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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 16:31:24 +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/t1291653162ppnyrhtj7xcrns2.htm/, Retrieved Mon, 29 Apr 2024 04:46:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105681, Retrieved Mon, 29 Apr 2024 04:46:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-12-01 21:03:34] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D    [Multiple Regression] [] [2010-12-02 12:01:33] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D        [Multiple Regression] [] [2010-12-06 16:31:24] [c474a97a96075919a678ad3d2290b00b] [Current]
-   PD          [Multiple Regression] [] [2010-12-06 16:36:51] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD          [Multiple Regression] [] [2010-12-06 16:38:41] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-   PD          [Multiple Regression] [] [2010-12-06 16:40:41] [acfa3f91ce5598ec4ba98aad4cfba2f0]
-    D          [Multiple Regression] [] [2010-12-06 16:44:05] [acfa3f91ce5598ec4ba98aad4cfba2f0]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3484,74	13830,14	9349,44	7977	-5,6	6	1	3,17
3411,13	14153,22	9327,78	8241	-6,2	3	1	3,17
3288,18	15418,03	9753,63	8444	-7,1	2	1,2	3,36
3280,37	16666,97	10443,5	8490	-1,4	2	1,2	3,11
3173,95	16505,21	10853,87	8388	-0,1	2	0,8	3,11
3165,26	17135,96	10704,02	8099	-0,9	-8	0,7	3,57
3092,71	18033,25	11052,23	7984	0	0	0,7	4,04
3053,05	17671	10935,47	7786	0,1	-2	0,9	4,21
3181,96	17544,22	10714,03	8086	2,6	3	1,2	4,36
2999,93	17677,9	10394,48	9315	6	5	1,3	4,75
3249,57	18470,97	10817,9	9113	6,4	8	1,5	4,43
3210,52	18409,96	11251,2	9023	8,6	8	1,9	4,7
3030,29	18941,6	11281,26	9026	6,4	9	1,8	4,81
2803,47	19685,53	10539,68	9787	7,7	11	1,9	5,01
2767,63	19834,71	10483,39	9536	9,2	13	2,2	5
2882,6	19598,93	10947,43	9490	8,6	12	2,1	4,81
2863,36	17039,97	10580,27	9736	7,4	13	2,2	5,11
2897,06	16969,28	10582,92	9694	8,6	15	2,7	5,1
3012,61	16973,38	10654,41	9647	6,2	13	2,8	5,11
3142,95	16329,89	11014,51	9753	6	16	2,9	5,21
3032,93	16153,34	10967,87	10070	6,6	10	3,4	5,21
3045,78	15311,7	10433,56	10137	5,1	14	3	5,21
3110,52	14760,87	10665,78	9984	4,7	14	3,1	5,06
3013,24	14452,93	10666,71	9732	5	15	2,5	4,58
2987,1	13720,95	10682,74	9103	3,6	13	2,2	4,37
2995,55	13266,27	10777,22	9155	1,9	8	2,3	4,37
2833,18	12708,47	10052,6	9308	-0,1	7	2,1	4,23
2848,96	13411,84	10213,97	9394	-5,7	3	2,8	4,23
2794,83	13975,55	10546,82	9948	-5,6	3	3,1	4,37
2845,26	12974,89	10767,2	10177	-6,4	4	2,9	4,31
2915,02	12151,11	10444,5	10002	-7,7	4	2,6	4,31
2892,63	11576,21	10314,68	9728	-8	0	2,7	4,28
2604,42	9996,83	9042,56	10002	-11,9	-4	2,3	3,98
2641,65	10438,9	9220,75	10063	-15,4	-14	2,3	3,79
2659,81	10511,22	9721,84	10018	-15,5	-18	2,1	3,55
2638,53	10496,2	9978,53	9960	-13,4	-8	2,2	4
2720,25	10300,79	9923,81	10236	-10,9	-1	2,9	4,02
2745,88	9981,65	9892,56	10893	-10,8	1	2,6	4,21
2735,7	11448,79	10500,98	10756	-7,3	2	2,7	4,5
2811,7	11384,49	10179,35	10940	-6,5	0	1,8	4,52
2799,43	11717,46	10080,48	10997	-5,1	1	1,3	4,45
2555,28	10965,88	9492,44	10827	-5,3	0	0,9	4,28
2304,98	10352,27	8616,49	10166	-6,8	-1	1,3	4,08
2214,95	9751,2	8685,4	10186	-8,4	-3	1,3	3,8
2065,81	9354,01	8160,67	10457	-8,4	-3	1,3	3,58
1940,49	8792,5	8048,1	10368	-9,7	-3	1,3	3,58
2042	8721,14	8641,21	10244	-8,8	-4	1,1	3,58
1995,37	8692,94	8526,63	10511	-9,6	-8	1,4	3,54
1946,81	8570,73	8474,21	10812	-11,5	-9	1,2	3,19
1765,9	8538,47	7916,13	10738	-11	-13	1,7	2,91
1635,25	8169,75	7977,64	10171	-14,9	-18	1,8	2,87
1833,42	7905,84	8334,59	9721	-16,2	-11	1,5	3,1
1910,43	8145,82	8623,36	9897	-14,4	-9	1	2,6
1959,67	8895,71	9098,03	9828	-17,3	-10	1,6	2,33
1969,6	9676,31	9154,34	9924	-15,7	-13	1,5	2,62
2061,41	9884,59	9284,73	10371	-12,6	-11	1,8	3,05
2093,48	10637,44	9492,49	10846	-9,4	-5	1,8	3,05
2120,88	10717,13	9682,35	10413	-8,1	-15	1,6	3,22
2174,56	10205,29	9762,12	10709	-5,4	-6	1,9	3,24
2196,72	10295,98	10124,63	10662	-4,6	-6	1,7	3,24
2350,44	10892,76	10540,05	10570	-4,9	-3	1,6	3,38
2440,25	10631,92	10601,61	10297	-4	-1	1,3	3,35
2408,64	11441,08	10323,73	10635	-3,1	-3	1,1	3,22
2472,81	11950,95	10418,4	10872	-1,3	-4	1,9	3,06
2407,6	11037,54	10092,96	10296	0	-6	2,6	3,17
2454,62	11527,72	10364,91	10383	-0,4	0	2,3	3,19
2448,05	11383,89	10152,09	10431	3	-4	2,4	3,35
2497,84	10989,34	10032,8	10574	0,4	-2	2,2	3,24
2645,64	11079,42	10204,59	10653	1,2	-2	2	3,23
2756,76	11028,93	10001,6	10805	0,6	-6	2,9	3,31
2849,27	10973	10411,75	10872	-1,3	-7	2,6	3,25
2921,44	11068,05	10673,38	10625	-3,2	-6	2,3	3,2
2981,85	11394,84	10539,51	10407	-1,8	-6	2,3	3,1
3080,58	11545,71	10723,78	10463	-3,6	-3	2,6	2,93
3106,22	11809,38	10682,06	10556	-4,2	-2	3,1	2,92
3119,31	11395,64	10283,19	10646	-6,9	-5	2,8	2,9
3061,26	11082,38	10377,18	10702	-8	-11	2,5	2,87
3097,31	11402,75	10486,64	11353	-7,5	-11	2,9	2,76
3161,69	11716,87	10545,38	11346	-8,2	-11	3,1	2,67
3257,16	12204,98	10554,27	11451	-7,6	-10	3,1	2,75
3277,01	12986,62	10532,54	11964	-3,7	-14	3,2	2,72
3295,32	13392,79	10324,31	12574	-1,7	-8	2,5	2,72
3363,99	14368,05	10695,25	13031	-0,7	-9	2,6	2,86
3494,17	15650,83	10827,81	13812	0,2	-5	2,9	2,99
3667,03	16102,64	10872,48	14544	0,6	-1	2,6	3,07
3813,06	16187,64	10971,19	14931	2,2	-2	2,4	2,96
3917,96	16311,54	11145,65	14886	3,3	-5	1,7	3,04
3895,51	17232,97	11234,68	16005	5,3	-4	2	3,3
3801,06	16397,83	11333,88	17064	5,5	-6	2,2	3,48
3570,12	14990,31	10997,97	15168	6,3	-2	1,9	3,46
3701,61	15147,55	11036,89	16050	7,7	-2	1,6	3,57
3862,27	15786,78	11257,35	15839	6,5	-2	1,6	3,6
3970,1	15934,09	11533,59	15137	5,5	-2	1,2	3,51
4138,52	16519,44	11963,12	14954	6,9	2	1,2	3,52
4199,75	16101,07	12185,15	15648	5,7	1	1,5	3,49
4290,89	16775,08	12377,62	15305	6,9	-8	1,6	3,5
4443,91	17286,32	12512,89	15579	6,1	-1	1,7	3,64
4502,64	17741,23	12631,48	16348	4,8	1	1,8	3,94
4356,98	17128,37	12268,53	15928	3,7	-1	1,8	3,94
4591,27	17460,53	12754,8	16171	5,8	2	1,8	3,91
4696,96	17611,14	13407,75	15937	6,8	2	1,3	3,88
4621,4	18001,37	13480,21	15713	8,5	1	1,3	4,21
4562,84	17974,77	13673,28	15594	7,2	-1	1,4	4,39
4202,52	16460,95	13239,71	15683	5	-2	1,1	4,33
4296,49	16235,39	13557,69	16438	4,7	-2	1,5	4,27
4435,23	16903,36	13901,28	17032	2,3	-1	2,2	4,29
4105,18	15543,76	13200,58	17696	2,4	-8	2,9	4,18
4116,68	15532,18	13406,97	17745	0,1	-4	3,1	4,14
3844,49	13731,31	12538,12	19394	1,9	-6	3,5	4,23
3720,98	13547,84	12419,57	20148	1,7	-3	3,6	4,07
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	3,66
3801,06	13995,33	12812,48	18441	0,5	-9	5,2	3,92
3504,37	14084,6	12056,67	18391	-1,3	-11	5,8	4,45
3032,6	13168,91	11322,38	19178	-3,3	-13	5,9	4,92
3047,03	12989,35	11530,75	18079	-2,8	-11	5,4	4,9
2962,34	12123,53	11114,08	18483	-8	-9	5,5	4,54
2197,82	9117,03	9181,73	19644	-13,9	-17	4,7	4,53
2014,45	8531,45	8614,55	19195	-21,9	-22	3,1	4,14
1862,83	8460,94	8595,56	19650	-28,8	-25	2,6	4,05
1905,41	8331,49	8396,2	20830	-27,6	-20	2,3	3,92
1810,99	7694,78	7690,5	23595	-31,4	-24	1,9	3,68
1670,07	7764,58	7235,47	22937	-31,8	-24	0,6	3,35
1864,44	8767,96	7992,12	21814	-29,4	-22	0,6	3,38
2052,02	9304,43	8398,37	21928	-27,6	-19	-0,4	3,44
2029,6	9810,31	8593	21777	-23,6	-18	-1,1	3,5
2070,83	9691,12	8679,75	21383	-22,8	-17	-1,7	3,54
2293,41	10430,35	9374,63	21467	-18,2	-11	-0,8	3,52
2443,27	10302,87	9634,97	22052	-17,8	-11	-1,2	3,53
2513,17	10066,24	9857,34	22680	-14,2	-12	-1	3,55
2466,92	9633,83	10238,83	24320	-8,8	-10	-0,1	3,37
2502,66	10169,02	10433,44	24977	-7,9	-15	0,3	3,36

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105681&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105681&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105681&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -1471.55250880347 + 0.0954355312113703Nikkei[t] + 0.369163039198861DJ_Indust[t] + 0.0238744986798515Goudprijs[t] -10.6388338055077Conjunct_Seizoenzuiver[t] + 13.4082779421853Cons_vertrouw[t] + 36.2029927235425Alg_consumptie_index_BE[t] -278.729546599587Gem_rente_kasbon_5j[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -1471.55250880347 +  0.0954355312113703Nikkei[t] +  0.369163039198861DJ_Indust[t] +  0.0238744986798515Goudprijs[t] -10.6388338055077Conjunct_Seizoenzuiver[t] +  13.4082779421853Cons_vertrouw[t] +  36.2029927235425Alg_consumptie_index_BE[t] -278.729546599587Gem_rente_kasbon_5j[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105681&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -1471.55250880347 +  0.0954355312113703Nikkei[t] +  0.369163039198861DJ_Indust[t] +  0.0238744986798515Goudprijs[t] -10.6388338055077Conjunct_Seizoenzuiver[t] +  13.4082779421853Cons_vertrouw[t] +  36.2029927235425Alg_consumptie_index_BE[t] -278.729546599587Gem_rente_kasbon_5j[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105681&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105681&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] = -1471.55250880347 + 0.0954355312113703Nikkei[t] + 0.369163039198861DJ_Indust[t] + 0.0238744986798515Goudprijs[t] -10.6388338055077Conjunct_Seizoenzuiver[t] + 13.4082779421853Cons_vertrouw[t] + 36.2029927235425Alg_consumptie_index_BE[t] -278.729546599587Gem_rente_kasbon_5j[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1471.55250880347334.820996-4.3952.4e-051.2e-05
Nikkei0.09543553121137030.014896.409400
DJ_Indust0.3691630391988610.03449810.700900
Goudprijs0.02387449867985150.0082272.90190.0043910.002195
Conjunct_Seizoenzuiver-10.63883380550776.686008-1.59120.1141090.057054
Cons_vertrouw13.40827794218536.245422.14690.0337480.016874
Alg_consumptie_index_BE36.202992723542522.7973871.5880.1148260.057413
Gem_rente_kasbon_5j-278.72954659958750.979851-5.467400

\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) & -1471.55250880347 & 334.820996 & -4.395 & 2.4e-05 & 1.2e-05 \tabularnewline
Nikkei & 0.0954355312113703 & 0.01489 & 6.4094 & 0 & 0 \tabularnewline
DJ_Indust & 0.369163039198861 & 0.034498 & 10.7009 & 0 & 0 \tabularnewline
