568-745.344624556815
1479269655359.741072774437336.2589272256
1489478555305.171999768239479.8280002318
149877314555.0204273025-5782.02042730249
1508320969581.234320939613627.7656790604
15193815106369.302082307-12554.3020823068
1528668793341.036403041-6654.03640304097
1533455330982.24405827843570.75594172164
154105547123724.823619712-18177.823619712
155103487111178.911889109-7691.91188910898
156213688159965.52774504853722.4722549518
1577122079093.7260560658-7873.72605606585
1582351727950.636652514-4433.63665251402
1595692647020.69853507789905.30146492221
1609172185568.99464919796152.00535080213
161115168117587.855869512-2419.85586951236
16211119448715.635083120262478.3649168798
1635100947300.36261611983708.63738388017
16413577797615.821886640238161.1781133598
1655151360055.3580191872-8542.35801918716
16674163101795.590898618-27632.5908986182
1675163373392.451279905-21759.451279905
1687534530540.208252454144804.7917475459
1693341655187.3422799639-21771.3422799639
1708330544873.507238583538431.4927614165
1719895277687.793889262321264.2061107377
17210237295501.94218062666870.05781937342
1733723818653.589318705218584.4106812948
174103772120016.569592291-16244.569592291
17512396994194.555064334829774.4449356652
1762714234910.9290535236-7768.92905352356
17713540099792.4919238835607.50807612
1782139925899.3967240271-4500.39672402713
179130115115793.07562920214321.9243707984
1802487441038.0215319037-16164.0215319037
1813498829193.67931478845794.3206852116
1824554922721.773410555722827.2265894443
18360236232.31084904104-209.310849041044
1846446666233.5290681204-1767.52906812036
1855499090227.5716034464-35237.5716034463
18616443828.5275323408-2184.5275323408
187617911593.0931961422-5414.09319614222
18839266996.87442581703-3070.87442581703
1893275546954.1025017398-14199.1025017398
1903477721422.913577221113354.0864227789
1917322448389.850732477524834.1492675225
1922711423623.43528803013490.56471196994
1932076038712.1883942871-17952.1883942871
1943763658660.203921446-21024.203921446
1956546171632.0626672083-6171.06266720831
1963008038403.1985948465-8323.19859484647
1972409431364.9640599467-7270.96405994675

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112285 & 109430.021787883 & 2854.97821211731 \tabularnewline
2 & 84786 & 91819.1386445416 & -7033.13864454164 \tabularnewline
3 & 83123 & 91335.0768676245 & -8212.07686762447 \tabularnewline
4 & 101193 & 99196.8096618387 & 1996.19033816127 \tabularnewline
5 & 38361 & 55088.6964461765 & -16727.6964461765 \tabularnewline
6 & 68504 & 50455.2255484877 & 18048.7744515123 \tabularnewline
7 & 119182 & 102381.767889175 & 16800.2321108251 \tabularnewline
8 & 22807 & 12072.252083034 & 10734.747916966 \tabularnewline
9 & 17140 & 22091.4712157128 & -4951.47121571275 \tabularnewline
10 & 116174 & 93037.8041273469 & 23136.1958726531 \tabularnewline
11 & 57635 & 60145.8926578285 & -2510.89265782853 \tabularnewline
12 & 66198 & 97861.0106150539 & -31663.0106150539 \tabularnewline
13 & 71701 & 95811.8137839478 & -24110.8137839478 \tabularnewline
14 & 57793 & 66756.5689607368 & -8963.5689607368 \tabularnewline
15 & 80444 & 81467.3544268822 & -1023.35442688215 \tabularnewline
16 & 53855 & 77932.679673867 & -24077.679673867 \tabularnewline
17 & 97668 & 116311.88854436 & -18643.8885443602 \tabularnewline
18 & 133824 & 109883.989808352 & 23940.010191648 \tabularnewline
19 & 101481 & 77758.2350222059 & 23722.7649777941 \tabularnewline
20 & 99645 & 76108.174767376 & 23536.825232624 \tabularnewline
21 & 114789 & 99220.7163907736 & 15568.2836092264 \tabularnewline
22 & 99052 & 108550.269391584 & -9498.26939158443 \tabularnewline
23 & 67654 & 71412.0248665141 & -3758.02486651412 \tabularnewline
24 & 65553 & 82111.1622890438 & -16558.1622890438 \tabularnewline
25 & 97500 & 97648.8000001838 & -148.800000183766 \tabularnewline
26 & 69112 & 90625.5111338183 & -21513.5111338183 \tabularnewline
27 & 82753 & 86364.3194577601 & -3611.31945776007 \tabularnewline
28 & 85323 & 94924.3183840939 & -9601.31838409389 \tabularnewline
29 & 72654 & 98596.1260371563 & -25942.1260371563 \tabularnewline
30 & 30727 & 42904.668243278 & -12177.668243278 \tabularnewline
31 & 77873 & 106119.953821568 & -28246.9538215678 \tabularnewline
32 & 117478 & 127227.358567237 & -9749.35856723699 \tabularnewline
33 & 74007 & 75364.2140938761 & -1357.21409387613 \tabularnewline
34 & 90183 & 121661.286282984 & -31478.2862829836 \tabularnewline
35 & 61542 & 45862.1743207337 & 15679.8256792663 \tabularnewline
36 & 101494 & 120379.160086863 & -18885.1600868627 \tabularnewline
37 & 27570 & 41250.2082343289 & -13680.2082343289 \tabularnewline
38 & 55813 & 46771.5825067204 & 9041.41749327958 \tabularnewline
39 & 79215 & 98393.9786766126 & -19178.9786766126 \tabularnewline
40 & 1423 & 3091.49302622469 & -1668.49302622469 \tabularnewline
41 & 55461 & 67468.397307263 & -12007.397307263 \tabularnewline
42 & 31081 & 45685.8392472863 & -14604.8392472863 \tabularnewline
43 & 22996 & 36504.7013793752 & -13508.7013793752 \tabularnewline
44 & 83122 & 103686.082341251 & -20564.0823412511 \tabularnewline
45 & 70106 & 94379.6728737496 & -24273.6728737496 \tabularnewline
46 & 60578 & 74735.1952417547 & -14157.1952417547 \tabularnewline
47 & 39992 & 46818.6799145082 & -6826.67991450824 \tabularnewline
48 & 79892 & 96065.6970791231 & -16173.697079123 \tabularnewline
49 & 49810 & 57059.6919568891 & -7249.69195688912 \tabularnewline
50 & 71570 & 56128.8163810248 & 15441.1836189752 \tabularnewline
51 & 100708 & 78941.1372236519 & 21766.8627763481 \tabularnewline
52 & 33032 & 52822.632726626 & -19790.6327266261 \tabularnewline
53 & 82875 & 85427.5873978297 & -2552.58739782968 \tabularnewline
54 & 139077 & 121573.058604889 & 17503.941395111 \tabularnewline
55 & 71595 & 84070.0328548076 & -12475.0328548076 \tabularnewline
56 & 72260 & 91563.3354263456 & -19303.3354263456 \tabularnewline
57 & 5950 & 11851.2214560107 & -5901.22145601069 \tabularnewline
58 & 115762 & 117642.078029822 & -1880.07802982221 \tabularnewline
59 & 32551 & 37018.7847528281 & -4467.78475282814 \tabularnewline
60 & 31701 & 38612.158362554 & -6911.15836255397 \tabularnewline
61 & 80670 & 82881.0771761833 & -2211.07717618333 \tabularnewline
62 & 143558 & 137816.508430581 & 5741.49156941911 \tabularnewline
63 & 117105 & 91781.0173665083 & 25323.9826334917 \tabularnewline
64 & 23789 & 36519.5138000277 & -12730.5138000277 \tabularnewline
65 & 120733 & 140007.985195332 & -19274.985195332 \tabularnewline
66 & 105195 & 127384.424793499 & -22189.4247934991 \tabularnewline
67 & 73107 & 81269.428519685 & -8162.42851968502 \tabularnewline
68 & 132068 & 130653.675833697 & 1414.32416630312 \tabularnewline
69 & 149193 & 152116.486642739 & -2923.48664273871 \tabularnewline
70 & 46821 & 66318.4034450566 & -19497.4034450566 \tabularnewline
71 & 87011 & 93110.5127432282 & -6099.51274322824 \tabularnewline
72 & 95260 & 102252.980504933 & -6992.98050493286 \tabularnewline
73 & 55183 & 80030.9137983012 & -24847.9137983012 \tabularnewline
74 & 106671 & 79951.1798966447 & 26719.8201033553 \tabularnewline
75 & 73511 & 109987.551951842 & -36476.5519518417 \tabularnewline
76 & 92945 & 74369.5204157544 & 18575.4795842456 \tabularnewline
77 & 78664 & 78220.8163194287 & 443.183680571251 \tabularnewline
78 & 70054 & 100592.444503775 & -30538.4445037747 \tabularnewline
79 & 22618 & 38714.7489291616 & -16096.7489291616 \tabularnewline
80 & 74011 & 96285.5056862452 & -22274.5056862452 \tabularnewline
81 & 83737 & 72234.2132424693 & 11502.7867575307 \tabularnewline
82 & 69094 & 75481.3784007143 & -6387.37840071434 \tabularnewline
83 & 93133 & 83661.2920316523 & 9471.70796834768 \tabularnewline
84 & 95536 & 94646.9821050289 & 889.017894971095 \tabularnewline
85 & 225920 & 97494.7096899657 & 128425.290310034 \tabularnewline
86 & 62133 & 71554.1294386445 & -9421.12943864448 \tabularnewline
87 & 61370 & 60897.010298505 & 472.989701494981 \tabularnewline
88 & 43836 & 51169.6587255544 & -7333.65872555444 \tabularnewline
89 & 106117 & 85626.1101928562 & 20490.8898071438 \tabularnewline
90 & 38692 & 61008.3420634413 & -22316.3420634413 \tabularnewline
91 & 84651 & 90192.6423279059 & -5541.64232790587 \tabularnewline
92 & 56622 & 34861.3673749836 & 21760.6326250164 \tabularnewline
93 & 15986 & 27744.9804066333 & -11758.9804066333 \tabularnewline
94 & 95364 & 100075.18538853 & -4711.18538852975 \tabularnewline
95 & 26706 & 45025.2809794329 & -18319.2809794329 \tabularnewline
96 & 89691 & 77879.9329763657 & 11811.0670236343 \tabularnewline
97 & 67267 & 93538.6664944519 & -26271.6664944519 \tabularnewline
98 & 126846 & 98351.8548526126 & 28494.1451473874 \tabularnewline
99 & 41140 & 55093.4404312681 & -13953.4404312681 \tabularnewline
100 & 102860 & 92247.9120739074 & 10612.0879260926 \tabularnewline
101 & 51715 & 62819.9120545236 & -11104.9120545236 \tabularnewline
102 & 55801 & 64115.2941261775 & -8314.2941261775 \tabularnewline
103 & 111813 & 103645.463655533 & 8167.53634446716 \tabularnewline
104 & 120293 & 88066.9674993582 & 32226.0325006418 \tabularnewline
105 & 138599 & 113681.46510487 & 24917.5348951296 \tabularnewline
106 & 161647 & 127080.181591659 & 34566.8184083408 \tabularnewline
107 & 115929 & 149044.848070442 & -33115.848070442 \tabularnewline
108 & 24266 & 30494.8439757332 & -6228.84397573319 \tabularnewline
109 & 162901 & 145262.365883489 & 17638.6341165105 \tabularnewline
110 & 109825 & 114278.006609457 & -4453.0066094566 \tabularnewline
