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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 06 Dec 2010 16:44:05 +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/t1291653750vcjg6r1te91v0af.htm/, Retrieved Mon, 29 Apr 2024 06:08:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105694, Retrieved Mon, 29 Apr 2024 06:08:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151

Tree of Dependent Computations

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

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

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=105694&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=105694&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105694&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] = -260.706690926404 + 0.0250525591118981Nikkei[t] + 0.102631890756772DJ_Indust[t] + 0.006957119094245Goudprijs[t] -10.2630504431514Conjunct_Seizoenzuiver[t] + 12.9959047466191Cons_vertrouw[t] -7.33394620617855Alg_consumptie_index_BE[t] -165.868025333682Gem_rente_kasbon_5j[t] + 0.806808871155089Y1[t] -0.114390996301673Y2[t] + 0.181991078938916Y3[t] -0.0730288844595162Y4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -260.706690926404 +  0.0250525591118981Nikkei[t] +  0.102631890756772DJ_Indust[t] +  0.006957119094245Goudprijs[t] -10.2630504431514Conjunct_Seizoenzuiver[t] +  12.9959047466191Cons_vertrouw[t] -7.33394620617855Alg_consumptie_index_BE[t] -165.868025333682Gem_rente_kasbon_5j[t] +  0.806808871155089Y1[t] -0.114390996301673Y2[t] +  0.181991078938916Y3[t] -0.0730288844595162Y4[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105694&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -260.706690926404 +  0.0250525591118981Nikkei[t] +  0.102631890756772DJ_Indust[t] +  0.006957119094245Goudprijs[t] -10.2630504431514Conjunct_Seizoenzuiver[t] +  12.9959047466191Cons_vertrouw[t] -7.33394620617855Alg_consumptie_index_BE[t] -165.868025333682Gem_rente_kasbon_5j[t] +  0.806808871155089Y1[t] -0.114390996301673Y2[t] +  0.181991078938916Y3[t] -0.0730288844595162Y4[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105694&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105694&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] = -260.706690926404 + 0.0250525591118981Nikkei[t] + 0.102631890756772DJ_Indust[t] + 0.006957119094245Goudprijs[t] -10.2630504431514Conjunct_Seizoenzuiver[t] + 12.9959047466191Cons_vertrouw[t] -7.33394620617855Alg_consumptie_index_BE[t] -165.868025333682Gem_rente_kasbon_5j[t] + 0.806808871155089Y1[t] -0.114390996301673Y2[t] + 0.181991078938916Y3[t] -0.0730288844595162Y4[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-260.706690926404157.937258-1.65070.1015050.050753
Nikkei0.02505255911189810.0072143.47270.0007250.000362
DJ_Indust0.1026318907567720.0194855.26731e-060
Goudprijs0.0069571190942450.0035851.94040.0547580.027379
Conjunct_Seizoenzuiver-10.26305044315143.080988-3.33110.0011610.00058
Cons_vertrouw12.99590474661912.9367314.42532.2e-051.1e-05
Alg_consumptie_index_BE-7.3339462061785510.299251-0.71210.4778420.238921
Gem_rente_kasbon_5j-165.86802533368225.569328-6.48700
Y10.8068088711550890.0853449.453600
Y2-0.1143909963016730.112468-1.01710.3112250.155613
Y30.1819910789389160.1114771.63250.1052760.052638
Y4-0.07302888445951620.073509-0.99350.3225470.161274

\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) & -260.706690926404 & 157.937258 & -1.6507 & 0.101505 & 0.050753 \tabularnewline
Nikkei & 0.0250525591118981 & 0.007214 & 3.4727 & 0.000725 & 0.000362 \tabularnewline
DJ_Indust & 0.102631890756772 & 0.019485 & 5.2673 & 1e-06 & 0 \tabularnewline
Goudprijs & 0.006957119094245 & 0.003585 & 1.9404 & 0.054758 & 0.027379 \tabularnewline
Conjunct_Seizoenzuiver & -10.2630504431514 & 3.080988 & -3.3311 & 0.001161 & 0.00058 \tabularnewline
Cons_vertrouw & 12.9959047466191 & 2.936731 & 4.4253 & 2.2e-05 & 1.1e-05 \tabularnewline
Alg_consumptie_index_BE & -7.33394620617855 & 10.299251 & -0.7121 & 0.477842 & 0.238921 \tabularnewline
Gem_rente_kasbon_5j & -165.868025333682 & 25.569328 & -6.487 & 0 & 0 \tabularnewline
Y1 & 0.806808871155089 & 0.085344 & 9.4536 & 0 & 0 \tabularnewline
Y2 & -0.114390996301673 & 0.112468 & -1.0171 & 0.311225 & 0.155613 \tabularnewline
Y3 & 0.181991078938916 & 0.111477 & 1.6325 & 0.105276 & 0.052638 \tabularnewline
Y4 & -0.0730288844595162 & 0.073509 & -0.9935 & 0.322547 & 0.161274 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105694&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]-260.706690926404[/C][C]157.937258[/C][C]-1.6507[/C][C]0.101505[/C][C]0.050753[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.0250525591118981[/C][C]0.007214[/C][C]3.4727[/C][C]0.000725[/C][C]0.000362[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.102631890756772[/C][C]0.019485[/C][C]5.2673[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.006957119094245[/C][C]0.003585[/C][C]1.9404[/C][C]0.054758[/C][C]0.027379[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-10.2630504431514[/C][C]3.080988[/C][C]-3.3311[/C][C]0.001161[/C][C]0.00058[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]12.9959047466191[/C][C]2.936731[/C][C]4.4253[/C][C]2.2e-05[/C][C]1.1e-05[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]-7.33394620617855[/C][C]10.299251[/C][C]-0.7121[/C][C]0.477842[/C][C]0.238921[/C][/ROW]
[ROW][C]Gem_rente_kasbon_5j[/C][C]-165.868025333682[/C][C]25.569328[/C][C]-6.487[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y1[/C][C]0.806808871155089[/C][C]0.085344[/C][C]9.4536[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.114390996301673[/C][C]0.112468[/C][C]-1.0171[/C][C]0.311225[/C][C]0.155613[/C][/ROW]
[ROW][C]Y3[/C][C]0.181991078938916[/C][C]0.111477[/C][C]1.6325[/C][C]0.105276[/C][C]0.052638[/C][/ROW]
[ROW][C]Y4[/C][C]-0.0730288844595162[/C][C]0.073509[/C][C]-0.9935[/C][C]0.322547[/C][C]0.161274[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105694&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105694&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)-260.706690926404157.937258-1.65070.1015050.050753
Nikkei0.02505255911189810.0072143.47270.0007250.000362
DJ_Indust0.1026318907567720.0194855.26731e-060
Goudprijs0.0069571190942450.0035851.94040.0547580.027379
Conjunct_Seizoenzuiver-10.26305044315143.080988-3.33110.0011610.00058
Cons_vertrouw12.99590474661912.9367314.42532.2e-051.1e-05
Alg_consumptie_index_BE-7.3339462061785510.299251-0.71210.4778420.238921
Gem_rente_kasbon_5j-165.86802533368225.569328-6.48700
Y10.8068088711550890.0853449.453600
Y2-0.1143909963016730.112468-1.01710.3112250.155613
Y30.1819910789389160.1114771.63250.1052760.052638
Y4-0.07302888445951620.073509-0.99350.3225470.161274






Multiple Linear Regression - Regression Statistics
Multiple R0.990305665787982
R-squared0.980705311691779
Adjusted R-squared0.978875642972896
F-TEST (value)536.001573164823
F-TEST (DF numerator)11
F-TEST (DF denominator)116
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation110.643967300128
Sum Squared Residuals1420082.14998977

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990305665787982 \tabularnewline
R-squared & 0.980705311691779 \tabularnewline
Adjusted R-squared & 0.978875642972896 \tabularnewline
F-TEST (value) & 536.001573164823 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 116 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 110.643967300128 \tabularnewline
Sum Squared Residuals & 1420082.14998977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105694&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990305665787982[/C][/ROW]
[ROW][C]R-squared[/C][C]0.980705311691779[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.978875642972896[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]536.001573164823[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]116[/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]110.643967300128[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1420082.14998977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105694&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105694&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 R0.990305665787982
R-squared0.980705311691779
Adjusted R-squared0.978875642972896
F-TEST (value)536.001573164823
F-TEST (DF numerator)11
F-TEST (DF denominator)116
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation110.643967300128
Sum Squared Residuals1420082.14998977






