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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 computationWed, 22 Dec 2010 15:44:40 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t129303522624nzf5nsq8teqx3.htm/, Retrieved Mon, 06 May 2024 01:49:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114369, Retrieved Mon, 06 May 2024 01:49:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [BEL20-MR1] [2010-12-22 15:44:40] [4c7d8c32b2e34fcaa7f14928b91d45ae] [Current]
-   PD    [Multiple Regression] [BEL20-MR2] [2010-12-22 17:26:34] [d672a41e0af7ff107c03f1d65e47fd32]
-   P       [Multiple Regression] [BEL20-MR3] [2010-12-22 17:35:13] [d672a41e0af7ff107c03f1d65e47fd32]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3,04	493	9	3.030	9.026	25,64	104,8
3,28	481	11	2.803	9.787	27,97	105,2
3,51	462	13	2.768	9.536	27,62	105,6
3,69	457	12	2.883	9.490	23,31	105,8
3,92	442	13	2.863	9.736	29,07	106,1
4,29	439	15	2.897	9.694	29,58	106,5
4,31	488	13	3.013	9.647	28,63	106,71
4,42	521	16	3.143	9.753	29,92	106,68
4,59	501	10	3.033	10.070	32,68	107,41
4,76	485	14	3.046	10.137	31,54	107,15
4,83	464	14	3.111	9.984	32,43	107,5
4,83	460	45	3.013	9.732	26,54	107,22
4,76	467	13	2.987	9.103	25,85	107,11
4,99	460	8	2.996	9.155	27,60	107,57
4,78	448	7	2.833	9.308	25,71	107,81
5,06	443	3	2.849	9.394	25,38	108,75
4,65	436	3	2.795	9.948	28,57	109,43
4,54	431	4	2.845	10.177	27,64	109,62
4,51	484	4	2.915	10.002	25,36	109,54
4,49	510	0	2.893	9.728	25,90	109,53
3,99	513	-4	2.604	10.002	26,29	109,84
3,97	503	-14	2.642	10.063	21,74	109,67
3,51	471	-18	2.660	10.018	19,20	109,79
3,34	471	-8	2.639	9.960	19,32	109,56
3,29	476	-1	2.720	10.236	19,82	110,22
3,28	475	1	2.746	10.893	20,36	110,4
3,26	470	2	2.736	10.756	24,31	110,69
3,32	461	0	2.812	10.940	25,97	110,72
3,31	455	1	2.799	10.997	25,61	110,89
3,35	456	0	2.555	10.827	24,67	110,58
3,30	517	-1	2.305	10.166	25,59	110,94
3,29	525	-3	2.215	10.186	26,09	110,91
3,32	523	-3	2.066	10.457	28,37	111,22
3,30	519	-3	1.940	10.368	27,34	111,09
3,30	509	-4	2.042	10.244	24,46	111
3,09	512	-8	1.995	10.511	27,46	111,06
2,79	519	-9	1.947	10.812	30,23	111,55
2,76	517	-13	1.766	10.738	32,33	112,32
2,75	510	-18	1.635	10.171	29,87	112,64
2,56	509	-11	1.833	9.721	24,87	112,36
2,56	501	-9	1.910	9.897	25,48	112,04
2,21	507	-10	1.960	9.828	27,28	112,37
2,08	569	-13	1.970	9.924	28,24	112,59
2,10	580	-11	2.061	10.371	29,58	112,89
2,02	578	-5	2.093	10.846	26,95	113,22
2,01	565	-15	2.121	10.413	29,08	112,85
1,97	547	-6	2.175	10.709	28,76	113,06
2,06	555	-6	2.197	10.662	29,59	112,99
2,02	562	-3	2.350	10.570	30,70	113,32
2,03	561	-1	2.440	10.297	30,52	113,74
2,01	555	-3	2.409	10.635	32,67	113,91
2,08	544	-4	2.473	10.872	33,19	114,52
2,02	537	-6	2.408	10.296	37,13	114,96
2,03	543	0	2.455	10.383	35,54	114,91
2,07	594	-4	2.448	10.431	37,75	115,3
2,04	611	-2	2.498	10.574	41,84	115,44
2,05	613	-2	2.646	10.653	42,94	115,52
2,11	611	-6	2.757	10.805	49,14	116,08
2,09	594	-7	2.849	10.872	44,61	115,94
2,05	595	-6	2.921	10.625	40,22	115,56
2,08	591	-6	2.982	10.407	44,23	115,88
2,06	589	-3	3.081	10.463	45,85	116,66
2,06	584	-2	3.106	10.556	53,38	117,41
2,08	573	-5	3.119	10.646	53,26	117,68
2,07	567	-11	3.061	10.702	51,80	117,85
2,06	569	-11	3.097	11.353	55,30	118,21
2,07	621	-11	3.162	11.346	57,81	118,92
2,06	629	-10	3.257	11.451	63,96	119,03
2,09	628	-14	3.277	11.964	63,77	119,17
2,07	612	-8	3.295	12.574	59,15	118,95
2,09	595	-9	3.364	13.031	56,12	118,92
2,28	597	-5	3.494	13.812	57,42	118,9
2,33	593	-1	3.667	14.544	63,52	118,92
2,35	590	-2	3.813	14.931	61,71	119,44
2,52	580	-5	3.918	14.886	63,01	119,40
2,63	574	-4	3.896	16.005	68,18	119,98
2,58	573	-6	3.801	17.064	72,03	120,43
2,70	573	-2	3.570	15.168	69,75	120,41
2,81	620	-2	3.702	16.050	74,41	120,82
2,97	626	-2	3.862	15.839	74,33	120,97
3,04	620	-2	3.970	15.137	64,24	120,63
3,28	588	2	4.139	14.954	60,03	120,38
3,33	566	1	4.200	15.648	59,44	120,68
3,50	557	-8	4.291	15.305	62,50	120,84
3,56	561	-1	4.444	15.579	55,04	120,90
3,57	549	1	4.503	16.348	58,34	121,56
3,69	532	-1	4.357	15.928	61,92	121,57
3,82	526	2	4.591	16.171	67,65	122,12
3,79	511	2	4.697	15.937	67,68	121,97
3,96	499	1	4.621	15.713	70,30	121,96
4,06	555	-1	4.563	15.594	75,26	122,48
4,05	565	-2	4.203	15.683	71,44	122,33
4,03	542	-2	4.296	16.438	76,36	122,44
3,94	527	-1	4.435	17.032	81,71	123,08
4,02	510	-8	4.105	17.696	92,60	124,23
3,88	514	-4	4.117	17.745	90,60	124,58
4,02	517	-6	3.844	19.394	92,23	125,08
4,03	508	-3	3.721	20.148	94,09	125,98
4,09	493	-3	3.674	20.108	102,79	126,90
3,99	490	-7	3.858	18.584	109,65	127,19
4,01	469	-9	3.801	18.441	124,05	128,33
4,01	478	-11	3.504	18.391	132,69	129,04
4,19	528	-13	3.033	19.178	135,81	129,72
4,30	534	-11	3.047	18.079	116,07	128,92
4,27	518	-9	2.962	18.483	101,42	129,13
3,82	506	-17	2.198	19.644	75,73	128,90
3,15	502	-22	2.014	19.195	55,48	128,13
2,49	516	-25	1.863	19.650	43,80	127,85
1,81	528	-20	1.905	20.830	45,29	127,98
1,26	533	-24	1.811	23.595	44,01	128,42
1,06	536	-24	1.670	22.937	47,48	127,68
0,84	537	-22	1.864	21.814	51,07	127,95
0,78	524	-19	2.052	21.928	57,84	127,85
0,70	536	-18	2.030	21.777	69,04	127,61
0,36	587	-17	2.071	21.383	65,61	127,53
0,35	597	-11	2.293	21.467	72,87	127,92
0,36	581	-11	2.443	22.052	68,41	127,59
0,36	564	-12	2.513	22.680	73,25	127,65
0,36	558	-10	2.467	24.320	77,43	127,98
0,35	575	-15	2.503	24.977	75,28	128,19
0,34	580	-15	2.540	25.204	77,33	128,77
0,34	575	-15	2.483	25.739	74,31	129,31
0,35	563	-13	2.626	26.434	79,70	129,80
0,35	552	-8	2.656	27.525	85,47	130,24
0,34	537	-13	2.447	30.695	77,98	130,76
0,35	545	-9	2.467	32.436	75,69	130,75
0,48	601	-7	2.462	30.160	75,20	130,81
0,43	604	-4	2.505	30.236	77,21	130,89
0,45	586	-4	2.579	31.293	77,85	131,30
0,70	564	-2	2.649	31.077	83,53	131,49
0,59	549	0	2.637	32.226	85,99	131,65

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114369&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114369&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114369&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
BEL20[t] = -6.52694269803798 + 0.38134548609792Eonia[t] + 0.0066331019909006Werkloosheid[t] + 0.0339479944182582Consumentenvertrouwen[t] -0.0176233167337368Goudprijs[t] + 0.00861723790694973Olieprijs[t] + 0.0406391768418570CPI[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL20[t] =  -6.52694269803798 +  0.38134548609792Eonia[t] +  0.0066331019909006Werkloosheid[t] +  0.0339479944182582Consumentenvertrouwen[t] -0.0176233167337368Goudprijs[t] +  0.00861723790694973Olieprijs[t] +  0.0406391768418570CPI[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114369&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL20[t] =  -6.52694269803798 +  0.38134548609792Eonia[t] +  0.0066331019909006Werkloosheid[t] +  0.0339479944182582Consumentenvertrouwen[t] -0.0176233167337368Goudprijs[t] +  0.00861723790694973Olieprijs[t] +  0.0406391768418570CPI[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114369&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114369&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
BEL20[t] = -6.52694269803798 + 0.38134548609792Eonia[t] + 0.0066331019909006Werkloosheid[t] + 0.0339479944182582Consumentenvertrouwen[t] -0.0176233167337368Goudprijs[t] + 0.00861723790694973Olieprijs[t] + 0.0406391768418570CPI[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-6.526942698037982.38488-2.73680.0071160.003558
Eonia0.381345486097920.0642675.933800
Werkloosheid0.00663310199090060.0013165.04032e-061e-06
Consumentenvertrouwen0.03394799441825820.0069714.86993e-062e-06
Goudprijs-0.01762331673373680.022191-0.79420.428610.214305
Olieprijs0.008617237906949730.0039432.18560.0307220.015361
CPI0.04063917684185700.0247861.63960.1036160.051808

\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) & -6.52694269803798 & 2.38488 & -2.7368 & 0.007116 & 0.003558 \tabularnewline
Eonia & 0.38134548609792 & 0.064267 & 5.9338 & 0 & 0 \tabularnewline
Werkloosheid & 0.0066331019909006 & 0.001316 & 5.0403 & 2e-06 & 1e-06 \tabularnewline
Consumentenvertrouwen & 0.0339479944182582 & 0.006971 & 4.8699 & 3e-06 & 2e-06 \tabularnewline
Goudprijs & -0.0176233167337368 & 0.022191 & -0.7942 & 0.42861 & 0.214305 \tabularnewline
Olieprijs & 0.00861723790694973 & 0.003943 & 2.1856 & 0.030722 & 0.015361 \tabularnewline
CPI & 0.0406391768418570 & 0.024786 & 1.6396 & 0.103616 & 0.051808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114369&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]-6.52694269803798[/C][C]2.38488[/C][C]-2.7368[/C][C]0.007116[/C][C]0.003558[/C][/ROW]
[ROW][C]Eonia[/C][C]0.38134548609792[/C][C]0.064267[/C][C]5.9338[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Werkloosheid[/C][C]0.0066331019909006[/C][C]0.001316[/C][C]5.0403[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Consumentenvertrouwen[/C][C]0.0339479944182582[/C][C]0.006971[/C][C]4.8699[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Goudprijs[/C][C]-0.0176233167337368[/C][C]0.022191[/C][C]-0.7942[/C][C]0.42861[/C][C]0.214305[/C][/ROW]
[ROW][C]Olieprijs[/C][C]0.00861723790694973[/C][C]0.003943[/C][C]2.1856[/C][C]0.030722[/C][C]0.015361[/C][/ROW]
[ROW][C]CPI[/C][C]0.0406391768418570[/C][C]0.024786[/C][C]1.6396[/C][C]0.103616[/C][C]0.051808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114369&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114369&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)-6.526942698037982.38488-2.73680.0071160.003558
Eonia0.381345486097920.0642675.933800
Werkloosheid0.00663310199090060.0013165.04032e-061e-06
Consumentenvertrouwen0.03394799441825820.0069714.86993e-062e-06
Goudprijs-0.01762331673373680.022191-0.79420.428610.214305