Goudprijs & 0.0238744986798515 & 0.008227 & 2.9019 & 0.004391 & 0.002195 \tabularnewline
Conjunct_Seizoenzuiver & -10.6388338055077 & 6.686008 & -1.5912 & 0.114109 & 0.057054 \tabularnewline
Cons_vertrouw & 13.4082779421853 & 6.24542 & 2.1469 & 0.033748 & 0.016874 \tabularnewline
Alg_consumptie_index_BE & 36.2029927235425 & 22.797387 & 1.588 & 0.114826 & 0.057413 \tabularnewline
Gem_rente_kasbon_5j & -278.729546599587 & 50.979851 & -5.4674 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105681&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]-1471.55250880347[/C][C]334.820996[/C][C]-4.395[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.0954355312113703[/C][C]0.01489[/C][C]6.4094[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.369163039198861[/C][C]0.034498[/C][C]10.7009[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.0238744986798515[/C][C]0.008227[/C][C]2.9019[/C][C]0.004391[/C][C]0.002195[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-10.6388338055077[/C][C]6.686008[/C][C]-1.5912[/C][C]0.114109[/C][C]0.057054[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]13.4082779421853[/C][C]6.24542[/C][C]2.1469[/C][C]0.033748[/C][C]0.016874[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]36.2029927235425[/C][C]22.797387[/C][C]1.588[/C][C]0.114826[/C][C]0.057413[/C][/ROW]
[ROW][C]Gem_rente_kasbon_5j[/C][C]-278.729546599587[/C][C]50.979851[/C][C]-5.4674[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105681&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105681&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)-1471.55250880347334.820996-4.3952.4e-051.2e-05
Nikkei0.09543553121137030.014896.409400
DJ_Indust0.3691630391988610.03449810.700900
Goudprijs0.02387449867985150.0082272.90190.0043910.002195
Conjunct_Seizoenzuiver-10.63883380550776.686008-1.59120.1141090.057054
Cons_vertrouw13.40827794218536.245422.14690.0337480.016874
Alg_consumptie_index_BE36.202992723542522.7973871.5880.1148260.057413
Gem_rente_kasbon_5j-278.72954659958750.979851-5.467400






Multiple Linear Regression - Regression Statistics
Multiple R0.941641426248589
R-squared0.886688575627476
Adjusted R-squared0.880291962961286
F-TEST (value)138.618456658170
F-TEST (DF numerator)7
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.514793987770
Sum Squared Residuals8415626.77792477

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.941641426248589 \tabularnewline
R-squared & 0.886688575627476 \tabularnewline
Adjusted R-squared & 0.880291962961286 \tabularnewline
F-TEST (value) & 138.618456658170 \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 & 260.514793987770 \tabularnewline
Sum Squared Residuals & 8415626.77792477 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105681&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.941641426248589[/C][/ROW]
[ROW][C]R-squared[/C][C]0.886688575627476[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.880291962961286[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]138.618456658170[/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]260.514793987770[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8415626.77792477[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105681&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105681&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 R0.941641426248589
R-squared0.886688575627476
Adjusted R-squared0.880291962961286
F-TEST (value)138.618456658170
F-TEST (DF numerator)7
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.514793987770
Sum Squared Residuals8415626.77792477






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13484.742782.9062769675701.8337230325
23411.132778.20485107048632.925148929522
33288.183011.41592595034276.764074049664
43280.373395.42294505136-115.052945051356
53173.953500.73185001672-326.781850016719
63165.263241.30139670025-76.0413967002547
73092.713419.42382524288-326.713825242876
83053.053268.99781322394-215.947813223945
93181.963191.80915380501-9.84915380500932
102999.933001.50298236305-1.57298236304665
113249.573361.08175818603-111.511758186028
123210.523428.88766156591-218.367661565914
133030.293493.32683474948-463.036834749478
142803.473269.58921825948-466.119218259476
152767.633281.56010261961-513.930102619609
162882.63471.57983976385-588.979839763845
172863.363043.87167181946-180.511671819459
182897.063072.03963437298-174.979634372979
193012.613097.24993334053-84.6399333405251
203142.953189.40437583695-46.4543758369476
213032.933094.17421316021-61.2442131602135
223045.782873.31410599627172.465894003733
233110.522952.50485978835158.015140211654
243013.243039.72840480046-26.4884048004578
252987.12996.52224692528-9.42224692528093
262995.552943.9145444997551.6354555002539
272833.182666.48140967185166.698590328153
282848.962826.5193982305622.4406017694427
292794.832987.19462930827-192.364629308267
302845.263009.92204066049-164.662040660487
312915.022810.96679487084104.053205129165
322892.632663.17527363360229.454726366404
332604.422106.36423852480498.055761475196
342641.652171.90268293308469.747317066924
352659.812369.79939967285290.010600327151
362638.532451.67492933541186.855070664595
372720.252506.44299736808213.807002631916
382745.882442.06806342782303.81193657218
392735.72702.3818090608833.318190939116
402811.72508.4193965516303.2806034484
412799.432504.98174463288294.448255367116
422555.282233.7353243904321.544675609602
432304.981908.80279944558396.177200554424
442214.951945.60573095747269.344269042525
452065.811781.77926015096284.030739849039
461940.491698.34022526250242.149774737498
4720421877.29997118634164.700028813658
481995.371815.57251427901179.797485720992
491946.811888.8642846509257.9457153490768
501765.91715.1895533338050.7104466662039
511635.251678.39048532208-43.1404853220774
521833.421806.9530527236726.4669472763262
531910.432069.59072607337-159.160726073366
541959.672429.16326929088-469.493269290875
551969.62385.04093186342-415.440931863422
562061.412348.56867776170-287.158677761696
572093.482554.86041680655-461.380416806548
582120.882419.67942614705-298.799426147051
592174.562504.58264818782-330.022648187820
602196.722630.18922282629-433.46922282629
612350.442839.07854317997-488.638543179968
622440.252845.13567181262-404.885671812615
632408.642820.44757767273-411.807577672728
642472.812950.71515592222-477.905155922219
652407.62683.68606159096-276.086061590964
662454.622901.20733260169-446.587332601692
672448.052679.27996319084-231.229963190844
682497.842678.89964407652-181.059644076521
692645.642739.8367105046-94.196710504604
702756.762626.74520724389130.014792756113
712849.272787.0876911893962.1823088106102
722921.442923.54360488827-2.10360488827106
732981.852913.0850726953268.7649273046758
743080.583114.46573186389-33.8857318638916
753106.223167.1284348135-60.9084348135003
763119.312965.75729212929153.552707870711
773061.262900.64980171626160.610198283741
783097.313026.9973980743570.3126019256458
793161.693118.2665639727843.4234360272214
803257.163155.36489682314101.795103176863
813277.013151.04445248029125.965547519714
823295.323161.33003187036133.989968129645
833363.993342.8035226973921.1864773026068
843494.173551.49276747278-57.3227674727801
853667.033644.7964575255622.2335424744379
863813.063691.5772318172121.482768182800
873917.963667.16351586527250.796484134733
883895.513745.20465282433150.305347175671
893801.063655.53164839026145.528351609735
903570.123391.76836134461178.351638655387
913701.613385.78426232281315.825737677191
923862.273527.54239550801334.727604491992
933970.13648.06199939585322.038000604154
944138.523894.07420353413244.445796465871
954199.753971.26211885784228.487881142157
964290.893965.84238011712325.047619882884
974443.914138.07831343244305.831686567559
984502.644204.27990036321298.360099636787
994356.983986.66242748394370.317572516063
1004591.274229.92187701452361.348122985484
1014696.964459.37535235478237.58464764522
1024621.44394.54378002604226.856219973956
1034562.844400.90134647826161.938653521738
1044202.524114.3559735057788.164026494235
1054296.494262.6378648205233.8521351794845
1064435.234526.11710050783-90.8871005078348
1074105.184114.62359388622-9.4435938862209
1084116.684287.37207046046-170.692070460459
1093844.493777.5559082441566.9340917558457
1103720.983824.85307235633-103.873072356332
1113674.43768.15519271046-93.7551927104564
1123857.623977.54863426004-119.928634260039
1133801.064003.90475196433-202.844751964330
1143504.373598.54192114657-94.1719211465738
1153032.63125.94758607654-93.3475860765388
1163047.033168.46584407277-121.435844072772
1172962.343127.76341408423-165.423414084230
1182197.822084.53038087081113.289619129190
1192014.451877.3927155733137.057284426701
1201862.831914.68332931617-51.85332931617
1211905.411936.5534971336-31.1434971335989
1221810.991720.4917229298390.4982770701676
1231670.071592.6358385098577.434161490152
1241864.441933.83156176222-69.3915617622216
1252052.022105.87220617235-53.8522061723506
1262029.62151.18336073787-121.583360737874
1272070.832134.4529743431-63.6229743431011
1282293.412533.19956917905-239.78956917905
1292443.272609.58390893535-166.313908935353
1302513.172634.04289735311-120.872897353108
1312466.922824.88267013467-357.962670134671
1322502.662904.13932919437-401.479329194367

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3484.74 & 2782.9062769675 & 701.8337230325 \tabularnewline
2 & 3411.13 & 2778.20485107048 & 632.925148929522 \tabularnewline
3 & 3288.18 & 3011.41592595034 & 276.764074049664 \tabularnewline
4 & 3280.37 & 3395.42294505136 & -115.052945051356 \tabularnewline
5 & 3173.95 & 3500.73185001672 & -326.781850016719 \tabularnewline
6 & 3165.26 & 3241.30139670025 & -76.0413967002547 \tabularnewline
7 & 3092.71 & 3419.42382524288 & -326.713825242876 \tabularnewline
8 & 3053.05 & 3268.99781322394 & -215.947813223945 \tabularnewline
