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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 06 Dec 2010 13:17:42 +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/t129164318074nava3veshv62k.htm/, Retrieved Mon, 29 Apr 2024 00:12:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105598, Retrieved Mon, 29 Apr 2024 00:12:10 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-12-06 13:17:42] [c474a97a96075919a678ad3d2290b00b] [Current]
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Dataset

Dataseries X:
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Boxplots 
3484,74	13830,14	9349,44	7977	-5,6	6	1	3,17	2,77
3411,13	14153,22	9327,78	8241	-6,2	3	1	3,17	2,76
3288,18	15418,03	9753,63	8444	-7,1	2	1,2	3,36	2,76
3280,37	16666,97	10443,5	8490	-1,4	2	1,2	3,11	2,46
3173,95	16505,21	10853,87	8388	-0,1	2	0,8	3,11	2,46
3165,26	17135,96	10704,02	8099	-0,9	-8	0,7	3,57	2,47
3092,71	18033,25	11052,23	7984	0	0	0,7	4,04	2,71
3053,05	17671	10935,47	7786	0,1	-2	0,9	4,21	2,8
3181,96	17544,22	10714,03	8086	2,6	3	1,2	4,36	2,89
2999,93	17677,9	10394,48	9315	6	5	1,3	4,75	3,36
3249,57	18470,97	10817,9	9113	6,4	8	1,5	4,43	3,31
3210,52	18409,96	11251,2	9023	8,6	8	1,9	4,7	3,5
3030,29	18941,6	11281,26	9026	6,4	9	1,8	4,81	3,51
2803,47	19685,53	10539,68	9787	7,7	11	1,9	5,01	3,71
2767,63	19834,71	10483,39	9536	9,2	13	2,2	5	3,71
2882,6	19598,93	10947,43	9490	8,6	12	2,1	4,81	3,71
2863,36	17039,97	10580,27	9736	7,4	13	2,2	5,11	4,21
2897,06	16969,28	10582,92	9694	8,6	15	2,7	5,1	4,21
3012,61	16973,38	10654,41	9647	6,2	13	2,8	5,11	4,21
3142,95	16329,89	11014,51	9753	6	16	2,9	5,21	4,5
3032,93	16153,34	10967,87	10070	6,6	10	3,4	5,21	4,51
3045,78	15311,7	10433,56	10137	5,1	14	3	5,21	4,51
3110,52	14760,87	10665,78	9984	4,7	14	3,1	5,06	4,51
3013,24	14452,93	10666,71	9732	5	15	2,5	4,58	4,32
2987,1	13720,95	10682,74	9103	3,6	13	2,2	4,37	4,02
2995,55	13266,27	10777,22	9155	1,9	8	2,3	4,37	4,02
2833,18	12708,47	10052,6	9308	-0,1	7	2,1	4,23	3,85
2848,96	13411,84	10213,97	9394	-5,7	3	2,8	4,23	3,84
2794,83	13975,55	10546,82	9948	-5,6	3	3,1	4,37	4,02
2845,26	12974,89	10767,2	10177	-6,4	4	2,9	4,31	3,82
2915,02	12151,11	10444,5	10002	-7,7	4	2,6	4,31	3,75
2892,63	11576,21	10314,68	9728	-8	0	2,7	4,28	3,74
2604,42	9996,83	9042,56	10002	-11,9	-4	2,3	3,98	3,14
2641,65	10438,9	9220,75	10063	-15,4	-14	2,3	3,79	2,91
2659,81	10511,22	9721,84	10018	-15,5	-18	2,1	3,55	2,84
2638,53	10496,2	9978,53	9960	-13,4	-8	2,2	4	2,85
2720,25	10300,79	9923,81	10236	-10,9	-1	2,9	4,02	2,85
2745,88	9981,65	9892,56	10893	-10,8	1	2,6	4,21	3,08
2735,7	11448,79	10500,98	10756	-7,3	2	2,7	4,5	3,3
2811,7	11384,49	10179,35	10940	-6,5	0	1,8	4,52	3,29
2799,43	11717,46	10080,48	10997	-5,1	1	1,3	4,45	3,26
2555,28	10965,88	9492,44	10827	-5,3	0	0,9	4,28	3,26
2304,98	10352,27	8616,49	10166	-6,8	-1	1,3	4,08	3,11
2214,95	9751,2	8685,4	10186	-8,4	-3	1,3	3,8	2,84
2065,81	9354,01	8160,67	10457	-8,4	-3	1,3	3,58	2,71
1940,49	8792,5	8048,1	10368	-9,7	-3	1,3	3,58	2,69
2042	8721,14	8641,21	10244	-8,8	-4	1,1	3,58	2,65
1995,37	8692,94	8526,63	10511	-9,6	-8	1,4	3,54	2,57
1946,81	8570,73	8474,21	10812	-11,5	-9	1,2	3,19	2,32
1765,9	8538,47	7916,13	10738	-11	-13	1,7	2,91	2,12
1635,25	8169,75	7977,64	10171	-14,9	-18	1,8	2,87	2,05
1833,42	7905,84	8334,59	9721	-16,2	-11	1,5	3,1	2,05
1910,43	8145,82	8623,36	9897	-14,4	-9	1	2,6	1,81
1959,67	8895,71	9098,03	9828	-17,3	-10	1,6	2,33	1,58
1969,6	9676,31	9154,34	9924	-15,7	-13	1,5	2,62	1,57
2061,41	9884,59	9284,73	10371	-12,6	-11	1,8	3,05	1,76
2093,48	10637,44	9492,49	10846	-9,4	-5	1,8	3,05	1,76
2120,88	10717,13	9682,35	10413	-8,1	-15	1,6	3,22	1,89
2174,56	10205,29	9762,12	10709	-5,4	-6	1,9	3,24	1,9
2196,72	10295,98	10124,63	10662	-4,6	-6	1,7	3,24	1,9
2350,44	10892,76	10540,05	10570	-4,9	-3	1,6	3,38	1,92
2440,25	10631,92	10601,61	10297	-4	-1	1,3	3,35	1,76
2408,64	11441,08	10323,73	10635	-3,1	-3	1,1	3,22	1,64
2472,81	11950,95	10418,4	10872	-1,3	-4	1,9	3,06	1,57
2407,6	11037,54	10092,96	10296	0	-6	2,6	3,17	1,69
2454,62	11527,72	10364,91	10383	-0,4	0	2,3	3,19	1,76
2448,05	11383,89	10152,09	10431	3	-4	2,4	3,35	1,89
2497,84	10989,34	10032,8	10574	0,4	-2	2,2	3,24	1,78
2645,64	11079,42	10204,59	10653	1,2	-2	2	3,23	1,88
2756,76	11028,93	10001,6	10805	0,6	-6	2,9	3,31	1,86
2849,27	10973	10411,75	10872	-1,3	-7	2,6	3,25	1,88
2921,44	11068,05	10673,38	10625	-3,2	-6	2,3	3,2	1,87
2981,85	11394,84	10539,51	10407	-1,8	-6	2,3	3,1	1,86
3080,58	11545,71	10723,78	10463	-3,6	-3	2,6	2,93	1,89
3106,22	11809,38	10682,06	10556	-4,2	-2	3,1	2,92	1,9
3119,31	11395,64	10283,19	10646	-6,9	-5	2,8	2,9	1,89
3061,26	11082,38	10377,18	10702	-8	-11	2,5	2,87	1,85
3097,31	11402,75	10486,64	11353	-7,5	-11	2,9	2,76	1,78
3161,69	11716,87	10545,38	11346	-8,2	-11	3,1	2,67	1,71
3257,16	12204,98	10554,27	11451	-7,6	-10	3,1	2,75	1,69
3277,01	12986,62	10532,54	11964	-3,7	-14	3,2	2,72	1,72
3295,32	13392,79	10324,31	12574	-1,7	-8	2,5	2,72	1,77
3363,99	14368,05	10695,25	13031	-0,7	-9	2,6	2,86	1,98
3494,17	15650,83	10827,81	13812	0,2	-5	2,9	2,99	2,2
3667,03	16102,64	10872,48	14544	0,6	-1	2,6	3,07	2,25
3813,06	16187,64	10971,19	14931	2,2	-2	2,4	2,96	2,24
3917,96	16311,54	11145,65	14886	3,3	-5	1,7	3,04	2,51
3895,51	17232,97	11234,68	16005	5,3	-4	2	3,3	2,79
3801,06	16397,83	11333,88	17064	5,5	-6	2,2	3,48	3,07
3570,12	14990,31	10997,97	15168	6,3	-2	1,9	3,46	3,08
3701,61	15147,55	11036,89	16050	7,7	-2	1,6	3,57	3,05
3862,27	15786,78	11257,35	15839	6,5	-2	1,6	3,6	3,08
3970,1	15934,09	11533,59	15137	5,5	-2	1,2	3,51	3,15
4138,52	16519,44	11963,12	14954	6,9	2	1,2	3,52	3,16
4199,75	16101,07	12185,15	15648	5,7	1	1,5	3,49	3,16
4290,89	16775,08	12377,62	15305	6,9	-8	1,6	3,5	3,19
4443,91	17286,32	12512,89	15579	6,1	-1	1,7	3,64	3,44
4502,64	17741,23	12631,48	16348	4,8	1	1,8	3,94	3,55
4356,98	17128,37	12268,53	15928	3,7	-1	1,8	3,94	3,6
4591,27	17460,53	12754,8	16171	5,8	2	1,8	3,91	3,62
4696,96	17611,14	13407,75	15937	6,8	2	1,3	3,88	3,69
4621,4	18001,37	13480,21	15713	8,5	1	1,3	4,21	3,99
4562,84	17974,77	13673,28	15594	7,2	-1	1,4	4,39	4,06
4202,52	16460,95	13239,71	15683	5	-2	1,1	4,33	4,05
4296,49	16235,39	13557,69	16438	4,7	-2	1,5	4,27	4,01
4435,23	16903,36	13901,28	17032	2,3	-1	2,2	4,29	3,98
4105,18	15543,76	13200,58	17696	2,4	-8	2,9	4,18	3,94
4116,68	15532,18	13406,97	17745	0,1	-4	3,1	4,14	3,92
3844,49	13731,31	12538,12	19394	1,9	-6	3,5	4,23	4,1
3720,98	13547,84	12419,57	20148	1,7	-3	3,6	4,07	3,88
3674,4	12602,93	12193,88	20108	2	-3	4,4	3,74	3,74
3857,62	13357,7	12656,63	18584	-1,9	-7	4,2	3,66	3,97
3801,06	13995,33	12812,48	18441	0,5	-9	5,2	3,92	4,26
3504,37	14084,6	12056,67	18391	-1,3	-11	5,8	4,45	4,63
3032,6	13168,91	11322,38	19178	-3,3	-13	5,9	4,92	4,82
3047,03	12989,35	11530,75	18079	-2,8	-11	5,4	4,9	4,94
2962,34	12123,53	11114,08	18483	-8	-9	5,5	4,54	4,98
2197,82	9117,03	9181,73	19644	-13,9	-17	4,7	4,53	5,02
2014,45	8531,45	8614,55	19195	-21,9	-22	3,1	4,14	4,96
1862,83	8460,94	8595,56	19650	-28,8	-25	2,6	4,05	4,49
1905,41	8331,49	8396,2	20830	-27,6	-20	2,3	3,92	3,5
1810,99	7694,78	7690,5	23595	-31,4	-24	1,9	3,68	2,95
1670,07	7764,58	7235,47	22937	-31,8	-24	0,6	3,35	2,37
1864,44	8767,96	7992,12	21814	-29,4	-22	0,6	3,38	2,16
2052,02	9304,43	8398,37	21928	-27,6	-19	-0,4	3,44	2,08
2029,6	9810,31	8593	21777	-23,6	-18	-1,1	3,5	1,98
2070,83	9691,12	8679,75	21383	-22,8	-17	-1,7	3,54	1,98
2293,41	10430,35	9374,63	21467	-18,2	-11	-0,8	3,52	1,85
2443,27	10302,87	9634,97	22052	-17,8	-11	-1,2	3,53	1,82
2513,17	10066,24	9857,34	22680	-14,2	-12	-1	3,55	1,65
2466,92	9633,83	10238,83	24320	-8,8	-10	-0,1	3,37	1,59
2502,66	10169,02	10433,44	24977	-7,9	-15	0,3	3,36	1,56

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105598&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105598&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105598&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -318.795051663729 + 0.0756871772943297Nikkei[t] + 0.340914254194191DJ_Indust[t] + 0.014098327989791Goudprijs[t] -1.85647687209124Conjunct_Seizoenzuiver[t] + 10.9888145807143Cons_vertrouw[t] -49.1725874574416Alg_consumptie_index_BE[t] -618.454798508847Gem_rente_kasbon_5j[t] + 340.993038108701Gem_rente_kasbon_1j[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -318.795051663729 +  0.0756871772943297Nikkei[t] +  0.340914254194191DJ_Indust[t] +  0.014098327989791Goudprijs[t] -1.85647687209124Conjunct_Seizoenzuiver[t] +  10.9888145807143Cons_vertrouw[t] -49.1725874574416Alg_consumptie_index_BE[t] -618.454798508847Gem_rente_kasbon_5j[t] +  340.993038108701Gem_rente_kasbon_1j[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105598&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -318.795051663729 +  0.0756871772943297Nikkei[t] +  0.340914254194191DJ_Indust[t] +  0.014098327989791Goudprijs[t] -1.85647687209124Conjunct_Seizoenzuiver[t] +  10.9888145807143Cons_vertrouw[t] -49.1725874574416Alg_consumptie_index_BE[t] -618.454798508847Gem_rente_kasbon_5j[t] +  340.993038108701Gem_rente_kasbon_1j[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105598&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105598&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] = -318.795051663729 + 0.0756871772943297Nikkei[t] + 0.340914254194191DJ_Indust[t] + 0.014098327989791Goudprijs[t] -1.85647687209124Conjunct_Seizoenzuiver[t] + 10.9888145807143Cons_vertrouw[t] -49.1725874574416Alg_consumptie_index_BE[t] -618.454798508847Gem_rente_kasbon_5j[t] + 340.993038108701Gem_rente_kasbon_1j[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-318.795051663729334.235028-0.95380.3420530.171027
Nikkei0.07568717729432970.0131055.775500
DJ_Indust0.3409142541941910.0298911.405700
Goudprijs0.0140983279897910.0072051.95670.0526480.026324
Conjunct_Seizoenzuiver-1.856476872091245.88166-0.31560.7528120.376406
Cons_vertrouw10.98881458071435.3697472.04640.0428450.021423
Alg_consumptie_index_BE-49.172587457441623.296096-2.11080.0368170.018409
Gem_rente_kasbon_5j-618.45479850884766.705749-9.271400