Olieprijs0.008617237906949730.0039432.18560.0307220.015361
CPI0.04063917684185700.0247861.63960.1036160.051808






Multiple Linear Regression - Regression Statistics
Multiple R0.821004574068137
R-squared0.674048510640803
Adjusted R-squared0.658276664381487
F-TEST (value)42.7374512506842
F-TEST (DF numerator)6
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.443811783973242
Sum Squared Residuals24.4241435495955

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.821004574068137 \tabularnewline
R-squared & 0.674048510640803 \tabularnewline
Adjusted R-squared & 0.658276664381487 \tabularnewline
F-TEST (value) & 42.7374512506842 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 124 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.443811783973242 \tabularnewline
Sum Squared Residuals & 24.4241435495955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114369&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.821004574068137[/C][/ROW]
[ROW][C]R-squared[/C][C]0.674048510640803[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.658276664381487[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]42.7374512506842[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]124[/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]0.443811783973242[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]24.4241435495955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114369&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114369&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 R0.821004574068137
R-squared0.674048510640803
Adjusted R-squared0.658276664381487
F-TEST (value)42.7374512506842
F-TEST (DF numerator)6
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.443811783973242
Sum Squared Residuals24.4241435495955






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13.032.528862467100140.501137532899857
22.8032.631606639734890.171393360265108
32.7682.678846242516290.0891537574837057
42.8832.652173138200330.230826861799671
52.8632.731825772037690.131174227962309
62.8972.94231092612984-0.0453109261298374
73.0133.20823999158108-0.195239991581082
83.1433.57895333402728-0.435953334027283
93.0333.35529564464951-0.322295644649509
103.0463.42821612369081-0.382216123690813
113.1113.34020458700085-0.229204587000851
123.0134.3083665810325-1.29536658103250
132.9873.24243715217481-0.255437152174809
142.9963.14683270316400-0.150832703163997
152.8332.94397538811202-0.110975388112018
162.8492.91563616907485-0.0666361690748498
172.7952.758213117543540.0367868824564586
182.8452.712719271350990.132280728649011
192.9153.03301895613899-0.118018956138994
202.8933.07113642599380-0.178136425993797
212.6042.77570109006415-0.171701090064148
222.6422.315071101390110.326928898609894
232.661.775382902593600.884617097406401
242.6392.04274332438530.596256675614701
252.722.320743961213300.399256038786698
262.7462.378583234405160.367416765594839
272.7362.419976654556070.316023345443931
282.8122.327544576919110.484455423080892
292.7992.320682429893770.478317570106226
302.5552.290918972301560.264081027698440
312.3052.67672989992180-0.371729899921805
322.2152.66082224946506-0.445822249465058
332.0662.68646593848017-0.620465938480174
341.942.63971624795032-0.699716247950316
352.0422.51314735381025-0.471147353810252
361.9952.34075676879281-0.345756768792811
371.9472.27731516979939-0.330315169799387
381.7662.16750911477274-0.401509114772736
391.6351.94932252581049-0.314322525810488
401.8332.06133507586850-0.228335075868503
411.912.06531652356652-0.155316523566523
421.961.96783418640435-0.00783418640434615
431.972.24318894228212-0.273188942282121
442.0612.40753719200839-0.346537192008392
452.0932.54982783286232-0.456827832862316
462.1212.13125322539307-0.0102532253930704
472.1752.30269572913065-0.127695729130647
482.1972.39521749977699-0.198217499776989
492.352.55283678509818-0.202836785098181
502.442.63824164372342-0.198241643723416
512.4092.54239917372659-0.133399173726592
522.4732.48727537695453-0.0142753769545326
532.4082.41205113061827-0.00405113061826799
542.4552.64208456826422-0.187084568264222
552.4482.8938820671105-0.445882067110501
562.4983.10151427871375-0.60351427871375
572.6463.12993179137956-0.483931791379559
582.7573.0772604088016-0.320260408801600
592.8492.87701643611857-0.0280164361185700
602.9212.853424110705630.0675758892943694
612.9822.889733610969180.0922663890308174
623.0813.015356058129020.0656439418709842
633.1063.10986675820726-0.00386675820725781
643.1192.951037973466970.167962026533034
653.0612.697078527132910.363921472867087
663.0972.739848933397470.357151066602535
673.1623.139190137706570.0228098622934251
683.2573.27800536751436-0.0210053675143613
693.2773.14203210050450.134967899495502
703.2953.172461044194780.122538955805216
713.3642.997993963742540.366006036257458
723.4943.216133603129130.27786639687087
733.6673.384938114063590.282061885936408
743.8133.337432671164820.47556732883518
753.9183.244456292096070.673543707903935
763.8963.328955029161850.567044970838153
773.8013.26815956712910.532840432870902
783.573.462666725696360.107333274303636
793.7023.85764514853185-0.155645148531854
803.8623.96758405557747-0.105584055577467
813.973.866085945398650.103914054601354
824.1393.837928277189910.301071722810089
834.23.671996314150980.52800368584902
844.2913.410510993034830.880489006965169
854.4443.630885058131740.81311494186826
864.5033.664703689178740.838296310821256
874.3573.568464321332140.788535678667865
884.5913.747530460337780.843469539662219
894.6973.634880062617961.06211993738204
904.6213.612281971441691.00871802855831
914.5634.019945289372950.543054710627052
924.2034.007932659482590.195067340517414
934.2963.881305919790740.41469408020926
944.4353.843079336437810.591920663562193
954.1053.652063172416140.452936827583862
964.1173.757124883559860.359875116440137
973.8443.767821405665080.0761785943349186
983.7213.85309626671009-0.132096266710090
993.6743.88954341116678-0.215543411166777
1003.8583.793475126939280.0645248730607213
1013.8013.766847927768530.0341520722314743
1023.5043.86283777376056-0.358837773760565
1033.0334.2358899442194-1.2028899442194
1043.0474.20228495580579-1.15528495580579
1052.9624.02778282004451-1.06578282004451
1062.1983.25381164883254-1.05581164883254
1072.0142.60415942852153-0.590159428521525
1081.8632.22344393493199-0.360443934931989
1091.9052.21079346409249-0.305793464092489
1101.8111.85654968154084-0.0455496815408419
1111.671.8116048573789-0.141604857378901
1121.8641.863937387790016.26122099874546e-05
1132.0521.908936040835420.143063959164578
1142.032.08139440319924-0.0513944031992398
1152.0712.29810846050504-0.227108460505042
1162.2932.64124405962976-0.348244059629764
1172.4432.47677443292429-0.0337744329242888
1182.5132.363142043832080.149857956167918
1192.4672.411768164088730.0552318359112712
1202.5032.329406117524550.173593882475448
1212.542.395993739997040.144006260002959
1222.4832.349320852605600.133679147394396
1232.6262.395544976253310.230455023746685
1242.6562.540696488421710.115303511578291
1252.4472.168369877594700.278630122405305
1262.4672.310218065047140.156781934952857
1272.4622.83746925148891-0.375469251488915
1282.5052.95937767668005-0.454377676680048
1292.5792.85115799954385-0.272157999543846
1302.6492.92893610743094-0.279936107430943
1312.6372.86283904555191-0.225839045551909

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.03 & 2.52886246710014 & 0.501137532899857 \tabularnewline
2 & 2.803 & 2.63160663973489 & 0.171393360265108 \tabularnewline
3 & 2.768 & 2.67884624251629 & 0.0891537574837057 \tabularnewline
4 & 2.883 & 2.65217313820033 & 0.230826861799671 \tabularnewline
5 & 2.863 & 2.73182577203769 & 0.131174227962309 \tabularnewline
6 & 2.897 & 2.94231092612984 & -0.0453109261298374 \tabularnewline
7 & 3.013 & 3.20823999158108 & -0.195239991581082 \tabularnewline
8 & 3.143 & 3.57895333402728 & -0.435953334027283 \tabularnewline
9 & 3.033 & 3.35529564464951 & -0.322295644649509 \tabularnewline
10 & 3.046 & 3.42821612369081 & -0.382216123690813 \tabularnewline
11 & 3.111 & 3.34020458700085 & -0.229204587000851 \tabularnewline
12 & 3.013 & 4.3083665810325 & -1.29536658103250 \tabularnewline
13 & 2.987 & 3.24243715217481 & -0.255437152174809 \tabularnewline
14 & 2.996 & 3.14683270316400 & -0.150832703163997 \tabularnewline
15 & 2.833 & 2.94397538811202 & -0.110975388112018 \tabularnewline
16 & 2.849 & 2.91563616907485 & -0.0666361690748498 \tabularnewline
17 & 2.795 & 2.75821311754354 & 0.0367868824564586 \tabularnewline
18 & 2.845 & 2.71271927135099 & 0.132280728649011 \tabularnewline
19 & 2.915 & 3.03301895613899 & -0.118018956138994 \tabularnewline
20 & 2.893 & 3.07113642599380 & -0.178136425993797 \tabularnewline
21 & 2.604 & 2.77570109006415 & -0.171701090064148 \tabularnewline
22 & 2.642 & 2.31507110139011 & 0.326928898609894 \tabularnewline
23 & 2.66 & 1.77538290259360 & 0.884617097406401 \tabularnewline
24 & 2.639 & 2.0427433243853 & 0.596256675614701 \tabularnewline
25 & 2.72 & 2.32074396121330 & 0.399256038786698 \tabularnewline
26 & 2.746 & 2.37858323440516 & 0.367416765594839 \tabularnewline
27 & 2.736 & 2.41997665455607 & 0.316023345443931 \tabularnewline
28 & 2.812 & 2.32754457691911 & 0.484455423080892 \tabularnewline
29 & 2.799 & 2.32068242989377 & 0.478317570106226 \tabularnewline
30 & 2.555 & 2.29091897230156 & 0.264081027698440 \tabularnewline
31 & 2.305 & 2.67672989992180 & -0.371729899921805 \tabularnewline
32 & 2.215 & 2.66082224946506 & -0.445822249465058 \tabularnewline
33 & 2.066 & 2.68646593848017 & -0.620465938480174 \tabularnewline
34 & 1.94 & 2.63971624795032 & -0.699716247950316 \tabularnewline
