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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 10 Dec 2011 12:32:46 -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/10/t1323538418s42w0ya3fcp6utc.htm/, Retrieved Sat, 04 May 2024 20:32:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153608, Retrieved Sat, 04 May 2024 20:32:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-10 17:32:46] [87b6e955a128bfb8d1e350b3ce0d281e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1587	252101	62	438	85	3	92	34	131	104	124252	25695	165119	147	148
951	134577	59	330	58	4	58	30	117	111	98956	19967	107269	126	124
1669	198520	62	609	51	14	62	38	146	93	98073	14338	93497	108	108
2283	189326	94	1015	131	2	108	34	132	119	106816	34117	100269	145	142
992	137449	43	294	44	1	55	25	80	57	41449	9713	91627	68	66
577	65295	27	164	42	3	8	31	117	80	76173	10024	47552	49	47
3916	439387	103	1912	94	0	134	29	112	107	177551	39981	233933	171	163
381	33186	19	111	46	0	1	18	67	22	22807	1271	6853	5	5
1790	178368	51	698	71	5	64	30	116	103	126938	30207	104380	106	106
1606	186657	38	556	65	0	77	29	107	72	61680	18035	98431	88	87
1886	261949	96	711	74	0	86	38	140	123	72117	21609	156949	145	141
1645	191051	95	495	55	7	93	49	186	164	79738	19836	81817	93	88
1433	138866	57	544	55	7	44	33	109	100	57793	9028	59238	60	60
2370	296878	66	959	98	3	106	46	159	143	91677	21750	101138	145	145
1645	192648	72	540	41	9	63	38	146	79	64631	10038	107158	95	95
4206	333462	162	1486	115	0	160	52	201	183	106385	30276	155499	144	137
1780	243571	58	635	45	4	104	32	124	123	161961	34972	156274	179	177
2352	263451	130	940	80	3	86	35	131	81	112669	19954	121777	102	102
1262	155679	48	452	37	3	93	25	96	74	114029	28113	105037	157	151
2026	227053	70	617	50	7	119	42	163	158	124550	18830	118661	170	156
2109	240028	63	695	93	0	107	40	151	133	105416	37144	131187	140	140
2451	388549	90	1046	93	1	86	35	128	128	72875	17916	145026	133	130
1253	156540	34	405	56	5	50	25	89	84	81964	16186	107016	74	71
1431	148421	43	477	57	9	92	46	184	184	104880	19195	87242	120	116
2631	177732	97	1012	138	0	123	36	136	127	76302	29124	91699	134	129
2293	191441	105	842	67	0	81	35	134	128	96740	29813	110087	108	107
2653	249893	122	994	88	5	93	38	146	118	93071	20270	145447	132	128
1709	236812	76	530	54	0	113	35	130	125	78912	26105	143307	125	119
1360	142329	45	515	47	0	52	28	105	89	35224	9155	61678	66	62
2051	259667	53	766	121	0	113	37	142	122	90694	18113	210080	130	124
1921	231625	65	734	44	3	112	40	155	151	125369	40546	165005	143	140
1623	176062	67	551	73	4	44	42	154	122	80849	10096	97806	150	144
1933	286683	79	718	49	1	123	44	169	162	104434	32338	184471	152	150
849	87485	33	280	36	4	38	33	125	121	65702	2871	27786	28	28
2641	322865	83	1055	61	2	111	35	135	132	108179	36592	184458	190	177
2235	247082	51	950	77	0	77	37	139	110	63583	4914	98765	73	73
2951	346011	106	1038	69	0	92	39	145	135	95066	30190	178441	115	111
1666	191653	74	552	63	2	74	32	124	80	62486	18153	100619	101	98
917	114673	31	275	36	1	33	17	55	46	31081	12558	58391	41	41
2745	284224	161	986	39	2	105	34	131	127	94584	32894	151672	147	139
2897	284195	72	1336	34	10	108	33	125	103	87408	24138	124437	107	107
1413	155363	59	565	65	6	66	35	128	95	68966	16628	79929	103	102
1535	177306	67	571	78	5	69	32	107	100	88766	26369	123064	84	80
1452	144571	49	404	67	5	62	35	130	102	57139	14171	50466	68	66
2369	140319	73	985	82	1	50	45	73	45	90586	8500	100991	52	51
4908	405267	135	1851	780	2	91	38	138	122	109249	11940	79367	70	69
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
2085	201970	69	611	72	0	101	45	173	159	96056	19456	106257	155	155
3655	302674	99	1249	112	9	129	44	169	153	146648	21347	178412	165	163
1923	164733	50	812	61	3	93	40	145	131	80613	24095	98520	124	121
1616	194221	68	501	39	0	89	33	134	113	87026	26204	153670	121	118
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2328	342263	279	785	73	8	79	41	151	147	131106	20366	174478	161	154
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
2598	246088	46	944	118	1	86	33	121	108	91072	29463	116772	125	122
2276	273108	75	600	72	5	116	49	186	184	159803	35838	189150	157	152
3185	282220	160	977	196	5	106	32	120	115	143950	42590	194404	256	255
2135	273495	119	863	58	0	127	37	135	132	112368	38665	185881	192	177
1863	214872	74	690	67	12	75	32	123	113	82124	19442	67508	86	83
3524	335121	124	1176	60	9	138	41	158	141	144068	25515	188597	164	164
2760	267171	106	1013	138	11	114	25	90	65	162627	51318	203618	213	202
2187	187938	88	890	69	9	55	40	157	87	55062	11807	87232	80	77
2161	229512	78	777	45	8	67	35	135	121	95329	24130	110875	122	118
1302	209798	61	521	54	2	45	33	125	112	105612	34053	144756	122	123
1540	201345	60	409	55	0	88	28	110	81	62853	22641	129825	113	109
1618	163833	114	493	46	6	67	31	121	116	125976	18898	92189	128	126
2403	204250	129	757	84	8	75	40	151	132	79146	24539	121158	117	114
1961	197813	67	736	71	2	114	32	123	104	108461	21664	96219	162	161
1402	132955	60	511	56	5	123	25	92	80	99971	21577	84128	87	85
2292	216092	59	789	55	13	86	42	162	145	77826	16643	97960	103	101
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
1935	213198	67	644	52	7	67	42	163	159	84892	18798	103515	104	102
1586	181713	49	664	94	2	77	38	133	90	92059	24648	91313	127	126
1494	148698	49	505	57	0	105	34	132	120	77993	20286	85407	132	130
1995	300103	70	878	82	4	119	38	144	126	104155	23999	95871	112	112
1904	251437	78	769	42	3	88	32	124	118	109840	26813	143846	155	150
1711	197295	101	499	45	6	78	37	140	112	238712	14718	155387	57	54
1746	158163	55	546	52	2	112	34	132	123	67486	16963	74429	109	106
1473	155529	57	551	63	0	66	33	122	98	68007	16673	74004	92	90
1538	132672	41	565	38	1	58	25	97	78	48194	14646	71987	57	55
3324	377205	100	1086	108	0	132	40	155	119	134796	31772	150629	145	139
1356	145905	66	649	45	5	30	26	99	99	38692	9648	68580	38	38
1931	223701	86	540	53	2	100	40	106	81	93587	23096	119855	152	148
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
1716	130805	47	732	169	0	26	27	101	77	15986	4527	25157	27	27
1091	135082	48	308	60	5	67	32	120	118	113402	37432	90895	104	104
3187	300805	156	1237	271	1	57	33	127	122	97967	21082	117510	80	75
2240	271806	95	783	84	0	95	50	178	103	74844	30437	144774	76	73
2677	150949	96	933	63	1	139	37	141	129	136051	36288	77529	163	157
2038	225805	79	710	54	1	73	33	122	69	50548	12369	103123	89	87
1783	197389	68	563	65	2	134	34	127	121	112215	23774	104669	199	186
1560	156583	56	508	80	6	37	28	102	81	59591	8108	82414	89	88
2540	222599	66	936	84	1	98	32	124	119	59938	15049	82390	107	107
2118	261601	70	838	115	4	58	32	124	116	137639	36021	128446	137	131
1521	178489	35	523	60	3	78	32	124	123	143372	30391	111542	123	123
1460	200657	43	500	62	3	88	31	111	111	138599	30910	136048	152	149
1866	259084	68	691	57	0	142	35	129	100	174110	40656	197257	202	201
3312	313075	130	1060	121	11	127	58	223	221	135062	35070	162079	159	145
3135	346933	100	1232	69	12	139	27	102	95	175681	47250	206286	282	273
2232	246440	104	735	60	8	108	45	174	153	130307	36236	109858	111	111
2061	252444	58	757	81	0	128	37	141	118	139141	29601	182125	197	195
1787	159965	159	574	100	0	62	32	122	50	44244	10443	74168	72	69
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
1973	172239	68	661	72	4	89	22	81	34	48029	18213	88634	82	82
1739	183738	120	631	61	0	83	35	131	76	95216	40856	128321	192	193
2282	227681	43	1015	101	0	116	36	139	112	92288	36471	118936	102	102
2339	260464	81	893	50	0	157	36	137	115	94588	26077	127044	127	124
923	106288	54	293	32	0	28	23	91	69	197426	24797	178377	60	59
1389	109632	77	446	74	0	83	36	142	108	151244	6816	69581	61	61
1673	268905	58	538	54	4	72	36	133	130	139206	25527	168019	106	102
2082	266805	78	627	65	0	134	42	155	110	106271	22139	113598	139	138
398	23623	11	156	9	0	12	1	0	0	1168	238	5841	11	11
1605	152474	65	577	45	0	106	32	123	83	71764	24459	93116	114	114
530	61857	25	192	25	4	23	11	32	30	25162	3913	24610	31	28
1503	144889	43	437	102	0	83	40	149	106	45635	9895	60611	132	101
2636	346600	99	1054	59	1	126	34	128	91	101817	25902	226620	210	208
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4
1849	224051	45	751	56	5	71	27	99	69	100174	12937	121996	98	93
449	31414	19	200	22	0	18	8	25	9	14116	3988	13155	39	39
2912	261043	105	1050	146	2	98	35	132	123	85008	23370	154158	119	114
1740	197819	57	590	63	7	66	41	155	143	124254	24015	78489	107	104
1401	154984	73	430	91	12	44	40	151	125	105793	3870	22007	41	40
1257	112933	45	467	46	2	29	28	103	81	117129	14648	72530	97	94
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
1512	158671	33	528	98	2	56	35	131	124	94747	16768	73397	65	64
2379	302148	70	898	105	0	112	47	178	168	107549	33400	143878	156	154
1180	177918	55	411	57	0	46	46	177	149	97392	23770	119956	158	145
3071	350552	70	1362	126	3	129	42	163	147	126893	34762	181558	160	150
2186	275578	91	743	120	0	139	48	187	145	118850	18793	208236	161	156
3065	368746	106	1069	104	3	136	49	182	172	234853	48186	237085	238	229
1127	172464	31	431	44	0	66	35	135	126	74783	20140	110297	85	84
1045	94381	35	380	48	0	42	32	118	89	66089	8728	61394	50	49
2419	243875	279	788	143	4	70	36	140	137	95684	19060	81420	106	101
3839	382487	153	1367	146	4	97	42	158	149	139537	26880	191154	184	179
1427	114525	40	449	91	14	49	35	132	121	144253	415	11798	15	15
3406	335681	119	1461	129	0	113	37	136	133	153824	38902	135724	155	154
1730	147989	72	651	67	4	55	34	123	93	63995	17375	68614	90	90
1507	216638	45	494	74	0	100	36	134	119	84891	31360	139926	133	133
1907	192862	72	667	168	1	80	36	129	102	61263	15051	105203	136	133
1552	184818	107	510	69	0	29	32	125	45	106221	16785	80338	109	97
3611	336707	105	1472	99	9	95	33	128	104	113587	15886	121376	118	116
1925	215836	76	675	61	1	114	35	129	111	113864	28548	124922	222	221
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
2503	271773	89	814	51	10	128	40	154	120	119906	34012	135471	162	157
1603	130908	52	556	121	5	142	49	188	176	135096	19215	66395	108	105
2272	204009	75	887	48	2	88	33	122	109	151611	34177	134041	153	150
2086	245514	92	663	52	1	147	39	144	132	144645	32990	153554	181	177
2	1	0	0	0	9	0	0	0	0	0	0	0	0	0
207	14688	10	85	0	0	4	0	0	0	6023	2065	7953	5	5
5	98	1	0	0	0	0	0	0	0	0	0	0	0	0
8	455	2	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1777	195765	75	607	51	2	56	33	120	78	77457	17428	98922	113	111
2781	326038	121	934	98	1	121	42	168	104	62464	19912	165395	165	165
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	203	4	0	0	0	0	0	0	0	0	0	0	0	0
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
976	107465	38	267	80	0	37	38	133	65	42087	1801	15167	33	33
29	969	2	0	0	0	0	0	0	0	0	0	0	0	0
1542	173102	58	517	43	2	47	28	101	55	87656	16541	63891	67	63

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153608&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153608&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153608&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 time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TimeRFC[t] = -4618.38756016574 + 24.7486131389241Pageviews[t] + 92.0803475617882Logins[t] + 76.4846704110998CCV[t] + 39.3618065603536`CV(PR)`[t] + 344.812562411198Shared[t] + 4.22707057620899BloggedComputations[t] -1000.68256963937RVC[t] + 453.807385653415FBM[t] + 81.4739577623583`FBM+120`[t] -0.144835141199124CCharacters[t] -0.460268664093618`#revisions`[t] + 0.760763167012572Ctime[t] -155.398378770009Hyperlinks[t] + 218.953794022795blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TimeRFC[t] =  -4618.38756016574 +  24.7486131389241Pageviews[t] +  92.0803475617882Logins[t] +  76.4846704110998CCV[t] +  39.3618065603536`CV(PR)`[t] +  344.812562411198Shared[t] +  4.22707057620899BloggedComputations[t] -1000.68256963937RVC[t] +  453.807385653415FBM[t] +  81.4739577623583`FBM+120`[t] -0.144835141199124CCharacters[t] -0.460268664093618`#revisions`[t] +  0.760763167012572Ctime[t] -155.398378770009Hyperlinks[t] +  218.953794022795blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153608&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TimeRFC[t] =  -4618.38756016574 +  24.7486131389241Pageviews[t] +  92.0803475617882Logins[t] +  76.4846704110998CCV[t] +  39.3618065603536`CV(PR)`[t] +  344.812562411198Shared[t] +  4.22707057620899BloggedComputations[t] -1000.68256963937RVC[t] +  453.807385653415FBM[t] +  81.4739577623583`FBM+120`[t] -0.144835141199124CCharacters[t] -0.460268664093618`#revisions`[t] +  0.760763167012572Ctime[t] -155.398378770009Hyperlinks[t] +  218.953794022795blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153608&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153608&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