Gem_rente_kasbon_1j340.99303810870150.556526.744800

\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) & -318.795051663729 & 334.235028 & -0.9538 & 0.342053 & 0.171027 \tabularnewline
Nikkei & 0.0756871772943297 & 0.013105 & 5.7755 & 0 & 0 \tabularnewline
DJ_Indust & 0.340914254194191 & 0.02989 & 11.4057 & 0 & 0 \tabularnewline
Goudprijs & 0.014098327989791 & 0.007205 & 1.9567 & 0.052648 & 0.026324 \tabularnewline
Conjunct_Seizoenzuiver & -1.85647687209124 & 5.88166 & -0.3156 & 0.752812 & 0.376406 \tabularnewline
Cons_vertrouw & 10.9888145807143 & 5.369747 & 2.0464 & 0.042845 & 0.021423 \tabularnewline
Alg_consumptie_index_BE & -49.1725874574416 & 23.296096 & -2.1108 & 0.036817 & 0.018409 \tabularnewline
Gem_rente_kasbon_5j & -618.454798508847 & 66.705749 & -9.2714 & 0 & 0 \tabularnewline
Gem_rente_kasbon_1j & 340.993038108701 & 50.55652 & 6.7448 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105598&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]-318.795051663729[/C][C]334.235028[/C][C]-0.9538[/C][C]0.342053[/C][C]0.171027[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.0756871772943297[/C][C]0.013105[/C][C]5.7755[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.340914254194191[/C][C]0.02989[/C][C]11.4057[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.014098327989791[/C][C]0.007205[/C][C]1.9567[/C][C]0.052648[/C][C]0.026324[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-1.85647687209124[/C][C]5.88166[/C][C]-0.3156[/C][C]0.752812[/C][C]0.376406[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]10.9888145807143[/C][C]5.369747[/C][C]2.0464[/C][C]0.042845[/C][C]0.021423[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]-49.1725874574416[/C][C]23.296096[/C][C]-2.1108[/C][C]0.036817[/C][C]0.018409[/C][/ROW]
[ROW][C]Gem_rente_kasbon_5j[/C][C]-618.454798508847[/C][C]66.705749[/C][C]-9.2714[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gem_rente_kasbon_1j[/C][C]340.993038108701[/C][C]50.55652[/C][C]6.7448[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105598&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105598&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)-318.795051663729334.235028-0.95380.3420530.171027
Nikkei0.07568717729432970.0131055.775500
DJ_Indust0.3409142541941910.0298911.405700
Goudprijs0.0140983279897910.0072051.95670.0526480.026324
Conjunct_Seizoenzuiver-1.856476872091245.88166-0.31560.7528120.376406
Cons_vertrouw10.98881458071435.3697472.04640.0428450.021423
Alg_consumptie_index_BE-49.172587457441623.296096-2.11080.0368170.018409
Gem_rente_kasbon_5j-618.45479850884766.705749-9.271400
Gem_rente_kasbon_1j340.99303810870150.556526.744800






Multiple Linear Regression - Regression Statistics
Multiple R0.957748495389464
R-squared0.917282180420782
Adjusted R-squared0.911902159635142
F-TEST (value)170.497887827701
F-TEST (DF numerator)8
F-TEST (DF denominator)123
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation223.487566843427
Sum Squared Residuals6143443.18163222

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.957748495389464 \tabularnewline
R-squared & 0.917282180420782 \tabularnewline
Adjusted R-squared & 0.911902159635142 \tabularnewline
F-TEST (value) & 170.497887827701 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 123 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 223.487566843427 \tabularnewline
Sum Squared Residuals & 6143443.18163222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105598&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.957748495389464[/C][/ROW]
[ROW][C]R-squared[/C][C]0.917282180420782[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.911902159635142[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]170.497887827701[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]123[/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]223.487566843427[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6143443.18163222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105598&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105598&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.957748495389464
R-squared0.917282180420782
Adjusted R-squared0.911902159635142
F-TEST (value)170.497887827701
F-TEST (DF numerator)8
F-TEST (DF denominator)123
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation223.487566843427
Sum Squared Residuals6143443.18163222






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13484.743038.99450842818445.745491571817
23411.133024.52278951192386.60721048808
33288.183131.63406935208156.545930647917
43280.373503.73172221422-223.361722214222
53173.953647.20713250306-473.257132503061
63165.263255.2214193047-89.9614193047016
73092.713317.62747266103-224.917472661026
83053.053141.16743876762-88.1174387676151
93181.963033.78152251798148.178477482016
102999.932882.1047850936117.825214906397
113249.573286.8772851666-37.3072851665971
123210.523302.76150484482-92.2415048448192
133030.293308.6602382368-278.370238236797
142803.473082.03443341821-278.564433418207
152767.633081.22238843409-313.592388434093
162882.63333.47493520534-450.874935205344
172863.363011.35199469497-147.991994694972
182897.063007.66107230137-110.601072301366
193012.613003.056836945859.55316305414726
203142.953144.07352250243-1.12352250242652
213032.933031.056743553091.87325644691486
223045.782852.55508908587193.224910914127
233110.522976.4679369228134.052063077205
243013.243221.9301491869-208.690149186897
252987.13186.07646729113-198.976467291127
262995.553127.90039234392-132.350392343916
272833.182871.9793541251-38.7993541250991
282848.962910.05150398461-61.0915039846075
292794.833033.85855705596-239.028557055961
302845.263027.69782053226-182.437820532262
312915.022846.163683897468.8563161025978
322892.632721.3588348894171.271165110597
332604.422135.89377478668468.526225213318
342641.652166.64785041228475.002149587717
352659.812432.94039177919226.86960822081
362638.532344.67270394404293.857296055959
372720.252340.60958585902379.640414140982
382745.882312.5295559028433.350444097198
392735.72524.30128439963211.398715600367
402811.72417.39393137458394.306068625416
412799.432475.73098862238323.69901137762
422555.282330.16691764008225.113082359924
432304.982020.45054274776284.529457252241
442214.952060.82357603482154.126423965181
452065.811947.42505708501118.384942914985
461940.491860.8880405482979.6019594517112
4720422039.473616412492.52638358750839
481995.371942.2784321697453.0915678302633
491946.812052.98550267133-106.175502671332
501765.91894.74207655299-128.842076552992
511635.251828.05819177089-192.808191770885
521833.421875.27097882927-41.8509788292654
531910.432264.97283717494-354.542837174939
541959.672536.02469166952-576.354691669517
551969.62401.87505346577-432.275053465769
562061.412268.71682947101-207.306829471011
572093.482463.21513363718-369.735133637177
582120.882364.59278003575-243.712780035746
592174.562427.39679219693-252.836792196925
602196.722565.53240317198-368.812403171978
612350.442709.70338466043-359.263384660428
622440.252706.15231335361-265.90231335361
632408.642703.09335001644-294.45335001644
642472.812798.71433945902-325.904339459019
652407.62524.59041946124-116.990419461243
662454.622748.55661715839-293.936617158386
672448.052555.98566737305-107.935667373049
682497.842554.63147125566-56.7914712556564
692645.642669.76198761532-24.1219876153179
702756.762455.48835787512301.271642124885
712849.272643.2431597683206.026840231697
722921.442792.92904170075128.510958299248
732981.852824.78770950689157.062290493115
743080.583036.7398426975143.840157302488
753106.223040.895367894265.3246321057995
763119.312870.62592174791248.684078252085
773061.262835.523129451225.736870549002
783097.312929.82875797557167.481242024426
793161.692996.78666461987164.90333538013
803257.162991.8200687023265.339931297703
813277.013023.47522770653253.53477229347
823295.323105.51889029683189.80110970317
833363.993279.4975521800784.4924478199342
843494.173454.941934644239.228065355799
853667.033540.22448587691126.805514123086
863813.063646.2610322808166.798967719195
873917.963756.48412890216161.475871097843
883895.513799.5560774053995.953922594608
893801.063737.0682564285763.9917435714334
903570.123562.290983061927.82901693808217
913701.613533.78803251698167.821967483018
923862.273648.25667556715214.013324432852
933970.13844.73923737262125.360762627381
944138.524071.4772052816867.0427947183191
954199.754120.3302177845379.4197822154683
964290.894130.12505309643160.764946903569
974443.914290.9519912033152.9580087967
984502.644248.10006564401254.539934355992
994356.984069.17244315522287.80755684478
1004591.274317.95631108202273.313688917978
1014696.964613.8094838603183.1505161396885
1024621.44548.952515106572.4474848935027
1034562.844499.1480311795263.6919688204818
1044202.524279.56039475249-77.0403947524937
1054296.494385.89202168778-89.4020216877807
1064435.234520.38258166968-85.1525816696783
1074105.184130.82311263087-25.6431126308655
1084116.684257.3077349278-140.627734927794
1093844.493808.779253531935.7107464681017
1103720.983817.46246133161-96.4824613316064
1113674.43784.89606463086-110.496064630863
1123857.624079.31899632472-221.698996324721
1133801.064041.17880912386-240.118809123863
1143504.373539.81094220333-35.4409422033285
1153032.62982.203395020950.3966049790982
1163047.033123.07919114892-76.04919114892
1172962.343184.19220065482-221.852200654817
1182197.822296.44623819843-98.626238198431
1192014.452311.75711609413-297.307116094134
1201862.832206.18493513003-343.354935130032
1211905.411955.34268431391-49.9326843139105
1221810.991649.20195882217161.788041177827
1231670.071561.06308674244109.006913257565
1241864.441806.4863375098457.9536624901642
1252052.022001.6045045407450.415495459255
1262029.62070.89355414891-41.2935541489147
1272070.832100.16096342772-29.3309634277243
1282293.412375.36771818163-81.9577181816276