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13173.953467.20395684971-293.253956849713
23165.263166.33400475918-1.07400475917596
33092.713253.24318993043-160.533189930426
43053.053097.78756228371-44.7375622837136
53181.963068.70449700179113.255502998209
62999.933069.456961988-69.5269619880009
73249.573054.34084232542195.229157674582
83210.523274.95114213856-64.431142138561
93030.293206.83415388342-176.544153883423
102803.473051.18046483841-247.710464838414
112767.632869.72876856238-102.098768562381
122882.62903.61905785664-21.0190578566352
132863.362847.0988190816216.2611809183754
142897.062838.3428010763758.7171989236346
153012.612894.63433382927117.975666170726
163142.953017.40273750787125.547262492127
173032.933022.0778954568910.8521045431094
183045.782931.82647547135113.953524528646
193110.523007.28256317961103.237436820385
203013.243113.06577998169-99.8257799816897
212987.13041.88708251668-54.7870825166776
222995.552993.170537084362.37946291564232
232833.182925.48578054612-92.3057805461233
242848.962831.0015634015917.9584365984119
252794.832891.44310757541-96.6131075754113
262845.262847.5657863162-2.30578631619529
272915.022869.7422068177545.277793182248
282892.632834.9581861888757.6718138111332
292604.422694.55960606494-90.1396060649419
302641.652440.86718632355200.782813676453
312659.812537.94838063762121.861619362381
322638.532556.1221340153782.4078659846257
332720.252612.97029490253107.27970509747
342745.882670.942119320974.937880679098
352735.72703.5595112311932.1404887688128
362811.72644.58139788928167.118602110724
372799.432718.2563437139881.1736562860207
382555.282635.76316212094-80.4831621209376
392304.982377.52624607223-72.546246072231
402214.952224.75260418944-9.80260418944235
412065.812111.78229724053-45.9722972405347
421940.491961.13332624318-20.6433262431819
4320421916.43449107842125.565508921578
441995.371942.1546453086853.2153546913235
451946.811940.682668516026.12733148397533
461765.91861.52507793266-95.6250779326628
471635.251679.2992266262-44.0492266261958
481833.421684.40793861533149.012061384672
491910.431960.85359447305-50.4235944730495
501959.672113.92544550314-154.255445503144
511969.62113.67604473085-144.07604473085
522061.412057.960912966133.44908703386985
532093.482222.85733855074-129.377338550744
542120.882084.8785993604336.0014006395703
552174.562200.45878078965-25.8987807896495
562196.722272.17210730731-75.452107307312
572350.442363.07965422396-12.6396542239555
582440.252514.1510039829-73.9010039828977
592408.642551.04381936523-142.403819365229
602472.812554.96536232685-82.1553623268483
612407.62492.46870431742-84.8687043174208
622454.622541.96451296828-87.3445129682793
632448.052462.0851575135-14.0351575135041
642497.842486.1072920684111.7327079315883
652645.642555.7017972826489.9382027173589
662756.762577.8866697528178.873330247205
672849.272719.98898167275129.281018327251
682921.442875.6816207673445.7583792326578
692981.852927.9053600262953.9446399737078
703080.583083.64995305582-3.0699530558192
713106.223182.89048690123-76.6704869012327
723119.313141.57100875059-22.261008750592
733061.263105.43361308601-44.1736130860067
743097.313088.52672821318.78327178690254
753161.693159.257263625942.43273637405727
763257.163202.9952252001254.1647747998809
773277.013216.6109047640760.3990952359311
783295.323286.420538724328.89946127567834
793363.993330.0641990291333.9258009708689
803494.173450.1728820479543.9971179520542
813667.033607.0359795915459.9940204084565
823813.063748.0178628000165.0421371999933
833917.963827.0225917609790.9374082390337
843895.513904.05484933636-8.54484933636026
853801.063825.15358404066-24.093584040661
863570.123726.30798898261-156.187988982612
873701.613522.69765525564178.912344744364
883862.273684.16509310029178.104906899715
893970.13818.89667219032151.203327809679
904138.524021.74361967929116.776380320708
914199.754184.1576802365815.592319763425
924290.894124.76619869132166.123801308677
934443.914317.89482363685126.015176363151
944502.644447.5286837633555.1113162366501
954356.984419.2951666984-62.3151666984014
964591.274398.5798222448192.6901777552
974696.964671.3213779044925.638622095512
984621.44629.46956744007-8.06956744006972
994562.844584.77454273744-21.9345427374353
1004202.524488.22698001557-285.706980015571
1014296.494225.0807520574571.4092479425464
1024435.234422.2808913506712.9491086493269
1034105.184281.92776215144-176.747762151443
1044116.684145.17490964838-28.4949096483841
1053844.494025.45204593649-180.962045936491
1063720.983789.66113769446-68.6811376944588
1073674.43746.02102646824-71.6210264682444
1083857.623730.77000425409126.849995745909
1093801.063811.2136841969-10.1536841968965
1103504.373569.6541459628-65.2841459627997
1113032.63196.51429632775-163.914296327754
1123047.032863.23668579991183.79331420009
1132962.342955.675564389886.66443561011828
1142197.822520.05182648897-322.231826488974
1152014.451967.5399936654846.9100063345203
1161862.831940.45550701668-77.6255070166772
1171905.411767.08456288793138.325437112074
1181810.991801.858557419389.13144258061918
1191670.071725.45280795337-55.382807953366
1201864.441732.7451791336131.694820866399
1212052.021959.2147827569392.8052172430666
1222029.62068.29470960025-38.6947096002482
1232070.832080.140736216-9.31073621600352
1242293.412253.8164293133839.5935706866216
1252443.272435.665687866877.60431213312592
1262513.172506.789818301836.38018169816949
1272466.922617.09648544227-150.17648544227
1282502.662545.26478975177-42.6047897517722