111 & 129838 & 114070.077694514 & 15767.9223054861 \tabularnewline
112 & 37510 & 46245.8172911089 & -8735.81729110889 \tabularnewline
113 & 43750 & 41289.4596072157 & 2460.54039278428 \tabularnewline
114 & 40652 & 60488.915121797 & -19836.915121797 \tabularnewline
115 & 87771 & 103037.694853017 & -15266.6948530172 \tabularnewline
116 & 85872 & 92634.0919151266 & -6762.09191512661 \tabularnewline
117 & 89275 & 88822.1478479743 & 452.852152025669 \tabularnewline
118 & 44418 & 52324.3515421344 & -7906.35154213439 \tabularnewline
119 & 192565 & 100461.552002652 & 92103.447997348 \tabularnewline
120 & 35232 & 36543.9720254507 & -1311.9720254507 \tabularnewline
121 & 40909 & 54856.6091093121 & -13947.6091093121 \tabularnewline
122 & 13294 & 23144.4093211783 & -9850.40932117833 \tabularnewline
123 & 32387 & 56027.1865402713 & -23640.1865402713 \tabularnewline
124 & 140867 & 59081.1176558903 & 81785.8823441097 \tabularnewline
125 & 120662 & 105033.877666875 & 15628.1223331248 \tabularnewline
126 & 21233 & 35479.5453029419 & -14246.5453029419 \tabularnewline
127 & 44332 & 37924.268453161 & 6407.73154683899 \tabularnewline
128 & 61056 & 62783.8396959776 & -1727.8396959776 \tabularnewline
129 & 101338 & 84142.2277751461 & 17195.7722248539 \tabularnewline
130 & 1168 & 3728.49658647283 & -2560.49658647283 \tabularnewline
131 & 13497 & 18774.4654215929 & -5277.46542159286 \tabularnewline
132 & 65567 & 76111.6097740206 & -10544.6097740206 \tabularnewline
133 & 25162 & 30434.8680825338 & -5272.86808253378 \tabularnewline
134 & 32334 & 47340.1156746685 & -15006.1156746685 \tabularnewline
135 & 40735 & 51978.7235932586 & -11243.7235932586 \tabularnewline
136 & 91413 & 111755.673535539 & -20342.6735355393 \tabularnewline
137 & 855 & 3024.46332653226 & -2169.46332653226 \tabularnewline
138 & 97068 & 71036.3047636155 & 26031.6952363845 \tabularnewline
139 & 44339 & 20436.0502882636 & 23902.9497117364 \tabularnewline
140 & 14116 & 14461.5140466695 & -345.514046669475 \tabularnewline
141 & 10288 & 18949.4110060669 & -8661.41100606688 \tabularnewline
142 & 65622 & 56786.8694963796 & 8835.13050362041 \tabularnewline
143 & 16563 & 14490.0452394203 & 2072.95476057967 \tabularnewline
144 & 76643 & 102703.627882247 & -26060.6278822472 \tabularnewline
145 & 110681 & 89440.2753358041 & 21240.7246641959 \tabularnewline
146 & 29011 & 29756.3446245568 & -745.344624556815 \tabularnewline
147 & 92696 & 55359.7410727744 & 37336.2589272256 \tabularnewline
148 & 94785 & 55305.1719997682 & 39479.8280002318 \tabularnewline
149 & 8773 & 14555.0204273025 & -5782.02042730249 \tabularnewline
150 & 83209 & 69581.2343209396 & 13627.7656790604 \tabularnewline
151 & 93815 & 106369.302082307 & -12554.3020823068 \tabularnewline
152 & 86687 & 93341.036403041 & -6654.03640304097 \tabularnewline
153 & 34553 & 30982.2440582784 & 3570.75594172164 \tabularnewline
154 & 105547 & 123724.823619712 & -18177.823619712 \tabularnewline
155 & 103487 & 111178.911889109 & -7691.91188910898 \tabularnewline
156 & 213688 & 159965.527745048 & 53722.4722549518 \tabularnewline
157 & 71220 & 79093.7260560658 & -7873.72605606585 \tabularnewline
158 & 23517 & 27950.636652514 & -4433.63665251402 \tabularnewline
159 & 56926 & 47020.6985350778 & 9905.30146492221 \tabularnewline
160 & 91721 & 85568.9946491979 & 6152.00535080213 \tabularnewline
161 & 115168 & 117587.855869512 & -2419.85586951236 \tabularnewline
162 & 111194 & 48715.6350831202 & 62478.3649168798 \tabularnewline
163 & 51009 & 47300.3626161198 & 3708.63738388017 \tabularnewline
164 & 135777 & 97615.8218866402 & 38161.1781133598 \tabularnewline
165 & 51513 & 60055.3580191872 & -8542.35801918716 \tabularnewline
166 & 74163 & 101795.590898618 & -27632.5908986182 \tabularnewline
167 & 51633 & 73392.451279905 & -21759.451279905 \tabularnewline
168 & 75345 & 30540.2082524541 & 44804.7917475459 \tabularnewline
169 & 33416 & 55187.3422799639 & -21771.3422799639 \tabularnewline
170 & 83305 & 44873.5072385835 & 38431.4927614165 \tabularnewline
171 & 98952 & 77687.7938892623 & 21264.2061107377 \tabularnewline
172 & 102372 & 95501.9421806266 & 6870.05781937342 \tabularnewline
173 & 37238 & 18653.5893187052 & 18584.4106812948 \tabularnewline
174 & 103772 & 120016.569592291 & -16244.569592291 \tabularnewline
175 & 123969 & 94194.5550643348 & 29774.4449356652 \tabularnewline
176 & 27142 & 34910.9290535236 & -7768.92905352356 \tabularnewline
177 & 135400 & 99792.49192388 & 35607.50807612 \tabularnewline
178 & 21399 & 25899.3967240271 & -4500.39672402713 \tabularnewline
179 & 130115 & 115793.075629202 & 14321.9243707984 \tabularnewline
180 & 24874 & 41038.0215319037 & -16164.0215319037 \tabularnewline
181 & 34988 & 29193.6793147884 & 5794.3206852116 \tabularnewline
182 & 45549 & 22721.7734105557 & 22827.2265894443 \tabularnewline
183 & 6023 & 6232.31084904104 & -209.310849041044 \tabularnewline
184 & 64466 & 66233.5290681204 & -1767.52906812036 \tabularnewline
185 & 54990 & 90227.5716034464 & -35237.5716034463 \tabularnewline
186 & 1644 & 3828.5275323408 & -2184.5275323408 \tabularnewline
187 & 6179 & 11593.0931961422 & -5414.09319614222 \tabularnewline
188 & 3926 & 6996.87442581703 & -3070.87442581703 \tabularnewline
189 & 32755 & 46954.1025017398 & -14199.1025017398 \tabularnewline
190 & 34777 & 21422.9135772211 & 13354.0864227789 \tabularnewline
191 & 73224 & 48389.8507324775 & 24834.1492675225 \tabularnewline
192 & 27114 & 23623.4352880301 & 3490.56471196994 \tabularnewline
193 & 20760 & 38712.1883942871 & -17952.1883942871 \tabularnewline
194 & 37636 & 58660.203921446 & -21024.203921446 \tabularnewline
195 & 65461 & 71632.0626672083 & -6171.06266720831 \tabularnewline
196 & 30080 & 38403.1985948465 & -8323.19859484647 \tabularnewline
197 & 24094 & 31364.9640599467 & -7270.96405994675 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160355&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]112285[/C][C]109430.021787883[/C][C]2854.97821211731[/C][/ROW]
[ROW][C]2[/C][C]84786[/C][C]91819.1386445416[/C][C]-7033.13864454164[/C][/ROW]
[ROW][C]3[/C][C]83123[/C][C]91335.0768676245[/C][C]-8212.07686762447[/C][/ROW]
[ROW][C]4[/C][C]101193[/C][C]99196.8096618387[/C][C]1996.19033816127[/C][/ROW]
[ROW][C]5[/C][C]38361[/C][C]55088.6964461765[/C][C]-16727.6964461765[/C][/ROW]
[ROW][C]6[/C][C]68504[/C][C]50455.2255484877[/C][C]18048.7744515123[/C][/ROW]
[ROW][C]7[/C][C]119182[/C][C]102381.767889175[/C][C]16800.2321108251[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]12072.252083034[/C][C]10734.747916966[/C][/ROW]
[ROW][C]9[/C][C]17140[/C][C]22091.4712157128[/C][C]-4951.47121571275[/C][/ROW]
[ROW][C]10[/C][C]116174[/C][C]93037.8041273469[/C][C]23136.1958726531[/C][/ROW]
[ROW][C]11[/C][C]57635[/C][C]60145.8926578285[/C][C]-2510.89265782853[/C][/ROW]
[ROW][C]12[/C][C]66198[/C][C]97861.0106150539[/C][C]-31663.0106150539[/C][/ROW]
[ROW][C]13[/C][C]71701[/C][C]95811.8137839478[/C][C]-24110.8137839478[/C][/ROW]
[ROW][C]14[/C][C]57793[/C][C]66756.5689607368[/C][C]-8963.5689607368[/C][/ROW]
[ROW][C]15[/C][C]80444[/C][C]81467.3544268822[/C][C]-1023.35442688215[/C][/ROW]
[ROW][C]16[/C][C]53855[/C][C]77932.679673867[/C][C]-24077.679673867[/C][/ROW]
[ROW][C]17[/C][C]97668[/C][C]116311.88854436[/C][C]-18643.8885443602[/C][/ROW]
[ROW][C]18[/C][C]133824[/C][C]109883.989808352[/C][C]23940.010191648[/C][/ROW]
[ROW][C]19[/C][C]101481[/C][C]77758.2350222059[/C][C]23722.7649777941[/C][/ROW]
[ROW][C]20[/C][C]99645[/C][C]76108.174767376[/C][C]23536.825232624[/C][/ROW]
[ROW][C]21[/C][C]114789[/C][C]99220.7163907736[/C][C]15568.2836092264[/C][/ROW]
[ROW][C]22[/C][C]99052[/C][C]108550.269391584[/C][C]-9498.26939158443[/C][/ROW]
[ROW][C]23[/C][C]67654[/C][C]71412.0248665141[/C][C]-3758.02486651412[/C][/ROW]
[ROW][C]24[/C][C]65553[/C][C]82111.1622890438[/C][C]-16558.1622890438[/C][/ROW]
[ROW][C]25[/C][C]97500[/C][C]97648.8000001838[/C][C]-148.800000183766[/C][/ROW]
[ROW][C]26[/C][C]69112[/C][C]90625.5111338183[/C][C]-21513.5111338183[/C][/ROW]
[ROW][C]27[/C][C]82753[/C][C]86364.3194577601[/C][C]-3611.31945776007[/C][/ROW]
[ROW][C]28[/C][C]85323[/C][C]94924.3183840939[/C][C]-9601.31838409389[/C][/ROW]
[ROW][C]29[/C][C]72654[/C][C]98596.1260371563[/C][C]-25942.1260371563[/C][/ROW]
[ROW][C]30[/C][C]30727[/C][C]42904.668243278[/C][C]-12177.668243278[/C][/ROW]
[ROW][C]31[/C][C]77873[/C][C]106119.953821568[/C][C]-28246.9538215678[/C][/ROW]
[ROW][C]32[/C][C]117478[/C][C]127227.358567237[/C][C]-9749.35856723699[/C][/ROW]
[ROW][C]33[/C][C]74007[/C][C]75364.2140938761[/C][C]-1357.21409387613[/C][/ROW]
[ROW][C]34[/C][C]90183[/C][C]121661.286282984[/C][C]-31478.2862829836[/C][/ROW]
[ROW][C]35[/C][C]61542[/C][C]45862.1743207337[/C][C]15679.8256792663[/C][/ROW]
[ROW][C]36[/C][C]101494[/C][C]120379.160086863[/C][C]-18885.1600868627[/C][/ROW]
[ROW][C]37[/C][C]27570[/C][C]41250.2082343289[/C][C]-13680.2082343289[/C][/ROW]
[ROW][C]38[/C][C]55813[/C][C]46771.5825067204[/C][C]9041.41749327958[/C][/ROW]
[ROW][C]39[/C][C]79215[/C][C]98393.9786766126[/C][C]-19178.9786766126[/C][/ROW]
[ROW][C]40[/C][C]1423[/C][C]3091.49302622469[/C][C]-1668.49302622469[/C][/ROW]
[ROW][C]41[/C][C]55461[/C][C]67468.397307263[/C][C]-12007.397307263[/C][/ROW]
[ROW][C]42[/C][C]31081[/C][C]45685.8392472863[/C][C]-14604.8392472863[/C][/ROW]
[ROW][C]43[/C][C]22996[/C][C]36504.7013793752[/C][C]-13508.7013793752[/C][/ROW]
[ROW][C]44[/C][C]83122[/C][C]103686.082341251[/C][C]-20564.0823412511[/C][/ROW]