9 & 3181.96 & 3191.80915380501 & -9.84915380500932 \tabularnewline
10 & 2999.93 & 3001.50298236305 & -1.57298236304665 \tabularnewline
11 & 3249.57 & 3361.08175818603 & -111.511758186028 \tabularnewline
12 & 3210.52 & 3428.88766156591 & -218.367661565914 \tabularnewline
13 & 3030.29 & 3493.32683474948 & -463.036834749478 \tabularnewline
14 & 2803.47 & 3269.58921825948 & -466.119218259476 \tabularnewline
15 & 2767.63 & 3281.56010261961 & -513.930102619609 \tabularnewline
16 & 2882.6 & 3471.57983976385 & -588.979839763845 \tabularnewline
17 & 2863.36 & 3043.87167181946 & -180.511671819459 \tabularnewline
18 & 2897.06 & 3072.03963437298 & -174.979634372979 \tabularnewline
19 & 3012.61 & 3097.24993334053 & -84.6399333405251 \tabularnewline
20 & 3142.95 & 3189.40437583695 & -46.4543758369476 \tabularnewline
21 & 3032.93 & 3094.17421316021 & -61.2442131602135 \tabularnewline
22 & 3045.78 & 2873.31410599627 & 172.465894003733 \tabularnewline
23 & 3110.52 & 2952.50485978835 & 158.015140211654 \tabularnewline
24 & 3013.24 & 3039.72840480046 & -26.4884048004578 \tabularnewline
25 & 2987.1 & 2996.52224692528 & -9.42224692528093 \tabularnewline
26 & 2995.55 & 2943.91454449975 & 51.6354555002539 \tabularnewline
27 & 2833.18 & 2666.48140967185 & 166.698590328153 \tabularnewline
28 & 2848.96 & 2826.51939823056 & 22.4406017694427 \tabularnewline
29 & 2794.83 & 2987.19462930827 & -192.364629308267 \tabularnewline
30 & 2845.26 & 3009.92204066049 & -164.662040660487 \tabularnewline
31 & 2915.02 & 2810.96679487084 & 104.053205129165 \tabularnewline
32 & 2892.63 & 2663.17527363360 & 229.454726366404 \tabularnewline
33 & 2604.42 & 2106.36423852480 & 498.055761475196 \tabularnewline
34 & 2641.65 & 2171.90268293308 & 469.747317066924 \tabularnewline
35 & 2659.81 & 2369.79939967285 & 290.010600327151 \tabularnewline
36 & 2638.53 & 2451.67492933541 & 186.855070664595 \tabularnewline
37 & 2720.25 & 2506.44299736808 & 213.807002631916 \tabularnewline
38 & 2745.88 & 2442.06806342782 & 303.81193657218 \tabularnewline
39 & 2735.7 & 2702.38180906088 & 33.318190939116 \tabularnewline
40 & 2811.7 & 2508.4193965516 & 303.2806034484 \tabularnewline
41 & 2799.43 & 2504.98174463288 & 294.448255367116 \tabularnewline
42 & 2555.28 & 2233.7353243904 & 321.544675609602 \tabularnewline
43 & 2304.98 & 1908.80279944558 & 396.177200554424 \tabularnewline
44 & 2214.95 & 1945.60573095747 & 269.344269042525 \tabularnewline
45 & 2065.81 & 1781.77926015096 & 284.030739849039 \tabularnewline
46 & 1940.49 & 1698.34022526250 & 242.149774737498 \tabularnewline
47 & 2042 & 1877.29997118634 & 164.700028813658 \tabularnewline
48 & 1995.37 & 1815.57251427901 & 179.797485720992 \tabularnewline
49 & 1946.81 & 1888.86428465092 & 57.9457153490768 \tabularnewline
50 & 1765.9 & 1715.18955333380 & 50.7104466662039 \tabularnewline
51 & 1635.25 & 1678.39048532208 & -43.1404853220774 \tabularnewline
52 & 1833.42 & 1806.95305272367 & 26.4669472763262 \tabularnewline
53 & 1910.43 & 2069.59072607337 & -159.160726073366 \tabularnewline
54 & 1959.67 & 2429.16326929088 & -469.493269290875 \tabularnewline
55 & 1969.6 & 2385.04093186342 & -415.440931863422 \tabularnewline
56 & 2061.41 & 2348.56867776170 & -287.158677761696 \tabularnewline
57 & 2093.48 & 2554.86041680655 & -461.380416806548 \tabularnewline
58 & 2120.88 & 2419.67942614705 & -298.799426147051 \tabularnewline
59 & 2174.56 & 2504.58264818782 & -330.022648187820 \tabularnewline
60 & 2196.72 & 2630.18922282629 & -433.46922282629 \tabularnewline
61 & 2350.44 & 2839.07854317997 & -488.638543179968 \tabularnewline
62 & 2440.25 & 2845.13567181262 & -404.885671812615 \tabularnewline
63 & 2408.64 & 2820.44757767273 & -411.807577672728 \tabularnewline
64 & 2472.81 & 2950.71515592222 & -477.905155922219 \tabularnewline
65 & 2407.6 & 2683.68606159096 & -276.086061590964 \tabularnewline
66 & 2454.62 & 2901.20733260169 & -446.587332601692 \tabularnewline
67 & 2448.05 & 2679.27996319084 & -231.229963190844 \tabularnewline
68 & 2497.84 & 2678.89964407652 & -181.059644076521 \tabularnewline
69 & 2645.64 & 2739.8367105046 & -94.196710504604 \tabularnewline
70 & 2756.76 & 2626.74520724389 & 130.014792756113 \tabularnewline
71 & 2849.27 & 2787.08769118939 & 62.1823088106102 \tabularnewline
72 & 2921.44 & 2923.54360488827 & -2.10360488827106 \tabularnewline
73 & 2981.85 & 2913.08507269532 & 68.7649273046758 \tabularnewline
74 & 3080.58 & 3114.46573186389 & -33.8857318638916 \tabularnewline
75 & 3106.22 & 3167.1284348135 & -60.9084348135003 \tabularnewline
76 & 3119.31 & 2965.75729212929 & 153.552707870711 \tabularnewline
77 & 3061.26 & 2900.64980171626 & 160.610198283741 \tabularnewline
78 & 3097.31 & 3026.99739807435 & 70.3126019256458 \tabularnewline
79 & 3161.69 & 3118.26656397278 & 43.4234360272214 \tabularnewline
80 & 3257.16 & 3155.36489682314 & 101.795103176863 \tabularnewline
81 & 3277.01 & 3151.04445248029 & 125.965547519714 \tabularnewline
82 & 3295.32 & 3161.33003187036 & 133.989968129645 \tabularnewline
83 & 3363.99 & 3342.80352269739 & 21.1864773026068 \tabularnewline
84 & 3494.17 & 3551.49276747278 & -57.3227674727801 \tabularnewline
85 & 3667.03 & 3644.79645752556 & 22.2335424744379 \tabularnewline
86 & 3813.06 & 3691.5772318172 & 121.482768182800 \tabularnewline
87 & 3917.96 & 3667.16351586527 & 250.796484134733 \tabularnewline
88 & 3895.51 & 3745.20465282433 & 150.305347175671 \tabularnewline
89 & 3801.06 & 3655.53164839026 & 145.528351609735 \tabularnewline
90 & 3570.12 & 3391.76836134461 & 178.351638655387 \tabularnewline
91 & 3701.61 & 3385.78426232281 & 315.825737677191 \tabularnewline
92 & 3862.27 & 3527.54239550801 & 334.727604491992 \tabularnewline
93 & 3970.1 & 3648.06199939585 & 322.038000604154 \tabularnewline
94 & 4138.52 & 3894.07420353413 & 244.445796465871 \tabularnewline
95 & 4199.75 & 3971.26211885784 & 228.487881142157 \tabularnewline
96 & 4290.89 & 3965.84238011712 & 325.047619882884 \tabularnewline
97 & 4443.91 & 4138.07831343244 & 305.831686567559 \tabularnewline
98 & 4502.64 & 4204.27990036321 & 298.360099636787 \tabularnewline
99 & 4356.98 & 3986.66242748394 & 370.317572516063 \tabularnewline
100 & 4591.27 & 4229.92187701452 & 361.348122985484 \tabularnewline
101 & 4696.96 & 4459.37535235478 & 237.58464764522 \tabularnewline
102 & 4621.4 & 4394.54378002604 & 226.856219973956 \tabularnewline
103 & 4562.84 & 4400.90134647826 & 161.938653521738 \tabularnewline
104 & 4202.52 & 4114.35597350577 & 88.164026494235 \tabularnewline
105 & 4296.49 & 4262.63786482052 & 33.8521351794845 \tabularnewline
106 & 4435.23 & 4526.11710050783 & -90.8871005078348 \tabularnewline
107 & 4105.18 & 4114.62359388622 & -9.4435938862209 \tabularnewline
108 & 4116.68 & 4287.37207046046 & -170.692070460459 \tabularnewline
109 & 3844.49 & 3777.55590824415 & 66.9340917558457 \tabularnewline
110 & 3720.98 & 3824.85307235633 & -103.873072356332 \tabularnewline
111 & 3674.4 & 3768.15519271046 & -93.7551927104564 \tabularnewline
112 & 3857.62 & 3977.54863426004 & -119.928634260039 \tabularnewline
113 & 3801.06 & 4003.90475196433 & -202.844751964330 \tabularnewline
114 & 3504.37 & 3598.54192114657 & -94.1719211465738 \tabularnewline
115 & 3032.6 & 3125.94758607654 & -93.3475860765388 \tabularnewline
116 & 3047.03 & 3168.46584407277 & -121.435844072772 \tabularnewline
117 & 2962.34 & 3127.76341408423 & -165.423414084230 \tabularnewline
118 & 2197.82 & 2084.53038087081 & 113.289619129190 \tabularnewline
119 & 2014.45 & 1877.3927155733 & 137.057284426701 \tabularnewline
120 & 1862.83 & 1914.68332931617 & -51.85332931617 \tabularnewline
121 & 1905.41 & 1936.5534971336 & -31.1434971335989 \tabularnewline
122 & 1810.99 & 1720.49172292983 & 90.4982770701676 \tabularnewline
123 & 1670.07 & 1592.63583850985 & 77.434161490152 \tabularnewline
124 & 1864.44 & 1933.83156176222 & -69.3915617622216 \tabularnewline
125 & 2052.02 & 2105.87220617235 & -53.8522061723506 \tabularnewline
126 & 2029.6 & 2151.18336073787 & -121.583360737874 \tabularnewline
127 & 2070.83 & 2134.4529743431 & -63.6229743431011 \tabularnewline
128 & 2293.41 & 2533.19956917905 & -239.78956917905 \tabularnewline
129 & 2443.27 & 2609.58390893535 & -166.313908935353 \tabularnewline
130 & 2513.17 & 2634.04289735311 & -120.872897353108 \tabularnewline
131 & 2466.92 & 2824.88267013467 & -357.962670134671 \tabularnewline
132 & 2502.66 & 2904.13932919437 & -401.479329194367 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105681&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]2782.9062769675[/C][C]701.8337230325[/C][/ROW]
[ROW][C]2[/C][C]3411.13[/C][C]2778.20485107048[/C][C]632.925148929522[/C][/ROW]
[ROW][C]3[/C][C]3288.18[/C][C]3011.41592595034[/C][C]276.764074049664[/C][/ROW]
[ROW][C]4[/C][C]3280.37[/C][C]3395.42294505136[/C][C]-115.052945051356[/C][/ROW]
[ROW][C]5[/C][C]3173.95[/C][C]3500.73185001672[/C][C]-326.781850016719[/C][/ROW]
[ROW][C]6[/C][C]3165.26[/C][C]3241.30139670025[/C][C]-76.0413967002547[/C][/ROW]
[ROW][C]7[/C][C]3092.71[/C][C]3419.42382524288[/C][C]-326.713825242876[/C][/ROW]
[ROW][C]8[/C][C]3053.05[/C][C]3268.99781322394[/C][C]-215.947813223945[/C][/ROW]
[ROW][C]9[/C][C]3181.96[/C][C]3191.80915380501[/C][C]-9.84915380500932[/C][/ROW]
[ROW][C]10[/C][C]2999.93[/C][C]3001.50298236305[/C][C]-1.57298236304665[/C][/ROW]
[ROW][C]11[/C][C]3249.57[/C][C]3361.08175818603[/C][C]-111.511758186028[/C][/ROW]
[ROW][C]12[/C][C]3210.52[/C][C]3428.88766156591[/C][C]-218.367661565914[/C][/ROW]
[ROW][C]13[/C][C]3030.29[/C][C]3493.32683474948[/C][C]-463.036834749478[/C][/ROW]
[ROW][C]14[/C][C]2803.47[/C][C]3269.58921825948[/C][C]-466.119218259476[/C][/ROW]
[ROW][C]15[/C][C]2767.63[/C][C]3281.56010261961[/C][C]-513.930102619609[/C][/ROW]
[ROW][C]16[/C][C]2882.6[/C][C]3471.57983976385[/C][C]-588.979839763845[/C][/ROW]
[ROW][C]17[/C][C]2863.36[/C][C]3043.87167181946[/C][C]-180.511671819459[/C][/ROW]
