FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSun, 05 Dec 2010 21:39:06 +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/05/t12915850991ebz0in7ewg9hgv.htm/, Retrieved Wed, 01 May 2024 15:56:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105504, Retrieved Wed, 01 May 2024 15:56:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2010-12-05 21:39:06] [c474a97a96075919a678ad3d2290b00b] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2293,41	10430,35	9374,63	-18,2	-11	3,3	-0,8	2443,27	2513,17	2466,92	2502,66
2070,83	9691,12	8679,75	-22,8	-17	3,47	-1,7	2293,41	2443,27	2513,17	2466,92
2029,6	9810,31	8593	-23,6	-18	3,72	-1,1	2070,83	2293,41	2443,27	2513,17
2052,02	9304,43	8398,37	-27,6	-19	3,67	-0,4	2029,6	2070,83	2293,41	2443,27
1864,44	8767,96	7992,12	-29,4	-22	3,82	0,6	2052,02	2029,6	2070,83	2293,41
1670,07	7764,58	7235,47	-31,8	-24	3,85	0,6	1864,44	2052,02	2029,6	2070,83
1810,99	7694,78	7690,5	-31,4	-24	3,9	1,9	1670,07	1864,44	2052,02	2029,6
1905,41	8331,49	8396,2	-27,6	-20	3,99	2,3	1810,99	1670,07	1864,44	2052,02
1862,83	8460,94	8595,56	-28,8	-25	4,35	2,6	1905,41	1810,99	1670,07	1864,44
2014,45	8531,45	8614,55	-21,9	-22	4,98	3,1	1862,83	1905,41	1810,99	1670,07
2197,82	9117,03	9181,73	-13,9	-17	5,46	4,7	2014,45	1862,83	1905,41	1810,99
2962,34	12123,53	11114,08	-8	-9	5,19	5,5	2197,82	2014,45	1862,83	1905,41
3047,03	12989,35	11530,75	-2,8	-11	5,03	5,4	2962,34	2197,82	2014,45	1862,83
3032,6	13168,91	11322,38	-3,3	-13	5,38	5,9	3047,03	2962,34	2197,82	2014,45
3504,37	14084,6	12056,67	-1,3	-11	5,37	5,8	3032,6	3047,03	2962,34	2197,82
3801,06	13995,33	12812,48	0,5	-9	4,87	5,2	3504,37	3032,6	3047,03	2962,34
3857,62	13357,7	12656,63	-1,9	-7	4,7	4,2	3801,06	3504,37	3032,6	3047,03
3674,4	12602,93	12193,88	2	-3	4,4	4,4	3857,62	3801,06	3504,37	3032,6
3720,98	13547,84	12419,57	1,7	-3	4,37	3,6	3674,4	3857,62	3801,06	3504,37
3844,49	13731,31	12538,12	1,9	-6	4,54	3,5	3720,98	3674,4	3857,62	3801,06
4116,68	15532,18	13406,97	0,1	-4	4,8	3,1	3844,49	3720,98	3674,4	3857,62
4105,18	15543,76	13200,58	2,4	-8	4,56	2,9	4116,68	3844,49	3720,98	3674,4
4435,23	16903,36	13901,28	2,3	-1	4,61	2,2	4105,18	4116,68	3844,49	3720,98
4296,49	16235,39	13557,69	4,7	-2	4,58	1,5	4435,23	4105,18	4116,68	3844,49
4202,52	16460,95	13239,71	5	-2	4,61	1,1	4296,49	4435,23	4105,18	4116,68
4562,84	17974,77	13673,28	7,2	-1	4,77	1,4	4202,52	4296,49	4435,23	4105,18
4621,4	18001,37	13480,21	8,5	1	4,76	1,3	4562,84	4202,52	4296,49	4435,23
4696,96	17611,14	13407,75	6,8	2	4,5	1,3	4621,4	4562,84	4202,52	4296,49
4591,27	17460,53	12754,8	5,8	2	4,37	1,8	4696,96	4621,4	4562,84	4202,52
4356,98	17128,37	12268,53	3,7	-1	4,15	1,8	4591,27	4696,96	4621,4	4562,84
4502,64	17741,23	12631,48	4,8	1	4,24	1,8	4356,98	4591,27	4696,96	4621,4
4443,91	17286,32	12512,89	6,1	-1	4,22	1,7	4502,64	4356,98	4591,27	4696,96
4290,89	16775,08	12377,62	6,9	-8	4,01	1,6	4443,91	4502,64	4356,98	4591,27
4199,75	16101,07	12185,15	5,7	1	3,93	1,5	4290,89	4443,91	4502,64	4356,98
4138,52	16519,44	11963,12	6,9	2	3,97	1,2	4199,75	4290,89	4443,91	4502,64
3970,1	15934,09	11533,59	5,5	-2	3,92	1,2	4138,52	4199,75	4290,89	4443,91
3862,27	15786,78	11257,35	6,5	-2	3,99	1,6	3970,1	4138,52	4199,75	4290,89
3701,61	15147,55	11036,89	7,7	-2	4,1	1,6	3862,27	3970,1	4138,52	4199,75
3570,12	14990,31	10997,97	6,3	-2	4,04	1,9	3701,61	3862,27	3970,1	4138,52
3801,06	16397,83	11333,88	5,5	-6	3,97	2,2	3570,12	3701,61	3862,27	3970,1
3895,51	17232,97	11234,68	5,3	-4	3,9	2	3801,06	3570,12	3701,61	3862,27
3917,96	16311,54	11145,65	3,3	-5	3,66	1,7	3895,51	3801,06	3570,12	3701,61
3813,06	16187,64	10971,19	2,2	-2	3,44	2,4	3917,96	3895,51	3801,06	3570,12
3667,03	16102,64	10872,48	0,6	-1	3,27	2,6	3813,06	3917,96	3895,51	3801,06
3494,17	15650,83	10827,81	0,2	-5	3,24	2,9	3667,03	3813,06	3917,96	3895,51
3363,99	14368,05	10695,25	-0,7	-9	3,27	2,6	3494,17	3667,03	3813,06	3917,96
3295,32	13392,79	10324,31	-1,7	-8	2,99	2,5	3363,99	3494,17	3667,03	3813,06
3277,01	12986,62	10532,54	-3,7	-14	2,77	3,2	3295,32	3363,99	3494,17	3667,03
3257,16	12204,98	10554,27	-7,6	-10	2,9	3,1	3277,01	3295,32	3363,99	3494,17
3161,69	11716,87	10545,38	-8,2	-11	2,87	3,1	3257,16	3277,01	3295,32	3363,99
3097,31	11402,75	10486,64	-7,5	-11	2,84	2,9	3161,69	3257,16	3277,01	3295,32
3061,26	11082,38	10377,18	-8	-11	3,02	2,5	3097,31	3161,69	3257,16	3277,01
3119,31	11395,64	10283,19	-6,9	-5	3,19	2,8	3061,26	3097,31	3161,69	3257,16
3106,22	11809,38	10682,06	-4,2	-2	3,39	3,1	3119,31	3061,26	3097,31	3161,69
3080,58	11545,71	10723,78	-3,6	-3	3,28	2,6	3106,22	3119,31	3061,26	3097,31
2981,85	11394,84	10539,51	-1,8	-6	3,28	2,3	3080,58	3106,22	3119,31	3061,26
2921,44	11068,05	10673,38	-3,2	-6	3,33	2,3	2981,85	3080,58	3106,22	3119,31
2849,27	10973	10411,75	-1,3	-7	3,51	2,6	2921,44	2981,85	3080,58	3106,22
2756,76	11028,93	10001,6	0,6	-6	3,65	2,9	2849,27	2921,44	2981,85	3080,58
2645,64	11079,42	10204,59	1,2	-2	3,76	2	2756,76	2849,27	2921,44	2981,85
2497,84	10989,34	10032,8	0,4	-2	3,67	2,2	2645,64	2756,76	2849,27	2921,44
2448,05	11383,89	10152,09	3	-4	3,87	2,4	2497,84	2645,64	2756,76	2849,27
2454,62	11527,72	10364,91	-0,4	0	3,99	2,3	2448,05	2497,84	2645,64	2756,76
2407,6	11037,54	10092,96	0	-6	3,9	2,6	2454,62	2448,05	2497,84	2645,64
2472,81	11950,95	10418,4	-1,3	-4	3,74	1,9	2407,6	2454,62	2448,05	2497,84
2408,64	11441,08	10323,73	-3,1	-3	3,55	1,1	2472,81	2407,6	2454,62	2448,05
2440,25	10631,92	10601,61	-4	-1	3,67	1,3	2408,64	2472,81	2407,6	2454,62
2350,44	10892,76	10540,05	-4,9	-3	3,6	1,6	2440,25	2408,64	2472,81	2407,6
2196,72	10295,98	10124,63	-4,6	-6	3,82	1,7	2350,44	2440,25	2408,64	2472,81
2174,56	10205,29	9762,12	-5,4	-6	3,91	1,9	2196,72	2350,44	2440,25	2408,64
2120,88	10717,13	9682,35	-8,1	-15	3,79	1,6	2174,56	2196,72	2350,44	2440,25
2093,48	10637,44	9492,49	-9,4	-5	3,73	1,8	2120,88	2174,56	2196,72	2350,44
2061,41	9884,59	9284,73	-12,6	-11	3,77	1,8	2093,48	2120,88	2174,56	2196,72
1969,6	9676,31	9154,34	-15,7	-13	3,47	1,5	2061,41	2093,48	2120,88	2174,56
1959,67	8895,71	9098,03	-17,3	-10	3,18	1,6	1969,6	2061,41	2093,48	2120,88
1910,43	8145,82	8623,36	-14,4	-9	3,44	1	1959,67	1969,6	2061,41	2093,48
1833,42	7905,84	8334,59	-16,2	-11	3,81	1,5	1910,43	1959,67	1969,6	2061,41
1635,25	8169,75	7977,64	-14,9	-18	3,6	1,8	1833,42	1910,43	1959,67	1969,6
1765,9	8538,47	7916,13	-11	-13	3,42	1,7	1635,25	1833,42	1910,43	1959,67
1946,81	8570,73	8474,21	-11,5	-9	3,73	1,2	1765,9	1635,25	1833,42	1910,43
1995,37	8692,94	8526,63	-9,6	-8	4,04	1,4	1946,81	1765,9	1635,25	1833,42
2042	8721,14	8641,21	-8,8	-4	4,22	1,1	1995,37	1946,81	1765,9	1635,25
1940,49	8792,5	8048,1	-9,7	-3	4,3	1,3	2042	1995,37	1946,81	1765,9
2065,81	9354,01	8160,67	-8,4	-3	4,28	1,3	1940,49	2042	1995,37	1946,81
2214,95	9751,2	8685,4	-8,4	-3	4,56	1,3	2065,81	1940,49	2042	1995,37
2304,98	10352,27	8616,49	-6,8	-1	4,79	1,3	2214,95	2065,81	1940,49	2042
2555,28	10965,88	9492,44	-5,3	0	4,93	0,9	2304,98	2214,95	2065,81	1940,49
2799,43	11717,46	10080,48	-5,1	1	5,12	1,3	2555,28	2304,98	2214,95	2065,81
2811,7	11384,49	10179,35	-6,5	0	5,13	1,8	2799,43	2555,28	2304,98	2214,95
2735,7	11448,79	10500,98	-7,3	2	5,15	2,7	2811,7	2799,43	2555,28	2304,98
2745,88	9981,65	9892,56	-10,8	1	4,92	2,6	2735,7	2811,7	2799,43	2555,28
2720,25	10300,79	9923,81	-10,9	-1	4,79	2,9	2745,88	2735,7	2811,7	2799,43
2638,53	10496,2	9978,53	-13,4	-8	4,68	2,2	2720,25	2745,88	2735,7	2811,7
2659,81	10511,22	9721,84	-15,5	-18	4,42	2,1	2638,53	2720,25	2745,88	2735,7
2641,65	10438,9	9220,75	-15,4	-14	4,53	2,3	2659,81	2638,53	2720,25	2745,88
2604,42	9996,83	9042,56	-11,9	-4	4,71	2,3	2641,65	2659,81	2638,53	2720,25
2892,63	11576,21	10314,68	-8	0	4,83	2,7	2604,42	2641,65	2659,81	2638,53
2915,02	12151,11	10444,5	-7,7	4	5,04	2,6	2892,63	2604,42	2641,65	2659,81
2845,26	12974,89	10767,2	-6,4	4	5,06	2,9	2915,02	2892,63	2604,42	2641,65
2794,83	13975,55	10546,82	-5,6	3	5,14	3,1	2845,26	2915,02	2892,63	2604,42
2848,96	13411,84	10213,97	-5,7	3	5,06	2,8	2794,83	2845,26	2915,02	2892,63
2833,18	12708,47	10052,6	-0,1	7	5,04	2,1	2848,96	2794,83	2845,26	2915,02
2995,55	13266,27	10777,22	1,9	8	5,19	2,3	2833,18	2848,96	2794,83	2845,26
2987,1	13720,95	10682,74	3,6	13	5,22	2,2	2995,55	2833,18	2848,96	2794,83
3013,24	14452,93	10666,71	5	15	5,4	2,5	2987,1	2995,55	2833,18	2848,96
3110,52	14760,87	10665,78	4,7	14	5,7	3,1	3013,24	2987,1	2995,55	2833,18
3045,78	15311,7	10433,56	5,1	14	5,61	3	3110,52	3013,24	2987,1	2995,55
3032,93	16153,34	10967,87	6,6	10	5,66	3,4	3045,78	3110,52	3013,24	2987,1
3142,95	16329,89	11014,51	6	16	5,65	2,9	3032,93	3045,78	3110,52	3013,24
3012,61	16973,38	10654,41	6,2	13	5,63	2,8	3142,95	3032,93	3045,78	3110,52
2897,06	16969,28	10582,92	8,6	15	5,5	2,7	3012,61	3142,95	3032,93	3045,78
2863,36	17039,97	10580,27	7,4	13	5,61	2,2	2897,06	3012,61	3142,95	3032,93
2882,6	19598,93	10947,43	8,6	12	5,3	2,1	2863,36	2897,06	3012,61	3142,95
2767,63	19834,71	10483,39	9,2	13	5,38	2,2	2882,6	2863,36	2897,06	3012,61
2803,47	19685,53	10539,68	7,7	11	5,5	1,9	2767,63	2882,6	2863,36	2897,06
3030,29	18941,6	11281,26	6,4	9	5,35	1,8	2803,47	2767,63	2882,6	2863,36
3210,52	18409,96	11251,2	8,6	8	4,99	1,9	3030,29	2803,47	2767,63	2882,6
3249,57	18470,97	10817,9	6,4	8	4,93	1,5	3210,52	3030,29	2803,47	2767,63
2999,93	17677,9	10394,48	6	5	5,16	1,3	3249,57	3210,52	3030,29	2803,47
3181,96	17544,22	10714,03	2,6	3	4,87	1,2	2999,93	3249,57	3210,52	3030,29
3053,05	17671	10935,47	0,1	-2	4,73	0,9	3181,96	2999,93	3249,57	3210,52
3092,71	18033,25	11052,23	0	0	4,4	0,7	3053,05	3181,96	2999,93	3249,57
3165,26	17135,96	10704,02	-0,9	-8	3,99	0,7	3092,71	3053,05	3181,96	2999,93
3173,95	16505,21	10853,87	-0,1	2	3,67	0,8	3165,26	3092,71	3053,05	3181,96
3280,37	16666,97	10443,5	-1,4	2	3,65	1,2	3173,95	3165,26	3092,71	3053,05
3288,18	15418,03	9753,63	-7,1	2	3,75	1,2	3280,37	3173,95	3165,26	3092,71
3411,13	14153,22	9327,78	-6,2	3	3,67	1	3288,18	3280,37	3173,95	3165,26
3484,74	13830,14	9349,44	-5,6	6	3,68	1	3411,13	3288,18	3280,37	3173,95
3361,13	14295,79	9018,68	-7,6	1	3,85	0,6	3484,74	3411,13	3288,18	3280,37
3230,66	14525,87	9005,73	-7,3	1	4,02	0,6	3361,13	3484,74	3411,13	3288,18
3006,84	13486,9	8164,47	-7,8	-4	3,99	0,9	3230,66	3361,13	3484,74	3411,13
3149,9	14144,81	7895,51	-3,7	1	4,12	0,8	3006,84	3230,66	3361,13	3484,74
3403,13	15243,98	8478,52	-0,3	2	4,47	0,4	3149,9	3006,84	3230,66	3361,13
3564,95	16370,17	9097,14	2	3	4,69	1	3403,13	3149,9	3006,84	3230,66
3327,7	15231,29	8872,96	2	5	4,77	1,6	3564,95	3403,13	3149,9	3006,84
3141,12	15514,27	9081,69	3,1	5	4,92	1,9	3327,7	3564,95	3403,13	3149,9
3064,42	15941,29	9037,44	2,7	3	4,84	1,5	3141,12	3327,7	3564,95	3403,13
2880,4	16840,31	8709,47	2,4	2	4,77	1	3064,42	3141,12	3327,7	3564,95
2661,39	16797,69	8323,61	2	3	4,88	0,7	2880,4	3064,42	3141,12	3327,7
2504,67	15929,69	7808,33	4,1	-1	5	0,4	2661,39	2880,4	3064,42	3141,12
2450,41	15917,07	7909,82	5,2	-9	5,3	1,1	2504,67	2661,39	2880,4	3064,42
2354,32	16135,96	7683,23	6	-5	5,5	1,4	2450,41	2504,67	2661,39	2880,4
2401,33	17274,75	7875,82	5,1	-1	5,44	1,3	2354,32	2450,41	2504,67	2661,39
2394,36	18233,45	7855,04	3,6	-9	5,36	1,6	2401,33	2354,32	2450,41	2504,67
2409,36	19081,79	7948,43	0,8	-8	5,32	1,8	2394,36	2401,33	2354,32	2450,41
2525,56	20152,53	7990,65	1,8	-12	5,26	1,9	2409,36	2394,36	2401,33	2354,32
2346,9	20482,48	7603,88	0,1	-13	5,42	1,7	2525,56	2409,36	2394,36	2401,33
2250,27	20021,79	7242,41	-2	-16	5,37	1,6	2346,9	2525,56	2409,36	2394,36
2152,18	18200,34	6657,9	-4,1	-21	5,32	1,3	2250,27	2346,9	2525,56	2409,36
2154,87	18255,96	6901,12	-3	-21	5,11	1,5	2152,18	2250,27	2346,9	2525,56
2097,76	18555,87	6921,03	-3,1	-16	4,82	2	2154,87	2152,18	2250,27	2346,9
1989,31	18223,28	6707,03	-3,9	-15	4,89	2,3	2097,76	2154,87	2152,18	2250,27
1877,1	20090,77	6435,87	-4,8	-8	5,05	2,5	1989,31	2097,76	2154,87	2152,18
1852,13	21021,37	6323,43	-5,1	-8	5,23	2,4	1877,1	1989,31	2097,76	2154,87
1795,65	21108,13	5996,21	-6,2	-9	5,3	2,5	1852,13	1877,1	1989,31	2097,76
1751,01	20824,57	5804,8	-6,6	-9	5,69	2	1795,65	1852,13	1877,1	1989,31
1745,74	20870,31	5685,5	-7,8	-11	5,86	1,9	1751,01	1795,65	1852,13	1877,1