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3173.95 & 3467.20395684971 & -293.253956849713 \tabularnewline
2 & 3165.26 & 3166.33400475918 & -1.07400475917596 \tabularnewline
3 & 3092.71 & 3253.24318993043 & -160.533189930426 \tabularnewline
4 & 3053.05 & 3097.78756228371 & -44.7375622837136 \tabularnewline
5 & 3181.96 & 3068.70449700179 & 113.255502998209 \tabularnewline
6 & 2999.93 & 3069.456961988 & -69.5269619880009 \tabularnewline
7 & 3249.57 & 3054.34084232542 & 195.229157674582 \tabularnewline
8 & 3210.52 & 3274.95114213856 & -64.431142138561 \tabularnewline
9 & 3030.29 & 3206.83415388342 & -176.544153883423 \tabularnewline
10 & 2803.47 & 3051.18046483841 & -247.710464838414 \tabularnewline
11 & 2767.63 & 2869.72876856238 & -102.098768562381 \tabularnewline
12 & 2882.6 & 2903.61905785664 & -21.0190578566352 \tabularnewline
13 & 2863.36 & 2847.09881908162 & 16.2611809183754 \tabularnewline
14 & 2897.06 & 2838.34280107637 & 58.7171989236346 \tabularnewline
15 & 3012.61 & 2894.63433382927 & 117.975666170726 \tabularnewline
16 & 3142.95 & 3017.40273750787 & 125.547262492127 \tabularnewline
17 & 3032.93 & 3022.07789545689 & 10.8521045431094 \tabularnewline
18 & 3045.78 & 2931.82647547135 & 113.953524528646 \tabularnewline
19 & 3110.52 & 3007.28256317961 & 103.237436820385 \tabularnewline
20 & 3013.24 & 3113.06577998169 & -99.8257799816897 \tabularnewline
21 & 2987.1 & 3041.88708251668 & -54.7870825166776 \tabularnewline
22 & 2995.55 & 2993.17053708436 & 2.37946291564232 \tabularnewline
23 & 2833.18 & 2925.48578054612 & -92.3057805461233 \tabularnewline
24 & 2848.96 & 2831.00156340159 & 17.9584365984119 \tabularnewline
25 & 2794.83 & 2891.44310757541 & -96.6131075754113 \tabularnewline
26 & 2845.26 & 2847.5657863162 & -2.30578631619529 \tabularnewline
27 & 2915.02 & 2869.74220681775 & 45.277793182248 \tabularnewline
28 & 2892.63 & 2834.95818618887 & 57.6718138111332 \tabularnewline
29 & 2604.42 & 2694.55960606494 & -90.1396060649419 \tabularnewline
30 & 2641.65 & 2440.86718632355 & 200.782813676453 \tabularnewline
31 & 2659.81 & 2537.94838063762 & 121.861619362381 \tabularnewline
32 & 2638.53 & 2556.12213401537 & 82.4078659846257 \tabularnewline
33 & 2720.25 & 2612.97029490253 & 107.27970509747 \tabularnewline
34 & 2745.88 & 2670.9421193209 & 74.937880679098 \tabularnewline
35 & 2735.7 & 2703.55951123119 & 32.1404887688128 \tabularnewline
36 & 2811.7 & 2644.58139788928 & 167.118602110724 \tabularnewline
37 & 2799.43 & 2718.25634371398 & 81.1736562860207 \tabularnewline
38 & 2555.28 & 2635.76316212094 & -80.4831621209376 \tabularnewline
39 & 2304.98 & 2377.52624607223 & -72.546246072231 \tabularnewline
40 & 2214.95 & 2224.75260418944 & -9.80260418944235 \tabularnewline
41 & 2065.81 & 2111.78229724053 & -45.9722972405347 \tabularnewline
42 & 1940.49 & 1961.13332624318 & -20.6433262431819 \tabularnewline
43 & 2042 & 1916.43449107842 & 125.565508921578 \tabularnewline
44 & 1995.37 & 1942.15464530868 & 53.2153546913235 \tabularnewline
45 & 1946.81 & 1940.68266851602 & 6.12733148397533 \tabularnewline
46 & 1765.9 & 1861.52507793266 & -95.6250779326628 \tabularnewline
47 & 1635.25 & 1679.2992266262 & -44.0492266261958 \tabularnewline
48 & 1833.42 & 1684.40793861533 & 149.012061384672 \tabularnewline
49 & 1910.43 & 1960.85359447305 & -50.4235944730495 \tabularnewline
50 & 1959.67 & 2113.92544550314 & -154.255445503144 \tabularnewline
51 & 1969.6 & 2113.67604473085 & -144.07604473085 \tabularnewline
52 & 2061.41 & 2057.96091296613 & 3.44908703386985 \tabularnewline
53 & 2093.48 & 2222.85733855074 & -129.377338550744 \tabularnewline
54 & 2120.88 & 2084.87859936043 & 36.0014006395703 \tabularnewline
55 & 2174.56 & 2200.45878078965 & -25.8987807896495 \tabularnewline
56 & 2196.72 & 2272.17210730731 & -75.452107307312 \tabularnewline
57 & 2350.44 & 2363.07965422396 & -12.6396542239555 \tabularnewline
58 & 2440.25 & 2514.1510039829 & -73.9010039828977 \tabularnewline
59 & 2408.64 & 2551.04381936523 & -142.403819365229 \tabularnewline
60 & 2472.81 & 2554.96536232685 & -82.1553623268483 \tabularnewline
61 & 2407.6 & 2492.46870431742 & -84.8687043174208 \tabularnewline
62 & 2454.62 & 2541.96451296828 & -87.3445129682793 \tabularnewline
63 & 2448.05 & 2462.0851575135 & -14.0351575135041 \tabularnewline
64 & 2497.84 & 2486.10729206841 & 11.7327079315883 \tabularnewline
65 & 2645.64 & 2555.70179728264 & 89.9382027173589 \tabularnewline
66 & 2756.76 & 2577.8866697528 & 178.873330247205 \tabularnewline
67 & 2849.27 & 2719.98898167275 & 129.281018327251 \tabularnewline
68 & 2921.44 & 2875.68162076734 & 45.7583792326578 \tabularnewline
69 & 2981.85 & 2927.90536002629 & 53.9446399737078 \tabularnewline
70 & 3080.58 & 3083.64995305582 & -3.0699530558192 \tabularnewline
71 & 3106.22 & 3182.89048690123 & -76.6704869012327 \tabularnewline
72 & 3119.31 & 3141.57100875059 & -22.261008750592 \tabularnewline
73 & 3061.26 & 3105.43361308601 & -44.1736130860067 \tabularnewline
74 & 3097.31 & 3088.5267282131 & 8.78327178690254 \tabularnewline
75 & 3161.69 & 3159.25726362594 & 2.43273637405727 \tabularnewline
76 & 3257.16 & 3202.99522520012 & 54.1647747998809 \tabularnewline
77 & 3277.01 & 3216.61090476407 & 60.3990952359311 \tabularnewline
78 & 3295.32 & 3286.42053872432 & 8.89946127567834 \tabularnewline
79 & 3363.99 & 3330.06419902913 & 33.9258009708689 \tabularnewline
80 & 3494.17 & 3450.17288204795 & 43.9971179520542 \tabularnewline
81 & 3667.03 & 3607.03597959154 & 59.9940204084565 \tabularnewline
82 & 3813.06 & 3748.01786280001 & 65.0421371999933 \tabularnewline
83 & 3917.96 & 3827.02259176097 & 90.9374082390337 \tabularnewline
84 & 3895.51 & 3904.05484933636 & -8.54484933636026 \tabularnewline
85 & 3801.06 & 3825.15358404066 & -24.093584040661 \tabularnewline
86 & 3570.12 & 3726.30798898261 & -156.187988982612 \tabularnewline
87 & 3701.61 & 3522.69765525564 & 178.912344744364 \tabularnewline
88 & 3862.27 & 3684.16509310029 & 178.104906899715 \tabularnewline
89 & 3970.1 & 3818.89667219032 & 151.203327809679 \tabularnewline
90 & 4138.52 & 4021.74361967929 & 116.776380320708 \tabularnewline
91 & 4199.75 & 4184.15768023658 & 15.592319763425 \tabularnewline
92 & 4290.89 & 4124.76619869132 & 166.123801308677 \tabularnewline
93 & 4443.91 & 4317.89482363685 & 126.015176363151 \tabularnewline
94 & 4502.64 & 4447.52868376335 & 55.1113162366501 \tabularnewline
95 & 4356.98 & 4419.2951666984 & -62.3151666984014 \tabularnewline
96 & 4591.27 & 4398.5798222448 & 192.6901777552 \tabularnewline
97 & 4696.96 & 4671.32137790449 & 25.638622095512 \tabularnewline
98 & 4621.4 & 4629.46956744007 & -8.06956744006972 \tabularnewline
99 & 4562.84 & 4584.77454273744 & -21.9345427374353 \tabularnewline
100 & 4202.52 & 4488.22698001557 & -285.706980015571 \tabularnewline
101 & 4296.49 & 4225.08075205745 & 71.4092479425464 \tabularnewline
102 & 4435.23 & 4422.28089135067 & 12.9491086493269 \tabularnewline
103 & 4105.18 & 4281.92776215144 & -176.747762151443 \tabularnewline
104 & 4116.68 & 4145.17490964838 & -28.4949096483841 \tabularnewline
105 & 3844.49 & 4025.45204593649 & -180.962045936491 \tabularnewline
106 & 3720.98 & 3789.66113769446 & -68.6811376944588 \tabularnewline
107 & 3674.4 & 3746.02102646824 & -71.6210264682444 \tabularnewline
108 & 3857.62 & 3730.77000425409 & 126.849995745909 \tabularnewline
109 & 3801.06 & 3811.2136841969 & -10.1536841968965 \tabularnewline
110 & 3504.37 & 3569.6541459628 & -65.2841459627997 \tabularnewline
111 & 3032.6 & 3196.51429632775 & -163.914296327754 \tabularnewline
112 & 3047.03 & 2863.23668579991 & 183.79331420009 \tabularnewline
113 & 2962.34 & 2955.67556438988 & 6.66443561011828 \tabularnewline
114 & 2197.82 & 2520.05182648897 & -322.231826488974 \tabularnewline
115 & 2014.45 & 1967.53999366548 & 46.9100063345203 \tabularnewline
116 & 1862.83 & 1940.45550701668 & -77.6255070166772 \tabularnewline
117 & 1905.41 & 1767.08456288793 & 138.325437112074 \tabularnewline
118 & 1810.99 & 1801.85855741938 & 9.13144258061918 \tabularnewline
119 & 1670.07 & 1725.45280795337 & -55.382807953366 \tabularnewline
120 & 1864.44 & 1732.7451791336 & 131.694820866399 \tabularnewline
121 & 2052.02 & 1959.21478275693 & 92.8052172430666 \tabularnewline
122 & 2029.6 & 2068.29470960025 & -38.6947096002482 \tabularnewline
