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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 computationMon, 06 Dec 2010 13:56:53 +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/06/t12916438383b0wittuxfmkk49.htm/, Retrieved Mon, 29 Apr 2024 00:04:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105605, Retrieved Mon, 29 Apr 2024 00:04:40 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-12-06 13:56:53] [c474a97a96075919a678ad3d2290b00b] [Current]
-   PD    [Multiple Regression] [] [2010-12-06 15:14:42] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD    [Multiple Regression] [] [2010-12-06 15:16:44] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD    [Multiple Regression] [] [2010-12-06 15:17:57] [acfa3f91ce5598ec4ba98aad4cfba2f0]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3484,74	13830,14	9349,44	7977	-5,6	6	1	2,77
3411,13	14153,22	9327,78	8241	-6,2	3	1	2,76
3288,18	15418,03	9753,63	8444	-7,1	2	1,2	2,76
3280,37	16666,97	10443,5	8490	-1,4	2	1,2	2,46
3173,95	16505,21	10853,87	8388	-0,1	2	0,8	2,46
3165,26	17135,96	10704,02	8099	-0,9	-8	0,7	2,47
3092,71	18033,25	11052,23	7984	0	0	0,7	2,71
3053,05	17671	10935,47	7786	0,1	-2	0,9	2,8
3181,96	17544,22	10714,03	8086	2,6	3	1,2	2,89
2999,93	17677,9	10394,48	9315	6	5	1,3	3,36
3249,57	18470,97	10817,9	9113	6,4	8	1,5	3,31
3210,52	18409,96	11251,2	9023	8,6	8	1,9	3,5
3030,29	18941,6	11281,26	9026	6,4	9	1,8	3,51
2803,47	19685,53	10539,68	9787	7,7	11	1,9	3,71
2767,63	19834,71	10483,39	9536	9,2	13	2,2	3,71
2882,6	19598,93	10947,43	9490	8,6	12	2,1	3,71
2863,36	17039,97	10580,27	9736	7,4	13	2,2	4,21
2897,06	16969,28	10582,92	9694	8,6	15	2,7	4,21
3012,61	16973,38	10654,41	9647	6,2	13	2,8	4,21
3142,95	16329,89	11014,51	9753	6	16	2,9	4,5
3032,93	16153,34	10967,87	10070	6,6	10	3,4	4,51
3045,78	15311,7	10433,56	10137	5,1	14	3	4,51
3110,52	14760,87	10665,78	9984	4,7	14	3,1	4,51
3013,24	14452,93	10666,71	9732	5	15	2,5	4,32
2987,1	13720,95	10682,74	9103	3,6	13	2,2	4,02
2995,55	13266,27	10777,22	9155	1,9	8	2,3	4,02
2833,18	12708,47	10052,6	9308	-0,1	7	2,1	3,85
2848,96	13411,84	10213,97	9394	-5,7	3	2,8	3,84
2794,83	13975,55	10546,82	9948	-5,6	3	3,1	4,02
2845,26	12974,89	10767,2	10177	-6,4	4	2,9	3,82
2915,02	12151,11	10444,5	10002	-7,7	4	2,6	3,75
2892,63	11576,21	10314,68	9728	-8	0	2,7	3,74
2604,42	9996,83	9042,56	10002	-11,9	-4	2,3	3,14
2641,65	10438,9	9220,75	10063	-15,4	-14	2,3	2,91
2659,81	10511,22	9721,84	10018	-15,5	-18	2,1	2,84
2638,53	10496,2	9978,53	9960	-13,4	-8	2,2	2,85
2720,25	10300,79	9923,81	10236	-10,9	-1	2,9	2,85
2745,88	9981,65	9892,56	10893	-10,8	1	2,6	3,08
2735,7	11448,79	10500,98	10756	-7,3	2	2,7	3,3
2811,7	11384,49	10179,35	10940	-6,5	0	1,8	3,29
2799,43	11717,46	10080,48	10997	-5,1	1	1,3	3,26
2555,28	10965,88	9492,44	10827	-5,3	0	0,9	3,26
2304,98	10352,27	8616,49	10166	-6,8	-1	1,3	3,11
2214,95	9751,2	8685,4	10186	-8,4	-3	1,3	2,84
2065,81	9354,01	8160,67	10457	-8,4	-3	1,3	2,71
1940,49	8792,5	8048,1	10368	-9,7	-3	1,3	2,69
2042	8721,14	8641,21	10244	-8,8	-4	1,1	2,65
1995,37	8692,94	8526,63	10511	-9,6	-8	1,4	2,57
1946,81	8570,73	8474,21	10812	-11,5	-9	1,2	2,32
1765,9	8538,47	7916,13	10738	-11	-13	1,7	2,12
1635,25	8169,75	7977,64	10171	-14,9	-18	1,8	2,05
1833,42	7905,84	8334,59	9721	-16,2	-11	1,5	2,05
1910,43	8145,82	8623,36	9897	-14,4	-9	1	1,81
1959,67	8895,71	9098,03	9828	-17,3	-10	1,6	1,58
1969,6	9676,31	9154,34	9924	-15,7	-13	1,5	1,57
2061,41	9884,59	9284,73	10371	-12,6	-11	1,8	1,76
2093,48	10637,44	9492,49	10846	-9,4	-5	1,8	1,76
2120,88	10717,13	9682,35	10413	-8,1	-15	1,6	1,89
2174,56	10205,29	9762,12	10709	-5,4	-6	1,9	1,9
2196,72	10295,98	10124,63	10662	-4,6	-6	1,7	1,9
2350,44	10892,76	10540,05	10570	-4,9	-3	1,6	1,92
2440,25	10631,92	10601,61	10297	-4	-1	1,3	1,76
2408,64	11441,08	10323,73	10635	-3,1	-3	1,1	1,64
2472,81	11950,95	10418,4	10872	-1,3	-4	1,9	1,57
2407,6	11037,54	10092,96	10296	0	-6	2,6	1,69
2454,62	11527,72	10364,91	10383	-0,4	0	2,3	1,76
2448,05	11383,89	10152,09	10431	3	-4	2,4	1,89
2497,84	10989,34	10032,8	10574	0,4	-2	2,2	1,78
2645,64	11079,42	10204,59	10653	1,2	-2	2	1,88
2756,76	11028,93	10001,6	10805	0,6	-6	2,9	1,86
2849,27	10973	10411,75	10872	-1,3	-7	2,6	1,88
2921,44	11068,05	10673,38	10625	-3,2	-6	2,3	1,87
2981,85	11394,84	10539,51	10407	-1,8	-6	2,3	1,86
3080,58	11545,71	10723,78	10463	-3,6	-3	2,6	1,89
3106,22	11809,38	10682,06	10556	-4,2	-2	3,1	1,9
3119,31	11395,64	10283,19	10646	-6,9	-5	2,8	1,89
3061,26	11082,38	10377,18	10702	-8	-11	2,5	1,85
3097,31	11402,75	10486,64	11353	-7,5	-11	2,9	1,78
3161,69	11716,87	10545,38	11346	-8,2	-11	3,1	1,71
3257,16	12204,98	10554,27	11451	-7,6	-10	3,1	1,69
3277,01	12986,62	10532,54	11964	-3,7	-14	3,2	1,72
3295,32	13392,79	10324,31	12574	-1,7	-8	2,5	1,77
3363,99	14368,05	10695,25	13031	-0,7	-9	2,6	1,98
3494,17	15650,83	10827,81	13812	0,2	-5	2,9	2,2
3667,03	16102,64	10872,48	14544	0,6	-1	2,6	2,25
3813,06	16187,64	10971,19	14931	2,2	-2	2,4	2,24
3917,96	16311,54	11145,65	14886	3,3	-5	1,7	2,51
3895,51	17232,97	11234,68	16005	5,3	-4	2	2,79
3801,06	16397,83	11333,88	17064	5,5	-6	2,2	3,07
3570,12	14990,31	10997,97	15168	6,3	-2	1,9	3,08
3701,61	15147,55	11036,89	16050	7,7	-2	1,6	3,05
3862,27	15786,78	11257,35	15839	6,5	-2	1,6	3,08
3970,1	15934,09	11533,59	15137	5,5	-2	1,2	3,15
4138,52	16519,44	11963,12	14954	6,9	2	1,2	3,16
4199,75	16101,07	12185,15	15648	5,7	1	1,5	3,16
4290,89	16775,08	12377,62	15305	6,9	-8	1,6	3,19
4443,91	17286,32	12512,89	15579	6,1	-1	1,7	3,44
4502,64	17741,23	12631,48	16348	4,8	1	1,8	3,55
4356,98	17128,37	12268,53	15928	3,7	-1	1,8	3,6
4591,27	17460,53	12754,8	16171	5,8	2	1,8	3,62
4696,96	17611,14	13407,75	15937	6,8	2	1,3	3,69
4621,4	18001,37	13480,21	15713	8,5	1	1,3	3,99
4562,84	17974,77	13673,28	15594	7,2	-1	1,4	4,06
4202,52	16460,95	13239,71	15683	5	-2	1,1	4,05
4296,49	16235,39	13557,69	16438	4,7	-2	1,5	4,01
4435,23	16903,36	13901,28	17032	2,3	-1	2,2	3,98
4105,18	15543,76	13200,58	17696	2,4	-8	2,9	3,94
4116,68	15532,18	13406,97	17745	0,1	-4	3,1	3,92
3844,49	13731,31	12538,12	19394	1,9	-6	3,5	4,1
3720,98	13547,84	12419,57	20148	1,7	-3	3,6	3,88
3674,4	12602,93	12193,88	20108	2	-3	4,4	3,74
3857,62	13357,7	12656,63	18584	-1,9	-7	4,2	3,97
3801,06	13995,33	12812,48	18441	0,5	-9	5,2	4,26
3504,37	14084,6	12056,67	18391	-1,3	-11	5,8	4,63
3032,6	13168,91	11322,38	19178	-3,3	-13	5,9	4,82
3047,03	12989,35	11530,75	18079	-2,8	-11	5,4	4,94
2962,34	12123,53	11114,08	18483	-8	-9	5,5	4,98
2197,82	9117,03	9181,73	19644	-13,9	-17	4,7	5,02
2014,45	8531,45	8614,55	19195	-21,9	-22	3,1	4,96
1862,83	8460,94	8595,56	19650	-28,8	-25	2,6	4,49
1905,41	8331,49	8396,2	20830	-27,6	-20	2,3	3,5
1810,99	7694,78	7690,5	23595	-31,4	-24	1,9	2,95
1670,07	7764,58	7235,47	22937	-31,8	-24	0,6	2,37
1864,44	8767,96	7992,12	21814	-29,4	-22	0,6	2,16
2052,02	9304,43	8398,37	21928	-27,6	-19	-0,4	2,08
2029,6	9810,31	8593	21777	-23,6	-18	-1,1	1,98
2070,83	9691,12	8679,75	21383	-22,8	-17	-1,7	1,98
2293,41	10430,35	9374,63	21467	-18,2	-11	-0,8	1,85
2443,27	10302,87	9634,97	22052	-17,8	-11	-1,2	1,82
2513,17	10066,24	9857,34	22680	-14,2	-12	-1	1,65
2466,92	9633,83	10238,83	24320	-8,8	-10	-0,1	1,59
2502,66	10169,02	10433,44	24977	-7,9	-15	0,3	1,56