[ROW][C]18[/C][C]2897.06[/C][C]3072.03963437298[/C][C]-174.979634372979[/C][/ROW]
[ROW][C]19[/C][C]3012.61[/C][C]3097.24993334053[/C][C]-84.6399333405251[/C][/ROW]
[ROW][C]20[/C][C]3142.95[/C][C]3189.40437583695[/C][C]-46.4543758369476[/C][/ROW]
[ROW][C]21[/C][C]3032.93[/C][C]3094.17421316021[/C][C]-61.2442131602135[/C][/ROW]
[ROW][C]22[/C][C]3045.78[/C][C]2873.31410599627[/C][C]172.465894003733[/C][/ROW]
[ROW][C]23[/C][C]3110.52[/C][C]2952.50485978835[/C][C]158.015140211654[/C][/ROW]
[ROW][C]24[/C][C]3013.24[/C][C]3039.72840480046[/C][C]-26.4884048004578[/C][/ROW]
[ROW][C]25[/C][C]2987.1[/C][C]2996.52224692528[/C][C]-9.42224692528093[/C][/ROW]
[ROW][C]26[/C][C]2995.55[/C][C]2943.91454449975[/C][C]51.6354555002539[/C][/ROW]
[ROW][C]27[/C][C]2833.18[/C][C]2666.48140967185[/C][C]166.698590328153[/C][/ROW]
[ROW][C]28[/C][C]2848.96[/C][C]2826.51939823056[/C][C]22.4406017694427[/C][/ROW]
[ROW][C]29[/C][C]2794.83[/C][C]2987.19462930827[/C][C]-192.364629308267[/C][/ROW]
[ROW][C]30[/C][C]2845.26[/C][C]3009.92204066049[/C][C]-164.662040660487[/C][/ROW]
[ROW][C]31[/C][C]2915.02[/C][C]2810.96679487084[/C][C]104.053205129165[/C][/ROW]
[ROW][C]32[/C][C]2892.63[/C][C]2663.17527363360[/C][C]229.454726366404[/C][/ROW]
[ROW][C]33[/C][C]2604.42[/C][C]2106.36423852480[/C][C]498.055761475196[/C][/ROW]
[ROW][C]34[/C][C]2641.65[/C][C]2171.90268293308[/C][C]469.747317066924[/C][/ROW]
[ROW][C]35[/C][C]2659.81[/C][C]2369.79939967285[/C][C]290.010600327151[/C][/ROW]
[ROW][C]36[/C][C]2638.53[/C][C]2451.67492933541[/C][C]186.855070664595[/C][/ROW]
[ROW][C]37[/C][C]2720.25[/C][C]2506.44299736808[/C][C]213.807002631916[/C][/ROW]
[ROW][C]38[/C][C]2745.88[/C][C]2442.06806342782[/C][C]303.81193657218[/C][/ROW]
[ROW][C]39[/C][C]2735.7[/C][C]2702.38180906088[/C][C]33.318190939116[/C][/ROW]
[ROW][C]40[/C][C]2811.7[/C][C]2508.4193965516[/C][C]303.2806034484[/C][/ROW]
[ROW][C]41[/C][C]2799.43[/C][C]2504.98174463288[/C][C]294.448255367116[/C][/ROW]
[ROW][C]42[/C][C]2555.28[/C][C]2233.7353243904[/C][C]321.544675609602[/C][/ROW]
[ROW][C]43[/C][C]2304.98[/C][C]1908.80279944558[/C][C]396.177200554424[/C][/ROW]
[ROW][C]44[/C][C]2214.95[/C][C]1945.60573095747[/C][C]269.344269042525[/C][/ROW]
[ROW][C]45[/C][C]2065.81[/C][C]1781.77926015096[/C][C]284.030739849039[/C][/ROW]
[ROW][C]46[/C][C]1940.49[/C][C]1698.34022526250[/C][C]242.149774737498[/C][/ROW]
[ROW][C]47[/C][C]2042[/C][C]1877.29997118634[/C][C]164.700028813658[/C][/ROW]
[ROW][C]48[/C][C]1995.37[/C][C]1815.57251427901[/C][C]179.797485720992[/C][/ROW]
[ROW][C]49[/C][C]1946.81[/C][C]1888.86428465092[/C][C]57.9457153490768[/C][/ROW]
[ROW][C]50[/C][C]1765.9[/C][C]1715.18955333380[/C][C]50.7104466662039[/C][/ROW]
[ROW][C]51[/C][C]1635.25[/C][C]1678.39048532208[/C][C]-43.1404853220774[/C][/ROW]
[ROW][C]52[/C][C]1833.42[/C][C]1806.95305272367[/C][C]26.4669472763262[/C][/ROW]
[ROW][C]53[/C][C]1910.43[/C][C]2069.59072607337[/C][C]-159.160726073366[/C][/ROW]
[ROW][C]54[/C][C]1959.67[/C][C]2429.16326929088[/C][C]-469.493269290875[/C][/ROW]
[ROW][C]55[/C][C]1969.6[/C][C]2385.04093186342[/C][C]-415.440931863422[/C][/ROW]
[ROW][C]56[/C][C]2061.41[/C][C]2348.56867776170[/C][C]-287.158677761696[/C][/ROW]
[ROW][C]57[/C][C]2093.48[/C][C]2554.86041680655[/C][C]-461.380416806548[/C][/ROW]
[ROW][C]58[/C][C]2120.88[/C][C]2419.67942614705[/C][C]-298.799426147051[/C][/ROW]
[ROW][C]59[/C][C]2174.56[/C][C]2504.58264818782[/C][C]-330.022648187820[/C][/ROW]
[ROW][C]60[/C][C]2196.72[/C][C]2630.18922282629[/C][C]-433.46922282629[/C][/ROW]
[ROW][C]61[/C][C]2350.44[/C][C]2839.07854317997[/C][C]-488.638543179968[/C][/ROW]
[ROW][C]62[/C][C]2440.25[/C][C]2845.13567181262[/C][C]-404.885671812615[/C][/ROW]
[ROW][C]63[/C][C]2408.64[/C][C]2820.44757767273[/C][C]-411.807577672728[/C][/ROW]
[ROW][C]64[/C][C]2472.81[/C][C]2950.71515592222[/C][C]-477.905155922219[/C][/ROW]
[ROW][C]65[/C][C]2407.6[/C][C]2683.68606159096[/C][C]-276.086061590964[/C][/ROW]
[ROW][C]66[/C][C]2454.62[/C][C]2901.20733260169[/C][C]-446.587332601692[/C][/ROW]
[ROW][C]67[/C][C]2448.05[/C][C]2679.27996319084[/C][C]-231.229963190844[/C][/ROW]
[ROW][C]68[/C][C]2497.84[/C][C]2678.89964407652[/C][C]-181.059644076521[/C][/ROW]
[ROW][C]69[/C][C]2645.64[/C][C]2739.8367105046[/C][C]-94.196710504604[/C][/ROW]
[ROW][C]70[/C][C]2756.76[/C][C]2626.74520724389[/C][C]130.014792756113[/C][/ROW]
[ROW][C]71[/C][C]2849.27[/C][C]2787.08769118939[/C][C]62.1823088106102[/C][/ROW]
[ROW][C]72[/C][C]2921.44[/C][C]2923.54360488827[/C][C]-2.10360488827106[/C][/ROW]
[ROW][C]73[/C][C]2981.85[/C][C]2913.08507269532[/C][C]68.7649273046758[/C][/ROW]
[ROW][C]74[/C][C]3080.58[/C][C]3114.46573186389[/C][C]-33.8857318638916[/C][/ROW]
[ROW][C]75[/C][C]3106.22[/C][C]3167.1284348135[/C][C]-60.9084348135003[/C][/ROW]
[ROW][C]76[/C][C]3119.31[/C][C]2965.75729212929[/C][C]153.552707870711[/C][/ROW]
[ROW][C]77[/C][C]3061.26[/C][C]2900.64980171626[/C][C]160.610198283741[/C][/ROW]
[ROW][C]78[/C][C]3097.31[/C][C]3026.99739807435[/C][C]70.3126019256458[/C][/ROW]
[ROW][C]79[/C][C]3161.69[/C][C]3118.26656397278[/C][C]43.4234360272214[/C][/ROW]
[ROW][C]80[/C][C]3257.16[/C][C]3155.36489682314[/C][C]101.795103176863[/C][/ROW]
[ROW][C]81[/C][C]3277.01[/C][C]3151.04445248029[/C][C]125.965547519714[/C][/ROW]
[ROW][C]82[/C][C]3295.32[/C][C]3161.33003187036[/C][C]133.989968129645[/C][/ROW]
[ROW][C]83[/C][C]3363.99[/C][C]3342.80352269739[/C][C]21.1864773026068[/C][/ROW]
[ROW][C]84[/C][C]3494.17[/C][C]3551.49276747278[/C][C]-57.3227674727801[/C][/ROW]
[ROW][C]85[/C][C]3667.03[/C][C]3644.79645752556[/C][C]22.2335424744379[/C][/ROW]
[ROW][C]86[/C][C]3813.06[/C][C]3691.5772318172[/C][C]121.482768182800[/C][/ROW]
[ROW][C]87[/C][C]3917.96[/C][C]3667.16351586527[/C][C]250.796484134733[/C][/ROW]
[ROW][C]88[/C][C]3895.51[/C][C]3745.20465282433[/C][C]150.305347175671[/C][/ROW]
[ROW][C]89[/C][C]3801.06[/C][C]3655.53164839026[/C][C]145.528351609735[/C][/ROW]
[ROW][C]90[/C][C]3570.12[/C][C]3391.76836134461[/C][C]178.351638655387[/C][/ROW]
[ROW][C]91[/C][C]3701.61[/C][C]3385.78426232281[/C][C]315.825737677191[/C][/ROW]
[ROW][C]92[/C][C]3862.27[/C][C]3527.54239550801[/C][C]334.727604491992[/C][/ROW]
[ROW][C]93[/C][C]3970.1[/C][C]3648.06199939585[/C][C]322.038000604154[/C][/ROW]
[ROW][C]94[/C][C]4138.52[/C][C]3894.07420353413[/C][C]244.445796465871[/C][/ROW]
[ROW][C]95[/C][C]4199.75[/C][C]3971.26211885784[/C][C]228.487881142157[/C][/ROW]
[ROW][C]96[/C][C]4290.89[/C][C]3965.84238011712[/C][C]325.047619882884[/C][/ROW]
[ROW][C]97[/C][C]4443.91[/C][C]4138.07831343244[/C][C]305.831686567559[/C][/ROW]
[ROW][C]98[/C][C]4502.64[/C][C]4204.27990036321[/C][C]298.360099636787[/C][/ROW]
[ROW][C]99[/C][C]4356.98[/C][C]3986.66242748394[/C][C]370.317572516063[/C][/ROW]
[ROW][C]100[/C][C]4591.27[/C][C]4229.92187701452[/C][C]361.348122985484[/C][/ROW]
[ROW][C]101[/C][C]4696.96[/C][C]4459.37535235478[/C][C]237.58464764522[/C][/ROW]
[ROW][C]102[/C][C]4621.4[/C][C]4394.54378002604[/C][C]226.856219973956[/C][/ROW]
[ROW][C]103[/C][C]4562.84[/C][C]4400.90134647826[/C][C]161.938653521738[/C][/ROW]
[ROW][C]104[/C][C]4202.52[/C][C]4114.35597350577[/C][C]88.164026494235[/C][/ROW]
[ROW][C]105[/C][C]4296.49[/C][C]4262.63786482052[/C][C]33.8521351794845[/C][/ROW]
[ROW][C]106[/C][C]4435.23[/C][C]4526.11710050783[/C][C]-90.8871005078348[/C][/ROW]
[ROW][C]107[/C][C]4105.18[/C][C]4114.62359388622[/C][C]-9.4435938862209[/C][/ROW]
[ROW][C]108[/C][C]4116.68[/C][C]4287.37207046046[/C][C]-170.692070460459[/C][/ROW]
[ROW][C]109[/C][C]3844.49[/C][C]3777.55590824415[/C][C]66.9340917558457[/C][/ROW]
[ROW][C]110[/C][C]3720.98[/C][C]3824.85307235633[/C][C]-103.873072356332[/C][/ROW]
[ROW][C]111[/C][C]3674.4[/C][C]3768.15519271046[/C][C]-93.7551927104564[/C][/ROW]
[ROW][C]112[/C][C]3857.62[/C][C]3977.54863426004[/C][C]-119.928634260039[/C][/ROW]
[ROW][C]113[/C][C]3801.06[/C][C]4003.90475196433[/C][C]-202.844751964330[/C][/ROW]
[ROW][C]114[/C][C]3504.37[/C][C]3598.54192114657[/C][C]-94.1719211465738[/C][/ROW]
[ROW][C]115[/C][C]3032.6[/C][C]3125.94758607654[/C][C]-93.3475860765388[/C][/ROW]
[ROW][C]116[/C][C]3047.03[/C][C]3168.46584407277[/C][C]-121.435844072772[/C][/ROW]
[ROW][C]117[/C][C]2962.34[/C][C]3127.76341408423[/C][C]-165.423414084230[/C][/ROW]
[ROW][C]118[/C][C]2197.82[/C][C]2084.53038087081[/C][C]113.289619129190[/C][/ROW]
[ROW][C]119[/C][C]2014.45[/C][C]1877.3927155733[/C][C]137.057284426701[/C][/ROW]
[ROW][C]120[/C][C]1862.83[/C][C]1914.68332931617[/C][C]-51.85332931617[/C][/ROW]
[ROW][C]121[/C][C]1905.41[/C][C]1936.5534971336[/C][C]-31.1434971335989[/C][/ROW]
[ROW][C]122[/C][C]1810.99[/C][C]1720.49172292983[/C][C]90.4982770701676[/C][/ROW]
[ROW][C]123[/C][C]1670.07[/C][C]1592.63583850985[/C][C]77.434161490152[/C][/ROW]
[ROW][C]124[/C][C]1864.44[/C][C]1933.83156176222[/C][C]-69.3915617622216[/C][/ROW]
[ROW][C]125[/C][C]2052.02[/C][C]2105.87220617235[/C][C]-53.8522061723506[/C][/ROW]
[ROW][C]126[/C][C]2029.6[/C][C]2151.18336073787[/C][C]-121.583360737874[/C][/ROW]
[ROW][C]127[/C][C]2070.83[/C][C]2134.4529743431[/C][C]-63.6229743431011[/C][/ROW]
[ROW][C]128[/C][C]2293.41[/C][C]2533.19956917905[/C][C]-239.78956917905[/C][/ROW]
[ROW][C]129[/C][C]2443.27[/C][C]2609.58390893535[/C][C]-166.313908935353[/C][/ROW]
[ROW][C]130[/C][C]2513.17[/C][C]2634.04289735311[/C][C]-120.872897353108[/C][/ROW]
[ROW][C]131[/C][C]2466.92[/C][C]2824.88267013467[/C][C]-357.962670134671[/C][/ROW]
[ROW][C]132[/C][C]2502.66[/C][C]2904.13932919437[/C][C]-401.479329194367[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105681&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105681&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
13484.742782.9062769675701.8337230325
23411.132778.20485107048632.925148929522
33288.183011.41592595034276.764074049664
43280.373395.42294505136-115.052945051356
53173.953500.73185001672-326.781850016719
63165.263241.30139670025-76.0413967002547
73092.713419.42382524288-326.713825242876