[ROW][C]45[/C][C]70106[/C][C]94379.6728737496[/C][C]-24273.6728737496[/C][/ROW]
[ROW][C]46[/C][C]60578[/C][C]74735.1952417547[/C][C]-14157.1952417547[/C][/ROW]
[ROW][C]47[/C][C]39992[/C][C]46818.6799145082[/C][C]-6826.67991450824[/C][/ROW]
[ROW][C]48[/C][C]79892[/C][C]96065.6970791231[/C][C]-16173.697079123[/C][/ROW]
[ROW][C]49[/C][C]49810[/C][C]57059.6919568891[/C][C]-7249.69195688912[/C][/ROW]
[ROW][C]50[/C][C]71570[/C][C]56128.8163810248[/C][C]15441.1836189752[/C][/ROW]
[ROW][C]51[/C][C]100708[/C][C]78941.1372236519[/C][C]21766.8627763481[/C][/ROW]
[ROW][C]52[/C][C]33032[/C][C]52822.632726626[/C][C]-19790.6327266261[/C][/ROW]
[ROW][C]53[/C][C]82875[/C][C]85427.5873978297[/C][C]-2552.58739782968[/C][/ROW]
[ROW][C]54[/C][C]139077[/C][C]121573.058604889[/C][C]17503.941395111[/C][/ROW]
[ROW][C]55[/C][C]71595[/C][C]84070.0328548076[/C][C]-12475.0328548076[/C][/ROW]
[ROW][C]56[/C][C]72260[/C][C]91563.3354263456[/C][C]-19303.3354263456[/C][/ROW]
[ROW][C]57[/C][C]5950[/C][C]11851.2214560107[/C][C]-5901.22145601069[/C][/ROW]
[ROW][C]58[/C][C]115762[/C][C]117642.078029822[/C][C]-1880.07802982221[/C][/ROW]
[ROW][C]59[/C][C]32551[/C][C]37018.7847528281[/C][C]-4467.78475282814[/C][/ROW]
[ROW][C]60[/C][C]31701[/C][C]38612.158362554[/C][C]-6911.15836255397[/C][/ROW]
[ROW][C]61[/C][C]80670[/C][C]82881.0771761833[/C][C]-2211.07717618333[/C][/ROW]
[ROW][C]62[/C][C]143558[/C][C]137816.508430581[/C][C]5741.49156941911[/C][/ROW]
[ROW][C]63[/C][C]117105[/C][C]91781.0173665083[/C][C]25323.9826334917[/C][/ROW]
[ROW][C]64[/C][C]23789[/C][C]36519.5138000277[/C][C]-12730.5138000277[/C][/ROW]
[ROW][C]65[/C][C]120733[/C][C]140007.985195332[/C][C]-19274.985195332[/C][/ROW]
[ROW][C]66[/C][C]105195[/C][C]127384.424793499[/C][C]-22189.4247934991[/C][/ROW]
[ROW][C]67[/C][C]73107[/C][C]81269.428519685[/C][C]-8162.42851968502[/C][/ROW]
[ROW][C]68[/C][C]132068[/C][C]130653.675833697[/C][C]1414.32416630312[/C][/ROW]
[ROW][C]69[/C][C]149193[/C][C]152116.486642739[/C][C]-2923.48664273871[/C][/ROW]
[ROW][C]70[/C][C]46821[/C][C]66318.4034450566[/C][C]-19497.4034450566[/C][/ROW]
[ROW][C]71[/C][C]87011[/C][C]93110.5127432282[/C][C]-6099.51274322824[/C][/ROW]
[ROW][C]72[/C][C]95260[/C][C]102252.980504933[/C][C]-6992.98050493286[/C][/ROW]
[ROW][C]73[/C][C]55183[/C][C]80030.9137983012[/C][C]-24847.9137983012[/C][/ROW]
[ROW][C]74[/C][C]106671[/C][C]79951.1798966447[/C][C]26719.8201033553[/C][/ROW]
[ROW][C]75[/C][C]73511[/C][C]109987.551951842[/C][C]-36476.5519518417[/C][/ROW]
[ROW][C]76[/C][C]92945[/C][C]74369.5204157544[/C][C]18575.4795842456[/C][/ROW]
[ROW][C]77[/C][C]78664[/C][C]78220.8163194287[/C][C]443.183680571251[/C][/ROW]
[ROW][C]78[/C][C]70054[/C][C]100592.444503775[/C][C]-30538.4445037747[/C][/ROW]
[ROW][C]79[/C][C]22618[/C][C]38714.7489291616[/C][C]-16096.7489291616[/C][/ROW]
[ROW][C]80[/C][C]74011[/C][C]96285.5056862452[/C][C]-22274.5056862452[/C][/ROW]
[ROW][C]81[/C][C]83737[/C][C]72234.2132424693[/C][C]11502.7867575307[/C][/ROW]
[ROW][C]82[/C][C]69094[/C][C]75481.3784007143[/C][C]-6387.37840071434[/C][/ROW]
[ROW][C]83[/C][C]93133[/C][C]83661.2920316523[/C][C]9471.70796834768[/C][/ROW]
[ROW][C]84[/C][C]95536[/C][C]94646.9821050289[/C][C]889.017894971095[/C][/ROW]
[ROW][C]85[/C][C]225920[/C][C]97494.7096899657[/C][C]128425.290310034[/C][/ROW]
[ROW][C]86[/C][C]62133[/C][C]71554.1294386445[/C][C]-9421.12943864448[/C][/ROW]
[ROW][C]87[/C][C]61370[/C][C]60897.010298505[/C][C]472.989701494981[/C][/ROW]
[ROW][C]88[/C][C]43836[/C][C]51169.6587255544[/C][C]-7333.65872555444[/C][/ROW]
[ROW][C]89[/C][C]106117[/C][C]85626.1101928562[/C][C]20490.8898071438[/C][/ROW]
[ROW][C]90[/C][C]38692[/C][C]61008.3420634413[/C][C]-22316.3420634413[/C][/ROW]
[ROW][C]91[/C][C]84651[/C][C]90192.6423279059[/C][C]-5541.64232790587[/C][/ROW]
[ROW][C]92[/C][C]56622[/C][C]34861.3673749836[/C][C]21760.6326250164[/C][/ROW]
[ROW][C]93[/C][C]15986[/C][C]27744.9804066333[/C][C]-11758.9804066333[/C][/ROW]
[ROW][C]94[/C][C]95364[/C][C]100075.18538853[/C][C]-4711.18538852975[/C][/ROW]
[ROW][C]95[/C][C]26706[/C][C]45025.2809794329[/C][C]-18319.2809794329[/C][/ROW]
[ROW][C]96[/C][C]89691[/C][C]77879.9329763657[/C][C]11811.0670236343[/C][/ROW]
[ROW][C]97[/C][C]67267[/C][C]93538.6664944519[/C][C]-26271.6664944519[/C][/ROW]
[ROW][C]98[/C][C]126846[/C][C]98351.8548526126[/C][C]28494.1451473874[/C][/ROW]
[ROW][C]99[/C][C]41140[/C][C]55093.4404312681[/C][C]-13953.4404312681[/C][/ROW]
[ROW][C]100[/C][C]102860[/C][C]92247.9120739074[/C][C]10612.0879260926[/C][/ROW]
[ROW][C]101[/C][C]51715[/C][C]62819.9120545236[/C][C]-11104.9120545236[/C][/ROW]
[ROW][C]102[/C][C]55801[/C][C]64115.2941261775[/C][C]-8314.2941261775[/C][/ROW]
[ROW][C]103[/C][C]111813[/C][C]103645.463655533[/C][C]8167.53634446716[/C][/ROW]
[ROW][C]104[/C][C]120293[/C][C]88066.9674993582[/C][C]32226.0325006418[/C][/ROW]
[ROW][C]105[/C][C]138599[/C][C]113681.46510487[/C][C]24917.5348951296[/C][/ROW]
[ROW][C]106[/C][C]161647[/C][C]127080.181591659[/C][C]34566.8184083408[/C][/ROW]
[ROW][C]107[/C][C]115929[/C][C]149044.848070442[/C][C]-33115.848070442[/C][/ROW]
[ROW][C]108[/C][C]24266[/C][C]30494.8439757332[/C][C]-6228.84397573319[/C][/ROW]
[ROW][C]109[/C][C]162901[/C][C]145262.365883489[/C][C]17638.6341165105[/C][/ROW]
[ROW][C]110[/C][C]109825[/C][C]114278.006609457[/C][C]-4453.0066094566[/C][/ROW]
[ROW][C]111[/C][C]129838[/C][C]114070.077694514[/C][C]15767.9223054861[/C][/ROW]
[ROW][C]112[/C][C]37510[/C][C]46245.8172911089[/C][C]-8735.81729110889[/C][/ROW]
[ROW][C]113[/C][C]43750[/C][C]41289.4596072157[/C][C]2460.54039278428[/C][/ROW]
[ROW][C]114[/C][C]40652[/C][C]60488.915121797[/C][C]-19836.915121797[/C][/ROW]
[ROW][C]115[/C][C]87771[/C][C]103037.694853017[/C][C]-15266.6948530172[/C][/ROW]
[ROW][C]116[/C][C]85872[/C][C]92634.0919151266[/C][C]-6762.09191512661[/C][/ROW]
[ROW][C]117[/C][C]89275[/C][C]88822.1478479743[/C][C]452.852152025669[/C][/ROW]
[ROW][C]118[/C][C]44418[/C][C]52324.3515421344[/C][C]-7906.35154213439[/C][/ROW]
[ROW][C]119[/C][C]192565[/C][C]100461.552002652[/C][C]92103.447997348[/C][/ROW]
[ROW][C]120[/C][C]35232[/C][C]36543.9720254507[/C][C]-1311.9720254507[/C][/ROW]
[ROW][C]121[/C][C]40909[/C][C]54856.6091093121[/C][C]-13947.6091093121[/C][/ROW]
[ROW][C]122[/C][C]13294[/C][C]23144.4093211783[/C][C]-9850.40932117833[/C][/ROW]
[ROW][C]123[/C][C]32387[/C][C]56027.1865402713[/C][C]-23640.1865402713[/C][/ROW]
[ROW][C]124[/C][C]140867[/C][C]59081.1176558903[/C][C]81785.8823441097[/C][/ROW]
[ROW][C]125[/C][C]120662[/C][C]105033.877666875[/C][C]15628.1223331248[/C][/ROW]
[ROW][C]126[/C][C]21233[/C][C]35479.5453029419[/C][C]-14246.5453029419[/C][/ROW]
[ROW][C]127[/C][C]44332[/C][C]37924.268453161[/C][C]6407.73154683899[/C][/ROW]
[ROW][C]128[/C][C]61056[/C][C]62783.8396959776[/C][C]-1727.8396959776[/C][/ROW]
[ROW][C]129[/C][C]101338[/C][C]84142.2277751461[/C][C]17195.7722248539[/C][/ROW]
[ROW][C]130[/C][C]1168[/C][C]3728.49658647283[/C][C]-2560.49658647283[/C][/ROW]
[ROW][C]131[/C][C]13497[/C][C]18774.4654215929[/C][C]-5277.46542159286[/C][/ROW]
[ROW][C]132[/C][C]65567[/C][C]76111.6097740206[/C][C]-10544.6097740206[/C][/ROW]
[ROW][C]133[/C][C]25162[/C][C]30434.8680825338[/C][C]-5272.86808253378[/C][/ROW]
[ROW][C]134[/C][C]32334[/C][C]47340.1156746685[/C][C]-15006.1156746685[/C][/ROW]
[ROW][C]135[/C][C]40735[/C][C]51978.7235932586[/C][C]-11243.7235932586[/C][/ROW]
[ROW][C]136[/C][C]91413[/C][C]111755.673535539[/C][C]-20342.6735355393[/C][/ROW]
[ROW][C]137[/C][C]855[/C][C]3024.46332653226[/C][C]-2169.46332653226[/C][/ROW]
[ROW][C]138[/C][C]97068[/C][C]71036.3047636155[/C][C]26031.6952363845[/C][/ROW]
[ROW][C]139[/C][C]44339[/C][C]20436.0502882636[/C][C]23902.9497117364[/C][/ROW]
[ROW][C]140[/C][C]14116[/C][C]14461.5140466695[/C][C]-345.514046669475[/C][/ROW]
[ROW][C]141[/C][C]10288[/C][C]18949.4110060669[/C][C]-8661.41100606688[/C][/ROW]
[ROW][C]142[/C][C]65622[/C][C]56786.8694963796[/C][C]8835.13050362041[/C][/ROW]
[ROW][C]143[/C][C]16563[/C][C]14490.0452394203[/C][C]2072.95476057967[/C][/ROW]
[ROW][C]144[/C][C]76643[/C][C]102703.627882247[/C][C]-26060.6278822472[/C][/ROW]
[ROW][C]145[/C][C]110681[/C][C]89440.2753358041[/C][C]21240.7246641959[/C][/ROW]
[ROW][C]146[/C][C]29011[/C][C]29756.3446245568[/C][C]-745.344624556815[/C][/ROW]
[ROW][C]147[/C][C]92696[/C][C]55359.7410727744[/C][C]37336.2589272256[/C][/ROW]
[ROW][C]148[/C][C]94785[/C][C]55305.1719997682[/C][C]39479.8280002318[/C][/ROW]
[ROW][C]149[/C][C]8773[/C][C]14555.0204273025[/C][C]-5782.02042730249[/C][/ROW]
[ROW][C]150[/C][C]83209[/C][C]69581.2343209396[/C][C]13627.7656790604[/C][/ROW]
[ROW][C]151[/C][C]93815[/C][C]106369.302082307[/C][C]-12554.3020823068[/C][/ROW]
[ROW][C]152[/C][C]86687[/C][C]93341.036403041[/C][C]-6654.03640304097[/C][/ROW]
[ROW][C]153[/C][C]34553[/C][C]30982.2440582784[/C][C]3570.75594172164[/C][/ROW]
[ROW][C]154[/C][C]105547[/C][C]123724.823619712[/C][C]-18177.823619712[/C][/ROW]
[ROW][C]155[/C][C]103487[/C][C]111178.911889109[/C][C]-7691.91188910898[/C][/ROW]
[ROW][C]156[/C][C]213688[/C][C]159965.527745048[/C][C]53722.4722549518[/C][/ROW]
[ROW][C]157[/C][C]71220[/C][C]79093.7260560658[/C][C]-7873.72605606585[/C][/ROW]
[ROW][C]158[/C][C]23517[/C][C]27950.636652514[/C][C]-4433.63665251402[/C][/ROW]
[ROW][C]159[/C][C]56926[/C][C]47020.6985350778[/C][C]9905.30146492221[/C][/ROW]
[ROW][C]160[/C][C]91721[/C][C]85568.9946491979[/C][C]6152.00535080213[/C][/ROW]
[ROW][C]161[/C][C]115168[/C][C]117587.855869512[/C][C]-2419.85586951236[/C][/ROW]