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105605&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105605&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105605&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -2047.16188772809 + 0.0723275143762075Nikkei[t] + 0.386310468950729DJ_Indust[t] + 0.00586235663459956Goudprijs[t] + 1.0731471514904Conjunct_Seizoenzuiver[t] -7.03174420326656Cons_vertrouw[t] -14.6090054577671Alg_consumptie_index_BE[t] -12.9375414285237Gem_rente_kasbon_1j[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -2047.16188772809 +  0.0723275143762075Nikkei[t] +  0.386310468950729DJ_Indust[t] +  0.00586235663459956Goudprijs[t] +  1.0731471514904Conjunct_Seizoenzuiver[t] -7.03174420326656Cons_vertrouw[t] -14.6090054577671Alg_consumptie_index_BE[t] -12.9375414285237Gem_rente_kasbon_1j[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105605&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -2047.16188772809 +  0.0723275143762075Nikkei[t] +  0.386310468950729DJ_Indust[t] +  0.00586235663459956Goudprijs[t] +  1.0731471514904Conjunct_Seizoenzuiver[t] -7.03174420326656Cons_vertrouw[t] -14.6090054577671Alg_consumptie_index_BE[t] -12.9375414285237Gem_rente_kasbon_1j[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105605&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105605&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
BEL_20[t] = -2047.16188772809 + 0.0723275143762075Nikkei[t] + 0.386310468950729DJ_Indust[t] + 0.00586235663459956Goudprijs[t] + 1.0731471514904Conjunct_Seizoenzuiver[t] -7.03174420326656Cons_vertrouw[t] -14.6090054577671Alg_consumptie_index_BE[t] -12.9375414285237Gem_rente_kasbon_1j[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-2047.16188772809360.12577-5.684600
Nikkei0.07232751437620750.0170054.25324.1e-052.1e-05
DJ_Indust0.3863104689507290.03827710.092500
Goudprijs0.005862356634599560.0092820.63160.5288170.264408
Conjunct_Seizoenzuiver1.07314715149047.6241520.14080.8882910.444145
Cons_vertrouw-7.031744203266566.497973-1.08210.2812880.140644
Alg_consumptie_index_BE-14.609005457767129.85171-0.48940.6254330.312717
Gem_rente_kasbon_1j-12.937541428523743.028156-0.30070.7641650.382082

\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) & -2047.16188772809 & 360.12577 & -5.6846 & 0 & 0 \tabularnewline
Nikkei & 0.0723275143762075 & 0.017005 & 4.2532 & 4.1e-05 & 2.1e-05 \tabularnewline
DJ_Indust & 0.386310468950729 & 0.038277 & 10.0925 & 0 & 0 \tabularnewline
Goudprijs & 0.00586235663459956 & 0.009282 & 0.6316 & 0.528817 & 0.264408 \tabularnewline
Conjunct_Seizoenzuiver & 1.0731471514904 & 7.624152 & 0.1408 & 0.888291 & 0.444145 \tabularnewline
Cons_vertrouw & -7.03174420326656 & 6.497973 & -1.0821 & 0.281288 & 0.140644 \tabularnewline
Alg_consumptie_index_BE & -14.6090054577671 & 29.85171 & -0.4894 & 0.625433 & 0.312717 \tabularnewline
Gem_rente_kasbon_1j & -12.9375414285237 & 43.028156 & -0.3007 & 0.764165 & 0.382082 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105605&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]-2047.16188772809[/C][C]360.12577[/C][C]-5.6846[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.0723275143762075[/C][C]0.017005[/C][C]4.2532[/C][C]4.1e-05[/C][C]2.1e-05[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.386310468950729[/C][C]0.038277[/C][C]10.0925[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.00586235663459956[/C][C]0.009282[/C][C]0.6316[/C][C]0.528817[/C][C]0.264408[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]1.0731471514904[/C][C]7.624152[/C][C]0.1408[/C][C]0.888291[/C][C]0.444145[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]-7.03174420326656[/C][C]6.497973[/C][C]-1.0821[/C][C]0.281288[/C][C]0.140644[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]-14.6090054577671[/C][C]29.85171[/C][C]-0.4894[/C][C]0.625433[/C][C]0.312717[/C][/ROW]
[ROW][C]Gem_rente_kasbon_1j[/C][C]-12.9375414285237[/C][C]43.028156[/C][C]-0.3007[/C][C]0.764165[/C][C]0.382082[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105605&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105605&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)-2047.16188772809360.12577-5.684600
Nikkei0.07232751437620750.0170054.25324.1e-052.1e-05
DJ_Indust0.3863104689507290.03827710.092500
Goudprijs0.005862356634599560.0092820.63160.5288170.264408
Conjunct_Seizoenzuiver1.07314715149047.6241520.14080.8882910.444145
Cons_vertrouw-7.031744203266566.497973-1.08210.2812880.140644
Alg_consumptie_index_BE-14.609005457767129.85171-0.48940.6254330.312717
Gem_rente_kasbon_1j-12.937541428523743.028156-0.30070.7641650.382082






Multiple Linear Regression - Regression Statistics
Multiple R0.927078632287769
R-squared0.85947479044456
Adjusted R-squared0.851541915711591
F-TEST (value)108.3434214425
F-TEST (DF numerator)7
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation290.116655497996
Sum Squared Residuals10436791.5508705

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.927078632287769 \tabularnewline
R-squared & 0.85947479044456 \tabularnewline
Adjusted R-squared & 0.851541915711591 \tabularnewline
F-TEST (value) & 108.3434214425 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 124 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 290.116655497996 \tabularnewline
Sum Squared Residuals & 10436791.5508705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105605&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.927078632287769[/C][/ROW]
[ROW][C]R-squared[/C][C]0.85947479044456[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.851541915711591[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]108.3434214425[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/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]290.116655497996[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10436791.5508705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105605&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105605&T=3

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

Multiple Linear Regression - Regression Statistics
Multiple R0.927078632287769
R-squared0.85947479044456
Adjusted R-squared0.851541915711591
F-TEST (value)108.3434214425
F-TEST (DF numerator)7
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation290.116655497996
Sum Squared Residuals10436791.5508705






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13484.742513.04224716505971.69775283495
23411.132550.17071763697860.959282363034
33288.182810.49576337477.684236630001
43280.373177.6003619873102.769638012696
53173.953331.07162350843-157.121623508434
63165.263387.89980780453-222.639807804526
73092.713528.24842942687-435.53842942687
83053.053465.86605367731-412.816053677312
93181.963334.88755464712-152.927554647124
102999.933210.359289611-210.429289611004
113249.573407.16655639094-157.59655639094
123210.523563.67375751684-353.153757516838
133030.293605.69489422131-575.404894221313
142803.473360.76583188458-557.295831884577
152767.633331.50231335007-563.87231335007
162882.63501.2915300753-618.691530075303
172863.363159.56350979423-296.203509794231
182897.063135.14796701341-238.087967013415
193012.613172.813349183-160.203349182998
203142.953239.48047702933-96.530477029331
213032.933245.95237651484-213.022376514843
223045.782955.1687831875190.6112168124899
233110.523002.25053557194108.269464428064
243013.242983.3736917473929.8663082526084
252987.12953.7615787287533.3384212712537
262995.552989.552630456665.99736954334172
272833.182680.18362452632152.996375473685
282848.962805.9201357168343.0398642831692
292794.832971.91891963235-177.088919632346
302845.262983.64029736613-138.380297366134
312915.022802.26327507224112.756724927764
322892.632735.39858379642157.231416203582
332604.422168.88679605968435.533203940322
342641.652339.59194808802302.058051911981
352659.812569.9822718551489.827728144863
362638.532598.0638212047640.4661787952357
372720.252507.6437578261212.606242173902
382745.882463.79141445968282.088585540318
392735.72796.55898772855-60.8589877285517
402811.72696.93745250031114.762547499694
412799.432685.32327400572114.106725994275
422555.282415.46146891726139.818531082738
432304.982030.33596231522274.644037684781
442214.952029.43955394705185.510446052952
452065.811801.27367517313264.536324826869
461940.491715.51599287712224.974007122883
4720421950.1892508562191.8107491437909
481995.371929.3721714086965.9978285913104
491946.811913.1961515055133.6138484944906
501765.91718.7834609343847.1165390656171
511635.251742.97103504667-107.721035046669
521833.421812.9039430515620.5160569484361
531910.431941.12543797604-30.6954379760365
541959.672176.45845413851-216.78845413851
551969.62279.63578461636-310.035784616361
562061.412330.11408802713-268.704088027127
572093.482428.85394532106-335.373945321062
582120.882578.37668418935-457.496684189354
592174.562509.00753533109-334.447535331088
602196.722659.11311376011-462.393113760114
612350.442842.00345895263-491.563458952635
622440.252838.67345150581-398.423451505808
632408.642811.33513337478-402.695133374777
642472.812884.35598635747-411.545986357472
652407.62692.87348592586-285.273485925856
662454.622794.75149363828-340.131493638285
672448.052731.04832255857-282.99832255857
682497.842644.75810256734-146.918102567335
692645.642720.58733136742-74.9473313674172
702756.762654.00316572147102.756834278535
712849.272817.9126200012631.3573799987422
722921.442919.851109406071.58889059393404
732981.852892.1254230306689.7245769693375
743080.582946.75347184702133.826528152979
753106.222935.14288332766171.077116672341
763119.312774.36786522877344.942134771225
773061.262834.25836767093227.001632329068
783097.312899.13047084474198.179529155257
793161.692941.73345391272219.956546087279
803257.162974.9579793867282.202020613305
813277.013056.56814410221220.441855897789
823295.322978.61527504628316.704724953718
833363.993199.05761616041164.932383839592
843494.173313.23561621932180.93438378068
853667.033343.49975080762323.530249192376
863813.063401.84898408881411.211015911192
873917.963506.95114359578411.008856404218
883895.513601.65842015465293.851579845351
893801.063593.51885913975207.541140860253
903570.123327.82072543168242.299274568322
913701.613365.67253968774335.937460312255
923862.273494.15960261278368.110397387219
933970.13611.27802547262358.82197452738
944138.523791.72011426174346.799885738257
954199.753852.66270698186347.087293018141
964290.894036.4790102024254.410989797599
974443.914072.54221845797371.367781542027
984502.644137.42282898134365.217171018658
994356.983962.65876349701394.321236502991
1004591.274156.85844065085434.411559349153
1014696.964426.09333882031270.866661179692
1024621.44485.9714253816135.428574618402
1034562.844568.23672396381-5.39672396381288
1044202.524300.95790338994-98.4379033899365
1054296.494406.2607467519-109.770746751903
1064435.234571.34253545309-136.112535453087
1074105.184245.83162809351-140.651628093511
1084116.684291.75368311439-175.073683114394
1093844.493843.34520108131.14479891869715
1103720.983768.77387935548-47.7938793554795
1113674.43603.4559793226170.9440206773902
1123857.623851.765424829415.85457517059325
1133801.063955.5299365064-154.469936506402
1143504.373668.29571027602-163.925710276023
1153032.63331.01404974883-298.414049748829
1163047.033384.30478666795-337.274786667948
1172962.343141.46433135622-179.124331356217
1182197.822245.42290890139-47.602908901391
1192014.452032.05379814711-17.6037981471105
1201862.832049.36098603662-186.530986036618
1211905.411953.22083825645-47.810838256453
1221810.991687.76757234031123.222427659686
1231670.071539.2419717546130.828028245398
1241864.441888.76129141735-24.3212914173478
1252052.022081.65021075726-29.6302107572624
1262029.62201.32254681594-171.722546815938
1272070.832226.49667183747-155.666671837467
1282293.412500.17498406255-206.764984062554
1292443.272601.61720593431-158.347205934307
1302513.172684.26042005449-171.090420054495
1312466.922789.33277902907-322.412779029072
1322502.662937.74226763193-435.082267631931