83053.053268.99781322394-215.947813223945
93181.963191.80915380501-9.84915380500932
102999.933001.50298236305-1.57298236304665
113249.573361.08175818603-111.511758186028
123210.523428.88766156591-218.367661565914
133030.293493.32683474948-463.036834749478
142803.473269.58921825948-466.119218259476
152767.633281.56010261961-513.930102619609
162882.63471.57983976385-588.979839763845
172863.363043.87167181946-180.511671819459
182897.063072.03963437298-174.979634372979
193012.613097.24993334053-84.6399333405251
203142.953189.40437583695-46.4543758369476
213032.933094.17421316021-61.2442131602135
223045.782873.31410599627172.465894003733
233110.522952.50485978835158.015140211654
243013.243039.72840480046-26.4884048004578
252987.12996.52224692528-9.42224692528093
262995.552943.9145444997551.6354555002539
272833.182666.48140967185166.698590328153
282848.962826.5193982305622.4406017694427
292794.832987.19462930827-192.364629308267
302845.263009.92204066049-164.662040660487
312915.022810.96679487084104.053205129165
322892.632663.17527363360229.454726366404
332604.422106.36423852480498.055761475196
342641.652171.90268293308469.747317066924
352659.812369.79939967285290.010600327151
362638.532451.67492933541186.855070664595
372720.252506.44299736808213.807002631916
382745.882442.06806342782303.81193657218
392735.72702.3818090608833.318190939116
402811.72508.4193965516303.2806034484
412799.432504.98174463288294.448255367116
422555.282233.7353243904321.544675609602
432304.981908.80279944558396.177200554424
442214.951945.60573095747269.344269042525
452065.811781.77926015096284.030739849039
461940.491698.34022526250242.149774737498
4720421877.29997118634164.700028813658
481995.371815.57251427901179.797485720992
491946.811888.8642846509257.9457153490768
501765.91715.1895533338050.7104466662039
511635.251678.39048532208-43.1404853220774
521833.421806.9530527236726.4669472763262
531910.432069.59072607337-159.160726073366
541959.672429.16326929088-469.493269290875
551969.62385.04093186342-415.440931863422
562061.412348.56867776170-287.158677761696
572093.482554.86041680655-461.380416806548
582120.882419.67942614705-298.799426147051
592174.562504.58264818782-330.022648187820
602196.722630.18922282629-433.46922282629
612350.442839.07854317997-488.638543179968
622440.252845.13567181262-404.885671812615
632408.642820.44757767273-411.807577672728
642472.812950.71515592222-477.905155922219
652407.62683.68606159096-276.086061590964
662454.622901.20733260169-446.587332601692
672448.052679.27996319084-231.229963190844
682497.842678.89964407652-181.059644076521
692645.642739.8367105046-94.196710504604
702756.762626.74520724389130.014792756113
712849.272787.0876911893962.1823088106102
722921.442923.54360488827-2.10360488827106
732981.852913.0850726953268.7649273046758
743080.583114.46573186389-33.8857318638916
753106.223167.1284348135-60.9084348135003
763119.312965.75729212929153.552707870711
773061.262900.64980171626160.610198283741
783097.313026.9973980743570.3126019256458
793161.693118.2665639727843.4234360272214
803257.163155.36489682314101.795103176863
813277.013151.04445248029125.965547519714
823295.323161.33003187036133.989968129645
833363.993342.8035226973921.1864773026068
843494.173551.49276747278-57.3227674727801
853667.033644.7964575255622.2335424744379
863813.063691.5772318172121.482768182800
873917.963667.16351586527250.796484134733
883895.513745.20465282433150.305347175671
893801.063655.53164839026145.528351609735
903570.123391.76836134461178.351638655387
913701.613385.78426232281315.825737677191
923862.273527.54239550801334.727604491992
933970.13648.06199939585322.038000604154
944138.523894.07420353413244.445796465871
954199.753971.26211885784228.487881142157
964290.893965.84238011712325.047619882884
974443.914138.07831343244305.831686567559
984502.644204.27990036321298.360099636787
994356.983986.66242748394370.317572516063
1004591.274229.92187701452361.348122985484
1014696.964459.37535235478237.58464764522
1024621.44394.54378002604226.856219973956
1034562.844400.90134647826161.938653521738
1044202.524114.3559735057788.164026494235
1054296.494262.6378648205233.8521351794845
1064435.234526.11710050783-90.8871005078348
1074105.184114.62359388622-9.4435938862209
1084116.684287.37207046046-170.692070460459
1093844.493777.5559082441566.9340917558457
1103720.983824.85307235633-103.873072356332
1113674.43768.15519271046-93.7551927104564
1123857.623977.54863426004-119.928634260039
1133801.064003.90475196433-202.844751964330
1143504.373598.54192114657-94.1719211465738
1153032.63125.94758607654-93.3475860765388
1163047.033168.46584407277-121.435844072772
1172962.343127.76341408423-165.423414084230
1182197.822084.53038087081113.289619129190
1192014.451877.3927155733137.057284426701
1201862.831914.68332931617-51.85332931617
1211905.411936.5534971336-31.1434971335989
1221810.991720.4917229298390.4982770701676
1231670.071592.6358385098577.434161490152
1241864.441933.83156176222-69.3915617622216
1252052.022105.87220617235-53.8522061723506
1262029.62151.18336073787-121.583360737874
1272070.832134.4529743431-63.6229743431011
1282293.412533.19956917905-239.78956917905
1292443.272609.58390893535-166.313908935353
1302513.172634.04289735311-120.872897353108
1312466.922824.88267013467-357.962670134671
1322502.662904.13932919437-401.479329194367






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.03239979735292950.06479959470585910.96760020264707
120.01528632640207160.03057265280414320.984713673597928
130.00579310282952340.01158620565904680.994206897170477
140.01492370535223760.02984741070447520.985076294647762
150.01830477285462250.0366095457092450.981695227145377
160.01429978006082650.02859956012165310.985700219939173
170.04451914503430030.08903829006860070.9554808549657
180.03957291035603550.0791458207120710.960427089643965
190.02934336785969130.05868673571938270.970656632140309
200.02433082052820030.04866164105640050.9756691794718
210.01743158226097190.03486316452194380.982568417739028
220.01005809813883150.02011619627766300.989941901861169
230.005554520861070980.01110904172214200.994445479138929
240.00858846718210040.01717693436420080.9914115328179
250.01757022658087830.03514045316175660.982429773419122
260.01908671122566860.03817342245133720.980913288774331
270.0409838550882410.0819677101764820.959016144911759
280.07386563813118210.1477312762623640.926134361868818
290.08528145127334870.1705629025466970.914718548726651
300.07959566156490180.1591913231298040.920404338435098
310.06183329319974190.1236665863994840.938166706800258
320.04328634076454550.0865726815290910.956713659235454
330.04688448974456340.09376897948912680.953115510255437
340.03991774693976420.07983549387952850.960082253060236
350.03340561950805510.06681123901611020.966594380491945
360.02445935970613340.04891871941226680.975540640293867
370.01851200840085860.03702401680171720.981487991599141
380.01913438577469290.03826877154938580.980865614225307
390.01381702083472160.02763404166944320.986182979165278
400.01570390674899410.03140781349798820.984296093251006
410.01172299774890050.02344599549780090.9882770022511
420.01279734039758770.02559468079517540.987202659602412
430.04623888177520090.09247776355040180.953761118224799
440.1119048180286570.2238096360573140.888095181971343
450.1794353675148180.3588707350296360.820564632485182
460.2980145183388530.5960290366777060.701985481661147
470.400206048975690.800412097951380.59979395102431
480.4346506964551880.8693013929103770.565349303544812
490.4509833145697620.9019666291395250.549016685430238
500.4148734210834870.8297468421669740.585126578916513
510.427696112371620.855392224743240.57230388762838
520.6603971332231790.6792057335536420.339602866776821
530.7705069916152730.4589860167694530.229493008384727
540.8832014378009720.2335971243980560.116798562199028
550.9153912162701250.1692175674597510.0846087837298755
560.9028797986302270.1942404027395450.0971202013697726
570.9146238134322670.1707523731354670.0853761865677333
580.927248530376460.1455029392470790.0727514696235395
590.9185825606891410.1628348786217180.081417439310859
600.920074721874450.1598505562510980.0799252781255492
610.9346848670811140.1306302658377720.0653151329188861
620.9317474338234180.1365051323531650.0682525661765824
630.9546966912519930.09060661749601320.0453033087480066
640.991810970937030.01637805812594050.00818902906297025
650.9959752901862680.00804941962746350.00402470981373175
660.9992853334667160.001429333066568570.000714666533284283
670.999818912438320.0003621751233608320.000181087561680416
680.9999326439173290.0001347121653418026.73560826709012e-05
690.9999750228827834.99542344347484e-052.49771172173742e-05
700.9999920983651331.58032697344862e-057.90163486724312e-06
710.9999959926290068.0147419883021e-064.00737099415105e-06
720.9999967718518186.45629636470691e-063.22814818235346e-06
730.9999973914476915.21710461742972e-062.60855230871486e-06
740.9999978093150344.38136993116523e-062.19068496558262e-06
750.9999984185720123.16285597597775e-061.58142798798887e-06
760.9999986613751822.67724963630439e-061.33862481815219e-06
770.9999988956760512.20864789754283e-061.10432394877142e-06
780.9999989765124052.04697519069756e-061.02348759534878e-06
790.9999987024435742.59511285117537e-061.29755642558768e-06
800.999998494707193.01058562045257e-061.50529281022628e-06
810.9999985475632512.90487349773240e-061.45243674886620e-06
820.9999985167722412.96645551698758e-061.48322775849379e-06
830.9999983205800463.3588399073991e-061.67941995369955e-06
840.9999995519759668.96048068353057e-074.48024034176528e-07
850.9999998767119672.46576065540835e-071.23288032770418e-07
860.9999999340084671.31983066215823e-076.59915331079114e-08
870.9999999241197621.51760475101662e-077.58802375508312e-08
880.999999968408336.31833410290195e-083.15916705145098e-08
890.9999999737255545.25488928073461e-082.62744464036730e-08