35 & 2.042 & 2.51314735381025 & -0.471147353810252 \tabularnewline
36 & 1.995 & 2.34075676879281 & -0.345756768792811 \tabularnewline
37 & 1.947 & 2.27731516979939 & -0.330315169799387 \tabularnewline
38 & 1.766 & 2.16750911477274 & -0.401509114772736 \tabularnewline
39 & 1.635 & 1.94932252581049 & -0.314322525810488 \tabularnewline
40 & 1.833 & 2.06133507586850 & -0.228335075868503 \tabularnewline
41 & 1.91 & 2.06531652356652 & -0.155316523566523 \tabularnewline
42 & 1.96 & 1.96783418640435 & -0.00783418640434615 \tabularnewline
43 & 1.97 & 2.24318894228212 & -0.273188942282121 \tabularnewline
44 & 2.061 & 2.40753719200839 & -0.346537192008392 \tabularnewline
45 & 2.093 & 2.54982783286232 & -0.456827832862316 \tabularnewline
46 & 2.121 & 2.13125322539307 & -0.0102532253930704 \tabularnewline
47 & 2.175 & 2.30269572913065 & -0.127695729130647 \tabularnewline
48 & 2.197 & 2.39521749977699 & -0.198217499776989 \tabularnewline
49 & 2.35 & 2.55283678509818 & -0.202836785098181 \tabularnewline
50 & 2.44 & 2.63824164372342 & -0.198241643723416 \tabularnewline
51 & 2.409 & 2.54239917372659 & -0.133399173726592 \tabularnewline
52 & 2.473 & 2.48727537695453 & -0.0142753769545326 \tabularnewline
53 & 2.408 & 2.41205113061827 & -0.00405113061826799 \tabularnewline
54 & 2.455 & 2.64208456826422 & -0.187084568264222 \tabularnewline
55 & 2.448 & 2.8938820671105 & -0.445882067110501 \tabularnewline
56 & 2.498 & 3.10151427871375 & -0.60351427871375 \tabularnewline
57 & 2.646 & 3.12993179137956 & -0.483931791379559 \tabularnewline
58 & 2.757 & 3.0772604088016 & -0.320260408801600 \tabularnewline
59 & 2.849 & 2.87701643611857 & -0.0280164361185700 \tabularnewline
60 & 2.921 & 2.85342411070563 & 0.0675758892943694 \tabularnewline
61 & 2.982 & 2.88973361096918 & 0.0922663890308174 \tabularnewline
62 & 3.081 & 3.01535605812902 & 0.0656439418709842 \tabularnewline
63 & 3.106 & 3.10986675820726 & -0.00386675820725781 \tabularnewline
64 & 3.119 & 2.95103797346697 & 0.167962026533034 \tabularnewline
65 & 3.061 & 2.69707852713291 & 0.363921472867087 \tabularnewline
66 & 3.097 & 2.73984893339747 & 0.357151066602535 \tabularnewline
67 & 3.162 & 3.13919013770657 & 0.0228098622934251 \tabularnewline
68 & 3.257 & 3.27800536751436 & -0.0210053675143613 \tabularnewline
69 & 3.277 & 3.1420321005045 & 0.134967899495502 \tabularnewline
70 & 3.295 & 3.17246104419478 & 0.122538955805216 \tabularnewline
71 & 3.364 & 2.99799396374254 & 0.366006036257458 \tabularnewline
72 & 3.494 & 3.21613360312913 & 0.27786639687087 \tabularnewline
73 & 3.667 & 3.38493811406359 & 0.282061885936408 \tabularnewline
74 & 3.813 & 3.33743267116482 & 0.47556732883518 \tabularnewline
75 & 3.918 & 3.24445629209607 & 0.673543707903935 \tabularnewline
76 & 3.896 & 3.32895502916185 & 0.567044970838153 \tabularnewline
77 & 3.801 & 3.2681595671291 & 0.532840432870902 \tabularnewline
78 & 3.57 & 3.46266672569636 & 0.107333274303636 \tabularnewline
79 & 3.702 & 3.85764514853185 & -0.155645148531854 \tabularnewline
80 & 3.862 & 3.96758405557747 & -0.105584055577467 \tabularnewline
81 & 3.97 & 3.86608594539865 & 0.103914054601354 \tabularnewline
82 & 4.139 & 3.83792827718991 & 0.301071722810089 \tabularnewline
83 & 4.2 & 3.67199631415098 & 0.52800368584902 \tabularnewline
84 & 4.291 & 3.41051099303483 & 0.880489006965169 \tabularnewline
85 & 4.444 & 3.63088505813174 & 0.81311494186826 \tabularnewline
86 & 4.503 & 3.66470368917874 & 0.838296310821256 \tabularnewline
87 & 4.357 & 3.56846432133214 & 0.788535678667865 \tabularnewline
88 & 4.591 & 3.74753046033778 & 0.843469539662219 \tabularnewline
89 & 4.697 & 3.63488006261796 & 1.06211993738204 \tabularnewline
90 & 4.621 & 3.61228197144169 & 1.00871802855831 \tabularnewline
91 & 4.563 & 4.01994528937295 & 0.543054710627052 \tabularnewline
92 & 4.203 & 4.00793265948259 & 0.195067340517414 \tabularnewline
93 & 4.296 & 3.88130591979074 & 0.41469408020926 \tabularnewline
94 & 4.435 & 3.84307933643781 & 0.591920663562193 \tabularnewline
95 & 4.105 & 3.65206317241614 & 0.452936827583862 \tabularnewline
96 & 4.117 & 3.75712488355986 & 0.359875116440137 \tabularnewline
97 & 3.844 & 3.76782140566508 & 0.0761785943349186 \tabularnewline
98 & 3.721 & 3.85309626671009 & -0.132096266710090 \tabularnewline
99 & 3.674 & 3.88954341116678 & -0.215543411166777 \tabularnewline
100 & 3.858 & 3.79347512693928 & 0.0645248730607213 \tabularnewline
101 & 3.801 & 3.76684792776853 & 0.0341520722314743 \tabularnewline
102 & 3.504 & 3.86283777376056 & -0.358837773760565 \tabularnewline
103 & 3.033 & 4.2358899442194 & -1.2028899442194 \tabularnewline
104 & 3.047 & 4.20228495580579 & -1.15528495580579 \tabularnewline
105 & 2.962 & 4.02778282004451 & -1.06578282004451 \tabularnewline
106 & 2.198 & 3.25381164883254 & -1.05581164883254 \tabularnewline
107 & 2.014 & 2.60415942852153 & -0.590159428521525 \tabularnewline
108 & 1.863 & 2.22344393493199 & -0.360443934931989 \tabularnewline
109 & 1.905 & 2.21079346409249 & -0.305793464092489 \tabularnewline
110 & 1.811 & 1.85654968154084 & -0.0455496815408419 \tabularnewline
111 & 1.67 & 1.8116048573789 & -0.141604857378901 \tabularnewline
112 & 1.864 & 1.86393738779001 & 6.26122099874546e-05 \tabularnewline
113 & 2.052 & 1.90893604083542 & 0.143063959164578 \tabularnewline
114 & 2.03 & 2.08139440319924 & -0.0513944031992398 \tabularnewline
115 & 2.071 & 2.29810846050504 & -0.227108460505042 \tabularnewline
116 & 2.293 & 2.64124405962976 & -0.348244059629764 \tabularnewline
117 & 2.443 & 2.47677443292429 & -0.0337744329242888 \tabularnewline
118 & 2.513 & 2.36314204383208 & 0.149857956167918 \tabularnewline
119 & 2.467 & 2.41176816408873 & 0.0552318359112712 \tabularnewline
120 & 2.503 & 2.32940611752455 & 0.173593882475448 \tabularnewline
121 & 2.54 & 2.39599373999704 & 0.144006260002959 \tabularnewline
122 & 2.483 & 2.34932085260560 & 0.133679147394396 \tabularnewline
123 & 2.626 & 2.39554497625331 & 0.230455023746685 \tabularnewline
124 & 2.656 & 2.54069648842171 & 0.115303511578291 \tabularnewline
125 & 2.447 & 2.16836987759470 & 0.278630122405305 \tabularnewline
126 & 2.467 & 2.31021806504714 & 0.156781934952857 \tabularnewline
127 & 2.462 & 2.83746925148891 & -0.375469251488915 \tabularnewline
128 & 2.505 & 2.95937767668005 & -0.454377676680048 \tabularnewline
129 & 2.579 & 2.85115799954385 & -0.272157999543846 \tabularnewline
130 & 2.649 & 2.92893610743094 & -0.279936107430943 \tabularnewline
131 & 2.637 & 2.86283904555191 & -0.225839045551909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114369&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]3.03[/C][C]2.52886246710014[/C][C]0.501137532899857[/C][/ROW]
[ROW][C]2[/C][C]2.803[/C][C]2.63160663973489[/C][C]0.171393360265108[/C][/ROW]
[ROW][C]3[/C][C]2.768[/C][C]2.67884624251629[/C][C]0.0891537574837057[/C][/ROW]
[ROW][C]4[/C][C]2.883[/C][C]2.65217313820033[/C][C]0.230826861799671[/C][/ROW]
[ROW][C]5[/C][C]2.863[/C][C]2.73182577203769[/C][C]0.131174227962309[/C][/ROW]
[ROW][C]6[/C][C]2.897[/C][C]2.94231092612984[/C][C]-0.0453109261298374[/C][/ROW]
[ROW][C]7[/C][C]3.013[/C][C]3.20823999158108[/C][C]-0.195239991581082[/C][/ROW]
[ROW][C]8[/C][C]3.143[/C][C]3.57895333402728[/C][C]-0.435953334027283[/C][/ROW]
[ROW][C]9[/C][C]3.033[/C][C]3.35529564464951[/C][C]-0.322295644649509[/C][/ROW]
[ROW][C]10[/C][C]3.046[/C][C]3.42821612369081[/C][C]-0.382216123690813[/C][/ROW]
[ROW][C]11[/C][C]3.111[/C][C]3.34020458700085[/C][C]-0.229204587000851[/C][/ROW]
[ROW][C]12[/C][C]3.013[/C][C]4.3083665810325[/C][C]-1.29536658103250[/C][/ROW]
[ROW][C]13[/C][C]2.987[/C][C]3.24243715217481[/C][C]-0.255437152174809[/C][/ROW]
[ROW][C]14[/C][C]2.996[/C][C]3.14683270316400[/C][C]-0.150832703163997[/C][/ROW]
[ROW][C]15[/C][C]2.833[/C][C]2.94397538811202[/C][C]-0.110975388112018[/C][/ROW]
[ROW][C]16[/C][C]2.849[/C][C]2.91563616907485[/C][C]-0.0666361690748498[/C][/ROW]
[ROW][C]17[/C][C]2.795[/C][C]2.75821311754354[/C][C]0.0367868824564586[/C][/ROW]
[ROW][C]18[/C][C]2.845[/C][C]2.71271927135099[/C][C]0.132280728649011[/C][/ROW]
[ROW][C]19[/C][C]2.915[/C][C]3.03301895613899[/C][C]-0.118018956138994[/C][/ROW]
[ROW][C]20[/C][C]2.893[/C][C]3.07113642599380[/C][C]-0.178136425993797[/C][/ROW]
[ROW][C]21[/C][C]2.604[/C][C]2.77570109006415[/C][C]-0.171701090064148[/C][/ROW]
[ROW][C]22[/C][C]2.642[/C][C]2.31507110139011[/C][C]0.326928898609894[/C][/ROW]
[ROW][C]23[/C][C]2.66[/C][C]1.77538290259360[/C][C]0.884617097406401[/C][/ROW]
[ROW][C]24[/C][C]2.639[/C][C]2.0427433243853[/C][C]0.596256675614701[/C][/ROW]
[ROW][C]25[/C][C]2.72[/C][C]2.32074396121330[/C][C]0.399256038786698[/C][/ROW]
[ROW][C]26[/C][C]2.746[/C][C]2.37858323440516[/C][C]0.367416765594839[/C][/ROW]
[ROW][C]27[/C][C]2.736[/C][C]2.41997665455607[/C][C]0.316023345443931[/C][/ROW]
[ROW][C]28[/C][C]2.812[/C][C]2.32754457691911[/C][C]0.484455423080892[/C][/ROW]
[ROW][C]29[/C][C]2.799[/C][C]2.32068242989377[/C][C]0.478317570106226[/C][/ROW]
[ROW][C]30[/C][C]2.555[/C][C]2.29091897230156[/C][C]0.264081027698440[/C][/ROW]
[ROW][C]31[/C][C]2.305[/C][C]2.67672989992180[/C][C]-0.371729899921805[/C][/ROW]
[ROW][C]32[/C][C]2.215[/C][C]2.66082224946506[/C][C]-0.445822249465058[/C][/ROW]
[ROW][C]33[/C][C]2.066[/C][C]2.68646593848017[/C][C]-0.620465938480174[/C][/ROW]
[ROW][C]34[/C][C]1.94[/C][C]2.63971624795032[/C][C]-0.699716247950316[/C][/ROW]
[ROW][C]35[/C][C]2.042[/C][C]2.51314735381025[/C][C]-0.471147353810252[/C][/ROW]
[ROW][C]36[/C][C]1.995[/C][C]2.34075676879281[/C][C]-0.345756768792811[/C][/ROW]
[ROW][C]37[/C][C]1.947[/C][C]2.27731516979939[/C][C]-0.330315169799387[/C][/ROW]