1703,45	21597,85	5496,26	-10,5	-12	5,96	1,9	1745,74	1751,01	1795,65	1852,13
1748,09	22204,88	5671,51	-10,8	-13	6,09	1,8	1703,45	1745,74	1751,01	1795,65
1734,1	21761,31	5600,81	-12,3	-13	6,11	1,9	1748,09	1703,45	1745,74	1751,01
1711,74	21837,95	5580,18	-13	-12	6,37	2	1734,1	1748,09	1703,45	1745,74
1690,6	20394,71	5610,95	-12,8	-15	6,45	2	1711,74	1734,1	1748,09	1703,45
1665,5	20675,4	5518,24	-15,1	-18	6,26	1,9	1690,6	1711,74	1734,1	1748,09
1631,59	20407,27	5174,59	-14	-16	6,07	2	1665,5	1690,6	1711,74	1734,1
1538,09	19417,95	5136,72	-12,4	-16	6,4	1,5	1631,59	1665,5	1690,6	1711,74
1452,46	18111,66	4935,8	-11,8	-20	6,72	1,5	1538,09	1631,59	1665,5	1690,6
1429,12	17961,87	4761,28	-9,9	-16	6,99	1,2	1452,46	1538,09	1631,59	1665,5
1471,16	18128,65	4744,66	-9,9	-12	6,94	1,2	1429,12	1452,46	1538,09	1631,59
1475,57	17410,71	4637,58	-9,4	-12	7,14	1,3	1471,16	1429,12	1452,46	1538,09
1464,65	16188,69	4684,76	-9,2	-6	7,35	1,2	1475,57	1471,16	1429,12	1452,46
1433,75	15039,44	4510,76	-7	-5	7,48	1,3	1464,65	1475,57	1471,16	1429,12
1451,04	16373,21	4392,16	-5,1	-7	7,74	1,4	1433,75	1464,65	1475,57	1471,16
1365,41	16322,08	4230,64	-2,2	-4	8,1	1,7	1451,04	1433,75	1464,65	1475,57
1299,88	16433,75	4064,33	0,4	-7	8,29	1,7	1365,41	1451,04	1433,75	1464,65
1349,03	18065,03	3953,66	4,3	-8	8,26	1,8	1299,88	1365,41	1451,04	1433,75
1368,43	19036,23	3872,33	3,7	-7	8,41	1,9	1349,03	1299,88	1365,41	1451,04

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105504&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]10 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=105504&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105504&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 time10 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -546.230265905034 + 0.00497848663458504Nikkei[t] + 0.064130924727598DJ_Indust[t] -2.4442084265653Conjunct_Seizoenzuiver[t] -0.956816977273839Cons_vertrouw[t] + 37.9019963078767Rend_oblig_EUR[t] + 11.9162996323731Alg_consumptie_index_BE[t] + 1.0619313863985Y1[t] -0.292689132011974Y2[t] + 0.13605984089728Y3[t] -0.0183487610508147Y4[t] -51.4908803453574M1[t] -56.7853158988978M2[t] -58.0309927479335M3[t] -37.7722465200661M4[t] -68.7764884632851M5[t] -120.354286713861M6[t] -2.68155983648654M7[t] -59.9732492342926M8[t] -85.7452911011672M9[t] -67.6665101098873M10[t] -76.3701833397756M11[t] + 0.22183130685296t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -546.230265905034 +  0.00497848663458504Nikkei[t] +  0.064130924727598DJ_Indust[t] -2.4442084265653Conjunct_Seizoenzuiver[t] -0.956816977273839Cons_vertrouw[t] +  37.9019963078767Rend_oblig_EUR[t] +  11.9162996323731Alg_consumptie_index_BE[t] +  1.0619313863985Y1[t] -0.292689132011974Y2[t] +  0.13605984089728Y3[t] -0.0183487610508147Y4[t] -51.4908803453574M1[t] -56.7853158988978M2[t] -58.0309927479335M3[t] -37.7722465200661M4[t] -68.7764884632851M5[t] -120.354286713861M6[t] -2.68155983648654M7[t] -59.9732492342926M8[t] -85.7452911011672M9[t] -67.6665101098873M10[t] -76.3701833397756M11[t] +  0.22183130685296t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105504&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -546.230265905034 +  0.00497848663458504Nikkei[t] +  0.064130924727598DJ_Indust[t] -2.4442084265653Conjunct_Seizoenzuiver[t] -0.956816977273839Cons_vertrouw[t] +  37.9019963078767Rend_oblig_EUR[t] +  11.9162996323731Alg_consumptie_index_BE[t] +  1.0619313863985Y1[t] -0.292689132011974Y2[t] +  0.13605984089728Y3[t] -0.0183487610508147Y4[t] -51.4908803453574M1[t] -56.7853158988978M2[t] -58.0309927479335M3[t] -37.7722465200661M4[t] -68.7764884632851M5[t] -120.354286713861M6[t] -2.68155983648654M7[t] -59.9732492342926M8[t] -85.7452911011672M9[t] -67.6665101098873M10[t] -76.3701833397756M11[t] +  0.22183130685296t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105504&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105504&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] = -546.230265905034 + 0.00497848663458504Nikkei[t] + 0.064130924727598DJ_Indust[t] -2.4442084265653Conjunct_Seizoenzuiver[t] -0.956816977273839Cons_vertrouw[t] + 37.9019963078767Rend_oblig_EUR[t] + 11.9162996323731Alg_consumptie_index_BE[t] + 1.0619313863985Y1[t] -0.292689132011974Y2[t] + 0.13605984089728Y3[t] -0.0183487610508147Y4[t] -51.4908803453574M1[t] -56.7853158988978M2[t] -58.0309927479335M3[t] -37.7722465200661M4[t] -68.7764884632851M5[t] -120.354286713861M6[t] -2.68155983648654M7[t] -59.9732492342926M8[t] -85.7452911011672M9[t] -67.6665101098873M10[t] -76.3701833397756M11[t] + 0.22183130685296t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-546.230265905034194.981219-2.80150.0057450.002872
Nikkei0.004978486634585040.004721.05490.2931510.146575
DJ_Indust0.0641309247275980.0142334.50561.3e-057e-06
Conjunct_Seizoenzuiver-2.44420842656532.172125-1.12530.262240.13112
Cons_vertrouw-0.9568169772738392.022311-0.47310.6367940.318397
Rend_oblig_EUR37.901996307876715.5057852.44440.0156460.007823
Alg_consumptie_index_BE11.916299632373110.6227031.12180.2637150.131857
Y11.06193138639850.08111513.091600
Y2-0.2926891320119740.117551-2.48990.0138480.006924
Y30.136059840897280.1170081.16280.246710.123355
Y4-0.01834876105081470.077425-0.2370.8129840.406492
M1-51.490880345357444.407475-1.15950.2480550.124027
M2-56.785315898897844.374047-1.27970.2025890.101294
M3-58.030992747933544.473916-1.30480.1939090.096955
M4-37.772246520066144.263057-0.85340.3947950.197397
M5-68.776488463285144.332146-1.55140.1228740.061437
M6-120.35428671386144.281582-2.71790.0073270.003664
M7-2.6815598364865444.350327-0.06050.9518660.475933
M8-59.973249234292644.730392-1.34080.1819820.090991
M9-85.745291101167245.283421-1.89350.0601760.030088
M10-67.666510109887345.069847-1.50140.1353210.06766
M11-76.370183339775644.836184-1.70330.0905390.04527
t0.221831306852960.4605990.48160.6307680.315384

\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) & -546.230265905034 & 194.981219 & -2.8015 & 0.005745 & 0.002872 \tabularnewline
Nikkei & 0.00497848663458504 & 0.00472 & 1.0549 & 0.293151 & 0.146575 \tabularnewline
DJ_Indust & 0.064130924727598 & 0.014233 & 4.5056 & 1.3e-05 & 7e-06 \tabularnewline
Conjunct_Seizoenzuiver & -2.4442084265653 & 2.172125 & -1.1253 & 0.26224 & 0.13112 \tabularnewline
Cons_vertrouw & -0.956816977273839 & 2.022311 & -0.4731 & 0.636794 & 0.318397 \tabularnewline
Rend_oblig_EUR & 37.9019963078767 & 15.505785 & 2.4444 & 0.015646 & 0.007823 \tabularnewline
Alg_consumptie_index_BE & 11.9162996323731 & 10.622703 & 1.1218 & 0.263715 & 0.131857 \tabularnewline
Y1 & 1.0619313863985 & 0.081115 & 13.0916 & 0 & 0 \tabularnewline
Y2 & -0.292689132011974 & 0.117551 & -2.4899 & 0.013848 & 0.006924 \tabularnewline
Y3 & 0.13605984089728 & 0.117008 & 1.1628 & 0.24671 & 0.123355 \tabularnewline
Y4 & -0.0183487610508147 & 0.077425 & -0.237 & 0.812984 & 0.406492 \tabularnewline
M1 & -51.4908803453574 & 44.407475 & -1.1595 & 0.248055 & 0.124027 \tabularnewline
M2 & -56.7853158988978 & 44.374047 & -1.2797 & 0.202589 & 0.101294 \tabularnewline
M3 & -58.0309927479335 & 44.473916 & -1.3048 & 0.193909 & 0.096955 \tabularnewline
M4 & -37.7722465200661 & 44.263057 & -0.8534 & 0.394795 & 0.197397 \tabularnewline
M5 & -68.7764884632851 & 44.332146 & -1.5514 & 0.122874 & 0.061437 \tabularnewline
M6 & -120.354286713861 & 44.281582 & -2.7179 & 0.007327 & 0.003664 \tabularnewline
M7 & -2.68155983648654 & 44.350327 & -0.0605 & 0.951866 & 0.475933 \tabularnewline
M8 & -59.9732492342926 & 44.730392 & -1.3408 & 0.181982 & 0.090991 \tabularnewline
M9 & -85.7452911011672 & 45.283421 & -1.8935 & 0.060176 & 0.030088 \tabularnewline
M10 & -67.6665101098873 & 45.069847 & -1.5014 & 0.135321 & 0.06766 \tabularnewline
M11 & -76.3701833397756 & 44.836184 & -1.7033 & 0.090539 & 0.04527 \tabularnewline
t & 0.22183130685296 & 0.460599 & 0.4816 & 0.630768 & 0.315384 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105504&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]-546.230265905034[/C][C]194.981219[/C][C]-2.8015[/C][C]0.005745[/C][C]0.002872[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.00497848663458504[/C][C]0.00472[/C][C]1.0549[/C][C]0.293151[/C][C]0.146575[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.064130924727598[/C][C]0.014233[/C][C]4.5056[/C][C]1.3e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]-2.4442084265653[/C][C]2.172125[/C][C]-1.1253[/C][C]0.26224[/C][C]0.13112[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]-0.956816977273839[/C][C]2.022311[/C][C]-0.4731[/C][C]0.636794[/C][C]0.318397[/C][/ROW]
[ROW][C]Rend_oblig_EUR[/C][C]37.9019963078767[/C][C]15.505785[/C][C]2.4444[/C][C]0.015646[/C][C]0.007823[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]11.9162996323731[/C][C]10.622703[/C][C]1.1218[/C][C]0.263715[/C][C]0.131857[/C][/ROW]
[ROW][C]Y1[/C][C]1.0619313863985[/C][C]0.081115[/C][C]13.0916[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.292689132011974[/C][C]0.117551[/C][C]-2.4899[/C][C]0.013848[/C][C]0.006924[/C][/ROW]
[ROW][C]Y3[/C][C]0.13605984089728[/C][C]0.117008[/C][C]1.1628[/C][C]0.24671[/C][C]0.123355[/C][/ROW]
[ROW][C]Y4[/C][C]-0.0183487610508147[/C][C]0.077425[/C][C]-0.237[/C][C]0.812984[/C][C]0.406492[/C][/ROW]
[ROW][C]M1[/C][C]-51.4908803453574[/C][C]44.407475[/C][C]-1.1595[/C][C]0.248055[/C][C]0.124027[/C][/ROW]
[ROW][C]M2[/C][C]-56.7853158988978[/C][C]44.374047[/C][C]-1.2797[/C][C]0.202589[/C][C]0.101294[/C][/ROW]
[ROW][C]M3[/C][C]-58.0309927479335[/C][C]44.473916[/C][C]-1.3048[/C][C]0.193909[/C][C]0.096955[/C][/ROW]
[ROW][C]M4[/C][C]-37.7722465200661[/C][C]44.263057[/C][C]-0.8534[/C][C]0.394795[/C][C]0.197397[/C][/ROW]
[ROW][C]M5[/C][C]-68.7764884632851[/C][C]44.332146[/C][C]-1.5514[/C][C]0.122874[/C][C]0.061437[/C][/ROW]
[ROW][C]M6[/C][C]-120.354286713861[/C][C]44.281582[/C][C]-2.7179[/C][C]0.007327[/C][C]0.003664[/C][/ROW]
[ROW][C]M7[/C][C]-2.68155983648654[/C][C]44.350327[/C][C]-0.0605[/C][C]0.951866[/C][C]0.475933[/C][/ROW]
[ROW][C]M8[/C][C]-59.9732492342926[/C][C]44.730392[/C][C]-1.3408[/C][C]0.181982[/C][C]0.090991[/C][/ROW]
[ROW][C]M9[/C][C]-85.7452911011672[/C][C]45.283421[/C][C]-1.8935[/C][C]0.060176[/C][C]0.030088[/C][/ROW]
[ROW][C]M10[/C][C]-67.6665101098873[/C][C]45.069847[/C][C]-1.5014[/C][C]0.135321[/C][C]0.06766[/C][/ROW]
[ROW][C]M11[/C][C]-76.3701833397756[/C][C]44.836184[/C][C]-1.7033[/C][C]0.090539[/C][C]0.04527[/C][/ROW]
[ROW][C]t[/C][C]0.22183130685296[/C][C]0.460599[/C][C]0.4816[/C][C]0.630768[/C][C]0.315384[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105504&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105504&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)-546.230265905034194.981219-2.80150.0057450.002872
Nikkei0.004978486634585040.004721.05490.2931510.146575
DJ_Indust0.0641309247275980.0142334.50561.3e-057e-06
Conjunct_Seizoenzuiver-2.44420842656532.172125-1.12530.262240.13112
Cons_vertrouw-0.9568169772738392.022311-0.47310.6367940.318397
Rend_oblig_EUR37.901996307876715.5057852.44440.0156460.007823
Alg_consumptie_index_BE11.916299632373110.6227031.12180.2637150.131857
Y11.06193138639850.08111513.091600
Y2-0.2926891320119740.117551-2.48990.0138480.006924
Y30.136059840897280.1170081.16280.246710.123355
Y4-0.01834876105081470.077425-0.2370.8129840.406492
M1-51.490880345357444.407475-1.15950.2480550.124027
M2-56.785315898897844.374047-1.27970.2025890.101294
M3-58.030992747933544.473916-1.30480.1939090.096955
M4-37.772246520066144.263057-0.85340.3947950.197397
M5-68.776488463285144.332146-1.55140.1228740.061437
M6-120.35428671386144.281582-2.71790.0073270.003664
M7-2.6815598364865444.350327-0.06050.9518660.475933
M8-59.973249234292644.730392-1.34080.1819820.090991
M9-85.745291101167245.283421-1.89350.0601760.030088
M10-67.666510109887345.069847-1.50140.1353210.06766
M11-76.370183339775644.836184-1.70330.0905390.04527
t0.221831306852960.4605990.48160.6307680.315384






Multiple Linear Regression - Regression Statistics