[ROW][C]162[/C][C]111194[/C][C]48715.6350831202[/C][C]62478.3649168798[/C][/ROW]
[ROW][C]163[/C][C]51009[/C][C]47300.3626161198[/C][C]3708.63738388017[/C][/ROW]
[ROW][C]164[/C][C]135777[/C][C]97615.8218866402[/C][C]38161.1781133598[/C][/ROW]
[ROW][C]165[/C][C]51513[/C][C]60055.3580191872[/C][C]-8542.35801918716[/C][/ROW]
[ROW][C]166[/C][C]74163[/C][C]101795.590898618[/C][C]-27632.5908986182[/C][/ROW]
[ROW][C]167[/C][C]51633[/C][C]73392.451279905[/C][C]-21759.451279905[/C][/ROW]
[ROW][C]168[/C][C]75345[/C][C]30540.2082524541[/C][C]44804.7917475459[/C][/ROW]
[ROW][C]169[/C][C]33416[/C][C]55187.3422799639[/C][C]-21771.3422799639[/C][/ROW]
[ROW][C]170[/C][C]83305[/C][C]44873.5072385835[/C][C]38431.4927614165[/C][/ROW]
[ROW][C]171[/C][C]98952[/C][C]77687.7938892623[/C][C]21264.2061107377[/C][/ROW]
[ROW][C]172[/C][C]102372[/C][C]95501.9421806266[/C][C]6870.05781937342[/C][/ROW]
[ROW][C]173[/C][C]37238[/C][C]18653.5893187052[/C][C]18584.4106812948[/C][/ROW]
[ROW][C]174[/C][C]103772[/C][C]120016.569592291[/C][C]-16244.569592291[/C][/ROW]
[ROW][C]175[/C][C]123969[/C][C]94194.5550643348[/C][C]29774.4449356652[/C][/ROW]
[ROW][C]176[/C][C]27142[/C][C]34910.9290535236[/C][C]-7768.92905352356[/C][/ROW]
[ROW][C]177[/C][C]135400[/C][C]99792.49192388[/C][C]35607.50807612[/C][/ROW]
[ROW][C]178[/C][C]21399[/C][C]25899.3967240271[/C][C]-4500.39672402713[/C][/ROW]
[ROW][C]179[/C][C]130115[/C][C]115793.075629202[/C][C]14321.9243707984[/C][/ROW]
[ROW][C]180[/C][C]24874[/C][C]41038.0215319037[/C][C]-16164.0215319037[/C][/ROW]
[ROW][C]181[/C][C]34988[/C][C]29193.6793147884[/C][C]5794.3206852116[/C][/ROW]
[ROW][C]182[/C][C]45549[/C][C]22721.7734105557[/C][C]22827.2265894443[/C][/ROW]
[ROW][C]183[/C][C]6023[/C][C]6232.31084904104[/C][C]-209.310849041044[/C][/ROW]
[ROW][C]184[/C][C]64466[/C][C]66233.5290681204[/C][C]-1767.52906812036[/C][/ROW]
[ROW][C]185[/C][C]54990[/C][C]90227.5716034464[/C][C]-35237.5716034463[/C][/ROW]
[ROW][C]186[/C][C]1644[/C][C]3828.5275323408[/C][C]-2184.5275323408[/C][/ROW]
[ROW][C]187[/C][C]6179[/C][C]11593.0931961422[/C][C]-5414.09319614222[/C][/ROW]
[ROW][C]188[/C][C]3926[/C][C]6996.87442581703[/C][C]-3070.87442581703[/C][/ROW]
[ROW][C]189[/C][C]32755[/C][C]46954.1025017398[/C][C]-14199.1025017398[/C][/ROW]
[ROW][C]190[/C][C]34777[/C][C]21422.9135772211[/C][C]13354.0864227789[/C][/ROW]
[ROW][C]191[/C][C]73224[/C][C]48389.8507324775[/C][C]24834.1492675225[/C][/ROW]
[ROW][C]192[/C][C]27114[/C][C]23623.4352880301[/C][C]3490.56471196994[/C][/ROW]
[ROW][C]193[/C][C]20760[/C][C]38712.1883942871[/C][C]-17952.1883942871[/C][/ROW]
[ROW][C]194[/C][C]37636[/C][C]58660.203921446[/C][C]-21024.203921446[/C][/ROW]
[ROW][C]195[/C][C]65461[/C][C]71632.0626672083[/C][C]-6171.06266720831[/C][/ROW]
[ROW][C]196[/C][C]30080[/C][C]38403.1985948465[/C][C]-8323.19859484647[/C][/ROW]
[ROW][C]197[/C][C]24094[/C][C]31364.9640599467[/C][C]-7270.96405994675[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160355&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160355&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
1112285109430.0217878832854.97821211731
28478691819.1386445416-7033.13864454164
38312391335.0768676245-8212.07686762447
410119399196.80966183871996.19033816127
53836155088.6964461765-16727.6964461765
66850450455.225548487718048.7744515123
7119182102381.76788917516800.2321108251
82280712072.25208303410734.747916966
91714022091.4712157128-4951.47121571275
1011617493037.804127346923136.1958726531
115763560145.8926578285-2510.89265782853
126619897861.0106150539-31663.0106150539
137170195811.8137839478-24110.8137839478
145779366756.5689607368-8963.5689607368
158044481467.3544268822-1023.35442688215
165385577932.679673867-24077.679673867
1797668116311.88854436-18643.8885443602
18133824109883.98980835223940.010191648
1910148177758.235022205923722.7649777941
209964576108.17476737623536.825232624
2111478999220.716390773615568.2836092264
2299052108550.269391584-9498.26939158443
236765471412.0248665141-3758.02486651412
246555382111.1622890438-16558.1622890438
259750097648.8000001838-148.800000183766
266911290625.5111338183-21513.5111338183
278275386364.3194577601-3611.31945776007
288532394924.3183840939-9601.31838409389
297265498596.1260371563-25942.1260371563
303072742904.668243278-12177.668243278
3177873106119.953821568-28246.9538215678
32117478127227.358567237-9749.35856723699
337400775364.2140938761-1357.21409387613
3490183121661.286282984-31478.2862829836
356154245862.174320733715679.8256792663
36101494120379.160086863-18885.1600868627
372757041250.2082343289-13680.2082343289
385581346771.58250672049041.41749327958
397921598393.9786766126-19178.9786766126
4014233091.49302622469-1668.49302622469
415546167468.397307263-12007.397307263
423108145685.8392472863-14604.8392472863
432299636504.7013793752-13508.7013793752
4483122103686.082341251-20564.0823412511
457010694379.6728737496-24273.6728737496
466057874735.1952417547-14157.1952417547
473999246818.6799145082-6826.67991450824
487989296065.6970791231-16173.697079123
494981057059.6919568891-7249.69195688912
507157056128.816381024815441.1836189752
5110070878941.137223651921766.8627763481
523303252822.632726626-19790.6327266261
538287585427.5873978297-2552.58739782968
54139077121573.05860488917503.941395111
557159584070.0328548076-12475.0328548076
567226091563.3354263456-19303.3354263456
57595011851.2214560107-5901.22145601069
58115762117642.078029822-1880.07802982221
593255137018.7847528281-4467.78475282814
603170138612.158362554-6911.15836255397
618067082881.0771761833-2211.07717618333
62143558137816.5084305815741.49156941911
6311710591781.017366508325323.9826334917
642378936519.5138000277-12730.5138000277
65120733140007.985195332-19274.985195332
66105195127384.424793499-22189.4247934991
677310781269.428519685-8162.42851968502
68132068130653.6758336971414.32416630312
69149193152116.486642739-2923.48664273871
704682166318.4034450566-19497.4034450566
718701193110.5127432282-6099.51274322824
7295260102252.980504933-6992.98050493286
735518380030.9137983012-24847.9137983012
7410667179951.179896644726719.8201033553
7573511109987.551951842-36476.5519518417
769294574369.520415754418575.4795842456
777866478220.8163194287443.183680571251
7870054100592.444503775-30538.4445037747
792261838714.7489291616-16096.7489291616
807401196285.5056862452-22274.5056862452
818373772234.213242469311502.7867575307
826909475481.3784007143-6387.37840071434
839313383661.29203165239471.70796834768
849553694646.9821050289889.017894971095
8522592097494.7096899657128425.290310034
866213371554.1294386445-9421.12943864448
876137060897.010298505472.989701494981
884383651169.6587255544-7333.65872555444
8910611785626.110192856220490.8898071438
903869261008.3420634413-22316.3420634413
918465190192.6423279059-5541.64232790587
925662234861.367374983621760.6326250164
931598627744.9804066333-11758.9804066333
9495364100075.18538853-4711.18538852975
952670645025.2809794329-18319.2809794329
968969177879.932976365711811.0670236343
976726793538.6664944519-26271.6664944519
9812684698351.854852612628494.1451473874
994114055093.4404312681-13953.4404312681
10010286092247.912073907410612.0879260926
1015171562819.9120545236-11104.9120545236
1025580164115.2941261775-8314.2941261775
103111813103645.4636555338167.53634446716
10412029388066.967499358232226.0325006418
105138599113681.4651048724917.5348951296
106161647127080.18159165934566.8184083408
107115929149044.848070442-33115.848070442
1082426630494.8439757332-6228.84397573319
109162901145262.36588348917638.6341165105
110109825114278.006609457-4453.0066094566
111129838114070.07769451415767.9223054861
1123751046245.8172911089-8735.81729110889
1134375041289.45960721572460.54039278428
1144065260488.915121797-19836.915121797
11587771103037.694853017-15266.6948530172
1168587292634.0919151266-6762.09191512661
1178927588822.1478479743452.852152025669
1184441852324.3515421344-7906.35154213439
119192565100461.55200265292103.447997348
1203523236543.9720254507-1311.9720254507
1214090954856.6091093121-13947.6091093121
1221329423144.4093211783-9850.40932117833
1233238756027.1865402713-23640.1865402713
12414086759081.117655890381785.8823441097
125120662105033.87766687515628.1223331248
1262123335479.5453029419-14246.5453029419
1274433237924.2684531616407.73154683899
1286105662783.8396959776-1727.8396959776
12910133884142.227775146117195.7722248539
13011683728.49658647283-2560.49658647283
1311349718774.4654215929-5277.46542159286
1326556776111.6097740206-10544.6097740206
1332516230434.8680825338-5272.86808253378
1343233447340.1156746685-15006.1156746685
1354073551978.7235932586-11243.7235932586
13691413111755.673535539-20342.6735355393
1378553024.46332653226-2169.46332653226
1389706871036.304763615526031.6952363845
1394433920436.050288263623902.9497117364
1401411614461.5140466695-345.514046669475
1411028818949.4110060669-8661.41100606688
1426562256786.86949637968835.13050362041
1431656314490.04523942032072.95476057967
14476643102703.627882247-26060.6278822472
14511068189440.275335804121240.7246641959
1462901129756.3446245568-745.344624556815
1479269655359.741072774437336.2589272256
1489478555305.171999768239479.8280002318
149877314555.0204273025-5782.02042730249
1508320969581.234320939613627.7656790604
15193815106369.302082307-12554.3020823068
1528668793341.036403041-6654.03640304097
1533455330982.24405827843570.75594172164
154105547123724.823619712-18177.823619712