1292443.272465.23236073688-21.9623607368806
1302513.172434.1407953960979.0292046039127
1312466.922613.14914658355-146.229146583554
1322502.662648.93491126813-146.274911268129

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3484.74 & 3038.99450842818 & 445.745491571817 \tabularnewline
2 & 3411.13 & 3024.52278951192 & 386.60721048808 \tabularnewline
3 & 3288.18 & 3131.63406935208 & 156.545930647917 \tabularnewline
4 & 3280.37 & 3503.73172221422 & -223.361722214222 \tabularnewline
5 & 3173.95 & 3647.20713250306 & -473.257132503061 \tabularnewline
6 & 3165.26 & 3255.2214193047 & -89.9614193047016 \tabularnewline
7 & 3092.71 & 3317.62747266103 & -224.917472661026 \tabularnewline
8 & 3053.05 & 3141.16743876762 & -88.1174387676151 \tabularnewline
9 & 3181.96 & 3033.78152251798 & 148.178477482016 \tabularnewline
10 & 2999.93 & 2882.1047850936 & 117.825214906397 \tabularnewline
11 & 3249.57 & 3286.8772851666 & -37.3072851665971 \tabularnewline
12 & 3210.52 & 3302.76150484482 & -92.2415048448192 \tabularnewline
13 & 3030.29 & 3308.6602382368 & -278.370238236797 \tabularnewline
14 & 2803.47 & 3082.03443341821 & -278.564433418207 \tabularnewline
15 & 2767.63 & 3081.22238843409 & -313.592388434093 \tabularnewline
16 & 2882.6 & 3333.47493520534 & -450.874935205344 \tabularnewline
17 & 2863.36 & 3011.35199469497 & -147.991994694972 \tabularnewline
18 & 2897.06 & 3007.66107230137 & -110.601072301366 \tabularnewline
19 & 3012.61 & 3003.05683694585 & 9.55316305414726 \tabularnewline
20 & 3142.95 & 3144.07352250243 & -1.12352250242652 \tabularnewline
21 & 3032.93 & 3031.05674355309 & 1.87325644691486 \tabularnewline
22 & 3045.78 & 2852.55508908587 & 193.224910914127 \tabularnewline
23 & 3110.52 & 2976.4679369228 & 134.052063077205 \tabularnewline
24 & 3013.24 & 3221.9301491869 & -208.690149186897 \tabularnewline
25 & 2987.1 & 3186.07646729113 & -198.976467291127 \tabularnewline
26 & 2995.55 & 3127.90039234392 & -132.350392343916 \tabularnewline
27 & 2833.18 & 2871.9793541251 & -38.7993541250991 \tabularnewline
28 & 2848.96 & 2910.05150398461 & -61.0915039846075 \tabularnewline
29 & 2794.83 & 3033.85855705596 & -239.028557055961 \tabularnewline
30 & 2845.26 & 3027.69782053226 & -182.437820532262 \tabularnewline
31 & 2915.02 & 2846.1636838974 & 68.8563161025978 \tabularnewline
32 & 2892.63 & 2721.3588348894 & 171.271165110597 \tabularnewline
33 & 2604.42 & 2135.89377478668 & 468.526225213318 \tabularnewline
34 & 2641.65 & 2166.64785041228 & 475.002149587717 \tabularnewline
35 & 2659.81 & 2432.94039177919 & 226.86960822081 \tabularnewline
36 & 2638.53 & 2344.67270394404 & 293.857296055959 \tabularnewline
37 & 2720.25 & 2340.60958585902 & 379.640414140982 \tabularnewline
38 & 2745.88 & 2312.5295559028 & 433.350444097198 \tabularnewline
39 & 2735.7 & 2524.30128439963 & 211.398715600367 \tabularnewline
40 & 2811.7 & 2417.39393137458 & 394.306068625416 \tabularnewline
41 & 2799.43 & 2475.73098862238 & 323.69901137762 \tabularnewline
42 & 2555.28 & 2330.16691764008 & 225.113082359924 \tabularnewline
43 & 2304.98 & 2020.45054274776 & 284.529457252241 \tabularnewline
44 & 2214.95 & 2060.82357603482 & 154.126423965181 \tabularnewline
45 & 2065.81 & 1947.42505708501 & 118.384942914985 \tabularnewline
46 & 1940.49 & 1860.88804054829 & 79.6019594517112 \tabularnewline
47 & 2042 & 2039.47361641249 & 2.52638358750839 \tabularnewline
48 & 1995.37 & 1942.27843216974 & 53.0915678302633 \tabularnewline
49 & 1946.81 & 2052.98550267133 & -106.175502671332 \tabularnewline
50 & 1765.9 & 1894.74207655299 & -128.842076552992 \tabularnewline
51 & 1635.25 & 1828.05819177089 & -192.808191770885 \tabularnewline
52 & 1833.42 & 1875.27097882927 & -41.8509788292654 \tabularnewline
53 & 1910.43 & 2264.97283717494 & -354.542837174939 \tabularnewline
54 & 1959.67 & 2536.02469166952 & -576.354691669517 \tabularnewline
55 & 1969.6 & 2401.87505346577 & -432.275053465769 \tabularnewline
56 & 2061.41 & 2268.71682947101 & -207.306829471011 \tabularnewline
57 & 2093.48 & 2463.21513363718 & -369.735133637177 \tabularnewline
58 & 2120.88 & 2364.59278003575 & -243.712780035746 \tabularnewline
59 & 2174.56 & 2427.39679219693 & -252.836792196925 \tabularnewline
60 & 2196.72 & 2565.53240317198 & -368.812403171978 \tabularnewline
61 & 2350.44 & 2709.70338466043 & -359.263384660428 \tabularnewline
62 & 2440.25 & 2706.15231335361 & -265.90231335361 \tabularnewline
63 & 2408.64 & 2703.09335001644 & -294.45335001644 \tabularnewline
64 & 2472.81 & 2798.71433945902 & -325.904339459019 \tabularnewline
65 & 2407.6 & 2524.59041946124 & -116.990419461243 \tabularnewline
66 & 2454.62 & 2748.55661715839 & -293.936617158386 \tabularnewline
67 & 2448.05 & 2555.98566737305 & -107.935667373049 \tabularnewline
68 & 2497.84 & 2554.63147125566 & -56.7914712556564 \tabularnewline
69 & 2645.64 & 2669.76198761532 & -24.1219876153179 \tabularnewline
70 & 2756.76 & 2455.48835787512 & 301.271642124885 \tabularnewline
71 & 2849.27 & 2643.2431597683 & 206.026840231697 \tabularnewline
72 & 2921.44 & 2792.92904170075 & 128.510958299248 \tabularnewline
73 & 2981.85 & 2824.78770950689 & 157.062290493115 \tabularnewline
74 & 3080.58 & 3036.73984269751 & 43.840157302488 \tabularnewline
75 & 3106.22 & 3040.8953678942 & 65.3246321057995 \tabularnewline
76 & 3119.31 & 2870.62592174791 & 248.684078252085 \tabularnewline
77 & 3061.26 & 2835.523129451 & 225.736870549002 \tabularnewline
78 & 3097.31 & 2929.82875797557 & 167.481242024426 \tabularnewline
79 & 3161.69 & 2996.78666461987 & 164.90333538013 \tabularnewline
80 & 3257.16 & 2991.8200687023 & 265.339931297703 \tabularnewline
81 & 3277.01 & 3023.47522770653 & 253.53477229347 \tabularnewline
82 & 3295.32 & 3105.51889029683 & 189.80110970317 \tabularnewline
83 & 3363.99 & 3279.49755218007 & 84.4924478199342 \tabularnewline
84 & 3494.17 & 3454.9419346442 & 39.228065355799 \tabularnewline
85 & 3667.03 & 3540.22448587691 & 126.805514123086 \tabularnewline
86 & 3813.06 & 3646.2610322808 & 166.798967719195 \tabularnewline
87 & 3917.96 & 3756.48412890216 & 161.475871097843 \tabularnewline
88 & 3895.51 & 3799.55607740539 & 95.953922594608 \tabularnewline
89 & 3801.06 & 3737.06825642857 & 63.9917435714334 \tabularnewline
90 & 3570.12 & 3562.29098306192 & 7.82901693808217 \tabularnewline
91 & 3701.61 & 3533.78803251698 & 167.821967483018 \tabularnewline
92 & 3862.27 & 3648.25667556715 & 214.013324432852 \tabularnewline
93 & 3970.1 & 3844.73923737262 & 125.360762627381 \tabularnewline
94 & 4138.52 & 4071.47720528168 & 67.0427947183191 \tabularnewline
95 & 4199.75 & 4120.33021778453 & 79.4197822154683 \tabularnewline
96 & 4290.89 & 4130.12505309643 & 160.764946903569 \tabularnewline
97 & 4443.91 & 4290.9519912033 & 152.9580087967 \tabularnewline
98 & 4502.64 & 4248.10006564401 & 254.539934355992 \tabularnewline
99 & 4356.98 & 4069.17244315522 & 287.80755684478 \tabularnewline
100 & 4591.27 & 4317.95631108202 & 273.313688917978 \tabularnewline
101 & 4696.96 & 4613.80948386031 & 83.1505161396885 \tabularnewline
102 & 4621.4 & 4548.9525151065 & 72.4474848935027 \tabularnewline
103 & 4562.84 & 4499.14803117952 & 63.6919688204818 \tabularnewline
104 & 4202.52 & 4279.56039475249 & -77.0403947524937 \tabularnewline
105 & 4296.49 & 4385.89202168778 & -89.4020216877807 \tabularnewline
106 & 4435.23 & 4520.38258166968 & -85.1525816696783 \tabularnewline
107 & 4105.18 & 4130.82311263087 & -25.6431126308655 \tabularnewline
108 & 4116.68 & 4257.3077349278 & -140.627734927794 \tabularnewline
109 & 3844.49 & 3808.7792535319 & 35.7107464681017 \tabularnewline
110 & 3720.98 & 3817.46246133161 & -96.4824613316064 \tabularnewline
111 & 3674.4 & 3784.89606463086 & -110.496064630863 \tabularnewline
112 & 3857.62 & 4079.31899632472 & -221.698996324721 \tabularnewline
113 & 3801.06 & 4041.17880912386 & -240.118809123863 \tabularnewline
114 & 3504.37 & 3539.81094220333 & -35.4409422033285 \tabularnewline
115 & 3032.6 & 2982.2033950209 & 50.3966049790982 \tabularnewline
116 & 3047.03 & 3123.07919114892 & -76.04919114892 \tabularnewline
117 & 2962.34 & 3184.19220065482 & -221.852200654817 \tabularnewline
118 & 2197.82 & 2296.44623819843 & -98.626238198431 \tabularnewline
119 & 2014.45 & 2311.75711609413 & -297.307116094134 \tabularnewline
120 & 1862.83 & 2206.18493513003 & -343.354935130032 \tabularnewline
121 & 1905.41 & 1955.34268431391 & -49.9326843139105 \tabularnewline
122 & 1810.99 & 1649.20195882217 & 161.788041177827 \tabularnewline
123 & 1670.07 & 1561.06308674244 & 109.006913257565 \tabularnewline
124 & 1864.44 & 1806.48633750984 & 57.9536624901642 \tabularnewline
125 & 2052.02 & 2001.60450454074 & 50.415495459255 \tabularnewline
126 & 2029.6 & 2070.89355414891 & -41.2935541489147 \tabularnewline
127 & 2070.83 & 2100.16096342772 & -29.3309634277243 \tabularnewline
128 & 2293.41 & 2375.36771818163 & -81.9577181816276 \tabularnewline
129 & 2443.27 & 2465.23236073688 & -21.9623607368806 \tabularnewline
130 & 2513.17 & 2434.14079539609 & 79.0292046039127 \tabularnewline
131 & 2466.92 & 2613.14914658355 & -146.229146583554 \tabularnewline
132 & 2502.66 & 2648.93491126813 & -146.274911268129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105598&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]3038.99450842818[/C][C]445.745491571817[/C][/ROW]
[ROW][C]2[/C][C]3411.13[/C][C]3024.52278951192[/C][C]386.60721048808[/C][/ROW]
[ROW][C]3[/C][C]3288.18[/C][C]3131.63406935208[/C][C]156.545930647917[/C][/ROW]
[ROW][C]4[/C][C]3280.37[/C][C]3503.73172221422[/C][C]-223.361722214222[/C][/ROW]
[ROW][C]5[/C][C]3173.95[/C][C]3647.20713250306[/C][C]-473.257132503061[/C][/ROW]
[ROW][C]6[/C][C]3165.26[/C][C]3255.2214193047[/C][C]-89.9614193047016[/C][/ROW]
[ROW][C]7[/C][C]3092.71[/C][C]3317.62747266103[/C][C]-224.917472661026[/C][/ROW]
[ROW][C]8[/C][C]3053.05[/C][C]3141.16743876762[/C][C]-88.1174387676151[/C][/ROW]