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3484.74 & 2513.04224716505 & 971.69775283495 \tabularnewline
2 & 3411.13 & 2550.17071763697 & 860.959282363034 \tabularnewline
3 & 3288.18 & 2810.49576337 & 477.684236630001 \tabularnewline
4 & 3280.37 & 3177.6003619873 & 102.769638012696 \tabularnewline
5 & 3173.95 & 3331.07162350843 & -157.121623508434 \tabularnewline
6 & 3165.26 & 3387.89980780453 & -222.639807804526 \tabularnewline
7 & 3092.71 & 3528.24842942687 & -435.53842942687 \tabularnewline
8 & 3053.05 & 3465.86605367731 & -412.816053677312 \tabularnewline
9 & 3181.96 & 3334.88755464712 & -152.927554647124 \tabularnewline
10 & 2999.93 & 3210.359289611 & -210.429289611004 \tabularnewline
11 & 3249.57 & 3407.16655639094 & -157.59655639094 \tabularnewline
12 & 3210.52 & 3563.67375751684 & -353.153757516838 \tabularnewline
13 & 3030.29 & 3605.69489422131 & -575.404894221313 \tabularnewline
14 & 2803.47 & 3360.76583188458 & -557.295831884577 \tabularnewline
15 & 2767.63 & 3331.50231335007 & -563.87231335007 \tabularnewline
16 & 2882.6 & 3501.2915300753 & -618.691530075303 \tabularnewline
17 & 2863.36 & 3159.56350979423 & -296.203509794231 \tabularnewline
18 & 2897.06 & 3135.14796701341 & -238.087967013415 \tabularnewline
19 & 3012.61 & 3172.813349183 & -160.203349182998 \tabularnewline
20 & 3142.95 & 3239.48047702933 & -96.530477029331 \tabularnewline
21 & 3032.93 & 3245.95237651484 & -213.022376514843 \tabularnewline
22 & 3045.78 & 2955.16878318751 & 90.6112168124899 \tabularnewline
23 & 3110.52 & 3002.25053557194 & 108.269464428064 \tabularnewline
24 & 3013.24 & 2983.37369174739 & 29.8663082526084 \tabularnewline
25 & 2987.1 & 2953.76157872875 & 33.3384212712537 \tabularnewline
26 & 2995.55 & 2989.55263045666 & 5.99736954334172 \tabularnewline
27 & 2833.18 & 2680.18362452632 & 152.996375473685 \tabularnewline
28 & 2848.96 & 2805.92013571683 & 43.0398642831692 \tabularnewline
29 & 2794.83 & 2971.91891963235 & -177.088919632346 \tabularnewline
30 & 2845.26 & 2983.64029736613 & -138.380297366134 \tabularnewline
31 & 2915.02 & 2802.26327507224 & 112.756724927764 \tabularnewline
32 & 2892.63 & 2735.39858379642 & 157.231416203582 \tabularnewline
33 & 2604.42 & 2168.88679605968 & 435.533203940322 \tabularnewline
34 & 2641.65 & 2339.59194808802 & 302.058051911981 \tabularnewline
35 & 2659.81 & 2569.98227185514 & 89.827728144863 \tabularnewline
36 & 2638.53 & 2598.06382120476 & 40.4661787952357 \tabularnewline
37 & 2720.25 & 2507.6437578261 & 212.606242173902 \tabularnewline
38 & 2745.88 & 2463.79141445968 & 282.088585540318 \tabularnewline
39 & 2735.7 & 2796.55898772855 & -60.8589877285517 \tabularnewline
40 & 2811.7 & 2696.93745250031 & 114.762547499694 \tabularnewline
41 & 2799.43 & 2685.32327400572 & 114.106725994275 \tabularnewline
42 & 2555.28 & 2415.46146891726 & 139.818531082738 \tabularnewline
43 & 2304.98 & 2030.33596231522 & 274.644037684781 \tabularnewline
44 & 2214.95 & 2029.43955394705 & 185.510446052952 \tabularnewline
45 & 2065.81 & 1801.27367517313 & 264.536324826869 \tabularnewline
46 & 1940.49 & 1715.51599287712 & 224.974007122883 \tabularnewline
47 & 2042 & 1950.18925085621 & 91.8107491437909 \tabularnewline
48 & 1995.37 & 1929.37217140869 & 65.9978285913104 \tabularnewline
49 & 1946.81 & 1913.19615150551 & 33.6138484944906 \tabularnewline
50 & 1765.9 & 1718.78346093438 & 47.1165390656171 \tabularnewline
51 & 1635.25 & 1742.97103504667 & -107.721035046669 \tabularnewline
52 & 1833.42 & 1812.90394305156 & 20.5160569484361 \tabularnewline
53 & 1910.43 & 1941.12543797604 & -30.6954379760365 \tabularnewline
54 & 1959.67 & 2176.45845413851 & -216.78845413851 \tabularnewline
55 & 1969.6 & 2279.63578461636 & -310.035784616361 \tabularnewline
56 & 2061.41 & 2330.11408802713 & -268.704088027127 \tabularnewline
57 & 2093.48 & 2428.85394532106 & -335.373945321062 \tabularnewline
58 & 2120.88 & 2578.37668418935 & -457.496684189354 \tabularnewline
59 & 2174.56 & 2509.00753533109 & -334.447535331088 \tabularnewline
60 & 2196.72 & 2659.11311376011 & -462.393113760114 \tabularnewline
61 & 2350.44 & 2842.00345895263 & -491.563458952635 \tabularnewline
62 & 2440.25 & 2838.67345150581 & -398.423451505808 \tabularnewline
63 & 2408.64 & 2811.33513337478 & -402.695133374777 \tabularnewline
64 & 2472.81 & 2884.35598635747 & -411.545986357472 \tabularnewline
65 & 2407.6 & 2692.87348592586 & -285.273485925856 \tabularnewline
66 & 2454.62 & 2794.75149363828 & -340.131493638285 \tabularnewline
67 & 2448.05 & 2731.04832255857 & -282.99832255857 \tabularnewline
68 & 2497.84 & 2644.75810256734 & -146.918102567335 \tabularnewline
69 & 2645.64 & 2720.58733136742 & -74.9473313674172 \tabularnewline
70 & 2756.76 & 2654.00316572147 & 102.756834278535 \tabularnewline
71 & 2849.27 & 2817.91262000126 & 31.3573799987422 \tabularnewline
72 & 2921.44 & 2919.85110940607 & 1.58889059393404 \tabularnewline
73 & 2981.85 & 2892.12542303066 & 89.7245769693375 \tabularnewline
74 & 3080.58 & 2946.75347184702 & 133.826528152979 \tabularnewline
75 & 3106.22 & 2935.14288332766 & 171.077116672341 \tabularnewline
76 & 3119.31 & 2774.36786522877 & 344.942134771225 \tabularnewline
77 & 3061.26 & 2834.25836767093 & 227.001632329068 \tabularnewline
78 & 3097.31 & 2899.13047084474 & 198.179529155257 \tabularnewline
79 & 3161.69 & 2941.73345391272 & 219.956546087279 \tabularnewline
80 & 3257.16 & 2974.9579793867 & 282.202020613305 \tabularnewline
81 & 3277.01 & 3056.56814410221 & 220.441855897789 \tabularnewline
82 & 3295.32 & 2978.61527504628 & 316.704724953718 \tabularnewline
83 & 3363.99 & 3199.05761616041 & 164.932383839592 \tabularnewline
84 & 3494.17 & 3313.23561621932 & 180.93438378068 \tabularnewline
85 & 3667.03 & 3343.49975080762 & 323.530249192376 \tabularnewline
86 & 3813.06 & 3401.84898408881 & 411.211015911192 \tabularnewline
87 & 3917.96 & 3506.95114359578 & 411.008856404218 \tabularnewline
88 & 3895.51 & 3601.65842015465 & 293.851579845351 \tabularnewline
89 & 3801.06 & 3593.51885913975 & 207.541140860253 \tabularnewline
90 & 3570.12 & 3327.82072543168 & 242.299274568322 \tabularnewline
91 & 3701.61 & 3365.67253968774 & 335.937460312255 \tabularnewline
92 & 3862.27 & 3494.15960261278 & 368.110397387219 \tabularnewline
93 & 3970.1 & 3611.27802547262 & 358.82197452738 \tabularnewline
94 & 4138.52 & 3791.72011426174 & 346.799885738257 \tabularnewline
95 & 4199.75 & 3852.66270698186 & 347.087293018141 \tabularnewline
96 & 4290.89 & 4036.4790102024 & 254.410989797599 \tabularnewline
97 & 4443.91 & 4072.54221845797 & 371.367781542027 \tabularnewline
98 & 4502.64 & 4137.42282898134 & 365.217171018658 \tabularnewline
99 & 4356.98 & 3962.65876349701 & 394.321236502991 \tabularnewline
100 & 4591.27 & 4156.85844065085 & 434.411559349153 \tabularnewline
101 & 4696.96 & 4426.09333882031 & 270.866661179692 \tabularnewline
102 & 4621.4 & 4485.9714253816 & 135.428574618402 \tabularnewline
103 & 4562.84 & 4568.23672396381 & -5.39672396381288 \tabularnewline
104 & 4202.52 & 4300.95790338994 & -98.4379033899365 \tabularnewline
105 & 4296.49 & 4406.2607467519 & -109.770746751903 \tabularnewline
106 & 4435.23 & 4571.34253545309 & -136.112535453087 \tabularnewline
107 & 4105.18 & 4245.83162809351 & -140.651628093511 \tabularnewline
108 & 4116.68 & 4291.75368311439 & -175.073683114394 \tabularnewline
109 & 3844.49 & 3843.3452010813 & 1.14479891869715 \tabularnewline
110 & 3720.98 & 3768.77387935548 & -47.7938793554795 \tabularnewline
111 & 3674.4 & 3603.45597932261 & 70.9440206773902 \tabularnewline
112 & 3857.62 & 3851.76542482941 & 5.85457517059325 \tabularnewline
113 & 3801.06 & 3955.5299365064 & -154.469936506402 \tabularnewline
114 & 3504.37 & 3668.29571027602 & -163.925710276023 \tabularnewline
115 & 3032.6 & 3331.01404974883 & -298.414049748829 \tabularnewline
116 & 3047.03 & 3384.30478666795 & -337.274786667948 \tabularnewline
117 & 2962.34 & 3141.46433135622 & -179.124331356217 \tabularnewline
118 & 2197.82 & 2245.42290890139 & -47.602908901391 \tabularnewline
119 & 2014.45 & 2032.05379814711 & -17.6037981471105 \tabularnewline
120 & 1862.83 & 2049.36098603662 & -186.530986036618 \tabularnewline
121 & 1905.41 & 1953.22083825645 & -47.810838256453 \tabularnewline
122 & 1810.99 & 1687.76757234031 & 123.222427659686 \tabularnewline
123 & 1670.07 & 1539.2419717546 & 130.828028245398 \tabularnewline
124 & 1864.44 & 1888.76129141735 & -24.3212914173478 \tabularnewline
125 & 2052.02 & 2081.65021075726 & -29.6302107572624 \tabularnewline
126 & 2029.6 & 2201.32254681594 & -171.722546815938 \tabularnewline