155103487111178.911889109-7691.91188910898
156213688159965.52774504853722.4722549518
1577122079093.7260560658-7873.72605606585
1582351727950.636652514-4433.63665251402
1595692647020.69853507789905.30146492221
1609172185568.99464919796152.00535080213
161115168117587.855869512-2419.85586951236
16211119448715.635083120262478.3649168798
1635100947300.36261611983708.63738388017
16413577797615.821886640238161.1781133598
1655151360055.3580191872-8542.35801918716
16674163101795.590898618-27632.5908986182
1675163373392.451279905-21759.451279905
1687534530540.208252454144804.7917475459
1693341655187.3422799639-21771.3422799639
1708330544873.507238583538431.4927614165
1719895277687.793889262321264.2061107377
17210237295501.94218062666870.05781937342
1733723818653.589318705218584.4106812948
174103772120016.569592291-16244.569592291
17512396994194.555064334829774.4449356652
1762714234910.9290535236-7768.92905352356
17713540099792.4919238835607.50807612
1782139925899.3967240271-4500.39672402713
179130115115793.07562920214321.9243707984
1802487441038.0215319037-16164.0215319037
1813498829193.67931478845794.3206852116
1824554922721.773410555722827.2265894443
18360236232.31084904104-209.310849041044
1846446666233.5290681204-1767.52906812036
1855499090227.5716034464-35237.5716034463
18616443828.5275323408-2184.5275323408
187617911593.0931961422-5414.09319614222
18839266996.87442581703-3070.87442581703
1893275546954.1025017398-14199.1025017398
1903477721422.913577221113354.0864227789
1917322448389.850732477524834.1492675225
1922711423623.43528803013490.56471196994
1932076038712.1883942871-17952.1883942871
1943763658660.203921446-21024.203921446
1956546171632.0626672083-6171.06266720831
1963008038403.1985948465-8323.19859484647
1972409431364.9640599467-7270.96405994675






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.1549029731399990.3098059462799980.845097026860001
190.1052987132133110.2105974264266220.894701286786689
200.04655692507327040.09311385014654080.95344307492673
210.263058325138530.526116650277060.73694167486147
220.2213400214249350.4426800428498690.778659978575065
230.1433885400201250.2867770800402490.856611459979875
240.08931389205233260.1786277841046650.910686107947667
250.06187050116578390.1237410023315680.938129498834216
260.0371226685764860.07424533715297190.962877331423514
270.02947909909952490.05895819819904970.970520900900475
280.01671632287841240.03343264575682480.983283677121588
290.01030114003550970.02060228007101940.98969885996449
300.007077243499117360.01415448699823470.992922756500883
310.01215046051075940.02430092102151880.987849539489241
320.007690570994954360.01538114198990870.992309429005046
330.004422158449818490.008844316899636980.995577841550181
340.002785963693688970.005571927387377950.997214036306311
350.004668540687655310.009337081375310630.995331459312345
360.005243960089680020.010487920179360.99475603991032
370.003961117742496270.007922235484992540.996038882257504
380.00254035150862490.005080703017249810.997459648491375
390.001659954443897320.003319908887794650.998340045556103
400.002442721776085290.004885443552170590.997557278223915
410.001733499971443580.003466999942887160.998266500028556
420.001179995858840910.002359991717681820.998820004141159
430.0007062324682612080.001412464936522420.999293767531739
440.0005073328351091810.001014665670218360.999492667164891
450.002041984566845310.004083969133690630.997958015433155
460.001386226046654090.002772452093308180.998613773953346
470.0008939267964182020.00178785359283640.999106073203582
480.0006082174023485390.001216434804697080.999391782597651
490.0003638508802773660.0007277017605547320.999636149119723
500.0005217880188888050.001043576037777610.999478211981111
510.0007754268711764620.001550853742352920.999224573128824
520.0006011417229955920.001202283445991180.999398858277004
530.0003629995904895210.0007259991809790420.999637000409511
540.000403551190083420.0008071023801668410.999596448809917
550.0003884628739587590.0007769257479175180.999611537126041
560.0002716165440935970.0005432330881871940.999728383455906
570.0001673645431097290.0003347290862194570.99983263545689
580.0001513635257468180.0003027270514936350.999848636474253
598.93973767658755e-050.0001787947535317510.999910602623234
605.33603139663402e-050.000106720627932680.999946639686034
613.1905824979008e-056.38116499580159e-050.999968094175021
623.53890409458978e-057.07780818917956e-050.999964610959054
633.3551137135775e-056.71022742715499e-050.999966448862864
642.10452181586577e-054.20904363173155e-050.999978954781841
657.42482853795874e-050.0001484965707591750.99992575171462
666.60055653659273e-050.0001320111307318550.999933994434634
674.10595333180706e-058.21190666361412e-050.999958940466682
682.8199194493483e-055.6398388986966e-050.999971800805507
691.62938476853923e-053.25876953707845e-050.999983706152315
701.39750611834663e-052.79501223669327e-050.999986024938816
718.99094176761277e-061.79818835352255e-050.999991009058232
725.40764884104096e-061.08152976820819e-050.999994592351159
734.71537492081689e-069.43074984163378e-060.999995284625079
749.10068361498889e-061.82013672299778e-050.999990899316385
752.2723042543777e-054.54460850875539e-050.999977276957456
762.05339465304687e-054.10678930609374e-050.99997946605347
771.53136206142802e-053.06272412285604e-050.999984686379386
782.88086734389129e-055.76173468778258e-050.999971191326561
792.35969598781858e-054.71939197563717e-050.999976403040122
802.52686351388164e-055.05372702776328e-050.999974731364861
811.78286583242233e-053.56573166484465e-050.999982171341676
821.10406830286841e-052.20813660573682e-050.999988959316971
837.6240109080932e-061.52480218161864e-050.999992375989092
844.66672641771109e-069.33345283542218e-060.999995333273582
850.4755698286824420.9511396573648830.524430171317558
860.4422730874876480.8845461749752960.557726912512352
870.4038092667213490.8076185334426980.596190733278651
880.369200651748660.7384013034973210.63079934825134
890.3717207126936140.7434414253872280.628279287306386
900.404591667769010.809183335538020.59540833223099
910.3695342983656820.7390685967313630.630465701634318
920.3589022032839290.7178044065678570.641097796716071
930.3239118140588720.6478236281177440.676088185941128
940.2926028118122710.5852056236245410.707397188187729
950.2838709955287150.5677419910574310.716129004471285
960.2835133201861060.5670266403722110.716486679813894
970.2955825773335210.5911651546670420.704417422666479
980.3237884034814140.6475768069628290.676211596518586
990.3046235807856180.6092471615712360.695376419214382
1000.275708715835690.5514174316713790.72429128416431
1010.2556262165388290.5112524330776580.744373783461171
1020.2274823622421380.4549647244842770.772517637757862
1030.2057046961027930.4114093922055850.794295303897207
1040.2411049528658340.4822099057316670.758895047134166
1050.241555473221890.4831109464437790.75844452677811
1060.2844131560924090.5688263121848180.715586843907591
1070.4318715821733340.8637431643466690.568128417826666
1080.3905608198272490.7811216396544970.609439180172751
1090.3872865605184820.7745731210369640.612713439481518
1100.3852442370790430.7704884741580860.614755762920957
1110.37325734139350.7465146827870.6267426586065
1120.3750719318525550.750143863705110.624928068147445
1130.3369861070878040.6739722141756090.663013892912196
1140.3346222994423530.6692445988847050.665377700557647
1150.3123230447421630.6246460894843260.687676955257837
1160.2788953154832140.5577906309664280.721104684516786
1170.2427980087359540.4855960174719070.757201991264046
1180.2109485015743750.421897003148750.789051498425625
1190.7686051732001520.4627896535996970.231394826799848
1200.7326550567974480.5346898864051030.267344943202552
1210.7009812627424670.5980374745150650.299018737257533
1220.6807487322885330.6385025354229330.319251267711467
1230.6597735390615640.6804529218768720.340226460938436
1240.9723128197007090.05537436059858220.0276871802992911
1250.9682709711643340.06345805767133140.0317290288356657
1260.9633615914031640.07327681719367150.0366384085968358
1270.9543516165682870.09129676686342540.0456483834317127
1280.9419971025214280.1160057949571440.058002897478572
1290.9321658799314630.1356682401370740.0678341200685371
1300.9150395888343910.1699208223312190.0849604111656093
1310.9002431030940040.1995137938119910.0997568969059956
1320.8857539340266610.2284921319466790.114246065973339
1330.8758570505083910.2482858989832190.124142949491609
1340.8591696202427390.2816607595145220.140830379757261
1350.8904521553202540.2190956893594910.109547844679746
1360.8799365844113980.2401268311772050.120063415588602
1370.8527755746281840.2944488507436330.147224425371816
1380.8569404232142420.2861191535715170.143059576785758
1390.8471145328557790.3057709342884420.152885467144221
1400.8148692656467090.3702614687065820.185130734353291
1410.7842423674500550.431515265099890.215757632549945
1420.7685253931764190.4629492136471630.231474606823581
1430.7291373490871350.5417253018257290.270862650912865
1440.7327136784745370.5345726430509270.267286321525463
1450.7092201843095530.5815596313808930.290779815690447
1460.6695207803521970.6609584392956050.330479219647803
1470.6823982673008720.6352034653982560.317601732699128
1480.7500886012373080.4998227975253840.249911398762692
1490.7046276584106820.5907446831786370.295372341589318
1500.6643394475563040.6713211048873920.335660552443696
1510.6329892548684150.7340214902631690.367010745131585