[ROW][C]9[/C][C]3181.96[/C][C]3033.78152251798[/C][C]148.178477482016[/C][/ROW]
[ROW][C]10[/C][C]2999.93[/C][C]2882.1047850936[/C][C]117.825214906397[/C][/ROW]
[ROW][C]11[/C][C]3249.57[/C][C]3286.8772851666[/C][C]-37.3072851665971[/C][/ROW]
[ROW][C]12[/C][C]3210.52[/C][C]3302.76150484482[/C][C]-92.2415048448192[/C][/ROW]
[ROW][C]13[/C][C]3030.29[/C][C]3308.6602382368[/C][C]-278.370238236797[/C][/ROW]
[ROW][C]14[/C][C]2803.47[/C][C]3082.03443341821[/C][C]-278.564433418207[/C][/ROW]
[ROW][C]15[/C][C]2767.63[/C][C]3081.22238843409[/C][C]-313.592388434093[/C][/ROW]
[ROW][C]16[/C][C]2882.6[/C][C]3333.47493520534[/C][C]-450.874935205344[/C][/ROW]
[ROW][C]17[/C][C]2863.36[/C][C]3011.35199469497[/C][C]-147.991994694972[/C][/ROW]
[ROW][C]18[/C][C]2897.06[/C][C]3007.66107230137[/C][C]-110.601072301366[/C][/ROW]
[ROW][C]19[/C][C]3012.61[/C][C]3003.05683694585[/C][C]9.55316305414726[/C][/ROW]
[ROW][C]20[/C][C]3142.95[/C][C]3144.07352250243[/C][C]-1.12352250242652[/C][/ROW]
[ROW][C]21[/C][C]3032.93[/C][C]3031.05674355309[/C][C]1.87325644691486[/C][/ROW]
[ROW][C]22[/C][C]3045.78[/C][C]2852.55508908587[/C][C]193.224910914127[/C][/ROW]
[ROW][C]23[/C][C]3110.52[/C][C]2976.4679369228[/C][C]134.052063077205[/C][/ROW]
[ROW][C]24[/C][C]3013.24[/C][C]3221.9301491869[/C][C]-208.690149186897[/C][/ROW]
[ROW][C]25[/C][C]2987.1[/C][C]3186.07646729113[/C][C]-198.976467291127[/C][/ROW]
[ROW][C]26[/C][C]2995.55[/C][C]3127.90039234392[/C][C]-132.350392343916[/C][/ROW]
[ROW][C]27[/C][C]2833.18[/C][C]2871.9793541251[/C][C]-38.7993541250991[/C][/ROW]
[ROW][C]28[/C][C]2848.96[/C][C]2910.05150398461[/C][C]-61.0915039846075[/C][/ROW]
[ROW][C]29[/C][C]2794.83[/C][C]3033.85855705596[/C][C]-239.028557055961[/C][/ROW]
[ROW][C]30[/C][C]2845.26[/C][C]3027.69782053226[/C][C]-182.437820532262[/C][/ROW]
[ROW][C]31[/C][C]2915.02[/C][C]2846.1636838974[/C][C]68.8563161025978[/C][/ROW]
[ROW][C]32[/C][C]2892.63[/C][C]2721.3588348894[/C][C]171.271165110597[/C][/ROW]
[ROW][C]33[/C][C]2604.42[/C][C]2135.89377478668[/C][C]468.526225213318[/C][/ROW]
[ROW][C]34[/C][C]2641.65[/C][C]2166.64785041228[/C][C]475.002149587717[/C][/ROW]
[ROW][C]35[/C][C]2659.81[/C][C]2432.94039177919[/C][C]226.86960822081[/C][/ROW]
[ROW][C]36[/C][C]2638.53[/C][C]2344.67270394404[/C][C]293.857296055959[/C][/ROW]
[ROW][C]37[/C][C]2720.25[/C][C]2340.60958585902[/C][C]379.640414140982[/C][/ROW]
[ROW][C]38[/C][C]2745.88[/C][C]2312.5295559028[/C][C]433.350444097198[/C][/ROW]
[ROW][C]39[/C][C]2735.7[/C][C]2524.30128439963[/C][C]211.398715600367[/C][/ROW]
[ROW][C]40[/C][C]2811.7[/C][C]2417.39393137458[/C][C]394.306068625416[/C][/ROW]
[ROW][C]41[/C][C]2799.43[/C][C]2475.73098862238[/C][C]323.69901137762[/C][/ROW]
[ROW][C]42[/C][C]2555.28[/C][C]2330.16691764008[/C][C]225.113082359924[/C][/ROW]
[ROW][C]43[/C][C]2304.98[/C][C]2020.45054274776[/C][C]284.529457252241[/C][/ROW]
[ROW][C]44[/C][C]2214.95[/C][C]2060.82357603482[/C][C]154.126423965181[/C][/ROW]
[ROW][C]45[/C][C]2065.81[/C][C]1947.42505708501[/C][C]118.384942914985[/C][/ROW]
[ROW][C]46[/C][C]1940.49[/C][C]1860.88804054829[/C][C]79.6019594517112[/C][/ROW]
[ROW][C]47[/C][C]2042[/C][C]2039.47361641249[/C][C]2.52638358750839[/C][/ROW]
[ROW][C]48[/C][C]1995.37[/C][C]1942.27843216974[/C][C]53.0915678302633[/C][/ROW]
[ROW][C]49[/C][C]1946.81[/C][C]2052.98550267133[/C][C]-106.175502671332[/C][/ROW]
[ROW][C]50[/C][C]1765.9[/C][C]1894.74207655299[/C][C]-128.842076552992[/C][/ROW]
[ROW][C]51[/C][C]1635.25[/C][C]1828.05819177089[/C][C]-192.808191770885[/C][/ROW]
[ROW][C]52[/C][C]1833.42[/C][C]1875.27097882927[/C][C]-41.8509788292654[/C][/ROW]
[ROW][C]53[/C][C]1910.43[/C][C]2264.97283717494[/C][C]-354.542837174939[/C][/ROW]
[ROW][C]54[/C][C]1959.67[/C][C]2536.02469166952[/C][C]-576.354691669517[/C][/ROW]
[ROW][C]55[/C][C]1969.6[/C][C]2401.87505346577[/C][C]-432.275053465769[/C][/ROW]
[ROW][C]56[/C][C]2061.41[/C][C]2268.71682947101[/C][C]-207.306829471011[/C][/ROW]
[ROW][C]57[/C][C]2093.48[/C][C]2463.21513363718[/C][C]-369.735133637177[/C][/ROW]
[ROW][C]58[/C][C]2120.88[/C][C]2364.59278003575[/C][C]-243.712780035746[/C][/ROW]
[ROW][C]59[/C][C]2174.56[/C][C]2427.39679219693[/C][C]-252.836792196925[/C][/ROW]
[ROW][C]60[/C][C]2196.72[/C][C]2565.53240317198[/C][C]-368.812403171978[/C][/ROW]
[ROW][C]61[/C][C]2350.44[/C][C]2709.70338466043[/C][C]-359.263384660428[/C][/ROW]
[ROW][C]62[/C][C]2440.25[/C][C]2706.15231335361[/C][C]-265.90231335361[/C][/ROW]
[ROW][C]63[/C][C]2408.64[/C][C]2703.09335001644[/C][C]-294.45335001644[/C][/ROW]
[ROW][C]64[/C][C]2472.81[/C][C]2798.71433945902[/C][C]-325.904339459019[/C][/ROW]
[ROW][C]65[/C][C]2407.6[/C][C]2524.59041946124[/C][C]-116.990419461243[/C][/ROW]
[ROW][C]66[/C][C]2454.62[/C][C]2748.55661715839[/C][C]-293.936617158386[/C][/ROW]
[ROW][C]67[/C][C]2448.05[/C][C]2555.98566737305[/C][C]-107.935667373049[/C][/ROW]
[ROW][C]68[/C][C]2497.84[/C][C]2554.63147125566[/C][C]-56.7914712556564[/C][/ROW]
[ROW][C]69[/C][C]2645.64[/C][C]2669.76198761532[/C][C]-24.1219876153179[/C][/ROW]
[ROW][C]70[/C][C]2756.76[/C][C]2455.48835787512[/C][C]301.271642124885[/C][/ROW]
[ROW][C]71[/C][C]2849.27[/C][C]2643.2431597683[/C][C]206.026840231697[/C][/ROW]
[ROW][C]72[/C][C]2921.44[/C][C]2792.92904170075[/C][C]128.510958299248[/C][/ROW]
[ROW][C]73[/C][C]2981.85[/C][C]2824.78770950689[/C][C]157.062290493115[/C][/ROW]
[ROW][C]74[/C][C]3080.58[/C][C]3036.73984269751[/C][C]43.840157302488[/C][/ROW]
[ROW][C]75[/C][C]3106.22[/C][C]3040.8953678942[/C][C]65.3246321057995[/C][/ROW]
[ROW][C]76[/C][C]3119.31[/C][C]2870.62592174791[/C][C]248.684078252085[/C][/ROW]
[ROW][C]77[/C][C]3061.26[/C][C]2835.523129451[/C][C]225.736870549002[/C][/ROW]
[ROW][C]78[/C][C]3097.31[/C][C]2929.82875797557[/C][C]167.481242024426[/C][/ROW]
[ROW][C]79[/C][C]3161.69[/C][C]2996.78666461987[/C][C]164.90333538013[/C][/ROW]
[ROW][C]80[/C][C]3257.16[/C][C]2991.8200687023[/C][C]265.339931297703[/C][/ROW]
[ROW][C]81[/C][C]3277.01[/C][C]3023.47522770653[/C][C]253.53477229347[/C][/ROW]
[ROW][C]82[/C][C]3295.32[/C][C]3105.51889029683[/C][C]189.80110970317[/C][/ROW]
[ROW][C]83[/C][C]3363.99[/C][C]3279.49755218007[/C][C]84.4924478199342[/C][/ROW]
[ROW][C]84[/C][C]3494.17[/C][C]3454.9419346442[/C][C]39.228065355799[/C][/ROW]
[ROW][C]85[/C][C]3667.03[/C][C]3540.22448587691[/C][C]126.805514123086[/C][/ROW]
[ROW][C]86[/C][C]3813.06[/C][C]3646.2610322808[/C][C]166.798967719195[/C][/ROW]
[ROW][C]87[/C][C]3917.96[/C][C]3756.48412890216[/C][C]161.475871097843[/C][/ROW]
[ROW][C]88[/C][C]3895.51[/C][C]3799.55607740539[/C][C]95.953922594608[/C][/ROW]
[ROW][C]89[/C][C]3801.06[/C][C]3737.06825642857[/C][C]63.9917435714334[/C][/ROW]
[ROW][C]90[/C][C]3570.12[/C][C]3562.29098306192[/C][C]7.82901693808217[/C][/ROW]
[ROW][C]91[/C][C]3701.61[/C][C]3533.78803251698[/C][C]167.821967483018[/C][/ROW]
[ROW][C]92[/C][C]3862.27[/C][C]3648.25667556715[/C][C]214.013324432852[/C][/ROW]
[ROW][C]93[/C][C]3970.1[/C][C]3844.73923737262[/C][C]125.360762627381[/C][/ROW]
[ROW][C]94[/C][C]4138.52[/C][C]4071.47720528168[/C][C]67.0427947183191[/C][/ROW]
[ROW][C]95[/C][C]4199.75[/C][C]4120.33021778453[/C][C]79.4197822154683[/C][/ROW]
[ROW][C]96[/C][C]4290.89[/C][C]4130.12505309643[/C][C]160.764946903569[/C][/ROW]
[ROW][C]97[/C][C]4443.91[/C][C]4290.9519912033[/C][C]152.9580087967[/C][/ROW]
[ROW][C]98[/C][C]4502.64[/C][C]4248.10006564401[/C][C]254.539934355992[/C][/ROW]
[ROW][C]99[/C][C]4356.98[/C][C]4069.17244315522[/C][C]287.80755684478[/C][/ROW]
[ROW][C]100[/C][C]4591.27[/C][C]4317.95631108202[/C][C]273.313688917978[/C][/ROW]
[ROW][C]101[/C][C]4696.96[/C][C]4613.80948386031[/C][C]83.1505161396885[/C][/ROW]
[ROW][C]102[/C][C]4621.4[/C][C]4548.9525151065[/C][C]72.4474848935027[/C][/ROW]
[ROW][C]103[/C][C]4562.84[/C][C]4499.14803117952[/C][C]63.6919688204818[/C][/ROW]
[ROW][C]104[/C][C]4202.52[/C][C]4279.56039475249[/C][C]-77.0403947524937[/C][/ROW]
[ROW][C]105[/C][C]4296.49[/C][C]4385.89202168778[/C][C]-89.4020216877807[/C][/ROW]
[ROW][C]106[/C][C]4435.23[/C][C]4520.38258166968[/C][C]-85.1525816696783[/C][/ROW]
[ROW][C]107[/C][C]4105.18[/C][C]4130.82311263087[/C][C]-25.6431126308655[/C][/ROW]
[ROW][C]108[/C][C]4116.68[/C][C]4257.3077349278[/C][C]-140.627734927794[/C][/ROW]
[ROW][C]109[/C][C]3844.49[/C][C]3808.7792535319[/C][C]35.7107464681017[/C][/ROW]
[ROW][C]110[/C][C]3720.98[/C][C]3817.46246133161[/C][C]-96.4824613316064[/C][/ROW]
[ROW][C]111[/C][C]3674.4[/C][C]3784.89606463086[/C][C]-110.496064630863[/C][/ROW]
[ROW][C]112[/C][C]3857.62[/C][C]4079.31899632472[/C][C]-221.698996324721[/C][/ROW]
[ROW][C]113[/C][C]3801.06[/C][C]4041.17880912386[/C][C]-240.118809123863[/C][/ROW]
[ROW][C]114[/C][C]3504.37[/C][C]3539.81094220333[/C][C]-35.4409422033285[/C][/ROW]
[ROW][C]115[/C][C]3032.6[/C][C]2982.2033950209[/C][C]50.3966049790982[/C][/ROW]
[ROW][C]116[/C][C]3047.03[/C][C]3123.07919114892[/C][C]-76.04919114892[/C][/ROW]
[ROW][C]117[/C][C]2962.34[/C][C]3184.19220065482[/C][C]-221.852200654817[/C][/ROW]
[ROW][C]118[/C][C]2197.82[/C][C]2296.44623819843[/C][C]-98.626238198431[/C][/ROW]
[ROW][C]119[/C][C]2014.45[/C][C]2311.75711609413[/C][C]-297.307116094134[/C][/ROW]
[ROW][C]120[/C][C]1862.83[/C][C]2206.18493513003[/C][C]-343.354935130032[/C][/ROW]
[ROW][C]121[/C][C]1905.41[/C][C]1955.34268431391[/C][C]-49.9326843139105[/C][/ROW]
[ROW][C]122[/C][C]1810.99[/C][C]1649.20195882217[/C][C]161.788041177827[/C][/ROW]
[ROW][C]123[/C][C]1670.07[/C][C]1561.06308674244[/C][C]109.006913257565[/C][/ROW]
[ROW][C]124[/C][C]1864.44[/C][C]1806.48633750984[/C][C]57.9536624901642[/C][/ROW]
[ROW][C]125[/C][C]2052.02[/C][C]2001.60450454074[/C][C]50.415495459255[/C][/ROW]
[ROW][C]126[/C][C]2029.6[/C][C]2070.89355414891[/C][C]-41.2935541489147[/C][/ROW]
[ROW][C]127[/C][C]2070.83[/C][C]2100.16096342772[/C][C]-29.3309634277243[/C][/ROW]
[ROW][C]128[/C][C]2293.41[/C][C]2375.36771818163[/C][C]-81.9577181816276[/C][/ROW]
[ROW][C]129[/C][C]2443.27[/C][C]2465.23236073688[/C][C]-21.9623607368806[/C][/ROW]