TimeRFC[t] = -4618.38756016574 + 24.7486131389241Pageviews[t] + 92.0803475617882Logins[t] + 76.4846704110998CCV[t] + 39.3618065603536`CV(PR)`[t] + 344.812562411198Shared[t] + 4.22707057620899BloggedComputations[t] -1000.68256963937RVC[t] + 453.807385653415FBM[t] + 81.4739577623583`FBM+120`[t] -0.144835141199124CCharacters[t] -0.460268664093618`#revisions`[t] + 0.760763167012572Ctime[t] -155.398378770009Hyperlinks[t] + 218.953794022795blogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4618.387560165745929.335125-0.77890.4372710.218636
Pageviews24.748613138924114.1272861.75180.081860.04093
Logins92.080347561788280.5965941.14250.2550850.127543
CCV76.484670411099827.7334282.75790.0065470.003273
`CV(PR)`39.361806560353645.0078450.87460.3832240.191612
Shared344.812562411198730.3085160.47210.6375130.318757
BloggedComputations4.22707057620899127.108770.03330.9735150.486758
RVC-1000.68256963937898.93663-1.11320.2674220.133711
FBM453.807385653415273.0994821.66170.0986770.049338
`FBM+120`81.4739577623583139.041360.5860.5587830.279391
CCharacters-0.1448351411991240.0858-1.68810.0934920.046746
`#revisions`-0.4602686640936180.400399-1.14950.2521810.12609
Ctime0.7607631670125720.0891728.531400
Hyperlinks-155.398378770009601.188745-0.25850.7963890.398195
blogs218.953794022795620.7740050.35270.7248040.362402

\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) & -4618.38756016574 & 5929.335125 & -0.7789 & 0.437271 & 0.218636 \tabularnewline
Pageviews & 24.7486131389241 & 14.127286 & 1.7518 & 0.08186 & 0.04093 \tabularnewline
Logins & 92.0803475617882 & 80.596594 & 1.1425 & 0.255085 & 0.127543 \tabularnewline
CCV & 76.4846704110998 & 27.733428 & 2.7579 & 0.006547 & 0.003273 \tabularnewline
`CV(PR)` & 39.3618065603536 & 45.007845 & 0.8746 & 0.383224 & 0.191612 \tabularnewline
Shared & 344.812562411198 & 730.308516 & 0.4721 & 0.637513 & 0.318757 \tabularnewline
BloggedComputations & 4.22707057620899 & 127.10877 & 0.0333 & 0.973515 & 0.486758 \tabularnewline
RVC & -1000.68256963937 & 898.93663 & -1.1132 & 0.267422 & 0.133711 \tabularnewline
FBM & 453.807385653415 & 273.099482 & 1.6617 & 0.098677 & 0.049338 \tabularnewline
`FBM+120` & 81.4739577623583 & 139.04136 & 0.586 & 0.558783 & 0.279391 \tabularnewline
CCharacters & -0.144835141199124 & 0.0858 & -1.6881 & 0.093492 & 0.046746 \tabularnewline
`#revisions` & -0.460268664093618 & 0.400399 & -1.1495 & 0.252181 & 0.12609 \tabularnewline
Ctime & 0.760763167012572 & 0.089172 & 8.5314 & 0 & 0 \tabularnewline
Hyperlinks & -155.398378770009 & 601.188745 & -0.2585 & 0.796389 & 0.398195 \tabularnewline
blogs & 218.953794022795 & 620.774005 & 0.3527 & 0.724804 & 0.362402 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153608&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]-4618.38756016574[/C][C]5929.335125[/C][C]-0.7789[/C][C]0.437271[/C][C]0.218636[/C][/ROW]
[ROW][C]Pageviews[/C][C]24.7486131389241[/C][C]14.127286[/C][C]1.7518[/C][C]0.08186[/C][C]0.04093[/C][/ROW]
[ROW][C]Logins[/C][C]92.0803475617882[/C][C]80.596594[/C][C]1.1425[/C][C]0.255085[/C][C]0.127543[/C][/ROW]
[ROW][C]CCV[/C][C]76.4846704110998[/C][C]27.733428[/C][C]2.7579[/C][C]0.006547[/C][C]0.003273[/C][/ROW]
[ROW][C]`CV(PR)`[/C][C]39.3618065603536[/C][C]45.007845[/C][C]0.8746[/C][C]0.383224[/C][C]0.191612[/C][/ROW]
[ROW][C]Shared[/C][C]344.812562411198[/C][C]730.308516[/C][C]0.4721[/C][C]0.637513[/C][C]0.318757[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]4.22707057620899[/C][C]127.10877[/C][C]0.0333[/C][C]0.973515[/C][C]0.486758[/C][/ROW]
[ROW][C]RVC[/C][C]-1000.68256963937[/C][C]898.93663[/C][C]-1.1132[/C][C]0.267422[/C][C]0.133711[/C][/ROW]
[ROW][C]FBM[/C][C]453.807385653415[/C][C]273.099482[/C][C]1.6617[/C][C]0.098677[/C][C]0.049338[/C][/ROW]
[ROW][C]`FBM+120`[/C][C]81.4739577623583[/C][C]139.04136[/C][C]0.586[/C][C]0.558783[/C][C]0.279391[/C][/ROW]
[ROW][C]CCharacters[/C][C]-0.144835141199124[/C][C]0.0858[/C][C]-1.6881[/C][C]0.093492[/C][C]0.046746[/C][/ROW]
[ROW][C]`#revisions`[/C][C]-0.460268664093618[/C][C]0.400399[/C][C]-1.1495[/C][C]0.252181[/C][C]0.12609[/C][/ROW]
[ROW][C]Ctime[/C][C]0.760763167012572[/C][C]0.089172[/C][C]8.5314[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Hyperlinks[/C][C]-155.398378770009[/C][C]601.188745[/C][C]-0.2585[/C][C]0.796389[/C][C]0.398195[/C][/ROW]
[ROW][C]blogs[/C][C]218.953794022795[/C][C]620.774005[/C][C]0.3527[/C][C]0.724804[/C][C]0.362402[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153608&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153608&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)-4618.387560165745929.335125-0.77890.4372710.218636
Pageviews24.748613138924114.1272861.75180.081860.04093
Logins92.080347561788280.5965941.14250.2550850.127543
CCV76.484670411099827.7334282.75790.0065470.003273
`CV(PR)`39.361806560353645.0078450.87460.3832240.191612
Shared344.812562411198730.3085160.47210.6375130.318757
BloggedComputations4.22707057620899127.108770.03330.9735150.486758
RVC-1000.68256963937898.93663-1.11320.2674220.133711
FBM453.807385653415273.0994821.66170.0986770.049338
`FBM+120`81.4739577623583139.041360.5860.5587830.279391
CCharacters-0.1448351411991240.0858-1.68810.0934920.046746
`#revisions`-0.4602686640936180.400399-1.14950.2521810.12609
Ctime0.7607631670125720.0891728.531400
Hyperlinks-155.398378770009601.188745-0.25850.7963890.398195
blogs218.953794022795620.7740050.35270.7248040.362402






Multiple Linear Regression - Regression Statistics
Multiple R0.963059080914616
R-squared0.927482793332105
Adjusted R-squared0.920669096061296
F-TEST (value)136.120340612372
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27351.3612755186
Sum Squared Residuals111466447579.967

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.963059080914616 \tabularnewline
R-squared & 0.927482793332105 \tabularnewline
Adjusted R-squared & 0.920669096061296 \tabularnewline
F-TEST (value) & 136.120340612372 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 27351.3612755186 \tabularnewline
Sum Squared Residuals & 111466447579.967 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153608&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.963059080914616[/C][/ROW]
[ROW][C]R-squared[/C][C]0.927482793332105[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.920669096061296[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]136.120340612372[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/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]27351.3612755186[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]111466447579.967[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153608&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153608&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 R0.963059080914616
R-squared0.927482793332105
Adjusted R-squared0.920669096061296
F-TEST (value)136.120340612372
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27351.3612755186
Sum Squared Residuals111466447579.967






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1252101217890.2561001934210.7438998099
2134577151270.114991834-16693.114991834
3198520189068.428477839451.57152217019
4189326233713.442660326-44387.4426603257
5137449127735.393907279713.60609273045
66529579210.7463412652-13915.7463412652
7439387425777.2605354713609.7394645305
83318632693.2954158744492.704584125588
9178368187423.223221721-9055.22322172065
10186657172462.36283626714194.6371637335
11261949251432.22264780310516.7773521967
12191051182789.2786527128261.72134728764
13138866143410.720587882-4544.72058788203
14296878239445.34944235557432.6505576445
15192648197253.031037848-4605.0310378475
16333462383913.81442525-50451.8144252498
17243571221476.14185836322094.8581416365
18263451246651.04145362216799.958546378
19155679152184.5686142433494.43138575732
20227053220164.4994151386888.50058486196
21240028226316.77399671213711.2260032883
22388549281517.184040757107031.815959243
23156540152992.8253963473547.17460365298
24148421178531.884341142-30110.884341142
25177732241548.580150546-63816.5801505459
26191441228055.136015464-36614.1360154636
27249893287078.757658619-37185.7576586191
28236812214179.33265312822632.6673468716
29142329142450.365785842-121.365785842302
30259667297501.27078158-37834.2707815802
31231625248045.266946872-16420.2669468724
32176062192367.336867263-16305.3368672629
33286683273618.06600036913064.9339996305
348748589446.9226489757-1961.92264897569
35322865306678.97572074316186.0242792571
36247082234729.25434929812352.7456507019
37346011312965.65404912133045.3459508789
38191653184800.7370579386852.2629420616
3911467392306.620540528922366.3794594711
40284224286135.029594064-1911.02959406387
41284195290927.616734237-6732.61673423701
42155363164200.088424828-8837.0884248283
43177306186073.896648886-8767.89664888558
44144571131111.47097456813459.5290254322
45140319194501.569593722-54182.5695937225
46405267380458.29302249524808.7069775052
4778800103035.078255192-24235.0782551916
48201970217581.683756271-15611.6837562708
49302674358382.034487145-55708.0344871446
50164733209367.875934464-44634.8759344643
51194221218135.713292676-23914.7132926757
522418839166.7197332187-14978.7197332187
53342263297240.31320951145022.686790489
546502968932.237734182-3903.23773418201
55101097111634.020758585-10537.0207585848
56246088241528.204774514559.79522549016
57273108273062.25918871145.7408112885536
58282220328850.693711664-46630.6937116637
59273495279255.488593147-5760.48859314739
60214872176495.21224184238376.7877581579
61335121353460.302483777-18339.3024837771
62267171300615.148374787-33444.1483747866
63187938227422.874070706-39484.8740707059
64229512222699.96257786812.03742219982
65209798196031.47347337113766.5265266295
66201345186885.25929922314459.740700777
67163833172019.488150146-8186.48815014593
68204250246457.898723516-42207.8987235165
69197813200208.370820153-2395.37082015298
70132955147069.407402228-14114.4074022278
71216092229897.891331082-13805.8913310816
727356690848.3801889004-17282.3801889004
73213198212310.480399774887.519600226372
74181713176950.961998024762.03800198013
75148698166129.213654975-17431.2136549753
76300103214974.28524607185128.7147539294
77251437235364.95021080216072.0497891983
78197295204825.568614113-7530.56861411323
79158163169839.914404613-11676.9144046126
80155529156498.002527515-969.002527515051
81132672152105.594193621-19433.5941936212
82377205303081.63456804874123.3654319519
83145905159797.458723069-13892.4587230693
84223701186046.820825337654.1791747003
858095395103.0362569831-14150.0362569831
86130805146471.992519471-15666.992519471
87135082128883.4004794586198.59952054173
88300805298529.6509732382275.34902676237
89271806251759.84137225320046.1586277466
90150949214337.279129321-63388.2791293209
91225805208796.8696899617008.1303100404
92197389188348.317901369040.68209864022
93156583164015.299026948-7432.29902694782
94222599227793.170995878-5194.17099587819
95261601226787.28731917434813.712680826
96178489172167.0082134776321.99178652289
97200657184161.25807827316495.7419217275
98259084253938.5919101175145.40808988267
99313075335291.317710068-22216.3177100676
100346933336551.24829452510381.751705475
101246440223466.36642559722973.6335744027
102252444266792.526362015-14348.5263620146
103159965178893.715379299-18928.7153792989
1044328757786.8989436848-14499.8989436848
105172239180433.329231854-8194.32923185432
106183738208547.85938856-24809.8593885603
107227681240906.739481358-13225.7394813577
108260464245539.20213571714924.7978642828
109106288170176.859343711-63888.8593437112
110109632143207.575037141-33575.5750371409
111268905223780.542785345124.4572146999
112266805211899.69746924154905.302530759
1132362322444.36725208181178.63274791825
114152474174431.049494288-21957.0494942885
1156185748494.872581589813362.1274184102