Multiple R0.991017486025751
R-squared0.9821156576088
Adjusted R-squared0.979544052820524
F-TEST (value)381.907695181581
F-TEST (DF numerator)22
F-TEST (DF denominator)153
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation118.049470789014
Sum Squared Residuals2132158.66569565

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.991017486025751 \tabularnewline
R-squared & 0.9821156576088 \tabularnewline
Adjusted R-squared & 0.979544052820524 \tabularnewline
F-TEST (value) & 381.907695181581 \tabularnewline
F-TEST (DF numerator) & 22 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 118.049470789014 \tabularnewline
Sum Squared Residuals & 2132158.66569565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105504&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.991017486025751[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9821156576088[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.979544052820524[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]381.907695181581[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]22[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/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]118.049470789014[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2132158.66569565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105504&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105504&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 R0.991017486025751
R-squared0.9821156576088
Adjusted R-squared0.979544052820524
F-TEST (value)381.907695181581
F-TEST (DF numerator)22
F-TEST (DF denominator)153
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation118.049470789014
Sum Squared Residuals2132158.66569565






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12293.412374.9204471504-81.5104471504024
22070.832202.57371470657-131.743714706575
32029.62013.2558521025916.3441478974135
42052.022038.1720381322713.8479618677283
51864.442011.87887631872-147.438876318724
61670.071708.63486552215-38.5648655221507
71810.991724.0740308445186.9159691554925
81905.411891.0975315317214.3124684682847
91862.831939.93138912236-77.1013891223556
102014.451919.7890543759794.6609456240305
112197.822147.2513807011550.5686192988492
122962.342482.78165706429479.558342935708
133047.033224.10037520864-177.070375208638
143032.63117.25386171145-84.6538617114457
153504.373220.051107333284.318892667005
163801.063758.8498499916642.2101500083401
173857.623873.95620071193-16.3362007119252
183674.43804.49764065487-130.097640654871
193720.983752.22292525193-31.2429252519323
203844.493816.6454273165527.8445726834464
214116.683954.91391412283161.766085877175
224105.184209.35864198702-104.178641987023
234435.234163.75328139702271.476718602975
244296.494589.22157073622-292.73157073622
254202.524263.82694919623-61.3069491962283
264562.844283.33681543981279.503184560186
274621.44648.60804914092-27.2080491409183
284696.964602.3278811383294.6321188616842
294591.274646.24544698546-54.9754469854603
304356.984428.72085025161-71.7408502516143
314502.644363.09234730264139.547652697363
324443.914500.42739811638-56.517398116376
334290.894324.30978991275-33.4197899127477
344199.754195.819780628593.93021937141131
354138.524106.572511248631.9474887513999
363970.14099.96964712113-129.869647121127
373862.273864.70524671014-2.43524671013965
383701.613771.67611451071-70.0661145107054
393570.123611.25520684942-41.135206849423
403801.063562.79875125954238.261248740461
413895.513787.19839548962108.311604510381
423917.963736.48258497383181.477415026173
433813.063872.42338578189-59.3633857818872
443667.033698.1397454553-31.1097454553004
453494.173551.6690102607-57.4990102607003
463363.993403.16267708351-39.1726770835065
473295.323250.1279755328545.1920244671476
483277.013293.02375837092-16.0137583709221
493257.163234.8121603025322.347839697471
503161.693205.35087759409-43.66087759409
513097.313096.960917142950.349082857051021
523061.263069.31577633949-8.05577633949236
533119.313003.58924867598115.720751324023
543106.223046.747242056359.4727579436985
553080.583120.75263737136-40.1726373713582
562981.853031.17338196733-49.3233819673311
572921.442917.712344935183.72765506482012
582849.272886.9687823469-37.6987823469014
592756.762783.8214812394-27.0614812393987
602645.642778.30985333788-132.66985333788
612497.842616.86660354774-119.026603547745
622448.052491.23828209012-43.1882820901185
632454.622489.38285036294-34.7628503629438
642407.62498.38874798934-90.7887479893355
652472.812423.9651588405448.8448411594604
662408.642435.52616212029-26.8861621202918
672440.252480.68220209258-40.4322020925778
682350.442488.08293811056-137.642938110562
692196.722330.03621112739-133.316211127386
702174.562200.91163908127-26.3516390812745
712120.882205.61008319479-84.7300831947874
722093.482193.56220403328-100.08220403328
732061.412126.71982718502-65.3098271850213
741969.62073.85992270252-104.259922702524
751959.671965.72653343761-6.05653343760915
761910.431959.15861293408-48.7286129340816
771833.421873.67116790276-40.2511679027626
781635.251732.83946066238-97.589460662383
791765.91631.87417005499134.025829945005
801946.811801.11047532871145.699524671287
811995.371916.3867259225978.983274077409
8220421959.6700771363682.32992286364
831940.491977.68739397482-37.1973939748218
842065.811942.20142822077123.608571779232
852214.952105.41933306558109.530666934419
862304.982208.8425852375796.1374147624267
872555.282333.83332997212221.446670027884
882799.432663.73254935437135.697450645627
892811.72843.8723466043-32.1723466042971
902735.72798.96105380288-63.2610538028835
912745.882814.46362948746-68.5836294874634
922720.252792.0368219694-71.7868219694018
932638.532730.50374825098-91.9737482509784
942659.812659.572413392450.237586607545997
952641.652663.91840467373-22.2684046737264
962604.422679.41992016769-74.9999201676892
972892.632683.72544724087208.90455275913
982915.023006.14246334576-91.1224633457558
992845.262965.75871842271-120.49871842271
1002794.832940.76805300687-145.938053006872
1012848.962844.093490070774.86650992922868
1022833.182814.613008505118.5669914948993
1032995.552945.7964145537449.7535854462584
1042987.13061.27195663224-74.1719566322435
1053013.242983.7621061641229.4778938358788
1063110.523076.3602994753734.159700524627
1073045.783141.6725783741-95.8925783741013
1083032.933170.03247039503-137.102470395026
1093142.953130.0810337140412.868966285963
1103012.613215.55169409775-202.941694097748
1112897.063024.84982791778-127.789827917784
1122863.362979.2181566094-115.858156609396
1132882.62948.08469363016-65.4846936301613
1142767.632886.90873411199-119.278734111993
1152803.472884.03721998166-80.567219981657
1163030.292943.9823379700186.3076620299932
1173210.523111.3655274105299.1544725894839
1183249.573232.5086530041917.0613469958054
1192999.933222.0270719571-222.097071957098
1203181.963060.31747045725121.642529542751
1213053.053294.27060727036-241.220607270362
1223092.713057.0749792368835.6350207631186
1233165.263132.7617753783132.49822462169
1243173.953181.80721821289-7.85721821289137
1253280.373128.45373017278151.916269827222
1263288.183163.97081792921124.209182070795
1273411.133216.68322204783194.446777952173
1283484.743298.03463367133186.70536632867
1293361.133306.2326247869954.8973752130068
1303230.663194.3333365935536.3266634064473
1313006.843040.56073411741-33.720734117409
1323149.92874.44644042819275.453559571811
1333403.133067.21656677318335.913433226817
1343564.953315.31490667249.635093330002
1353327.73423.80820567826-96.1082056782637
1363141.123198.17848633517-57.0584863351692
1373064.423050.4529185986113.9670814013898
1382880.42913.52888842161-33.1288884216134
1392661.392813.08074213255-151.690742132549
1402504.672532.58673780743-27.9167378074278
1412450.412412.2058394569238.2041605430753
1422354.322384.26533795801-29.945337958006
1432401.332285.24628083222116.083719167783
1442394.362450.68101267712-56.3210126771239
1452409.362383.139382545526.2206174545002
1462525.562412.53394000328113.026059996723
1472346.92514.33703692578-167.437036925776
1482250.272292.69287878474-42.4228787847382
1492152.182185.0165022864-32.8365022864033
1502154.872038.94769763982115.922302360184
1512097.762171.73630691704-73.9763069170388
1521989.312033.50591120422-44.1959112042209
1531877.11907.52772959679-30.4277295967861
1541852.131834.377512098917.752487901105
1551795.651805.45132722119-9.80132722119151
1561751.011812.21086532155-61.2008653215477
1571745.741731.4050717547514.3349282452514
1581703.451729.40753203478-25.9575320347792
1591748.091699.6664509763448.4235490236638
1601734.11778.90514115895-44.8051411589505
1611711.741725.4022030362-13.6622030361979
1621690.61661.4477781064529.1522218935463
1631665.51756.2660243397-90.7660243396975
1641631.591641.95805136251-10.3680513625139
1651538.091580.56303892989-42.4730389298938
1661452.461501.5717948379-49.111794837901
1671429.121411.6194955356217.500504464379
1681471.161470.431701668690.728298331314978
1691475.571457.8109483350217.7590516649845
1701464.651440.9923106187123.6576893812863
1711433.751416.1341383592817.6158616407206
1721451.041414.1758587529136.8641412470865
1731365.411405.87962067577-40.469620675774
1741299.881248.1332152414951.7467847585055
1751349.031316.9247418401332.105258159869
1761368.431326.266651556342.1633484436966

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2293.41 & 2374.9204471504 & -81.5104471504024 \tabularnewline
2 & 2070.83 & 2202.57371470657 & -131.743714706575 \tabularnewline
3 & 2029.6 & 2013.25585210259 & 16.3441478974135 \tabularnewline
4 & 2052.02 & 2038.17203813227 & 13.8479618677283 \tabularnewline
5 & 1864.44 & 2011.87887631872 & -147.438876318724 \tabularnewline
6 & 1670.07 & 1708.63486552215 & -38.5648655221507 \tabularnewline
7 & 1810.99 & 1724.07403084451 & 86.9159691554925 \tabularnewline
8 & 1905.41 & 1891.09753153172 & 14.3124684682847 \tabularnewline
9 & 1862.83 & 1939.93138912236 & -77.1013891223556 \tabularnewline
10 & 2014.45 & 1919.78905437597 & 94.6609456240305 \tabularnewline
11 & 2197.82 & 2147.25138070115 & 50.5686192988492 \tabularnewline
12 & 2962.34 & 2482.78165706429 & 479.558342935708 \tabularnewline
13 & 3047.03 & 3224.10037520864 & -177.070375208638 \tabularnewline
14 & 3032.6 & 3117.25386171145 & -84.6538617114457 \tabularnewline
15 & 3504.37 & 3220.051107333 & 284.318892667005 \tabularnewline
16 & 3801.06 & 3758.84984999166 & 42.2101500083401 \tabularnewline
17 & 3857.62 & 3873.95620071193 & -16.3362007119252 \tabularnewline
18 & 3674.4 & 3804.49764065487 & -130.097640654871 \tabularnewline
19 & 3720.98 & 3752.22292525193 & -31.2429252519323 \tabularnewline
20 & 3844.49 & 3816.64542731655 & 27.8445726834464 \tabularnewline
21 & 4116.68 & 3954.91391412283 & 161.766085877175 \tabularnewline
22 & 4105.18 & 4209.35864198702 & -104.178641987023 \tabularnewline
23 & 4435.23 & 4163.75328139702 & 271.476718602975 \tabularnewline
24 & 4296.49 & 4589.22157073622 & -292.73157073622 \tabularnewline
25 & 4202.52 & 4263.82694919623 & -61.3069491962283 \tabularnewline
26 & 4562.84 & 4283.33681543981 & 279.503184560186 \tabularnewline
27 & 4621.4 & 4648.60804914092 & -27.2080491409183 \tabularnewline
28 & 4696.96 & 4602.32788113832 & 94.6321188616842 \tabularnewline
29 & 4591.27 & 4646.24544698546 & -54.9754469854603 \tabularnewline
30 & 4356.98 & 4428.72085025161 & -71.7408502516143 \tabularnewline
31 & 4502.64 & 4363.09234730264 & 139.547652697363 \tabularnewline
32 & 4443.91 & 4500.42739811638 & -56.517398116376 \tabularnewline
33 & 4290.89 & 4324.30978991275 & -33.4197899127477 \tabularnewline
34 & 4199.75 & 4195.81978062859 & 3.93021937141131 \tabularnewline
35 & 4138.52 & 4106.5725112486 & 31.9474887513999 \tabularnewline
36 & 3970.1 & 4099.96964712113 & -129.869647121127 \tabularnewline
37 & 3862.27 & 3864.70524671014 & -2.43524671013965 \tabularnewline
38 & 3701.61 & 3771.67611451071 & -70.0661145107054 \tabularnewline