1520.6715230130007890.6569539739984230.328476986999211
1530.6173657374342340.7652685251315320.382634262565766
1540.714222029390350.57155594121930.28577797060965
1550.6745282522300740.6509434955398510.325471747769926
1560.8727919392873720.2544161214252560.127208060712628
1570.8382379967705780.3235240064588440.161762003229422
1580.8030194276211810.3939611447576380.196980572378819
1590.7603113110191760.4793773779616480.239688688980824
1600.7489349716893840.5021300566212310.251065028310616
1610.6917816132870290.6164367734259420.308218386712971
1620.9323350651537440.1353298696925110.0676649348462557
1630.9444874339241990.1110251321516020.0555125660758008
1640.9522622958188540.09547540836229140.0477377041811457
1650.9601376571786350.07972468564273020.0398623428213651
1660.9515891276419170.09682174471616650.0484108723580833
1670.9507761262217810.09844774755643830.0492238737782192
1680.965337410607120.069325178785760.03466258939288
1690.959379011434590.08124197713081920.0406209885654096
1700.9991062921081460.001787415783707830.000893707891853914
1710.9996901528635010.0006196942729978110.000309847136498906
1720.9991066698724030.001786660255193820.000893330127596908
1730.9976981379520570.004603724095885340.00230186204794267
1740.9985355269930890.002928946013822610.00146447300691131
1750.996009443227710.007981113544580770.00399055677229038
1760.9887265501600540.0225468996798920.011273449839946
1770.9984374097947530.003125180410493520.00156259020524676
1780.9926758278108940.01464834437821220.00732417218910608
1790.973620902081850.05275819583629990.0263790979181499

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.154902973139999 & 0.309805946279998 & 0.845097026860001 \tabularnewline
19 & 0.105298713213311 & 0.210597426426622 & 0.894701286786689 \tabularnewline
20 & 0.0465569250732704 & 0.0931138501465408 & 0.95344307492673 \tabularnewline
21 & 0.26305832513853 & 0.52611665027706 & 0.73694167486147 \tabularnewline
22 & 0.221340021424935 & 0.442680042849869 & 0.778659978575065 \tabularnewline
23 & 0.143388540020125 & 0.286777080040249 & 0.856611459979875 \tabularnewline
24 & 0.0893138920523326 & 0.178627784104665 & 0.910686107947667 \tabularnewline
25 & 0.0618705011657839 & 0.123741002331568 & 0.938129498834216 \tabularnewline
26 & 0.037122668576486 & 0.0742453371529719 & 0.962877331423514 \tabularnewline
27 & 0.0294790990995249 & 0.0589581981990497 & 0.970520900900475 \tabularnewline
28 & 0.0167163228784124 & 0.0334326457568248 & 0.983283677121588 \tabularnewline
29 & 0.0103011400355097 & 0.0206022800710194 & 0.98969885996449 \tabularnewline
30 & 0.00707724349911736 & 0.0141544869982347 & 0.992922756500883 \tabularnewline
31 & 0.0121504605107594 & 0.0243009210215188 & 0.987849539489241 \tabularnewline
32 & 0.00769057099495436 & 0.0153811419899087 & 0.992309429005046 \tabularnewline
33 & 0.00442215844981849 & 0.00884431689963698 & 0.995577841550181 \tabularnewline
34 & 0.00278596369368897 & 0.00557192738737795 & 0.997214036306311 \tabularnewline
35 & 0.00466854068765531 & 0.00933708137531063 & 0.995331459312345 \tabularnewline
36 & 0.00524396008968002 & 0.01048792017936 & 0.99475603991032 \tabularnewline
37 & 0.00396111774249627 & 0.00792223548499254 & 0.996038882257504 \tabularnewline
38 & 0.0025403515086249 & 0.00508070301724981 & 0.997459648491375 \tabularnewline
39 & 0.00165995444389732 & 0.00331990888779465 & 0.998340045556103 \tabularnewline
40 & 0.00244272177608529 & 0.00488544355217059 & 0.997557278223915 \tabularnewline
41 & 0.00173349997144358 & 0.00346699994288716 & 0.998266500028556 \tabularnewline
42 & 0.00117999585884091 & 0.00235999171768182 & 0.998820004141159 \tabularnewline
43 & 0.000706232468261208 & 0.00141246493652242 & 0.999293767531739 \tabularnewline
44 & 0.000507332835109181 & 0.00101466567021836 & 0.999492667164891 \tabularnewline
45 & 0.00204198456684531 & 0.00408396913369063 & 0.997958015433155 \tabularnewline
46 & 0.00138622604665409 & 0.00277245209330818 & 0.998613773953346 \tabularnewline
47 & 0.000893926796418202 & 0.0017878535928364 & 0.999106073203582 \tabularnewline
48 & 0.000608217402348539 & 0.00121643480469708 & 0.999391782597651 \tabularnewline
49 & 0.000363850880277366 & 0.000727701760554732 & 0.999636149119723 \tabularnewline
50 & 0.000521788018888805 & 0.00104357603777761 & 0.999478211981111 \tabularnewline
51 & 0.000775426871176462 & 0.00155085374235292 & 0.999224573128824 \tabularnewline
52 & 0.000601141722995592 & 0.00120228344599118 & 0.999398858277004 \tabularnewline
53 & 0.000362999590489521 & 0.000725999180979042 & 0.999637000409511 \tabularnewline
54 & 0.00040355119008342 & 0.000807102380166841 & 0.999596448809917 \tabularnewline
55 & 0.000388462873958759 & 0.000776925747917518 & 0.999611537126041 \tabularnewline
56 & 0.000271616544093597 & 0.000543233088187194 & 0.999728383455906 \tabularnewline
57 & 0.000167364543109729 & 0.000334729086219457 & 0.99983263545689 \tabularnewline
58 & 0.000151363525746818 & 0.000302727051493635 & 0.999848636474253 \tabularnewline
59 & 8.93973767658755e-05 & 0.000178794753531751 & 0.999910602623234 \tabularnewline
60 & 5.33603139663402e-05 & 0.00010672062793268 & 0.999946639686034 \tabularnewline
61 & 3.1905824979008e-05 & 6.38116499580159e-05 & 0.999968094175021 \tabularnewline
62 & 3.53890409458978e-05 & 7.07780818917956e-05 & 0.999964610959054 \tabularnewline
63 & 3.3551137135775e-05 & 6.71022742715499e-05 & 0.999966448862864 \tabularnewline
64 & 2.10452181586577e-05 & 4.20904363173155e-05 & 0.999978954781841 \tabularnewline
65 & 7.42482853795874e-05 & 0.000148496570759175 & 0.99992575171462 \tabularnewline
66 & 6.60055653659273e-05 & 0.000132011130731855 & 0.999933994434634 \tabularnewline
67 & 4.10595333180706e-05 & 8.21190666361412e-05 & 0.999958940466682 \tabularnewline
68 & 2.8199194493483e-05 & 5.6398388986966e-05 & 0.999971800805507 \tabularnewline
69 & 1.62938476853923e-05 & 3.25876953707845e-05 & 0.999983706152315 \tabularnewline
70 & 1.39750611834663e-05 & 2.79501223669327e-05 & 0.999986024938816 \tabularnewline
71 & 8.99094176761277e-06 & 1.79818835352255e-05 & 0.999991009058232 \tabularnewline
72 & 5.40764884104096e-06 & 1.08152976820819e-05 & 0.999994592351159 \tabularnewline
73 & 4.71537492081689e-06 & 9.43074984163378e-06 & 0.999995284625079 \tabularnewline
74 & 9.10068361498889e-06 & 1.82013672299778e-05 & 0.999990899316385 \tabularnewline
75 & 2.2723042543777e-05 & 4.54460850875539e-05 & 0.999977276957456 \tabularnewline
76 & 2.05339465304687e-05 & 4.10678930609374e-05 & 0.99997946605347 \tabularnewline
77 & 1.53136206142802e-05 & 3.06272412285604e-05 & 0.999984686379386 \tabularnewline
78 & 2.88086734389129e-05 & 5.76173468778258e-05 & 0.999971191326561 \tabularnewline
79 & 2.35969598781858e-05 & 4.71939197563717e-05 & 0.999976403040122 \tabularnewline
80 & 2.52686351388164e-05 & 5.05372702776328e-05 & 0.999974731364861 \tabularnewline
81 & 1.78286583242233e-05 & 3.56573166484465e-05 & 0.999982171341676 \tabularnewline
82 & 1.10406830286841e-05 & 2.20813660573682e-05 & 0.999988959316971 \tabularnewline
83 & 7.6240109080932e-06 & 1.52480218161864e-05 & 0.999992375989092 \tabularnewline
84 & 4.66672641771109e-06 & 9.33345283542218e-06 & 0.999995333273582 \tabularnewline
85 & 0.475569828682442 & 0.951139657364883 & 0.524430171317558 \tabularnewline
86 & 0.442273087487648 & 0.884546174975296 & 0.557726912512352 \tabularnewline
87 & 0.403809266721349 & 0.807618533442698 & 0.596190733278651 \tabularnewline
88 & 0.36920065174866 & 0.738401303497321 & 0.63079934825134 \tabularnewline
89 & 0.371720712693614 & 0.743441425387228 & 0.628279287306386 \tabularnewline
90 & 0.40459166776901 & 0.80918333553802 & 0.59540833223099 \tabularnewline
91 & 0.369534298365682 & 0.739068596731363 & 0.630465701634318 \tabularnewline
92 & 0.358902203283929 & 0.717804406567857 & 0.641097796716071 \tabularnewline
93 & 0.323911814058872 & 0.647823628117744 & 0.676088185941128 \tabularnewline
94 & 0.292602811812271 & 0.585205623624541 & 0.707397188187729 \tabularnewline
95 & 0.283870995528715 & 0.567741991057431 & 0.716129004471285 \tabularnewline
96 & 0.283513320186106 & 0.567026640372211 & 0.716486679813894 \tabularnewline
97 & 0.295582577333521 & 0.591165154667042 & 0.704417422666479 \tabularnewline
98 & 0.323788403481414 & 0.647576806962829 & 0.676211596518586 \tabularnewline
99 & 0.304623580785618 & 0.609247161571236 & 0.695376419214382 \tabularnewline
100 & 0.27570871583569 & 0.551417431671379 & 0.72429128416431 \tabularnewline
101 & 0.255626216538829 & 0.511252433077658 & 0.744373783461171 \tabularnewline
102 & 0.227482362242138 & 0.454964724484277 & 0.772517637757862 \tabularnewline
103 & 0.205704696102793 & 0.411409392205585 & 0.794295303897207 \tabularnewline
104 & 0.241104952865834 & 0.482209905731667 & 0.758895047134166 \tabularnewline
105 & 0.24155547322189 & 0.483110946443779 & 0.75844452677811 \tabularnewline
106 & 0.284413156092409 & 0.568826312184818 & 0.715586843907591 \tabularnewline
107 & 0.431871582173334 & 0.863743164346669 & 0.568128417826666 \tabularnewline
108 & 0.390560819827249 & 0.781121639654497 & 0.609439180172751 \tabularnewline
109 & 0.387286560518482 & 0.774573121036964 & 0.612713439481518 \tabularnewline
110 & 0.385244237079043 & 0.770488474158086 & 0.614755762920957 \tabularnewline
111 & 0.3732573413935 & 0.746514682787 & 0.6267426586065 \tabularnewline