[ROW][C]38[/C][C]1.766[/C][C]2.16750911477274[/C][C]-0.401509114772736[/C][/ROW]
[ROW][C]39[/C][C]1.635[/C][C]1.94932252581049[/C][C]-0.314322525810488[/C][/ROW]
[ROW][C]40[/C][C]1.833[/C][C]2.06133507586850[/C][C]-0.228335075868503[/C][/ROW]
[ROW][C]41[/C][C]1.91[/C][C]2.06531652356652[/C][C]-0.155316523566523[/C][/ROW]
[ROW][C]42[/C][C]1.96[/C][C]1.96783418640435[/C][C]-0.00783418640434615[/C][/ROW]
[ROW][C]43[/C][C]1.97[/C][C]2.24318894228212[/C][C]-0.273188942282121[/C][/ROW]
[ROW][C]44[/C][C]2.061[/C][C]2.40753719200839[/C][C]-0.346537192008392[/C][/ROW]
[ROW][C]45[/C][C]2.093[/C][C]2.54982783286232[/C][C]-0.456827832862316[/C][/ROW]
[ROW][C]46[/C][C]2.121[/C][C]2.13125322539307[/C][C]-0.0102532253930704[/C][/ROW]
[ROW][C]47[/C][C]2.175[/C][C]2.30269572913065[/C][C]-0.127695729130647[/C][/ROW]
[ROW][C]48[/C][C]2.197[/C][C]2.39521749977699[/C][C]-0.198217499776989[/C][/ROW]
[ROW][C]49[/C][C]2.35[/C][C]2.55283678509818[/C][C]-0.202836785098181[/C][/ROW]
[ROW][C]50[/C][C]2.44[/C][C]2.63824164372342[/C][C]-0.198241643723416[/C][/ROW]
[ROW][C]51[/C][C]2.409[/C][C]2.54239917372659[/C][C]-0.133399173726592[/C][/ROW]
[ROW][C]52[/C][C]2.473[/C][C]2.48727537695453[/C][C]-0.0142753769545326[/C][/ROW]
[ROW][C]53[/C][C]2.408[/C][C]2.41205113061827[/C][C]-0.00405113061826799[/C][/ROW]
[ROW][C]54[/C][C]2.455[/C][C]2.64208456826422[/C][C]-0.187084568264222[/C][/ROW]
[ROW][C]55[/C][C]2.448[/C][C]2.8938820671105[/C][C]-0.445882067110501[/C][/ROW]
[ROW][C]56[/C][C]2.498[/C][C]3.10151427871375[/C][C]-0.60351427871375[/C][/ROW]
[ROW][C]57[/C][C]2.646[/C][C]3.12993179137956[/C][C]-0.483931791379559[/C][/ROW]
[ROW][C]58[/C][C]2.757[/C][C]3.0772604088016[/C][C]-0.320260408801600[/C][/ROW]
[ROW][C]59[/C][C]2.849[/C][C]2.87701643611857[/C][C]-0.0280164361185700[/C][/ROW]
[ROW][C]60[/C][C]2.921[/C][C]2.85342411070563[/C][C]0.0675758892943694[/C][/ROW]
[ROW][C]61[/C][C]2.982[/C][C]2.88973361096918[/C][C]0.0922663890308174[/C][/ROW]
[ROW][C]62[/C][C]3.081[/C][C]3.01535605812902[/C][C]0.0656439418709842[/C][/ROW]
[ROW][C]63[/C][C]3.106[/C][C]3.10986675820726[/C][C]-0.00386675820725781[/C][/ROW]
[ROW][C]64[/C][C]3.119[/C][C]2.95103797346697[/C][C]0.167962026533034[/C][/ROW]
[ROW][C]65[/C][C]3.061[/C][C]2.69707852713291[/C][C]0.363921472867087[/C][/ROW]
[ROW][C]66[/C][C]3.097[/C][C]2.73984893339747[/C][C]0.357151066602535[/C][/ROW]
[ROW][C]67[/C][C]3.162[/C][C]3.13919013770657[/C][C]0.0228098622934251[/C][/ROW]
[ROW][C]68[/C][C]3.257[/C][C]3.27800536751436[/C][C]-0.0210053675143613[/C][/ROW]
[ROW][C]69[/C][C]3.277[/C][C]3.1420321005045[/C][C]0.134967899495502[/C][/ROW]
[ROW][C]70[/C][C]3.295[/C][C]3.17246104419478[/C][C]0.122538955805216[/C][/ROW]
[ROW][C]71[/C][C]3.364[/C][C]2.99799396374254[/C][C]0.366006036257458[/C][/ROW]
[ROW][C]72[/C][C]3.494[/C][C]3.21613360312913[/C][C]0.27786639687087[/C][/ROW]
[ROW][C]73[/C][C]3.667[/C][C]3.38493811406359[/C][C]0.282061885936408[/C][/ROW]
[ROW][C]74[/C][C]3.813[/C][C]3.33743267116482[/C][C]0.47556732883518[/C][/ROW]
[ROW][C]75[/C][C]3.918[/C][C]3.24445629209607[/C][C]0.673543707903935[/C][/ROW]
[ROW][C]76[/C][C]3.896[/C][C]3.32895502916185[/C][C]0.567044970838153[/C][/ROW]
[ROW][C]77[/C][C]3.801[/C][C]3.2681595671291[/C][C]0.532840432870902[/C][/ROW]
[ROW][C]78[/C][C]3.57[/C][C]3.46266672569636[/C][C]0.107333274303636[/C][/ROW]
[ROW][C]79[/C][C]3.702[/C][C]3.85764514853185[/C][C]-0.155645148531854[/C][/ROW]
[ROW][C]80[/C][C]3.862[/C][C]3.96758405557747[/C][C]-0.105584055577467[/C][/ROW]
[ROW][C]81[/C][C]3.97[/C][C]3.86608594539865[/C][C]0.103914054601354[/C][/ROW]
[ROW][C]82[/C][C]4.139[/C][C]3.83792827718991[/C][C]0.301071722810089[/C][/ROW]
[ROW][C]83[/C][C]4.2[/C][C]3.67199631415098[/C][C]0.52800368584902[/C][/ROW]
[ROW][C]84[/C][C]4.291[/C][C]3.41051099303483[/C][C]0.880489006965169[/C][/ROW]
[ROW][C]85[/C][C]4.444[/C][C]3.63088505813174[/C][C]0.81311494186826[/C][/ROW]
[ROW][C]86[/C][C]4.503[/C][C]3.66470368917874[/C][C]0.838296310821256[/C][/ROW]
[ROW][C]87[/C][C]4.357[/C][C]3.56846432133214[/C][C]0.788535678667865[/C][/ROW]
[ROW][C]88[/C][C]4.591[/C][C]3.74753046033778[/C][C]0.843469539662219[/C][/ROW]
[ROW][C]89[/C][C]4.697[/C][C]3.63488006261796[/C][C]1.06211993738204[/C][/ROW]
[ROW][C]90[/C][C]4.621[/C][C]3.61228197144169[/C][C]1.00871802855831[/C][/ROW]
[ROW][C]91[/C][C]4.563[/C][C]4.01994528937295[/C][C]0.543054710627052[/C][/ROW]
[ROW][C]92[/C][C]4.203[/C][C]4.00793265948259[/C][C]0.195067340517414[/C][/ROW]
[ROW][C]93[/C][C]4.296[/C][C]3.88130591979074[/C][C]0.41469408020926[/C][/ROW]
[ROW][C]94[/C][C]4.435[/C][C]3.84307933643781[/C][C]0.591920663562193[/C][/ROW]
[ROW][C]95[/C][C]4.105[/C][C]3.65206317241614[/C][C]0.452936827583862[/C][/ROW]
[ROW][C]96[/C][C]4.117[/C][C]3.75712488355986[/C][C]0.359875116440137[/C][/ROW]
[ROW][C]97[/C][C]3.844[/C][C]3.76782140566508[/C][C]0.0761785943349186[/C][/ROW]
[ROW][C]98[/C][C]3.721[/C][C]3.85309626671009[/C][C]-0.132096266710090[/C][/ROW]
[ROW][C]99[/C][C]3.674[/C][C]3.88954341116678[/C][C]-0.215543411166777[/C][/ROW]
[ROW][C]100[/C][C]3.858[/C][C]3.79347512693928[/C][C]0.0645248730607213[/C][/ROW]
[ROW][C]101[/C][C]3.801[/C][C]3.76684792776853[/C][C]0.0341520722314743[/C][/ROW]
[ROW][C]102[/C][C]3.504[/C][C]3.86283777376056[/C][C]-0.358837773760565[/C][/ROW]
[ROW][C]103[/C][C]3.033[/C][C]4.2358899442194[/C][C]-1.2028899442194[/C][/ROW]
[ROW][C]104[/C][C]3.047[/C][C]4.20228495580579[/C][C]-1.15528495580579[/C][/ROW]
[ROW][C]105[/C][C]2.962[/C][C]4.02778282004451[/C][C]-1.06578282004451[/C][/ROW]
[ROW][C]106[/C][C]2.198[/C][C]3.25381164883254[/C][C]-1.05581164883254[/C][/ROW]
[ROW][C]107[/C][C]2.014[/C][C]2.60415942852153[/C][C]-0.590159428521525[/C][/ROW]
[ROW][C]108[/C][C]1.863[/C][C]2.22344393493199[/C][C]-0.360443934931989[/C][/ROW]
[ROW][C]109[/C][C]1.905[/C][C]2.21079346409249[/C][C]-0.305793464092489[/C][/ROW]
[ROW][C]110[/C][C]1.811[/C][C]1.85654968154084[/C][C]-0.0455496815408419[/C][/ROW]
[ROW][C]111[/C][C]1.67[/C][C]1.8116048573789[/C][C]-0.141604857378901[/C][/ROW]
[ROW][C]112[/C][C]1.864[/C][C]1.86393738779001[/C][C]6.26122099874546e-05[/C][/ROW]
[ROW][C]113[/C][C]2.052[/C][C]1.90893604083542[/C][C]0.143063959164578[/C][/ROW]
[ROW][C]114[/C][C]2.03[/C][C]2.08139440319924[/C][C]-0.0513944031992398[/C][/ROW]
[ROW][C]115[/C][C]2.071[/C][C]2.29810846050504[/C][C]-0.227108460505042[/C][/ROW]
[ROW][C]116[/C][C]2.293[/C][C]2.64124405962976[/C][C]-0.348244059629764[/C][/ROW]
[ROW][C]117[/C][C]2.443[/C][C]2.47677443292429[/C][C]-0.0337744329242888[/C][/ROW]
[ROW][C]118[/C][C]2.513[/C][C]2.36314204383208[/C][C]0.149857956167918[/C][/ROW]
[ROW][C]119[/C][C]2.467[/C][C]2.41176816408873[/C][C]0.0552318359112712[/C][/ROW]
[ROW][C]120[/C][C]2.503[/C][C]2.32940611752455[/C][C]0.173593882475448[/C][/ROW]
[ROW][C]121[/C][C]2.54[/C][C]2.39599373999704[/C][C]0.144006260002959[/C][/ROW]
[ROW][C]122[/C][C]2.483[/C][C]2.34932085260560[/C][C]0.133679147394396[/C][/ROW]
[ROW][C]123[/C][C]2.626[/C][C]2.39554497625331[/C][C]0.230455023746685[/C][/ROW]
[ROW][C]124[/C][C]2.656[/C][C]2.54069648842171[/C][C]0.115303511578291[/C][/ROW]
[ROW][C]125[/C][C]2.447[/C][C]2.16836987759470[/C][C]0.278630122405305[/C][/ROW]
[ROW][C]126[/C][C]2.467[/C][C]2.31021806504714[/C][C]0.156781934952857[/C][/ROW]
[ROW][C]127[/C][C]2.462[/C][C]2.83746925148891[/C][C]-0.375469251488915[/C][/ROW]
[ROW][C]128[/C][C]2.505[/C][C]2.95937767668005[/C][C]-0.454377676680048[/C][/ROW]
[ROW][C]129[/C][C]2.579[/C][C]2.85115799954385[/C][C]-0.272157999543846[/C][/ROW]
[ROW][C]130[/C][C]2.649[/C][C]2.92893610743094[/C][C]-0.279936107430943[/C][/ROW]
[ROW][C]131[/C][C]2.637[/C][C]2.86283904555191[/C][C]-0.225839045551909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114369&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114369&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
13.032.528862467100140.501137532899857
22.8032.631606639734890.171393360265108
32.7682.678846242516290.0891537574837057
42.8832.652173138200330.230826861799671
52.8632.731825772037690.131174227962309
62.8972.94231092612984-0.0453109261298374
73.0133.20823999158108-0.195239991581082
83.1433.57895333402728-0.435953334027283
93.0333.35529564464951-0.322295644649509
103.0463.42821612369081-0.382216123690813
113.1113.34020458700085-0.229204587000851
123.0134.3083665810325-1.29536658103250
132.9873.24243715217481-0.255437152174809
142.9963.14683270316400-0.150832703163997
152.8332.94397538811202-0.110975388112018
162.8492.91563616907485-0.0666361690748498
172.7952.758213117543540.0367868824564586
182.8452.712719271350990.132280728649011
192.9153.03301895613899-0.118018956138994
202.8933.07113642599380-0.178136425993797
212.6042.77570109006415-0.171701090064148
222.6422.315071101390110.326928898609894
232.661.775382902593600.884617097406401
242.6392.04274332438530.596256675614701
252.722.320743961213300.399256038786698
262.7462.378583234405160.367416765594839
272.7362.419976654556070.316023345443931
282.8122.327544576919110.484455423080892
292.7992.320682429893770.478317570106226
302.5552.290918972301560.264081027698440
312.3052.67672989992180-0.371729899921805
322.2152.66082224946506-0.445822249465058
332.0662.68646593848017-0.620465938480174
341.942.63971624795032-0.699716247950316
352.0422.51314735381025-0.471147353810252
361.9952.34075676879281-0.345756768792811
371.9472.27731516979939-0.330315169799387
381.7662.16750911477274-0.401509114772736
391.6351.94932252581049-0.314322525810488
401.8332.06133507586850-0.228335075868503
411.912.06531652356652-0.155316523566523
421.961.96783418640435-0.00783418640434615
431.972.24318894228212-0.273188942282121
442.0612.40753719200839-0.346537192008392
452.0932.54982783286232-0.456827832862316