[ROW][C]130[/C][C]2513.17[/C][C]2434.14079539609[/C][C]79.0292046039127[/C][/ROW]
[ROW][C]131[/C][C]2466.92[/C][C]2613.14914658355[/C][C]-146.229146583554[/C][/ROW]
[ROW][C]132[/C][C]2502.66[/C][C]2648.93491126813[/C][C]-146.274911268129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105598&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105598&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
13484.743038.99450842818445.745491571817
23411.133024.52278951192386.60721048808
33288.183131.63406935208156.545930647917
43280.373503.73172221422-223.361722214222
53173.953647.20713250306-473.257132503061
63165.263255.2214193047-89.9614193047016
73092.713317.62747266103-224.917472661026
83053.053141.16743876762-88.1174387676151
93181.963033.78152251798148.178477482016
102999.932882.1047850936117.825214906397
113249.573286.8772851666-37.3072851665971
123210.523302.76150484482-92.2415048448192
133030.293308.6602382368-278.370238236797
142803.473082.03443341821-278.564433418207
152767.633081.22238843409-313.592388434093
162882.63333.47493520534-450.874935205344
172863.363011.35199469497-147.991994694972
182897.063007.66107230137-110.601072301366
193012.613003.056836945859.55316305414726
203142.953144.07352250243-1.12352250242652
213032.933031.056743553091.87325644691486
223045.782852.55508908587193.224910914127
233110.522976.4679369228134.052063077205
243013.243221.9301491869-208.690149186897
252987.13186.07646729113-198.976467291127
262995.553127.90039234392-132.350392343916
272833.182871.9793541251-38.7993541250991
282848.962910.05150398461-61.0915039846075
292794.833033.85855705596-239.028557055961
302845.263027.69782053226-182.437820532262
312915.022846.163683897468.8563161025978
322892.632721.3588348894171.271165110597
332604.422135.89377478668468.526225213318
342641.652166.64785041228475.002149587717
352659.812432.94039177919226.86960822081
362638.532344.67270394404293.857296055959
372720.252340.60958585902379.640414140982
382745.882312.5295559028433.350444097198
392735.72524.30128439963211.398715600367
402811.72417.39393137458394.306068625416
412799.432475.73098862238323.69901137762
422555.282330.16691764008225.113082359924
432304.982020.45054274776284.529457252241
442214.952060.82357603482154.126423965181
452065.811947.42505708501118.384942914985
461940.491860.8880405482979.6019594517112
4720422039.473616412492.52638358750839
481995.371942.2784321697453.0915678302633
491946.812052.98550267133-106.175502671332
501765.91894.74207655299-128.842076552992
511635.251828.05819177089-192.808191770885
521833.421875.27097882927-41.8509788292654
531910.432264.97283717494-354.542837174939
541959.672536.02469166952-576.354691669517
551969.62401.87505346577-432.275053465769
562061.412268.71682947101-207.306829471011
572093.482463.21513363718-369.735133637177
582120.882364.59278003575-243.712780035746
592174.562427.39679219693-252.836792196925
602196.722565.53240317198-368.812403171978
612350.442709.70338466043-359.263384660428
622440.252706.15231335361-265.90231335361
632408.642703.09335001644-294.45335001644
642472.812798.71433945902-325.904339459019
652407.62524.59041946124-116.990419461243
662454.622748.55661715839-293.936617158386
672448.052555.98566737305-107.935667373049
682497.842554.63147125566-56.7914712556564
692645.642669.76198761532-24.1219876153179
702756.762455.48835787512301.271642124885
712849.272643.2431597683206.026840231697
722921.442792.92904170075128.510958299248
732981.852824.78770950689157.062290493115
743080.583036.7398426975143.840157302488
753106.223040.895367894265.3246321057995
763119.312870.62592174791248.684078252085
773061.262835.523129451225.736870549002
783097.312929.82875797557167.481242024426
793161.692996.78666461987164.90333538013
803257.162991.8200687023265.339931297703
813277.013023.47522770653253.53477229347
823295.323105.51889029683189.80110970317
833363.993279.4975521800784.4924478199342
843494.173454.941934644239.228065355799
853667.033540.22448587691126.805514123086
863813.063646.2610322808166.798967719195
873917.963756.48412890216161.475871097843
883895.513799.5560774053995.953922594608
893801.063737.0682564285763.9917435714334
903570.123562.290983061927.82901693808217
913701.613533.78803251698167.821967483018
923862.273648.25667556715214.013324432852
933970.13844.73923737262125.360762627381
944138.524071.4772052816867.0427947183191
954199.754120.3302177845379.4197822154683
964290.894130.12505309643160.764946903569
974443.914290.9519912033152.9580087967
984502.644248.10006564401254.539934355992
994356.984069.17244315522287.80755684478
1004591.274317.95631108202273.313688917978
1014696.964613.8094838603183.1505161396885
1024621.44548.952515106572.4474848935027
1034562.844499.1480311795263.6919688204818
1044202.524279.56039475249-77.0403947524937
1054296.494385.89202168778-89.4020216877807
1064435.234520.38258166968-85.1525816696783
1074105.184130.82311263087-25.6431126308655
1084116.684257.3077349278-140.627734927794
1093844.493808.779253531935.7107464681017
1103720.983817.46246133161-96.4824613316064
1113674.43784.89606463086-110.496064630863
1123857.624079.31899632472-221.698996324721
1133801.064041.17880912386-240.118809123863
1143504.373539.81094220333-35.4409422033285
1153032.62982.203395020950.3966049790982
1163047.033123.07919114892-76.04919114892
1172962.343184.19220065482-221.852200654817
1182197.822296.44623819843-98.626238198431
1192014.452311.75711609413-297.307116094134
1201862.832206.18493513003-343.354935130032
1211905.411955.34268431391-49.9326843139105
1221810.991649.20195882217161.788041177827
1231670.071561.06308674244109.006913257565
1241864.441806.4863375098457.9536624901642
1252052.022001.6045045407450.415495459255
1262029.62070.89355414891-41.2935541489147
1272070.832100.16096342772-29.3309634277243
1282293.412375.36771818163-81.9577181816276
1292443.272465.23236073688-21.9623607368806
1302513.172434.1407953960979.0292046039127
1312466.922613.14914658355-146.229146583554
1322502.662648.93491126813-146.274911268129






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.02211403794093870.04422807588187740.977885962059061
130.01012115870862080.02024231741724160.989878841291379
140.05552728197030770.1110545639406150.944472718029692
150.06076314296755580.1215262859351120.939236857032444
160.04469261575187370.08938523150374750.955307384248126
170.09034604335781190.1806920867156240.909653956642188
180.08478905306837780.1695781061367560.915210946931622
190.0585154469431020.1170308938862040.941484553056898
200.04793916507894170.09587833015788340.952060834921058
210.03208048089220050.0641609617844010.9679195191078
220.01885244159299670.03770488318599340.981147558407003
230.01076548159600870.02153096319201740.989234518403991
240.01185812980226230.02371625960452460.988141870197738
250.02341840324004760.04683680648009520.976581596759952
260.02443423493496610.04886846986993220.975565765065034
270.06664192769391680.1332838553878340.933358072306083
280.1269337571623990.2538675143247970.873066242837601
290.1469840623082180.2939681246164350.853015937691782
300.1387848248559010.2775696497118020.861215175144099
310.1083368677164050.2166737354328110.891663132283595
320.07894483715108040.1578896743021610.92105516284892
330.09364213601786340.1872842720357270.906357863982137
340.08680032760731660.1736006552146330.913199672392683
350.07590427357316650.1518085471463330.924095726426833
360.06032561916981930.1206512383396390.93967438083018
370.04948522785913350.0989704557182670.950514772140867
380.06067394197964770.1213478839592950.939326058020352
390.04674994786870890.09349989573741770.95325005213129
400.05847462165404350.1169492433080870.941525378345957
410.05086742673031140.1017348534606230.949132573269689
420.06118640015473960.1223728003094790.93881359984526
430.1726783179212190.3453566358424370.827321682078781
440.3190492859649080.6380985719298160.680950714035092
450.4176056988503110.8352113977006210.58239430114969
460.5501124280450950.899775143909810.449887571954905
470.6293933458472670.7412133083054650.370606654152733
480.6462207551548460.7075584896903070.353779244845154
490.6368947685797040.7262104628405920.363105231420296
500.5850143106916310.8299713786167390.414985689308369
510.5891859579487270.8216280841025450.410814042051273
520.7300112512496080.5399774975007830.269988748750392
530.7871146360631330.4257707278737340.212885363936867
540.8984664500220190.2030670999559630.101533549977981
550.9367997605397210.1264004789205580.0632002394602788
560.9281568628310140.1436862743379720.0718431371689859
570.9471351357237740.1057297285524520.0528648642762261
580.959999045508560.08000190898287830.0400009544914392
590.9661094107492180.06778117850156450.0338905892507822
600.9743874396292960.05122512074140730.0256125603707037
610.9807946325682050.03841073486358940.0192053674317947
620.980079538496240.03984092300752150.0199204615037607
630.990146187316330.01970762536733820.00985381268366912
640.9992531731495850.001493653700829930.000746826850414964
650.9997968266507340.0004063466985316710.000203173349265836
660.9999852118518882.95762962242739e-051.47881481121369e-05
670.9999973425046785.31499064382068e-062.65749532191034e-06
680.9999993136278761.37274424808602e-066.8637212404301e-07
690.9999998386671643.22665672603342e-071.61332836301671e-07
700.999999969206666.15866796893579e-083.07933398446789e-08
710.9999999843065163.13869672644522e-081.56934836322261e-08
720.9999999840245653.19508706764648e-081.59754353382324e-08
730.9999999842909683.14180634620664e-081.57090317310332e-08
740.9999999857554232.84891536664721e-081.42445768332361e-08
750.999999988299222.34015585345344e-081.17007792672672e-08
760.9999999905191091.89617822399972e-089.48089111999861e-09
770.999999991736851.65263018677202e-088.26315093386008e-09
780.9999999899754252.00491498338234e-081.00245749169117e-08
790.999999985431622.91367586025276e-081.45683793012638e-08
800.999999992912871.41742613432005e-087.08713067160023e-09