900.9999999838185383.23629239456539e-081.61814619728270e-08
910.99999996705286.58943995559013e-083.29471997779507e-08
920.9999999324741771.35051645973568e-076.75258229867841e-08
930.9999998739537662.52092468638194e-071.26046234319097e-07
940.9999998918156462.16368708985461e-071.08184354492731e-07
950.999999837881893.24236222101166e-071.62118111050583e-07
960.9999996951806366.09638726899165e-073.04819363449582e-07
970.9999994608164661.07836706715209e-065.39183533576043e-07
980.9999986325351372.73492972584283e-061.36746486292141e-06
990.9999966146331946.77073361146977e-063.38536680573488e-06
1000.999993308852591.33822948188023e-056.69114740940116e-06
1010.999986651950612.66960987830777e-051.33480493915389e-05
1020.9999751802028264.96395943488386e-052.48197971744193e-05
1030.9999581999615698.36000768627274e-054.18000384313637e-05
1040.9999155987991050.0001688024017903878.44012008951936e-05
1050.9998766726475040.0002466547049919230.000123327352495962
1060.9997953782771640.0004092434456717170.000204621722835859
1070.999784380685140.0004312386297201750.000215619314860088
1080.9996097416494990.0007805167010027930.000390258350501396
1090.9999705175529145.89648941717101e-052.94824470858551e-05
1100.9999759055708354.81888583306757e-052.40944291653379e-05
1110.99995168395029.66320995995522e-054.83160497997761e-05
1120.9999481560730690.0001036878538627165.18439269313579e-05
1130.9999247558638480.0001504882723031587.52441361515789e-05
1140.999984790068173.04198636579714e-051.52099318289857e-05
1150.9999364336334280.0001271327331446186.35663665723088e-05
1160.9997435982929430.0005128034141134660.000256401707056733
1170.9994831645039850.001033670992030540.000516835496015269
1180.9979720447804350.004055910439130220.00202795521956511
1190.9994855883136630.001028823372674640.000514411686337322
1200.9972920380615220.00541592387695590.00270796193847795
1210.9863266974486950.0273466051026090.0136733025513045

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0323997973529295 & 0.0647995947058591 & 0.96760020264707 \tabularnewline
12 & 0.0152863264020716 & 0.0305726528041432 & 0.984713673597928 \tabularnewline
13 & 0.0057931028295234 & 0.0115862056590468 & 0.994206897170477 \tabularnewline
14 & 0.0149237053522376 & 0.0298474107044752 & 0.985076294647762 \tabularnewline
15 & 0.0183047728546225 & 0.036609545709245 & 0.981695227145377 \tabularnewline
16 & 0.0142997800608265 & 0.0285995601216531 & 0.985700219939173 \tabularnewline
17 & 0.0445191450343003 & 0.0890382900686007 & 0.9554808549657 \tabularnewline
18 & 0.0395729103560355 & 0.079145820712071 & 0.960427089643965 \tabularnewline
19 & 0.0293433678596913 & 0.0586867357193827 & 0.970656632140309 \tabularnewline
20 & 0.0243308205282003 & 0.0486616410564005 & 0.9756691794718 \tabularnewline
21 & 0.0174315822609719 & 0.0348631645219438 & 0.982568417739028 \tabularnewline
22 & 0.0100580981388315 & 0.0201161962776630 & 0.989941901861169 \tabularnewline
23 & 0.00555452086107098 & 0.0111090417221420 & 0.994445479138929 \tabularnewline
24 & 0.0085884671821004 & 0.0171769343642008 & 0.9914115328179 \tabularnewline
25 & 0.0175702265808783 & 0.0351404531617566 & 0.982429773419122 \tabularnewline
26 & 0.0190867112256686 & 0.0381734224513372 & 0.980913288774331 \tabularnewline
27 & 0.040983855088241 & 0.081967710176482 & 0.959016144911759 \tabularnewline
28 & 0.0738656381311821 & 0.147731276262364 & 0.926134361868818 \tabularnewline
29 & 0.0852814512733487 & 0.170562902546697 & 0.914718548726651 \tabularnewline
30 & 0.0795956615649018 & 0.159191323129804 & 0.920404338435098 \tabularnewline
31 & 0.0618332931997419 & 0.123666586399484 & 0.938166706800258 \tabularnewline
32 & 0.0432863407645455 & 0.086572681529091 & 0.956713659235454 \tabularnewline
33 & 0.0468844897445634 & 0.0937689794891268 & 0.953115510255437 \tabularnewline
34 & 0.0399177469397642 & 0.0798354938795285 & 0.960082253060236 \tabularnewline
35 & 0.0334056195080551 & 0.0668112390161102 & 0.966594380491945 \tabularnewline
36 & 0.0244593597061334 & 0.0489187194122668 & 0.975540640293867 \tabularnewline
37 & 0.0185120084008586 & 0.0370240168017172 & 0.981487991599141 \tabularnewline
38 & 0.0191343857746929 & 0.0382687715493858 & 0.980865614225307 \tabularnewline
39 & 0.0138170208347216 & 0.0276340416694432 & 0.986182979165278 \tabularnewline
40 & 0.0157039067489941 & 0.0314078134979882 & 0.984296093251006 \tabularnewline
41 & 0.0117229977489005 & 0.0234459954978009 & 0.9882770022511 \tabularnewline
42 & 0.0127973403975877 & 0.0255946807951754 & 0.987202659602412 \tabularnewline
43 & 0.0462388817752009 & 0.0924777635504018 & 0.953761118224799 \tabularnewline
44 & 0.111904818028657 & 0.223809636057314 & 0.888095181971343 \tabularnewline
45 & 0.179435367514818 & 0.358870735029636 & 0.820564632485182 \tabularnewline
46 & 0.298014518338853 & 0.596029036677706 & 0.701985481661147 \tabularnewline
47 & 0.40020604897569 & 0.80041209795138 & 0.59979395102431 \tabularnewline
48 & 0.434650696455188 & 0.869301392910377 & 0.565349303544812 \tabularnewline
49 & 0.450983314569762 & 0.901966629139525 & 0.549016685430238 \tabularnewline
50 & 0.414873421083487 & 0.829746842166974 & 0.585126578916513 \tabularnewline
51 & 0.42769611237162 & 0.85539222474324 & 0.57230388762838 \tabularnewline
52 & 0.660397133223179 & 0.679205733553642 & 0.339602866776821 \tabularnewline
53 & 0.770506991615273 & 0.458986016769453 & 0.229493008384727 \tabularnewline
54 & 0.883201437800972 & 0.233597124398056 & 0.116798562199028 \tabularnewline
55 & 0.915391216270125 & 0.169217567459751 & 0.0846087837298755 \tabularnewline
56 & 0.902879798630227 & 0.194240402739545 & 0.0971202013697726 \tabularnewline
57 & 0.914623813432267 & 0.170752373135467 & 0.0853761865677333 \tabularnewline
58 & 0.92724853037646 & 0.145502939247079 & 0.0727514696235395 \tabularnewline
59 & 0.918582560689141 & 0.162834878621718 & 0.081417439310859 \tabularnewline
60 & 0.92007472187445 & 0.159850556251098 & 0.0799252781255492 \tabularnewline
61 & 0.934684867081114 & 0.130630265837772 & 0.0653151329188861 \tabularnewline
62 & 0.931747433823418 & 0.136505132353165 & 0.0682525661765824 \tabularnewline
63 & 0.954696691251993 & 0.0906066174960132 & 0.0453033087480066 \tabularnewline
64 & 0.99181097093703 & 0.0163780581259405 & 0.00818902906297025 \tabularnewline
65 & 0.995975290186268 & 0.0080494196274635 & 0.00402470981373175 \tabularnewline
66 & 0.999285333466716 & 0.00142933306656857 & 0.000714666533284283 \tabularnewline
67 & 0.99981891243832 & 0.000362175123360832 & 0.000181087561680416 \tabularnewline
68 & 0.999932643917329 & 0.000134712165341802 & 6.73560826709012e-05 \tabularnewline
69 & 0.999975022882783 & 4.99542344347484e-05 & 2.49771172173742e-05 \tabularnewline
70 & 0.999992098365133 & 1.58032697344862e-05 & 7.90163486724312e-06 \tabularnewline
71 & 0.999995992629006 & 8.0147419883021e-06 & 4.00737099415105e-06 \tabularnewline
72 & 0.999996771851818 & 6.45629636470691e-06 & 3.22814818235346e-06 \tabularnewline
73 & 0.999997391447691 & 5.21710461742972e-06 & 2.60855230871486e-06 \tabularnewline
74 & 0.999997809315034 & 4.38136993116523e-06 & 2.19068496558262e-06 \tabularnewline
75 & 0.999998418572012 & 3.16285597597775e-06 & 1.58142798798887e-06 \tabularnewline
76 & 0.999998661375182 & 2.67724963630439e-06 & 1.33862481815219e-06 \tabularnewline
77 & 0.999998895676051 & 2.20864789754283e-06 & 1.10432394877142e-06 \tabularnewline
78 & 0.999998976512405 & 2.04697519069756e-06 & 1.02348759534878e-06 \tabularnewline
79 & 0.999998702443574 & 2.59511285117537e-06 & 1.29755642558768e-06 \tabularnewline
80 & 0.99999849470719 & 3.01058562045257e-06 & 1.50529281022628e-06 \tabularnewline
81 & 0.999998547563251 & 2.90487349773240e-06 & 1.45243674886620e-06 \tabularnewline
82 & 0.999998516772241 & 2.96645551698758e-06 & 1.48322775849379e-06 \tabularnewline
83 & 0.999998320580046 & 3.3588399073991e-06 & 1.67941995369955e-06 \tabularnewline
84 & 0.999999551975966 & 8.96048068353057e-07 & 4.48024034176528e-07 \tabularnewline
85 & 0.999999876711967 & 2.46576065540835e-07 & 1.23288032770418e-07 \tabularnewline
86 & 0.999999934008467 & 1.31983066215823e-07 & 6.59915331079114e-08 \tabularnewline
87 & 0.999999924119762 & 1.51760475101662e-07 & 7.58802375508312e-08 \tabularnewline
88 & 0.99999996840833 & 6.31833410290195e-08 & 3.15916705145098e-08 \tabularnewline
89 & 0.999999973725554 & 5.25488928073461e-08 & 2.62744464036730e-08 \tabularnewline
90 & 0.999999983818538 & 3.23629239456539e-08 & 1.61814619728270e-08 \tabularnewline
91 & 0.9999999670528 & 6.58943995559013e-08 & 3.29471997779507e-08 \tabularnewline
92 & 0.999999932474177 & 1.35051645973568e-07 & 6.75258229867841e-08 \tabularnewline
93 & 0.999999873953766 & 2.52092468638194e-07 & 1.26046234319097e-07 \tabularnewline
94 & 0.999999891815646 & 2.16368708985461e-07 & 1.08184354492731e-07 \tabularnewline
95 & 0.99999983788189 & 3.24236222101166e-07 & 1.62118111050583e-07 \tabularnewline
96 & 0.999999695180636 & 6.09638726899165e-07 & 3.04819363449582e-07 \tabularnewline
97 & 0.999999460816466 & 1.07836706715209e-06 & 5.39183533576043e-07 \tabularnewline
98 & 0.999998632535137 & 2.73492972584283e-06 & 1.36746486292141e-06 \tabularnewline
99 & 0.999996614633194 & 6.77073361146977e-06 & 3.38536680573488e-06 \tabularnewline
100 & 0.99999330885259 & 1.33822948188023e-05 & 6.69114740940116e-06 \tabularnewline
101 & 0.99998665195061 & 2.66960987830777e-05 & 1.33480493915389e-05 \tabularnewline
102 & 0.999975180202826 & 4.96395943488386e-05 & 2.48197971744193e-05 \tabularnewline
103 & 0.999958199961569 & 8.36000768627274e-05 & 4.18000384313637e-05 \tabularnewline
104 & 0.999915598799105 & 0.000168802401790387 & 8.44012008951936e-05 \tabularnewline
105 & 0.999876672647504 & 0.000246654704991923 & 0.000123327352495962 \tabularnewline