123 & 2070.83 & 2080.140736216 & -9.31073621600352 \tabularnewline
124 & 2293.41 & 2253.81642931338 & 39.5935706866216 \tabularnewline
125 & 2443.27 & 2435.66568786687 & 7.60431213312592 \tabularnewline
126 & 2513.17 & 2506.78981830183 & 6.38018169816949 \tabularnewline
127 & 2466.92 & 2617.09648544227 & -150.17648544227 \tabularnewline
128 & 2502.66 & 2545.26478975177 & -42.6047897517722 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105694&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]3173.95[/C][C]3467.20395684971[/C][C]-293.253956849713[/C][/ROW]
[ROW][C]2[/C][C]3165.26[/C][C]3166.33400475918[/C][C]-1.07400475917596[/C][/ROW]
[ROW][C]3[/C][C]3092.71[/C][C]3253.24318993043[/C][C]-160.533189930426[/C][/ROW]
[ROW][C]4[/C][C]3053.05[/C][C]3097.78756228371[/C][C]-44.7375622837136[/C][/ROW]
[ROW][C]5[/C][C]3181.96[/C][C]3068.70449700179[/C][C]113.255502998209[/C][/ROW]
[ROW][C]6[/C][C]2999.93[/C][C]3069.456961988[/C][C]-69.5269619880009[/C][/ROW]
[ROW][C]7[/C][C]3249.57[/C][C]3054.34084232542[/C][C]195.229157674582[/C][/ROW]
[ROW][C]8[/C][C]3210.52[/C][C]3274.95114213856[/C][C]-64.431142138561[/C][/ROW]
[ROW][C]9[/C][C]3030.29[/C][C]3206.83415388342[/C][C]-176.544153883423[/C][/ROW]
[ROW][C]10[/C][C]2803.47[/C][C]3051.18046483841[/C][C]-247.710464838414[/C][/ROW]
[ROW][C]11[/C][C]2767.63[/C][C]2869.72876856238[/C][C]-102.098768562381[/C][/ROW]
[ROW][C]12[/C][C]2882.6[/C][C]2903.61905785664[/C][C]-21.0190578566352[/C][/ROW]
[ROW][C]13[/C][C]2863.36[/C][C]2847.09881908162[/C][C]16.2611809183754[/C][/ROW]
[ROW][C]14[/C][C]2897.06[/C][C]2838.34280107637[/C][C]58.7171989236346[/C][/ROW]
[ROW][C]15[/C][C]3012.61[/C][C]2894.63433382927[/C][C]117.975666170726[/C][/ROW]
[ROW][C]16[/C][C]3142.95[/C][C]3017.40273750787[/C][C]125.547262492127[/C][/ROW]
[ROW][C]17[/C][C]3032.93[/C][C]3022.07789545689[/C][C]10.8521045431094[/C][/ROW]
[ROW][C]18[/C][C]3045.78[/C][C]2931.82647547135[/C][C]113.953524528646[/C][/ROW]
[ROW][C]19[/C][C]3110.52[/C][C]3007.28256317961[/C][C]103.237436820385[/C][/ROW]
[ROW][C]20[/C][C]3013.24[/C][C]3113.06577998169[/C][C]-99.8257799816897[/C][/ROW]
[ROW][C]21[/C][C]2987.1[/C][C]3041.88708251668[/C][C]-54.7870825166776[/C][/ROW]
[ROW][C]22[/C][C]2995.55[/C][C]2993.17053708436[/C][C]2.37946291564232[/C][/ROW]
[ROW][C]23[/C][C]2833.18[/C][C]2925.48578054612[/C][C]-92.3057805461233[/C][/ROW]
[ROW][C]24[/C][C]2848.96[/C][C]2831.00156340159[/C][C]17.9584365984119[/C][/ROW]
[ROW][C]25[/C][C]2794.83[/C][C]2891.44310757541[/C][C]-96.6131075754113[/C][/ROW]
[ROW][C]26[/C][C]2845.26[/C][C]2847.5657863162[/C][C]-2.30578631619529[/C][/ROW]
[ROW][C]27[/C][C]2915.02[/C][C]2869.74220681775[/C][C]45.277793182248[/C][/ROW]
[ROW][C]28[/C][C]2892.63[/C][C]2834.95818618887[/C][C]57.6718138111332[/C][/ROW]
[ROW][C]29[/C][C]2604.42[/C][C]2694.55960606494[/C][C]-90.1396060649419[/C][/ROW]
[ROW][C]30[/C][C]2641.65[/C][C]2440.86718632355[/C][C]200.782813676453[/C][/ROW]
[ROW][C]31[/C][C]2659.81[/C][C]2537.94838063762[/C][C]121.861619362381[/C][/ROW]
[ROW][C]32[/C][C]2638.53[/C][C]2556.12213401537[/C][C]82.4078659846257[/C][/ROW]
[ROW][C]33[/C][C]2720.25[/C][C]2612.97029490253[/C][C]107.27970509747[/C][/ROW]
[ROW][C]34[/C][C]2745.88[/C][C]2670.9421193209[/C][C]74.937880679098[/C][/ROW]
[ROW][C]35[/C][C]2735.7[/C][C]2703.55951123119[/C][C]32.1404887688128[/C][/ROW]
[ROW][C]36[/C][C]2811.7[/C][C]2644.58139788928[/C][C]167.118602110724[/C][/ROW]
[ROW][C]37[/C][C]2799.43[/C][C]2718.25634371398[/C][C]81.1736562860207[/C][/ROW]
[ROW][C]38[/C][C]2555.28[/C][C]2635.76316212094[/C][C]-80.4831621209376[/C][/ROW]
[ROW][C]39[/C][C]2304.98[/C][C]2377.52624607223[/C][C]-72.546246072231[/C][/ROW]
[ROW][C]40[/C][C]2214.95[/C][C]2224.75260418944[/C][C]-9.80260418944235[/C][/ROW]
[ROW][C]41[/C][C]2065.81[/C][C]2111.78229724053[/C][C]-45.9722972405347[/C][/ROW]
[ROW][C]42[/C][C]1940.49[/C][C]1961.13332624318[/C][C]-20.6433262431819[/C][/ROW]
[ROW][C]43[/C][C]2042[/C][C]1916.43449107842[/C][C]125.565508921578[/C][/ROW]
[ROW][C]44[/C][C]1995.37[/C][C]1942.15464530868[/C][C]53.2153546913235[/C][/ROW]
[ROW][C]45[/C][C]1946.81[/C][C]1940.68266851602[/C][C]6.12733148397533[/C][/ROW]
[ROW][C]46[/C][C]1765.9[/C][C]1861.52507793266[/C][C]-95.6250779326628[/C][/ROW]
[ROW][C]47[/C][C]1635.25[/C][C]1679.2992266262[/C][C]-44.0492266261958[/C][/ROW]
[ROW][C]48[/C][C]1833.42[/C][C]1684.40793861533[/C][C]149.012061384672[/C][/ROW]
[ROW][C]49[/C][C]1910.43[/C][C]1960.85359447305[/C][C]-50.4235944730495[/C][/ROW]
[ROW][C]50[/C][C]1959.67[/C][C]2113.92544550314[/C][C]-154.255445503144[/C][/ROW]
[ROW][C]51[/C][C]1969.6[/C][C]2113.67604473085[/C][C]-144.07604473085[/C][/ROW]
[ROW][C]52[/C][C]2061.41[/C][C]2057.96091296613[/C][C]3.44908703386985[/C][/ROW]
[ROW][C]53[/C][C]2093.48[/C][C]2222.85733855074[/C][C]-129.377338550744[/C][/ROW]
[ROW][C]54[/C][C]2120.88[/C][C]2084.87859936043[/C][C]36.0014006395703[/C][/ROW]
[ROW][C]55[/C][C]2174.56[/C][C]2200.45878078965[/C][C]-25.8987807896495[/C][/ROW]
[ROW][C]56[/C][C]2196.72[/C][C]2272.17210730731[/C][C]-75.452107307312[/C][/ROW]
[ROW][C]57[/C][C]2350.44[/C][C]2363.07965422396[/C][C]-12.6396542239555[/C][/ROW]
[ROW][C]58[/C][C]2440.25[/C][C]2514.1510039829[/C][C]-73.9010039828977[/C][/ROW]
[ROW][C]59[/C][C]2408.64[/C][C]2551.04381936523[/C][C]-142.403819365229[/C][/ROW]
[ROW][C]60[/C][C]2472.81[/C][C]2554.96536232685[/C][C]-82.1553623268483[/C][/ROW]
[ROW][C]61[/C][C]2407.6[/C][C]2492.46870431742[/C][C]-84.8687043174208[/C][/ROW]
[ROW][C]62[/C][C]2454.62[/C][C]2541.96451296828[/C][C]-87.3445129682793[/C][/ROW]
[ROW][C]63[/C][C]2448.05[/C][C]2462.0851575135[/C][C]-14.0351575135041[/C][/ROW]
[ROW][C]64[/C][C]2497.84[/C][C]2486.10729206841[/C][C]11.7327079315883[/C][/ROW]
[ROW][C]65[/C][C]2645.64[/C][C]2555.70179728264[/C][C]89.9382027173589[/C][/ROW]
[ROW][C]66[/C][C]2756.76[/C][C]2577.8866697528[/C][C]178.873330247205[/C][/ROW]
[ROW][C]67[/C][C]2849.27[/C][C]2719.98898167275[/C][C]129.281018327251[/C][/ROW]
[ROW][C]68[/C][C]2921.44[/C][C]2875.68162076734[/C][C]45.7583792326578[/C][/ROW]
[ROW][C]69[/C][C]2981.85[/C][C]2927.90536002629[/C][C]53.9446399737078[/C][/ROW]
[ROW][C]70[/C][C]3080.58[/C][C]3083.64995305582[/C][C]-3.0699530558192[/C][/ROW]
[ROW][C]71[/C][C]3106.22[/C][C]3182.89048690123[/C][C]-76.6704869012327[/C][/ROW]
[ROW][C]72[/C][C]3119.31[/C][C]3141.57100875059[/C][C]-22.261008750592[/C][/ROW]
[ROW][C]73[/C][C]3061.26[/C][C]3105.43361308601[/C][C]-44.1736130860067[/C][/ROW]
[ROW][C]74[/C][C]3097.31[/C][C]3088.5267282131[/C][C]8.78327178690254[/C][/ROW]
[ROW][C]75[/C][C]3161.69[/C][C]3159.25726362594[/C][C]2.43273637405727[/C][/ROW]
[ROW][C]76[/C][C]3257.16[/C][C]3202.99522520012[/C][C]54.1647747998809[/C][/ROW]
[ROW][C]77[/C][C]3277.01[/C][C]3216.61090476407[/C][C]60.3990952359311[/C][/ROW]
[ROW][C]78[/C][C]3295.32[/C][C]3286.42053872432[/C][C]8.89946127567834[/C][/ROW]
[ROW][C]79[/C][C]3363.99[/C][C]3330.06419902913[/C][C]33.9258009708689[/C][/ROW]
[ROW][C]80[/C][C]3494.17[/C][C]3450.17288204795[/C][C]43.9971179520542[/C][/ROW]
[ROW][C]81[/C][C]3667.03[/C][C]3607.03597959154[/C][C]59.9940204084565[/C][/ROW]
[ROW][C]82[/C][C]3813.06[/C][C]3748.01786280001[/C][C]65.0421371999933[/C][/ROW]
[ROW][C]83[/C][C]3917.96[/C][C]3827.02259176097[/C][C]90.9374082390337[/C][/ROW]
[ROW][C]84[/C][C]3895.51[/C][C]3904.05484933636[/C][C]-8.54484933636026[/C][/ROW]
[ROW][C]85[/C][C]3801.06[/C][C]3825.15358404066[/C][C]-24.093584040661[/C][/ROW]
[ROW][C]86[/C][C]3570.12[/C][C]3726.30798898261[/C][C]-156.187988982612[/C][/ROW]
[ROW][C]87[/C][C]3701.61[/C][C]3522.69765525564[/C][C]178.912344744364[/C][/ROW]
[ROW][C]88[/C][C]3862.27[/C][C]3684.16509310029[/C][C]178.104906899715[/C][/ROW]
[ROW][C]89[/C][C]3970.1[/C][C]3818.89667219032[/C][C]151.203327809679[/C][/ROW]
[ROW][C]90[/C][C]4138.52[/C][C]4021.74361967929[/C][C]116.776380320708[/C][/ROW]
[ROW][C]91[/C][C]4199.75[/C][C]4184.15768023658[/C][C]15.592319763425[/C][/ROW]
[ROW][C]92[/C][C]4290.89[/C][C]4124.76619869132[/C][C]166.123801308677[/C][/ROW]
[ROW][C]93[/C][C]4443.91[/C][C]4317.89482363685[/C][C]126.015176363151[/C][/ROW]
[ROW][C]94[/C][C]4502.64[/C][C]4447.52868376335[/C][C]55.1113162366501[/C][/ROW]
[ROW][C]95[/C][C]4356.98[/C][C]4419.2951666984[/C][C]-62.3151666984014[/C][/ROW]
[ROW][C]96[/C][C]4591.27[/C][C]4398.5798222448[/C][C]192.6901777552[/C][/ROW]