462.1212.13125322539307-0.0102532253930704
472.1752.30269572913065-0.127695729130647
482.1972.39521749977699-0.198217499776989
492.352.55283678509818-0.202836785098181
502.442.63824164372342-0.198241643723416
512.4092.54239917372659-0.133399173726592
522.4732.48727537695453-0.0142753769545326
532.4082.41205113061827-0.00405113061826799
542.4552.64208456826422-0.187084568264222
552.4482.8938820671105-0.445882067110501
562.4983.10151427871375-0.60351427871375
572.6463.12993179137956-0.483931791379559
582.7573.0772604088016-0.320260408801600
592.8492.87701643611857-0.0280164361185700
602.9212.853424110705630.0675758892943694
612.9822.889733610969180.0922663890308174
623.0813.015356058129020.0656439418709842
633.1063.10986675820726-0.00386675820725781
643.1192.951037973466970.167962026533034
653.0612.697078527132910.363921472867087
663.0972.739848933397470.357151066602535
673.1623.139190137706570.0228098622934251
683.2573.27800536751436-0.0210053675143613
693.2773.14203210050450.134967899495502
703.2953.172461044194780.122538955805216
713.3642.997993963742540.366006036257458
723.4943.216133603129130.27786639687087
733.6673.384938114063590.282061885936408
743.8133.337432671164820.47556732883518
753.9183.244456292096070.673543707903935
763.8963.328955029161850.567044970838153
773.8013.26815956712910.532840432870902
783.573.462666725696360.107333274303636
793.7023.85764514853185-0.155645148531854
803.8623.96758405557747-0.105584055577467
813.973.866085945398650.103914054601354
824.1393.837928277189910.301071722810089
834.23.671996314150980.52800368584902
844.2913.410510993034830.880489006965169
854.4443.630885058131740.81311494186826
864.5033.664703689178740.838296310821256
874.3573.568464321332140.788535678667865
884.5913.747530460337780.843469539662219
894.6973.634880062617961.06211993738204
904.6213.612281971441691.00871802855831
914.5634.019945289372950.543054710627052
924.2034.007932659482590.195067340517414
934.2963.881305919790740.41469408020926
944.4353.843079336437810.591920663562193
954.1053.652063172416140.452936827583862
964.1173.757124883559860.359875116440137
973.8443.767821405665080.0761785943349186
983.7213.85309626671009-0.132096266710090
993.6743.88954341116678-0.215543411166777
1003.8583.793475126939280.0645248730607213
1013.8013.766847927768530.0341520722314743
1023.5043.86283777376056-0.358837773760565
1033.0334.2358899442194-1.2028899442194
1043.0474.20228495580579-1.15528495580579
1052.9624.02778282004451-1.06578282004451
1062.1983.25381164883254-1.05581164883254
1072.0142.60415942852153-0.590159428521525
1081.8632.22344393493199-0.360443934931989
1091.9052.21079346409249-0.305793464092489
1101.8111.85654968154084-0.0455496815408419
1111.671.8116048573789-0.141604857378901
1121.8641.863937387790016.26122099874546e-05
1132.0521.908936040835420.143063959164578
1142.032.08139440319924-0.0513944031992398
1152.0712.29810846050504-0.227108460505042
1162.2932.64124405962976-0.348244059629764
1172.4432.47677443292429-0.0337744329242888
1182.5132.363142043832080.149857956167918
1192.4672.411768164088730.0552318359112712
1202.5032.329406117524550.173593882475448
1212.542.395993739997040.144006260002959
1222.4832.349320852605600.133679147394396
1232.6262.395544976253310.230455023746685
1242.6562.540696488421710.115303511578291
1252.4472.168369877594700.278630122405305
1262.4672.310218065047140.156781934952857
1272.4622.83746925148891-0.375469251488915
1282.5052.95937767668005-0.454377676680048
1292.5792.85115799954385-0.272157999543846
1302.6492.92893610743094-0.279936107430943
1312.6372.86283904555191-0.225839045551909






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.0008787235816516720.001757447163303340.999121276418348
110.0006153582725018050.001230716545003610.999384641727498
120.0002062655593766060.0004125311187532120.999793734440623
130.0002210070608548180.0004420141217096360.999778992939145
144.71259844848096e-059.42519689696192e-050.999952874015515
159.00616697596201e-061.80123339519240e-050.999990993833024
161.73030505214405e-063.46061010428809e-060.999998269694948
173.65545430783622e-077.31090861567244e-070.99999963445457
181.39548733097121e-072.79097466194241e-070.999999860451267
192.30153054991982e-084.60306109983964e-080.999999976984695
207.47544960303117e-091.49508992060623e-080.99999999252455
213.98377021393674e-087.96754042787349e-080.999999960162298
228.79613583082916e-091.75922716616583e-080.999999991203864
238.67518727119655e-091.73503745423931e-080.999999991324813
242.80595923888905e-095.6119184777781e-090.99999999719404
251.44509635548742e-092.89019271097484e-090.999999998554904
265.04217689203774e-101.00843537840755e-090.999999999495782
271.31953193165719e-102.63906386331437e-100.999999999868047
285.76717683575943e-111.15343536715189e-100.999999999942328
291.76875382461664e-113.53750764923327e-110.999999999982312
301.66760542193812e-113.33521084387624e-110.999999999983324
314.14879514602423e-108.29759029204845e-100.99999999958512
321.18712006636003e-092.37424013272006e-090.99999999881288
336.46080540198683e-091.29216108039737e-080.999999993539195
345.17214660795943e-081.03442932159189e-070.999999948278534
358.67148857002095e-081.73429771400419e-070.999999913285114
367.22392259002392e-081.44478451800478e-070.999999927760774
372.98424106293431e-085.96848212586862e-080.99999997015759
381.17777933364393e-082.35555866728786e-080.999999988222207
394.73820144124059e-099.47640288248118e-090.999999995261799
404.88743918498313e-099.77487836996627e-090.99999999511256
412.84311663633253e-095.68623327266506e-090.999999997156883
421.03235783760647e-082.06471567521294e-080.999999989676422
434.55525824298141e-089.11051648596281e-080.999999954447418
441.21866330095137e-072.43732660190273e-070.99999987813367
451.35009733581721e-072.70019467163442e-070.999999864990266
462.2333647090806e-074.4667294181612e-070.999999776663529
473.06822292808928e-076.13644585617855e-070.999999693177707
484.48516944038307e-078.97033888076613e-070.999999551483056
492.1088194129996e-064.2176388259992e-060.999997891180587
501.47882523602673e-052.95765047205347e-050.99998521174764
515.15177380113457e-050.0001030354760226910.999948482261989
520.0001645657754246140.0003291315508492270.999835434224575
530.000493947460166830.000987894920333660.999506052539833
540.001580628979159260.003161257958318520.99841937102084
550.00474597484770820.00949194969541640.995254025152292
560.0164381060738180.0328762121476360.983561893926182
570.05263957312987540.1052791462597510.947360426870125
580.1010025428360810.2020050856721620.898997457163919
590.1794530092789770.3589060185579540.820546990721023
600.3351388328428990.6702776656857990.664861167157101
610.4986601516482920.9973203032965840.501339848351708
620.652584965653410.694830068693180.34741503434659
630.7739894742031050.452021051593790.226010525796895
640.8575399109843130.2849201780313740.142460089015687
650.8800389529267560.2399220941464870.119961047073244
660.8981778187599180.2036443624801640.101822181240082
670.8770175381004240.2459649237991520.122982461899576
680.8487825272131230.3024349455737540.151217472786877
690.8290550922512930.3418898154974140.170944907748707
700.809639619310930.380720761378140.19036038068907
710.792406147148090.4151877057038190.207593852851909
720.8037194743376160.3925610513247680.196280525662384
730.8728307172580660.2543385654838680.127169282741934
740.8785181919461930.2429636161076130.121481808053807
750.8606921901363250.2786156197273490.139307809863675
760.8644446719807240.2711106560385510.135555328019276
770.8926399593724650.2147200812550700.107360040627535
780.9860359923457470.02792801530850610.0139640076542530
790.9960124250822560.007975149835487760.00398757491774388
800.9976255603597990.004748879280403060.00237443964020153
810.998057615734920.003884768530159680.00194238426507984
820.9994608149342720.001078370131456450.000539185065728224
830.9998325340682880.0003349318634239220.000167465931711961
840.9998101586297650.0003796827404697610.000189841370234880
850.9998392224007740.0003215551984523340.000160777599226167
860.9997976274138530.0004047451722936660.000202372586146833
870.9996573053147630.0006853893704742970.000342694685237149
880.9994683656329340.001063268734132120.000531634367066058
890.9993995418613480.001200916277304130.000600458138652066
900.9991344513688370.001731097262325020.000865548631162511
910.999519452117940.000961095764120440.00048054788206022
920.9993183499889280.001363300022143660.00068165001107183
930.999077331066340.001845337867319350.000922668933659677
940.9992268540417980.001546291916403550.000773145958201775
950.9996102050033090.000779589993382410.000389794996691205
960.9998315902833220.0003368194333562230.000168409716678112
970.9999104462572320.0001791074855361828.95537427680908e-05
980.9999535949586249.28100827521577e-054.64050413760788e-05
990.9999719335275225.6132944955891e-052.80664724779455e-05
1000.999990822473861.83550522807422e-059.1775261403711e-06
1010.999999528450779.43098461443915e-074.71549230721957e-07
1020.9999997890304334.21939134837489e-072.10969567418744e-07
1030.999999965764476.84710584228158e-083.42355292114079e-08
1040.999999953399899.32002214404243e-084.66001107202121e-08
1050.9999999575205748.49588514838488e-084.24794257419244e-08
1060.9999999899344532.01310944525951e-081.00655472262975e-08
1070.9999999815247163.69505688458387e-081.84752844229194e-08
1080.999999943303891.13392221562619e-075.66961107813096e-08
1090.999999935952871.28094260290691e-076.40471301453456e-08
1100.9999999362730681.27453864478889e-076.37269322394447e-08
1110.999999711115345.77769320118724e-072.88884660059362e-07