116144889147102.475643651-2213.47564365146
117346600343672.1119049062927.88809509405
1182105422707.595559812-1653.59555981205
119224051207964.5660337616086.4339662399
1203141437161.693515223-5747.69351522301
121261043299855.027281137-38812.0272811375
122197819171759.54910729126059.4508927092
123154984118277.36594078136706.6340592187
124112933131277.624257903-18344.624257903
1253821451267.0766690346-13053.0766690346
126158671153844.9217021584826.07829784165
127302148269411.07397980632736.9260201943
128177918183361.884115539-5443.88411553877
129350552344183.8166678856368.18333211469
130275578310335.869948255-34757.8699482553
131368746353362.37427232515383.6257276752
132172464166601.5493948875862.45060511348
13394381120452.291586749-26071.2915867493
134243875232135.43781931411739.5621806857
135382487381823.461552598663.538447402211
136114525100921.07484744513603.9251525545
137335681316162.43471686719518.565283133
138147989168892.276357145-20903.2763571449
139216638200595.50600941516042.4939905848
140192862210565.667162075-17703.6671620747
141184818156168.34089272728649.6591072734
142336707323579.34600227913127.6539977207
143215836216731.739007864-895.739007864503
144173260132571.26626440340688.7337355968
145271773252654.91115859719118.0888414027
146130908168383.422030719-37475.4220307186
147204009233883.372007461-29874.3720074614
148245514237589.1596318297924.84036817105
1491-1465.577272187181466.57727218718
1501468812488.8137991772199.18620082304
15198-4402.56414690944500.5641469094
152455-4236.237959930834691.23795993083
1530-4273.574997754614273.57499775461
1540-4618.38756016584618.3875601658
155195765186175.2529248699589.74707513083
156326038312285.13504083813752.8649591623
1570-4618.38756016584618.3875601658
158203-4151.071717362964354.07171736296
15971998385.26365219319-1186.26365219319
1604666047285.7463165455-625.746316545491
1611754710101.24197553837445.75802446171
16210746581099.520312586426365.4796874136
163969-3716.517084013424685.51708401342
164173102134983.90950292238118.0904970783

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 252101 & 217890.25610019 & 34210.7438998099 \tabularnewline
2 & 134577 & 151270.114991834 & -16693.114991834 \tabularnewline
3 & 198520 & 189068.42847783 & 9451.57152217019 \tabularnewline
4 & 189326 & 233713.442660326 & -44387.4426603257 \tabularnewline
5 & 137449 & 127735.39390727 & 9713.60609273045 \tabularnewline
6 & 65295 & 79210.7463412652 & -13915.7463412652 \tabularnewline
7 & 439387 & 425777.26053547 & 13609.7394645305 \tabularnewline
8 & 33186 & 32693.2954158744 & 492.704584125588 \tabularnewline
9 & 178368 & 187423.223221721 & -9055.22322172065 \tabularnewline
10 & 186657 & 172462.362836267 & 14194.6371637335 \tabularnewline
11 & 261949 & 251432.222647803 & 10516.7773521967 \tabularnewline
12 & 191051 & 182789.278652712 & 8261.72134728764 \tabularnewline
13 & 138866 & 143410.720587882 & -4544.72058788203 \tabularnewline
14 & 296878 & 239445.349442355 & 57432.6505576445 \tabularnewline
15 & 192648 & 197253.031037848 & -4605.0310378475 \tabularnewline
16 & 333462 & 383913.81442525 & -50451.8144252498 \tabularnewline
17 & 243571 & 221476.141858363 & 22094.8581416365 \tabularnewline
18 & 263451 & 246651.041453622 & 16799.958546378 \tabularnewline
19 & 155679 & 152184.568614243 & 3494.43138575732 \tabularnewline
20 & 227053 & 220164.499415138 & 6888.50058486196 \tabularnewline
21 & 240028 & 226316.773996712 & 13711.2260032883 \tabularnewline
22 & 388549 & 281517.184040757 & 107031.815959243 \tabularnewline
23 & 156540 & 152992.825396347 & 3547.17460365298 \tabularnewline
24 & 148421 & 178531.884341142 & -30110.884341142 \tabularnewline
25 & 177732 & 241548.580150546 & -63816.5801505459 \tabularnewline
26 & 191441 & 228055.136015464 & -36614.1360154636 \tabularnewline
27 & 249893 & 287078.757658619 & -37185.7576586191 \tabularnewline
28 & 236812 & 214179.332653128 & 22632.6673468716 \tabularnewline
29 & 142329 & 142450.365785842 & -121.365785842302 \tabularnewline
30 & 259667 & 297501.27078158 & -37834.2707815802 \tabularnewline
31 & 231625 & 248045.266946872 & -16420.2669468724 \tabularnewline
32 & 176062 & 192367.336867263 & -16305.3368672629 \tabularnewline
33 & 286683 & 273618.066000369 & 13064.9339996305 \tabularnewline
34 & 87485 & 89446.9226489757 & -1961.92264897569 \tabularnewline
35 & 322865 & 306678.975720743 & 16186.0242792571 \tabularnewline
36 & 247082 & 234729.254349298 & 12352.7456507019 \tabularnewline
37 & 346011 & 312965.654049121 & 33045.3459508789 \tabularnewline
38 & 191653 & 184800.737057938 & 6852.2629420616 \tabularnewline
39 & 114673 & 92306.6205405289 & 22366.3794594711 \tabularnewline
40 & 284224 & 286135.029594064 & -1911.02959406387 \tabularnewline
41 & 284195 & 290927.616734237 & -6732.61673423701 \tabularnewline
42 & 155363 & 164200.088424828 & -8837.0884248283 \tabularnewline
43 & 177306 & 186073.896648886 & -8767.89664888558 \tabularnewline
44 & 144571 & 131111.470974568 & 13459.5290254322 \tabularnewline
45 & 140319 & 194501.569593722 & -54182.5695937225 \tabularnewline
46 & 405267 & 380458.293022495 & 24808.7069775052 \tabularnewline
47 & 78800 & 103035.078255192 & -24235.0782551916 \tabularnewline
48 & 201970 & 217581.683756271 & -15611.6837562708 \tabularnewline
49 & 302674 & 358382.034487145 & -55708.0344871446 \tabularnewline
50 & 164733 & 209367.875934464 & -44634.8759344643 \tabularnewline
51 & 194221 & 218135.713292676 & -23914.7132926757 \tabularnewline
52 & 24188 & 39166.7197332187 & -14978.7197332187 \tabularnewline
53 & 342263 & 297240.313209511 & 45022.686790489 \tabularnewline
54 & 65029 & 68932.237734182 & -3903.23773418201 \tabularnewline
55 & 101097 & 111634.020758585 & -10537.0207585848 \tabularnewline
56 & 246088 & 241528.20477451 & 4559.79522549016 \tabularnewline
57 & 273108 & 273062.259188711 & 45.7408112885536 \tabularnewline
58 & 282220 & 328850.693711664 & -46630.6937116637 \tabularnewline
59 & 273495 & 279255.488593147 & -5760.48859314739 \tabularnewline
60 & 214872 & 176495.212241842 & 38376.7877581579 \tabularnewline
61 & 335121 & 353460.302483777 & -18339.3024837771 \tabularnewline
62 & 267171 & 300615.148374787 & -33444.1483747866 \tabularnewline
63 & 187938 & 227422.874070706 & -39484.8740707059 \tabularnewline
64 & 229512 & 222699.9625778 & 6812.03742219982 \tabularnewline
65 & 209798 & 196031.473473371 & 13766.5265266295 \tabularnewline
66 & 201345 & 186885.259299223 & 14459.740700777 \tabularnewline
67 & 163833 & 172019.488150146 & -8186.48815014593 \tabularnewline
68 & 204250 & 246457.898723516 & -42207.8987235165 \tabularnewline
69 & 197813 & 200208.370820153 & -2395.37082015298 \tabularnewline
70 & 132955 & 147069.407402228 & -14114.4074022278 \tabularnewline
71 & 216092 & 229897.891331082 & -13805.8913310816 \tabularnewline
72 & 73566 & 90848.3801889004 & -17282.3801889004 \tabularnewline
73 & 213198 & 212310.480399774 & 887.519600226372 \tabularnewline
74 & 181713 & 176950.96199802 & 4762.03800198013 \tabularnewline
75 & 148698 & 166129.213654975 & -17431.2136549753 \tabularnewline
76 & 300103 & 214974.285246071 & 85128.7147539294 \tabularnewline
77 & 251437 & 235364.950210802 & 16072.0497891983 \tabularnewline
78 & 197295 & 204825.568614113 & -7530.56861411323 \tabularnewline
79 & 158163 & 169839.914404613 & -11676.9144046126 \tabularnewline
80 & 155529 & 156498.002527515 & -969.002527515051 \tabularnewline
81 & 132672 & 152105.594193621 & -19433.5941936212 \tabularnewline
82 & 377205 & 303081.634568048 & 74123.3654319519 \tabularnewline
83 & 145905 & 159797.458723069 & -13892.4587230693 \tabularnewline
84 & 223701 & 186046.8208253 & 37654.1791747003 \tabularnewline
85 & 80953 & 95103.0362569831 & -14150.0362569831 \tabularnewline
86 & 130805 & 146471.992519471 & -15666.992519471 \tabularnewline
87 & 135082 & 128883.400479458 & 6198.59952054173 \tabularnewline
88 & 300805 & 298529.650973238 & 2275.34902676237 \tabularnewline
89 & 271806 & 251759.841372253 & 20046.1586277466 \tabularnewline
90 & 150949 & 214337.279129321 & -63388.2791293209 \tabularnewline
91 & 225805 & 208796.86968996 & 17008.1303100404 \tabularnewline
92 & 197389 & 188348.31790136 & 9040.68209864022 \tabularnewline
93 & 156583 & 164015.299026948 & -7432.29902694782 \tabularnewline
94 & 222599 & 227793.170995878 & -5194.17099587819 \tabularnewline
95 & 261601 & 226787.287319174 & 34813.712680826 \tabularnewline
96 & 178489 & 172167.008213477 & 6321.99178652289 \tabularnewline
97 & 200657 & 184161.258078273 & 16495.7419217275 \tabularnewline
98 & 259084 & 253938.591910117 & 5145.40808988267 \tabularnewline
99 & 313075 & 335291.317710068 & -22216.3177100676 \tabularnewline
100 & 346933 & 336551.248294525 & 10381.751705475 \tabularnewline
101 & 246440 & 223466.366425597 & 22973.6335744027 \tabularnewline
102 & 252444 & 266792.526362015 & -14348.5263620146 \tabularnewline
103 & 159965 & 178893.715379299 & -18928.7153792989 \tabularnewline
104 & 43287 & 57786.8989436848 & -14499.8989436848 \tabularnewline
105 & 172239 & 180433.329231854 & -8194.32923185432 \tabularnewline
106 & 183738 & 208547.85938856 & -24809.8593885603 \tabularnewline
107 & 227681 & 240906.739481358 & -13225.7394813577 \tabularnewline
108 & 260464 & 245539.202135717 & 14924.7978642828 \tabularnewline
109 & 106288 & 170176.859343711 & -63888.8593437112 \tabularnewline
110 & 109632 & 143207.575037141 & -33575.5750371409 \tabularnewline
111 & 268905 & 223780.5427853 & 45124.4572146999 \tabularnewline
112 & 266805 & 211899.697469241 & 54905.302530759 \tabularnewline
113 & 23623 & 22444.3672520818 & 1178.63274791825 \tabularnewline
114 & 152474 & 174431.049494288 & -21957.0494942885 \tabularnewline
115 & 61857 & 48494.8725815898 & 13362.1274184102 \tabularnewline
116 & 144889 & 147102.475643651 & -2213.47564365146 \tabularnewline
117 & 346600 & 343672.111904906 & 2927.88809509405 \tabularnewline
118 & 21054 & 22707.595559812 & -1653.59555981205 \tabularnewline
119 & 224051 & 207964.56603376 & 16086.4339662399 \tabularnewline
120 & 31414 & 37161.693515223 & -5747.69351522301 \tabularnewline
121 & 261043 & 299855.027281137 & -38812.0272811375 \tabularnewline
122 & 197819 & 171759.549107291 & 26059.4508927092 \tabularnewline
123 & 154984 & 118277.365940781 & 36706.6340592187 \tabularnewline
124 & 112933 & 131277.624257903 & -18344.624257903 \tabularnewline
125 & 38214 & 51267.0766690346 & -13053.0766690346 \tabularnewline
126 & 158671 & 153844.921702158 & 4826.07829784165 \tabularnewline
127 & 302148 & 269411.073979806 & 32736.9260201943 \tabularnewline
128 & 177918 & 183361.884115539 & -5443.88411553877 \tabularnewline
129 & 350552 & 344183.816667885 & 6368.18333211469 \tabularnewline
130 & 275578 & 310335.869948255 & -34757.8699482553 \tabularnewline
131 & 368746 & 353362.374272325 & 15383.6257276752 \tabularnewline
132 & 172464 & 166601.549394887 & 5862.45060511348 \tabularnewline
133 & 94381 & 120452.291586749 & -26071.2915867493 \tabularnewline
134 & 243875 & 232135.437819314 & 11739.5621806857 \tabularnewline
135 & 382487 & 381823.461552598 & 663.538447402211 \tabularnewline
136 & 114525 & 100921.074847445 & 13603.9251525545 \tabularnewline
137 & 335681 & 316162.434716867 & 19518.565283133 \tabularnewline
138 & 147989 & 168892.276357145 & -20903.2763571449 \tabularnewline
139 & 216638 & 200595.506009415 & 16042.4939905848 \tabularnewline
140 & 192862 & 210565.667162075 & -17703.6671620747 \tabularnewline
141 & 184818 & 156168.340892727 & 28649.6591072734 \tabularnewline
142 & 336707 & 323579.346002279 & 13127.6539977207 \tabularnewline
143 & 215836 & 216731.739007864 & -895.739007864503 \tabularnewline
144 & 173260 & 132571.266264403 & 40688.7337355968 \tabularnewline
145 & 271773 & 252654.911158597 & 19118.0888414027 \tabularnewline
146 & 130908 & 168383.422030719 & -37475.4220307186 \tabularnewline
147 & 204009 & 233883.372007461 & -29874.3720074614 \tabularnewline
148 & 245514 & 237589.159631829 & 7924.84036817105 \tabularnewline
149 & 1 & -1465.57727218718 & 1466.57727218718 \tabularnewline
150 & 14688 & 12488.813799177 & 2199.18620082304 \tabularnewline
151 & 98 & -4402.5641469094 & 4500.5641469094 \tabularnewline