[ROW][C]97[/C][C]4696.96[/C][C]4671.32137790449[/C][C]25.638622095512[/C][/ROW]
[ROW][C]98[/C][C]4621.4[/C][C]4629.46956744007[/C][C]-8.06956744006972[/C][/ROW]
[ROW][C]99[/C][C]4562.84[/C][C]4584.77454273744[/C][C]-21.9345427374353[/C][/ROW]
[ROW][C]100[/C][C]4202.52[/C][C]4488.22698001557[/C][C]-285.706980015571[/C][/ROW]
[ROW][C]101[/C][C]4296.49[/C][C]4225.08075205745[/C][C]71.4092479425464[/C][/ROW]
[ROW][C]102[/C][C]4435.23[/C][C]4422.28089135067[/C][C]12.9491086493269[/C][/ROW]
[ROW][C]103[/C][C]4105.18[/C][C]4281.92776215144[/C][C]-176.747762151443[/C][/ROW]
[ROW][C]104[/C][C]4116.68[/C][C]4145.17490964838[/C][C]-28.4949096483841[/C][/ROW]
[ROW][C]105[/C][C]3844.49[/C][C]4025.45204593649[/C][C]-180.962045936491[/C][/ROW]
[ROW][C]106[/C][C]3720.98[/C][C]3789.66113769446[/C][C]-68.6811376944588[/C][/ROW]
[ROW][C]107[/C][C]3674.4[/C][C]3746.02102646824[/C][C]-71.6210264682444[/C][/ROW]
[ROW][C]108[/C][C]3857.62[/C][C]3730.77000425409[/C][C]126.849995745909[/C][/ROW]
[ROW][C]109[/C][C]3801.06[/C][C]3811.2136841969[/C][C]-10.1536841968965[/C][/ROW]
[ROW][C]110[/C][C]3504.37[/C][C]3569.6541459628[/C][C]-65.2841459627997[/C][/ROW]
[ROW][C]111[/C][C]3032.6[/C][C]3196.51429632775[/C][C]-163.914296327754[/C][/ROW]
[ROW][C]112[/C][C]3047.03[/C][C]2863.23668579991[/C][C]183.79331420009[/C][/ROW]
[ROW][C]113[/C][C]2962.34[/C][C]2955.67556438988[/C][C]6.66443561011828[/C][/ROW]
[ROW][C]114[/C][C]2197.82[/C][C]2520.05182648897[/C][C]-322.231826488974[/C][/ROW]
[ROW][C]115[/C][C]2014.45[/C][C]1967.53999366548[/C][C]46.9100063345203[/C][/ROW]
[ROW][C]116[/C][C]1862.83[/C][C]1940.45550701668[/C][C]-77.6255070166772[/C][/ROW]
[ROW][C]117[/C][C]1905.41[/C][C]1767.08456288793[/C][C]138.325437112074[/C][/ROW]
[ROW][C]118[/C][C]1810.99[/C][C]1801.85855741938[/C][C]9.13144258061918[/C][/ROW]
[ROW][C]119[/C][C]1670.07[/C][C]1725.45280795337[/C][C]-55.382807953366[/C][/ROW]
[ROW][C]120[/C][C]1864.44[/C][C]1732.7451791336[/C][C]131.694820866399[/C][/ROW]
[ROW][C]121[/C][C]2052.02[/C][C]1959.21478275693[/C][C]92.8052172430666[/C][/ROW]
[ROW][C]122[/C][C]2029.6[/C][C]2068.29470960025[/C][C]-38.6947096002482[/C][/ROW]
[ROW][C]123[/C][C]2070.83[/C][C]2080.140736216[/C][C]-9.31073621600352[/C][/ROW]
[ROW][C]124[/C][C]2293.41[/C][C]2253.81642931338[/C][C]39.5935706866216[/C][/ROW]
[ROW][C]125[/C][C]2443.27[/C][C]2435.66568786687[/C][C]7.60431213312592[/C][/ROW]
[ROW][C]126[/C][C]2513.17[/C][C]2506.78981830183[/C][C]6.38018169816949[/C][/ROW]
[ROW][C]127[/C][C]2466.92[/C][C]2617.09648544227[/C][C]-150.17648544227[/C][/ROW]
[ROW][C]128[/C][C]2502.66[/C][C]2545.26478975177[/C][C]-42.6047897517722[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105694&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105694&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
13173.953467.20395684971-293.253956849713
23165.263166.33400475918-1.07400475917596
33092.713253.24318993043-160.533189930426
43053.053097.78756228371-44.7375622837136
53181.963068.70449700179113.255502998209
62999.933069.456961988-69.5269619880009
73249.573054.34084232542195.229157674582
83210.523274.95114213856-64.431142138561
93030.293206.83415388342-176.544153883423
102803.473051.18046483841-247.710464838414
112767.632869.72876856238-102.098768562381
122882.62903.61905785664-21.0190578566352
132863.362847.0988190816216.2611809183754
142897.062838.3428010763758.7171989236346
153012.612894.63433382927117.975666170726
163142.953017.40273750787125.547262492127
173032.933022.0778954568910.8521045431094
183045.782931.82647547135113.953524528646
193110.523007.28256317961103.237436820385
203013.243113.06577998169-99.8257799816897
212987.13041.88708251668-54.7870825166776
222995.552993.170537084362.37946291564232
232833.182925.48578054612-92.3057805461233
242848.962831.0015634015917.9584365984119
252794.832891.44310757541-96.6131075754113
262845.262847.5657863162-2.30578631619529
272915.022869.7422068177545.277793182248
282892.632834.9581861888757.6718138111332
292604.422694.55960606494-90.1396060649419
302641.652440.86718632355200.782813676453
312659.812537.94838063762121.861619362381
322638.532556.1221340153782.4078659846257
332720.252612.97029490253107.27970509747
342745.882670.942119320974.937880679098
352735.72703.5595112311932.1404887688128
362811.72644.58139788928167.118602110724
372799.432718.2563437139881.1736562860207
382555.282635.76316212094-80.4831621209376
392304.982377.52624607223-72.546246072231
402214.952224.75260418944-9.80260418944235
412065.812111.78229724053-45.9722972405347
421940.491961.13332624318-20.6433262431819
4320421916.43449107842125.565508921578
441995.371942.1546453086853.2153546913235
451946.811940.682668516026.12733148397533
461765.91861.52507793266-95.6250779326628
471635.251679.2992266262-44.0492266261958
481833.421684.40793861533149.012061384672
491910.431960.85359447305-50.4235944730495
501959.672113.92544550314-154.255445503144
511969.62113.67604473085-144.07604473085
522061.412057.960912966133.44908703386985
532093.482222.85733855074-129.377338550744
542120.882084.8785993604336.0014006395703
552174.562200.45878078965-25.8987807896495
562196.722272.17210730731-75.452107307312
572350.442363.07965422396-12.6396542239555
582440.252514.1510039829-73.9010039828977
592408.642551.04381936523-142.403819365229
602472.812554.96536232685-82.1553623268483
612407.62492.46870431742-84.8687043174208
622454.622541.96451296828-87.3445129682793
632448.052462.0851575135-14.0351575135041
642497.842486.1072920684111.7327079315883
652645.642555.7017972826489.9382027173589
662756.762577.8866697528178.873330247205
672849.272719.98898167275129.281018327251
682921.442875.6816207673445.7583792326578
692981.852927.9053600262953.9446399737078
703080.583083.64995305582-3.0699530558192
713106.223182.89048690123-76.6704869012327
723119.313141.57100875059-22.261008750592
733061.263105.43361308601-44.1736130860067
743097.313088.52672821318.78327178690254
753161.693159.257263625942.43273637405727
763257.163202.9952252001254.1647747998809
773277.013216.6109047640760.3990952359311
783295.323286.420538724328.89946127567834
793363.993330.0641990291333.9258009708689
803494.173450.1728820479543.9971179520542
813667.033607.0359795915459.9940204084565
823813.063748.0178628000165.0421371999933
833917.963827.0225917609790.9374082390337
843895.513904.05484933636-8.54484933636026
853801.063825.15358404066-24.093584040661
863570.123726.30798898261-156.187988982612
873701.613522.69765525564178.912344744364
883862.273684.16509310029178.104906899715
893970.13818.89667219032151.203327809679
904138.524021.74361967929116.776380320708
914199.754184.1576802365815.592319763425
924290.894124.76619869132166.123801308677
934443.914317.89482363685126.015176363151
944502.644447.5286837633555.1113162366501
954356.984419.2951666984-62.3151666984014
964591.274398.5798222448192.6901777552
974696.964671.3213779044925.638622095512
984621.44629.46956744007-8.06956744006972
994562.844584.77454273744-21.9345427374353
1004202.524488.22698001557-285.706980015571
1014296.494225.0807520574571.4092479425464
1024435.234422.2808913506712.9491086493269
1034105.184281.92776215144-176.747762151443
1044116.684145.17490964838-28.4949096483841
1053844.494025.45204593649-180.962045936491
1063720.983789.66113769446-68.6811376944588
1073674.43746.02102646824-71.6210264682444
1083857.623730.77000425409126.849995745909
1093801.063811.2136841969-10.1536841968965
1103504.373569.6541459628-65.2841459627997
1113032.63196.51429632775-163.914296327754
1123047.032863.23668579991183.79331420009
1132962.342955.675564389886.66443561011828
1142197.822520.05182648897-322.231826488974
1152014.451967.5399936654846.9100063345203
1161862.831940.45550701668-77.6255070166772
1171905.411767.08456288793138.325437112074
1181810.991801.858557419389.13144258061918
1191670.071725.45280795337-55.382807953366
1201864.441732.7451791336131.694820866399
1212052.021959.2147827569392.8052172430666
1222029.62068.29470960025-38.6947096002482
1232070.832080.140736216-9.31073621600352
1242293.412253.8164293133839.5935706866216
1252443.272435.665687866877.60431213312592
1262513.172506.789818301836.38018169816949
1272466.922617.09648544227-150.17648544227
1282502.662545.26478975177-42.6047897517722