1120.9999988050021482.38999570331684e-061.19499785165842e-06
1130.999998962434812.07513037799125e-061.03756518899562e-06
1140.9999965466191716.90676165800907e-063.45338082900453e-06
1150.9999987042388162.59152236717197e-061.29576118358599e-06
1160.9999999368713171.26257366764203e-076.31286833821014e-08
1170.9999992976357761.40472844721956e-067.02364223609781e-07
1180.9999953254794439.34904111482786e-064.67452055741393e-06
1190.999979381510324.12369793587486e-052.06184896793743e-05
1200.9997708010464040.0004583979071911990.000229198953595599
1210.9979426727991350.004114654401729240.00205732720086462

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.000878723581651672 & 0.00175744716330334 & 0.999121276418348 \tabularnewline
11 & 0.000615358272501805 & 0.00123071654500361 & 0.999384641727498 \tabularnewline
12 & 0.000206265559376606 & 0.000412531118753212 & 0.999793734440623 \tabularnewline
13 & 0.000221007060854818 & 0.000442014121709636 & 0.999778992939145 \tabularnewline
14 & 4.71259844848096e-05 & 9.42519689696192e-05 & 0.999952874015515 \tabularnewline
15 & 9.00616697596201e-06 & 1.80123339519240e-05 & 0.999990993833024 \tabularnewline
16 & 1.73030505214405e-06 & 3.46061010428809e-06 & 0.999998269694948 \tabularnewline
17 & 3.65545430783622e-07 & 7.31090861567244e-07 & 0.99999963445457 \tabularnewline
18 & 1.39548733097121e-07 & 2.79097466194241e-07 & 0.999999860451267 \tabularnewline
19 & 2.30153054991982e-08 & 4.60306109983964e-08 & 0.999999976984695 \tabularnewline
20 & 7.47544960303117e-09 & 1.49508992060623e-08 & 0.99999999252455 \tabularnewline
21 & 3.98377021393674e-08 & 7.96754042787349e-08 & 0.999999960162298 \tabularnewline
22 & 8.79613583082916e-09 & 1.75922716616583e-08 & 0.999999991203864 \tabularnewline
23 & 8.67518727119655e-09 & 1.73503745423931e-08 & 0.999999991324813 \tabularnewline
24 & 2.80595923888905e-09 & 5.6119184777781e-09 & 0.99999999719404 \tabularnewline
25 & 1.44509635548742e-09 & 2.89019271097484e-09 & 0.999999998554904 \tabularnewline
26 & 5.04217689203774e-10 & 1.00843537840755e-09 & 0.999999999495782 \tabularnewline
27 & 1.31953193165719e-10 & 2.63906386331437e-10 & 0.999999999868047 \tabularnewline
28 & 5.76717683575943e-11 & 1.15343536715189e-10 & 0.999999999942328 \tabularnewline
29 & 1.76875382461664e-11 & 3.53750764923327e-11 & 0.999999999982312 \tabularnewline
30 & 1.66760542193812e-11 & 3.33521084387624e-11 & 0.999999999983324 \tabularnewline
31 & 4.14879514602423e-10 & 8.29759029204845e-10 & 0.99999999958512 \tabularnewline
32 & 1.18712006636003e-09 & 2.37424013272006e-09 & 0.99999999881288 \tabularnewline
33 & 6.46080540198683e-09 & 1.29216108039737e-08 & 0.999999993539195 \tabularnewline
34 & 5.17214660795943e-08 & 1.03442932159189e-07 & 0.999999948278534 \tabularnewline
35 & 8.67148857002095e-08 & 1.73429771400419e-07 & 0.999999913285114 \tabularnewline
36 & 7.22392259002392e-08 & 1.44478451800478e-07 & 0.999999927760774 \tabularnewline
37 & 2.98424106293431e-08 & 5.96848212586862e-08 & 0.99999997015759 \tabularnewline
38 & 1.17777933364393e-08 & 2.35555866728786e-08 & 0.999999988222207 \tabularnewline
39 & 4.73820144124059e-09 & 9.47640288248118e-09 & 0.999999995261799 \tabularnewline
40 & 4.88743918498313e-09 & 9.77487836996627e-09 & 0.99999999511256 \tabularnewline
41 & 2.84311663633253e-09 & 5.68623327266506e-09 & 0.999999997156883 \tabularnewline
42 & 1.03235783760647e-08 & 2.06471567521294e-08 & 0.999999989676422 \tabularnewline
43 & 4.55525824298141e-08 & 9.11051648596281e-08 & 0.999999954447418 \tabularnewline
44 & 1.21866330095137e-07 & 2.43732660190273e-07 & 0.99999987813367 \tabularnewline
45 & 1.35009733581721e-07 & 2.70019467163442e-07 & 0.999999864990266 \tabularnewline
46 & 2.2333647090806e-07 & 4.4667294181612e-07 & 0.999999776663529 \tabularnewline
47 & 3.06822292808928e-07 & 6.13644585617855e-07 & 0.999999693177707 \tabularnewline
48 & 4.48516944038307e-07 & 8.97033888076613e-07 & 0.999999551483056 \tabularnewline
49 & 2.1088194129996e-06 & 4.2176388259992e-06 & 0.999997891180587 \tabularnewline
50 & 1.47882523602673e-05 & 2.95765047205347e-05 & 0.99998521174764 \tabularnewline
51 & 5.15177380113457e-05 & 0.000103035476022691 & 0.999948482261989 \tabularnewline
52 & 0.000164565775424614 & 0.000329131550849227 & 0.999835434224575 \tabularnewline
53 & 0.00049394746016683 & 0.00098789492033366 & 0.999506052539833 \tabularnewline
54 & 0.00158062897915926 & 0.00316125795831852 & 0.99841937102084 \tabularnewline
55 & 0.0047459748477082 & 0.0094919496954164 & 0.995254025152292 \tabularnewline
56 & 0.016438106073818 & 0.032876212147636 & 0.983561893926182 \tabularnewline
57 & 0.0526395731298754 & 0.105279146259751 & 0.947360426870125 \tabularnewline
58 & 0.101002542836081 & 0.202005085672162 & 0.898997457163919 \tabularnewline
59 & 0.179453009278977 & 0.358906018557954 & 0.820546990721023 \tabularnewline
60 & 0.335138832842899 & 0.670277665685799 & 0.664861167157101 \tabularnewline
61 & 0.498660151648292 & 0.997320303296584 & 0.501339848351708 \tabularnewline
62 & 0.65258496565341 & 0.69483006869318 & 0.34741503434659 \tabularnewline
63 & 0.773989474203105 & 0.45202105159379 & 0.226010525796895 \tabularnewline
64 & 0.857539910984313 & 0.284920178031374 & 0.142460089015687 \tabularnewline
65 & 0.880038952926756 & 0.239922094146487 & 0.119961047073244 \tabularnewline
66 & 0.898177818759918 & 0.203644362480164 & 0.101822181240082 \tabularnewline
67 & 0.877017538100424 & 0.245964923799152 & 0.122982461899576 \tabularnewline
68 & 0.848782527213123 & 0.302434945573754 & 0.151217472786877 \tabularnewline
69 & 0.829055092251293 & 0.341889815497414 & 0.170944907748707 \tabularnewline
70 & 0.80963961931093 & 0.38072076137814 & 0.19036038068907 \tabularnewline
71 & 0.79240614714809 & 0.415187705703819 & 0.207593852851909 \tabularnewline
72 & 0.803719474337616 & 0.392561051324768 & 0.196280525662384 \tabularnewline
73 & 0.872830717258066 & 0.254338565483868 & 0.127169282741934 \tabularnewline
74 & 0.878518191946193 & 0.242963616107613 & 0.121481808053807 \tabularnewline
75 & 0.860692190136325 & 0.278615619727349 & 0.139307809863675 \tabularnewline
76 & 0.864444671980724 & 0.271110656038551 & 0.135555328019276 \tabularnewline
77 & 0.892639959372465 & 0.214720081255070 & 0.107360040627535 \tabularnewline
78 & 0.986035992345747 & 0.0279280153085061 & 0.0139640076542530 \tabularnewline
79 & 0.996012425082256 & 0.00797514983548776 & 0.00398757491774388 \tabularnewline
80 & 0.997625560359799 & 0.00474887928040306 & 0.00237443964020153 \tabularnewline
81 & 0.99805761573492 & 0.00388476853015968 & 0.00194238426507984 \tabularnewline
82 & 0.999460814934272 & 0.00107837013145645 & 0.000539185065728224 \tabularnewline
83 & 0.999832534068288 & 0.000334931863423922 & 0.000167465931711961 \tabularnewline
84 & 0.999810158629765 & 0.000379682740469761 & 0.000189841370234880 \tabularnewline
85 & 0.999839222400774 & 0.000321555198452334 & 0.000160777599226167 \tabularnewline
86 & 0.999797627413853 & 0.000404745172293666 & 0.000202372586146833 \tabularnewline
87 & 0.999657305314763 & 0.000685389370474297 & 0.000342694685237149 \tabularnewline
88 & 0.999468365632934 & 0.00106326873413212 & 0.000531634367066058 \tabularnewline
89 & 0.999399541861348 & 0.00120091627730413 & 0.000600458138652066 \tabularnewline
90 & 0.999134451368837 & 0.00173109726232502 & 0.000865548631162511 \tabularnewline
91 & 0.99951945211794 & 0.00096109576412044 & 0.00048054788206022 \tabularnewline
92 & 0.999318349988928 & 0.00136330002214366 & 0.00068165001107183 \tabularnewline
93 & 0.99907733106634 & 0.00184533786731935 & 0.000922668933659677 \tabularnewline
94 & 0.999226854041798 & 0.00154629191640355 & 0.000773145958201775 \tabularnewline
95 & 0.999610205003309 & 0.00077958999338241 & 0.000389794996691205 \tabularnewline
96 & 0.999831590283322 & 0.000336819433356223 & 0.000168409716678112 \tabularnewline
97 & 0.999910446257232 & 0.000179107485536182 & 8.95537427680908e-05 \tabularnewline
98 & 0.999953594958624 & 9.28100827521577e-05 & 4.64050413760788e-05 \tabularnewline
99 & 0.999971933527522 & 5.6132944955891e-05 & 2.80664724779455e-05 \tabularnewline
100 & 0.99999082247386 & 1.83550522807422e-05 & 9.1775261403711e-06 \tabularnewline
101 & 0.99999952845077 & 9.43098461443915e-07 & 4.71549230721957e-07 \tabularnewline
102 & 0.999999789030433 & 4.21939134837489e-07 & 2.10969567418744e-07 \tabularnewline
103 & 0.99999996576447 & 6.84710584228158e-08 & 3.42355292114079e-08 \tabularnewline
104 & 0.99999995339989 & 9.32002214404243e-08 & 4.66001107202121e-08 \tabularnewline
105 & 0.999999957520574 & 8.49588514838488e-08 & 4.24794257419244e-08 \tabularnewline
106 & 0.999999989934453 & 2.01310944525951e-08 & 1.00655472262975e-08 \tabularnewline
107 & 0.999999981524716 & 3.69505688458387e-08 & 1.84752844229194e-08 \tabularnewline
108 & 0.99999994330389 & 1.13392221562619e-07 & 5.66961107813096e-08 \tabularnewline
109 & 0.99999993595287 & 1.28094260290691e-07 & 6.40471301453456e-08 \tabularnewline
110 & 0.999999936273068 & 1.27453864478889e-07 & 6.37269322394447e-08 \tabularnewline
111 & 0.99999971111534 & 5.77769320118724e-07 & 2.88884660059362e-07 \tabularnewline
112 & 0.999998805002148 & 2.38999570331684e-06 & 1.19499785165842e-06 \tabularnewline
113 & 0.99999896243481 & 2.07513037799125e-06 & 1.03756518899562e-06 \tabularnewline
114 & 0.999996546619171 & 6.90676165800907e-06 & 3.45338082900453e-06 \tabularnewline
115 & 0.999998704238816 & 2.59152236717197e-06 & 1.29576118358599e-06 \tabularnewline
116 & 0.999999936871317 & 1.26257366764203e-07 & 6.31286833821014e-08 \tabularnewline