39 & 3570.12 & 3611.25520684942 & -41.135206849423 \tabularnewline
40 & 3801.06 & 3562.79875125954 & 238.261248740461 \tabularnewline
41 & 3895.51 & 3787.19839548962 & 108.311604510381 \tabularnewline
42 & 3917.96 & 3736.48258497383 & 181.477415026173 \tabularnewline
43 & 3813.06 & 3872.42338578189 & -59.3633857818872 \tabularnewline
44 & 3667.03 & 3698.1397454553 & -31.1097454553004 \tabularnewline
45 & 3494.17 & 3551.6690102607 & -57.4990102607003 \tabularnewline
46 & 3363.99 & 3403.16267708351 & -39.1726770835065 \tabularnewline
47 & 3295.32 & 3250.12797553285 & 45.1920244671476 \tabularnewline
48 & 3277.01 & 3293.02375837092 & -16.0137583709221 \tabularnewline
49 & 3257.16 & 3234.81216030253 & 22.347839697471 \tabularnewline
50 & 3161.69 & 3205.35087759409 & -43.66087759409 \tabularnewline
51 & 3097.31 & 3096.96091714295 & 0.349082857051021 \tabularnewline
52 & 3061.26 & 3069.31577633949 & -8.05577633949236 \tabularnewline
53 & 3119.31 & 3003.58924867598 & 115.720751324023 \tabularnewline
54 & 3106.22 & 3046.7472420563 & 59.4727579436985 \tabularnewline
55 & 3080.58 & 3120.75263737136 & -40.1726373713582 \tabularnewline
56 & 2981.85 & 3031.17338196733 & -49.3233819673311 \tabularnewline
57 & 2921.44 & 2917.71234493518 & 3.72765506482012 \tabularnewline
58 & 2849.27 & 2886.9687823469 & -37.6987823469014 \tabularnewline
59 & 2756.76 & 2783.8214812394 & -27.0614812393987 \tabularnewline
60 & 2645.64 & 2778.30985333788 & -132.66985333788 \tabularnewline
61 & 2497.84 & 2616.86660354774 & -119.026603547745 \tabularnewline
62 & 2448.05 & 2491.23828209012 & -43.1882820901185 \tabularnewline
63 & 2454.62 & 2489.38285036294 & -34.7628503629438 \tabularnewline
64 & 2407.6 & 2498.38874798934 & -90.7887479893355 \tabularnewline
65 & 2472.81 & 2423.96515884054 & 48.8448411594604 \tabularnewline
66 & 2408.64 & 2435.52616212029 & -26.8861621202918 \tabularnewline
67 & 2440.25 & 2480.68220209258 & -40.4322020925778 \tabularnewline
68 & 2350.44 & 2488.08293811056 & -137.642938110562 \tabularnewline
69 & 2196.72 & 2330.03621112739 & -133.316211127386 \tabularnewline
70 & 2174.56 & 2200.91163908127 & -26.3516390812745 \tabularnewline
71 & 2120.88 & 2205.61008319479 & -84.7300831947874 \tabularnewline
72 & 2093.48 & 2193.56220403328 & -100.08220403328 \tabularnewline
73 & 2061.41 & 2126.71982718502 & -65.3098271850213 \tabularnewline
74 & 1969.6 & 2073.85992270252 & -104.259922702524 \tabularnewline
75 & 1959.67 & 1965.72653343761 & -6.05653343760915 \tabularnewline
76 & 1910.43 & 1959.15861293408 & -48.7286129340816 \tabularnewline
77 & 1833.42 & 1873.67116790276 & -40.2511679027626 \tabularnewline
78 & 1635.25 & 1732.83946066238 & -97.589460662383 \tabularnewline
79 & 1765.9 & 1631.87417005499 & 134.025829945005 \tabularnewline
80 & 1946.81 & 1801.11047532871 & 145.699524671287 \tabularnewline
81 & 1995.37 & 1916.38672592259 & 78.983274077409 \tabularnewline
82 & 2042 & 1959.67007713636 & 82.32992286364 \tabularnewline
83 & 1940.49 & 1977.68739397482 & -37.1973939748218 \tabularnewline
84 & 2065.81 & 1942.20142822077 & 123.608571779232 \tabularnewline
85 & 2214.95 & 2105.41933306558 & 109.530666934419 \tabularnewline
86 & 2304.98 & 2208.84258523757 & 96.1374147624267 \tabularnewline
87 & 2555.28 & 2333.83332997212 & 221.446670027884 \tabularnewline
88 & 2799.43 & 2663.73254935437 & 135.697450645627 \tabularnewline
89 & 2811.7 & 2843.8723466043 & -32.1723466042971 \tabularnewline
90 & 2735.7 & 2798.96105380288 & -63.2610538028835 \tabularnewline
91 & 2745.88 & 2814.46362948746 & -68.5836294874634 \tabularnewline
92 & 2720.25 & 2792.0368219694 & -71.7868219694018 \tabularnewline
93 & 2638.53 & 2730.50374825098 & -91.9737482509784 \tabularnewline
94 & 2659.81 & 2659.57241339245 & 0.237586607545997 \tabularnewline
95 & 2641.65 & 2663.91840467373 & -22.2684046737264 \tabularnewline
96 & 2604.42 & 2679.41992016769 & -74.9999201676892 \tabularnewline
97 & 2892.63 & 2683.72544724087 & 208.90455275913 \tabularnewline
98 & 2915.02 & 3006.14246334576 & -91.1224633457558 \tabularnewline
99 & 2845.26 & 2965.75871842271 & -120.49871842271 \tabularnewline
100 & 2794.83 & 2940.76805300687 & -145.938053006872 \tabularnewline
101 & 2848.96 & 2844.09349007077 & 4.86650992922868 \tabularnewline
102 & 2833.18 & 2814.6130085051 & 18.5669914948993 \tabularnewline
103 & 2995.55 & 2945.79641455374 & 49.7535854462584 \tabularnewline
104 & 2987.1 & 3061.27195663224 & -74.1719566322435 \tabularnewline
105 & 3013.24 & 2983.76210616412 & 29.4778938358788 \tabularnewline
106 & 3110.52 & 3076.36029947537 & 34.159700524627 \tabularnewline
107 & 3045.78 & 3141.6725783741 & -95.8925783741013 \tabularnewline
108 & 3032.93 & 3170.03247039503 & -137.102470395026 \tabularnewline
109 & 3142.95 & 3130.08103371404 & 12.868966285963 \tabularnewline
110 & 3012.61 & 3215.55169409775 & -202.941694097748 \tabularnewline
111 & 2897.06 & 3024.84982791778 & -127.789827917784 \tabularnewline
112 & 2863.36 & 2979.2181566094 & -115.858156609396 \tabularnewline
113 & 2882.6 & 2948.08469363016 & -65.4846936301613 \tabularnewline
114 & 2767.63 & 2886.90873411199 & -119.278734111993 \tabularnewline
115 & 2803.47 & 2884.03721998166 & -80.567219981657 \tabularnewline
116 & 3030.29 & 2943.98233797001 & 86.3076620299932 \tabularnewline
117 & 3210.52 & 3111.36552741052 & 99.1544725894839 \tabularnewline
118 & 3249.57 & 3232.50865300419 & 17.0613469958054 \tabularnewline
119 & 2999.93 & 3222.0270719571 & -222.097071957098 \tabularnewline
120 & 3181.96 & 3060.31747045725 & 121.642529542751 \tabularnewline
121 & 3053.05 & 3294.27060727036 & -241.220607270362 \tabularnewline
122 & 3092.71 & 3057.07497923688 & 35.6350207631186 \tabularnewline
123 & 3165.26 & 3132.76177537831 & 32.49822462169 \tabularnewline
124 & 3173.95 & 3181.80721821289 & -7.85721821289137 \tabularnewline
125 & 3280.37 & 3128.45373017278 & 151.916269827222 \tabularnewline
126 & 3288.18 & 3163.97081792921 & 124.209182070795 \tabularnewline
127 & 3411.13 & 3216.68322204783 & 194.446777952173 \tabularnewline
128 & 3484.74 & 3298.03463367133 & 186.70536632867 \tabularnewline
129 & 3361.13 & 3306.23262478699 & 54.8973752130068 \tabularnewline
130 & 3230.66 & 3194.33333659355 & 36.3266634064473 \tabularnewline
131 & 3006.84 & 3040.56073411741 & -33.720734117409 \tabularnewline
132 & 3149.9 & 2874.44644042819 & 275.453559571811 \tabularnewline
133 & 3403.13 & 3067.21656677318 & 335.913433226817 \tabularnewline
134 & 3564.95 & 3315.31490667 & 249.635093330002 \tabularnewline
135 & 3327.7 & 3423.80820567826 & -96.1082056782637 \tabularnewline
136 & 3141.12 & 3198.17848633517 & -57.0584863351692 \tabularnewline
137 & 3064.42 & 3050.45291859861 & 13.9670814013898 \tabularnewline
138 & 2880.4 & 2913.52888842161 & -33.1288884216134 \tabularnewline
139 & 2661.39 & 2813.08074213255 & -151.690742132549 \tabularnewline
140 & 2504.67 & 2532.58673780743 & -27.9167378074278 \tabularnewline
141 & 2450.41 & 2412.20583945692 & 38.2041605430753 \tabularnewline
142 & 2354.32 & 2384.26533795801 & -29.945337958006 \tabularnewline
143 & 2401.33 & 2285.24628083222 & 116.083719167783 \tabularnewline
144 & 2394.36 & 2450.68101267712 & -56.3210126771239 \tabularnewline
145 & 2409.36 & 2383.1393825455 & 26.2206174545002 \tabularnewline
146 & 2525.56 & 2412.53394000328 & 113.026059996723 \tabularnewline
147 & 2346.9 & 2514.33703692578 & -167.437036925776 \tabularnewline
148 & 2250.27 & 2292.69287878474 & -42.4228787847382 \tabularnewline
149 & 2152.18 & 2185.0165022864 & -32.8365022864033 \tabularnewline
150 & 2154.87 & 2038.94769763982 & 115.922302360184 \tabularnewline
151 & 2097.76 & 2171.73630691704 & -73.9763069170388 \tabularnewline
152 & 1989.31 & 2033.50591120422 & -44.1959112042209 \tabularnewline
153 & 1877.1 & 1907.52772959679 & -30.4277295967861 \tabularnewline
154 & 1852.13 & 1834.3775120989 & 17.752487901105 \tabularnewline
155 & 1795.65 & 1805.45132722119 & -9.80132722119151 \tabularnewline
156 & 1751.01 & 1812.21086532155 & -61.2008653215477 \tabularnewline
157 & 1745.74 & 1731.40507175475 & 14.3349282452514 \tabularnewline
158 & 1703.45 & 1729.40753203478 & -25.9575320347792 \tabularnewline
159 & 1748.09 & 1699.66645097634 & 48.4235490236638 \tabularnewline
160 & 1734.1 & 1778.90514115895 & -44.8051411589505 \tabularnewline
161 & 1711.74 & 1725.4022030362 & -13.6622030361979 \tabularnewline
162 & 1690.6 & 1661.44777810645 & 29.1522218935463 \tabularnewline
163 & 1665.5 & 1756.2660243397 & -90.7660243396975 \tabularnewline
164 & 1631.59 & 1641.95805136251 & -10.3680513625139 \tabularnewline
165 & 1538.09 & 1580.56303892989 & -42.4730389298938 \tabularnewline
166 & 1452.46 & 1501.5717948379 & -49.111794837901 \tabularnewline
167 & 1429.12 & 1411.61949553562 & 17.500504464379 \tabularnewline
168 & 1471.16 & 1470.43170166869 & 0.728298331314978 \tabularnewline
169 & 1475.57 & 1457.81094833502 & 17.7590516649845 \tabularnewline
170 & 1464.65 & 1440.99231061871 & 23.6576893812863 \tabularnewline
171 & 1433.75 & 1416.13413835928 & 17.6158616407206 \tabularnewline
172 & 1451.04 & 1414.17585875291 & 36.8641412470865 \tabularnewline
173 & 1365.41 & 1405.87962067577 & -40.469620675774 \tabularnewline
174 & 1299.88 & 1248.13321524149 & 51.7467847585055 \tabularnewline
175 & 1349.03 & 1316.92474184013 & 32.105258159869 \tabularnewline
176 & 1368.43 & 1326.2666515563 & 42.1633484436966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105504&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]2293.41[/C][C]2374.9204471504[/C][C]-81.5104471504024[/C][/ROW]
[ROW][C]2[/C][C]2070.83[/C][C]2202.57371470657[/C][C]-131.743714706575[/C][/ROW]
[ROW][C]3[/C][C]2029.6[/C][C]2013.25585210259[/C][C]16.3441478974135[/C][/ROW]
[ROW][C]4[/C][C]2052.02[/C][C]2038.17203813227[/C][C]13.8479618677283[/C][/ROW]
[ROW][C]5[/C][C]1864.44[/C][C]2011.87887631872[/C][C]-147.438876318724[/C][/ROW]
[ROW][C]6[/C][C]1670.07[/C][C]1708.63486552215[/C][C]-38.5648655221507[/C][/ROW]
[ROW][C]7[/C][C]1810.99[/C][C]1724.07403084451[/C][C]86.9159691554925[/C][/ROW]
[ROW][C]8[/C][C]1905.41[/C][C]1891.09753153172[/C][C]14.3124684682847[/C][/ROW]
[ROW][C]9[/C][C]1862.83[/C][C]1939.93138912236[/C][C]-77.1013891223556[/C][/ROW]
[ROW][C]10[/C][C]2014.45[/C][C]1919.78905437597[/C][C]94.6609456240305[/C][/ROW]
[ROW][C]11[/C][C]2197.82[/C][C]2147.25138070115[/C][C]50.5686192988492[/C][/ROW]
[ROW][C]12[/C][C]2962.34[/C][C]2482.78165706429[/C][C]479.558342935708[/C][/ROW]
[ROW][C]13[/C][C]3047.03[/C][C]3224.10037520864[/C][C]-177.070375208638[/C][/ROW]
[ROW][C]14[/C][C]3032.6[/C][C]3117.25386171145[/C][C]-84.6538617114457[/C][/ROW]
[ROW][C]15[/C][C]3504.37[/C][C]3220.051107333[/C][C]284.318892667005[/C][/ROW]
[ROW][C]16[/C][C]3801.06[/C][C]3758.84984999166[/C][C]42.2101500083401[/C][/ROW]
[ROW][C]17[/C][C]3857.62[/C][C]3873.95620071193[/C][C]-16.3362007119252[/C][/ROW]
[ROW][C]18[/C][C]3674.4[/C][C]3804.49764065487[/C][C]-130.097640654871[/C][/ROW]
[ROW][C]19[/C][C]3720.98[/C][C]3752.22292525193[/C][C]-31.2429252519323[/C][/ROW]
[ROW][C]20[/C][C]3844.49[/C][C]3816.64542731655[/C][C]27.8445726834464[/C][/ROW]
[ROW][C]21[/C][C]4116.68[/C][C]3954.91391412283[/C][C]161.766085877175[/C][/ROW]
[ROW][C]22[/C][C]4105.18[/C][C]4209.35864198702[/C][C]-104.178641987023[/C][/ROW]
[ROW][C]23[/C][C]4435.23[/C][C]4163.75328139702[/C][C]271.476718602975[/C][/ROW]
[ROW][C]24[/C][C]4296.49[/C][C]4589.22157073622[/C][C]-292.73157073622[/C][/ROW]
[ROW][C]25[/C][C]4202.52[/C][C]4263.82694919623[/C][C]-61.3069491962283[/C][/ROW]
[ROW][C]26[/C][C]4562.84[/C][C]4283.33681543981[/C][C]279.503184560186[/C][/ROW]
[ROW][C]27[/C][C]4621.4[/C][C]4648.60804914092[/C][C]-27.2080491409183[/C][/ROW]
[ROW][C]28[/C][C]4696.96[/C][C]4602.32788113832[/C][C]94.6321188616842[/C][/ROW]
[ROW][C]29[/C][C]4591.27[/C][C]4646.24544698546[/C][C]-54.9754469854603[/C][/ROW]
[ROW][C]30[/C][C]4356.98[/C][C]4428.72085025161[/C][C]-71.7408502516143[/C][/ROW]