106 & 0.999795378277164 & 0.000409243445671717 & 0.000204621722835859 \tabularnewline
107 & 0.99978438068514 & 0.000431238629720175 & 0.000215619314860088 \tabularnewline
108 & 0.999609741649499 & 0.000780516701002793 & 0.000390258350501396 \tabularnewline
109 & 0.999970517552914 & 5.89648941717101e-05 & 2.94824470858551e-05 \tabularnewline
110 & 0.999975905570835 & 4.81888583306757e-05 & 2.40944291653379e-05 \tabularnewline
111 & 0.9999516839502 & 9.66320995995522e-05 & 4.83160497997761e-05 \tabularnewline
112 & 0.999948156073069 & 0.000103687853862716 & 5.18439269313579e-05 \tabularnewline
113 & 0.999924755863848 & 0.000150488272303158 & 7.52441361515789e-05 \tabularnewline
114 & 0.99998479006817 & 3.04198636579714e-05 & 1.52099318289857e-05 \tabularnewline
115 & 0.999936433633428 & 0.000127132733144618 & 6.35663665723088e-05 \tabularnewline
116 & 0.999743598292943 & 0.000512803414113466 & 0.000256401707056733 \tabularnewline
117 & 0.999483164503985 & 0.00103367099203054 & 0.000516835496015269 \tabularnewline
118 & 0.997972044780435 & 0.00405591043913022 & 0.00202795521956511 \tabularnewline
119 & 0.999485588313663 & 0.00102882337267464 & 0.000514411686337322 \tabularnewline
120 & 0.997292038061522 & 0.0054159238769559 & 0.00270796193847795 \tabularnewline
121 & 0.986326697448695 & 0.027346605102609 & 0.0136733025513045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105681&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]0.0323997973529295[/C][C]0.0647995947058591[/C][C]0.96760020264707[/C][/ROW]
[ROW][C]12[/C][C]0.0152863264020716[/C][C]0.0305726528041432[/C][C]0.984713673597928[/C][/ROW]
[ROW][C]13[/C][C]0.0057931028295234[/C][C]0.0115862056590468[/C][C]0.994206897170477[/C][/ROW]
[ROW][C]14[/C][C]0.0149237053522376[/C][C]0.0298474107044752[/C][C]0.985076294647762[/C][/ROW]
[ROW][C]15[/C][C]0.0183047728546225[/C][C]0.036609545709245[/C][C]0.981695227145377[/C][/ROW]
[ROW][C]16[/C][C]0.0142997800608265[/C][C]0.0285995601216531[/C][C]0.985700219939173[/C][/ROW]
[ROW][C]17[/C][C]0.0445191450343003[/C][C]0.0890382900686007[/C][C]0.9554808549657[/C][/ROW]
[ROW][C]18[/C][C]0.0395729103560355[/C][C]0.079145820712071[/C][C]0.960427089643965[/C][/ROW]
[ROW][C]19[/C][C]0.0293433678596913[/C][C]0.0586867357193827[/C][C]0.970656632140309[/C][/ROW]
[ROW][C]20[/C][C]0.0243308205282003[/C][C]0.0486616410564005[/C][C]0.9756691794718[/C][/ROW]
[ROW][C]21[/C][C]0.0174315822609719[/C][C]0.0348631645219438[/C][C]0.982568417739028[/C][/ROW]
[ROW][C]22[/C][C]0.0100580981388315[/C][C]0.0201161962776630[/C][C]0.989941901861169[/C][/ROW]
[ROW][C]23[/C][C]0.00555452086107098[/C][C]0.0111090417221420[/C][C]0.994445479138929[/C][/ROW]
[ROW][C]24[/C][C]0.0085884671821004[/C][C]0.0171769343642008[/C][C]0.9914115328179[/C][/ROW]
[ROW][C]25[/C][C]0.0175702265808783[/C][C]0.0351404531617566[/C][C]0.982429773419122[/C][/ROW]
[ROW][C]26[/C][C]0.0190867112256686[/C][C]0.0381734224513372[/C][C]0.980913288774331[/C][/ROW]
[ROW][C]27[/C][C]0.040983855088241[/C][C]0.081967710176482[/C][C]0.959016144911759[/C][/ROW]
[ROW][C]28[/C][C]0.0738656381311821[/C][C]0.147731276262364[/C][C]0.926134361868818[/C][/ROW]
[ROW][C]29[/C][C]0.0852814512733487[/C][C]0.170562902546697[/C][C]0.914718548726651[/C][/ROW]
[ROW][C]30[/C][C]0.0795956615649018[/C][C]0.159191323129804[/C][C]0.920404338435098[/C][/ROW]
[ROW][C]31[/C][C]0.0618332931997419[/C][C]0.123666586399484[/C][C]0.938166706800258[/C][/ROW]
[ROW][C]32[/C][C]0.0432863407645455[/C][C]0.086572681529091[/C][C]0.956713659235454[/C][/ROW]
[ROW][C]33[/C][C]0.0468844897445634[/C][C]0.0937689794891268[/C][C]0.953115510255437[/C][/ROW]
[ROW][C]34[/C][C]0.0399177469397642[/C][C]0.0798354938795285[/C][C]0.960082253060236[/C][/ROW]
[ROW][C]35[/C][C]0.0334056195080551[/C][C]0.0668112390161102[/C][C]0.966594380491945[/C][/ROW]
[ROW][C]36[/C][C]0.0244593597061334[/C][C]0.0489187194122668[/C][C]0.975540640293867[/C][/ROW]
[ROW][C]37[/C][C]0.0185120084008586[/C][C]0.0370240168017172[/C][C]0.981487991599141[/C][/ROW]
[ROW][C]38[/C][C]0.0191343857746929[/C][C]0.0382687715493858[/C][C]0.980865614225307[/C][/ROW]
[ROW][C]39[/C][C]0.0138170208347216[/C][C]0.0276340416694432[/C][C]0.986182979165278[/C][/ROW]
[ROW][C]40[/C][C]0.0157039067489941[/C][C]0.0314078134979882[/C][C]0.984296093251006[/C][/ROW]
[ROW][C]41[/C][C]0.0117229977489005[/C][C]0.0234459954978009[/C][C]0.9882770022511[/C][/ROW]
[ROW][C]42[/C][C]0.0127973403975877[/C][C]0.0255946807951754[/C][C]0.987202659602412[/C][/ROW]
[ROW][C]43[/C][C]0.0462388817752009[/C][C]0.0924777635504018[/C][C]0.953761118224799[/C][/ROW]
[ROW][C]44[/C][C]0.111904818028657[/C][C]0.223809636057314[/C][C]0.888095181971343[/C][/ROW]
[ROW][C]45[/C][C]0.179435367514818[/C][C]0.358870735029636[/C][C]0.820564632485182[/C][/ROW]
[ROW][C]46[/C][C]0.298014518338853[/C][C]0.596029036677706[/C][C]0.701985481661147[/C][/ROW]
[ROW][C]47[/C][C]0.40020604897569[/C][C]0.80041209795138[/C][C]0.59979395102431[/C][/ROW]
[ROW][C]48[/C][C]0.434650696455188[/C][C]0.869301392910377[/C][C]0.565349303544812[/C][/ROW]
[ROW][C]49[/C][C]0.450983314569762[/C][C]0.901966629139525[/C][C]0.549016685430238[/C][/ROW]
[ROW][C]50[/C][C]0.414873421083487[/C][C]0.829746842166974[/C][C]0.585126578916513[/C][/ROW]
[ROW][C]51[/C][C]0.42769611237162[/C][C]0.85539222474324[/C][C]0.57230388762838[/C][/ROW]
[ROW][C]52[/C][C]0.660397133223179[/C][C]0.679205733553642[/C][C]0.339602866776821[/C][/ROW]
[ROW][C]53[/C][C]0.770506991615273[/C][C]0.458986016769453[/C][C]0.229493008384727[/C][/ROW]
[ROW][C]54[/C][C]0.883201437800972[/C][C]0.233597124398056[/C][C]0.116798562199028[/C][/ROW]
[ROW][C]55[/C][C]0.915391216270125[/C][C]0.169217567459751[/C][C]0.0846087837298755[/C][/ROW]
[ROW][C]56[/C][C]0.902879798630227[/C][C]0.194240402739545[/C][C]0.0971202013697726[/C][/ROW]
[ROW][C]57[/C][C]0.914623813432267[/C][C]0.170752373135467[/C][C]0.0853761865677333[/C][/ROW]
[ROW][C]58[/C][C]0.92724853037646[/C][C]0.145502939247079[/C][C]0.0727514696235395[/C][/ROW]
[ROW][C]59[/C][C]0.918582560689141[/C][C]0.162834878621718[/C][C]0.081417439310859[/C][/ROW]
[ROW][C]60[/C][C]0.92007472187445[/C][C]0.159850556251098[/C][C]0.0799252781255492[/C][/ROW]
[ROW][C]61[/C][C]0.934684867081114[/C][C]0.130630265837772[/C][C]0.0653151329188861[/C][/ROW]
[ROW][C]62[/C][C]0.931747433823418[/C][C]0.136505132353165[/C][C]0.0682525661765824[/C][/ROW]
[ROW][C]63[/C][C]0.954696691251993[/C][C]0.0906066174960132[/C][C]0.0453033087480066[/C][/ROW]
[ROW][C]64[/C][C]0.99181097093703[/C][C]0.0163780581259405[/C][C]0.00818902906297025[/C][/ROW]
[ROW][C]65[/C][C]0.995975290186268[/C][C]0.0080494196274635[/C][C]0.00402470981373175[/C][/ROW]
[ROW][C]66[/C][C]0.999285333466716[/C][C]0.00142933306656857[/C][C]0.000714666533284283[/C][/ROW]
[ROW][C]67[/C][C]0.99981891243832[/C][C]0.000362175123360832[/C][C]0.000181087561680416[/C][/ROW]
[ROW][C]68[/C][C]0.999932643917329[/C][C]0.000134712165341802[/C][C]6.73560826709012e-05[/C][/ROW]
[ROW][C]69[/C][C]0.999975022882783[/C][C]4.99542344347484e-05[/C][C]2.49771172173742e-05[/C][/ROW]
[ROW][C]70[/C][C]0.999992098365133[/C][C]1.58032697344862e-05[/C][C]7.90163486724312e-06[/C][/ROW]
[ROW][C]71[/C][C]0.999995992629006[/C][C]8.0147419883021e-06[/C][C]4.00737099415105e-06[/C][/ROW]
[ROW][C]72[/C][C]0.999996771851818[/C][C]6.45629636470691e-06[/C][C]3.22814818235346e-06[/C][/ROW]
[ROW][C]73[/C][C]0.999997391447691[/C][C]5.21710461742972e-06[/C][C]2.60855230871486e-06[/C][/ROW]
[ROW][C]74[/C][C]0.999997809315034[/C][C]4.38136993116523e-06[/C][C]2.19068496558262e-06[/C][/ROW]
[ROW][C]75[/C][C]0.999998418572012[/C][C]3.16285597597775e-06[/C][C]1.58142798798887e-06[/C][/ROW]
[ROW][C]76[/C][C]0.999998661375182[/C][C]2.67724963630439e-06[/C][C]1.33862481815219e-06[/C][/ROW]
[ROW][C]77[/C][C]0.999998895676051[/C][C]2.20864789754283e-06[/C][C]1.10432394877142e-06[/C][/ROW]
[ROW][C]78[/C][C]0.999998976512405[/C][C]2.04697519069756e-06[/C][C]1.02348759534878e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999998702443574[/C][C]2.59511285117537e-06[/C][C]1.29755642558768e-06[/C][/ROW]
[ROW][C]80[/C][C]0.99999849470719[/C][C]3.01058562045257e-06[/C][C]1.50529281022628e-06[/C][/ROW]
[ROW][C]81[/C][C]0.999998547563251[/C][C]2.90487349773240e-06[/C][C]1.45243674886620e-06[/C][/ROW]
[ROW][C]82[/C][C]0.999998516772241[/C][C]2.96645551698758e-06[/C][C]1.48322775849379e-06[/C][/ROW]
[ROW][C]83[/C][C]0.999998320580046[/C][C]3.3588399073991e-06[/C][C]1.67941995369955e-06[/C][/ROW]
[ROW][C]84[/C][C]0.999999551975966[/C][C]8.96048068353057e-07[/C][C]4.48024034176528e-07[/C][/ROW]
[ROW][C]85[/C][C]0.999999876711967[/C][C]2.46576065540835e-07[/C][C]1.23288032770418e-07[/C][/ROW]
[ROW][C]86[/C][C]0.999999934008467[/C][C]1.31983066215823e-07[/C][C]6.59915331079114e-08[/C][/ROW]
[ROW][C]87[/C][C]0.999999924119762[/C][C]1.51760475101662e-07[/C][C]7.58802375508312e-08[/C][/ROW]
[ROW][C]88[/C][C]0.99999996840833[/C][C]6.31833410290195e-08[/C][C]3.15916705145098e-08[/C][/ROW]
[ROW][C]89[/C][C]0.999999973725554[/C][C]5.25488928073461e-08[/C][C]2.62744464036730e-08[/C][/ROW]
[ROW][C]90[/C][C]0.999999983818538[/C][C]3.23629239456539e-08[/C][C]1.61814619728270e-08[/C][/ROW]
[ROW][C]91[/C][C]0.9999999670528[/C][C]6.58943995559013e-08[/C][C]3.29471997779507e-08[/C][/ROW]
[ROW][C]92[/C][C]0.999999932474177[/C][C]1.35051645973568e-07[/C][C]6.75258229867841e-08[/C][/ROW]
[ROW][C]93[/C][C]0.999999873953766[/C][C]2.52092468638194e-07[/C][C]1.26046234319097e-07[/C][/ROW]
[ROW][C]94[/C][C]0.999999891815646[/C][C]2.16368708985461e-07[/C][C]1.08184354492731e-07[/C][/ROW]
[ROW][C]95[/C][C]0.99999983788189[/C][C]3.24236222101166e-07[/C][C]1.62118111050583e-07[/C][/ROW]
[ROW][C]96[/C][C]0.999999695180636[/C][C]6.09638726899165e-07[/C][C]3.04819363449582e-07[/C][/ROW]
[ROW][C]97[/C][C]0.999999460816466[/C][C]1.07836706715209e-06[/C][C]5.39183533576043e-07[/C][/ROW]