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.8002349189794880.3995301620410240.199765081020512
160.8230517611766340.3538964776467330.176948238823366
170.7320926496611970.5358147006776060.267907350338803
180.6455304261977920.7089391476044150.354469573802208
190.5565251699935370.8869496600129270.443474830006463
200.6213601350885310.7572797298229380.378639864911469
210.8515798843924030.2968402312151950.148420115607598
220.8495037346495040.3009925307009910.150496265350496
230.8535314943871460.2929370112257090.146468505612854
240.8054615178944220.3890769642111570.194538482105578
250.7902434361572160.4195131276855680.209756563842784
260.7383098257063580.5233803485872840.261690174293642
270.682916938365540.634166123268920.31708306163446
280.6135166507817890.7729666984364220.386483349218211
290.5618172169881330.8763655660237340.438182783011867
300.5442051044268560.9115897911462870.455794895573144
310.4939165542202930.9878331084405860.506083445779707
320.4410782908065470.8821565816130950.558921709193453
330.4033456039440570.8066912078881140.596654396055943
340.3739186523567060.7478373047134120.626081347643294
350.3637777867762460.7275555735524920.636222213223754
360.3806316132544660.7612632265089310.619368386745534
370.3567115515655870.7134231031311730.643288448434413
380.3756147346906290.7512294693812580.624385265309371
390.4071528034118030.8143056068236070.592847196588197
400.3705143337785120.7410286675570240.629485666221488
410.3181634180211430.6363268360422850.681836581978857
420.2808633806815930.5617267613631850.719136619318407
430.2745249217551020.5490498435102030.725475078244898
440.2678344898916060.5356689797832130.732165510108394
450.2378852254813870.4757704509627730.762114774518613
460.2297458841571730.4594917683143460.770254115842827
470.2511732355144760.5023464710289530.748826764485524
480.2508542091793850.5017084183587690.749145790820615
490.2086163478653020.4172326957306040.791383652134698
500.2124842214698930.4249684429397870.787515778530107
510.2384027141328390.4768054282656770.761597285867161
520.1966696433165290.3933392866330580.803330356683471
530.2035494165656120.4070988331312240.796450583434388
540.1799372764926280.3598745529852570.820062723507372
550.1491970213273180.2983940426546350.850802978672682
560.1516323312566740.3032646625133470.848367668743326
570.1278098079778730.2556196159557470.872190192022127
580.1105587472069270.2211174944138540.889441252793073
590.1343608768619170.2687217537238330.865639123138083
600.1922759465842940.3845518931685890.807724053415706
610.2011155320704120.4022310641408250.798884467929588
620.3304073228541910.6608146457083820.669592677145809
630.3643645440620770.7287290881241550.635635455937923
640.4390995716802680.8781991433605360.560900428319732
650.5297050959309430.9405898081381140.470294904069057
660.6786844623665060.6426310752669880.321315537633494
670.7272300461654530.5455399076690940.272769953834547
680.7108622562773520.5782754874452970.289137743722648
690.7091512894368850.581697421126230.290848710563115
700.6876720891553930.6246558216892140.312327910844607
710.6594780058683220.6810439882633570.340521994131678
720.6285771145856470.7428457708287070.371422885414353
730.5816960318674120.8366079362651760.418303968132588
740.5313119270183010.9373761459633980.468688072981699
750.4807103253175210.9614206506350420.519289674682479
760.4632129183736930.9264258367473870.536787081626307
770.4454465577009050.890893115401810.554553442299095
780.4141867590737930.8283735181475870.585813240926207
790.3747358538833820.7494717077667630.625264146116618
800.460759150765490.921518301530980.53924084923451
810.5817371512163410.8365256975673170.418262848783659
820.6441524059922170.7116951880155660.355847594007783
830.6117084322879150.776583135424170.388291567712085
840.777441560870680.4451168782586410.222558439129321
850.9166392834793160.1667214330413690.0833607165206845
860.9956184703718170.008763059256365270.00438152962818263
870.995073068065860.00985386386827940.0049269319341397
880.9931929404071650.01361411918566950.00680705959283473
890.9903160982674280.0193678034651440.00968390173257202
900.9896986614922040.02060267701559250.0103013385077963
910.9870088849723250.02598223005535070.0129911150276754
920.9814774280613230.03704514387735460.0185225719386773
930.97217794434760.05564411130479960.0278220556523998
940.960132247482180.07973550503563930.0398677525178196
950.9595908262487090.08081834750258260.0404091737512913
960.9573200166458790.0853599667082420.042679983354121
970.9496327643363960.1007344713272090.0503672356636043
980.9511345839382770.09773083212344540.0488654160617227
990.9644375512898730.07112489742025430.0355624487101272
1000.982135743937860.03572851212427920.0178642560621396
1010.9756294675110930.04874106497781310.0243705324889066
1020.9616434712477920.07671305750441620.0383565287522081
1030.965576933608040.0688461327839210.0344230663919605
1040.980594335714480.03881132857103840.0194056642855192
1050.9901150366293060.01976992674138870.00988496337069437
1060.9903714776734140.01925704465317280.00962852232658638
1070.9832362576116050.03352748477678910.0167637423883946
1080.971596302264950.05680739547010010.02840369773505
1090.9558481773072170.0883036453855650.0441518226927825
1100.9176741842860310.1646516314279380.082325815713969
1110.9781752697163170.04364946056736620.0218247302836831
1120.9489599529761410.1020800940477170.0510400470238586
1130.906675212779650.1866495744406990.0933247872203497