152 & 455 & -4236.23795993083 & 4691.23795993083 \tabularnewline
153 & 0 & -4273.57499775461 & 4273.57499775461 \tabularnewline
154 & 0 & -4618.3875601658 & 4618.3875601658 \tabularnewline
155 & 195765 & 186175.252924869 & 9589.74707513083 \tabularnewline
156 & 326038 & 312285.135040838 & 13752.8649591623 \tabularnewline
157 & 0 & -4618.3875601658 & 4618.3875601658 \tabularnewline
158 & 203 & -4151.07171736296 & 4354.07171736296 \tabularnewline
159 & 7199 & 8385.26365219319 & -1186.26365219319 \tabularnewline
160 & 46660 & 47285.7463165455 & -625.746316545491 \tabularnewline
161 & 17547 & 10101.2419755383 & 7445.75802446171 \tabularnewline
162 & 107465 & 81099.5203125864 & 26365.4796874136 \tabularnewline
163 & 969 & -3716.51708401342 & 4685.51708401342 \tabularnewline
164 & 173102 & 134983.909502922 & 38118.0904970783 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153608&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]252101[/C][C]217890.25610019[/C][C]34210.7438998099[/C][/ROW]
[ROW][C]2[/C][C]134577[/C][C]151270.114991834[/C][C]-16693.114991834[/C][/ROW]
[ROW][C]3[/C][C]198520[/C][C]189068.42847783[/C][C]9451.57152217019[/C][/ROW]
[ROW][C]4[/C][C]189326[/C][C]233713.442660326[/C][C]-44387.4426603257[/C][/ROW]
[ROW][C]5[/C][C]137449[/C][C]127735.39390727[/C][C]9713.60609273045[/C][/ROW]
[ROW][C]6[/C][C]65295[/C][C]79210.7463412652[/C][C]-13915.7463412652[/C][/ROW]
[ROW][C]7[/C][C]439387[/C][C]425777.26053547[/C][C]13609.7394645305[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]32693.2954158744[/C][C]492.704584125588[/C][/ROW]
[ROW][C]9[/C][C]178368[/C][C]187423.223221721[/C][C]-9055.22322172065[/C][/ROW]
[ROW][C]10[/C][C]186657[/C][C]172462.362836267[/C][C]14194.6371637335[/C][/ROW]
[ROW][C]11[/C][C]261949[/C][C]251432.222647803[/C][C]10516.7773521967[/C][/ROW]
[ROW][C]12[/C][C]191051[/C][C]182789.278652712[/C][C]8261.72134728764[/C][/ROW]
[ROW][C]13[/C][C]138866[/C][C]143410.720587882[/C][C]-4544.72058788203[/C][/ROW]
[ROW][C]14[/C][C]296878[/C][C]239445.349442355[/C][C]57432.6505576445[/C][/ROW]
[ROW][C]15[/C][C]192648[/C][C]197253.031037848[/C][C]-4605.0310378475[/C][/ROW]
[ROW][C]16[/C][C]333462[/C][C]383913.81442525[/C][C]-50451.8144252498[/C][/ROW]
[ROW][C]17[/C][C]243571[/C][C]221476.141858363[/C][C]22094.8581416365[/C][/ROW]
[ROW][C]18[/C][C]263451[/C][C]246651.041453622[/C][C]16799.958546378[/C][/ROW]
[ROW][C]19[/C][C]155679[/C][C]152184.568614243[/C][C]3494.43138575732[/C][/ROW]
[ROW][C]20[/C][C]227053[/C][C]220164.499415138[/C][C]6888.50058486196[/C][/ROW]
[ROW][C]21[/C][C]240028[/C][C]226316.773996712[/C][C]13711.2260032883[/C][/ROW]
[ROW][C]22[/C][C]388549[/C][C]281517.184040757[/C][C]107031.815959243[/C][/ROW]
[ROW][C]23[/C][C]156540[/C][C]152992.825396347[/C][C]3547.17460365298[/C][/ROW]
[ROW][C]24[/C][C]148421[/C][C]178531.884341142[/C][C]-30110.884341142[/C][/ROW]
[ROW][C]25[/C][C]177732[/C][C]241548.580150546[/C][C]-63816.5801505459[/C][/ROW]
[ROW][C]26[/C][C]191441[/C][C]228055.136015464[/C][C]-36614.1360154636[/C][/ROW]
[ROW][C]27[/C][C]249893[/C][C]287078.757658619[/C][C]-37185.7576586191[/C][/ROW]
[ROW][C]28[/C][C]236812[/C][C]214179.332653128[/C][C]22632.6673468716[/C][/ROW]
[ROW][C]29[/C][C]142329[/C][C]142450.365785842[/C][C]-121.365785842302[/C][/ROW]
[ROW][C]30[/C][C]259667[/C][C]297501.27078158[/C][C]-37834.2707815802[/C][/ROW]
[ROW][C]31[/C][C]231625[/C][C]248045.266946872[/C][C]-16420.2669468724[/C][/ROW]
[ROW][C]32[/C][C]176062[/C][C]192367.336867263[/C][C]-16305.3368672629[/C][/ROW]
[ROW][C]33[/C][C]286683[/C][C]273618.066000369[/C][C]13064.9339996305[/C][/ROW]
[ROW][C]34[/C][C]87485[/C][C]89446.9226489757[/C][C]-1961.92264897569[/C][/ROW]
[ROW][C]35[/C][C]322865[/C][C]306678.975720743[/C][C]16186.0242792571[/C][/ROW]
[ROW][C]36[/C][C]247082[/C][C]234729.254349298[/C][C]12352.7456507019[/C][/ROW]
[ROW][C]37[/C][C]346011[/C][C]312965.654049121[/C][C]33045.3459508789[/C][/ROW]
[ROW][C]38[/C][C]191653[/C][C]184800.737057938[/C][C]6852.2629420616[/C][/ROW]
[ROW][C]39[/C][C]114673[/C][C]92306.6205405289[/C][C]22366.3794594711[/C][/ROW]
[ROW][C]40[/C][C]284224[/C][C]286135.029594064[/C][C]-1911.02959406387[/C][/ROW]
[ROW][C]41[/C][C]284195[/C][C]290927.616734237[/C][C]-6732.61673423701[/C][/ROW]
[ROW][C]42[/C][C]155363[/C][C]164200.088424828[/C][C]-8837.0884248283[/C][/ROW]
[ROW][C]43[/C][C]177306[/C][C]186073.896648886[/C][C]-8767.89664888558[/C][/ROW]
[ROW][C]44[/C][C]144571[/C][C]131111.470974568[/C][C]13459.5290254322[/C][/ROW]
[ROW][C]45[/C][C]140319[/C][C]194501.569593722[/C][C]-54182.5695937225[/C][/ROW]
[ROW][C]46[/C][C]405267[/C][C]380458.293022495[/C][C]24808.7069775052[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]103035.078255192[/C][C]-24235.0782551916[/C][/ROW]
[ROW][C]48[/C][C]201970[/C][C]217581.683756271[/C][C]-15611.6837562708[/C][/ROW]
[ROW][C]49[/C][C]302674[/C][C]358382.034487145[/C][C]-55708.0344871446[/C][/ROW]
[ROW][C]50[/C][C]164733[/C][C]209367.875934464[/C][C]-44634.8759344643[/C][/ROW]
[ROW][C]51[/C][C]194221[/C][C]218135.713292676[/C][C]-23914.7132926757[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]39166.7197332187[/C][C]-14978.7197332187[/C][/ROW]
[ROW][C]53[/C][C]342263[/C][C]297240.313209511[/C][C]45022.686790489[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]68932.237734182[/C][C]-3903.23773418201[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]111634.020758585[/C][C]-10537.0207585848[/C][/ROW]
[ROW][C]56[/C][C]246088[/C][C]241528.20477451[/C][C]4559.79522549016[/C][/ROW]
[ROW][C]57[/C][C]273108[/C][C]273062.259188711[/C][C]45.7408112885536[/C][/ROW]
[ROW][C]58[/C][C]282220[/C][C]328850.693711664[/C][C]-46630.6937116637[/C][/ROW]
[ROW][C]59[/C][C]273495[/C][C]279255.488593147[/C][C]-5760.48859314739[/C][/ROW]
[ROW][C]60[/C][C]214872[/C][C]176495.212241842[/C][C]38376.7877581579[/C][/ROW]
[ROW][C]61[/C][C]335121[/C][C]353460.302483777[/C][C]-18339.3024837771[/C][/ROW]
[ROW][C]62[/C][C]267171[/C][C]300615.148374787[/C][C]-33444.1483747866[/C][/ROW]
[ROW][C]63[/C][C]187938[/C][C]227422.874070706[/C][C]-39484.8740707059[/C][/ROW]
[ROW][C]64[/C][C]229512[/C][C]222699.9625778[/C][C]6812.03742219982[/C][/ROW]
[ROW][C]65[/C][C]209798[/C][C]196031.473473371[/C][C]13766.5265266295[/C][/ROW]
[ROW][C]66[/C][C]201345[/C][C]186885.259299223[/C][C]14459.740700777[/C][/ROW]
[ROW][C]67[/C][C]163833[/C][C]172019.488150146[/C][C]-8186.48815014593[/C][/ROW]
[ROW][C]68[/C][C]204250[/C][C]246457.898723516[/C][C]-42207.8987235165[/C][/ROW]
[ROW][C]69[/C][C]197813[/C][C]200208.370820153[/C][C]-2395.37082015298[/C][/ROW]
[ROW][C]70[/C][C]132955[/C][C]147069.407402228[/C][C]-14114.4074022278[/C][/ROW]
[ROW][C]71[/C][C]216092[/C][C]229897.891331082[/C][C]-13805.8913310816[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]90848.3801889004[/C][C]-17282.3801889004[/C][/ROW]
[ROW][C]73[/C][C]213198[/C][C]212310.480399774[/C][C]887.519600226372[/C][/ROW]
[ROW][C]74[/C][C]181713[/C][C]176950.96199802[/C][C]4762.03800198013[/C][/ROW]
[ROW][C]75[/C][C]148698[/C][C]166129.213654975[/C][C]-17431.2136549753[/C][/ROW]
[ROW][C]76[/C][C]300103[/C][C]214974.285246071[/C][C]85128.7147539294[/C][/ROW]
[ROW][C]77[/C][C]251437[/C][C]235364.950210802[/C][C]16072.0497891983[/C][/ROW]
[ROW][C]78[/C][C]197295[/C][C]204825.568614113[/C][C]-7530.56861411323[/C][/ROW]
[ROW][C]79[/C][C]158163[/C][C]169839.914404613[/C][C]-11676.9144046126[/C][/ROW]
[ROW][C]80[/C][C]155529[/C][C]156498.002527515[/C][C]-969.002527515051[/C][/ROW]
[ROW][C]81[/C][C]132672[/C][C]152105.594193621[/C][C]-19433.5941936212[/C][/ROW]
[ROW][C]82[/C][C]377205[/C][C]303081.634568048[/C][C]74123.3654319519[/C][/ROW]
[ROW][C]83[/C][C]145905[/C][C]159797.458723069[/C][C]-13892.4587230693[/C][/ROW]
[ROW][C]84[/C][C]223701[/C][C]186046.8208253[/C][C]37654.1791747003[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]95103.0362569831[/C][C]-14150.0362569831[/C][/ROW]
[ROW][C]86[/C][C]130805[/C][C]146471.992519471[/C][C]-15666.992519471[/C][/ROW]
[ROW][C]87[/C][C]135082[/C][C]128883.400479458[/C][C]6198.59952054173[/C][/ROW]
[ROW][C]88[/C][C]300805[/C][C]298529.650973238[/C][C]2275.34902676237[/C][/ROW]
[ROW][C]89[/C][C]271806[/C][C]251759.841372253[/C][C]20046.1586277466[/C][/ROW]
[ROW][C]90[/C][C]150949[/C][C]214337.279129321[/C][C]-63388.2791293209[/C][/ROW]
[ROW][C]91[/C][C]225805[/C][C]208796.86968996[/C][C]17008.1303100404[/C][/ROW]
[ROW][C]92[/C][C]197389[/C][C]188348.31790136[/C][C]9040.68209864022[/C][/ROW]
[ROW][C]93[/C][C]156583[/C][C]164015.299026948[/C][C]-7432.29902694782[/C][/ROW]
[ROW][C]94[/C][C]222599[/C][C]227793.170995878[/C][C]-5194.17099587819[/C][/ROW]
[ROW][C]95[/C][C]261601[/C][C]226787.287319174[/C][C]34813.712680826[/C][/ROW]
[ROW][C]96[/C][C]178489[/C][C]172167.008213477[/C][C]6321.99178652289[/C][/ROW]
[ROW][C]97[/C][C]200657[/C][C]184161.258078273[/C][C]16495.7419217275[/C][/ROW]
[ROW][C]98[/C][C]259084[/C][C]253938.591910117[/C][C]5145.40808988267[/C][/ROW]
[ROW][C]99[/C][C]313075[/C][C]335291.317710068[/C][C]-22216.3177100676[/C][/ROW]
[ROW][C]100[/C][C]346933[/C][C]336551.248294525[/C][C]10381.751705475[/C][/ROW]
[ROW][C]101[/C][C]246440[/C][C]223466.366425597[/C][C]22973.6335744027[/C][/ROW]
[ROW][C]102[/C][C]252444[/C][C]266792.526362015[/C][C]-14348.5263620146[/C][/ROW]
[ROW][C]103[/C][C]159965[/C][C]178893.715379299[/C][C]-18928.7153792989[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]57786.8989436848[/C][C]-14499.8989436848[/C][/ROW]
[ROW][C]105[/C][C]172239[/C][C]180433.329231854[/C][C]-8194.32923185432[/C][/ROW]
[ROW][C]106[/C][C]183738[/C][C]208547.85938856[/C][C]-24809.8593885603[/C][/ROW]
[ROW][C]107[/C][C]227681[/C][C]240906.739481358[/C][C]-13225.7394813577[/C][/ROW]
[ROW][C]108[/C][C]260464[/C][C]245539.202135717[/C][C]14924.7978642828[/C][/ROW]
[ROW][C]109[/C][C]106288[/C][C]170176.859343711[/C][C]-63888.8593437112[/C][/ROW]
[ROW][C]110[/C][C]109632[/C][C]143207.575037141[/C][C]-33575.5750371409[/C][/ROW]
[ROW][C]111[/C][C]268905[/C][C]223780.5427853[/C][C]45124.4572146999[/C][/ROW]
[ROW][C]112[/C][C]266805[/C][C]211899.697469241[/C][C]54905.302530759[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]22444.3672520818[/C][C]1178.63274791825[/C][/ROW]
[ROW][C]114[/C][C]152474[/C][C]174431.049494288[/C][C]-21957.0494942885[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]48494.8725815898[/C][C]13362.1274184102[/C][/ROW]
[ROW][C]116[/C][C]144889[/C][C]147102.475643651[/C][C]-2213.47564365146[/C][/ROW]
[ROW][C]117[/C][C]346600[/C][C]343672.111904906[/C][C]2927.88809509405[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]22707.595559812[/C][C]-1653.59555981205[/C][/ROW]
[ROW][C]119[/C][C]224051[/C][C]207964.56603376[/C][C]16086.4339662399[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]37161.693515223[/C][C]-5747.69351522301[/C][/ROW]
[ROW][C]121[/C][C]261043[/C][C]299855.027281137[/C][C]-38812.0272811375[/C][/ROW]
[ROW][C]122[/C][C]197819[/C][C]171759.549107291[/C][C]26059.4508927092[/C][/ROW]
[ROW][C]123[/C][C]154984[/C][C]118277.365940781[/C][C]36706.6340592187[/C][/ROW]
[ROW][C]124[/C][C]112933[/C][C]131277.624257903[/C][C]-18344.624257903[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]51267.0766690346[/C][C]-13053.0766690346[/C][/ROW]
[ROW][C]126[/C][C]158671[/C][C]153844.921702158[/C][C]4826.07829784165[/C][/ROW]
[ROW][C]127[/C][C]302148[/C][C]269411.073979806[/C][C]32736.9260201943[/C][/ROW]
[ROW][C]128[/C][C]177918[/C][C]183361.884115539[/C][C]-5443.88411553877[/C][/ROW]
[ROW][C]129[/C][C]350552[/C][C]344183.816667885[/C][C]6368.18333211469[/C][/ROW]
[ROW][C]130[/C][C]275578[/C][C]310335.869948255[/C][C]-34757.8699482553[/C][/ROW]
[ROW][C]131[/C][C]368746[/C][C]353362.374272325[/C][C]15383.6257276752[/C][/ROW]
[ROW][C]132[/C][C]172464[/C][C]166601.549394887[/C][C]5862.45060511348[/C][/ROW]
[ROW][C]133[/C][C]94381[/C][C]120452.291586749[/C][C]-26071.2915867493[/C][/ROW]
[ROW][C]134[/C][C]243875[/C][C]232135.437819314[/C][C]11739.5621806857[/C][/ROW]
[ROW][C]135[/C][C]382487[/C][C]381823.461552598[/C][C]663.538447402211[/C][/ROW]
[ROW][C]136[/C][C]114525[/C][C]100921.074847445[/C][C]13603.9251525545[/C][/ROW]
[ROW][C]137[/C][C]335681[/C][C]316162.434716867[/C][C]19518.565283133[/C][/ROW]