117 & 0.999999297635776 & 1.40472844721956e-06 & 7.02364223609781e-07 \tabularnewline
118 & 0.999995325479443 & 9.34904111482786e-06 & 4.67452055741393e-06 \tabularnewline
119 & 0.99997938151032 & 4.12369793587486e-05 & 2.06184896793743e-05 \tabularnewline
120 & 0.999770801046404 & 0.000458397907191199 & 0.000229198953595599 \tabularnewline
121 & 0.997942672799135 & 0.00411465440172924 & 0.00205732720086462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114369&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]10[/C][C]0.000878723581651672[/C][C]0.00175744716330334[/C][C]0.999121276418348[/C][/ROW]
[ROW][C]11[/C][C]0.000615358272501805[/C][C]0.00123071654500361[/C][C]0.999384641727498[/C][/ROW]
[ROW][C]12[/C][C]0.000206265559376606[/C][C]0.000412531118753212[/C][C]0.999793734440623[/C][/ROW]
[ROW][C]13[/C][C]0.000221007060854818[/C][C]0.000442014121709636[/C][C]0.999778992939145[/C][/ROW]
[ROW][C]14[/C][C]4.71259844848096e-05[/C][C]9.42519689696192e-05[/C][C]0.999952874015515[/C][/ROW]
[ROW][C]15[/C][C]9.00616697596201e-06[/C][C]1.80123339519240e-05[/C][C]0.999990993833024[/C][/ROW]
[ROW][C]16[/C][C]1.73030505214405e-06[/C][C]3.46061010428809e-06[/C][C]0.999998269694948[/C][/ROW]
[ROW][C]17[/C][C]3.65545430783622e-07[/C][C]7.31090861567244e-07[/C][C]0.99999963445457[/C][/ROW]
[ROW][C]18[/C][C]1.39548733097121e-07[/C][C]2.79097466194241e-07[/C][C]0.999999860451267[/C][/ROW]
[ROW][C]19[/C][C]2.30153054991982e-08[/C][C]4.60306109983964e-08[/C][C]0.999999976984695[/C][/ROW]
[ROW][C]20[/C][C]7.47544960303117e-09[/C][C]1.49508992060623e-08[/C][C]0.99999999252455[/C][/ROW]
[ROW][C]21[/C][C]3.98377021393674e-08[/C][C]7.96754042787349e-08[/C][C]0.999999960162298[/C][/ROW]
[ROW][C]22[/C][C]8.79613583082916e-09[/C][C]1.75922716616583e-08[/C][C]0.999999991203864[/C][/ROW]
[ROW][C]23[/C][C]8.67518727119655e-09[/C][C]1.73503745423931e-08[/C][C]0.999999991324813[/C][/ROW]
[ROW][C]24[/C][C]2.80595923888905e-09[/C][C]5.6119184777781e-09[/C][C]0.99999999719404[/C][/ROW]
[ROW][C]25[/C][C]1.44509635548742e-09[/C][C]2.89019271097484e-09[/C][C]0.999999998554904[/C][/ROW]
[ROW][C]26[/C][C]5.04217689203774e-10[/C][C]1.00843537840755e-09[/C][C]0.999999999495782[/C][/ROW]
[ROW][C]27[/C][C]1.31953193165719e-10[/C][C]2.63906386331437e-10[/C][C]0.999999999868047[/C][/ROW]
[ROW][C]28[/C][C]5.76717683575943e-11[/C][C]1.15343536715189e-10[/C][C]0.999999999942328[/C][/ROW]
[ROW][C]29[/C][C]1.76875382461664e-11[/C][C]3.53750764923327e-11[/C][C]0.999999999982312[/C][/ROW]
[ROW][C]30[/C][C]1.66760542193812e-11[/C][C]3.33521084387624e-11[/C][C]0.999999999983324[/C][/ROW]
[ROW][C]31[/C][C]4.14879514602423e-10[/C][C]8.29759029204845e-10[/C][C]0.99999999958512[/C][/ROW]
[ROW][C]32[/C][C]1.18712006636003e-09[/C][C]2.37424013272006e-09[/C][C]0.99999999881288[/C][/ROW]
[ROW][C]33[/C][C]6.46080540198683e-09[/C][C]1.29216108039737e-08[/C][C]0.999999993539195[/C][/ROW]
[ROW][C]34[/C][C]5.17214660795943e-08[/C][C]1.03442932159189e-07[/C][C]0.999999948278534[/C][/ROW]
[ROW][C]35[/C][C]8.67148857002095e-08[/C][C]1.73429771400419e-07[/C][C]0.999999913285114[/C][/ROW]
[ROW][C]36[/C][C]7.22392259002392e-08[/C][C]1.44478451800478e-07[/C][C]0.999999927760774[/C][/ROW]
[ROW][C]37[/C][C]2.98424106293431e-08[/C][C]5.96848212586862e-08[/C][C]0.99999997015759[/C][/ROW]
[ROW][C]38[/C][C]1.17777933364393e-08[/C][C]2.35555866728786e-08[/C][C]0.999999988222207[/C][/ROW]
[ROW][C]39[/C][C]4.73820144124059e-09[/C][C]9.47640288248118e-09[/C][C]0.999999995261799[/C][/ROW]
[ROW][C]40[/C][C]4.88743918498313e-09[/C][C]9.77487836996627e-09[/C][C]0.99999999511256[/C][/ROW]
[ROW][C]41[/C][C]2.84311663633253e-09[/C][C]5.68623327266506e-09[/C][C]0.999999997156883[/C][/ROW]
[ROW][C]42[/C][C]1.03235783760647e-08[/C][C]2.06471567521294e-08[/C][C]0.999999989676422[/C][/ROW]
[ROW][C]43[/C][C]4.55525824298141e-08[/C][C]9.11051648596281e-08[/C][C]0.999999954447418[/C][/ROW]
[ROW][C]44[/C][C]1.21866330095137e-07[/C][C]2.43732660190273e-07[/C][C]0.99999987813367[/C][/ROW]
[ROW][C]45[/C][C]1.35009733581721e-07[/C][C]2.70019467163442e-07[/C][C]0.999999864990266[/C][/ROW]
[ROW][C]46[/C][C]2.2333647090806e-07[/C][C]4.4667294181612e-07[/C][C]0.999999776663529[/C][/ROW]
[ROW][C]47[/C][C]3.06822292808928e-07[/C][C]6.13644585617855e-07[/C][C]0.999999693177707[/C][/ROW]
[ROW][C]48[/C][C]4.48516944038307e-07[/C][C]8.97033888076613e-07[/C][C]0.999999551483056[/C][/ROW]
[ROW][C]49[/C][C]2.1088194129996e-06[/C][C]4.2176388259992e-06[/C][C]0.999997891180587[/C][/ROW]
[ROW][C]50[/C][C]1.47882523602673e-05[/C][C]2.95765047205347e-05[/C][C]0.99998521174764[/C][/ROW]
[ROW][C]51[/C][C]5.15177380113457e-05[/C][C]0.000103035476022691[/C][C]0.999948482261989[/C][/ROW]
[ROW][C]52[/C][C]0.000164565775424614[/C][C]0.000329131550849227[/C][C]0.999835434224575[/C][/ROW]
[ROW][C]53[/C][C]0.00049394746016683[/C][C]0.00098789492033366[/C][C]0.999506052539833[/C][/ROW]
[ROW][C]54[/C][C]0.00158062897915926[/C][C]0.00316125795831852[/C][C]0.99841937102084[/C][/ROW]
[ROW][C]55[/C][C]0.0047459748477082[/C][C]0.0094919496954164[/C][C]0.995254025152292[/C][/ROW]
[ROW][C]56[/C][C]0.016438106073818[/C][C]0.032876212147636[/C][C]0.983561893926182[/C][/ROW]
[ROW][C]57[/C][C]0.0526395731298754[/C][C]0.105279146259751[/C][C]0.947360426870125[/C][/ROW]
[ROW][C]58[/C][C]0.101002542836081[/C][C]0.202005085672162[/C][C]0.898997457163919[/C][/ROW]
[ROW][C]59[/C][C]0.179453009278977[/C][C]0.358906018557954[/C][C]0.820546990721023[/C][/ROW]
[ROW][C]60[/C][C]0.335138832842899[/C][C]0.670277665685799[/C][C]0.664861167157101[/C][/ROW]
[ROW][C]61[/C][C]0.498660151648292[/C][C]0.997320303296584[/C][C]0.501339848351708[/C][/ROW]
[ROW][C]62[/C][C]0.65258496565341[/C][C]0.69483006869318[/C][C]0.34741503434659[/C][/ROW]
[ROW][C]63[/C][C]0.773989474203105[/C][C]0.45202105159379[/C][C]0.226010525796895[/C][/ROW]
[ROW][C]64[/C][C]0.857539910984313[/C][C]0.284920178031374[/C][C]0.142460089015687[/C][/ROW]
[ROW][C]65[/C][C]0.880038952926756[/C][C]0.239922094146487[/C][C]0.119961047073244[/C][/ROW]
[ROW][C]66[/C][C]0.898177818759918[/C][C]0.203644362480164[/C][C]0.101822181240082[/C][/ROW]
[ROW][C]67[/C][C]0.877017538100424[/C][C]0.245964923799152[/C][C]0.122982461899576[/C][/ROW]
[ROW][C]68[/C][C]0.848782527213123[/C][C]0.302434945573754[/C][C]0.151217472786877[/C][/ROW]
[ROW][C]69[/C][C]0.829055092251293[/C][C]0.341889815497414[/C][C]0.170944907748707[/C][/ROW]
[ROW][C]70[/C][C]0.80963961931093[/C][C]0.38072076137814[/C][C]0.19036038068907[/C][/ROW]
[ROW][C]71[/C][C]0.79240614714809[/C][C]0.415187705703819[/C][C]0.207593852851909[/C][/ROW]
[ROW][C]72[/C][C]0.803719474337616[/C][C]0.392561051324768[/C][C]0.196280525662384[/C][/ROW]
[ROW][C]73[/C][C]0.872830717258066[/C][C]0.254338565483868[/C][C]0.127169282741934[/C][/ROW]
[ROW][C]74[/C][C]0.878518191946193[/C][C]0.242963616107613[/C][C]0.121481808053807[/C][/ROW]
[ROW][C]75[/C][C]0.860692190136325[/C][C]0.278615619727349[/C][C]0.139307809863675[/C][/ROW]
[ROW][C]76[/C][C]0.864444671980724[/C][C]0.271110656038551[/C][C]0.135555328019276[/C][/ROW]
[ROW][C]77[/C][C]0.892639959372465[/C][C]0.214720081255070[/C][C]0.107360040627535[/C][/ROW]
[ROW][C]78[/C][C]0.986035992345747[/C][C]0.0279280153085061[/C][C]0.0139640076542530[/C][/ROW]
[ROW][C]79[/C][C]0.996012425082256[/C][C]0.00797514983548776[/C][C]0.00398757491774388[/C][/ROW]
[ROW][C]80[/C][C]0.997625560359799[/C][C]0.00474887928040306[/C][C]0.00237443964020153[/C][/ROW]
[ROW][C]81[/C][C]0.99805761573492[/C][C]0.00388476853015968[/C][C]0.00194238426507984[/C][/ROW]
[ROW][C]82[/C][C]0.999460814934272[/C][C]0.00107837013145645[/C][C]0.000539185065728224[/C][/ROW]
[ROW][C]83[/C][C]0.999832534068288[/C][C]0.000334931863423922[/C][C]0.000167465931711961[/C][/ROW]
[ROW][C]84[/C][C]0.999810158629765[/C][C]0.000379682740469761[/C][C]0.000189841370234880[/C][/ROW]
[ROW][C]85[/C][C]0.999839222400774[/C][C]0.000321555198452334[/C][C]0.000160777599226167[/C][/ROW]
[ROW][C]86[/C][C]0.999797627413853[/C][C]0.000404745172293666[/C][C]0.000202372586146833[/C][/ROW]
[ROW][C]87[/C][C]0.999657305314763[/C][C]0.000685389370474297[/C][C]0.000342694685237149[/C][/ROW]
[ROW][C]88[/C][C]0.999468365632934[/C][C]0.00106326873413212[/C][C]0.000531634367066058[/C][/ROW]
[ROW][C]89[/C][C]0.999399541861348[/C][C]0.00120091627730413[/C][C]0.000600458138652066[/C][/ROW]
[ROW][C]90[/C][C]0.999134451368837[/C][C]0.00173109726232502[/C][C]0.000865548631162511[/C][/ROW]
[ROW][C]91[/C][C]0.99951945211794[/C][C]0.00096109576412044[/C][C]0.00048054788206022[/C][/ROW]
[ROW][C]92[/C][C]0.999318349988928[/C][C]0.00136330002214366[/C][C]0.00068165001107183[/C][/ROW]
[ROW][C]93[/C][C]0.99907733106634[/C][C]0.00184533786731935[/C][C]0.000922668933659677[/C][/ROW]
[ROW][C]94[/C][C]0.999226854041798[/C][C]0.00154629191640355[/C][C]0.000773145958201775[/C][/ROW]
[ROW][C]95[/C][C]0.999610205003309[/C][C]0.00077958999338241[/C][C]0.000389794996691205[/C][/ROW]
[ROW][C]96[/C][C]0.999831590283322[/C][C]0.000336819433356223[/C][C]0.000168409716678112[/C][/ROW]
[ROW][C]97[/C][C]0.999910446257232[/C][C]0.000179107485536182[/C][C]8.95537427680908e-05[/C][/ROW]
[ROW][C]98[/C][C]0.999953594958624[/C][C]9.28100827521577e-05[/C][C]4.64050413760788e-05[/C][/ROW]
[ROW][C]99[/C][C]0.999971933527522[/C][C]5.6132944955891e-05[/C][C]2.80664724779455e-05[/C][/ROW]
[ROW][C]100[/C][C]0.99999082247386[/C][C]1.83550522807422e-05[/C][C]9.1775261403711e-06[/C][/ROW]
[ROW][C]101[/C][C]0.99999952845077[/C][C]9.43098461443915e-07[/C][C]4.71549230721957e-07[/C][/ROW]
[ROW][C]102[/C][C]0.999999789030433[/C][C]4.21939134837489e-07[/C][C]2.10969567418744e-07[/C][/ROW]