810.9999999983046593.39068271277371e-091.69534135638685e-09
820.9999999996070367.85927704882804e-103.92963852441402e-10
830.9999999996996266.00747675331449e-103.00373837665724e-10
840.99999999920951.58100193990149e-097.90500969950747e-10
850.9999999983481153.30376919261876e-091.65188459630938e-09
860.9999999959823758.03524981311005e-094.01762490655503e-09
870.9999999897886942.04226120176796e-081.02113060088398e-08
880.9999999982049413.59011796606406e-091.79505898303203e-09
890.9999999999134591.73082571538773e-108.65412857693864e-11
900.9999999998587932.82414042781231e-101.41207021390616e-10
910.9999999995731548.53692667584624e-104.26846333792312e-10
920.9999999987438672.51226538508602e-091.25613269254301e-09
930.9999999962599557.48008951351497e-093.74004475675749e-09
940.9999999939555621.2088876001421e-086.04443800071051e-09
950.9999999831425683.3714863437189e-081.68574317185945e-08
960.9999999633859187.32281638239122e-083.66140819119561e-08
970.9999999014684021.97063196312632e-079.85315981563158e-08
980.9999997676485944.64702811813533e-072.32351405906767e-07
990.9999994020683671.19586326645857e-065.97931633229283e-07
1000.9999986833599792.63328004240163e-061.31664002120081e-06
1010.9999965791084456.8417831106665e-063.42089155533325e-06
1020.9999914740199811.70519600374571e-058.52598001872856e-06
1030.9999788712404184.2257519164283e-052.11287595821415e-05
1040.9999532493522839.3501295434825e-054.67506477174125e-05
1050.999916885457290.0001662290854217248.3114542710862e-05
1060.9998336551143060.000332689771388310.000166344885694155
1070.999817588141370.0003648237172607140.000182411858630357
1080.999639662289850.0007206754202990040.000360337710149502
1090.9999261828717120.0001476342565759277.38171282879633e-05
1100.9998450460425220.0003099079149553130.000154953957477657
1110.9996880044892330.0006239910215349280.000311995510767464
1120.9996878399067560.0006243201864871690.000312160093243584
1130.9996185393112450.0007629213775097610.000381460688754881
1140.9998967746313060.0002064507373878790.000103225368693939
1150.999610817006530.0007783659869414640.000389182993470732
1160.9986426338002420.00271473239951620.0013573661997581
1170.9969940637177070.006011872564586890.00300593628229345
1180.9898347835805230.02033043283895370.0101652164194768
1190.997852143090190.00429571381962190.00214785690981095
1200.9922216216627490.0155567566745030.0077783783372515

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0221140379409387 & 0.0442280758818774 & 0.977885962059061 \tabularnewline
13 & 0.0101211587086208 & 0.0202423174172416 & 0.989878841291379 \tabularnewline
14 & 0.0555272819703077 & 0.111054563940615 & 0.944472718029692 \tabularnewline
15 & 0.0607631429675558 & 0.121526285935112 & 0.939236857032444 \tabularnewline
16 & 0.0446926157518737 & 0.0893852315037475 & 0.955307384248126 \tabularnewline
17 & 0.0903460433578119 & 0.180692086715624 & 0.909653956642188 \tabularnewline
18 & 0.0847890530683778 & 0.169578106136756 & 0.915210946931622 \tabularnewline
19 & 0.058515446943102 & 0.117030893886204 & 0.941484553056898 \tabularnewline
20 & 0.0479391650789417 & 0.0958783301578834 & 0.952060834921058 \tabularnewline
21 & 0.0320804808922005 & 0.064160961784401 & 0.9679195191078 \tabularnewline
22 & 0.0188524415929967 & 0.0377048831859934 & 0.981147558407003 \tabularnewline
23 & 0.0107654815960087 & 0.0215309631920174 & 0.989234518403991 \tabularnewline
24 & 0.0118581298022623 & 0.0237162596045246 & 0.988141870197738 \tabularnewline
25 & 0.0234184032400476 & 0.0468368064800952 & 0.976581596759952 \tabularnewline
26 & 0.0244342349349661 & 0.0488684698699322 & 0.975565765065034 \tabularnewline
27 & 0.0666419276939168 & 0.133283855387834 & 0.933358072306083 \tabularnewline
28 & 0.126933757162399 & 0.253867514324797 & 0.873066242837601 \tabularnewline
29 & 0.146984062308218 & 0.293968124616435 & 0.853015937691782 \tabularnewline
30 & 0.138784824855901 & 0.277569649711802 & 0.861215175144099 \tabularnewline
31 & 0.108336867716405 & 0.216673735432811 & 0.891663132283595 \tabularnewline
32 & 0.0789448371510804 & 0.157889674302161 & 0.92105516284892 \tabularnewline
33 & 0.0936421360178634 & 0.187284272035727 & 0.906357863982137 \tabularnewline
34 & 0.0868003276073166 & 0.173600655214633 & 0.913199672392683 \tabularnewline
35 & 0.0759042735731665 & 0.151808547146333 & 0.924095726426833 \tabularnewline
36 & 0.0603256191698193 & 0.120651238339639 & 0.93967438083018 \tabularnewline
37 & 0.0494852278591335 & 0.098970455718267 & 0.950514772140867 \tabularnewline
38 & 0.0606739419796477 & 0.121347883959295 & 0.939326058020352 \tabularnewline
39 & 0.0467499478687089 & 0.0934998957374177 & 0.95325005213129 \tabularnewline
40 & 0.0584746216540435 & 0.116949243308087 & 0.941525378345957 \tabularnewline
41 & 0.0508674267303114 & 0.101734853460623 & 0.949132573269689 \tabularnewline
42 & 0.0611864001547396 & 0.122372800309479 & 0.93881359984526 \tabularnewline
43 & 0.172678317921219 & 0.345356635842437 & 0.827321682078781 \tabularnewline
44 & 0.319049285964908 & 0.638098571929816 & 0.680950714035092 \tabularnewline
45 & 0.417605698850311 & 0.835211397700621 & 0.58239430114969 \tabularnewline
46 & 0.550112428045095 & 0.89977514390981 & 0.449887571954905 \tabularnewline
47 & 0.629393345847267 & 0.741213308305465 & 0.370606654152733 \tabularnewline
48 & 0.646220755154846 & 0.707558489690307 & 0.353779244845154 \tabularnewline
49 & 0.636894768579704 & 0.726210462840592 & 0.363105231420296 \tabularnewline
50 & 0.585014310691631 & 0.829971378616739 & 0.414985689308369 \tabularnewline
51 & 0.589185957948727 & 0.821628084102545 & 0.410814042051273 \tabularnewline
52 & 0.730011251249608 & 0.539977497500783 & 0.269988748750392 \tabularnewline
53 & 0.787114636063133 & 0.425770727873734 & 0.212885363936867 \tabularnewline
54 & 0.898466450022019 & 0.203067099955963 & 0.101533549977981 \tabularnewline
55 & 0.936799760539721 & 0.126400478920558 & 0.0632002394602788 \tabularnewline
56 & 0.928156862831014 & 0.143686274337972 & 0.0718431371689859 \tabularnewline
57 & 0.947135135723774 & 0.105729728552452 & 0.0528648642762261 \tabularnewline
58 & 0.95999904550856 & 0.0800019089828783 & 0.0400009544914392 \tabularnewline
59 & 0.966109410749218 & 0.0677811785015645 & 0.0338905892507822 \tabularnewline
60 & 0.974387439629296 & 0.0512251207414073 & 0.0256125603707037 \tabularnewline
61 & 0.980794632568205 & 0.0384107348635894 & 0.0192053674317947 \tabularnewline
62 & 0.98007953849624 & 0.0398409230075215 & 0.0199204615037607 \tabularnewline
63 & 0.99014618731633 & 0.0197076253673382 & 0.00985381268366912 \tabularnewline
64 & 0.999253173149585 & 0.00149365370082993 & 0.000746826850414964 \tabularnewline
65 & 0.999796826650734 & 0.000406346698531671 & 0.000203173349265836 \tabularnewline
66 & 0.999985211851888 & 2.95762962242739e-05 & 1.47881481121369e-05 \tabularnewline
67 & 0.999997342504678 & 5.31499064382068e-06 & 2.65749532191034e-06 \tabularnewline
68 & 0.999999313627876 & 1.37274424808602e-06 & 6.8637212404301e-07 \tabularnewline
69 & 0.999999838667164 & 3.22665672603342e-07 & 1.61332836301671e-07 \tabularnewline
70 & 0.99999996920666 & 6.15866796893579e-08 & 3.07933398446789e-08 \tabularnewline
71 & 0.999999984306516 & 3.13869672644522e-08 & 1.56934836322261e-08 \tabularnewline
72 & 0.999999984024565 & 3.19508706764648e-08 & 1.59754353382324e-08 \tabularnewline
73 & 0.999999984290968 & 3.14180634620664e-08 & 1.57090317310332e-08 \tabularnewline
74 & 0.999999985755423 & 2.84891536664721e-08 & 1.42445768332361e-08 \tabularnewline
75 & 0.99999998829922 & 2.34015585345344e-08 & 1.17007792672672e-08 \tabularnewline
76 & 0.999999990519109 & 1.89617822399972e-08 & 9.48089111999861e-09 \tabularnewline
77 & 0.99999999173685 & 1.65263018677202e-08 & 8.26315093386008e-09 \tabularnewline
78 & 0.999999989975425 & 2.00491498338234e-08 & 1.00245749169117e-08 \tabularnewline
79 & 0.99999998543162 & 2.91367586025276e-08 & 1.45683793012638e-08 \tabularnewline
80 & 0.99999999291287 & 1.41742613432005e-08 & 7.08713067160023e-09 \tabularnewline
81 & 0.999999998304659 & 3.39068271277371e-09 & 1.69534135638685e-09 \tabularnewline
82 & 0.999999999607036 & 7.85927704882804e-10 & 3.92963852441402e-10 \tabularnewline
83 & 0.999999999699626 & 6.00747675331449e-10 & 3.00373837665724e-10 \tabularnewline
84 & 0.9999999992095 & 1.58100193990149e-09 & 7.90500969950747e-10 \tabularnewline
85 & 0.999999998348115 & 3.30376919261876e-09 & 1.65188459630938e-09 \tabularnewline
86 & 0.999999995982375 & 8.03524981311005e-09 & 4.01762490655503e-09 \tabularnewline
87 & 0.999999989788694 & 2.04226120176796e-08 & 1.02113060088398e-08 \tabularnewline
88 & 0.999999998204941 & 3.59011796606406e-09 & 1.79505898303203e-09 \tabularnewline
89 & 0.999999999913459 & 1.73082571538773e-10 & 8.65412857693864e-11 \tabularnewline
90 & 0.999999999858793 & 2.82414042781231e-10 & 1.41207021390616e-10 \tabularnewline
91 & 0.999999999573154 & 8.53692667584624e-10 & 4.26846333792312e-10 \tabularnewline
92 & 0.999999998743867 & 2.51226538508602e-09 & 1.25613269254301e-09 \tabularnewline
93 & 0.999999996259955 & 7.48008951351497e-09 & 3.74004475675749e-09 \tabularnewline
94 & 0.999999993955562 & 1.2088876001421e-08 & 6.04443800071051e-09 \tabularnewline
95 & 0.999999983142568 & 3.3714863437189e-08 & 1.68574317185945e-08 \tabularnewline
96 & 0.999999963385918 & 7.32281638239122e-08 & 3.66140819119561e-08 \tabularnewline
97 & 0.999999901468402 & 1.97063196312632e-07 & 9.85315981563158e-08 \tabularnewline
98 & 0.999999767648594 & 4.64702811813533e-07 & 2.32351405906767e-07 \tabularnewline
99 & 0.999999402068367 & 1.19586326645857e-06 & 5.97931633229283e-07 \tabularnewline
100 & 0.999998683359979 & 2.63328004240163e-06 & 1.31664002120081e-06 \tabularnewline
101 & 0.999996579108445 & 6.8417831106665e-06 & 3.42089155533325e-06 \tabularnewline