[ROW][C]31[/C][C]4502.64[/C][C]4363.09234730264[/C][C]139.547652697363[/C][/ROW]
[ROW][C]32[/C][C]4443.91[/C][C]4500.42739811638[/C][C]-56.517398116376[/C][/ROW]
[ROW][C]33[/C][C]4290.89[/C][C]4324.30978991275[/C][C]-33.4197899127477[/C][/ROW]
[ROW][C]34[/C][C]4199.75[/C][C]4195.81978062859[/C][C]3.93021937141131[/C][/ROW]
[ROW][C]35[/C][C]4138.52[/C][C]4106.5725112486[/C][C]31.9474887513999[/C][/ROW]
[ROW][C]36[/C][C]3970.1[/C][C]4099.96964712113[/C][C]-129.869647121127[/C][/ROW]
[ROW][C]37[/C][C]3862.27[/C][C]3864.70524671014[/C][C]-2.43524671013965[/C][/ROW]
[ROW][C]38[/C][C]3701.61[/C][C]3771.67611451071[/C][C]-70.0661145107054[/C][/ROW]
[ROW][C]39[/C][C]3570.12[/C][C]3611.25520684942[/C][C]-41.135206849423[/C][/ROW]
[ROW][C]40[/C][C]3801.06[/C][C]3562.79875125954[/C][C]238.261248740461[/C][/ROW]
[ROW][C]41[/C][C]3895.51[/C][C]3787.19839548962[/C][C]108.311604510381[/C][/ROW]
[ROW][C]42[/C][C]3917.96[/C][C]3736.48258497383[/C][C]181.477415026173[/C][/ROW]
[ROW][C]43[/C][C]3813.06[/C][C]3872.42338578189[/C][C]-59.3633857818872[/C][/ROW]
[ROW][C]44[/C][C]3667.03[/C][C]3698.1397454553[/C][C]-31.1097454553004[/C][/ROW]
[ROW][C]45[/C][C]3494.17[/C][C]3551.6690102607[/C][C]-57.4990102607003[/C][/ROW]
[ROW][C]46[/C][C]3363.99[/C][C]3403.16267708351[/C][C]-39.1726770835065[/C][/ROW]
[ROW][C]47[/C][C]3295.32[/C][C]3250.12797553285[/C][C]45.1920244671476[/C][/ROW]
[ROW][C]48[/C][C]3277.01[/C][C]3293.02375837092[/C][C]-16.0137583709221[/C][/ROW]
[ROW][C]49[/C][C]3257.16[/C][C]3234.81216030253[/C][C]22.347839697471[/C][/ROW]
[ROW][C]50[/C][C]3161.69[/C][C]3205.35087759409[/C][C]-43.66087759409[/C][/ROW]
[ROW][C]51[/C][C]3097.31[/C][C]3096.96091714295[/C][C]0.349082857051021[/C][/ROW]
[ROW][C]52[/C][C]3061.26[/C][C]3069.31577633949[/C][C]-8.05577633949236[/C][/ROW]
[ROW][C]53[/C][C]3119.31[/C][C]3003.58924867598[/C][C]115.720751324023[/C][/ROW]
[ROW][C]54[/C][C]3106.22[/C][C]3046.7472420563[/C][C]59.4727579436985[/C][/ROW]
[ROW][C]55[/C][C]3080.58[/C][C]3120.75263737136[/C][C]-40.1726373713582[/C][/ROW]
[ROW][C]56[/C][C]2981.85[/C][C]3031.17338196733[/C][C]-49.3233819673311[/C][/ROW]
[ROW][C]57[/C][C]2921.44[/C][C]2917.71234493518[/C][C]3.72765506482012[/C][/ROW]
[ROW][C]58[/C][C]2849.27[/C][C]2886.9687823469[/C][C]-37.6987823469014[/C][/ROW]
[ROW][C]59[/C][C]2756.76[/C][C]2783.8214812394[/C][C]-27.0614812393987[/C][/ROW]
[ROW][C]60[/C][C]2645.64[/C][C]2778.30985333788[/C][C]-132.66985333788[/C][/ROW]
[ROW][C]61[/C][C]2497.84[/C][C]2616.86660354774[/C][C]-119.026603547745[/C][/ROW]
[ROW][C]62[/C][C]2448.05[/C][C]2491.23828209012[/C][C]-43.1882820901185[/C][/ROW]
[ROW][C]63[/C][C]2454.62[/C][C]2489.38285036294[/C][C]-34.7628503629438[/C][/ROW]
[ROW][C]64[/C][C]2407.6[/C][C]2498.38874798934[/C][C]-90.7887479893355[/C][/ROW]
[ROW][C]65[/C][C]2472.81[/C][C]2423.96515884054[/C][C]48.8448411594604[/C][/ROW]
[ROW][C]66[/C][C]2408.64[/C][C]2435.52616212029[/C][C]-26.8861621202918[/C][/ROW]
[ROW][C]67[/C][C]2440.25[/C][C]2480.68220209258[/C][C]-40.4322020925778[/C][/ROW]
[ROW][C]68[/C][C]2350.44[/C][C]2488.08293811056[/C][C]-137.642938110562[/C][/ROW]
[ROW][C]69[/C][C]2196.72[/C][C]2330.03621112739[/C][C]-133.316211127386[/C][/ROW]
[ROW][C]70[/C][C]2174.56[/C][C]2200.91163908127[/C][C]-26.3516390812745[/C][/ROW]
[ROW][C]71[/C][C]2120.88[/C][C]2205.61008319479[/C][C]-84.7300831947874[/C][/ROW]
[ROW][C]72[/C][C]2093.48[/C][C]2193.56220403328[/C][C]-100.08220403328[/C][/ROW]
[ROW][C]73[/C][C]2061.41[/C][C]2126.71982718502[/C][C]-65.3098271850213[/C][/ROW]
[ROW][C]74[/C][C]1969.6[/C][C]2073.85992270252[/C][C]-104.259922702524[/C][/ROW]
[ROW][C]75[/C][C]1959.67[/C][C]1965.72653343761[/C][C]-6.05653343760915[/C][/ROW]
[ROW][C]76[/C][C]1910.43[/C][C]1959.15861293408[/C][C]-48.7286129340816[/C][/ROW]
[ROW][C]77[/C][C]1833.42[/C][C]1873.67116790276[/C][C]-40.2511679027626[/C][/ROW]
[ROW][C]78[/C][C]1635.25[/C][C]1732.83946066238[/C][C]-97.589460662383[/C][/ROW]
[ROW][C]79[/C][C]1765.9[/C][C]1631.87417005499[/C][C]134.025829945005[/C][/ROW]
[ROW][C]80[/C][C]1946.81[/C][C]1801.11047532871[/C][C]145.699524671287[/C][/ROW]
[ROW][C]81[/C][C]1995.37[/C][C]1916.38672592259[/C][C]78.983274077409[/C][/ROW]
[ROW][C]82[/C][C]2042[/C][C]1959.67007713636[/C][C]82.32992286364[/C][/ROW]
[ROW][C]83[/C][C]1940.49[/C][C]1977.68739397482[/C][C]-37.1973939748218[/C][/ROW]
[ROW][C]84[/C][C]2065.81[/C][C]1942.20142822077[/C][C]123.608571779232[/C][/ROW]
[ROW][C]85[/C][C]2214.95[/C][C]2105.41933306558[/C][C]109.530666934419[/C][/ROW]
[ROW][C]86[/C][C]2304.98[/C][C]2208.84258523757[/C][C]96.1374147624267[/C][/ROW]
[ROW][C]87[/C][C]2555.28[/C][C]2333.83332997212[/C][C]221.446670027884[/C][/ROW]
[ROW][C]88[/C][C]2799.43[/C][C]2663.73254935437[/C][C]135.697450645627[/C][/ROW]
[ROW][C]89[/C][C]2811.7[/C][C]2843.8723466043[/C][C]-32.1723466042971[/C][/ROW]
[ROW][C]90[/C][C]2735.7[/C][C]2798.96105380288[/C][C]-63.2610538028835[/C][/ROW]
[ROW][C]91[/C][C]2745.88[/C][C]2814.46362948746[/C][C]-68.5836294874634[/C][/ROW]
[ROW][C]92[/C][C]2720.25[/C][C]2792.0368219694[/C][C]-71.7868219694018[/C][/ROW]
[ROW][C]93[/C][C]2638.53[/C][C]2730.50374825098[/C][C]-91.9737482509784[/C][/ROW]
[ROW][C]94[/C][C]2659.81[/C][C]2659.57241339245[/C][C]0.237586607545997[/C][/ROW]
[ROW][C]95[/C][C]2641.65[/C][C]2663.91840467373[/C][C]-22.2684046737264[/C][/ROW]
[ROW][C]96[/C][C]2604.42[/C][C]2679.41992016769[/C][C]-74.9999201676892[/C][/ROW]
[ROW][C]97[/C][C]2892.63[/C][C]2683.72544724087[/C][C]208.90455275913[/C][/ROW]
[ROW][C]98[/C][C]2915.02[/C][C]3006.14246334576[/C][C]-91.1224633457558[/C][/ROW]
[ROW][C]99[/C][C]2845.26[/C][C]2965.75871842271[/C][C]-120.49871842271[/C][/ROW]
[ROW][C]100[/C][C]2794.83[/C][C]2940.76805300687[/C][C]-145.938053006872[/C][/ROW]
[ROW][C]101[/C][C]2848.96[/C][C]2844.09349007077[/C][C]4.86650992922868[/C][/ROW]
[ROW][C]102[/C][C]2833.18[/C][C]2814.6130085051[/C][C]18.5669914948993[/C][/ROW]
[ROW][C]103[/C][C]2995.55[/C][C]2945.79641455374[/C][C]49.7535854462584[/C][/ROW]
[ROW][C]104[/C][C]2987.1[/C][C]3061.27195663224[/C][C]-74.1719566322435[/C][/ROW]
[ROW][C]105[/C][C]3013.24[/C][C]2983.76210616412[/C][C]29.4778938358788[/C][/ROW]
[ROW][C]106[/C][C]3110.52[/C][C]3076.36029947537[/C][C]34.159700524627[/C][/ROW]
[ROW][C]107[/C][C]3045.78[/C][C]3141.6725783741[/C][C]-95.8925783741013[/C][/ROW]
[ROW][C]108[/C][C]3032.93[/C][C]3170.03247039503[/C][C]-137.102470395026[/C][/ROW]
[ROW][C]109[/C][C]3142.95[/C][C]3130.08103371404[/C][C]12.868966285963[/C][/ROW]
[ROW][C]110[/C][C]3012.61[/C][C]3215.55169409775[/C][C]-202.941694097748[/C][/ROW]
[ROW][C]111[/C][C]2897.06[/C][C]3024.84982791778[/C][C]-127.789827917784[/C][/ROW]
[ROW][C]112[/C][C]2863.36[/C][C]2979.2181566094[/C][C]-115.858156609396[/C][/ROW]
[ROW][C]113[/C][C]2882.6[/C][C]2948.08469363016[/C][C]-65.4846936301613[/C][/ROW]
[ROW][C]114[/C][C]2767.63[/C][C]2886.90873411199[/C][C]-119.278734111993[/C][/ROW]
[ROW][C]115[/C][C]2803.47[/C][C]2884.03721998166[/C][C]-80.567219981657[/C][/ROW]
[ROW][C]116[/C][C]3030.29[/C][C]2943.98233797001[/C][C]86.3076620299932[/C][/ROW]
[ROW][C]117[/C][C]3210.52[/C][C]3111.36552741052[/C][C]99.1544725894839[/C][/ROW]
[ROW][C]118[/C][C]3249.57[/C][C]3232.50865300419[/C][C]17.0613469958054[/C][/ROW]
[ROW][C]119[/C][C]2999.93[/C][C]3222.0270719571[/C][C]-222.097071957098[/C][/ROW]
[ROW][C]120[/C][C]3181.96[/C][C]3060.31747045725[/C][C]121.642529542751[/C][/ROW]
[ROW][C]121[/C][C]3053.05[/C][C]3294.27060727036[/C][C]-241.220607270362[/C][/ROW]
[ROW][C]122[/C][C]3092.71[/C][C]3057.07497923688[/C][C]35.6350207631186[/C][/ROW]
[ROW][C]123[/C][C]3165.26[/C][C]3132.76177537831[/C][C]32.49822462169[/C][/ROW]
[ROW][C]124[/C][C]3173.95[/C][C]3181.80721821289[/C][C]-7.85721821289137[/C][/ROW]
[ROW][C]125[/C][C]3280.37[/C][C]3128.45373017278[/C][C]151.916269827222[/C][/ROW]
[ROW][C]126[/C][C]3288.18[/C][C]3163.97081792921[/C][C]124.209182070795[/C][/ROW]
[ROW][C]127[/C][C]3411.13[/C][C]3216.68322204783[/C][C]194.446777952173[/C][/ROW]
[ROW][C]128[/C][C]3484.74[/C][C]3298.03463367133[/C][C]186.70536632867[/C][/ROW]
[ROW][C]129[/C][C]3361.13[/C][C]3306.23262478699[/C][C]54.8973752130068[/C][/ROW]
[ROW][C]130[/C][C]3230.66[/C][C]3194.33333659355[/C][C]36.3266634064473[/C][/ROW]
[ROW][C]131[/C][C]3006.84[/C][C]3040.56073411741[/C][C]-33.720734117409[/C][/ROW]
[ROW][C]132[/C][C]3149.9[/C][C]2874.44644042819[/C][C]275.453559571811[/C][/ROW]
[ROW][C]133[/C][C]3403.13[/C][C]3067.21656677318[/C][C]335.913433226817[/C][/ROW]
[ROW][C]134[/C][C]3564.95[/C][C]3315.31490667[/C][C]249.635093330002[/C][/ROW]
[ROW][C]135[/C][C]3327.7[/C][C]3423.80820567826[/C][C]-96.1082056782637[/C][/ROW]
[ROW][C]136[/C][C]3141.12[/C][C]3198.17848633517[/C][C]-57.0584863351692[/C][/ROW]
[ROW][C]137[/C][C]3064.42[/C][C]3050.45291859861[/C][C]13.9670814013898[/C][/ROW]
[ROW][C]138[/C][C]2880.4[/C][C]2913.52888842161[/C][C]-33.1288884216134[/C][/ROW]
[ROW][C]139[/C][C]2661.39[/C][C]2813.08074213255[/C][C]-151.690742132549[/C][/ROW]
[ROW][C]140[/C][C]2504.67[/C][C]2532.58673780743[/C][C]-27.9167378074278[/C][/ROW]
[ROW][C]141[/C][C]2450.41[/C][C]2412.20583945692[/C][C]38.2041605430753[/C][/ROW]
[ROW][C]142[/C][C]2354.32[/C][C]2384.26533795801[/C][C]-29.945337958006[/C][/ROW]
[ROW][C]143[/C][C]2401.33[/C][C]2285.24628083222[/C][C]116.083719167783[/C][/ROW]
[ROW][C]144[/C][C]2394.36[/C][C]2450.68101267712[/C][C]-56.3210126771239[/C][/ROW]
[ROW][C]145[/C][C]2409.36[/C][C]2383.1393825455[/C][C]26.2206174545002[/C][/ROW]
[ROW][C]146[/C][C]2525.56[/C][C]2412.53394000328[/C][C]113.026059996723[/C][/ROW]
[ROW][C]147[/C][C]2346.9[/C][C]2514.33703692578[/C][C]-167.437036925776[/C][/ROW]
[ROW][C]148[/C][C]2250.27[/C][C]2292.69287878474[/C][C]-42.4228787847382[/C][/ROW]
[ROW][C]149[/C][C]2152.18[/C][C]2185.0165022864[/C][C]-32.8365022864033[/C][/ROW]
[ROW][C]150[/C][C]2154.87[/C][C]2038.94769763982[/C][C]115.922302360184[/C][/ROW]
[ROW][C]151[/C][C]2097.76[/C][C]2171.73630691704[/C][C]-73.9763069170388[/C][/ROW]
[ROW][C]152[/C][C]1989.31[/C][C]2033.50591120422[/C][C]-44.1959112042209[/C][/ROW]
[ROW][C]153[/C][C]1877.1[/C][C]1907.52772959679[/C][C]-30.4277295967861[/C][/ROW]
[ROW][C]154[/C][C]1852.13[/C][C]1834.3775120989[/C][C]17.752487901105[/C][/ROW]
[ROW][C]155[/C][C]1795.65[/C][C]1805.45132722119[/C][C]-9.80132722119151[/C][/ROW]
[ROW][C]156[/C][C]1751.01[/C][C]1812.21086532155[/C][C]-61.2008653215477[/C][/ROW]
[ROW][C]157[/C][C]1745.74[/C][C]1731.40507175475[/C][C]14.3349282452514[/C][/ROW]
[ROW][C]158[/C][C]1703.45[/C][C]1729.40753203478[/C][C]-25.9575320347792[/C][/ROW]
[ROW][C]159[/C][C]1748.09[/C][C]1699.66645097634[/C][C]48.4235490236638[/C][/ROW]
[ROW][C]160[/C][C]1734.1[/C][C]1778.90514115895[/C][C]-44.8051411589505[/C][/ROW]
[ROW][C]161[/C][C]1711.74[/C][C]1725.4022030362[/C][C]-13.6622030361979[/C][/ROW]
[ROW][C]162[/C][C]1690.6[/C][C]1661.44777810645[/C][C]29.1522218935463[/C][/ROW]
[ROW][C]163[/C][C]1665.5[/C][C]1756.2660243397[/C][C]-90.7660243396975[/C][/ROW]
[ROW][C]164[/C][C]1631.59[/C][C]1641.95805136251[/C][C]-10.3680513625139[/C][/ROW]
[ROW][C]165[/C][C]1538.09[/C][C]1580.56303892989[/C][C]-42.4730389298938[/C][/ROW]
[ROW][C]166[/C][C]1452.46[/C][C]1501.5717948379[/C][C]-49.111794837901[/C][/ROW]
[ROW][C]167[/C][C]1429.12[/C][C]1411.61949553562[/C][C]17.500504464379[/C][/ROW]
[ROW][C]168[/C][C]1471.16[/C][C]1470.43170166869[/C][C]0.728298331314978[/C][/ROW]
[ROW][C]169[/C][C]1475.57[/C][C]1457.81094833502[/C][C]17.7590516649845[/C][/ROW]
[ROW][C]170[/C][C]1464.65[/C][C]1440.99231061871[/C][C]23.6576893812863[/C][/ROW]
[ROW][C]171[/C][C]1433.75[/C][C]1416.13413835928[/C][C]17.6158616407206[/C][/ROW]
[ROW][C]172[/C][C]1451.04[/C][C]1414.17585875291[/C][C]36.8641412470865[/C][/ROW]
[ROW][C]173[/C][C]1365.41[/C][C]1405.87962067577[/C][C]-40.469620675774[/C][/ROW]
[ROW][C]174[/C][C]1299.88[/C][C]1248.13321524149[/C][C]51.7467847585055[/C][/ROW]
[ROW][C]175[/C][C]1349.03[/C][C]1316.92474184013[/C][C]32.105258159869[/C][/ROW]