[ROW][C]138[/C][C]147989[/C][C]168892.276357145[/C][C]-20903.2763571449[/C][/ROW]
[ROW][C]139[/C][C]216638[/C][C]200595.506009415[/C][C]16042.4939905848[/C][/ROW]
[ROW][C]140[/C][C]192862[/C][C]210565.667162075[/C][C]-17703.6671620747[/C][/ROW]
[ROW][C]141[/C][C]184818[/C][C]156168.340892727[/C][C]28649.6591072734[/C][/ROW]
[ROW][C]142[/C][C]336707[/C][C]323579.346002279[/C][C]13127.6539977207[/C][/ROW]
[ROW][C]143[/C][C]215836[/C][C]216731.739007864[/C][C]-895.739007864503[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]132571.266264403[/C][C]40688.7337355968[/C][/ROW]
[ROW][C]145[/C][C]271773[/C][C]252654.911158597[/C][C]19118.0888414027[/C][/ROW]
[ROW][C]146[/C][C]130908[/C][C]168383.422030719[/C][C]-37475.4220307186[/C][/ROW]
[ROW][C]147[/C][C]204009[/C][C]233883.372007461[/C][C]-29874.3720074614[/C][/ROW]
[ROW][C]148[/C][C]245514[/C][C]237589.159631829[/C][C]7924.84036817105[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]-1465.57727218718[/C][C]1466.57727218718[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]12488.813799177[/C][C]2199.18620082304[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]-4402.5641469094[/C][C]4500.5641469094[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]-4236.23795993083[/C][C]4691.23795993083[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-4273.57499775461[/C][C]4273.57499775461[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-4618.3875601658[/C][C]4618.3875601658[/C][/ROW]
[ROW][C]155[/C][C]195765[/C][C]186175.252924869[/C][C]9589.74707513083[/C][/ROW]
[ROW][C]156[/C][C]326038[/C][C]312285.135040838[/C][C]13752.8649591623[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-4618.3875601658[/C][C]4618.3875601658[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]-4151.07171736296[/C][C]4354.07171736296[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]8385.26365219319[/C][C]-1186.26365219319[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]47285.7463165455[/C][C]-625.746316545491[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]10101.2419755383[/C][C]7445.75802446171[/C][/ROW]
[ROW][C]162[/C][C]107465[/C][C]81099.5203125864[/C][C]26365.4796874136[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]-3716.51708401342[/C][C]4685.51708401342[/C][/ROW]
[ROW][C]164[/C][C]173102[/C][C]134983.909502922[/C][C]38118.0904970783[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153608&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153608&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
1252101217890.2561001934210.7438998099
2134577151270.114991834-16693.114991834
3198520189068.428477839451.57152217019
4189326233713.442660326-44387.4426603257
5137449127735.393907279713.60609273045
66529579210.7463412652-13915.7463412652
7439387425777.2605354713609.7394645305
83318632693.2954158744492.704584125588
9178368187423.223221721-9055.22322172065
10186657172462.36283626714194.6371637335
11261949251432.22264780310516.7773521967
12191051182789.2786527128261.72134728764
13138866143410.720587882-4544.72058788203
14296878239445.34944235557432.6505576445
15192648197253.031037848-4605.0310378475
16333462383913.81442525-50451.8144252498
17243571221476.14185836322094.8581416365
18263451246651.04145362216799.958546378
19155679152184.5686142433494.43138575732
20227053220164.4994151386888.50058486196
21240028226316.77399671213711.2260032883
22388549281517.184040757107031.815959243
23156540152992.8253963473547.17460365298
24148421178531.884341142-30110.884341142
25177732241548.580150546-63816.5801505459
26191441228055.136015464-36614.1360154636
27249893287078.757658619-37185.7576586191
28236812214179.33265312822632.6673468716
29142329142450.365785842-121.365785842302
30259667297501.27078158-37834.2707815802
31231625248045.266946872-16420.2669468724
32176062192367.336867263-16305.3368672629
33286683273618.06600036913064.9339996305
348748589446.9226489757-1961.92264897569
35322865306678.97572074316186.0242792571
36247082234729.25434929812352.7456507019
37346011312965.65404912133045.3459508789
38191653184800.7370579386852.2629420616
3911467392306.620540528922366.3794594711
40284224286135.029594064-1911.02959406387
41284195290927.616734237-6732.61673423701
42155363164200.088424828-8837.0884248283
43177306186073.896648886-8767.89664888558
44144571131111.47097456813459.5290254322
45140319194501.569593722-54182.5695937225
46405267380458.29302249524808.7069775052
4778800103035.078255192-24235.0782551916
48201970217581.683756271-15611.6837562708
49302674358382.034487145-55708.0344871446
50164733209367.875934464-44634.8759344643
51194221218135.713292676-23914.7132926757
522418839166.7197332187-14978.7197332187
53342263297240.31320951145022.686790489
546502968932.237734182-3903.23773418201
55101097111634.020758585-10537.0207585848
56246088241528.204774514559.79522549016
57273108273062.25918871145.7408112885536
58282220328850.693711664-46630.6937116637
59273495279255.488593147-5760.48859314739
60214872176495.21224184238376.7877581579
61335121353460.302483777-18339.3024837771
62267171300615.148374787-33444.1483747866
63187938227422.874070706-39484.8740707059
64229512222699.96257786812.03742219982
65209798196031.47347337113766.5265266295
66201345186885.25929922314459.740700777
67163833172019.488150146-8186.48815014593
68204250246457.898723516-42207.8987235165
69197813200208.370820153-2395.37082015298
70132955147069.407402228-14114.4074022278
71216092229897.891331082-13805.8913310816
727356690848.3801889004-17282.3801889004
73213198212310.480399774887.519600226372
74181713176950.961998024762.03800198013
75148698166129.213654975-17431.2136549753
76300103214974.28524607185128.7147539294
77251437235364.95021080216072.0497891983
78197295204825.568614113-7530.56861411323
79158163169839.914404613-11676.9144046126
80155529156498.002527515-969.002527515051
81132672152105.594193621-19433.5941936212
82377205303081.63456804874123.3654319519
83145905159797.458723069-13892.4587230693
84223701186046.820825337654.1791747003
858095395103.0362569831-14150.0362569831
86130805146471.992519471-15666.992519471
87135082128883.4004794586198.59952054173
88300805298529.6509732382275.34902676237
89271806251759.84137225320046.1586277466
90150949214337.279129321-63388.2791293209
91225805208796.8696899617008.1303100404
92197389188348.317901369040.68209864022
93156583164015.299026948-7432.29902694782
94222599227793.170995878-5194.17099587819
95261601226787.28731917434813.712680826
96178489172167.0082134776321.99178652289
97200657184161.25807827316495.7419217275
98259084253938.5919101175145.40808988267
99313075335291.317710068-22216.3177100676
100346933336551.24829452510381.751705475
101246440223466.36642559722973.6335744027
102252444266792.526362015-14348.5263620146
103159965178893.715379299-18928.7153792989
1044328757786.8989436848-14499.8989436848
105172239180433.329231854-8194.32923185432
106183738208547.85938856-24809.8593885603
107227681240906.739481358-13225.7394813577
108260464245539.20213571714924.7978642828
109106288170176.859343711-63888.8593437112
110109632143207.575037141-33575.5750371409
111268905223780.542785345124.4572146999
112266805211899.69746924154905.302530759
1132362322444.36725208181178.63274791825
114152474174431.049494288-21957.0494942885
1156185748494.872581589813362.1274184102
116144889147102.475643651-2213.47564365146
117346600343672.1119049062927.88809509405
1182105422707.595559812-1653.59555981205
119224051207964.5660337616086.4339662399
1203141437161.693515223-5747.69351522301
121261043299855.027281137-38812.0272811375
122197819171759.54910729126059.4508927092
123154984118277.36594078136706.6340592187
124112933131277.624257903-18344.624257903
1253821451267.0766690346-13053.0766690346
126158671153844.9217021584826.07829784165
127302148269411.07397980632736.9260201943
128177918183361.884115539-5443.88411553877
129350552344183.8166678856368.18333211469
130275578310335.869948255-34757.8699482553
131368746353362.37427232515383.6257276752
132172464166601.5493948875862.45060511348
13394381120452.291586749-26071.2915867493
134243875232135.43781931411739.5621806857
135382487381823.461552598663.538447402211
136114525100921.07484744513603.9251525545
137335681316162.43471686719518.565283133
138147989168892.276357145-20903.2763571449
139216638200595.50600941516042.4939905848
140192862210565.667162075-17703.6671620747
141184818156168.34089272728649.6591072734
142336707323579.34600227913127.6539977207
143215836216731.739007864-895.739007864503
144173260132571.26626440340688.7337355968
145271773252654.91115859719118.0888414027
146130908168383.422030719-37475.4220307186
147204009233883.372007461-29874.3720074614
148245514237589.1596318297924.84036817105
1491-1465.577272187181466.57727218718
1501468812488.8137991772199.18620082304
15198-4402.56414690944500.5641469094
152455-4236.237959930834691.23795993083
1530-4273.574997754614273.57499775461
1540-4618.38756016584618.3875601658
155195765186175.2529248699589.74707513083
156326038312285.13504083813752.8649591623
1570-4618.38756016584618.3875601658
158203-4151.071717362964354.07171736296
15971998385.26365219319-1186.26365219319
1604666047285.7463165455-625.746316545491
1611754710101.24197553837445.75802446171
16210746581099.520312586426365.4796874136
163969-3716.517084013424685.51708401342
164173102134983.90950292238118.0904970783






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.3689946332176120.7379892664352240.631005366782388
190.2585384564220350.5170769128440690.741461543577965
200.1778988415335190.3557976830670390.822101158466481
210.1158216421305380.2316432842610760.884178357869462
220.921494183489770.1570116330204610.0785058165102304
230.8826710492405480.2346579015189040.117328950759452
240.9206191478336180.1587617043327640.079380852166382
250.9300421215837380.1399157568325250.0699578784162623
260.9135039857595510.1729920284808990.0864960142404493
270.9120346238415820.1759307523168360.0879653761584181
280.8906716683742250.2186566632515490.109328331625775
290.8495469894285270.3009060211429460.150453010571473
300.9175423925942220.1649152148115550.0824576074057776
310.8977553369354870.2044893261290260.102244663064513
320.9017487641373820.1965024717252360.0982512358626182
330.8829753726297560.2340492547404870.117024627370244
340.8595722336303940.2808555327392120.140427766369606
350.8589116340097160.2821767319805680.141088365990284
360.8299077290196530.3401845419606950.170092270980347
370.8352999281076470.3294001437847060.164700071892353
380.8087685704343660.3824628591312670.191231429565634
390.7704844655850240.4590310688299520.229515534414976
400.7210850973389980.5578298053220040.278914902661002
410.7209400525703150.558119894859370.279059947429685
420.6742837968126350.651432406374730.325716203187365
430.6201178088563320.7597643822873360.379882191143668
440.6222829965539030.7554340068921950.377717003446097
450.8621530774077920.2756938451844150.137846922592208
460.9306077458176290.1387845083647420.0693922541823709
470.9297879011336130.1404241977327750.0702120988663874
480.9337100202348410.1325799595303190.0662899797651594
490.9761007115247320.04779857695053690.0238992884752685
500.9866126484711420.02677470305771590.013387351528858
510.9857203323132530.02855933537349320.0142796676867466
520.984873775737040.03025244852591940.0151262242629597
530.9862449003347710.02751019933045840.0137550996652292
540.9811236787324180.03775264253516310.0188763212675816
550.9766994423961140.04660111520777120.0233005576038856
560.9700118027220640.05997639455587150.0299881972779358
570.9609421748427970.07811565031440560.0390578251572028
580.9785311597675510.04293768046489710.0214688402324485
590.9715083057932780.05698338841344380.0284916942067219
600.9814027352763770.0371945294472450.0185972647236225
610.9778291284442080.0443417431115840.022170871555792
620.9771601837728320.04567963245433690.0228398162271684
630.9811382885632190.03772342287356230.0188617114367812
640.9757346220261660.04853075594766750.0242653779738338
650.9693745185279590.06125096294408220.0306254814720411
660.9648831759966650.07023364800667020.0351168240033351
670.9545478910546190.09090421789076280.0454521089453814
680.9688652881517480.06226942369650410.0311347118482521