102 & 0.999991474019981 & 1.70519600374571e-05 & 8.52598001872856e-06 \tabularnewline
103 & 0.999978871240418 & 4.2257519164283e-05 & 2.11287595821415e-05 \tabularnewline
104 & 0.999953249352283 & 9.3501295434825e-05 & 4.67506477174125e-05 \tabularnewline
105 & 0.99991688545729 & 0.000166229085421724 & 8.3114542710862e-05 \tabularnewline
106 & 0.999833655114306 & 0.00033268977138831 & 0.000166344885694155 \tabularnewline
107 & 0.99981758814137 & 0.000364823717260714 & 0.000182411858630357 \tabularnewline
108 & 0.99963966228985 & 0.000720675420299004 & 0.000360337710149502 \tabularnewline
109 & 0.999926182871712 & 0.000147634256575927 & 7.38171282879633e-05 \tabularnewline
110 & 0.999845046042522 & 0.000309907914955313 & 0.000154953957477657 \tabularnewline
111 & 0.999688004489233 & 0.000623991021534928 & 0.000311995510767464 \tabularnewline
112 & 0.999687839906756 & 0.000624320186487169 & 0.000312160093243584 \tabularnewline
113 & 0.999618539311245 & 0.000762921377509761 & 0.000381460688754881 \tabularnewline
114 & 0.999896774631306 & 0.000206450737387879 & 0.000103225368693939 \tabularnewline
115 & 0.99961081700653 & 0.000778365986941464 & 0.000389182993470732 \tabularnewline
116 & 0.998642633800242 & 0.0027147323995162 & 0.0013573661997581 \tabularnewline
117 & 0.996994063717707 & 0.00601187256458689 & 0.00300593628229345 \tabularnewline
118 & 0.989834783580523 & 0.0203304328389537 & 0.0101652164194768 \tabularnewline
119 & 0.99785214309019 & 0.0042957138196219 & 0.00214785690981095 \tabularnewline
120 & 0.992221621662749 & 0.015556756674503 & 0.0077783783372515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105598&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]12[/C][C]0.0221140379409387[/C][C]0.0442280758818774[/C][C]0.977885962059061[/C][/ROW]
[ROW][C]13[/C][C]0.0101211587086208[/C][C]0.0202423174172416[/C][C]0.989878841291379[/C][/ROW]
[ROW][C]14[/C][C]0.0555272819703077[/C][C]0.111054563940615[/C][C]0.944472718029692[/C][/ROW]
[ROW][C]15[/C][C]0.0607631429675558[/C][C]0.121526285935112[/C][C]0.939236857032444[/C][/ROW]
[ROW][C]16[/C][C]0.0446926157518737[/C][C]0.0893852315037475[/C][C]0.955307384248126[/C][/ROW]
[ROW][C]17[/C][C]0.0903460433578119[/C][C]0.180692086715624[/C][C]0.909653956642188[/C][/ROW]
[ROW][C]18[/C][C]0.0847890530683778[/C][C]0.169578106136756[/C][C]0.915210946931622[/C][/ROW]
[ROW][C]19[/C][C]0.058515446943102[/C][C]0.117030893886204[/C][C]0.941484553056898[/C][/ROW]
[ROW][C]20[/C][C]0.0479391650789417[/C][C]0.0958783301578834[/C][C]0.952060834921058[/C][/ROW]
[ROW][C]21[/C][C]0.0320804808922005[/C][C]0.064160961784401[/C][C]0.9679195191078[/C][/ROW]
[ROW][C]22[/C][C]0.0188524415929967[/C][C]0.0377048831859934[/C][C]0.981147558407003[/C][/ROW]
[ROW][C]23[/C][C]0.0107654815960087[/C][C]0.0215309631920174[/C][C]0.989234518403991[/C][/ROW]
[ROW][C]24[/C][C]0.0118581298022623[/C][C]0.0237162596045246[/C][C]0.988141870197738[/C][/ROW]
[ROW][C]25[/C][C]0.0234184032400476[/C][C]0.0468368064800952[/C][C]0.976581596759952[/C][/ROW]
[ROW][C]26[/C][C]0.0244342349349661[/C][C]0.0488684698699322[/C][C]0.975565765065034[/C][/ROW]
[ROW][C]27[/C][C]0.0666419276939168[/C][C]0.133283855387834[/C][C]0.933358072306083[/C][/ROW]
[ROW][C]28[/C][C]0.126933757162399[/C][C]0.253867514324797[/C][C]0.873066242837601[/C][/ROW]
[ROW][C]29[/C][C]0.146984062308218[/C][C]0.293968124616435[/C][C]0.853015937691782[/C][/ROW]
[ROW][C]30[/C][C]0.138784824855901[/C][C]0.277569649711802[/C][C]0.861215175144099[/C][/ROW]
[ROW][C]31[/C][C]0.108336867716405[/C][C]0.216673735432811[/C][C]0.891663132283595[/C][/ROW]
[ROW][C]32[/C][C]0.0789448371510804[/C][C]0.157889674302161[/C][C]0.92105516284892[/C][/ROW]
[ROW][C]33[/C][C]0.0936421360178634[/C][C]0.187284272035727[/C][C]0.906357863982137[/C][/ROW]
[ROW][C]34[/C][C]0.0868003276073166[/C][C]0.173600655214633[/C][C]0.913199672392683[/C][/ROW]
[ROW][C]35[/C][C]0.0759042735731665[/C][C]0.151808547146333[/C][C]0.924095726426833[/C][/ROW]
[ROW][C]36[/C][C]0.0603256191698193[/C][C]0.120651238339639[/C][C]0.93967438083018[/C][/ROW]
[ROW][C]37[/C][C]0.0494852278591335[/C][C]0.098970455718267[/C][C]0.950514772140867[/C][/ROW]
[ROW][C]38[/C][C]0.0606739419796477[/C][C]0.121347883959295[/C][C]0.939326058020352[/C][/ROW]
[ROW][C]39[/C][C]0.0467499478687089[/C][C]0.0934998957374177[/C][C]0.95325005213129[/C][/ROW]
[ROW][C]40[/C][C]0.0584746216540435[/C][C]0.116949243308087[/C][C]0.941525378345957[/C][/ROW]
[ROW][C]41[/C][C]0.0508674267303114[/C][C]0.101734853460623[/C][C]0.949132573269689[/C][/ROW]
[ROW][C]42[/C][C]0.0611864001547396[/C][C]0.122372800309479[/C][C]0.93881359984526[/C][/ROW]
[ROW][C]43[/C][C]0.172678317921219[/C][C]0.345356635842437[/C][C]0.827321682078781[/C][/ROW]
[ROW][C]44[/C][C]0.319049285964908[/C][C]0.638098571929816[/C][C]0.680950714035092[/C][/ROW]
[ROW][C]45[/C][C]0.417605698850311[/C][C]0.835211397700621[/C][C]0.58239430114969[/C][/ROW]
[ROW][C]46[/C][C]0.550112428045095[/C][C]0.89977514390981[/C][C]0.449887571954905[/C][/ROW]
[ROW][C]47[/C][C]0.629393345847267[/C][C]0.741213308305465[/C][C]0.370606654152733[/C][/ROW]
[ROW][C]48[/C][C]0.646220755154846[/C][C]0.707558489690307[/C][C]0.353779244845154[/C][/ROW]
[ROW][C]49[/C][C]0.636894768579704[/C][C]0.726210462840592[/C][C]0.363105231420296[/C][/ROW]
[ROW][C]50[/C][C]0.585014310691631[/C][C]0.829971378616739[/C][C]0.414985689308369[/C][/ROW]
[ROW][C]51[/C][C]0.589185957948727[/C][C]0.821628084102545[/C][C]0.410814042051273[/C][/ROW]
[ROW][C]52[/C][C]0.730011251249608[/C][C]0.539977497500783[/C][C]0.269988748750392[/C][/ROW]
[ROW][C]53[/C][C]0.787114636063133[/C][C]0.425770727873734[/C][C]0.212885363936867[/C][/ROW]
[ROW][C]54[/C][C]0.898466450022019[/C][C]0.203067099955963[/C][C]0.101533549977981[/C][/ROW]
[ROW][C]55[/C][C]0.936799760539721[/C][C]0.126400478920558[/C][C]0.0632002394602788[/C][/ROW]
[ROW][C]56[/C][C]0.928156862831014[/C][C]0.143686274337972[/C][C]0.0718431371689859[/C][/ROW]
[ROW][C]57[/C][C]0.947135135723774[/C][C]0.105729728552452[/C][C]0.0528648642762261[/C][/ROW]
[ROW][C]58[/C][C]0.95999904550856[/C][C]0.0800019089828783[/C][C]0.0400009544914392[/C][/ROW]
[ROW][C]59[/C][C]0.966109410749218[/C][C]0.0677811785015645[/C][C]0.0338905892507822[/C][/ROW]
[ROW][C]60[/C][C]0.974387439629296[/C][C]0.0512251207414073[/C][C]0.0256125603707037[/C][/ROW]
[ROW][C]61[/C][C]0.980794632568205[/C][C]0.0384107348635894[/C][C]0.0192053674317947[/C][/ROW]
[ROW][C]62[/C][C]0.98007953849624[/C][C]0.0398409230075215[/C][C]0.0199204615037607[/C][/ROW]
[ROW][C]63[/C][C]0.99014618731633[/C][C]0.0197076253673382[/C][C]0.00985381268366912[/C][/ROW]
[ROW][C]64[/C][C]0.999253173149585[/C][C]0.00149365370082993[/C][C]0.000746826850414964[/C][/ROW]
[ROW][C]65[/C][C]0.999796826650734[/C][C]0.000406346698531671[/C][C]0.000203173349265836[/C][/ROW]
[ROW][C]66[/C][C]0.999985211851888[/C][C]2.95762962242739e-05[/C][C]1.47881481121369e-05[/C][/ROW]
[ROW][C]67[/C][C]0.999997342504678[/C][C]5.31499064382068e-06[/C][C]2.65749532191034e-06[/C][/ROW]
[ROW][C]68[/C][C]0.999999313627876[/C][C]1.37274424808602e-06[/C][C]6.8637212404301e-07[/C][/ROW]
[ROW][C]69[/C][C]0.999999838667164[/C][C]3.22665672603342e-07[/C][C]1.61332836301671e-07[/C][/ROW]
[ROW][C]70[/C][C]0.99999996920666[/C][C]6.15866796893579e-08[/C][C]3.07933398446789e-08[/C][/ROW]
[ROW][C]71[/C][C]0.999999984306516[/C][C]3.13869672644522e-08[/C][C]1.56934836322261e-08[/C][/ROW]
[ROW][C]72[/C][C]0.999999984024565[/C][C]3.19508706764648e-08[/C][C]1.59754353382324e-08[/C][/ROW]
[ROW][C]73[/C][C]0.999999984290968[/C][C]3.14180634620664e-08[/C][C]1.57090317310332e-08[/C][/ROW]
[ROW][C]74[/C][C]0.999999985755423[/C][C]2.84891536664721e-08[/C][C]1.42445768332361e-08[/C][/ROW]
[ROW][C]75[/C][C]0.99999998829922[/C][C]2.34015585345344e-08[/C][C]1.17007792672672e-08[/C][/ROW]
[ROW][C]76[/C][C]0.999999990519109[/C][C]1.89617822399972e-08[/C][C]9.48089111999861e-09[/C][/ROW]
[ROW][C]77[/C][C]0.99999999173685[/C][C]1.65263018677202e-08[/C][C]8.26315093386008e-09[/C][/ROW]
[ROW][C]78[/C][C]0.999999989975425[/C][C]2.00491498338234e-08[/C][C]1.00245749169117e-08[/C][/ROW]
[ROW][C]79[/C][C]0.99999998543162[/C][C]2.91367586025276e-08[/C][C]1.45683793012638e-08[/C][/ROW]
[ROW][C]80[/C][C]0.99999999291287[/C][C]1.41742613432005e-08[/C][C]7.08713067160023e-09[/C][/ROW]
[ROW][C]81[/C][C]0.999999998304659[/C][C]3.39068271277371e-09[/C][C]1.69534135638685e-09[/C][/ROW]
[ROW][C]82[/C][C]0.999999999607036[/C][C]7.85927704882804e-10[/C][C]3.92963852441402e-10[/C][/ROW]
[ROW][C]83[/C][C]0.999999999699626[/C][C]6.00747675331449e-10[/C][C]3.00373837665724e-10[/C][/ROW]
[ROW][C]84[/C][C]0.9999999992095[/C][C]1.58100193990149e-09[/C][C]7.90500969950747e-10[/C][/ROW]
[ROW][C]85[/C][C]0.999999998348115[/C][C]3.30376919261876e-09[/C][C]1.65188459630938e-09[/C][/ROW]
[ROW][C]86[/C][C]0.999999995982375[/C][C]8.03524981311005e-09[/C][C]4.01762490655503e-09[/C][/ROW]
[ROW][C]87[/C][C]0.999999989788694[/C][C]2.04226120176796e-08[/C][C]1.02113060088398e-08[/C][/ROW]
[ROW][C]88[/C][C]0.999999998204941[/C][C]3.59011796606406e-09[/C][C]1.79505898303203e-09[/C][/ROW]
[ROW][C]89[/C][C]0.999999999913459[/C][C]1.73082571538773e-10[/C][C]8.65412857693864e-11[/C][/ROW]
[ROW][C]90[/C][C]0.999999999858793[/C][C]2.82414042781231e-10[/C][C]1.41207021390616e-10[/C][/ROW]
[ROW][C]91[/C][C]0.999999999573154[/C][C]8.53692667584624e-10[/C][C]4.26846333792312e-10[/C][/ROW]
[ROW][C]92[/C][C]0.999999998743867[/C][C]2.51226538508602e-09[/C][C]1.25613269254301e-09[/C][/ROW]
[ROW][C]93[/C][C]0.999999996259955[/C][C]7.48008951351497e-09[/C][C]3.74004475675749e-09[/C][/ROW]
[ROW][C]94[/C][C]0.999999993955562[/C][C]1.2088876001421e-08[/C][C]6.04443800071051e-09[/C][/ROW]
[ROW][C]95[/C][C]0.999999983142568[/C][C]3.3714863437189e-08[/C][C]1.68574317185945e-08[/C][/ROW]
[ROW][C]96[/C][C]0.999999963385918[/C][C]7.32281638239122e-08[/C][C]3.66140819119561e-08[/C][/ROW]