[ROW][C]176[/C][C]1368.43[/C][C]1326.2666515563[/C][C]42.1633484436966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105504&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105504&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
12293.412374.9204471504-81.5104471504024
22070.832202.57371470657-131.743714706575
32029.62013.2558521025916.3441478974135
42052.022038.1720381322713.8479618677283
51864.442011.87887631872-147.438876318724
61670.071708.63486552215-38.5648655221507
71810.991724.0740308445186.9159691554925
81905.411891.0975315317214.3124684682847
91862.831939.93138912236-77.1013891223556
102014.451919.7890543759794.6609456240305
112197.822147.2513807011550.5686192988492
122962.342482.78165706429479.558342935708
133047.033224.10037520864-177.070375208638
143032.63117.25386171145-84.6538617114457
153504.373220.051107333284.318892667005
163801.063758.8498499916642.2101500083401
173857.623873.95620071193-16.3362007119252
183674.43804.49764065487-130.097640654871
193720.983752.22292525193-31.2429252519323
203844.493816.6454273165527.8445726834464
214116.683954.91391412283161.766085877175
224105.184209.35864198702-104.178641987023
234435.234163.75328139702271.476718602975
244296.494589.22157073622-292.73157073622
254202.524263.82694919623-61.3069491962283
264562.844283.33681543981279.503184560186
274621.44648.60804914092-27.2080491409183
284696.964602.3278811383294.6321188616842
294591.274646.24544698546-54.9754469854603
304356.984428.72085025161-71.7408502516143
314502.644363.09234730264139.547652697363
324443.914500.42739811638-56.517398116376
334290.894324.30978991275-33.4197899127477
344199.754195.819780628593.93021937141131
354138.524106.572511248631.9474887513999
363970.14099.96964712113-129.869647121127
373862.273864.70524671014-2.43524671013965
383701.613771.67611451071-70.0661145107054
393570.123611.25520684942-41.135206849423
403801.063562.79875125954238.261248740461
413895.513787.19839548962108.311604510381
423917.963736.48258497383181.477415026173
433813.063872.42338578189-59.3633857818872
443667.033698.1397454553-31.1097454553004
453494.173551.6690102607-57.4990102607003
463363.993403.16267708351-39.1726770835065
473295.323250.1279755328545.1920244671476
483277.013293.02375837092-16.0137583709221
493257.163234.8121603025322.347839697471
503161.693205.35087759409-43.66087759409
513097.313096.960917142950.349082857051021
523061.263069.31577633949-8.05577633949236
533119.313003.58924867598115.720751324023
543106.223046.747242056359.4727579436985
553080.583120.75263737136-40.1726373713582
562981.853031.17338196733-49.3233819673311
572921.442917.712344935183.72765506482012
582849.272886.9687823469-37.6987823469014
592756.762783.8214812394-27.0614812393987
602645.642778.30985333788-132.66985333788
612497.842616.86660354774-119.026603547745
622448.052491.23828209012-43.1882820901185
632454.622489.38285036294-34.7628503629438
642407.62498.38874798934-90.7887479893355
652472.812423.9651588405448.8448411594604
662408.642435.52616212029-26.8861621202918
672440.252480.68220209258-40.4322020925778
682350.442488.08293811056-137.642938110562
692196.722330.03621112739-133.316211127386
702174.562200.91163908127-26.3516390812745
712120.882205.61008319479-84.7300831947874
722093.482193.56220403328-100.08220403328
732061.412126.71982718502-65.3098271850213
741969.62073.85992270252-104.259922702524
751959.671965.72653343761-6.05653343760915
761910.431959.15861293408-48.7286129340816
771833.421873.67116790276-40.2511679027626
781635.251732.83946066238-97.589460662383
791765.91631.87417005499134.025829945005
801946.811801.11047532871145.699524671287
811995.371916.3867259225978.983274077409
8220421959.6700771363682.32992286364
831940.491977.68739397482-37.1973939748218
842065.811942.20142822077123.608571779232
852214.952105.41933306558109.530666934419
862304.982208.8425852375796.1374147624267
872555.282333.83332997212221.446670027884
882799.432663.73254935437135.697450645627
892811.72843.8723466043-32.1723466042971
902735.72798.96105380288-63.2610538028835
912745.882814.46362948746-68.5836294874634
922720.252792.0368219694-71.7868219694018
932638.532730.50374825098-91.9737482509784
942659.812659.572413392450.237586607545997
952641.652663.91840467373-22.2684046737264
962604.422679.41992016769-74.9999201676892
972892.632683.72544724087208.90455275913
982915.023006.14246334576-91.1224633457558
992845.262965.75871842271-120.49871842271
1002794.832940.76805300687-145.938053006872
1012848.962844.093490070774.86650992922868
1022833.182814.613008505118.5669914948993
1032995.552945.7964145537449.7535854462584
1042987.13061.27195663224-74.1719566322435
1053013.242983.7621061641229.4778938358788
1063110.523076.3602994753734.159700524627
1073045.783141.6725783741-95.8925783741013
1083032.933170.03247039503-137.102470395026
1093142.953130.0810337140412.868966285963
1103012.613215.55169409775-202.941694097748
1112897.063024.84982791778-127.789827917784
1122863.362979.2181566094-115.858156609396
1132882.62948.08469363016-65.4846936301613
1142767.632886.90873411199-119.278734111993
1152803.472884.03721998166-80.567219981657
1163030.292943.9823379700186.3076620299932
1173210.523111.3655274105299.1544725894839
1183249.573232.5086530041917.0613469958054
1192999.933222.0270719571-222.097071957098
1203181.963060.31747045725121.642529542751
1213053.053294.27060727036-241.220607270362
1223092.713057.0749792368835.6350207631186
1233165.263132.7617753783132.49822462169
1243173.953181.80721821289-7.85721821289137
1253280.373128.45373017278151.916269827222
1263288.183163.97081792921124.209182070795
1273411.133216.68322204783194.446777952173
1283484.743298.03463367133186.70536632867
1293361.133306.2326247869954.8973752130068
1303230.663194.3333365935536.3266634064473
1313006.843040.56073411741-33.720734117409
1323149.92874.44644042819275.453559571811
1333403.133067.21656677318335.913433226817
1343564.953315.31490667249.635093330002
1353327.73423.80820567826-96.1082056782637
1363141.123198.17848633517-57.0584863351692
1373064.423050.4529185986113.9670814013898
1382880.42913.52888842161-33.1288884216134
1392661.392813.08074213255-151.690742132549
1402504.672532.58673780743-27.9167378074278
1412450.412412.2058394569238.2041605430753
1422354.322384.26533795801-29.945337958006
1432401.332285.24628083222116.083719167783
1442394.362450.68101267712-56.3210126771239
1452409.362383.139382545526.2206174545002
1462525.562412.53394000328113.026059996723
1472346.92514.33703692578-167.437036925776
1482250.272292.69287878474-42.4228787847382
1492152.182185.0165022864-32.8365022864033
1502154.872038.94769763982115.922302360184
1512097.762171.73630691704-73.9763069170388
1521989.312033.50591120422-44.1959112042209
1531877.11907.52772959679-30.4277295967861
1541852.131834.377512098917.752487901105
1551795.651805.45132722119-9.80132722119151
1561751.011812.21086532155-61.2008653215477
1571745.741731.4050717547514.3349282452514
1581703.451729.40753203478-25.9575320347792
1591748.091699.6664509763448.4235490236638
1601734.11778.90514115895-44.8051411589505
1611711.741725.4022030362-13.6622030361979
1621690.61661.4477781064529.1522218935463
1631665.51756.2660243397-90.7660243396975
1641631.591641.95805136251-10.3680513625139
1651538.091580.56303892989-42.4730389298938
1661452.461501.5717948379-49.111794837901
1671429.121411.6194955356217.500504464379
1681471.161470.431701668690.728298331314978
1691475.571457.8109483350217.7590516649845
1701464.651440.9923106187123.6576893812863
1711433.751416.1341383592817.6158616407206
1721451.041414.1758587529136.8641412470865
1731365.411405.87962067577-40.469620675774
1741299.881248.1332152414951.7467847585055
1751349.031316.9247418401332.105258159869
1761368.431326.266651556342.1633484436966






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
260.2333656745542590.4667313491085180.766634325445741
270.3696428602917970.7392857205835950.630357139708203
280.5317505549841650.936498890031670.468249445015835
290.4065651092164890.8131302184329770.593434890783511
300.299606489852270.5992129797045390.70039351014773
310.221994876168250.4439897523365010.77800512383175
320.1696274562833520.3392549125667040.830372543716648
330.134928940759120.269857881518240.86507105924088
340.1138373366974720.2276746733949440.886162663302528
350.085756338294770.171512676589540.91424366170523
360.05950296489856130.1190059297971230.940497035101439
370.03774675162472980.07549350324945960.96225324837527
380.03417163559428760.06834327118857530.965828364405712
390.0795977360774480.1591954721548960.920402263922552
400.1455358276923970.2910716553847940.854464172307603
410.115682353508440.231364707016880.88431764649156
420.1477996485846670.2955992971693330.852200351415333
430.1115116863328340.2230233726656680.888488313667166
440.1126823414152420.2253646828304830.887317658584758
450.1053035865567760.2106071731135520.894696413443224
460.07944123620579520.158882472411590.920558763794205
470.1034984268532540.2069968537065080.896501573146746
480.1096598593413160.2193197186826320.890340140658684
490.125159426558060.2503188531161190.87484057344194
500.09705589110952360.1941117822190470.902944108890476
510.07581583306622610.1516316661324520.924184166933774
520.06990344241630610.1398068848326120.930096557583694
530.06469816121147310.1293963224229460.935301838788527
540.09823993197562590.1964798639512520.901760068024374
550.0850143123281480.1700286246562960.914985687671852
560.06554700014056590.1310940002811320.934452999859434
570.05434109222524390.1086821844504880.945658907774756
580.04615368438940020.09230736877880040.9538463156106
590.04794145933533570.09588291867067130.952058540664664
600.06498581483378620.1299716296675720.935014185166214
610.0895555765045750.179111153009150.910444423495425
620.12394129476290.2478825895257990.8760587052371
630.19848402131720.3969680426344010.8015159786828
640.240696363206110.481392726412220.75930363679389
650.2723491107690390.5446982215380770.727650889230961
660.2480948040531260.4961896081062530.751905195946874
670.2097793548704070.4195587097408140.790220645129593
680.2179656699547080.4359313399094150.782034330045292
690.1957073422561940.3914146845123880.804292657743806
700.1620331923954410.3240663847908810.83796680760456
710.1556581676303510.3113163352607020.84434183236965
720.1536456077326680.3072912154653360.846354392267332
730.1425359683304490.2850719366608980.857464031669551
740.1352408273428090.2704816546856170.864759172657191
750.1148981209005970.2297962418011940.885101879099403
760.1404774562123420.2809549124246840.859522543787658
770.1542389744439090.3084779488878190.84576102555609
780.1688473817889030.3376947635778070.831152618211097
790.265767405346090.5315348106921810.73423259465391
800.5183857908987490.9632284182025020.481614209101251
810.6976149119825350.604770176034930.302385088017465
820.7628293026615340.4743413946769320.237170697338466
830.773424121718920.4531517565621610.22657587828108
840.7877804359720330.4244391280559350.212219564027967
850.8057810608255890.3884378783488220.194218939174411
860.8010532540114530.3978934919770940.198946745988547
870.8046014442278760.3907971115442490.195398555772124
880.7942319897448880.4115360205102240.205768010255112
890.7781536054012460.4436927891975080.221846394598754
900.8157760681657520.3684478636684960.184223931834248
910.8005974568751470.3988050862497050.199402543124853
920.780059360587980.4398812788240390.219940639412019
930.7602645350734680.4794709298530630.239735464926532
940.7241062366732450.5517875266535090.275893763326755
950.6919673856703440.6160652286593120.308032614329656
960.6802216320987980.6395567358024040.319778367901202
970.7207927245516020.5584145508967970.279207275448398
980.7278280952057060.5443438095885880.272171904794294
990.7896860401767880.4206279196464230.210313959823212
1000.8769809814994090.2460380370011830.123019018500591
1010.8615579112528370.2768841774943270.138442088747163