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.800234918979488 & 0.399530162041024 & 0.199765081020512 \tabularnewline
16 & 0.823051761176634 & 0.353896477646733 & 0.176948238823366 \tabularnewline
17 & 0.732092649661197 & 0.535814700677606 & 0.267907350338803 \tabularnewline
18 & 0.645530426197792 & 0.708939147604415 & 0.354469573802208 \tabularnewline
19 & 0.556525169993537 & 0.886949660012927 & 0.443474830006463 \tabularnewline
20 & 0.621360135088531 & 0.757279729822938 & 0.378639864911469 \tabularnewline
21 & 0.851579884392403 & 0.296840231215195 & 0.148420115607598 \tabularnewline
22 & 0.849503734649504 & 0.300992530700991 & 0.150496265350496 \tabularnewline
23 & 0.853531494387146 & 0.292937011225709 & 0.146468505612854 \tabularnewline
24 & 0.805461517894422 & 0.389076964211157 & 0.194538482105578 \tabularnewline
25 & 0.790243436157216 & 0.419513127685568 & 0.209756563842784 \tabularnewline
26 & 0.738309825706358 & 0.523380348587284 & 0.261690174293642 \tabularnewline
27 & 0.68291693836554 & 0.63416612326892 & 0.31708306163446 \tabularnewline
28 & 0.613516650781789 & 0.772966698436422 & 0.386483349218211 \tabularnewline
29 & 0.561817216988133 & 0.876365566023734 & 0.438182783011867 \tabularnewline
30 & 0.544205104426856 & 0.911589791146287 & 0.455794895573144 \tabularnewline
31 & 0.493916554220293 & 0.987833108440586 & 0.506083445779707 \tabularnewline
32 & 0.441078290806547 & 0.882156581613095 & 0.558921709193453 \tabularnewline
33 & 0.403345603944057 & 0.806691207888114 & 0.596654396055943 \tabularnewline
34 & 0.373918652356706 & 0.747837304713412 & 0.626081347643294 \tabularnewline
35 & 0.363777786776246 & 0.727555573552492 & 0.636222213223754 \tabularnewline
36 & 0.380631613254466 & 0.761263226508931 & 0.619368386745534 \tabularnewline
37 & 0.356711551565587 & 0.713423103131173 & 0.643288448434413 \tabularnewline
38 & 0.375614734690629 & 0.751229469381258 & 0.624385265309371 \tabularnewline
39 & 0.407152803411803 & 0.814305606823607 & 0.592847196588197 \tabularnewline
40 & 0.370514333778512 & 0.741028667557024 & 0.629485666221488 \tabularnewline
41 & 0.318163418021143 & 0.636326836042285 & 0.681836581978857 \tabularnewline
42 & 0.280863380681593 & 0.561726761363185 & 0.719136619318407 \tabularnewline
43 & 0.274524921755102 & 0.549049843510203 & 0.725475078244898 \tabularnewline
44 & 0.267834489891606 & 0.535668979783213 & 0.732165510108394 \tabularnewline
45 & 0.237885225481387 & 0.475770450962773 & 0.762114774518613 \tabularnewline
46 & 0.229745884157173 & 0.459491768314346 & 0.770254115842827 \tabularnewline
47 & 0.251173235514476 & 0.502346471028953 & 0.748826764485524 \tabularnewline
48 & 0.250854209179385 & 0.501708418358769 & 0.749145790820615 \tabularnewline
49 & 0.208616347865302 & 0.417232695730604 & 0.791383652134698 \tabularnewline
50 & 0.212484221469893 & 0.424968442939787 & 0.787515778530107 \tabularnewline
51 & 0.238402714132839 & 0.476805428265677 & 0.761597285867161 \tabularnewline
52 & 0.196669643316529 & 0.393339286633058 & 0.803330356683471 \tabularnewline
53 & 0.203549416565612 & 0.407098833131224 & 0.796450583434388 \tabularnewline
54 & 0.179937276492628 & 0.359874552985257 & 0.820062723507372 \tabularnewline
55 & 0.149197021327318 & 0.298394042654635 & 0.850802978672682 \tabularnewline
56 & 0.151632331256674 & 0.303264662513347 & 0.848367668743326 \tabularnewline
57 & 0.127809807977873 & 0.255619615955747 & 0.872190192022127 \tabularnewline
58 & 0.110558747206927 & 0.221117494413854 & 0.889441252793073 \tabularnewline
59 & 0.134360876861917 & 0.268721753723833 & 0.865639123138083 \tabularnewline
60 & 0.192275946584294 & 0.384551893168589 & 0.807724053415706 \tabularnewline
61 & 0.201115532070412 & 0.402231064140825 & 0.798884467929588 \tabularnewline
62 & 0.330407322854191 & 0.660814645708382 & 0.669592677145809 \tabularnewline
63 & 0.364364544062077 & 0.728729088124155 & 0.635635455937923 \tabularnewline
64 & 0.439099571680268 & 0.878199143360536 & 0.560900428319732 \tabularnewline
65 & 0.529705095930943 & 0.940589808138114 & 0.470294904069057 \tabularnewline
66 & 0.678684462366506 & 0.642631075266988 & 0.321315537633494 \tabularnewline
67 & 0.727230046165453 & 0.545539907669094 & 0.272769953834547 \tabularnewline
68 & 0.710862256277352 & 0.578275487445297 & 0.289137743722648 \tabularnewline
69 & 0.709151289436885 & 0.58169742112623 & 0.290848710563115 \tabularnewline
70 & 0.687672089155393 & 0.624655821689214 & 0.312327910844607 \tabularnewline
71 & 0.659478005868322 & 0.681043988263357 & 0.340521994131678 \tabularnewline
72 & 0.628577114585647 & 0.742845770828707 & 0.371422885414353 \tabularnewline
73 & 0.581696031867412 & 0.836607936265176 & 0.418303968132588 \tabularnewline
74 & 0.531311927018301 & 0.937376145963398 & 0.468688072981699 \tabularnewline
75 & 0.480710325317521 & 0.961420650635042 & 0.519289674682479 \tabularnewline
76 & 0.463212918373693 & 0.926425836747387 & 0.536787081626307 \tabularnewline
77 & 0.445446557700905 & 0.89089311540181 & 0.554553442299095 \tabularnewline
78 & 0.414186759073793 & 0.828373518147587 & 0.585813240926207 \tabularnewline
79 & 0.374735853883382 & 0.749471707766763 & 0.625264146116618 \tabularnewline
80 & 0.46075915076549 & 0.92151830153098 & 0.53924084923451 \tabularnewline
81 & 0.581737151216341 & 0.836525697567317 & 0.418262848783659 \tabularnewline
82 & 0.644152405992217 & 0.711695188015566 & 0.355847594007783 \tabularnewline
83 & 0.611708432287915 & 0.77658313542417 & 0.388291567712085 \tabularnewline
84 & 0.77744156087068 & 0.445116878258641 & 0.222558439129321 \tabularnewline
85 & 0.916639283479316 & 0.166721433041369 & 0.0833607165206845 \tabularnewline
86 & 0.995618470371817 & 0.00876305925636527 & 0.00438152962818263 \tabularnewline
87 & 0.99507306806586 & 0.0098538638682794 & 0.0049269319341397 \tabularnewline
88 & 0.993192940407165 & 0.0136141191856695 & 0.00680705959283473 \tabularnewline
89 & 0.990316098267428 & 0.019367803465144 & 0.00968390173257202 \tabularnewline
90 & 0.989698661492204 & 0.0206026770155925 & 0.0103013385077963 \tabularnewline
91 & 0.987008884972325 & 0.0259822300553507 & 0.0129911150276754 \tabularnewline
92 & 0.981477428061323 & 0.0370451438773546 & 0.0185225719386773 \tabularnewline
93 & 0.9721779443476 & 0.0556441113047996 & 0.0278220556523998 \tabularnewline
94 & 0.96013224748218 & 0.0797355050356393 & 0.0398677525178196 \tabularnewline
95 & 0.959590826248709 & 0.0808183475025826 & 0.0404091737512913 \tabularnewline
96 & 0.957320016645879 & 0.085359966708242 & 0.042679983354121 \tabularnewline
97 & 0.949632764336396 & 0.100734471327209 & 0.0503672356636043 \tabularnewline
98 & 0.951134583938277 & 0.0977308321234454 & 0.0488654160617227 \tabularnewline
99 & 0.964437551289873 & 0.0711248974202543 & 0.0355624487101272 \tabularnewline
100 & 0.98213574393786 & 0.0357285121242792 & 0.0178642560621396 \tabularnewline
101 & 0.975629467511093 & 0.0487410649778131 & 0.0243705324889066 \tabularnewline
102 & 0.961643471247792 & 0.0767130575044162 & 0.0383565287522081 \tabularnewline
103 & 0.96557693360804 & 0.068846132783921 & 0.0344230663919605 \tabularnewline
104 & 0.98059433571448 & 0.0388113285710384 & 0.0194056642855192 \tabularnewline
105 & 0.990115036629306 & 0.0197699267413887 & 0.00988496337069437 \tabularnewline
106 & 0.990371477673414 & 0.0192570446531728 & 0.00962852232658638 \tabularnewline
107 & 0.983236257611605 & 0.0335274847767891 & 0.0167637423883946 \tabularnewline
108 & 0.97159630226495 & 0.0568073954701001 & 0.02840369773505 \tabularnewline
109 & 0.955848177307217 & 0.088303645385565 & 0.0441518226927825 \tabularnewline
110 & 0.917674184286031 & 0.164651631427938 & 0.082325815713969 \tabularnewline
111 & 0.978175269716317 & 0.0436494605673662 & 0.0218247302836831 \tabularnewline
112 & 0.948959952976141 & 0.102080094047717 & 0.0510400470238586 \tabularnewline
113 & 0.90667521277965 & 0.186649574440699 & 0.0933247872203497 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105694&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]15[/C][C]0.800234918979488[/C][C]0.399530162041024[/C][C]0.199765081020512[/C][/ROW]
[ROW][C]16[/C][C]0.823051761176634[/C][C]0.353896477646733[/C][C]0.176948238823366[/C][/ROW]
[ROW][C]17[/C][C]0.732092649661197[/C][C]0.535814700677606[/C][C]0.267907350338803[/C][/ROW]
[ROW][C]18[/C][C]0.645530426197792[/C][C]0.708939147604415[/C][C]0.354469573802208[/C][/ROW]
[ROW][C]19[/C][C]0.556525169993537[/C][C]0.886949660012927[/C][C]0.443474830006463[/C][/ROW]
[ROW][C]20[/C][C]0.621360135088531[/C][C]0.757279729822938[/C][C]0.378639864911469[/C][/ROW]
[ROW][C]21[/C][C]0.851579884392403[/C][C]0.296840231215195[/C][C]0.148420115607598[/C][/ROW]
[ROW][C]22[/C][C]0.849503734649504[/C][C]0.300992530700991[/C][C]0.150496265350496[/C][/ROW]
[ROW][C]23[/C][C]0.853531494387146[/C][C]0.292937011225709[/C][C]0.146468505612854[/C][/ROW]
[ROW][C]24[/C][C]0.805461517894422[/C][C]0.389076964211157[/C][C]0.194538482105578[/C][/ROW]
[ROW][C]25[/C][C]0.790243436157216[/C][C]0.419513127685568[/C][C]0.209756563842784[/C][/ROW]
[ROW][C]26[/C][C]0.738309825706358[/C][C]0.523380348587284[/C][C]0.261690174293642[/C][/ROW]
[ROW][C]27[/C][C]0.68291693836554[/C][C]0.63416612326892[/C][C]0.31708306163446[/C][/ROW]
[ROW][C]28[/C][C]0.613516650781789[/C][C]0.772966698436422[/C][C]0.386483349218211[/C][/ROW]
[ROW][C]29[/C][C]0.561817216988133[/C][C]0.876365566023734[/C][C]0.438182783011867[/C][/ROW]
[ROW][C]30[/C][C]0.544205104426856[/C][C]0.911589791146287[/C][C]0.455794895573144[/C][/ROW]
[ROW][C]31[/C][C]0.493916554220293[/C][C]0.987833108440586[/C][C]0.506083445779707[/C][/ROW]
[ROW][C]32[/C][C]0.441078290806547[/C][C]0.882156581613095[/C][C]0.558921709193453[/C][/ROW]
[ROW][C]33[/C][C]0.403345603944057[/C][C]0.806691207888114[/C][C]0.596654396055943[/C][/ROW]
[ROW][C]34[/C][C]0.373918652356706[/C][C]0.747837304713412[/C][C]0.626081347643294[/C][/ROW]
[ROW][C]35[/C][C]0.363777786776246[/C][C]0.727555573552492[/C][C]0.636222213223754[/C][/ROW]
[ROW][C]36[/C][C]0.380631613254466[/C][C]0.761263226508931[/C][C]0.619368386745534[/C][/ROW]
[ROW][C]37[/C][C]0.356711551565587[/C][C]0.713423103131173[/C][C]0.643288448434413[/C][/ROW]
[ROW][C]38[/C][C]0.375614734690629[/C][C]0.751229469381258[/C][C]0.624385265309371[/C][/ROW]
[ROW][C]39[/C][C]0.407152803411803[/C][C]0.814305606823607[/C][C]0.592847196588197[/C][/ROW]
[ROW][C]40[/C][C]0.370514333778512[/C][C]0.741028667557024[/C][C]0.629485666221488[/C][/ROW]
[ROW][C]41[/C][C]0.318163418021143[/C][C]0.636326836042285[/C][C]0.681836581978857[/C][/ROW]
[ROW][C]42[/C][C]0.280863380681593[/C][C]0.561726761363185[/C][C]0.719136619318407[/C][/ROW]
[ROW][C]43[/C][C]0.274524921755102[/C][C]0.549049843510203[/C][C]0.725475078244898[/C][/ROW]
[ROW][C]44[/C][C]0.267834489891606[/C][C]0.535668979783213[/C][C]0.732165510108394[/C][/ROW]
[ROW][C]45[/C][C]0.237885225481387[/C][C]0.475770450962773[/C][C]0.762114774518613[/C][/ROW]
[ROW][C]46[/C][C]0.229745884157173[/C][C]0.459491768314346[/C][C]0.770254115842827[/C][/ROW]
[ROW][C]47[/C][C]0.251173235514476[/C][C]0.502346471028953[/C][C]0.748826764485524[/C][/ROW]
[ROW][C]48[/C][C]0.250854209179385[/C][C]0.501708418358769[/C][C]0.749145790820615[/C][/ROW]
[ROW][C]49[/C][C]0.208616347865302[/C][C]0.417232695730604[/C][C]0.791383652134698[/C][/ROW]
[ROW][C]50[/C][C]0.212484221469893[/C][C]0.424968442939787[/C][C]0.787515778530107[/C][/ROW]
[ROW][C]51[/C][C]0.238402714132839[/C][C]0.476805428265677[/C][C]0.761597285867161[/C][/ROW]
[ROW][C]52[/C][C]0.196669643316529[/C][C]0.393339286633058[/C][C]0.803330356683471[/C][/ROW]
[ROW][C]53[/C][C]0.203549416565612[/C][C]0.407098833131224[/C][C]0.796450583434388[/C][/ROW]
[ROW][C]54[/C][C]0.179937276492628[/C][C]0.359874552985257[/C][C]0.820062723507372[/C][/ROW]
[ROW][C]55[/C][C]0.149197021327318[/C][C]0.298394042654635[/C][C]0.850802978672682[/C][/ROW]
[ROW][C]56[/C][C]0.151632331256674[/C][C]0.303264662513347[/C][C]0.848367668743326[/C][/ROW]
[ROW][C]57[/C][C]0.127809807977873[/C][C]0.255619615955747[/C][C]0.872190192022127[/C][/ROW]