127 & 2070.83 & 2226.49667183747 & -155.666671837467 \tabularnewline
128 & 2293.41 & 2500.17498406255 & -206.764984062554 \tabularnewline
129 & 2443.27 & 2601.61720593431 & -158.347205934307 \tabularnewline
130 & 2513.17 & 2684.26042005449 & -171.090420054495 \tabularnewline
131 & 2466.92 & 2789.33277902907 & -322.412779029072 \tabularnewline
132 & 2502.66 & 2937.74226763193 & -435.082267631931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105605&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]3484.74[/C][C]2513.04224716505[/C][C]971.69775283495[/C][/ROW]
[ROW][C]2[/C][C]3411.13[/C][C]2550.17071763697[/C][C]860.959282363034[/C][/ROW]
[ROW][C]3[/C][C]3288.18[/C][C]2810.49576337[/C][C]477.684236630001[/C][/ROW]
[ROW][C]4[/C][C]3280.37[/C][C]3177.6003619873[/C][C]102.769638012696[/C][/ROW]
[ROW][C]5[/C][C]3173.95[/C][C]3331.07162350843[/C][C]-157.121623508434[/C][/ROW]
[ROW][C]6[/C][C]3165.26[/C][C]3387.89980780453[/C][C]-222.639807804526[/C][/ROW]
[ROW][C]7[/C][C]3092.71[/C][C]3528.24842942687[/C][C]-435.53842942687[/C][/ROW]
[ROW][C]8[/C][C]3053.05[/C][C]3465.86605367731[/C][C]-412.816053677312[/C][/ROW]
[ROW][C]9[/C][C]3181.96[/C][C]3334.88755464712[/C][C]-152.927554647124[/C][/ROW]
[ROW][C]10[/C][C]2999.93[/C][C]3210.359289611[/C][C]-210.429289611004[/C][/ROW]
[ROW][C]11[/C][C]3249.57[/C][C]3407.16655639094[/C][C]-157.59655639094[/C][/ROW]
[ROW][C]12[/C][C]3210.52[/C][C]3563.67375751684[/C][C]-353.153757516838[/C][/ROW]
[ROW][C]13[/C][C]3030.29[/C][C]3605.69489422131[/C][C]-575.404894221313[/C][/ROW]
[ROW][C]14[/C][C]2803.47[/C][C]3360.76583188458[/C][C]-557.295831884577[/C][/ROW]
[ROW][C]15[/C][C]2767.63[/C][C]3331.50231335007[/C][C]-563.87231335007[/C][/ROW]
[ROW][C]16[/C][C]2882.6[/C][C]3501.2915300753[/C][C]-618.691530075303[/C][/ROW]
[ROW][C]17[/C][C]2863.36[/C][C]3159.56350979423[/C][C]-296.203509794231[/C][/ROW]
[ROW][C]18[/C][C]2897.06[/C][C]3135.14796701341[/C][C]-238.087967013415[/C][/ROW]
[ROW][C]19[/C][C]3012.61[/C][C]3172.813349183[/C][C]-160.203349182998[/C][/ROW]
[ROW][C]20[/C][C]3142.95[/C][C]3239.48047702933[/C][C]-96.530477029331[/C][/ROW]
[ROW][C]21[/C][C]3032.93[/C][C]3245.95237651484[/C][C]-213.022376514843[/C][/ROW]
[ROW][C]22[/C][C]3045.78[/C][C]2955.16878318751[/C][C]90.6112168124899[/C][/ROW]
[ROW][C]23[/C][C]3110.52[/C][C]3002.25053557194[/C][C]108.269464428064[/C][/ROW]
[ROW][C]24[/C][C]3013.24[/C][C]2983.37369174739[/C][C]29.8663082526084[/C][/ROW]
[ROW][C]25[/C][C]2987.1[/C][C]2953.76157872875[/C][C]33.3384212712537[/C][/ROW]
[ROW][C]26[/C][C]2995.55[/C][C]2989.55263045666[/C][C]5.99736954334172[/C][/ROW]
[ROW][C]27[/C][C]2833.18[/C][C]2680.18362452632[/C][C]152.996375473685[/C][/ROW]
[ROW][C]28[/C][C]2848.96[/C][C]2805.92013571683[/C][C]43.0398642831692[/C][/ROW]
[ROW][C]29[/C][C]2794.83[/C][C]2971.91891963235[/C][C]-177.088919632346[/C][/ROW]
[ROW][C]30[/C][C]2845.26[/C][C]2983.64029736613[/C][C]-138.380297366134[/C][/ROW]
[ROW][C]31[/C][C]2915.02[/C][C]2802.26327507224[/C][C]112.756724927764[/C][/ROW]
[ROW][C]32[/C][C]2892.63[/C][C]2735.39858379642[/C][C]157.231416203582[/C][/ROW]
[ROW][C]33[/C][C]2604.42[/C][C]2168.88679605968[/C][C]435.533203940322[/C][/ROW]
[ROW][C]34[/C][C]2641.65[/C][C]2339.59194808802[/C][C]302.058051911981[/C][/ROW]
[ROW][C]35[/C][C]2659.81[/C][C]2569.98227185514[/C][C]89.827728144863[/C][/ROW]
[ROW][C]36[/C][C]2638.53[/C][C]2598.06382120476[/C][C]40.4661787952357[/C][/ROW]
[ROW][C]37[/C][C]2720.25[/C][C]2507.6437578261[/C][C]212.606242173902[/C][/ROW]
[ROW][C]38[/C][C]2745.88[/C][C]2463.79141445968[/C][C]282.088585540318[/C][/ROW]
[ROW][C]39[/C][C]2735.7[/C][C]2796.55898772855[/C][C]-60.8589877285517[/C][/ROW]
[ROW][C]40[/C][C]2811.7[/C][C]2696.93745250031[/C][C]114.762547499694[/C][/ROW]
[ROW][C]41[/C][C]2799.43[/C][C]2685.32327400572[/C][C]114.106725994275[/C][/ROW]
[ROW][C]42[/C][C]2555.28[/C][C]2415.46146891726[/C][C]139.818531082738[/C][/ROW]
[ROW][C]43[/C][C]2304.98[/C][C]2030.33596231522[/C][C]274.644037684781[/C][/ROW]
[ROW][C]44[/C][C]2214.95[/C][C]2029.43955394705[/C][C]185.510446052952[/C][/ROW]
[ROW][C]45[/C][C]2065.81[/C][C]1801.27367517313[/C][C]264.536324826869[/C][/ROW]
[ROW][C]46[/C][C]1940.49[/C][C]1715.51599287712[/C][C]224.974007122883[/C][/ROW]
[ROW][C]47[/C][C]2042[/C][C]1950.18925085621[/C][C]91.8107491437909[/C][/ROW]
[ROW][C]48[/C][C]1995.37[/C][C]1929.37217140869[/C][C]65.9978285913104[/C][/ROW]
[ROW][C]49[/C][C]1946.81[/C][C]1913.19615150551[/C][C]33.6138484944906[/C][/ROW]
[ROW][C]50[/C][C]1765.9[/C][C]1718.78346093438[/C][C]47.1165390656171[/C][/ROW]
[ROW][C]51[/C][C]1635.25[/C][C]1742.97103504667[/C][C]-107.721035046669[/C][/ROW]
[ROW][C]52[/C][C]1833.42[/C][C]1812.90394305156[/C][C]20.5160569484361[/C][/ROW]
[ROW][C]53[/C][C]1910.43[/C][C]1941.12543797604[/C][C]-30.6954379760365[/C][/ROW]
[ROW][C]54[/C][C]1959.67[/C][C]2176.45845413851[/C][C]-216.78845413851[/C][/ROW]
[ROW][C]55[/C][C]1969.6[/C][C]2279.63578461636[/C][C]-310.035784616361[/C][/ROW]
[ROW][C]56[/C][C]2061.41[/C][C]2330.11408802713[/C][C]-268.704088027127[/C][/ROW]
[ROW][C]57[/C][C]2093.48[/C][C]2428.85394532106[/C][C]-335.373945321062[/C][/ROW]
[ROW][C]58[/C][C]2120.88[/C][C]2578.37668418935[/C][C]-457.496684189354[/C][/ROW]
[ROW][C]59[/C][C]2174.56[/C][C]2509.00753533109[/C][C]-334.447535331088[/C][/ROW]
[ROW][C]60[/C][C]2196.72[/C][C]2659.11311376011[/C][C]-462.393113760114[/C][/ROW]
[ROW][C]61[/C][C]2350.44[/C][C]2842.00345895263[/C][C]-491.563458952635[/C][/ROW]
[ROW][C]62[/C][C]2440.25[/C][C]2838.67345150581[/C][C]-398.423451505808[/C][/ROW]
[ROW][C]63[/C][C]2408.64[/C][C]2811.33513337478[/C][C]-402.695133374777[/C][/ROW]
[ROW][C]64[/C][C]2472.81[/C][C]2884.35598635747[/C][C]-411.545986357472[/C][/ROW]
[ROW][C]65[/C][C]2407.6[/C][C]2692.87348592586[/C][C]-285.273485925856[/C][/ROW]
[ROW][C]66[/C][C]2454.62[/C][C]2794.75149363828[/C][C]-340.131493638285[/C][/ROW]
[ROW][C]67[/C][C]2448.05[/C][C]2731.04832255857[/C][C]-282.99832255857[/C][/ROW]
[ROW][C]68[/C][C]2497.84[/C][C]2644.75810256734[/C][C]-146.918102567335[/C][/ROW]
[ROW][C]69[/C][C]2645.64[/C][C]2720.58733136742[/C][C]-74.9473313674172[/C][/ROW]
[ROW][C]70[/C][C]2756.76[/C][C]2654.00316572147[/C][C]102.756834278535[/C][/ROW]
[ROW][C]71[/C][C]2849.27[/C][C]2817.91262000126[/C][C]31.3573799987422[/C][/ROW]
[ROW][C]72[/C][C]2921.44[/C][C]2919.85110940607[/C][C]1.58889059393404[/C][/ROW]
[ROW][C]73[/C][C]2981.85[/C][C]2892.12542303066[/C][C]89.7245769693375[/C][/ROW]
[ROW][C]74[/C][C]3080.58[/C][C]2946.75347184702[/C][C]133.826528152979[/C][/ROW]
[ROW][C]75[/C][C]3106.22[/C][C]2935.14288332766[/C][C]171.077116672341[/C][/ROW]
[ROW][C]76[/C][C]3119.31[/C][C]2774.36786522877[/C][C]344.942134771225[/C][/ROW]
[ROW][C]77[/C][C]3061.26[/C][C]2834.25836767093[/C][C]227.001632329068[/C][/ROW]
[ROW][C]78[/C][C]3097.31[/C][C]2899.13047084474[/C][C]198.179529155257[/C][/ROW]
[ROW][C]79[/C][C]3161.69[/C][C]2941.73345391272[/C][C]219.956546087279[/C][/ROW]
[ROW][C]80[/C][C]3257.16[/C][C]2974.9579793867[/C][C]282.202020613305[/C][/ROW]
[ROW][C]81[/C][C]3277.01[/C][C]3056.56814410221[/C][C]220.441855897789[/C][/ROW]
[ROW][C]82[/C][C]3295.32[/C][C]2978.61527504628[/C][C]316.704724953718[/C][/ROW]
[ROW][C]83[/C][C]3363.99[/C][C]3199.05761616041[/C][C]164.932383839592[/C][/ROW]
[ROW][C]84[/C][C]3494.17[/C][C]3313.23561621932[/C][C]180.93438378068[/C][/ROW]
[ROW][C]85[/C][C]3667.03[/C][C]3343.49975080762[/C][C]323.530249192376[/C][/ROW]
[ROW][C]86[/C][C]3813.06[/C][C]3401.84898408881[/C][C]411.211015911192[/C][/ROW]
[ROW][C]87[/C][C]3917.96[/C][C]3506.95114359578[/C][C]411.008856404218[/C][/ROW]
[ROW][C]88[/C][C]3895.51[/C][C]3601.65842015465[/C][C]293.851579845351[/C][/ROW]
[ROW][C]89[/C][C]3801.06[/C][C]3593.51885913975[/C][C]207.541140860253[/C][/ROW]
[ROW][C]90[/C][C]3570.12[/C][C]3327.82072543168[/C][C]242.299274568322[/C][/ROW]
[ROW][C]91[/C][C]3701.61[/C][C]3365.67253968774[/C][C]335.937460312255[/C][/ROW]
[ROW][C]92[/C][C]3862.27[/C][C]3494.15960261278[/C][C]368.110397387219[/C][/ROW]
[ROW][C]93[/C][C]3970.1[/C][C]3611.27802547262[/C][C]358.82197452738[/C][/ROW]
[ROW][C]94[/C][C]4138.52[/C][C]3791.72011426174[/C][C]346.799885738257[/C][/ROW]
[ROW][C]95[/C][C]4199.75[/C][C]3852.66270698186[/C][C]347.087293018141[/C][/ROW]
[ROW][C]96[/C][C]4290.89[/C][C]4036.4790102024[/C][C]254.410989797599[/C][/ROW]
[ROW][C]97[/C][C]4443.91[/C][C]4072.54221845797[/C][C]371.367781542027[/C][/ROW]
[ROW][C]98[/C][C]4502.64[/C][C]4137.42282898134[/C][C]365.217171018658[/C][/ROW]
[ROW][C]99[/C][C]4356.98[/C][C]3962.65876349701[/C][C]394.321236502991[/C][/ROW]