1020.8370496665252850.3259006669494290.162950333474715
1030.8148114658097750.3703770683804490.185188534190225
1040.8233049215691250.3533901568617490.176695078430875
1050.7897676880712990.4204646238574020.210232311928701
1060.7873966619145960.4252066761708070.212603338085404
1070.7884084115722240.4231831768555520.211591588427776
1080.8020634007296950.3958731985406090.197936599270305
1090.7977798412924040.4044403174151910.202220158707596
1100.838014774170130.3239704516597410.161985225829871
1110.8364074041699140.3271851916601730.163592595830086
1120.8316078134980420.3367843730039150.168392186501958
1130.815311133734910.3693777325301810.184688866265091
1140.8552077000700540.2895845998598920.144792299929946
1150.8460417175137040.3079165649725920.153958282486296
1160.8279275421266580.3441449157466840.172072457873342
1170.8303351415150730.3393297169698550.169664858484927
1180.8004780298812520.3990439402374950.199521970118748
1190.8783948006874030.2432103986251950.121605199312597
1200.8993841971430040.2012316057139920.100615802856996
1210.9674698040705560.06506039185888870.0325301959294444
1220.9592420521449340.08151589571013140.0407579478550657
1230.9449078446403330.1101843107193330.0550921553596666
1240.9421404505321750.115719098935650.0578595494678251
1250.9364278662206960.1271442675586080.0635721337793038
1260.9376817683362650.124636463327470.0623182316637352
1270.9450339368159950.1099321263680090.0549660631840046
1280.9445252179021050.1109495641957910.0554747820978953
1290.9249374384393580.1501251231212840.0750625615606418
1300.9024222772777970.1951554454444060.097577722722203
1310.930693808304920.1386123833901590.0693061916950793
1320.9744303881096050.05113922378079080.0255696118903954
1330.996928429398620.006143141202760620.00307157060138031
1340.999975462565034.90748699407833e-052.45374349703917e-05
1350.9999998396190393.2076192275549e-071.60380961377745e-07
1360.9999999972984295.40314262823092e-092.70157131411546e-09
1370.9999999925440351.4911930388853e-087.45596519442648e-09
1380.99999997600584.7988400345572e-082.3994200172786e-08
1390.9999999415406361.16918728134318e-075.84593640671591e-08
1400.9999999056956351.8860873044122e-079.43043652206102e-08
1410.999999732291815.3541638007725e-072.67708190038625e-07
1420.9999987777462462.44450750808191e-061.22225375404095e-06
1430.9999973873393855.22532123064641e-062.61266061532321e-06
1440.9999910351668931.79296662132601e-058.96483310663005e-06
1450.9999780759555444.38480889128657e-052.19240444564329e-05
1460.9999812990218323.74019563356824e-051.87009781678412e-05
1470.999917527581970.0001649448360609238.24724180304616e-05
1480.9998865955311480.0002268089377036590.00011340446885183
1490.9999390922147050.0001218155705890536.09077852945267e-05
1500.9992732813515170.001453437296966590.000726718648483293

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
26 & 0.233365674554259 & 0.466731349108518 & 0.766634325445741 \tabularnewline
27 & 0.369642860291797 & 0.739285720583595 & 0.630357139708203 \tabularnewline
28 & 0.531750554984165 & 0.93649889003167 & 0.468249445015835 \tabularnewline
29 & 0.406565109216489 & 0.813130218432977 & 0.593434890783511 \tabularnewline
30 & 0.29960648985227 & 0.599212979704539 & 0.70039351014773 \tabularnewline
31 & 0.22199487616825 & 0.443989752336501 & 0.77800512383175 \tabularnewline
32 & 0.169627456283352 & 0.339254912566704 & 0.830372543716648 \tabularnewline
33 & 0.13492894075912 & 0.26985788151824 & 0.86507105924088 \tabularnewline
34 & 0.113837336697472 & 0.227674673394944 & 0.886162663302528 \tabularnewline
35 & 0.08575633829477 & 0.17151267658954 & 0.91424366170523 \tabularnewline
36 & 0.0595029648985613 & 0.119005929797123 & 0.940497035101439 \tabularnewline
37 & 0.0377467516247298 & 0.0754935032494596 & 0.96225324837527 \tabularnewline
38 & 0.0341716355942876 & 0.0683432711885753 & 0.965828364405712 \tabularnewline
39 & 0.079597736077448 & 0.159195472154896 & 0.920402263922552 \tabularnewline
40 & 0.145535827692397 & 0.291071655384794 & 0.854464172307603 \tabularnewline
41 & 0.11568235350844 & 0.23136470701688 & 0.88431764649156 \tabularnewline
42 & 0.147799648584667 & 0.295599297169333 & 0.852200351415333 \tabularnewline
43 & 0.111511686332834 & 0.223023372665668 & 0.888488313667166 \tabularnewline
44 & 0.112682341415242 & 0.225364682830483 & 0.887317658584758 \tabularnewline
45 & 0.105303586556776 & 0.210607173113552 & 0.894696413443224 \tabularnewline
46 & 0.0794412362057952 & 0.15888247241159 & 0.920558763794205 \tabularnewline
47 & 0.103498426853254 & 0.206996853706508 & 0.896501573146746 \tabularnewline
48 & 0.109659859341316 & 0.219319718682632 & 0.890340140658684 \tabularnewline
49 & 0.12515942655806 & 0.250318853116119 & 0.87484057344194 \tabularnewline
50 & 0.0970558911095236 & 0.194111782219047 & 0.902944108890476 \tabularnewline
51 & 0.0758158330662261 & 0.151631666132452 & 0.924184166933774 \tabularnewline
52 & 0.0699034424163061 & 0.139806884832612 & 0.930096557583694 \tabularnewline
53 & 0.0646981612114731 & 0.129396322422946 & 0.935301838788527 \tabularnewline
54 & 0.0982399319756259 & 0.196479863951252 & 0.901760068024374 \tabularnewline
55 & 0.085014312328148 & 0.170028624656296 & 0.914985687671852 \tabularnewline
56 & 0.0655470001405659 & 0.131094000281132 & 0.934452999859434 \tabularnewline
57 & 0.0543410922252439 & 0.108682184450488 & 0.945658907774756 \tabularnewline
58 & 0.0461536843894002 & 0.0923073687788004 & 0.9538463156106 \tabularnewline
59 & 0.0479414593353357 & 0.0958829186706713 & 0.952058540664664 \tabularnewline
60 & 0.0649858148337862 & 0.129971629667572 & 0.935014185166214 \tabularnewline
61 & 0.089555576504575 & 0.17911115300915 & 0.910444423495425 \tabularnewline
62 & 0.1239412947629 & 0.247882589525799 & 0.8760587052371 \tabularnewline
63 & 0.1984840213172 & 0.396968042634401 & 0.8015159786828 \tabularnewline
64 & 0.24069636320611 & 0.48139272641222 & 0.75930363679389 \tabularnewline
65 & 0.272349110769039 & 0.544698221538077 & 0.727650889230961 \tabularnewline
66 & 0.248094804053126 & 0.496189608106253 & 0.751905195946874 \tabularnewline
67 & 0.209779354870407 & 0.419558709740814 & 0.790220645129593 \tabularnewline
68 & 0.217965669954708 & 0.435931339909415 & 0.782034330045292 \tabularnewline
69 & 0.195707342256194 & 0.391414684512388 & 0.804292657743806 \tabularnewline
70 & 0.162033192395441 & 0.324066384790881 & 0.83796680760456 \tabularnewline
71 & 0.155658167630351 & 0.311316335260702 & 0.84434183236965 \tabularnewline
72 & 0.153645607732668 & 0.307291215465336 & 0.846354392267332 \tabularnewline
73 & 0.142535968330449 & 0.285071936660898 & 0.857464031669551 \tabularnewline
74 & 0.135240827342809 & 0.270481654685617 & 0.864759172657191 \tabularnewline
75 & 0.114898120900597 & 0.229796241801194 & 0.885101879099403 \tabularnewline
76 & 0.140477456212342 & 0.280954912424684 & 0.859522543787658 \tabularnewline
77 & 0.154238974443909 & 0.308477948887819 & 0.84576102555609 \tabularnewline
78 & 0.168847381788903 & 0.337694763577807 & 0.831152618211097 \tabularnewline
79 & 0.26576740534609 & 0.531534810692181 & 0.73423259465391 \tabularnewline
80 & 0.518385790898749 & 0.963228418202502 & 0.481614209101251 \tabularnewline
81 & 0.697614911982535 & 0.60477017603493 & 0.302385088017465 \tabularnewline
82 & 0.762829302661534 & 0.474341394676932 & 0.237170697338466 \tabularnewline
83 & 0.77342412171892 & 0.453151756562161 & 0.22657587828108 \tabularnewline
84 & 0.787780435972033 & 0.424439128055935 & 0.212219564027967 \tabularnewline
85 & 0.805781060825589 & 0.388437878348822 & 0.194218939174411 \tabularnewline
86 & 0.801053254011453 & 0.397893491977094 & 0.198946745988547 \tabularnewline
87 & 0.804601444227876 & 0.390797111544249 & 0.195398555772124 \tabularnewline
88 & 0.794231989744888 & 0.411536020510224 & 0.205768010255112 \tabularnewline
89 & 0.778153605401246 & 0.443692789197508 & 0.221846394598754 \tabularnewline
90 & 0.815776068165752 & 0.368447863668496 & 0.184223931834248 \tabularnewline
91 & 0.800597456875147 & 0.398805086249705 & 0.199402543124853 \tabularnewline
92 & 0.78005936058798 & 0.439881278824039 & 0.219940639412019 \tabularnewline
93 & 0.760264535073468 & 0.479470929853063 & 0.239735464926532 \tabularnewline
94 & 0.724106236673245 & 0.551787526653509 & 0.275893763326755 \tabularnewline
95 & 0.691967385670344 & 0.616065228659312 & 0.308032614329656 \tabularnewline
96 & 0.680221632098798 & 0.639556735802404 & 0.319778367901202 \tabularnewline
97 & 0.720792724551602 & 0.558414550896797 & 0.279207275448398 \tabularnewline
98 & 0.727828095205706 & 0.544343809588588 & 0.272171904794294 \tabularnewline
99 & 0.789686040176788 & 0.420627919646423 & 0.210313959823212 \tabularnewline
100 & 0.876980981499409 & 0.246038037001183 & 0.123019018500591 \tabularnewline
101 & 0.861557911252837 & 0.276884177494327 & 0.138442088747163 \tabularnewline
102 & 0.837049666525285 & 0.325900666949429 & 0.162950333474715 \tabularnewline
103 & 0.814811465809775 & 0.370377068380449 & 0.185188534190225 \tabularnewline
104 & 0.823304921569125 & 0.353390156861749 & 0.176695078430875 \tabularnewline
105 & 0.789767688071299 & 0.420464623857402 & 0.210232311928701 \tabularnewline
106 & 0.787396661914596 & 0.425206676170807 & 0.212603338085404 \tabularnewline
107 & 0.788408411572224 & 0.423183176855552 & 0.211591588427776 \tabularnewline
108 & 0.802063400729695 & 0.395873198540609 & 0.197936599270305 \tabularnewline
109 & 0.797779841292404 & 0.404440317415191 & 0.202220158707596 \tabularnewline
110 & 0.83801477417013 & 0.323970451659741 & 0.161985225829871 \tabularnewline
111 & 0.836407404169914 & 0.327185191660173 & 0.163592595830086 \tabularnewline
112 & 0.831607813498042 & 0.336784373003915 & 0.168392186501958 \tabularnewline
113 & 0.81531113373491 & 0.369377732530181 & 0.184688866265091 \tabularnewline
114 & 0.855207700070054 & 0.289584599859892 & 0.144792299929946 \tabularnewline
115 & 0.846041717513704 & 0.307916564972592 & 0.153958282486296 \tabularnewline
116 & 0.827927542126658 & 0.344144915746684 & 0.172072457873342 \tabularnewline
117 & 0.830335141515073 & 0.339329716969855 & 0.169664858484927 \tabularnewline
118 & 0.800478029881252 & 0.399043940237495 & 0.199521970118748 \tabularnewline
119 & 0.878394800687403 & 0.243210398625195 & 0.121605199312597 \tabularnewline
120 & 0.899384197143004 & 0.201231605713992 & 0.100615802856996 \tabularnewline
121 & 0.967469804070556 & 0.0650603918588887 & 0.0325301959294444 \tabularnewline
122 & 0.959242052144934 & 0.0815158957101314 & 0.0407579478550657 \tabularnewline
123 & 0.944907844640333 & 0.110184310719333 & 0.0550921553596666 \tabularnewline
124 & 0.942140450532175 & 0.11571909893565 & 0.0578595494678251 \tabularnewline
125 & 0.936427866220696 & 0.127144267558608 & 0.0635721337793038 \tabularnewline
126 & 0.937681768336265 & 0.12463646332747 & 0.0623182316637352 \tabularnewline
127 & 0.945033936815995 & 0.109932126368009 & 0.0549660631840046 \tabularnewline
128 & 0.944525217902105 & 0.110949564195791 & 0.0554747820978953 \tabularnewline
129 & 0.924937438439358 & 0.150125123121284 & 0.0750625615606418 \tabularnewline
130 & 0.902422277277797 & 0.195155445444406 & 0.097577722722203 \tabularnewline
131 & 0.93069380830492 & 0.138612383390159 & 0.0693061916950793 \tabularnewline
132 & 0.974430388109605 & 0.0511392237807908 & 0.0255696118903954 \tabularnewline
133 & 0.99692842939862 & 0.00614314120276062 & 0.00307157060138031 \tabularnewline
134 & 0.99997546256503 & 4.90748699407833e-05 & 2.45374349703917e-05 \tabularnewline
135 & 0.999999839619039 & 3.2076192275549e-07 & 1.60380961377745e-07 \tabularnewline
136 & 0.999999997298429 & 5.40314262823092e-09 & 2.70157131411546e-09 \tabularnewline
137 & 0.999999992544035 & 1.4911930388853e-08 & 7.45596519442648e-09 \tabularnewline
138 & 0.9999999760058 & 4.7988400345572e-08 & 2.3994200172786e-08 \tabularnewline
139 & 0.999999941540636 & 1.16918728134318e-07 & 5.84593640671591e-08 \tabularnewline