690.9591662402427530.08166751951449350.0408337597572468
700.9505093287017640.09898134259647220.0494906712982361
710.9451277830540580.1097444338918850.0548722169459424
720.9392089949650930.1215820100698130.0607910050349067
730.9235757499125740.1528485001748520.0764242500874258
740.9071599268146040.1856801463707910.0928400731853956
750.8911174882818570.2177650234362860.108882511718143
760.9911335305725390.01773293885492160.00886646942746082
770.9894569560349430.02108608793011490.0105430439650574
780.9859037638953090.02819247220938260.0140962361046913
790.9819508152760260.03609836944794850.0180491847239743
800.9757361613630960.04852767727380750.0242638386369037
810.9736550366979450.05268992660411020.0263449633020551
820.997824231292590.004351537414820830.00217576870741041
830.9974363701327780.005127259734443150.00256362986722158
840.9978455003993520.004308999201295040.00215449960064752
850.9970963904997780.00580721900044380.0029036095002219
860.9960219219284850.0079561561430290.0039780780715145
870.9943869739172060.01122605216558770.00561302608279384
880.9935015789174910.01299684216501890.00649842108250946
890.9912730762262250.01745384754755070.00872692377377535
900.9991344369796030.001731126040794740.000865563020397371
910.9987609480573590.002478103885282070.00123905194264104
920.9982114966028440.003577006794312340.00178850339715617
930.9975382031098790.00492359378024310.00246179689012155
940.9967133000703760.006573399859248030.00328669992962402
950.9986948622950320.002610275409935870.00130513770496793
960.9981692617901850.003661476419629870.00183073820981493
970.9976097961820070.004780407635985340.00239020381799267
980.9969102952731940.006179409453611660.00308970472680583
990.9991224363643890.001755127271221210.000877563635610604
1000.9987637285977690.002472542804462320.00123627140223116
1010.9983411350378060.003317729924388320.00165886496219416
1020.9977684871631410.004463025673718130.00223151283685906
1030.9971872942877990.00562541142440290.00281270571220145
1040.9962930955870140.007413808825971460.00370690441298573
1050.9956200773400550.008759845319890660.00437992265994533
1060.995839383082960.008321233834079060.00416061691703953
1070.9941621900604540.01167561987909220.0058378099395461
1080.9915961813826080.01680763723478390.00840381861739195
1090.9979435320323650.004112935935270250.00205646796763512
1100.9979693228440130.004061354311973970.00203067715598698
1110.9986470125513460.002705974897308630.00135298744865431
1120.9996436393291330.0007127213417337730.000356360670866886
1130.9993965140143090.001206971971381810.000603485985690904
1140.9996021961989330.0007956076021342820.000397803801067141
1150.9994236602225680.001152679554864340.000576339777432169
1160.9990231091294390.001953781741121820.000976890870560909
1170.9988232991393590.002353401721282540.00117670086064127
1180.9981167258358660.003766548328268390.00188327416413419
1190.9984023305664440.003195338867112750.00159766943355637
1200.9973353184540760.005329363091848650.00266468154592432
1210.9996073056112220.0007853887775564460.000392694388778223
1220.9993773519055250.001245296188949650.000622648094474824
1230.9997768055430890.0004463889138221820.000223194456911091
1240.9996454624240520.0007090751518954770.000354537575947739
1250.9993682225332810.001263554933438970.000631777466719483
1260.9989121265638460.002175746872307480.00108787343615374
1270.9992803284117480.001439343176504690.000719671588252346
1280.9987246618400640.002550676319871040.00127533815993552
1290.9988991326452970.002201734709407080.00110086735470354
1300.9983205127932680.003358974413464160.00167948720673208
1310.9976277004529040.004744599094191150.00237229954709558
1320.9997659620788690.0004680758422621180.000234037921131059
1330.9994844453998760.001031109200248280.000515554600124139
1340.998873212449940.002253575100120980.00112678755006049
1350.9978221428838860.004355714232228650.00217785711611433
1360.9979297606708550.004140478658289960.00207023932914498
1370.9997642263067550.0004715473864901760.000235773693245088
1380.9993624805670910.001275038865817420.000637519432908708
1390.999996705424466.5891510792079e-063.29457553960395e-06
1400.9999978220554334.35588913377091e-062.17794456688545e-06
1410.9999985483969242.90320615156513e-061.45160307578257e-06
1420.9999968304944886.33901102467073e-063.16950551233536e-06
1430.9999995176694199.64661161455957e-074.82330580727978e-07
1440.9999999999983173.36504137145787e-121.68252068572893e-12
1450.9999999998246993.50601717743808e-101.75300858871904e-10
1460.9999999402027891.1959442146608e-075.97972107330401e-08

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.368994633217612 & 0.737989266435224 & 0.631005366782388 \tabularnewline
19 & 0.258538456422035 & 0.517076912844069 & 0.741461543577965 \tabularnewline
20 & 0.177898841533519 & 0.355797683067039 & 0.822101158466481 \tabularnewline
21 & 0.115821642130538 & 0.231643284261076 & 0.884178357869462 \tabularnewline
22 & 0.92149418348977 & 0.157011633020461 & 0.0785058165102304 \tabularnewline
23 & 0.882671049240548 & 0.234657901518904 & 0.117328950759452 \tabularnewline
24 & 0.920619147833618 & 0.158761704332764 & 0.079380852166382 \tabularnewline
25 & 0.930042121583738 & 0.139915756832525 & 0.0699578784162623 \tabularnewline
26 & 0.913503985759551 & 0.172992028480899 & 0.0864960142404493 \tabularnewline
27 & 0.912034623841582 & 0.175930752316836 & 0.0879653761584181 \tabularnewline
28 & 0.890671668374225 & 0.218656663251549 & 0.109328331625775 \tabularnewline
29 & 0.849546989428527 & 0.300906021142946 & 0.150453010571473 \tabularnewline
30 & 0.917542392594222 & 0.164915214811555 & 0.0824576074057776 \tabularnewline
31 & 0.897755336935487 & 0.204489326129026 & 0.102244663064513 \tabularnewline
32 & 0.901748764137382 & 0.196502471725236 & 0.0982512358626182 \tabularnewline
33 & 0.882975372629756 & 0.234049254740487 & 0.117024627370244 \tabularnewline
34 & 0.859572233630394 & 0.280855532739212 & 0.140427766369606 \tabularnewline
35 & 0.858911634009716 & 0.282176731980568 & 0.141088365990284 \tabularnewline
36 & 0.829907729019653 & 0.340184541960695 & 0.170092270980347 \tabularnewline
37 & 0.835299928107647 & 0.329400143784706 & 0.164700071892353 \tabularnewline
38 & 0.808768570434366 & 0.382462859131267 & 0.191231429565634 \tabularnewline
39 & 0.770484465585024 & 0.459031068829952 & 0.229515534414976 \tabularnewline
40 & 0.721085097338998 & 0.557829805322004 & 0.278914902661002 \tabularnewline
41 & 0.720940052570315 & 0.55811989485937 & 0.279059947429685 \tabularnewline
42 & 0.674283796812635 & 0.65143240637473 & 0.325716203187365 \tabularnewline
43 & 0.620117808856332 & 0.759764382287336 & 0.379882191143668 \tabularnewline
44 & 0.622282996553903 & 0.755434006892195 & 0.377717003446097 \tabularnewline
45 & 0.862153077407792 & 0.275693845184415 & 0.137846922592208 \tabularnewline
46 & 0.930607745817629 & 0.138784508364742 & 0.0693922541823709 \tabularnewline
47 & 0.929787901133613 & 0.140424197732775 & 0.0702120988663874 \tabularnewline
48 & 0.933710020234841 & 0.132579959530319 & 0.0662899797651594 \tabularnewline
49 & 0.976100711524732 & 0.0477985769505369 & 0.0238992884752685 \tabularnewline
50 & 0.986612648471142 & 0.0267747030577159 & 0.013387351528858 \tabularnewline
51 & 0.985720332313253 & 0.0285593353734932 & 0.0142796676867466 \tabularnewline
52 & 0.98487377573704 & 0.0302524485259194 & 0.0151262242629597 \tabularnewline
53 & 0.986244900334771 & 0.0275101993304584 & 0.0137550996652292 \tabularnewline
54 & 0.981123678732418 & 0.0377526425351631 & 0.0188763212675816 \tabularnewline
55 & 0.976699442396114 & 0.0466011152077712 & 0.0233005576038856 \tabularnewline
56 & 0.970011802722064 & 0.0599763945558715 & 0.0299881972779358 \tabularnewline
57 & 0.960942174842797 & 0.0781156503144056 & 0.0390578251572028 \tabularnewline
58 & 0.978531159767551 & 0.0429376804648971 & 0.0214688402324485 \tabularnewline
59 & 0.971508305793278 & 0.0569833884134438 & 0.0284916942067219 \tabularnewline
60 & 0.981402735276377 & 0.037194529447245 & 0.0185972647236225 \tabularnewline
61 & 0.977829128444208 & 0.044341743111584 & 0.022170871555792 \tabularnewline
62 & 0.977160183772832 & 0.0456796324543369 & 0.0228398162271684 \tabularnewline
63 & 0.981138288563219 & 0.0377234228735623 & 0.0188617114367812 \tabularnewline
64 & 0.975734622026166 & 0.0485307559476675 & 0.0242653779738338 \tabularnewline
65 & 0.969374518527959 & 0.0612509629440822 & 0.0306254814720411 \tabularnewline
66 & 0.964883175996665 & 0.0702336480066702 & 0.0351168240033351 \tabularnewline
67 & 0.954547891054619 & 0.0909042178907628 & 0.0454521089453814 \tabularnewline
68 & 0.968865288151748 & 0.0622694236965041 & 0.0311347118482521 \tabularnewline
69 & 0.959166240242753 & 0.0816675195144935 & 0.0408337597572468 \tabularnewline
70 & 0.950509328701764 & 0.0989813425964722 & 0.0494906712982361 \tabularnewline
71 & 0.945127783054058 & 0.109744433891885 & 0.0548722169459424 \tabularnewline
72 & 0.939208994965093 & 0.121582010069813 & 0.0607910050349067 \tabularnewline
73 & 0.923575749912574 & 0.152848500174852 & 0.0764242500874258 \tabularnewline
74 & 0.907159926814604 & 0.185680146370791 & 0.0928400731853956 \tabularnewline
75 & 0.891117488281857 & 0.217765023436286 & 0.108882511718143 \tabularnewline
76 & 0.991133530572539 & 0.0177329388549216 & 0.00886646942746082 \tabularnewline
77 & 0.989456956034943 & 0.0210860879301149 & 0.0105430439650574 \tabularnewline
78 & 0.985903763895309 & 0.0281924722093826 & 0.0140962361046913 \tabularnewline
79 & 0.981950815276026 & 0.0360983694479485 & 0.0180491847239743 \tabularnewline
80 & 0.975736161363096 & 0.0485276772738075 & 0.0242638386369037 \tabularnewline
81 & 0.973655036697945 & 0.0526899266041102 & 0.0263449633020551 \tabularnewline
82 & 0.99782423129259 & 0.00435153741482083 & 0.00217576870741041 \tabularnewline
83 & 0.997436370132778 & 0.00512725973444315 & 0.00256362986722158 \tabularnewline
84 & 0.997845500399352 & 0.00430899920129504 & 0.00215449960064752 \tabularnewline
85 & 0.997096390499778 & 0.0058072190004438 & 0.0029036095002219 \tabularnewline
86 & 0.996021921928485 & 0.007956156143029 & 0.0039780780715145 \tabularnewline
87 & 0.994386973917206 & 0.0112260521655877 & 0.00561302608279384 \tabularnewline
88 & 0.993501578917491 & 0.0129968421650189 & 0.00649842108250946 \tabularnewline
89 & 0.991273076226225 & 0.0174538475475507 & 0.00872692377377535 \tabularnewline
90 & 0.999134436979603 & 0.00173112604079474 & 0.000865563020397371 \tabularnewline
91 & 0.998760948057359 & 0.00247810388528207 & 0.00123905194264104 \tabularnewline
92 & 0.998211496602844 & 0.00357700679431234 & 0.00178850339715617 \tabularnewline
93 & 0.997538203109879 & 0.0049235937802431 & 0.00246179689012155 \tabularnewline
94 & 0.996713300070376 & 0.00657339985924803 & 0.00328669992962402 \tabularnewline
95 & 0.998694862295032 & 0.00261027540993587 & 0.00130513770496793 \tabularnewline
96 & 0.998169261790185 & 0.00366147641962987 & 0.00183073820981493 \tabularnewline
97 & 0.997609796182007 & 0.00478040763598534 & 0.00239020381799267 \tabularnewline
98 & 0.996910295273194 & 0.00617940945361166 & 0.00308970472680583 \tabularnewline
99 & 0.999122436364389 & 0.00175512727122121 & 0.000877563635610604 \tabularnewline
100 & 0.998763728597769 & 0.00247254280446232 & 0.00123627140223116 \tabularnewline
101 & 0.998341135037806 & 0.00331772992438832 & 0.00165886496219416 \tabularnewline
102 & 0.997768487163141 & 0.00446302567371813 & 0.00223151283685906 \tabularnewline
103 & 0.997187294287799 & 0.0056254114244029 & 0.00281270571220145 \tabularnewline
104 & 0.996293095587014 & 0.00741380882597146 & 0.00370690441298573 \tabularnewline
105 & 0.995620077340055 & 0.00875984531989066 & 0.00437992265994533 \tabularnewline
106 & 0.99583938308296 & 0.00832123383407906 & 0.00416061691703953 \tabularnewline
107 & 0.994162190060454 & 0.0116756198790922 & 0.0058378099395461 \tabularnewline
108 & 0.991596181382608 & 0.0168076372347839 & 0.00840381861739195 \tabularnewline
109 & 0.997943532032365 & 0.00411293593527025 & 0.00205646796763512 \tabularnewline