[ROW][C]97[/C][C]0.999999901468402[/C][C]1.97063196312632e-07[/C][C]9.85315981563158e-08[/C][/ROW]
[ROW][C]98[/C][C]0.999999767648594[/C][C]4.64702811813533e-07[/C][C]2.32351405906767e-07[/C][/ROW]
[ROW][C]99[/C][C]0.999999402068367[/C][C]1.19586326645857e-06[/C][C]5.97931633229283e-07[/C][/ROW]
[ROW][C]100[/C][C]0.999998683359979[/C][C]2.63328004240163e-06[/C][C]1.31664002120081e-06[/C][/ROW]
[ROW][C]101[/C][C]0.999996579108445[/C][C]6.8417831106665e-06[/C][C]3.42089155533325e-06[/C][/ROW]
[ROW][C]102[/C][C]0.999991474019981[/C][C]1.70519600374571e-05[/C][C]8.52598001872856e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999978871240418[/C][C]4.2257519164283e-05[/C][C]2.11287595821415e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999953249352283[/C][C]9.3501295434825e-05[/C][C]4.67506477174125e-05[/C][/ROW]
[ROW][C]105[/C][C]0.99991688545729[/C][C]0.000166229085421724[/C][C]8.3114542710862e-05[/C][/ROW]
[ROW][C]106[/C][C]0.999833655114306[/C][C]0.00033268977138831[/C][C]0.000166344885694155[/C][/ROW]
[ROW][C]107[/C][C]0.99981758814137[/C][C]0.000364823717260714[/C][C]0.000182411858630357[/C][/ROW]
[ROW][C]108[/C][C]0.99963966228985[/C][C]0.000720675420299004[/C][C]0.000360337710149502[/C][/ROW]
[ROW][C]109[/C][C]0.999926182871712[/C][C]0.000147634256575927[/C][C]7.38171282879633e-05[/C][/ROW]
[ROW][C]110[/C][C]0.999845046042522[/C][C]0.000309907914955313[/C][C]0.000154953957477657[/C][/ROW]
[ROW][C]111[/C][C]0.999688004489233[/C][C]0.000623991021534928[/C][C]0.000311995510767464[/C][/ROW]
[ROW][C]112[/C][C]0.999687839906756[/C][C]0.000624320186487169[/C][C]0.000312160093243584[/C][/ROW]
[ROW][C]113[/C][C]0.999618539311245[/C][C]0.000762921377509761[/C][C]0.000381460688754881[/C][/ROW]
[ROW][C]114[/C][C]0.999896774631306[/C][C]0.000206450737387879[/C][C]0.000103225368693939[/C][/ROW]
[ROW][C]115[/C][C]0.99961081700653[/C][C]0.000778365986941464[/C][C]0.000389182993470732[/C][/ROW]
[ROW][C]116[/C][C]0.998642633800242[/C][C]0.0027147323995162[/C][C]0.0013573661997581[/C][/ROW]
[ROW][C]117[/C][C]0.996994063717707[/C][C]0.00601187256458689[/C][C]0.00300593628229345[/C][/ROW]
[ROW][C]118[/C][C]0.989834783580523[/C][C]0.0203304328389537[/C][C]0.0101652164194768[/C][/ROW]
[ROW][C]119[/C][C]0.99785214309019[/C][C]0.0042957138196219[/C][C]0.00214785690981095[/C][/ROW]
[ROW][C]120[/C][C]0.992221621662749[/C][C]0.015556756674503[/C][C]0.0077783783372515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105598&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105598&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
120.02211403794093870.04422807588187740.977885962059061
130.01012115870862080.02024231741724160.989878841291379
140.05552728197030770.1110545639406150.944472718029692
150.06076314296755580.1215262859351120.939236857032444
160.04469261575187370.08938523150374750.955307384248126
170.09034604335781190.1806920867156240.909653956642188
180.08478905306837780.1695781061367560.915210946931622
190.0585154469431020.1170308938862040.941484553056898
200.04793916507894170.09587833015788340.952060834921058
210.03208048089220050.0641609617844010.9679195191078
220.01885244159299670.03770488318599340.981147558407003
230.01076548159600870.02153096319201740.989234518403991
240.01185812980226230.02371625960452460.988141870197738
250.02341840324004760.04683680648009520.976581596759952
260.02443423493496610.04886846986993220.975565765065034
270.06664192769391680.1332838553878340.933358072306083
280.1269337571623990.2538675143247970.873066242837601
290.1469840623082180.2939681246164350.853015937691782
300.1387848248559010.2775696497118020.861215175144099
310.1083368677164050.2166737354328110.891663132283595
320.07894483715108040.1578896743021610.92105516284892
330.09364213601786340.1872842720357270.906357863982137
340.08680032760731660.1736006552146330.913199672392683
350.07590427357316650.1518085471463330.924095726426833
360.06032561916981930.1206512383396390.93967438083018
370.04948522785913350.0989704557182670.950514772140867
380.06067394197964770.1213478839592950.939326058020352
390.04674994786870890.09349989573741770.95325005213129
400.05847462165404350.1169492433080870.941525378345957
410.05086742673031140.1017348534606230.949132573269689
420.06118640015473960.1223728003094790.93881359984526
430.1726783179212190.3453566358424370.827321682078781
440.3190492859649080.6380985719298160.680950714035092
450.4176056988503110.8352113977006210.58239430114969
460.5501124280450950.899775143909810.449887571954905
470.6293933458472670.7412133083054650.370606654152733
480.6462207551548460.7075584896903070.353779244845154
490.6368947685797040.7262104628405920.363105231420296
500.5850143106916310.8299713786167390.414985689308369
510.5891859579487270.8216280841025450.410814042051273
520.7300112512496080.5399774975007830.269988748750392
530.7871146360631330.4257707278737340.212885363936867
540.8984664500220190.2030670999559630.101533549977981
550.9367997605397210.1264004789205580.0632002394602788
560.9281568628310140.1436862743379720.0718431371689859
570.9471351357237740.1057297285524520.0528648642762261
580.959999045508560.08000190898287830.0400009544914392
590.9661094107492180.06778117850156450.0338905892507822
600.9743874396292960.05122512074140730.0256125603707037
610.9807946325682050.03841073486358940.0192053674317947
620.980079538496240.03984092300752150.0199204615037607
630.990146187316330.01970762536733820.00985381268366912
640.9992531731495850.001493653700829930.000746826850414964
650.9997968266507340.0004063466985316710.000203173349265836
660.9999852118518882.95762962242739e-051.47881481121369e-05
670.9999973425046785.31499064382068e-062.65749532191034e-06
680.9999993136278761.37274424808602e-066.8637212404301e-07
690.9999998386671643.22665672603342e-071.61332836301671e-07
700.999999969206666.15866796893579e-083.07933398446789e-08
710.9999999843065163.13869672644522e-081.56934836322261e-08
720.9999999840245653.19508706764648e-081.59754353382324e-08
730.9999999842909683.14180634620664e-081.57090317310332e-08
740.9999999857554232.84891536664721e-081.42445768332361e-08
750.999999988299222.34015585345344e-081.17007792672672e-08
760.9999999905191091.89617822399972e-089.48089111999861e-09
770.999999991736851.65263018677202e-088.26315093386008e-09
780.9999999899754252.00491498338234e-081.00245749169117e-08
790.999999985431622.91367586025276e-081.45683793012638e-08
800.999999992912871.41742613432005e-087.08713067160023e-09
810.9999999983046593.39068271277371e-091.69534135638685e-09
820.9999999996070367.85927704882804e-103.92963852441402e-10
830.9999999996996266.00747675331449e-103.00373837665724e-10
840.99999999920951.58100193990149e-097.90500969950747e-10
850.9999999983481153.30376919261876e-091.65188459630938e-09
860.9999999959823758.03524981311005e-094.01762490655503e-09
870.9999999897886942.04226120176796e-081.02113060088398e-08
880.9999999982049413.59011796606406e-091.79505898303203e-09
890.9999999999134591.73082571538773e-108.65412857693864e-11
900.9999999998587932.82414042781231e-101.41207021390616e-10
910.9999999995731548.53692667584624e-104.26846333792312e-10
920.9999999987438672.51226538508602e-091.25613269254301e-09
930.9999999962599557.48008951351497e-093.74004475675749e-09
940.9999999939555621.2088876001421e-086.04443800071051e-09
950.9999999831425683.3714863437189e-081.68574317185945e-08
960.9999999633859187.32281638239122e-083.66140819119561e-08
970.9999999014684021.97063196312632e-079.85315981563158e-08
980.9999997676485944.64702811813533e-072.32351405906767e-07
990.9999994020683671.19586326645857e-065.97931633229283e-07
1000.9999986833599792.63328004240163e-061.31664002120081e-06
1010.9999965791084456.8417831106665e-063.42089155533325e-06
1020.9999914740199811.70519600374571e-058.52598001872856e-06
1030.9999788712404184.2257519164283e-052.11287595821415e-05
1040.9999532493522839.3501295434825e-054.67506477174125e-05
1050.999916885457290.0001662290854217248.3114542710862e-05
1060.9998336551143060.000332689771388310.000166344885694155
1070.999817588141370.0003648237172607140.000182411858630357
1080.999639662289850.0007206754202990040.000360337710149502
1090.9999261828717120.0001476342565759277.38171282879633e-05
1100.9998450460425220.0003099079149553130.000154953957477657
1110.9996880044892330.0006239910215349280.000311995510767464
1120.9996878399067560.0006243201864871690.000312160093243584
1130.9996185393112450.0007629213775097610.000381460688754881
1140.9998967746313060.0002064507373878790.000103225368693939
1150.999610817006530.0007783659869414640.000389182993470732
1160.9986426338002420.00271473239951620.0013573661997581
1170.9969940637177070.006011872564586890.00300593628229345
1180.9898347835805230.02033043283895370.0101652164194768
1190.997852143090190.00429571381962190.00214785690981095
1200.9922216216627490.0155567566745030.0077783783372515






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.504587155963303NOK
5% type I error level670.614678899082569NOK
10% type I error level750.688073394495413NOK

\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 & 55 & 0.504587155963303 & NOK \tabularnewline
5% type I error level & 67 & 0.614678899082569 & NOK \tabularnewline
10% type I error level & 75 & 0.688073394495413 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105598&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]55[/C][C]0.504587155963303[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]67[/C][C]0.614678899082569[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]75[/C][C]0.688073394495413[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105598&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105598&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 level550.504587155963303NOK
5% type I error level670.614678899082569NOK
10% type I error level750.688073394495413NOK


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')
}