112 & 0.375071931852555 & 0.75014386370511 & 0.624928068147445 \tabularnewline
113 & 0.336986107087804 & 0.673972214175609 & 0.663013892912196 \tabularnewline
114 & 0.334622299442353 & 0.669244598884705 & 0.665377700557647 \tabularnewline
115 & 0.312323044742163 & 0.624646089484326 & 0.687676955257837 \tabularnewline
116 & 0.278895315483214 & 0.557790630966428 & 0.721104684516786 \tabularnewline
117 & 0.242798008735954 & 0.485596017471907 & 0.757201991264046 \tabularnewline
118 & 0.210948501574375 & 0.42189700314875 & 0.789051498425625 \tabularnewline
119 & 0.768605173200152 & 0.462789653599697 & 0.231394826799848 \tabularnewline
120 & 0.732655056797448 & 0.534689886405103 & 0.267344943202552 \tabularnewline
121 & 0.700981262742467 & 0.598037474515065 & 0.299018737257533 \tabularnewline
122 & 0.680748732288533 & 0.638502535422933 & 0.319251267711467 \tabularnewline
123 & 0.659773539061564 & 0.680452921876872 & 0.340226460938436 \tabularnewline
124 & 0.972312819700709 & 0.0553743605985822 & 0.0276871802992911 \tabularnewline
125 & 0.968270971164334 & 0.0634580576713314 & 0.0317290288356657 \tabularnewline
126 & 0.963361591403164 & 0.0732768171936715 & 0.0366384085968358 \tabularnewline
127 & 0.954351616568287 & 0.0912967668634254 & 0.0456483834317127 \tabularnewline
128 & 0.941997102521428 & 0.116005794957144 & 0.058002897478572 \tabularnewline
129 & 0.932165879931463 & 0.135668240137074 & 0.0678341200685371 \tabularnewline
130 & 0.915039588834391 & 0.169920822331219 & 0.0849604111656093 \tabularnewline
131 & 0.900243103094004 & 0.199513793811991 & 0.0997568969059956 \tabularnewline
132 & 0.885753934026661 & 0.228492131946679 & 0.114246065973339 \tabularnewline
133 & 0.875857050508391 & 0.248285898983219 & 0.124142949491609 \tabularnewline
134 & 0.859169620242739 & 0.281660759514522 & 0.140830379757261 \tabularnewline
135 & 0.890452155320254 & 0.219095689359491 & 0.109547844679746 \tabularnewline
136 & 0.879936584411398 & 0.240126831177205 & 0.120063415588602 \tabularnewline
137 & 0.852775574628184 & 0.294448850743633 & 0.147224425371816 \tabularnewline
138 & 0.856940423214242 & 0.286119153571517 & 0.143059576785758 \tabularnewline
139 & 0.847114532855779 & 0.305770934288442 & 0.152885467144221 \tabularnewline
140 & 0.814869265646709 & 0.370261468706582 & 0.185130734353291 \tabularnewline
141 & 0.784242367450055 & 0.43151526509989 & 0.215757632549945 \tabularnewline
142 & 0.768525393176419 & 0.462949213647163 & 0.231474606823581 \tabularnewline
143 & 0.729137349087135 & 0.541725301825729 & 0.270862650912865 \tabularnewline
144 & 0.732713678474537 & 0.534572643050927 & 0.267286321525463 \tabularnewline
145 & 0.709220184309553 & 0.581559631380893 & 0.290779815690447 \tabularnewline
146 & 0.669520780352197 & 0.660958439295605 & 0.330479219647803 \tabularnewline
147 & 0.682398267300872 & 0.635203465398256 & 0.317601732699128 \tabularnewline
148 & 0.750088601237308 & 0.499822797525384 & 0.249911398762692 \tabularnewline
149 & 0.704627658410682 & 0.590744683178637 & 0.295372341589318 \tabularnewline
150 & 0.664339447556304 & 0.671321104887392 & 0.335660552443696 \tabularnewline
151 & 0.632989254868415 & 0.734021490263169 & 0.367010745131585 \tabularnewline
152 & 0.671523013000789 & 0.656953973998423 & 0.328476986999211 \tabularnewline
153 & 0.617365737434234 & 0.765268525131532 & 0.382634262565766 \tabularnewline
154 & 0.71422202939035 & 0.5715559412193 & 0.28577797060965 \tabularnewline
155 & 0.674528252230074 & 0.650943495539851 & 0.325471747769926 \tabularnewline
156 & 0.872791939287372 & 0.254416121425256 & 0.127208060712628 \tabularnewline
157 & 0.838237996770578 & 0.323524006458844 & 0.161762003229422 \tabularnewline
158 & 0.803019427621181 & 0.393961144757638 & 0.196980572378819 \tabularnewline
159 & 0.760311311019176 & 0.479377377961648 & 0.239688688980824 \tabularnewline
160 & 0.748934971689384 & 0.502130056621231 & 0.251065028310616 \tabularnewline
161 & 0.691781613287029 & 0.616436773425942 & 0.308218386712971 \tabularnewline
162 & 0.932335065153744 & 0.135329869692511 & 0.0676649348462557 \tabularnewline
163 & 0.944487433924199 & 0.111025132151602 & 0.0555125660758008 \tabularnewline
164 & 0.952262295818854 & 0.0954754083622914 & 0.0477377041811457 \tabularnewline
165 & 0.960137657178635 & 0.0797246856427302 & 0.0398623428213651 \tabularnewline
166 & 0.951589127641917 & 0.0968217447161665 & 0.0484108723580833 \tabularnewline
167 & 0.950776126221781 & 0.0984477475564383 & 0.0492238737782192 \tabularnewline
168 & 0.96533741060712 & 0.06932517878576 & 0.03466258939288 \tabularnewline
169 & 0.95937901143459 & 0.0812419771308192 & 0.0406209885654096 \tabularnewline
170 & 0.999106292108146 & 0.00178741578370783 & 0.000893707891853914 \tabularnewline
171 & 0.999690152863501 & 0.000619694272997811 & 0.000309847136498906 \tabularnewline
172 & 0.999106669872403 & 0.00178666025519382 & 0.000893330127596908 \tabularnewline
173 & 0.997698137952057 & 0.00460372409588534 & 0.00230186204794267 \tabularnewline
174 & 0.998535526993089 & 0.00292894601382261 & 0.00146447300691131 \tabularnewline
175 & 0.99600944322771 & 0.00798111354458077 & 0.00399055677229038 \tabularnewline
176 & 0.988726550160054 & 0.022546899679892 & 0.011273449839946 \tabularnewline
177 & 0.998437409794753 & 0.00312518041049352 & 0.00156259020524676 \tabularnewline
178 & 0.992675827810894 & 0.0146483443782122 & 0.00732417218910608 \tabularnewline
179 & 0.97362090208185 & 0.0527581958362999 & 0.0263790979181499 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160355&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]18[/C][C]0.154902973139999[/C][C]0.309805946279998[/C][C]0.845097026860001[/C][/ROW]
[ROW][C]19[/C][C]0.105298713213311[/C][C]0.210597426426622[/C][C]0.894701286786689[/C][/ROW]
[ROW][C]20[/C][C]0.0465569250732704[/C][C]0.0931138501465408[/C][C]0.95344307492673[/C][/ROW]
[ROW][C]21[/C][C]0.26305832513853[/C][C]0.52611665027706[/C][C]0.73694167486147[/C][/ROW]
[ROW][C]22[/C][C]0.221340021424935[/C][C]0.442680042849869[/C][C]0.778659978575065[/C][/ROW]
[ROW][C]23[/C][C]0.143388540020125[/C][C]0.286777080040249[/C][C]0.856611459979875[/C][/ROW]
[ROW][C]24[/C][C]0.0893138920523326[/C][C]0.178627784104665[/C][C]0.910686107947667[/C][/ROW]
[ROW][C]25[/C][C]0.0618705011657839[/C][C]0.123741002331568[/C][C]0.938129498834216[/C][/ROW]
[ROW][C]26[/C][C]0.037122668576486[/C][C]0.0742453371529719[/C][C]0.962877331423514[/C][/ROW]
[ROW][C]27[/C][C]0.0294790990995249[/C][C]0.0589581981990497[/C][C]0.970520900900475[/C][/ROW]
[ROW][C]28[/C][C]0.0167163228784124[/C][C]0.0334326457568248[/C][C]0.983283677121588[/C][/ROW]
[ROW][C]29[/C][C]0.0103011400355097[/C][C]0.0206022800710194[/C][C]0.98969885996449[/C][/ROW]
[ROW][C]30[/C][C]0.00707724349911736[/C][C]0.0141544869982347[/C][C]0.992922756500883[/C][/ROW]
[ROW][C]31[/C][C]0.0121504605107594[/C][C]0.0243009210215188[/C][C]0.987849539489241[/C][/ROW]
[ROW][C]32[/C][C]0.00769057099495436[/C][C]0.0153811419899087[/C][C]0.992309429005046[/C][/ROW]
[ROW][C]33[/C][C]0.00442215844981849[/C][C]0.00884431689963698[/C][C]0.995577841550181[/C][/ROW]
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[ROW][C]64[/C][C]2.10452181586577e-05[/C][C]4.20904363173155e-05[/C][C]0.999978954781841[/C][/ROW]
[ROW][C]65[/C][C]7.42482853795874e-05[/C][C]0.000148496570759175[/C][C]0.99992575171462[/C][/ROW]
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[ROW][C]67[/C][C]4.10595333180706e-05[/C][C]8.21190666361412e-05[/C][C]0.999958940466682[/C][/ROW]
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[ROW][C]74[/C][C]9.10068361498889e-06[/C][C]1.82013672299778e-05[/C][C]0.999990899316385[/C][/ROW]
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[ROW][C]76[/C][C]2.05339465304687e-05[/C][C]4.10678930609374e-05[/C][C]0.99997946605347[/C][/ROW]
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[ROW][C]79[/C][C]2.35969598781858e-05[/C][C]4.71939197563717e-05[/C][C]0.999976403040122[/C][/ROW]
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[ROW][C]81[/C][C]1.78286583242233e-05[/C][C]3.56573166484465e-05[/C][C]0.999982171341676[/C][/ROW]
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[ROW][C]178[/C][C]0.992675827810894[/C][C]0.0146483443782122[/C][C]0.00732417218910608[/C][/ROW]
[ROW][C]179[/C][C]0.97362090208185[/C][C]0.0527581958362999[/C][C]0.0263790979181499[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160355&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160355&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
180.1549029731399990.3098059462799980.845097026860001
190.1052987132133110.2105974264266220.894701286786689
200.04655692507327040.09311385014654080.95344307492673
210.263058325138530.526116650277060.73694167486147
220.2213400214249350.4426800428498690.778659978575065
230.1433885400201250.2867770800402490.856611459979875
240.08931389205233260.1786277841046650.910686107947667
250.06187050116578390.1237410023315680.938129498834216
260.0371226685764860.07424533715297190.962877331423514
270.02947909909952490.05895819819904970.970520900900475
280.01671632287841240.03343264575682480.983283677121588
290.01030114003550970.02060228007101940.98969885996449
300.007077243499117360.01415448699823470.992922756500883
310.01215046051075940.02430092102151880.987849539489241
320.007690570994954360.01538114198990870.992309429005046
330.004422158449818490.008844316899636980.995577841550181