110 & 0.997969322844013 & 0.00406135431197397 & 0.00203067715598698 \tabularnewline
111 & 0.998647012551346 & 0.00270597489730863 & 0.00135298744865431 \tabularnewline
112 & 0.999643639329133 & 0.000712721341733773 & 0.000356360670866886 \tabularnewline
113 & 0.999396514014309 & 0.00120697197138181 & 0.000603485985690904 \tabularnewline
114 & 0.999602196198933 & 0.000795607602134282 & 0.000397803801067141 \tabularnewline
115 & 0.999423660222568 & 0.00115267955486434 & 0.000576339777432169 \tabularnewline
116 & 0.999023109129439 & 0.00195378174112182 & 0.000976890870560909 \tabularnewline
117 & 0.998823299139359 & 0.00235340172128254 & 0.00117670086064127 \tabularnewline
118 & 0.998116725835866 & 0.00376654832826839 & 0.00188327416413419 \tabularnewline
119 & 0.998402330566444 & 0.00319533886711275 & 0.00159766943355637 \tabularnewline
120 & 0.997335318454076 & 0.00532936309184865 & 0.00266468154592432 \tabularnewline
121 & 0.999607305611222 & 0.000785388777556446 & 0.000392694388778223 \tabularnewline
122 & 0.999377351905525 & 0.00124529618894965 & 0.000622648094474824 \tabularnewline
123 & 0.999776805543089 & 0.000446388913822182 & 0.000223194456911091 \tabularnewline
124 & 0.999645462424052 & 0.000709075151895477 & 0.000354537575947739 \tabularnewline
125 & 0.999368222533281 & 0.00126355493343897 & 0.000631777466719483 \tabularnewline
126 & 0.998912126563846 & 0.00217574687230748 & 0.00108787343615374 \tabularnewline
127 & 0.999280328411748 & 0.00143934317650469 & 0.000719671588252346 \tabularnewline
128 & 0.998724661840064 & 0.00255067631987104 & 0.00127533815993552 \tabularnewline
129 & 0.998899132645297 & 0.00220173470940708 & 0.00110086735470354 \tabularnewline
130 & 0.998320512793268 & 0.00335897441346416 & 0.00167948720673208 \tabularnewline
131 & 0.997627700452904 & 0.00474459909419115 & 0.00237229954709558 \tabularnewline
132 & 0.999765962078869 & 0.000468075842262118 & 0.000234037921131059 \tabularnewline
133 & 0.999484445399876 & 0.00103110920024828 & 0.000515554600124139 \tabularnewline
134 & 0.99887321244994 & 0.00225357510012098 & 0.00112678755006049 \tabularnewline
135 & 0.997822142883886 & 0.00435571423222865 & 0.00217785711611433 \tabularnewline
136 & 0.997929760670855 & 0.00414047865828996 & 0.00207023932914498 \tabularnewline
137 & 0.999764226306755 & 0.000471547386490176 & 0.000235773693245088 \tabularnewline
138 & 0.999362480567091 & 0.00127503886581742 & 0.000637519432908708 \tabularnewline
139 & 0.99999670542446 & 6.5891510792079e-06 & 3.29457553960395e-06 \tabularnewline
140 & 0.999997822055433 & 4.35588913377091e-06 & 2.17794456688545e-06 \tabularnewline
141 & 0.999998548396924 & 2.90320615156513e-06 & 1.45160307578257e-06 \tabularnewline
142 & 0.999996830494488 & 6.33901102467073e-06 & 3.16950551233536e-06 \tabularnewline
143 & 0.999999517669419 & 9.64661161455957e-07 & 4.82330580727978e-07 \tabularnewline
144 & 0.999999999998317 & 3.36504137145787e-12 & 1.68252068572893e-12 \tabularnewline
145 & 0.999999999824699 & 3.50601717743808e-10 & 1.75300858871904e-10 \tabularnewline
146 & 0.999999940202789 & 1.1959442146608e-07 & 5.97972107330401e-08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153608&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]0.368994633217612[/C][C]0.737989266435224[/C][C]0.631005366782388[/C][/ROW]
[ROW][C]19[/C][C]0.258538456422035[/C][C]0.517076912844069[/C][C]0.741461543577965[/C][/ROW]
[ROW][C]20[/C][C]0.177898841533519[/C][C]0.355797683067039[/C][C]0.822101158466481[/C][/ROW]
[ROW][C]21[/C][C]0.115821642130538[/C][C]0.231643284261076[/C][C]0.884178357869462[/C][/ROW]
[ROW][C]22[/C][C]0.92149418348977[/C][C]0.157011633020461[/C][C]0.0785058165102304[/C][/ROW]
[ROW][C]23[/C][C]0.882671049240548[/C][C]0.234657901518904[/C][C]0.117328950759452[/C][/ROW]
[ROW][C]24[/C][C]0.920619147833618[/C][C]0.158761704332764[/C][C]0.079380852166382[/C][/ROW]
[ROW][C]25[/C][C]0.930042121583738[/C][C]0.139915756832525[/C][C]0.0699578784162623[/C][/ROW]
[ROW][C]26[/C][C]0.913503985759551[/C][C]0.172992028480899[/C][C]0.0864960142404493[/C][/ROW]
[ROW][C]27[/C][C]0.912034623841582[/C][C]0.175930752316836[/C][C]0.0879653761584181[/C][/ROW]
[ROW][C]28[/C][C]0.890671668374225[/C][C]0.218656663251549[/C][C]0.109328331625775[/C][/ROW]
[ROW][C]29[/C][C]0.849546989428527[/C][C]0.300906021142946[/C][C]0.150453010571473[/C][/ROW]
[ROW][C]30[/C][C]0.917542392594222[/C][C]0.164915214811555[/C][C]0.0824576074057776[/C][/ROW]
[ROW][C]31[/C][C]0.897755336935487[/C][C]0.204489326129026[/C][C]0.102244663064513[/C][/ROW]
[ROW][C]32[/C][C]0.901748764137382[/C][C]0.196502471725236[/C][C]0.0982512358626182[/C][/ROW]
[ROW][C]33[/C][C]0.882975372629756[/C][C]0.234049254740487[/C][C]0.117024627370244[/C][/ROW]
[ROW][C]34[/C][C]0.859572233630394[/C][C]0.280855532739212[/C][C]0.140427766369606[/C][/ROW]
[ROW][C]35[/C][C]0.858911634009716[/C][C]0.282176731980568[/C][C]0.141088365990284[/C][/ROW]
[ROW][C]36[/C][C]0.829907729019653[/C][C]0.340184541960695[/C][C]0.170092270980347[/C][/ROW]
[ROW][C]37[/C][C]0.835299928107647[/C][C]0.329400143784706[/C][C]0.164700071892353[/C][/ROW]
[ROW][C]38[/C][C]0.808768570434366[/C][C]0.382462859131267[/C][C]0.191231429565634[/C][/ROW]
[ROW][C]39[/C][C]0.770484465585024[/C][C]0.459031068829952[/C][C]0.229515534414976[/C][/ROW]
[ROW][C]40[/C][C]0.721085097338998[/C][C]0.557829805322004[/C][C]0.278914902661002[/C][/ROW]
[ROW][C]41[/C][C]0.720940052570315[/C][C]0.55811989485937[/C][C]0.279059947429685[/C][/ROW]
[ROW][C]42[/C][C]0.674283796812635[/C][C]0.65143240637473[/C][C]0.325716203187365[/C][/ROW]
[ROW][C]43[/C][C]0.620117808856332[/C][C]0.759764382287336[/C][C]0.379882191143668[/C][/ROW]
[ROW][C]44[/C][C]0.622282996553903[/C][C]0.755434006892195[/C][C]0.377717003446097[/C][/ROW]
[ROW][C]45[/C][C]0.862153077407792[/C][C]0.275693845184415[/C][C]0.137846922592208[/C][/ROW]
[ROW][C]46[/C][C]0.930607745817629[/C][C]0.138784508364742[/C][C]0.0693922541823709[/C][/ROW]
[ROW][C]47[/C][C]0.929787901133613[/C][C]0.140424197732775[/C][C]0.0702120988663874[/C][/ROW]
[ROW][C]48[/C][C]0.933710020234841[/C][C]0.132579959530319[/C][C]0.0662899797651594[/C][/ROW]
[ROW][C]49[/C][C]0.976100711524732[/C][C]0.0477985769505369[/C][C]0.0238992884752685[/C][/ROW]
[ROW][C]50[/C][C]0.986612648471142[/C][C]0.0267747030577159[/C][C]0.013387351528858[/C][/ROW]
[ROW][C]51[/C][C]0.985720332313253[/C][C]0.0285593353734932[/C][C]0.0142796676867466[/C][/ROW]
[ROW][C]52[/C][C]0.98487377573704[/C][C]0.0302524485259194[/C][C]0.0151262242629597[/C][/ROW]
[ROW][C]53[/C][C]0.986244900334771[/C][C]0.0275101993304584[/C][C]0.0137550996652292[/C][/ROW]
[ROW][C]54[/C][C]0.981123678732418[/C][C]0.0377526425351631[/C][C]0.0188763212675816[/C][/ROW]
[ROW][C]55[/C][C]0.976699442396114[/C][C]0.0466011152077712[/C][C]0.0233005576038856[/C][/ROW]
[ROW][C]56[/C][C]0.970011802722064[/C][C]0.0599763945558715[/C][C]0.0299881972779358[/C][/ROW]
[ROW][C]57[/C][C]0.960942174842797[/C][C]0.0781156503144056[/C][C]0.0390578251572028[/C][/ROW]
[ROW][C]58[/C][C]0.978531159767551[/C][C]0.0429376804648971[/C][C]0.0214688402324485[/C][/ROW]
[ROW][C]59[/C][C]0.971508305793278[/C][C]0.0569833884134438[/C][C]0.0284916942067219[/C][/ROW]
[ROW][C]60[/C][C]0.981402735276377[/C][C]0.037194529447245[/C][C]0.0185972647236225[/C][/ROW]
[ROW][C]61[/C][C]0.977829128444208[/C][C]0.044341743111584[/C][C]0.022170871555792[/C][/ROW]
[ROW][C]62[/C][C]0.977160183772832[/C][C]0.0456796324543369[/C][C]0.0228398162271684[/C][/ROW]
[ROW][C]63[/C][C]0.981138288563219[/C][C]0.0377234228735623[/C][C]0.0188617114367812[/C][/ROW]
[ROW][C]64[/C][C]0.975734622026166[/C][C]0.0485307559476675[/C][C]0.0242653779738338[/C][/ROW]
[ROW][C]65[/C][C]0.969374518527959[/C][C]0.0612509629440822[/C][C]0.0306254814720411[/C][/ROW]
[ROW][C]66[/C][C]0.964883175996665[/C][C]0.0702336480066702[/C][C]0.0351168240033351[/C][/ROW]
[ROW][C]67[/C][C]0.954547891054619[/C][C]0.0909042178907628[/C][C]0.0454521089453814[/C][/ROW]
[ROW][C]68[/C][C]0.968865288151748[/C][C]0.0622694236965041[/C][C]0.0311347118482521[/C][/ROW]
[ROW][C]69[/C][C]0.959166240242753[/C][C]0.0816675195144935[/C][C]0.0408337597572468[/C][/ROW]
[ROW][C]70[/C][C]0.950509328701764[/C][C]0.0989813425964722[/C][C]0.0494906712982361[/C][/ROW]
[ROW][C]71[/C][C]0.945127783054058[/C][C]0.109744433891885[/C][C]0.0548722169459424[/C][/ROW]
[ROW][C]72[/C][C]0.939208994965093[/C][C]0.121582010069813[/C][C]0.0607910050349067[/C][/ROW]
[ROW][C]73[/C][C]0.923575749912574[/C][C]0.152848500174852[/C][C]0.0764242500874258[/C][/ROW]
[ROW][C]74[/C][C]0.907159926814604[/C][C]0.185680146370791[/C][C]0.0928400731853956[/C][/ROW]
[ROW][C]75[/C][C]0.891117488281857[/C][C]0.217765023436286[/C][C]0.108882511718143[/C][/ROW]
[ROW][C]76[/C][C]0.991133530572539[/C][C]0.0177329388549216[/C][C]0.00886646942746082[/C][/ROW]
[ROW][C]77[/C][C]0.989456956034943[/C][C]0.0210860879301149[/C][C]0.0105430439650574[/C][/ROW]
[ROW][C]78[/C][C]0.985903763895309[/C][C]0.0281924722093826[/C][C]0.0140962361046913[/C][/ROW]
[ROW][C]79[/C][C]0.981950815276026[/C][C]0.0360983694479485[/C][C]0.0180491847239743[/C][/ROW]
[ROW][C]80[/C][C]0.975736161363096[/C][C]0.0485276772738075[/C][C]0.0242638386369037[/C][/ROW]
[ROW][C]81[/C][C]0.973655036697945[/C][C]0.0526899266041102[/C][C]0.0263449633020551[/C][/ROW]
[ROW][C]82[/C][C]0.99782423129259[/C][C]0.00435153741482083[/C][C]0.00217576870741041[/C][/ROW]
[ROW][C]83[/C][C]0.997436370132778[/C][C]0.00512725973444315[/C][C]0.00256362986722158[/C][/ROW]
[ROW][C]84[/C][C]0.997845500399352[/C][C]0.00430899920129504[/C][C]0.00215449960064752[/C][/ROW]
[ROW][C]85[/C][C]0.997096390499778[/C][C]0.0058072190004438[/C][C]0.0029036095002219[/C][/ROW]
[ROW][C]86[/C][C]0.996021921928485[/C][C]0.007956156143029[/C][C]0.0039780780715145[/C][/ROW]
[ROW][C]87[/C][C]0.994386973917206[/C][C]0.0112260521655877[/C][C]0.00561302608279384[/C][/ROW]
[ROW][C]88[/C][C]0.993501578917491[/C][C]0.0129968421650189[/C][C]0.00649842108250946[/C][/ROW]
[ROW][C]89[/C][C]0.991273076226225[/C][C]0.0174538475475507[/C][C]0.00872692377377535[/C][/ROW]
[ROW][C]90[/C][C]0.999134436979603[/C][C]0.00173112604079474[/C][C]0.000865563020397371[/C][/ROW]
[ROW][C]91[/C][C]0.998760948057359[/C][C]0.00247810388528207[/C][C]0.00123905194264104[/C][/ROW]
[ROW][C]92[/C][C]0.998211496602844[/C][C]0.00357700679431234[/C][C]0.00178850339715617[/C][/ROW]
[ROW][C]93[/C][C]0.997538203109879[/C][C]0.0049235937802431[/C][C]0.00246179689012155[/C][/ROW]
[ROW][C]94[/C][C]0.996713300070376[/C][C]0.00657339985924803[/C][C]0.00328669992962402[/C][/ROW]
[ROW][C]95[/C][C]0.998694862295032[/C][C]0.00261027540993587[/C][C]0.00130513770496793[/C][/ROW]
[ROW][C]96[/C][C]0.998169261790185[/C][C]0.00366147641962987[/C][C]0.00183073820981493[/C][/ROW]
[ROW][C]97[/C][C]0.997609796182007[/C][C]0.00478040763598534[/C][C]0.00239020381799267[/C][/ROW]
[ROW][C]98[/C][C]0.996910295273194[/C][C]0.00617940945361166[/C][C]0.00308970472680583[/C][/ROW]
[ROW][C]99[/C][C]0.999122436364389[/C][C]0.00175512727122121[/C][C]0.000877563635610604[/C][/ROW]
[ROW][C]100[/C][C]0.998763728597769[/C][C]0.00247254280446232[/C][C]0.00123627140223116[/C][/ROW]
[ROW][C]101[/C][C]0.998341135037806[/C][C]0.00331772992438832[/C][C]0.00165886496219416[/C][/ROW]
[ROW][C]102[/C][C]0.997768487163141[/C][C]0.00446302567371813[/C][C]0.00223151283685906[/C][/ROW]
[ROW][C]103[/C][C]0.997187294287799[/C][C]0.0056254114244029[/C][C]0.00281270571220145[/C][/ROW]
[ROW][C]104[/C][C]0.996293095587014[/C][C]0.00741380882597146[/C][C]0.00370690441298573[/C][/ROW]
[ROW][C]105[/C][C]0.995620077340055[/C][C]0.00875984531989066[/C][C]0.00437992265994533[/C][/ROW]
[ROW][C]106[/C][C]0.99583938308296[/C][C]0.00832123383407906[/C][C]0.00416061691703953[/C][/ROW]
[ROW][C]107[/C][C]0.994162190060454[/C][C]0.0116756198790922[/C][C]0.0058378099395461[/C][/ROW]
[ROW][C]108[/C][C]0.991596181382608[/C][C]0.0168076372347839[/C][C]0.00840381861739195[/C][/ROW]
[ROW][C]109[/C][C]0.997943532032365[/C][C]0.00411293593527025[/C][C]0.00205646796763512[/C][/ROW]
[ROW][C]110[/C][C]0.997969322844013[/C][C]0.00406135431197397[/C][C]0.00203067715598698[/C][/ROW]
[ROW][C]111[/C][C]0.998647012551346[/C][C]0.00270597489730863[/C][C]0.00135298744865431[/C][/ROW]