[ROW][C]103[/C][C]0.99999996576447[/C][C]6.84710584228158e-08[/C][C]3.42355292114079e-08[/C][/ROW]
[ROW][C]104[/C][C]0.99999995339989[/C][C]9.32002214404243e-08[/C][C]4.66001107202121e-08[/C][/ROW]
[ROW][C]105[/C][C]0.999999957520574[/C][C]8.49588514838488e-08[/C][C]4.24794257419244e-08[/C][/ROW]
[ROW][C]106[/C][C]0.999999989934453[/C][C]2.01310944525951e-08[/C][C]1.00655472262975e-08[/C][/ROW]
[ROW][C]107[/C][C]0.999999981524716[/C][C]3.69505688458387e-08[/C][C]1.84752844229194e-08[/C][/ROW]
[ROW][C]108[/C][C]0.99999994330389[/C][C]1.13392221562619e-07[/C][C]5.66961107813096e-08[/C][/ROW]
[ROW][C]109[/C][C]0.99999993595287[/C][C]1.28094260290691e-07[/C][C]6.40471301453456e-08[/C][/ROW]
[ROW][C]110[/C][C]0.999999936273068[/C][C]1.27453864478889e-07[/C][C]6.37269322394447e-08[/C][/ROW]
[ROW][C]111[/C][C]0.99999971111534[/C][C]5.77769320118724e-07[/C][C]2.88884660059362e-07[/C][/ROW]
[ROW][C]112[/C][C]0.999998805002148[/C][C]2.38999570331684e-06[/C][C]1.19499785165842e-06[/C][/ROW]
[ROW][C]113[/C][C]0.99999896243481[/C][C]2.07513037799125e-06[/C][C]1.03756518899562e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999996546619171[/C][C]6.90676165800907e-06[/C][C]3.45338082900453e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999998704238816[/C][C]2.59152236717197e-06[/C][C]1.29576118358599e-06[/C][/ROW]
[ROW][C]116[/C][C]0.999999936871317[/C][C]1.26257366764203e-07[/C][C]6.31286833821014e-08[/C][/ROW]
[ROW][C]117[/C][C]0.999999297635776[/C][C]1.40472844721956e-06[/C][C]7.02364223609781e-07[/C][/ROW]
[ROW][C]118[/C][C]0.999995325479443[/C][C]9.34904111482786e-06[/C][C]4.67452055741393e-06[/C][/ROW]
[ROW][C]119[/C][C]0.99997938151032[/C][C]4.12369793587486e-05[/C][C]2.06184896793743e-05[/C][/ROW]
[ROW][C]120[/C][C]0.999770801046404[/C][C]0.000458397907191199[/C][C]0.000229198953595599[/C][/ROW]
[ROW][C]121[/C][C]0.997942672799135[/C][C]0.00411465440172924[/C][C]0.00205732720086462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114369&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114369&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
100.0008787235816516720.001757447163303340.999121276418348
110.0006153582725018050.001230716545003610.999384641727498
120.0002062655593766060.0004125311187532120.999793734440623
130.0002210070608548180.0004420141217096360.999778992939145
144.71259844848096e-059.42519689696192e-050.999952874015515
159.00616697596201e-061.80123339519240e-050.999990993833024
161.73030505214405e-063.46061010428809e-060.999998269694948
173.65545430783622e-077.31090861567244e-070.99999963445457
181.39548733097121e-072.79097466194241e-070.999999860451267
192.30153054991982e-084.60306109983964e-080.999999976984695
207.47544960303117e-091.49508992060623e-080.99999999252455
213.98377021393674e-087.96754042787349e-080.999999960162298
228.79613583082916e-091.75922716616583e-080.999999991203864
238.67518727119655e-091.73503745423931e-080.999999991324813
242.80595923888905e-095.6119184777781e-090.99999999719404
251.44509635548742e-092.89019271097484e-090.999999998554904
265.04217689203774e-101.00843537840755e-090.999999999495782
271.31953193165719e-102.63906386331437e-100.999999999868047
285.76717683575943e-111.15343536715189e-100.999999999942328
291.76875382461664e-113.53750764923327e-110.999999999982312
301.66760542193812e-113.33521084387624e-110.999999999983324
314.14879514602423e-108.29759029204845e-100.99999999958512
321.18712006636003e-092.37424013272006e-090.99999999881288
336.46080540198683e-091.29216108039737e-080.999999993539195
345.17214660795943e-081.03442932159189e-070.999999948278534
358.67148857002095e-081.73429771400419e-070.999999913285114
367.22392259002392e-081.44478451800478e-070.999999927760774
372.98424106293431e-085.96848212586862e-080.99999997015759
381.17777933364393e-082.35555866728786e-080.999999988222207
394.73820144124059e-099.47640288248118e-090.999999995261799
404.88743918498313e-099.77487836996627e-090.99999999511256
412.84311663633253e-095.68623327266506e-090.999999997156883
421.03235783760647e-082.06471567521294e-080.999999989676422
434.55525824298141e-089.11051648596281e-080.999999954447418
441.21866330095137e-072.43732660190273e-070.99999987813367
451.35009733581721e-072.70019467163442e-070.999999864990266
462.2333647090806e-074.4667294181612e-070.999999776663529
473.06822292808928e-076.13644585617855e-070.999999693177707
484.48516944038307e-078.97033888076613e-070.999999551483056
492.1088194129996e-064.2176388259992e-060.999997891180587
501.47882523602673e-052.95765047205347e-050.99998521174764
515.15177380113457e-050.0001030354760226910.999948482261989
520.0001645657754246140.0003291315508492270.999835434224575
530.000493947460166830.000987894920333660.999506052539833
540.001580628979159260.003161257958318520.99841937102084
550.00474597484770820.00949194969541640.995254025152292
560.0164381060738180.0328762121476360.983561893926182
570.05263957312987540.1052791462597510.947360426870125
580.1010025428360810.2020050856721620.898997457163919
590.1794530092789770.3589060185579540.820546990721023
600.3351388328428990.6702776656857990.664861167157101
610.4986601516482920.9973203032965840.501339848351708
620.652584965653410.694830068693180.34741503434659
630.7739894742031050.452021051593790.226010525796895
640.8575399109843130.2849201780313740.142460089015687
650.8800389529267560.2399220941464870.119961047073244
660.8981778187599180.2036443624801640.101822181240082
670.8770175381004240.2459649237991520.122982461899576
680.8487825272131230.3024349455737540.151217472786877
690.8290550922512930.3418898154974140.170944907748707
700.809639619310930.380720761378140.19036038068907
710.792406147148090.4151877057038190.207593852851909
720.8037194743376160.3925610513247680.196280525662384
730.8728307172580660.2543385654838680.127169282741934
740.8785181919461930.2429636161076130.121481808053807
750.8606921901363250.2786156197273490.139307809863675
760.8644446719807240.2711106560385510.135555328019276
770.8926399593724650.2147200812550700.107360040627535
780.9860359923457470.02792801530850610.0139640076542530
790.9960124250822560.007975149835487760.00398757491774388
800.9976255603597990.004748879280403060.00237443964020153
810.998057615734920.003884768530159680.00194238426507984
820.9994608149342720.001078370131456450.000539185065728224
830.9998325340682880.0003349318634239220.000167465931711961
840.9998101586297650.0003796827404697610.000189841370234880
850.9998392224007740.0003215551984523340.000160777599226167
860.9997976274138530.0004047451722936660.000202372586146833
870.9996573053147630.0006853893704742970.000342694685237149
880.9994683656329340.001063268734132120.000531634367066058
890.9993995418613480.001200916277304130.000600458138652066
900.9991344513688370.001731097262325020.000865548631162511
910.999519452117940.000961095764120440.00048054788206022
920.9993183499889280.001363300022143660.00068165001107183
930.999077331066340.001845337867319350.000922668933659677
940.9992268540417980.001546291916403550.000773145958201775
950.9996102050033090.000779589993382410.000389794996691205
960.9998315902833220.0003368194333562230.000168409716678112
970.9999104462572320.0001791074855361828.95537427680908e-05
980.9999535949586249.28100827521577e-054.64050413760788e-05
990.9999719335275225.6132944955891e-052.80664724779455e-05
1000.999990822473861.83550522807422e-059.1775261403711e-06
1010.999999528450779.43098461443915e-074.71549230721957e-07
1020.9999997890304334.21939134837489e-072.10969567418744e-07
1030.999999965764476.84710584228158e-083.42355292114079e-08
1040.999999953399899.32002214404243e-084.66001107202121e-08
1050.9999999575205748.49588514838488e-084.24794257419244e-08
1060.9999999899344532.01310944525951e-081.00655472262975e-08
1070.9999999815247163.69505688458387e-081.84752844229194e-08
1080.999999943303891.13392221562619e-075.66961107813096e-08
1090.999999935952871.28094260290691e-076.40471301453456e-08
1100.9999999362730681.27453864478889e-076.37269322394447e-08
1110.999999711115345.77769320118724e-072.88884660059362e-07
1120.9999988050021482.38999570331684e-061.19499785165842e-06
1130.999998962434812.07513037799125e-061.03756518899562e-06
1140.9999965466191716.90676165800907e-063.45338082900453e-06
1150.9999987042388162.59152236717197e-061.29576118358599e-06
1160.9999999368713171.26257366764203e-076.31286833821014e-08
1170.9999992976357761.40472844721956e-067.02364223609781e-07
1180.9999953254794439.34904111482786e-064.67452055741393e-06
1190.999979381510324.12369793587486e-052.06184896793743e-05
1200.9997708010464040.0004583979071911990.000229198953595599
1210.9979426727991350.004114654401729240.00205732720086462






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level890.794642857142857NOK
5% type I error level910.8125NOK
10% type I error level910.8125NOK

\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 & 89 & 0.794642857142857 & NOK \tabularnewline
5% type I error level & 91 & 0.8125 & NOK \tabularnewline
10% type I error level & 91 & 0.8125 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114369&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]89[/C][C]0.794642857142857[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]91[/C][C]0.8125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]91[/C][C]0.8125[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114369&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114369&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 level890.794642857142857NOK
5% type I error level910.8125NOK
10% type I error level910.8125NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}