[ROW][C]100[/C][C]4591.27[/C][C]4156.85844065085[/C][C]434.411559349153[/C][/ROW]
[ROW][C]101[/C][C]4696.96[/C][C]4426.09333882031[/C][C]270.866661179692[/C][/ROW]
[ROW][C]102[/C][C]4621.4[/C][C]4485.9714253816[/C][C]135.428574618402[/C][/ROW]
[ROW][C]103[/C][C]4562.84[/C][C]4568.23672396381[/C][C]-5.39672396381288[/C][/ROW]
[ROW][C]104[/C][C]4202.52[/C][C]4300.95790338994[/C][C]-98.4379033899365[/C][/ROW]
[ROW][C]105[/C][C]4296.49[/C][C]4406.2607467519[/C][C]-109.770746751903[/C][/ROW]
[ROW][C]106[/C][C]4435.23[/C][C]4571.34253545309[/C][C]-136.112535453087[/C][/ROW]
[ROW][C]107[/C][C]4105.18[/C][C]4245.83162809351[/C][C]-140.651628093511[/C][/ROW]
[ROW][C]108[/C][C]4116.68[/C][C]4291.75368311439[/C][C]-175.073683114394[/C][/ROW]
[ROW][C]109[/C][C]3844.49[/C][C]3843.3452010813[/C][C]1.14479891869715[/C][/ROW]
[ROW][C]110[/C][C]3720.98[/C][C]3768.77387935548[/C][C]-47.7938793554795[/C][/ROW]
[ROW][C]111[/C][C]3674.4[/C][C]3603.45597932261[/C][C]70.9440206773902[/C][/ROW]
[ROW][C]112[/C][C]3857.62[/C][C]3851.76542482941[/C][C]5.85457517059325[/C][/ROW]
[ROW][C]113[/C][C]3801.06[/C][C]3955.5299365064[/C][C]-154.469936506402[/C][/ROW]
[ROW][C]114[/C][C]3504.37[/C][C]3668.29571027602[/C][C]-163.925710276023[/C][/ROW]
[ROW][C]115[/C][C]3032.6[/C][C]3331.01404974883[/C][C]-298.414049748829[/C][/ROW]
[ROW][C]116[/C][C]3047.03[/C][C]3384.30478666795[/C][C]-337.274786667948[/C][/ROW]
[ROW][C]117[/C][C]2962.34[/C][C]3141.46433135622[/C][C]-179.124331356217[/C][/ROW]
[ROW][C]118[/C][C]2197.82[/C][C]2245.42290890139[/C][C]-47.602908901391[/C][/ROW]
[ROW][C]119[/C][C]2014.45[/C][C]2032.05379814711[/C][C]-17.6037981471105[/C][/ROW]
[ROW][C]120[/C][C]1862.83[/C][C]2049.36098603662[/C][C]-186.530986036618[/C][/ROW]
[ROW][C]121[/C][C]1905.41[/C][C]1953.22083825645[/C][C]-47.810838256453[/C][/ROW]
[ROW][C]122[/C][C]1810.99[/C][C]1687.76757234031[/C][C]123.222427659686[/C][/ROW]
[ROW][C]123[/C][C]1670.07[/C][C]1539.2419717546[/C][C]130.828028245398[/C][/ROW]
[ROW][C]124[/C][C]1864.44[/C][C]1888.76129141735[/C][C]-24.3212914173478[/C][/ROW]
[ROW][C]125[/C][C]2052.02[/C][C]2081.65021075726[/C][C]-29.6302107572624[/C][/ROW]
[ROW][C]126[/C][C]2029.6[/C][C]2201.32254681594[/C][C]-171.722546815938[/C][/ROW]
[ROW][C]127[/C][C]2070.83[/C][C]2226.49667183747[/C][C]-155.666671837467[/C][/ROW]
[ROW][C]128[/C][C]2293.41[/C][C]2500.17498406255[/C][C]-206.764984062554[/C][/ROW]
[ROW][C]129[/C][C]2443.27[/C][C]2601.61720593431[/C][C]-158.347205934307[/C][/ROW]
[ROW][C]130[/C][C]2513.17[/C][C]2684.26042005449[/C][C]-171.090420054495[/C][/ROW]
[ROW][C]131[/C][C]2466.92[/C][C]2789.33277902907[/C][C]-322.412779029072[/C][/ROW]
[ROW][C]132[/C][C]2502.66[/C][C]2937.74226763193[/C][C]-435.082267631931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105605&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105605&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
13484.742513.04224716505971.69775283495
23411.132550.17071763697860.959282363034
33288.182810.49576337477.684236630001
43280.373177.6003619873102.769638012696
53173.953331.07162350843-157.121623508434
63165.263387.89980780453-222.639807804526
73092.713528.24842942687-435.53842942687
83053.053465.86605367731-412.816053677312
93181.963334.88755464712-152.927554647124
102999.933210.359289611-210.429289611004
113249.573407.16655639094-157.59655639094
123210.523563.67375751684-353.153757516838
133030.293605.69489422131-575.404894221313
142803.473360.76583188458-557.295831884577
152767.633331.50231335007-563.87231335007
162882.63501.2915300753-618.691530075303
172863.363159.56350979423-296.203509794231
182897.063135.14796701341-238.087967013415
193012.613172.813349183-160.203349182998
203142.953239.48047702933-96.530477029331
213032.933245.95237651484-213.022376514843
223045.782955.1687831875190.6112168124899
233110.523002.25053557194108.269464428064
243013.242983.3736917473929.8663082526084
252987.12953.7615787287533.3384212712537
262995.552989.552630456665.99736954334172
272833.182680.18362452632152.996375473685
282848.962805.9201357168343.0398642831692
292794.832971.91891963235-177.088919632346
302845.262983.64029736613-138.380297366134
312915.022802.26327507224112.756724927764
322892.632735.39858379642157.231416203582
332604.422168.88679605968435.533203940322
342641.652339.59194808802302.058051911981
352659.812569.9822718551489.827728144863
362638.532598.0638212047640.4661787952357
372720.252507.6437578261212.606242173902
382745.882463.79141445968282.088585540318
392735.72796.55898772855-60.8589877285517
402811.72696.93745250031114.762547499694
412799.432685.32327400572114.106725994275
422555.282415.46146891726139.818531082738
432304.982030.33596231522274.644037684781
442214.952029.43955394705185.510446052952
452065.811801.27367517313264.536324826869
461940.491715.51599287712224.974007122883
4720421950.1892508562191.8107491437909
481995.371929.3721714086965.9978285913104
491946.811913.1961515055133.6138484944906
501765.91718.7834609343847.1165390656171
511635.251742.97103504667-107.721035046669
521833.421812.9039430515620.5160569484361
531910.431941.12543797604-30.6954379760365
541959.672176.45845413851-216.78845413851
551969.62279.63578461636-310.035784616361
562061.412330.11408802713-268.704088027127
572093.482428.85394532106-335.373945321062
582120.882578.37668418935-457.496684189354
592174.562509.00753533109-334.447535331088
602196.722659.11311376011-462.393113760114
612350.442842.00345895263-491.563458952635
622440.252838.67345150581-398.423451505808
632408.642811.33513337478-402.695133374777
642472.812884.35598635747-411.545986357472
652407.62692.87348592586-285.273485925856
662454.622794.75149363828-340.131493638285
672448.052731.04832255857-282.99832255857
682497.842644.75810256734-146.918102567335
692645.642720.58733136742-74.9473313674172
702756.762654.00316572147102.756834278535
712849.272817.9126200012631.3573799987422
722921.442919.851109406071.58889059393404
732981.852892.1254230306689.7245769693375
743080.582946.75347184702133.826528152979
753106.222935.14288332766171.077116672341
763119.312774.36786522877344.942134771225
773061.262834.25836767093227.001632329068
783097.312899.13047084474198.179529155257
793161.692941.73345391272219.956546087279
803257.162974.9579793867282.202020613305
813277.013056.56814410221220.441855897789
823295.322978.61527504628316.704724953718
833363.993199.05761616041164.932383839592
843494.173313.23561621932180.93438378068
853667.033343.49975080762323.530249192376
863813.063401.84898408881411.211015911192
873917.963506.95114359578411.008856404218
883895.513601.65842015465293.851579845351
893801.063593.51885913975207.541140860253
903570.123327.82072543168242.299274568322
913701.613365.67253968774335.937460312255
923862.273494.15960261278368.110397387219
933970.13611.27802547262358.82197452738
944138.523791.72011426174346.799885738257
954199.753852.66270698186347.087293018141
964290.894036.4790102024254.410989797599
974443.914072.54221845797371.367781542027
984502.644137.42282898134365.217171018658
994356.983962.65876349701394.321236502991
1004591.274156.85844065085434.411559349153
1014696.964426.09333882031270.866661179692
1024621.44485.9714253816135.428574618402
1034562.844568.23672396381-5.39672396381288
1044202.524300.95790338994-98.4379033899365
1054296.494406.2607467519-109.770746751903
1064435.234571.34253545309-136.112535453087
1074105.184245.83162809351-140.651628093511
1084116.684291.75368311439-175.073683114394
1093844.493843.34520108131.14479891869715
1103720.983768.77387935548-47.7938793554795
1113674.43603.4559793226170.9440206773902
1123857.623851.765424829415.85457517059325
1133801.063955.5299365064-154.469936506402
1143504.373668.29571027602-163.925710276023
1153032.63331.01404974883-298.414049748829
1163047.033384.30478666795-337.274786667948
1172962.343141.46433135622-179.124331356217
1182197.822245.42290890139-47.602908901391
1192014.452032.05379814711-17.6037981471105
1201862.832049.36098603662-186.530986036618
1211905.411953.22083825645-47.810838256453
1221810.991687.76757234031123.222427659686
1231670.071539.2419717546130.828028245398
1241864.441888.76129141735-24.3212914173478
1252052.022081.65021075726-29.6302107572624
1262029.62201.32254681594-171.722546815938
1272070.832226.49667183747-155.666671837467
1282293.412500.17498406255-206.764984062554
1292443.272601.61720593431-158.347205934307
1302513.172684.26042005449-171.090420054495
1312466.922789.33277902907-322.412779029072
1322502.662937.74226763193-435.082267631931