340.002785963693688970.005571927387377950.997214036306311
350.004668540687655310.009337081375310630.995331459312345
360.005243960089680020.010487920179360.99475603991032
370.003961117742496270.007922235484992540.996038882257504
380.00254035150862490.005080703017249810.997459648491375
390.001659954443897320.003319908887794650.998340045556103
400.002442721776085290.004885443552170590.997557278223915
410.001733499971443580.003466999942887160.998266500028556
420.001179995858840910.002359991717681820.998820004141159
430.0007062324682612080.001412464936522420.999293767531739
440.0005073328351091810.001014665670218360.999492667164891
450.002041984566845310.004083969133690630.997958015433155
460.001386226046654090.002772452093308180.998613773953346
470.0008939267964182020.00178785359283640.999106073203582
480.0006082174023485390.001216434804697080.999391782597651
490.0003638508802773660.0007277017605547320.999636149119723
500.0005217880188888050.001043576037777610.999478211981111
510.0007754268711764620.001550853742352920.999224573128824
520.0006011417229955920.001202283445991180.999398858277004
530.0003629995904895210.0007259991809790420.999637000409511
540.000403551190083420.0008071023801668410.999596448809917
550.0003884628739587590.0007769257479175180.999611537126041
560.0002716165440935970.0005432330881871940.999728383455906
570.0001673645431097290.0003347290862194570.99983263545689
580.0001513635257468180.0003027270514936350.999848636474253
598.93973767658755e-050.0001787947535317510.999910602623234
605.33603139663402e-050.000106720627932680.999946639686034
613.1905824979008e-056.38116499580159e-050.999968094175021
623.53890409458978e-057.07780818917956e-050.999964610959054
633.3551137135775e-056.71022742715499e-050.999966448862864
642.10452181586577e-054.20904363173155e-050.999978954781841
657.42482853795874e-050.0001484965707591750.99992575171462
666.60055653659273e-050.0001320111307318550.999933994434634
674.10595333180706e-058.21190666361412e-050.999958940466682
682.8199194493483e-055.6398388986966e-050.999971800805507
691.62938476853923e-053.25876953707845e-050.999983706152315
701.39750611834663e-052.79501223669327e-050.999986024938816
718.99094176761277e-061.79818835352255e-050.999991009058232
725.40764884104096e-061.08152976820819e-050.999994592351159
734.71537492081689e-069.43074984163378e-060.999995284625079
749.10068361498889e-061.82013672299778e-050.999990899316385
752.2723042543777e-054.54460850875539e-050.999977276957456
762.05339465304687e-054.10678930609374e-050.99997946605347
771.53136206142802e-053.06272412285604e-050.999984686379386
782.88086734389129e-055.76173468778258e-050.999971191326561
792.35969598781858e-054.71939197563717e-050.999976403040122
802.52686351388164e-055.05372702776328e-050.999974731364861
811.78286583242233e-053.56573166484465e-050.999982171341676
821.10406830286841e-052.20813660573682e-050.999988959316971
837.6240109080932e-061.52480218161864e-050.999992375989092
844.66672641771109e-069.33345283542218e-060.999995333273582
850.4755698286824420.9511396573648830.524430171317558
860.4422730874876480.8845461749752960.557726912512352
870.4038092667213490.8076185334426980.596190733278651
880.369200651748660.7384013034973210.63079934825134
890.3717207126936140.7434414253872280.628279287306386
900.404591667769010.809183335538020.59540833223099
910.3695342983656820.7390685967313630.630465701634318
920.3589022032839290.7178044065678570.641097796716071
930.3239118140588720.6478236281177440.676088185941128
940.2926028118122710.5852056236245410.707397188187729
950.2838709955287150.5677419910574310.716129004471285
960.2835133201861060.5670266403722110.716486679813894
970.2955825773335210.5911651546670420.704417422666479
980.3237884034814140.6475768069628290.676211596518586
990.3046235807856180.6092471615712360.695376419214382
1000.275708715835690.5514174316713790.72429128416431
1010.2556262165388290.5112524330776580.744373783461171
1020.2274823622421380.4549647244842770.772517637757862
1030.2057046961027930.4114093922055850.794295303897207
1040.2411049528658340.4822099057316670.758895047134166
1050.241555473221890.4831109464437790.75844452677811
1060.2844131560924090.5688263121848180.715586843907591
1070.4318715821733340.8637431643466690.568128417826666
1080.3905608198272490.7811216396544970.609439180172751
1090.3872865605184820.7745731210369640.612713439481518
1100.3852442370790430.7704884741580860.614755762920957
1110.37325734139350.7465146827870.6267426586065
1120.3750719318525550.750143863705110.624928068147445
1130.3369861070878040.6739722141756090.663013892912196
1140.3346222994423530.6692445988847050.665377700557647
1150.3123230447421630.6246460894843260.687676955257837
1160.2788953154832140.5577906309664280.721104684516786
1170.2427980087359540.4855960174719070.757201991264046
1180.2109485015743750.421897003148750.789051498425625
1190.7686051732001520.4627896535996970.231394826799848
1200.7326550567974480.5346898864051030.267344943202552
1210.7009812627424670.5980374745150650.299018737257533
1220.6807487322885330.6385025354229330.319251267711467
1230.6597735390615640.6804529218768720.340226460938436
1240.9723128197007090.05537436059858220.0276871802992911
1250.9682709711643340.06345805767133140.0317290288356657
1260.9633615914031640.07327681719367150.0366384085968358
1270.9543516165682870.09129676686342540.0456483834317127
1280.9419971025214280.1160057949571440.058002897478572
1290.9321658799314630.1356682401370740.0678341200685371
1300.9150395888343910.1699208223312190.0849604111656093
1310.9002431030940040.1995137938119910.0997568969059956
1320.8857539340266610.2284921319466790.114246065973339
1330.8758570505083910.2482858989832190.124142949491609
1340.8591696202427390.2816607595145220.140830379757261
1350.8904521553202540.2190956893594910.109547844679746
1360.8799365844113980.2401268311772050.120063415588602
1370.8527755746281840.2944488507436330.147224425371816
1380.8569404232142420.2861191535715170.143059576785758
1390.8471145328557790.3057709342884420.152885467144221
1400.8148692656467090.3702614687065820.185130734353291
1410.7842423674500550.431515265099890.215757632549945
1420.7685253931764190.4629492136471630.231474606823581
1430.7291373490871350.5417253018257290.270862650912865
1440.7327136784745370.5345726430509270.267286321525463
1450.7092201843095530.5815596313808930.290779815690447
1460.6695207803521970.6609584392956050.330479219647803
1470.6823982673008720.6352034653982560.317601732699128
1480.7500886012373080.4998227975253840.249911398762692
1490.7046276584106820.5907446831786370.295372341589318
1500.6643394475563040.6713211048873920.335660552443696
1510.6329892548684150.7340214902631690.367010745131585
1520.6715230130007890.6569539739984230.328476986999211
1530.6173657374342340.7652685251315320.382634262565766
1540.714222029390350.57155594121930.28577797060965
1550.6745282522300740.6509434955398510.325471747769926
1560.8727919392873720.2544161214252560.127208060712628
1570.8382379967705780.3235240064588440.161762003229422
1580.8030194276211810.3939611447576380.196980572378819
1590.7603113110191760.4793773779616480.239688688980824
1600.7489349716893840.5021300566212310.251065028310616
1610.6917816132870290.6164367734259420.308218386712971
1620.9323350651537440.1353298696925110.0676649348462557
1630.9444874339241990.1110251321516020.0555125660758008
1640.9522622958188540.09547540836229140.0477377041811457
1650.9601376571786350.07972468564273020.0398623428213651
1660.9515891276419170.09682174471616650.0484108723580833
1670.9507761262217810.09844774755643830.0492238737782192
1680.965337410607120.069325178785760.03466258939288
1690.959379011434590.08124197713081920.0406209885654096
1700.9991062921081460.001787415783707830.000893707891853914
1710.9996901528635010.0006196942729978110.000309847136498906
1720.9991066698724030.001786660255193820.000893330127596908
1730.9976981379520570.004603724095885340.00230186204794267
1740.9985355269930890.002928946013822610.00146447300691131
1750.996009443227710.007981113544580770.00399055677229038
1760.9887265501600540.0225468996798920.011273449839946
1770.9984374097947530.003125180410493520.00156259020524676
1780.9926758278108940.01464834437821220.00732417218910608
1790.973620902081850.05275819583629990.0263790979181499






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level580.358024691358025NOK
5% type I error level660.407407407407407NOK
10% type I error level800.493827160493827NOK

\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 & 58 & 0.358024691358025 & NOK \tabularnewline
5% type I error level & 66 & 0.407407407407407 & NOK \tabularnewline
10% type I error level & 80 & 0.493827160493827 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160355&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]58[/C][C]0.358024691358025[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.407407407407407[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]80[/C][C]0.493827160493827[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160355&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160355&T=6

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


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 level580.358024691358025NOK
5% type I error level660.407407407407407NOK
10% type I error level800.493827160493827NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 11 ; 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')
}