[ROW][C]112[/C][C]0.999643639329133[/C][C]0.000712721341733773[/C][C]0.000356360670866886[/C][/ROW]
[ROW][C]113[/C][C]0.999396514014309[/C][C]0.00120697197138181[/C][C]0.000603485985690904[/C][/ROW]
[ROW][C]114[/C][C]0.999602196198933[/C][C]0.000795607602134282[/C][C]0.000397803801067141[/C][/ROW]
[ROW][C]115[/C][C]0.999423660222568[/C][C]0.00115267955486434[/C][C]0.000576339777432169[/C][/ROW]
[ROW][C]116[/C][C]0.999023109129439[/C][C]0.00195378174112182[/C][C]0.000976890870560909[/C][/ROW]
[ROW][C]117[/C][C]0.998823299139359[/C][C]0.00235340172128254[/C][C]0.00117670086064127[/C][/ROW]
[ROW][C]118[/C][C]0.998116725835866[/C][C]0.00376654832826839[/C][C]0.00188327416413419[/C][/ROW]
[ROW][C]119[/C][C]0.998402330566444[/C][C]0.00319533886711275[/C][C]0.00159766943355637[/C][/ROW]
[ROW][C]120[/C][C]0.997335318454076[/C][C]0.00532936309184865[/C][C]0.00266468154592432[/C][/ROW]
[ROW][C]121[/C][C]0.999607305611222[/C][C]0.000785388777556446[/C][C]0.000392694388778223[/C][/ROW]
[ROW][C]122[/C][C]0.999377351905525[/C][C]0.00124529618894965[/C][C]0.000622648094474824[/C][/ROW]
[ROW][C]123[/C][C]0.999776805543089[/C][C]0.000446388913822182[/C][C]0.000223194456911091[/C][/ROW]
[ROW][C]124[/C][C]0.999645462424052[/C][C]0.000709075151895477[/C][C]0.000354537575947739[/C][/ROW]
[ROW][C]125[/C][C]0.999368222533281[/C][C]0.00126355493343897[/C][C]0.000631777466719483[/C][/ROW]
[ROW][C]126[/C][C]0.998912126563846[/C][C]0.00217574687230748[/C][C]0.00108787343615374[/C][/ROW]
[ROW][C]127[/C][C]0.999280328411748[/C][C]0.00143934317650469[/C][C]0.000719671588252346[/C][/ROW]
[ROW][C]128[/C][C]0.998724661840064[/C][C]0.00255067631987104[/C][C]0.00127533815993552[/C][/ROW]
[ROW][C]129[/C][C]0.998899132645297[/C][C]0.00220173470940708[/C][C]0.00110086735470354[/C][/ROW]
[ROW][C]130[/C][C]0.998320512793268[/C][C]0.00335897441346416[/C][C]0.00167948720673208[/C][/ROW]
[ROW][C]131[/C][C]0.997627700452904[/C][C]0.00474459909419115[/C][C]0.00237229954709558[/C][/ROW]
[ROW][C]132[/C][C]0.999765962078869[/C][C]0.000468075842262118[/C][C]0.000234037921131059[/C][/ROW]
[ROW][C]133[/C][C]0.999484445399876[/C][C]0.00103110920024828[/C][C]0.000515554600124139[/C][/ROW]
[ROW][C]134[/C][C]0.99887321244994[/C][C]0.00225357510012098[/C][C]0.00112678755006049[/C][/ROW]
[ROW][C]135[/C][C]0.997822142883886[/C][C]0.00435571423222865[/C][C]0.00217785711611433[/C][/ROW]
[ROW][C]136[/C][C]0.997929760670855[/C][C]0.00414047865828996[/C][C]0.00207023932914498[/C][/ROW]
[ROW][C]137[/C][C]0.999764226306755[/C][C]0.000471547386490176[/C][C]0.000235773693245088[/C][/ROW]
[ROW][C]138[/C][C]0.999362480567091[/C][C]0.00127503886581742[/C][C]0.000637519432908708[/C][/ROW]
[ROW][C]139[/C][C]0.99999670542446[/C][C]6.5891510792079e-06[/C][C]3.29457553960395e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999997822055433[/C][C]4.35588913377091e-06[/C][C]2.17794456688545e-06[/C][/ROW]
[ROW][C]141[/C][C]0.999998548396924[/C][C]2.90320615156513e-06[/C][C]1.45160307578257e-06[/C][/ROW]
[ROW][C]142[/C][C]0.999996830494488[/C][C]6.33901102467073e-06[/C][C]3.16950551233536e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999999517669419[/C][C]9.64661161455957e-07[/C][C]4.82330580727978e-07[/C][/ROW]
[ROW][C]144[/C][C]0.999999999998317[/C][C]3.36504137145787e-12[/C][C]1.68252068572893e-12[/C][/ROW]
[ROW][C]145[/C][C]0.999999999824699[/C][C]3.50601717743808e-10[/C][C]1.75300858871904e-10[/C][/ROW]
[ROW][C]146[/C][C]0.999999940202789[/C][C]1.1959442146608e-07[/C][C]5.97972107330401e-08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153608&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153608&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
180.3689946332176120.7379892664352240.631005366782388
190.2585384564220350.5170769128440690.741461543577965
200.1778988415335190.3557976830670390.822101158466481
210.1158216421305380.2316432842610760.884178357869462
220.921494183489770.1570116330204610.0785058165102304
230.8826710492405480.2346579015189040.117328950759452
240.9206191478336180.1587617043327640.079380852166382
250.9300421215837380.1399157568325250.0699578784162623
260.9135039857595510.1729920284808990.0864960142404493
270.9120346238415820.1759307523168360.0879653761584181
280.8906716683742250.2186566632515490.109328331625775
290.8495469894285270.3009060211429460.150453010571473
300.9175423925942220.1649152148115550.0824576074057776
310.8977553369354870.2044893261290260.102244663064513
320.9017487641373820.1965024717252360.0982512358626182
330.8829753726297560.2340492547404870.117024627370244
340.8595722336303940.2808555327392120.140427766369606
350.8589116340097160.2821767319805680.141088365990284
360.8299077290196530.3401845419606950.170092270980347
370.8352999281076470.3294001437847060.164700071892353
380.8087685704343660.3824628591312670.191231429565634
390.7704844655850240.4590310688299520.229515534414976
400.7210850973389980.5578298053220040.278914902661002
410.7209400525703150.558119894859370.279059947429685
420.6742837968126350.651432406374730.325716203187365
430.6201178088563320.7597643822873360.379882191143668
440.6222829965539030.7554340068921950.377717003446097
450.8621530774077920.2756938451844150.137846922592208
460.9306077458176290.1387845083647420.0693922541823709
470.9297879011336130.1404241977327750.0702120988663874
480.9337100202348410.1325799595303190.0662899797651594
490.9761007115247320.04779857695053690.0238992884752685
500.9866126484711420.02677470305771590.013387351528858
510.9857203323132530.02855933537349320.0142796676867466
520.984873775737040.03025244852591940.0151262242629597
530.9862449003347710.02751019933045840.0137550996652292
540.9811236787324180.03775264253516310.0188763212675816
550.9766994423961140.04660111520777120.0233005576038856
560.9700118027220640.05997639455587150.0299881972779358
570.9609421748427970.07811565031440560.0390578251572028
580.9785311597675510.04293768046489710.0214688402324485
590.9715083057932780.05698338841344380.0284916942067219
600.9814027352763770.0371945294472450.0185972647236225
610.9778291284442080.0443417431115840.022170871555792
620.9771601837728320.04567963245433690.0228398162271684
630.9811382885632190.03772342287356230.0188617114367812
640.9757346220261660.04853075594766750.0242653779738338
650.9693745185279590.06125096294408220.0306254814720411
660.9648831759966650.07023364800667020.0351168240033351
670.9545478910546190.09090421789076280.0454521089453814
680.9688652881517480.06226942369650410.0311347118482521
690.9591662402427530.08166751951449350.0408337597572468
700.9505093287017640.09898134259647220.0494906712982361
710.9451277830540580.1097444338918850.0548722169459424
720.9392089949650930.1215820100698130.0607910050349067
730.9235757499125740.1528485001748520.0764242500874258
740.9071599268146040.1856801463707910.0928400731853956
750.8911174882818570.2177650234362860.108882511718143
760.9911335305725390.01773293885492160.00886646942746082
770.9894569560349430.02108608793011490.0105430439650574
780.9859037638953090.02819247220938260.0140962361046913
790.9819508152760260.03609836944794850.0180491847239743
800.9757361613630960.04852767727380750.0242638386369037
810.9736550366979450.05268992660411020.0263449633020551
820.997824231292590.004351537414820830.00217576870741041
830.9974363701327780.005127259734443150.00256362986722158
840.9978455003993520.004308999201295040.00215449960064752
850.9970963904997780.00580721900044380.0029036095002219
860.9960219219284850.0079561561430290.0039780780715145
870.9943869739172060.01122605216558770.00561302608279384
880.9935015789174910.01299684216501890.00649842108250946
890.9912730762262250.01745384754755070.00872692377377535
900.9991344369796030.001731126040794740.000865563020397371
910.9987609480573590.002478103885282070.00123905194264104
920.9982114966028440.003577006794312340.00178850339715617
930.9975382031098790.00492359378024310.00246179689012155
940.9967133000703760.006573399859248030.00328669992962402
950.9986948622950320.002610275409935870.00130513770496793
960.9981692617901850.003661476419629870.00183073820981493
970.9976097961820070.004780407635985340.00239020381799267
980.9969102952731940.006179409453611660.00308970472680583
990.9991224363643890.001755127271221210.000877563635610604
1000.9987637285977690.002472542804462320.00123627140223116
1010.9983411350378060.003317729924388320.00165886496219416
1020.9977684871631410.004463025673718130.00223151283685906
1030.9971872942877990.00562541142440290.00281270571220145
1040.9962930955870140.007413808825971460.00370690441298573
1050.9956200773400550.008759845319890660.00437992265994533
1060.995839383082960.008321233834079060.00416061691703953
1070.9941621900604540.01167561987909220.0058378099395461
1080.9915961813826080.01680763723478390.00840381861739195
1090.9979435320323650.004112935935270250.00205646796763512
1100.9979693228440130.004061354311973970.00203067715598698
1110.9986470125513460.002705974897308630.00135298744865431
1120.9996436393291330.0007127213417337730.000356360670866886
1130.9993965140143090.001206971971381810.000603485985690904
1140.9996021961989330.0007956076021342820.000397803801067141
1150.9994236602225680.001152679554864340.000576339777432169
1160.9990231091294390.001953781741121820.000976890870560909
1170.9988232991393590.002353401721282540.00117670086064127
1180.9981167258358660.003766548328268390.00188327416413419
1190.9984023305664440.003195338867112750.00159766943355637
1200.9973353184540760.005329363091848650.00266468154592432
1210.9996073056112220.0007853887775564460.000392694388778223
1220.9993773519055250.001245296188949650.000622648094474824
1230.9997768055430890.0004463889138221820.000223194456911091
1240.9996454624240520.0007090751518954770.000354537575947739
1250.9993682225332810.001263554933438970.000631777466719483
1260.9989121265638460.002175746872307480.00108787343615374
1270.9992803284117480.001439343176504690.000719671588252346
1280.9987246618400640.002550676319871040.00127533815993552
1290.9988991326452970.002201734709407080.00110086735470354
1300.9983205127932680.003358974413464160.00167948720673208
1310.9976277004529040.004744599094191150.00237229954709558
1320.9997659620788690.0004680758422621180.000234037921131059
1330.9994844453998760.001031109200248280.000515554600124139
1340.998873212449940.002253575100120980.00112678755006049
1350.9978221428838860.004355714232228650.00217785711611433
1360.9979297606708550.004140478658289960.00207023932914498
1370.9997642263067550.0004715473864901760.000235773693245088
1380.9993624805670910.001275038865817420.000637519432908708
1390.999996705424466.5891510792079e-063.29457553960395e-06
1400.9999978220554334.35588913377091e-062.17794456688545e-06
1410.9999985483969242.90320615156513e-061.45160307578257e-06
1420.9999968304944886.33901102467073e-063.16950551233536e-06
1430.9999995176694199.64661161455957e-074.82330580727978e-07
1440.9999999999983173.36504137145787e-121.68252068572893e-12
1450.9999999998246993.50601717743808e-101.75300858871904e-10
1460.9999999402027891.1959442146608e-075.97972107330401e-08






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level600.465116279069767NOK
5% type I error level830.643410852713178NOK
10% type I error level930.720930232558139NOK

\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 & 60 & 0.465116279069767 & NOK \tabularnewline
5% type I error level & 83 & 0.643410852713178 & NOK \tabularnewline
10% type I error level & 93 & 0.720930232558139 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153608&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]60[/C][C]0.465116279069767[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]83[/C][C]0.643410852713178[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]93[/C][C]0.720930232558139[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153608&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153608&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level600.465116279069767NOK
5% type I error level830.643410852713178NOK
10% type I error level930.720930232558139NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 2 ; 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')
}