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.08750930447361540.1750186089472310.912490695526385
120.03357603415075670.06715206830151330.966423965849243
130.01610270022977890.03220540045955790.98389729977022
140.01521905778850780.03043811557701560.984780942211492
150.01471864192050870.02943728384101750.985281358079491
160.01088639465813550.0217727893162710.989113605341864
170.02839878214387330.05679756428774660.971601217856127
180.0280808358782630.0561616717565260.971919164121737
190.02005043360318210.04010086720636410.979949566396818
200.01698528354219880.03397056708439750.9830147164578
210.01260710039444520.02521420078889050.987392899605555
220.007376989326878350.01475397865375670.992623010673122
230.004118515193455120.008237030386910240.995881484806545
240.004956139605686210.009912279211372420.995043860394314
250.01063689316231350.02127378632462710.989363106837686
260.01127801573412480.02255603146824960.988721984265875
270.03041012495029020.06082024990058030.96958987504971
280.06486872109895560.1297374421979110.935131278901044
290.07837659800774110.1567531960154820.921623401992259
300.07300371594551020.146007431891020.92699628405449
310.05328759627268440.1065751925453690.946712403727316
320.0371409735762230.0742819471524460.962859026423777
330.06123116776109910.1224623355221980.9387688322389
340.04996119353514990.09992238707029990.95003880646485
350.04322180381719360.08644360763438710.956778196182806
360.03856261867249810.07712523734499620.961437381327502
370.0316302211121620.06326044222432410.968369778887838
380.02842644866254950.0568528973250990.97157355133745
390.02064662652074820.04129325304149640.979353373479252
400.019242953475350.03848590695070.98075704652465
410.01364386446215030.02728772892430060.98635613553785
420.01387795448008850.0277559089601770.986122045519912
430.04738553098500130.09477106197000260.952614469014999
440.0936680404460880.1873360808921760.906331959553912
450.1172109754015260.2344219508030520.882789024598474
460.1731692808873310.3463385617746630.826830719112668
470.2087011347263930.4174022694527870.791298865273607
480.1929910548814070.3859821097628130.807008945118593
490.1788857704898060.3577715409796120.821114229510194
500.1529842057244430.3059684114488870.847015794275557
510.1508018037857280.3016036075714560.849198196214272
520.2723371817753930.5446743635507860.727662818224607
530.3496495477436510.6992990954873010.65035045225635
540.4097011013425640.8194022026851270.590298898657436
550.4074627171800010.8149254343600030.592537282819999
560.3762732909970030.7525465819940060.623726709002997
570.3980099495035830.7960198990071670.601990050496417
580.462217416933150.9244348338662990.53778258306685
590.486591095928120.973182191856240.51340890407188
600.5010087227279640.9979825545440710.498991277272036
610.5464499927503080.9071000144993840.453550007249692
620.5462157292225550.907568541554890.453784270777445
630.6478704023216880.7042591953566250.352129597678312
640.8682355238485270.2635289523029470.131764476151473
650.9372316872614550.1255366254770890.0627683127385445
660.980454475107790.03909104978442120.0195455248922106
670.9951370902005940.009725819598812870.00486290979940643
680.998453539230050.003092921539897550.00154646076994877
690.9995245501326950.000950899734610710.000475449867305355
700.999872620218950.0002547595620986260.000127379781049313
710.9999424753131450.0001150493737098685.7524686854934e-05
720.999963006704367.39865912818429e-053.69932956409214e-05
730.9999758538700064.82922599885831e-052.41461299942916e-05
740.9999832848027153.34303945696871e-051.67151972848435e-05
750.9999899602658642.00794682716592e-051.00397341358296e-05
760.9999929103920621.4179215875988e-057.08960793799402e-06
770.999994428584211.11428315800221e-055.57141579001103e-06
780.9999958899929278.22001414596243e-064.11000707298122e-06
790.9999960797182087.84056358475542e-063.92028179237771e-06
800.9999968224986.3550040000917e-063.17750200004585e-06
810.9999982240097273.55198054537045e-061.77599027268522e-06
820.9999991047198151.79056037082588e-068.9528018541294e-07
830.9999988281168422.34376631524907e-061.17188315762453e-06
840.999998757477422.48504515847819e-061.2425225792391e-06
850.9999989579664252.08406714998244e-061.04203357499122e-06
860.9999986297965822.74040683598103e-061.37020341799051e-06
870.9999979526080024.09478399580471e-062.04739199790236e-06
880.999997367706585.26458684149547e-062.63229342074773e-06
890.9999954854955639.02900887351383e-064.51450443675692e-06
900.99999450313391.09937322002118e-055.49686610010591e-06
910.9999898179857822.0364028435404e-051.0182014217702e-05
920.9999809821882653.80356234691745e-051.90178117345872e-05
930.9999646697919177.06604161658346e-053.53302080829173e-05
940.9999464655864450.0001070688271104465.35344135552229e-05
950.9998953114936060.0002093770127875010.000104688506393751
960.9998523316617380.0002953366765245830.000147668338262292
970.9997720666499670.0004558667000657530.000227933350032877
980.9995749776922240.0008500446155512850.000425022307775643
990.999391905967030.001216188065939830.000608094032969913
1000.9995344998322790.0009310003354423730.000465500167721187
1010.9996822666740260.0006354666519482840.000317733325974142
1020.999706163078920.0005876738421592320.000293836921079616
1030.9996416949108450.0007166101783101250.000358305089155063
1040.9994727379159080.001054524168185010.000527262084092506
1050.9992513748638850.001497250272229210.000748625136114606
1060.9987791997428390.002441600514321960.00122080025716098
1070.9987070800970890.002585839805822490.00129291990291124
1080.9979689011510820.004062197697834980.00203109884891749
1090.9988908457424340.00221830851513130.00110915425756565
1100.9989955044477280.002008991104543360.00100449555227168
1110.9993725279835930.00125494403281410.000627472016407048
1120.999727913158670.0005441736826604980.000272086841330249
1130.9999290871300730.000141825739853157.09128699265752e-05
1140.9999942850699361.14298601280183e-055.71493006400913e-06
1150.9999751287289844.9742542032518e-052.4871271016259e-05
1160.9998954056402480.0002091887195042790.000104594359752139
1170.9997041211079720.0005917577840552130.000295878892027606
1180.9987937218658950.00241255626821070.00120627813410535
1190.9997124557900220.0005750884199552930.000287544209977647
1200.9982549407329990.003490118534002280.00174505926700114
1210.989773644134650.02045271173070080.0102263558653504