[ROW][C]58[/C][C]0.110558747206927[/C][C]0.221117494413854[/C][C]0.889441252793073[/C][/ROW]
[ROW][C]59[/C][C]0.134360876861917[/C][C]0.268721753723833[/C][C]0.865639123138083[/C][/ROW]
[ROW][C]60[/C][C]0.192275946584294[/C][C]0.384551893168589[/C][C]0.807724053415706[/C][/ROW]
[ROW][C]61[/C][C]0.201115532070412[/C][C]0.402231064140825[/C][C]0.798884467929588[/C][/ROW]
[ROW][C]62[/C][C]0.330407322854191[/C][C]0.660814645708382[/C][C]0.669592677145809[/C][/ROW]
[ROW][C]63[/C][C]0.364364544062077[/C][C]0.728729088124155[/C][C]0.635635455937923[/C][/ROW]
[ROW][C]64[/C][C]0.439099571680268[/C][C]0.878199143360536[/C][C]0.560900428319732[/C][/ROW]
[ROW][C]65[/C][C]0.529705095930943[/C][C]0.940589808138114[/C][C]0.470294904069057[/C][/ROW]
[ROW][C]66[/C][C]0.678684462366506[/C][C]0.642631075266988[/C][C]0.321315537633494[/C][/ROW]
[ROW][C]67[/C][C]0.727230046165453[/C][C]0.545539907669094[/C][C]0.272769953834547[/C][/ROW]
[ROW][C]68[/C][C]0.710862256277352[/C][C]0.578275487445297[/C][C]0.289137743722648[/C][/ROW]
[ROW][C]69[/C][C]0.709151289436885[/C][C]0.58169742112623[/C][C]0.290848710563115[/C][/ROW]
[ROW][C]70[/C][C]0.687672089155393[/C][C]0.624655821689214[/C][C]0.312327910844607[/C][/ROW]
[ROW][C]71[/C][C]0.659478005868322[/C][C]0.681043988263357[/C][C]0.340521994131678[/C][/ROW]
[ROW][C]72[/C][C]0.628577114585647[/C][C]0.742845770828707[/C][C]0.371422885414353[/C][/ROW]
[ROW][C]73[/C][C]0.581696031867412[/C][C]0.836607936265176[/C][C]0.418303968132588[/C][/ROW]
[ROW][C]74[/C][C]0.531311927018301[/C][C]0.937376145963398[/C][C]0.468688072981699[/C][/ROW]
[ROW][C]75[/C][C]0.480710325317521[/C][C]0.961420650635042[/C][C]0.519289674682479[/C][/ROW]
[ROW][C]76[/C][C]0.463212918373693[/C][C]0.926425836747387[/C][C]0.536787081626307[/C][/ROW]
[ROW][C]77[/C][C]0.445446557700905[/C][C]0.89089311540181[/C][C]0.554553442299095[/C][/ROW]
[ROW][C]78[/C][C]0.414186759073793[/C][C]0.828373518147587[/C][C]0.585813240926207[/C][/ROW]
[ROW][C]79[/C][C]0.374735853883382[/C][C]0.749471707766763[/C][C]0.625264146116618[/C][/ROW]
[ROW][C]80[/C][C]0.46075915076549[/C][C]0.92151830153098[/C][C]0.53924084923451[/C][/ROW]
[ROW][C]81[/C][C]0.581737151216341[/C][C]0.836525697567317[/C][C]0.418262848783659[/C][/ROW]
[ROW][C]82[/C][C]0.644152405992217[/C][C]0.711695188015566[/C][C]0.355847594007783[/C][/ROW]
[ROW][C]83[/C][C]0.611708432287915[/C][C]0.77658313542417[/C][C]0.388291567712085[/C][/ROW]
[ROW][C]84[/C][C]0.77744156087068[/C][C]0.445116878258641[/C][C]0.222558439129321[/C][/ROW]
[ROW][C]85[/C][C]0.916639283479316[/C][C]0.166721433041369[/C][C]0.0833607165206845[/C][/ROW]
[ROW][C]86[/C][C]0.995618470371817[/C][C]0.00876305925636527[/C][C]0.00438152962818263[/C][/ROW]
[ROW][C]87[/C][C]0.99507306806586[/C][C]0.0098538638682794[/C][C]0.0049269319341397[/C][/ROW]
[ROW][C]88[/C][C]0.993192940407165[/C][C]0.0136141191856695[/C][C]0.00680705959283473[/C][/ROW]
[ROW][C]89[/C][C]0.990316098267428[/C][C]0.019367803465144[/C][C]0.00968390173257202[/C][/ROW]
[ROW][C]90[/C][C]0.989698661492204[/C][C]0.0206026770155925[/C][C]0.0103013385077963[/C][/ROW]
[ROW][C]91[/C][C]0.987008884972325[/C][C]0.0259822300553507[/C][C]0.0129911150276754[/C][/ROW]
[ROW][C]92[/C][C]0.981477428061323[/C][C]0.0370451438773546[/C][C]0.0185225719386773[/C][/ROW]
[ROW][C]93[/C][C]0.9721779443476[/C][C]0.0556441113047996[/C][C]0.0278220556523998[/C][/ROW]
[ROW][C]94[/C][C]0.96013224748218[/C][C]0.0797355050356393[/C][C]0.0398677525178196[/C][/ROW]
[ROW][C]95[/C][C]0.959590826248709[/C][C]0.0808183475025826[/C][C]0.0404091737512913[/C][/ROW]
[ROW][C]96[/C][C]0.957320016645879[/C][C]0.085359966708242[/C][C]0.042679983354121[/C][/ROW]
[ROW][C]97[/C][C]0.949632764336396[/C][C]0.100734471327209[/C][C]0.0503672356636043[/C][/ROW]
[ROW][C]98[/C][C]0.951134583938277[/C][C]0.0977308321234454[/C][C]0.0488654160617227[/C][/ROW]
[ROW][C]99[/C][C]0.964437551289873[/C][C]0.0711248974202543[/C][C]0.0355624487101272[/C][/ROW]
[ROW][C]100[/C][C]0.98213574393786[/C][C]0.0357285121242792[/C][C]0.0178642560621396[/C][/ROW]
[ROW][C]101[/C][C]0.975629467511093[/C][C]0.0487410649778131[/C][C]0.0243705324889066[/C][/ROW]
[ROW][C]102[/C][C]0.961643471247792[/C][C]0.0767130575044162[/C][C]0.0383565287522081[/C][/ROW]
[ROW][C]103[/C][C]0.96557693360804[/C][C]0.068846132783921[/C][C]0.0344230663919605[/C][/ROW]
[ROW][C]104[/C][C]0.98059433571448[/C][C]0.0388113285710384[/C][C]0.0194056642855192[/C][/ROW]
[ROW][C]105[/C][C]0.990115036629306[/C][C]0.0197699267413887[/C][C]0.00988496337069437[/C][/ROW]
[ROW][C]106[/C][C]0.990371477673414[/C][C]0.0192570446531728[/C][C]0.00962852232658638[/C][/ROW]
[ROW][C]107[/C][C]0.983236257611605[/C][C]0.0335274847767891[/C][C]0.0167637423883946[/C][/ROW]
[ROW][C]108[/C][C]0.97159630226495[/C][C]0.0568073954701001[/C][C]0.02840369773505[/C][/ROW]
[ROW][C]109[/C][C]0.955848177307217[/C][C]0.088303645385565[/C][C]0.0441518226927825[/C][/ROW]
[ROW][C]110[/C][C]0.917674184286031[/C][C]0.164651631427938[/C][C]0.082325815713969[/C][/ROW]
[ROW][C]111[/C][C]0.978175269716317[/C][C]0.0436494605673662[/C][C]0.0218247302836831[/C][/ROW]
[ROW][C]112[/C][C]0.948959952976141[/C][C]0.102080094047717[/C][C]0.0510400470238586[/C][/ROW]
[ROW][C]113[/C][C]0.90667521277965[/C][C]0.186649574440699[/C][C]0.0933247872203497[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105694&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105694&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.8002349189794880.3995301620410240.199765081020512
160.8230517611766340.3538964776467330.176948238823366
170.7320926496611970.5358147006776060.267907350338803
180.6455304261977920.7089391476044150.354469573802208
190.5565251699935370.8869496600129270.443474830006463
200.6213601350885310.7572797298229380.378639864911469
210.8515798843924030.2968402312151950.148420115607598
220.8495037346495040.3009925307009910.150496265350496
230.8535314943871460.2929370112257090.146468505612854
240.8054615178944220.3890769642111570.194538482105578
250.7902434361572160.4195131276855680.209756563842784
260.7383098257063580.5233803485872840.261690174293642
270.682916938365540.634166123268920.31708306163446
280.6135166507817890.7729666984364220.386483349218211
290.5618172169881330.8763655660237340.438182783011867
300.5442051044268560.9115897911462870.455794895573144
310.4939165542202930.9878331084405860.506083445779707
320.4410782908065470.8821565816130950.558921709193453
330.4033456039440570.8066912078881140.596654396055943
340.3739186523567060.7478373047134120.626081347643294
350.3637777867762460.7275555735524920.636222213223754
360.3806316132544660.7612632265089310.619368386745534
370.3567115515655870.7134231031311730.643288448434413
380.3756147346906290.7512294693812580.624385265309371
390.4071528034118030.8143056068236070.592847196588197
400.3705143337785120.7410286675570240.629485666221488
410.3181634180211430.6363268360422850.681836581978857
420.2808633806815930.5617267613631850.719136619318407
430.2745249217551020.5490498435102030.725475078244898
440.2678344898916060.5356689797832130.732165510108394
450.2378852254813870.4757704509627730.762114774518613
460.2297458841571730.4594917683143460.770254115842827
470.2511732355144760.5023464710289530.748826764485524
480.2508542091793850.5017084183587690.749145790820615
490.2086163478653020.4172326957306040.791383652134698
500.2124842214698930.4249684429397870.787515778530107
510.2384027141328390.4768054282656770.761597285867161
520.1966696433165290.3933392866330580.803330356683471
530.2035494165656120.4070988331312240.796450583434388
540.1799372764926280.3598745529852570.820062723507372
550.1491970213273180.2983940426546350.850802978672682
560.1516323312566740.3032646625133470.848367668743326
570.1278098079778730.2556196159557470.872190192022127
580.1105587472069270.2211174944138540.889441252793073
590.1343608768619170.2687217537238330.865639123138083
600.1922759465842940.3845518931685890.807724053415706
610.2011155320704120.4022310641408250.798884467929588
620.3304073228541910.6608146457083820.669592677145809
630.3643645440620770.7287290881241550.635635455937923
640.4390995716802680.8781991433605360.560900428319732
650.5297050959309430.9405898081381140.470294904069057
660.6786844623665060.6426310752669880.321315537633494
670.7272300461654530.5455399076690940.272769953834547
680.7108622562773520.5782754874452970.289137743722648
690.7091512894368850.581697421126230.290848710563115
700.6876720891553930.6246558216892140.312327910844607
710.6594780058683220.6810439882633570.340521994131678
720.6285771145856470.7428457708287070.371422885414353
730.5816960318674120.8366079362651760.418303968132588
740.5313119270183010.9373761459633980.468688072981699
750.4807103253175210.9614206506350420.519289674682479
760.4632129183736930.9264258367473870.536787081626307
770.4454465577009050.890893115401810.554553442299095
780.4141867590737930.8283735181475870.585813240926207
790.3747358538833820.7494717077667630.625264146116618
800.460759150765490.921518301530980.53924084923451
810.5817371512163410.8365256975673170.418262848783659
820.6441524059922170.7116951880155660.355847594007783
830.6117084322879150.776583135424170.388291567712085
840.777441560870680.4451168782586410.222558439129321
850.9166392834793160.1667214330413690.0833607165206845
860.9956184703718170.008763059256365270.00438152962818263
870.995073068065860.00985386386827940.0049269319341397
880.9931929404071650.01361411918566950.00680705959283473
890.9903160982674280.0193678034651440.00968390173257202
900.9896986614922040.02060267701559250.0103013385077963
910.9870088849723250.02598223005535070.0129911150276754
920.9814774280613230.03704514387735460.0185225719386773
930.97217794434760.05564411130479960.0278220556523998
940.960132247482180.07973550503563930.0398677525178196
950.9595908262487090.08081834750258260.0404091737512913
960.9573200166458790.0853599667082420.042679983354121
970.9496327643363960.1007344713272090.0503672356636043
980.9511345839382770.09773083212344540.0488654160617227
990.9644375512898730.07112489742025430.0355624487101272
1000.982135743937860.03572851212427920.0178642560621396
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1020.9616434712477920.07671305750441620.0383565287522081
1030.965576933608040.0688461327839210.0344230663919605
1040.980594335714480.03881132857103840.0194056642855192
1050.9901150366293060.01976992674138870.00988496337069437
1060.9903714776734140.01925704465317280.00962852232658638
1070.9832362576116050.03352748477678910.0167637423883946
1080.971596302264950.05680739547010010.02840369773505
1090.9558481773072170.0883036453855650.0441518226927825
1100.9176741842860310.1646516314279380.082325815713969
1110.9781752697163170.04364946056736620.0218247302836831
1120.9489599529761410.1020800940477170.0510400470238586
1130.906675212779650.1866495744406990.0933247872203497






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0202020202020202NOK
5% type I error level140.141414141414141NOK
10% type I error level240.242424242424242NOK

\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 & 2 & 0.0202020202020202 & NOK \tabularnewline
5% type I error level & 14 & 0.141414141414141 & NOK \tabularnewline
10% type I error level & 24 & 0.242424242424242 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105694&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]2[/C][C]0.0202020202020202[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.141414141414141[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.242424242424242[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105694&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105694&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 level20.0202020202020202NOK
5% type I error level140.141414141414141NOK
10% type I error level240.242424242424242NOK


Figures (Output of Computation)

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Figure 10
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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')
}