[ROW][C]98[/C][C]0.999998632535137[/C][C]2.73492972584283e-06[/C][C]1.36746486292141e-06[/C][/ROW]
[ROW][C]99[/C][C]0.999996614633194[/C][C]6.77073361146977e-06[/C][C]3.38536680573488e-06[/C][/ROW]
[ROW][C]100[/C][C]0.99999330885259[/C][C]1.33822948188023e-05[/C][C]6.69114740940116e-06[/C][/ROW]
[ROW][C]101[/C][C]0.99998665195061[/C][C]2.66960987830777e-05[/C][C]1.33480493915389e-05[/C][/ROW]
[ROW][C]102[/C][C]0.999975180202826[/C][C]4.96395943488386e-05[/C][C]2.48197971744193e-05[/C][/ROW]
[ROW][C]103[/C][C]0.999958199961569[/C][C]8.36000768627274e-05[/C][C]4.18000384313637e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999915598799105[/C][C]0.000168802401790387[/C][C]8.44012008951936e-05[/C][/ROW]
[ROW][C]105[/C][C]0.999876672647504[/C][C]0.000246654704991923[/C][C]0.000123327352495962[/C][/ROW]
[ROW][C]106[/C][C]0.999795378277164[/C][C]0.000409243445671717[/C][C]0.000204621722835859[/C][/ROW]
[ROW][C]107[/C][C]0.99978438068514[/C][C]0.000431238629720175[/C][C]0.000215619314860088[/C][/ROW]
[ROW][C]108[/C][C]0.999609741649499[/C][C]0.000780516701002793[/C][C]0.000390258350501396[/C][/ROW]
[ROW][C]109[/C][C]0.999970517552914[/C][C]5.89648941717101e-05[/C][C]2.94824470858551e-05[/C][/ROW]
[ROW][C]110[/C][C]0.999975905570835[/C][C]4.81888583306757e-05[/C][C]2.40944291653379e-05[/C][/ROW]
[ROW][C]111[/C][C]0.9999516839502[/C][C]9.66320995995522e-05[/C][C]4.83160497997761e-05[/C][/ROW]
[ROW][C]112[/C][C]0.999948156073069[/C][C]0.000103687853862716[/C][C]5.18439269313579e-05[/C][/ROW]
[ROW][C]113[/C][C]0.999924755863848[/C][C]0.000150488272303158[/C][C]7.52441361515789e-05[/C][/ROW]
[ROW][C]114[/C][C]0.99998479006817[/C][C]3.04198636579714e-05[/C][C]1.52099318289857e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999936433633428[/C][C]0.000127132733144618[/C][C]6.35663665723088e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999743598292943[/C][C]0.000512803414113466[/C][C]0.000256401707056733[/C][/ROW]
[ROW][C]117[/C][C]0.999483164503985[/C][C]0.00103367099203054[/C][C]0.000516835496015269[/C][/ROW]
[ROW][C]118[/C][C]0.997972044780435[/C][C]0.00405591043913022[/C][C]0.00202795521956511[/C][/ROW]
[ROW][C]119[/C][C]0.999485588313663[/C][C]0.00102882337267464[/C][C]0.000514411686337322[/C][/ROW]
[ROW][C]120[/C][C]0.997292038061522[/C][C]0.0054159238769559[/C][C]0.00270796193847795[/C][/ROW]
[ROW][C]121[/C][C]0.986326697448695[/C][C]0.027346605102609[/C][C]0.0136733025513045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105681&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105681&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
110.03239979735292950.06479959470585910.96760020264707
120.01528632640207160.03057265280414320.984713673597928
130.00579310282952340.01158620565904680.994206897170477
140.01492370535223760.02984741070447520.985076294647762
150.01830477285462250.0366095457092450.981695227145377
160.01429978006082650.02859956012165310.985700219939173
170.04451914503430030.08903829006860070.9554808549657
180.03957291035603550.0791458207120710.960427089643965
190.02934336785969130.05868673571938270.970656632140309
200.02433082052820030.04866164105640050.9756691794718
210.01743158226097190.03486316452194380.982568417739028
220.01005809813883150.02011619627766300.989941901861169
230.005554520861070980.01110904172214200.994445479138929
240.00858846718210040.01717693436420080.9914115328179
250.01757022658087830.03514045316175660.982429773419122
260.01908671122566860.03817342245133720.980913288774331
270.0409838550882410.0819677101764820.959016144911759
280.07386563813118210.1477312762623640.926134361868818
290.08528145127334870.1705629025466970.914718548726651
300.07959566156490180.1591913231298040.920404338435098
310.06183329319974190.1236665863994840.938166706800258
320.04328634076454550.0865726815290910.956713659235454
330.04688448974456340.09376897948912680.953115510255437
340.03991774693976420.07983549387952850.960082253060236
350.03340561950805510.06681123901611020.966594380491945
360.02445935970613340.04891871941226680.975540640293867
370.01851200840085860.03702401680171720.981487991599141
380.01913438577469290.03826877154938580.980865614225307
390.01381702083472160.02763404166944320.986182979165278
400.01570390674899410.03140781349798820.984296093251006
410.01172299774890050.02344599549780090.9882770022511
420.01279734039758770.02559468079517540.987202659602412
430.04623888177520090.09247776355040180.953761118224799
440.1119048180286570.2238096360573140.888095181971343
450.1794353675148180.3588707350296360.820564632485182
460.2980145183388530.5960290366777060.701985481661147
470.400206048975690.800412097951380.59979395102431
480.4346506964551880.8693013929103770.565349303544812
490.4509833145697620.9019666291395250.549016685430238
500.4148734210834870.8297468421669740.585126578916513
510.427696112371620.855392224743240.57230388762838
520.6603971332231790.6792057335536420.339602866776821
530.7705069916152730.4589860167694530.229493008384727
540.8832014378009720.2335971243980560.116798562199028
550.9153912162701250.1692175674597510.0846087837298755
560.9028797986302270.1942404027395450.0971202013697726
570.9146238134322670.1707523731354670.0853761865677333
580.927248530376460.1455029392470790.0727514696235395
590.9185825606891410.1628348786217180.081417439310859
600.920074721874450.1598505562510980.0799252781255492
610.9346848670811140.1306302658377720.0653151329188861
620.9317474338234180.1365051323531650.0682525661765824
630.9546966912519930.09060661749601320.0453033087480066
640.991810970937030.01637805812594050.00818902906297025
650.9959752901862680.00804941962746350.00402470981373175
660.9992853334667160.001429333066568570.000714666533284283
670.999818912438320.0003621751233608320.000181087561680416
680.9999326439173290.0001347121653418026.73560826709012e-05
690.9999750228827834.99542344347484e-052.49771172173742e-05
700.9999920983651331.58032697344862e-057.90163486724312e-06
710.9999959926290068.0147419883021e-064.00737099415105e-06
720.9999967718518186.45629636470691e-063.22814818235346e-06
730.9999973914476915.21710461742972e-062.60855230871486e-06
740.9999978093150344.38136993116523e-062.19068496558262e-06
750.9999984185720123.16285597597775e-061.58142798798887e-06
760.9999986613751822.67724963630439e-061.33862481815219e-06
770.9999988956760512.20864789754283e-061.10432394877142e-06
780.9999989765124052.04697519069756e-061.02348759534878e-06
790.9999987024435742.59511285117537e-061.29755642558768e-06
800.999998494707193.01058562045257e-061.50529281022628e-06
810.9999985475632512.90487349773240e-061.45243674886620e-06
820.9999985167722412.96645551698758e-061.48322775849379e-06
830.9999983205800463.3588399073991e-061.67941995369955e-06
840.9999995519759668.96048068353057e-074.48024034176528e-07
850.9999998767119672.46576065540835e-071.23288032770418e-07
860.9999999340084671.31983066215823e-076.59915331079114e-08
870.9999999241197621.51760475101662e-077.58802375508312e-08
880.999999968408336.31833410290195e-083.15916705145098e-08
890.9999999737255545.25488928073461e-082.62744464036730e-08
900.9999999838185383.23629239456539e-081.61814619728270e-08
910.99999996705286.58943995559013e-083.29471997779507e-08
920.9999999324741771.35051645973568e-076.75258229867841e-08
930.9999998739537662.52092468638194e-071.26046234319097e-07
940.9999998918156462.16368708985461e-071.08184354492731e-07
950.999999837881893.24236222101166e-071.62118111050583e-07
960.9999996951806366.09638726899165e-073.04819363449582e-07
970.9999994608164661.07836706715209e-065.39183533576043e-07
980.9999986325351372.73492972584283e-061.36746486292141e-06
990.9999966146331946.77073361146977e-063.38536680573488e-06
1000.999993308852591.33822948188023e-056.69114740940116e-06
1010.999986651950612.66960987830777e-051.33480493915389e-05
1020.9999751802028264.96395943488386e-052.48197971744193e-05
1030.9999581999615698.36000768627274e-054.18000384313637e-05
1040.9999155987991050.0001688024017903878.44012008951936e-05
1050.9998766726475040.0002466547049919230.000123327352495962
1060.9997953782771640.0004092434456717170.000204621722835859
1070.999784380685140.0004312386297201750.000215619314860088
1080.9996097416494990.0007805167010027930.000390258350501396
1090.9999705175529145.89648941717101e-052.94824470858551e-05
1100.9999759055708354.81888583306757e-052.40944291653379e-05
1110.99995168395029.66320995995522e-054.83160497997761e-05
1120.9999481560730690.0001036878538627165.18439269313579e-05
1130.9999247558638480.0001504882723031587.52441361515789e-05
1140.999984790068173.04198636579714e-051.52099318289857e-05
1150.9999364336334280.0001271327331446186.35663665723088e-05
1160.9997435982929430.0005128034141134660.000256401707056733
1170.9994831645039850.001033670992030540.000516835496015269
1180.9979720447804350.004055910439130220.00202795521956511
1190.9994855883136630.001028823372674640.000514411686337322
1200.9972920380615220.00541592387695590.00270796193847795
1210.9863266974486950.0273466051026090.0136733025513045






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level560.504504504504504NOK
5% type I error level770.693693693693694NOK
10% type I error level880.792792792792793NOK

\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 & 77 & 0.693693693693694 & NOK \tabularnewline
10% type I error level & 88 & 0.792792792792793 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105681&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]77[/C][C]0.693693693693694[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]88[/C][C]0.792792792792793[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105681&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105681&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 level770.693693693693694NOK
10% type I error level880.792792792792793NOK


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')
}