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0875093044736154 & 0.175018608947231 & 0.912490695526385 \tabularnewline
12 & 0.0335760341507567 & 0.0671520683015133 & 0.966423965849243 \tabularnewline
13 & 0.0161027002297789 & 0.0322054004595579 & 0.98389729977022 \tabularnewline
14 & 0.0152190577885078 & 0.0304381155770156 & 0.984780942211492 \tabularnewline
15 & 0.0147186419205087 & 0.0294372838410175 & 0.985281358079491 \tabularnewline
16 & 0.0108863946581355 & 0.021772789316271 & 0.989113605341864 \tabularnewline
17 & 0.0283987821438733 & 0.0567975642877466 & 0.971601217856127 \tabularnewline
18 & 0.028080835878263 & 0.056161671756526 & 0.971919164121737 \tabularnewline
19 & 0.0200504336031821 & 0.0401008672063641 & 0.979949566396818 \tabularnewline
20 & 0.0169852835421988 & 0.0339705670843975 & 0.9830147164578 \tabularnewline
21 & 0.0126071003944452 & 0.0252142007888905 & 0.987392899605555 \tabularnewline
22 & 0.00737698932687835 & 0.0147539786537567 & 0.992623010673122 \tabularnewline
23 & 0.00411851519345512 & 0.00823703038691024 & 0.995881484806545 \tabularnewline
24 & 0.00495613960568621 & 0.00991227921137242 & 0.995043860394314 \tabularnewline
25 & 0.0106368931623135 & 0.0212737863246271 & 0.989363106837686 \tabularnewline
26 & 0.0112780157341248 & 0.0225560314682496 & 0.988721984265875 \tabularnewline
27 & 0.0304101249502902 & 0.0608202499005803 & 0.96958987504971 \tabularnewline
28 & 0.0648687210989556 & 0.129737442197911 & 0.935131278901044 \tabularnewline
29 & 0.0783765980077411 & 0.156753196015482 & 0.921623401992259 \tabularnewline
30 & 0.0730037159455102 & 0.14600743189102 & 0.92699628405449 \tabularnewline
31 & 0.0532875962726844 & 0.106575192545369 & 0.946712403727316 \tabularnewline
32 & 0.037140973576223 & 0.074281947152446 & 0.962859026423777 \tabularnewline
33 & 0.0612311677610991 & 0.122462335522198 & 0.9387688322389 \tabularnewline
34 & 0.0499611935351499 & 0.0999223870702999 & 0.95003880646485 \tabularnewline
35 & 0.0432218038171936 & 0.0864436076343871 & 0.956778196182806 \tabularnewline
36 & 0.0385626186724981 & 0.0771252373449962 & 0.961437381327502 \tabularnewline
37 & 0.031630221112162 & 0.0632604422243241 & 0.968369778887838 \tabularnewline
38 & 0.0284264486625495 & 0.056852897325099 & 0.97157355133745 \tabularnewline
39 & 0.0206466265207482 & 0.0412932530414964 & 0.979353373479252 \tabularnewline
40 & 0.01924295347535 & 0.0384859069507 & 0.98075704652465 \tabularnewline
41 & 0.0136438644621503 & 0.0272877289243006 & 0.98635613553785 \tabularnewline
42 & 0.0138779544800885 & 0.027755908960177 & 0.986122045519912 \tabularnewline
43 & 0.0473855309850013 & 0.0947710619700026 & 0.952614469014999 \tabularnewline
44 & 0.093668040446088 & 0.187336080892176 & 0.906331959553912 \tabularnewline
45 & 0.117210975401526 & 0.234421950803052 & 0.882789024598474 \tabularnewline
46 & 0.173169280887331 & 0.346338561774663 & 0.826830719112668 \tabularnewline
47 & 0.208701134726393 & 0.417402269452787 & 0.791298865273607 \tabularnewline
48 & 0.192991054881407 & 0.385982109762813 & 0.807008945118593 \tabularnewline
49 & 0.178885770489806 & 0.357771540979612 & 0.821114229510194 \tabularnewline
50 & 0.152984205724443 & 0.305968411448887 & 0.847015794275557 \tabularnewline
51 & 0.150801803785728 & 0.301603607571456 & 0.849198196214272 \tabularnewline
52 & 0.272337181775393 & 0.544674363550786 & 0.727662818224607 \tabularnewline
53 & 0.349649547743651 & 0.699299095487301 & 0.65035045225635 \tabularnewline
54 & 0.409701101342564 & 0.819402202685127 & 0.590298898657436 \tabularnewline
55 & 0.407462717180001 & 0.814925434360003 & 0.592537282819999 \tabularnewline
56 & 0.376273290997003 & 0.752546581994006 & 0.623726709002997 \tabularnewline
57 & 0.398009949503583 & 0.796019899007167 & 0.601990050496417 \tabularnewline
58 & 0.46221741693315 & 0.924434833866299 & 0.53778258306685 \tabularnewline
59 & 0.48659109592812 & 0.97318219185624 & 0.51340890407188 \tabularnewline
60 & 0.501008722727964 & 0.997982554544071 & 0.498991277272036 \tabularnewline
61 & 0.546449992750308 & 0.907100014499384 & 0.453550007249692 \tabularnewline
62 & 0.546215729222555 & 0.90756854155489 & 0.453784270777445 \tabularnewline
63 & 0.647870402321688 & 0.704259195356625 & 0.352129597678312 \tabularnewline
64 & 0.868235523848527 & 0.263528952302947 & 0.131764476151473 \tabularnewline
65 & 0.937231687261455 & 0.125536625477089 & 0.0627683127385445 \tabularnewline
66 & 0.98045447510779 & 0.0390910497844212 & 0.0195455248922106 \tabularnewline
67 & 0.995137090200594 & 0.00972581959881287 & 0.00486290979940643 \tabularnewline
68 & 0.99845353923005 & 0.00309292153989755 & 0.00154646076994877 \tabularnewline
69 & 0.999524550132695 & 0.00095089973461071 & 0.000475449867305355 \tabularnewline
70 & 0.99987262021895 & 0.000254759562098626 & 0.000127379781049313 \tabularnewline
71 & 0.999942475313145 & 0.000115049373709868 & 5.7524686854934e-05 \tabularnewline
72 & 0.99996300670436 & 7.39865912818429e-05 & 3.69932956409214e-05 \tabularnewline
73 & 0.999975853870006 & 4.82922599885831e-05 & 2.41461299942916e-05 \tabularnewline
74 & 0.999983284802715 & 3.34303945696871e-05 & 1.67151972848435e-05 \tabularnewline
75 & 0.999989960265864 & 2.00794682716592e-05 & 1.00397341358296e-05 \tabularnewline
76 & 0.999992910392062 & 1.4179215875988e-05 & 7.08960793799402e-06 \tabularnewline
77 & 0.99999442858421 & 1.11428315800221e-05 & 5.57141579001103e-06 \tabularnewline
78 & 0.999995889992927 & 8.22001414596243e-06 & 4.11000707298122e-06 \tabularnewline
79 & 0.999996079718208 & 7.84056358475542e-06 & 3.92028179237771e-06 \tabularnewline
80 & 0.999996822498 & 6.3550040000917e-06 & 3.17750200004585e-06 \tabularnewline
81 & 0.999998224009727 & 3.55198054537045e-06 & 1.77599027268522e-06 \tabularnewline
82 & 0.999999104719815 & 1.79056037082588e-06 & 8.9528018541294e-07 \tabularnewline
83 & 0.999998828116842 & 2.34376631524907e-06 & 1.17188315762453e-06 \tabularnewline
84 & 0.99999875747742 & 2.48504515847819e-06 & 1.2425225792391e-06 \tabularnewline
85 & 0.999998957966425 & 2.08406714998244e-06 & 1.04203357499122e-06 \tabularnewline
86 & 0.999998629796582 & 2.74040683598103e-06 & 1.37020341799051e-06 \tabularnewline
87 & 0.999997952608002 & 4.09478399580471e-06 & 2.04739199790236e-06 \tabularnewline
88 & 0.99999736770658 & 5.26458684149547e-06 & 2.63229342074773e-06 \tabularnewline
89 & 0.999995485495563 & 9.02900887351383e-06 & 4.51450443675692e-06 \tabularnewline
90 & 0.9999945031339 & 1.09937322002118e-05 & 5.49686610010591e-06 \tabularnewline
91 & 0.999989817985782 & 2.0364028435404e-05 & 1.0182014217702e-05 \tabularnewline
92 & 0.999980982188265 & 3.80356234691745e-05 & 1.90178117345872e-05 \tabularnewline
93 & 0.999964669791917 & 7.06604161658346e-05 & 3.53302080829173e-05 \tabularnewline
94 & 0.999946465586445 & 0.000107068827110446 & 5.35344135552229e-05 \tabularnewline
95 & 0.999895311493606 & 0.000209377012787501 & 0.000104688506393751 \tabularnewline
96 & 0.999852331661738 & 0.000295336676524583 & 0.000147668338262292 \tabularnewline
97 & 0.999772066649967 & 0.000455866700065753 & 0.000227933350032877 \tabularnewline
98 & 0.999574977692224 & 0.000850044615551285 & 0.000425022307775643 \tabularnewline
99 & 0.99939190596703 & 0.00121618806593983 & 0.000608094032969913 \tabularnewline
100 & 0.999534499832279 & 0.000931000335442373 & 0.000465500167721187 \tabularnewline
101 & 0.999682266674026 & 0.000635466651948284 & 0.000317733325974142 \tabularnewline
102 & 0.99970616307892 & 0.000587673842159232 & 0.000293836921079616 \tabularnewline
103 & 0.999641694910845 & 0.000716610178310125 & 0.000358305089155063 \tabularnewline
104 & 0.999472737915908 & 0.00105452416818501 & 0.000527262084092506 \tabularnewline
105 & 0.999251374863885 & 0.00149725027222921 & 0.000748625136114606 \tabularnewline
106 & 0.998779199742839 & 0.00244160051432196 & 0.00122080025716098 \tabularnewline
107 & 0.998707080097089 & 0.00258583980582249 & 0.00129291990291124 \tabularnewline
108 & 0.997968901151082 & 0.00406219769783498 & 0.00203109884891749 \tabularnewline
109 & 0.998890845742434 & 0.0022183085151313 & 0.00110915425756565 \tabularnewline
110 & 0.998995504447728 & 0.00200899110454336 & 0.00100449555227168 \tabularnewline
111 & 0.999372527983593 & 0.0012549440328141 & 0.000627472016407048 \tabularnewline
112 & 0.99972791315867 & 0.000544173682660498 & 0.000272086841330249 \tabularnewline
113 & 0.999929087130073 & 0.00014182573985315 & 7.09128699265752e-05 \tabularnewline
114 & 0.999994285069936 & 1.14298601280183e-05 & 5.71493006400913e-06 \tabularnewline
115 & 0.999975128728984 & 4.9742542032518e-05 & 2.4871271016259e-05 \tabularnewline
116 & 0.999895405640248 & 0.000209188719504279 & 0.000104594359752139 \tabularnewline
117 & 0.999704121107972 & 0.000591757784055213 & 0.000295878892027606 \tabularnewline
118 & 0.998793721865895 & 0.0024125562682107 & 0.00120627813410535 \tabularnewline
119 & 0.999712455790022 & 0.000575088419955293 & 0.000287544209977647 \tabularnewline
120 & 0.998254940732999 & 0.00349011853400228 & 0.00174505926700114 \tabularnewline
121 & 0.98977364413465 & 0.0204527117307008 & 0.0102263558653504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105605&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]11[/C][C]0.0875093044736154[/C][C]0.175018608947231[/C][C]0.912490695526385[/C][/ROW]
[ROW][C]12[/C][C]0.0335760341507567[/C][C]0.0671520683015133[/C][C]0.966423965849243[/C][/ROW]
[ROW][C]13[/C][C]0.0161027002297789[/C][C]0.0322054004595579[/C][C]0.98389729977022[/C][/ROW]
[ROW][C]14[/C][C]0.0152190577885078[/C][C]0.0304381155770156[/C][C]0.984780942211492[/C][/ROW]
[ROW][C]15[/C][C]0.0147186419205087[/C][C]0.0294372838410175[/C][C]0.985281358079491[/C][/ROW]
[ROW][C]16[/C][C]0.0108863946581355[/C][C]0.021772789316271[/C][C]0.989113605341864[/C][/ROW]
[ROW][C]17[/C][C]0.0283987821438733[/C][C]0.0567975642877466[/C][C]0.971601217856127[/C][/ROW]
[ROW][C]18[/C][C]0.028080835878263[/C][C]0.056161671756526[/C][C]0.971919164121737[/C][/ROW]
[ROW][C]19[/C][C]0.0200504336031821[/C][C]0.0401008672063641[/C][C]0.979949566396818[/C][/ROW]
[ROW][C]20[/C][C]0.0169852835421988[/C][C]0.0339705670843975[/C][C]0.9830147164578[/C][/ROW]
[ROW][C]21[/C][C]0.0126071003944452[/C][C]0.0252142007888905[/C][C]0.987392899605555[/C][/ROW]
[ROW][C]22[/C][C]0.00737698932687835[/C][C]0.0147539786537567[/C][C]0.992623010673122[/C][/ROW]
[ROW][C]23[/C][C]0.00411851519345512[/C][C]0.00823703038691024[/C][C]0.995881484806545[/C][/ROW]
[ROW][C]24[/C][C]0.00495613960568621[/C][C]0.00991227921137242[/C][C]0.995043860394314[/C][/ROW]
[ROW][C]25[/C][C]0.0106368931623135[/C][C]0.0212737863246271[/C][C]0.989363106837686[/C][/ROW]
[ROW][C]26[/C][C]0.0112780157341248[/C][C]0.0225560314682496[/C][C]0.988721984265875[/C][/ROW]
[ROW][C]27[/C][C]0.0304101249502902[/C][C]0.0608202499005803[/C][C]0.96958987504971[/C][/ROW]
[ROW][C]28[/C][C]0.0648687210989556[/C][C]0.129737442197911[/C][C]0.935131278901044[/C][/ROW]
[ROW][C]29[/C][C]0.0783765980077411[/C][C]0.156753196015482[/C][C]0.921623401992259[/C][/ROW]
[ROW][C]30[/C][C]0.0730037159455102[/C][C]0.14600743189102[/C][C]0.92699628405449[/C][/ROW]
[ROW][C]31[/C][C]0.0532875962726844[/C][C]0.106575192545369[/C][C]0.946712403727316[/C][/ROW]
[ROW][C]32[/C][C]0.037140973576223[/C][C]0.074281947152446[/C][C]0.962859026423777[/C][/ROW]
[ROW][C]33[/C][C]0.0612311677610991[/C][C]0.122462335522198[/C][C]0.9387688322389[/C][/ROW]
[ROW][C]34[/C][C]0.0499611935351499[/C][C]0.0999223870702999[/C][C]0.95003880646485[/C][/ROW]
[ROW][C]35[/C][C]0.0432218038171936[/C][C]0.0864436076343871[/C][C]0.956778196182806[/C][/ROW]
[ROW][C]36[/C][C]0.0385626186724981[/C][C]0.0771252373449962[/C][C]0.961437381327502[/C][/ROW]
[ROW][C]37[/C][C]0.031630221112162[/C][C]0.0632604422243241[/C][C]0.968369778887838[/C][/ROW]
[ROW][C]38[/C][C]0.0284264486625495[/C][C]0.056852897325099[/C][C]0.97157355133745[/C][/ROW]
[ROW][C]39[/C][C]0.0206466265207482[/C][C]0.0412932530414964[/C][C]0.979353373479252[/C][/ROW]
[ROW][C]40[/C][C]0.01924295347535[/C][C]0.0384859069507[/C][C]0.98075704652465[/C][/ROW]
[ROW][C]41[/C][C]0.0136438644621503[/C][C]0.0272877289243006[/C][C]0.98635613553785[/C][/ROW]
[ROW][C]42[/C][C]0.0138779544800885[/C][C]0.027755908960177[/C][C]0.986122045519912[/C][/ROW]
[ROW][C]43[/C][C]0.0473855309850013[/C][C]0.0947710619700026[/C][C]0.952614469014999[/C][/ROW]
[ROW][C]44[/C][C]0.093668040446088[/C][C]0.187336080892176[/C][C]0.906331959553912[/C][/ROW]
[ROW][C]45[/C][C]0.117210975401526[/C][C]0.234421950803052[/C][C]0.882789024598474[/C][/ROW]
[ROW][C]46[/C][C]0.173169280887331[/C][C]0.346338561774663[/C][C]0.826830719112668[/C][/ROW]
[ROW][C]47[/C][C]0.208701134726393[/C][C]0.417402269452787[/C][C]0.791298865273607[/C][/ROW]
[ROW][C]48[/C][C]0.192991054881407[/C][C]0.385982109762813[/C][C]0.807008945118593[/C][/ROW]
[ROW][C]49[/C][C]0.178885770489806[/C][C]0.357771540979612[/C][C]0.821114229510194[/C][/ROW]
[ROW][C]50[/C][C]0.152984205724443[/C][C]0.305968411448887[/C][C]0.847015794275557[/C][/ROW]
[ROW][C]51[/C][C]0.150801803785728[/C][C]0.301603607571456[/C][C]0.849198196214272[/C][/ROW]
[ROW][C]52[/C][C]0.272337181775393[/C][C]0.544674363550786[/C][C]0.727662818224607[/C][/ROW]
[ROW][C]53[/C][C]0.349649547743651[/C][C]0.699299095487301[/C][C]0.65035045225635[/C][/ROW]
[ROW][C]54[/C][C]0.409701101342564[/C][C]0.819402202685127[/C][C]0.590298898657436[/C][/ROW]
[ROW][C]55[/C][C]0.407462717180001[/C][C]0.814925434360003[/C][C]0.592537282819999[/C][/ROW]
[ROW][C]56[/C][C]0.376273290997003[/C][C]0.752546581994006[/C][C]0.623726709002997[/C][/ROW]
[ROW][C]57[/C][C]0.398009949503583[/C][C]0.796019899007167[/C][C]0.601990050496417[/C][/ROW]
[ROW][C]58[/C][C]0.46221741693315[/C][C]0.924434833866299[/C][C]0.53778258306685[/C][/ROW]
[ROW][C]59[/C][C]0.48659109592812[/C][C]0.97318219185624[/C][C]0.51340890407188[/C][/ROW]
[ROW][C]60[/C][C]0.501008722727964[/C][C]0.997982554544071[/C][C]0.498991277272036[/C][/ROW]
[ROW][C]61[/C][C]0.546449992750308[/C][C]0.907100014499384[/C][C]0.453550007249692[/C][/ROW]
[ROW][C]62[/C][C]0.546215729222555[/C][C]0.90756854155489[/C][C]0.453784270777445[/C][/ROW]
[ROW][C]63[/C][C]0.647870402321688[/C][C]0.704259195356625[/C][C]0.352129597678312[/C][/ROW]
[ROW][C]64[/C][C]0.868235523848527[/C][C]0.263528952302947[/C][C]0.131764476151473[/C][/ROW]
[ROW][C]65[/C][C]0.937231687261455[/C][C]0.125536625477089[/C][C]0.0627683127385445[/C][/ROW]
[ROW][C]66[/C][C]0.98045447510779[/C][C]0.0390910497844212[/C][C]0.0195455248922106[/C][/ROW]
[ROW][C]67[/C][C]0.995137090200594[/C][C]0.00972581959881287[/C][C]0.00486290979940643[/C][/ROW]
[ROW][C]68[/C][C]0.99845353923005[/C][C]0.00309292153989755[/C][C]0.00154646076994877[/C][/ROW]
[ROW][C]69[/C][C]0.999524550132695[/C][C]0.00095089973461071[/C][C]0.000475449867305355[/C][/ROW]
[ROW][C]70[/C][C]0.99987262021895[/C][C]0.000254759562098626[/C][C]0.000127379781049313[/C][/ROW]
[ROW][C]71[/C][C]0.999942475313145[/C][C]0.000115049373709868[/C][C]5.7524686854934e-05[/C][/ROW]
[ROW][C]72[/C][C]0.99996300670436[/C][C]7.39865912818429e-05[/C][C]3.69932956409214e-05[/C][/ROW]
[ROW][C]73[/C][C]0.999975853870006[/C][C]4.82922599885831e-05[/C][C]2.41461299942916e-05[/C][/ROW]
[ROW][C]74[/C][C]0.999983284802715[/C][C]3.34303945696871e-05[/C][C]1.67151972848435e-05[/C][/ROW]
[ROW][C]75[/C][C]0.999989960265864[/C][C]2.00794682716592e-05[/C][C]1.00397341358296e-05[/C][/ROW]
[ROW][C]76[/C][C]0.999992910392062[/C][C]1.4179215875988e-05[/C][C]7.08960793799402e-06[/C][/ROW]
[ROW][C]77[/C][C]0.99999442858421[/C][C]1.11428315800221e-05[/C][C]5.57141579001103e-06[/C][/ROW]
[ROW][C]78[/C][C]0.999995889992927[/C][C]8.22001414596243e-06[/C][C]4.11000707298122e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999996079718208[/C][C]7.84056358475542e-06[/C][C]3.92028179237771e-06[/C][/ROW]
[ROW][C]80[/C][C]0.999996822498[/C][C]6.3550040000917e-06[/C][C]3.17750200004585e-06[/C][/ROW]
[ROW][C]81[/C][C]0.999998224009727[/C][C]3.55198054537045e-06[/C][C]1.77599027268522e-06[/C][/ROW]
[ROW][C]82[/C][C]0.999999104719815[/C][C]1.79056037082588e-06[/C][C]8.9528018541294e-07[/C][/ROW]
[ROW][C]83[/C][C]0.999998828116842[/C][C]2.34376631524907e-06[/C][C]1.17188315762453e-06[/C][/ROW]
[ROW][C]84[/C][C]0.99999875747742[/C][C]2.48504515847819e-06[/C][C]1.2425225792391e-06[/C][/ROW]
[ROW][C]85[/C][C]0.999998957966425[/C][C]2.08406714998244e-06[/C][C]1.04203357499122e-06[/C][/ROW]
[ROW][C]86[/C][C]0.999998629796582[/C][C]2.74040683598103e-06[/C][C]1.37020341799051e-06[/C][/ROW]
[ROW][C]87[/C][C]0.999997952608002[/C][C]4.09478399580471e-06[/C][C]2.04739199790236e-06[/C][/ROW]
[ROW][C]88[/C][C]0.99999736770658[/C][C]5.26458684149547e-06[/C][C]2.63229342074773e-06[/C][/ROW]
[ROW][C]89[/C][C]0.999995485495563[/C][C]9.02900887351383e-06[/C][C]4.51450443675692e-06[/C][/ROW]
[ROW][C]90[/C][C]0.9999945031339[/C][C]1.09937322002118e-05[/C][C]5.49686610010591e-06[/C][/ROW]
[ROW][C]91[/C][C]0.999989817985782[/C][C]2.0364028435404e-05[/C][C]1.0182014217702e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999980982188265[/C][C]3.80356234691745e-05[/C][C]1.90178117345872e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999964669791917[/C][C]7.06604161658346e-05[/C][C]3.53302080829173e-05[/C][/ROW]
[ROW][C]94[/C][C]0.999946465586445[/C][C]0.000107068827110446[/C][C]5.35344135552229e-05[/C][/ROW]
[ROW][C]95[/C][C]0.999895311493606[/C][C]0.000209377012787501[/C][C]0.000104688506393751[/C][/ROW]
[ROW][C]96[/C][C]0.999852331661738[/C][C]0.000295336676524583[/C][C]0.000147668338262292[/C][/ROW]
[ROW][C]97[/C][C]0.999772066649967[/C][C]0.000455866700065753[/C][C]0.000227933350032877[/C][/ROW]
[ROW][C]98[/C][C]0.999574977692224[/C][C]0.000850044615551285[/C][C]0.000425022307775643[/C][/ROW]
[ROW][C]99[/C][C]0.99939190596703[/C][C]0.00121618806593983[/C][C]0.000608094032969913[/C][/ROW]
[ROW][C]100[/C][C]0.999534499832279[/C][C]0.000931000335442373[/C][C]0.000465500167721187[/C][/ROW]
[ROW][C]101[/C][C]0.999682266674026[/C][C]0.000635466651948284[/C][C]0.000317733325974142[/C][/ROW]
[ROW][C]102[/C][C]0.99970616307892[/C][C]0.000587673842159232[/C][C]0.000293836921079616[/C][/ROW]
[ROW][C]103[/C][C]0.999641694910845[/C][C]0.000716610178310125[/C][C]0.000358305089155063[/C][/ROW]
[ROW][C]104[/C][C]0.999472737915908[/C][C]0.00105452416818501[/C][C]0.000527262084092506[/C][/ROW]
[ROW][C]105[/C][C]0.999251374863885[/C][C]0.00149725027222921[/C][C]0.000748625136114606[/C][/ROW]
[ROW][C]106[/C][C]0.998779199742839[/C][C]0.00244160051432196[/C][C]0.00122080025716098[/C][/ROW]
[ROW][C]107[/C][C]0.998707080097089[/C][C]0.00258583980582249[/C][C]0.00129291990291124[/C][/ROW]
[ROW][C]108[/C][C]0.997968901151082[/C][C]0.00406219769783498[/C][C]0.00203109884891749[/C][/ROW]
[ROW][C]109[/C][C]0.998890845742434[/C][C]0.0022183085151313[/C][C]0.00110915425756565[/C][/ROW]
[ROW][C]110[/C][C]0.998995504447728[/C][C]0.00200899110454336[/C][C]0.00100449555227168[/C][/ROW]
[ROW][C]111[/C][C]0.999372527983593[/C][C]0.0012549440328141[/C][C]0.000627472016407048[/C][/ROW]
[ROW][C]112[/C][C]0.99972791315867[/C][C]0.000544173682660498[/C][C]0.000272086841330249[/C][/ROW]
[ROW][C]113[/C][C]0.999929087130073[/C][C]0.00014182573985315[/C][C]7.09128699265752e-05[/C][/ROW]
[ROW][C]114[/C][C]0.999994285069936[/C][C]1.14298601280183e-05[/C][C]5.71493006400913e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999975128728984[/C][C]4.9742542032518e-05[/C][C]2.4871271016259e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999895405640248[/C][C]0.000209188719504279[/C][C]0.000104594359752139[/C][/ROW]
[ROW][C]117[/C][C]0.999704121107972[/C][C]0.000591757784055213[/C][C]0.000295878892027606[/C][/ROW]
[ROW][C]118[/C][C]0.998793721865895[/C][C]0.0024125562682107[/C][C]0.00120627813410535[/C][/ROW]
[ROW][C]119[/C][C]0.999712455790022[/C][C]0.000575088419955293[/C][C]0.000287544209977647[/C][/ROW]
[ROW][C]120[/C][C]0.998254940732999[/C][C]0.00349011853400228[/C][C]0.00174505926700114[/C][/ROW]
[ROW][C]121[/C][C]0.98977364413465[/C][C]0.0204527117307008[/C][C]0.0102263558653504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105605&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105605&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
110.08750930447361540.1750186089472310.912490695526385
120.03357603415075670.06715206830151330.966423965849243
130.01610270022977890.03220540045955790.98389729977022
140.01521905778850780.03043811557701560.984780942211492
150.01471864192050870.02943728384101750.985281358079491
160.01088639465813550.0217727893162710.989113605341864
170.02839878214387330.05679756428774660.971601217856127
180.0280808358782630.0561616717565260.971919164121737
190.02005043360318210.04010086720636410.979949566396818
200.01698528354219880.03397056708439750.9830147164578
210.01260710039444520.02521420078889050.987392899605555
220.007376989326878350.01475397865375670.992623010673122
230.004118515193455120.008237030386910240.995881484806545
240.004956139605686210.009912279211372420.995043860394314
250.01063689316231350.02127378632462710.989363106837686
260.01127801573412480.02255603146824960.988721984265875
270.03041012495029020.06082024990058030.96958987504971
280.06486872109895560.1297374421979110.935131278901044
290.07837659800774110.1567531960154820.921623401992259
300.07300371594551020.146007431891020.92699628405449
310.05328759627268440.1065751925453690.946712403727316
320.0371409735762230.0742819471524460.962859026423777
330.06123116776109910.1224623355221980.9387688322389
340.04996119353514990.09992238707029990.95003880646485
350.04322180381719360.08644360763438710.956778196182806
360.03856261867249810.07712523734499620.961437381327502
370.0316302211121620.06326044222432410.968369778887838
380.02842644866254950.0568528973250990.97157355133745
390.02064662652074820.04129325304149640.979353373479252
400.019242953475350.03848590695070.98075704652465
410.01364386446215030.02728772892430060.98635613553785
420.01387795448008850.0277559089601770.986122045519912
430.04738553098500130.09477106197000260.952614469014999
440.0936680404460880.1873360808921760.906331959553912
450.1172109754015260.2344219508030520.882789024598474
460.1731692808873310.3463385617746630.826830719112668
470.2087011347263930.4174022694527870.791298865273607
480.1929910548814070.3859821097628130.807008945118593
490.1788857704898060.3577715409796120.821114229510194
500.1529842057244430.3059684114488870.847015794275557
510.1508018037857280.3016036075714560.849198196214272
520.2723371817753930.5446743635507860.727662818224607
530.3496495477436510.6992990954873010.65035045225635
540.4097011013425640.8194022026851270.590298898657436
550.4074627171800010.8149254343600030.592537282819999
560.3762732909970030.7525465819940060.623726709002997
570.3980099495035830.7960198990071670.601990050496417
580.462217416933150.9244348338662990.53778258306685
590.486591095928120.973182191856240.51340890407188
600.5010087227279640.9979825545440710.498991277272036
610.5464499927503080.9071000144993840.453550007249692
620.5462157292225550.907568541554890.453784270777445
630.6478704023216880.7042591953566250.352129597678312
640.8682355238485270.2635289523029470.131764476151473
650.9372316872614550.1255366254770890.0627683127385445
660.980454475107790.03909104978442120.0195455248922106
670.9951370902005940.009725819598812870.00486290979940643
680.998453539230050.003092921539897550.00154646076994877
690.9995245501326950.000950899734610710.000475449867305355
700.999872620218950.0002547595620986260.000127379781049313
710.9999424753131450.0001150493737098685.7524686854934e-05
720.999963006704367.39865912818429e-053.69932956409214e-05
730.9999758538700064.82922599885831e-052.41461299942916e-05
740.9999832848027153.34303945696871e-051.67151972848435e-05
750.9999899602658642.00794682716592e-051.00397341358296e-05
760.9999929103920621.4179215875988e-057.08960793799402e-06
770.999994428584211.11428315800221e-055.57141579001103e-06
780.9999958899929278.22001414596243e-064.11000707298122e-06
790.9999960797182087.84056358475542e-063.92028179237771e-06
800.9999968224986.3550040000917e-063.17750200004585e-06
810.9999982240097273.55198054537045e-061.77599027268522e-06
820.9999991047198151.79056037082588e-068.9528018541294e-07
830.9999988281168422.34376631524907e-061.17188315762453e-06
840.999998757477422.48504515847819e-061.2425225792391e-06
850.9999989579664252.08406714998244e-061.04203357499122e-06
860.9999986297965822.74040683598103e-061.37020341799051e-06
870.9999979526080024.09478399580471e-062.04739199790236e-06
880.999997367706585.26458684149547e-062.63229342074773e-06
890.9999954854955639.02900887351383e-064.51450443675692e-06
900.99999450313391.09937322002118e-055.49686610010591e-06
910.9999898179857822.0364028435404e-051.0182014217702e-05
920.9999809821882653.80356234691745e-051.90178117345872e-05
930.9999646697919177.06604161658346e-053.53302080829173e-05
940.9999464655864450.0001070688271104465.35344135552229e-05
950.9998953114936060.0002093770127875010.000104688506393751
960.9998523316617380.0002953366765245830.000147668338262292
970.9997720666499670.0004558667000657530.000227933350032877
980.9995749776922240.0008500446155512850.000425022307775643
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1000.9995344998322790.0009310003354423730.000465500167721187
1010.9996822666740260.0006354666519482840.000317733325974142
1020.999706163078920.0005876738421592320.000293836921079616
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1080.9979689011510820.004062197697834980.00203109884891749
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1100.9989955044477280.002008991104543360.00100449555227168
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1120.999727913158670.0005441736826604980.000272086841330249
1130.9999290871300730.000141825739853157.09128699265752e-05
1140.9999942850699361.14298601280183e-055.71493006400913e-06
1150.9999751287289844.9742542032518e-052.4871271016259e-05
1160.9998954056402480.0002091887195042790.000104594359752139
1170.9997041211079720.0005917577840552130.000295878892027606
1180.9987937218658950.00241255626821070.00120627813410535
1190.9997124557900220.0005750884199552930.000287544209977647
1200.9982549407329990.003490118534002280.00174505926700114
1210.989773644134650.02045271173070080.0102263558653504






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level560.504504504504504NOK
5% type I error level720.648648648648649NOK
10% type I error level830.747747747747748NOK

\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 & 56 & 0.504504504504504 & NOK \tabularnewline
5% type I error level & 72 & 0.648648648648649 & NOK \tabularnewline
10% type I error level & 83 & 0.747747747747748 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105605&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]56[/C][C]0.504504504504504[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]72[/C][C]0.648648648648649[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]83[/C][C]0.747747747747748[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105605&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105605&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 level560.504504504504504NOK
5% type I error level720.648648648648649NOK
10% type I error level830.747747747747748NOK


Figures (Output of Computation)

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Input Parameters & R Code

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