140 & 0.999999905695635 & 1.8860873044122e-07 & 9.43043652206102e-08 \tabularnewline
141 & 0.99999973229181 & 5.3541638007725e-07 & 2.67708190038625e-07 \tabularnewline
142 & 0.999998777746246 & 2.44450750808191e-06 & 1.22225375404095e-06 \tabularnewline
143 & 0.999997387339385 & 5.22532123064641e-06 & 2.61266061532321e-06 \tabularnewline
144 & 0.999991035166893 & 1.79296662132601e-05 & 8.96483310663005e-06 \tabularnewline
145 & 0.999978075955544 & 4.38480889128657e-05 & 2.19240444564329e-05 \tabularnewline
146 & 0.999981299021832 & 3.74019563356824e-05 & 1.87009781678412e-05 \tabularnewline
147 & 0.99991752758197 & 0.000164944836060923 & 8.24724180304616e-05 \tabularnewline
148 & 0.999886595531148 & 0.000226808937703659 & 0.00011340446885183 \tabularnewline
149 & 0.999939092214705 & 0.000121815570589053 & 6.09077852945267e-05 \tabularnewline
150 & 0.999273281351517 & 0.00145343729696659 & 0.000726718648483293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105504&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]26[/C][C]0.233365674554259[/C][C]0.466731349108518[/C][C]0.766634325445741[/C][/ROW]
[ROW][C]27[/C][C]0.369642860291797[/C][C]0.739285720583595[/C][C]0.630357139708203[/C][/ROW]
[ROW][C]28[/C][C]0.531750554984165[/C][C]0.93649889003167[/C][C]0.468249445015835[/C][/ROW]
[ROW][C]29[/C][C]0.406565109216489[/C][C]0.813130218432977[/C][C]0.593434890783511[/C][/ROW]
[ROW][C]30[/C][C]0.29960648985227[/C][C]0.599212979704539[/C][C]0.70039351014773[/C][/ROW]
[ROW][C]31[/C][C]0.22199487616825[/C][C]0.443989752336501[/C][C]0.77800512383175[/C][/ROW]
[ROW][C]32[/C][C]0.169627456283352[/C][C]0.339254912566704[/C][C]0.830372543716648[/C][/ROW]
[ROW][C]33[/C][C]0.13492894075912[/C][C]0.26985788151824[/C][C]0.86507105924088[/C][/ROW]
[ROW][C]34[/C][C]0.113837336697472[/C][C]0.227674673394944[/C][C]0.886162663302528[/C][/ROW]
[ROW][C]35[/C][C]0.08575633829477[/C][C]0.17151267658954[/C][C]0.91424366170523[/C][/ROW]
[ROW][C]36[/C][C]0.0595029648985613[/C][C]0.119005929797123[/C][C]0.940497035101439[/C][/ROW]
[ROW][C]37[/C][C]0.0377467516247298[/C][C]0.0754935032494596[/C][C]0.96225324837527[/C][/ROW]
[ROW][C]38[/C][C]0.0341716355942876[/C][C]0.0683432711885753[/C][C]0.965828364405712[/C][/ROW]
[ROW][C]39[/C][C]0.079597736077448[/C][C]0.159195472154896[/C][C]0.920402263922552[/C][/ROW]
[ROW][C]40[/C][C]0.145535827692397[/C][C]0.291071655384794[/C][C]0.854464172307603[/C][/ROW]
[ROW][C]41[/C][C]0.11568235350844[/C][C]0.23136470701688[/C][C]0.88431764649156[/C][/ROW]
[ROW][C]42[/C][C]0.147799648584667[/C][C]0.295599297169333[/C][C]0.852200351415333[/C][/ROW]
[ROW][C]43[/C][C]0.111511686332834[/C][C]0.223023372665668[/C][C]0.888488313667166[/C][/ROW]
[ROW][C]44[/C][C]0.112682341415242[/C][C]0.225364682830483[/C][C]0.887317658584758[/C][/ROW]
[ROW][C]45[/C][C]0.105303586556776[/C][C]0.210607173113552[/C][C]0.894696413443224[/C][/ROW]
[ROW][C]46[/C][C]0.0794412362057952[/C][C]0.15888247241159[/C][C]0.920558763794205[/C][/ROW]
[ROW][C]47[/C][C]0.103498426853254[/C][C]0.206996853706508[/C][C]0.896501573146746[/C][/ROW]
[ROW][C]48[/C][C]0.109659859341316[/C][C]0.219319718682632[/C][C]0.890340140658684[/C][/ROW]
[ROW][C]49[/C][C]0.12515942655806[/C][C]0.250318853116119[/C][C]0.87484057344194[/C][/ROW]
[ROW][C]50[/C][C]0.0970558911095236[/C][C]0.194111782219047[/C][C]0.902944108890476[/C][/ROW]
[ROW][C]51[/C][C]0.0758158330662261[/C][C]0.151631666132452[/C][C]0.924184166933774[/C][/ROW]
[ROW][C]52[/C][C]0.0699034424163061[/C][C]0.139806884832612[/C][C]0.930096557583694[/C][/ROW]
[ROW][C]53[/C][C]0.0646981612114731[/C][C]0.129396322422946[/C][C]0.935301838788527[/C][/ROW]
[ROW][C]54[/C][C]0.0982399319756259[/C][C]0.196479863951252[/C][C]0.901760068024374[/C][/ROW]
[ROW][C]55[/C][C]0.085014312328148[/C][C]0.170028624656296[/C][C]0.914985687671852[/C][/ROW]
[ROW][C]56[/C][C]0.0655470001405659[/C][C]0.131094000281132[/C][C]0.934452999859434[/C][/ROW]
[ROW][C]57[/C][C]0.0543410922252439[/C][C]0.108682184450488[/C][C]0.945658907774756[/C][/ROW]
[ROW][C]58[/C][C]0.0461536843894002[/C][C]0.0923073687788004[/C][C]0.9538463156106[/C][/ROW]
[ROW][C]59[/C][C]0.0479414593353357[/C][C]0.0958829186706713[/C][C]0.952058540664664[/C][/ROW]
[ROW][C]60[/C][C]0.0649858148337862[/C][C]0.129971629667572[/C][C]0.935014185166214[/C][/ROW]
[ROW][C]61[/C][C]0.089555576504575[/C][C]0.17911115300915[/C][C]0.910444423495425[/C][/ROW]
[ROW][C]62[/C][C]0.1239412947629[/C][C]0.247882589525799[/C][C]0.8760587052371[/C][/ROW]
[ROW][C]63[/C][C]0.1984840213172[/C][C]0.396968042634401[/C][C]0.8015159786828[/C][/ROW]
[ROW][C]64[/C][C]0.24069636320611[/C][C]0.48139272641222[/C][C]0.75930363679389[/C][/ROW]
[ROW][C]65[/C][C]0.272349110769039[/C][C]0.544698221538077[/C][C]0.727650889230961[/C][/ROW]
[ROW][C]66[/C][C]0.248094804053126[/C][C]0.496189608106253[/C][C]0.751905195946874[/C][/ROW]
[ROW][C]67[/C][C]0.209779354870407[/C][C]0.419558709740814[/C][C]0.790220645129593[/C][/ROW]
[ROW][C]68[/C][C]0.217965669954708[/C][C]0.435931339909415[/C][C]0.782034330045292[/C][/ROW]
[ROW][C]69[/C][C]0.195707342256194[/C][C]0.391414684512388[/C][C]0.804292657743806[/C][/ROW]
[ROW][C]70[/C][C]0.162033192395441[/C][C]0.324066384790881[/C][C]0.83796680760456[/C][/ROW]
[ROW][C]71[/C][C]0.155658167630351[/C][C]0.311316335260702[/C][C]0.84434183236965[/C][/ROW]
[ROW][C]72[/C][C]0.153645607732668[/C][C]0.307291215465336[/C][C]0.846354392267332[/C][/ROW]
[ROW][C]73[/C][C]0.142535968330449[/C][C]0.285071936660898[/C][C]0.857464031669551[/C][/ROW]
[ROW][C]74[/C][C]0.135240827342809[/C][C]0.270481654685617[/C][C]0.864759172657191[/C][/ROW]
[ROW][C]75[/C][C]0.114898120900597[/C][C]0.229796241801194[/C][C]0.885101879099403[/C][/ROW]
[ROW][C]76[/C][C]0.140477456212342[/C][C]0.280954912424684[/C][C]0.859522543787658[/C][/ROW]
[ROW][C]77[/C][C]0.154238974443909[/C][C]0.308477948887819[/C][C]0.84576102555609[/C][/ROW]
[ROW][C]78[/C][C]0.168847381788903[/C][C]0.337694763577807[/C][C]0.831152618211097[/C][/ROW]
[ROW][C]79[/C][C]0.26576740534609[/C][C]0.531534810692181[/C][C]0.73423259465391[/C][/ROW]
[ROW][C]80[/C][C]0.518385790898749[/C][C]0.963228418202502[/C][C]0.481614209101251[/C][/ROW]
[ROW][C]81[/C][C]0.697614911982535[/C][C]0.60477017603493[/C][C]0.302385088017465[/C][/ROW]
[ROW][C]82[/C][C]0.762829302661534[/C][C]0.474341394676932[/C][C]0.237170697338466[/C][/ROW]
[ROW][C]83[/C][C]0.77342412171892[/C][C]0.453151756562161[/C][C]0.22657587828108[/C][/ROW]
[ROW][C]84[/C][C]0.787780435972033[/C][C]0.424439128055935[/C][C]0.212219564027967[/C][/ROW]
[ROW][C]85[/C][C]0.805781060825589[/C][C]0.388437878348822[/C][C]0.194218939174411[/C][/ROW]
[ROW][C]86[/C][C]0.801053254011453[/C][C]0.397893491977094[/C][C]0.198946745988547[/C][/ROW]
[ROW][C]87[/C][C]0.804601444227876[/C][C]0.390797111544249[/C][C]0.195398555772124[/C][/ROW]
[ROW][C]88[/C][C]0.794231989744888[/C][C]0.411536020510224[/C][C]0.205768010255112[/C][/ROW]
[ROW][C]89[/C][C]0.778153605401246[/C][C]0.443692789197508[/C][C]0.221846394598754[/C][/ROW]
[ROW][C]90[/C][C]0.815776068165752[/C][C]0.368447863668496[/C][C]0.184223931834248[/C][/ROW]
[ROW][C]91[/C][C]0.800597456875147[/C][C]0.398805086249705[/C][C]0.199402543124853[/C][/ROW]
[ROW][C]92[/C][C]0.78005936058798[/C][C]0.439881278824039[/C][C]0.219940639412019[/C][/ROW]
[ROW][C]93[/C][C]0.760264535073468[/C][C]0.479470929853063[/C][C]0.239735464926532[/C][/ROW]
[ROW][C]94[/C][C]0.724106236673245[/C][C]0.551787526653509[/C][C]0.275893763326755[/C][/ROW]
[ROW][C]95[/C][C]0.691967385670344[/C][C]0.616065228659312[/C][C]0.308032614329656[/C][/ROW]
[ROW][C]96[/C][C]0.680221632098798[/C][C]0.639556735802404[/C][C]0.319778367901202[/C][/ROW]
[ROW][C]97[/C][C]0.720792724551602[/C][C]0.558414550896797[/C][C]0.279207275448398[/C][/ROW]
[ROW][C]98[/C][C]0.727828095205706[/C][C]0.544343809588588[/C][C]0.272171904794294[/C][/ROW]
[ROW][C]99[/C][C]0.789686040176788[/C][C]0.420627919646423[/C][C]0.210313959823212[/C][/ROW]
[ROW][C]100[/C][C]0.876980981499409[/C][C]0.246038037001183[/C][C]0.123019018500591[/C][/ROW]
[ROW][C]101[/C][C]0.861557911252837[/C][C]0.276884177494327[/C][C]0.138442088747163[/C][/ROW]
[ROW][C]102[/C][C]0.837049666525285[/C][C]0.325900666949429[/C][C]0.162950333474715[/C][/ROW]
[ROW][C]103[/C][C]0.814811465809775[/C][C]0.370377068380449[/C][C]0.185188534190225[/C][/ROW]
[ROW][C]104[/C][C]0.823304921569125[/C][C]0.353390156861749[/C][C]0.176695078430875[/C][/ROW]
[ROW][C]105[/C][C]0.789767688071299[/C][C]0.420464623857402[/C][C]0.210232311928701[/C][/ROW]
[ROW][C]106[/C][C]0.787396661914596[/C][C]0.425206676170807[/C][C]0.212603338085404[/C][/ROW]
[ROW][C]107[/C][C]0.788408411572224[/C][C]0.423183176855552[/C][C]0.211591588427776[/C][/ROW]
[ROW][C]108[/C][C]0.802063400729695[/C][C]0.395873198540609[/C][C]0.197936599270305[/C][/ROW]
[ROW][C]109[/C][C]0.797779841292404[/C][C]0.404440317415191[/C][C]0.202220158707596[/C][/ROW]
[ROW][C]110[/C][C]0.83801477417013[/C][C]0.323970451659741[/C][C]0.161985225829871[/C][/ROW]
[ROW][C]111[/C][C]0.836407404169914[/C][C]0.327185191660173[/C][C]0.163592595830086[/C][/ROW]
[ROW][C]112[/C][C]0.831607813498042[/C][C]0.336784373003915[/C][C]0.168392186501958[/C][/ROW]
[ROW][C]113[/C][C]0.81531113373491[/C][C]0.369377732530181[/C][C]0.184688866265091[/C][/ROW]
[ROW][C]114[/C][C]0.855207700070054[/C][C]0.289584599859892[/C][C]0.144792299929946[/C][/ROW]
[ROW][C]115[/C][C]0.846041717513704[/C][C]0.307916564972592[/C][C]0.153958282486296[/C][/ROW]
[ROW][C]116[/C][C]0.827927542126658[/C][C]0.344144915746684[/C][C]0.172072457873342[/C][/ROW]
[ROW][C]117[/C][C]0.830335141515073[/C][C]0.339329716969855[/C][C]0.169664858484927[/C][/ROW]
[ROW][C]118[/C][C]0.800478029881252[/C][C]0.399043940237495[/C][C]0.199521970118748[/C][/ROW]
[ROW][C]119[/C][C]0.878394800687403[/C][C]0.243210398625195[/C][C]0.121605199312597[/C][/ROW]
[ROW][C]120[/C][C]0.899384197143004[/C][C]0.201231605713992[/C][C]0.100615802856996[/C][/ROW]
[ROW][C]121[/C][C]0.967469804070556[/C][C]0.0650603918588887[/C][C]0.0325301959294444[/C][/ROW]
[ROW][C]122[/C][C]0.959242052144934[/C][C]0.0815158957101314[/C][C]0.0407579478550657[/C][/ROW]
[ROW][C]123[/C][C]0.944907844640333[/C][C]0.110184310719333[/C][C]0.0550921553596666[/C][/ROW]
[ROW][C]124[/C][C]0.942140450532175[/C][C]0.11571909893565[/C][C]0.0578595494678251[/C][/ROW]
[ROW][C]125[/C][C]0.936427866220696[/C][C]0.127144267558608[/C][C]0.0635721337793038[/C][/ROW]
[ROW][C]126[/C][C]0.937681768336265[/C][C]0.12463646332747[/C][C]0.0623182316637352[/C][/ROW]
[ROW][C]127[/C][C]0.945033936815995[/C][C]0.109932126368009[/C][C]0.0549660631840046[/C][/ROW]
[ROW][C]128[/C][C]0.944525217902105[/C][C]0.110949564195791[/C][C]0.0554747820978953[/C][/ROW]
[ROW][C]129[/C][C]0.924937438439358[/C][C]0.150125123121284[/C][C]0.0750625615606418[/C][/ROW]
[ROW][C]130[/C][C]0.902422277277797[/C][C]0.195155445444406[/C][C]0.097577722722203[/C][/ROW]
[ROW][C]131[/C][C]0.93069380830492[/C][C]0.138612383390159[/C][C]0.0693061916950793[/C][/ROW]
[ROW][C]132[/C][C]0.974430388109605[/C][C]0.0511392237807908[/C][C]0.0255696118903954[/C][/ROW]
[ROW][C]133[/C][C]0.99692842939862[/C][C]0.00614314120276062[/C][C]0.00307157060138031[/C][/ROW]
[ROW][C]134[/C][C]0.99997546256503[/C][C]4.90748699407833e-05[/C][C]2.45374349703917e-05[/C][/ROW]
[ROW][C]135[/C][C]0.999999839619039[/C][C]3.2076192275549e-07[/C][C]1.60380961377745e-07[/C][/ROW]
[ROW][C]136[/C][C]0.999999997298429[/C][C]5.40314262823092e-09[/C][C]2.70157131411546e-09[/C][/ROW]
[ROW][C]137[/C][C]0.999999992544035[/C][C]1.4911930388853e-08[/C][C]7.45596519442648e-09[/C][/ROW]
[ROW][C]138[/C][C]0.9999999760058[/C][C]4.7988400345572e-08[/C][C]2.3994200172786e-08[/C][/ROW]
[ROW][C]139[/C][C]0.999999941540636[/C][C]1.16918728134318e-07[/C][C]5.84593640671591e-08[/C][/ROW]
[ROW][C]140[/C][C]0.999999905695635[/C][C]1.8860873044122e-07[/C][C]9.43043652206102e-08[/C][/ROW]
[ROW][C]141[/C][C]0.99999973229181[/C][C]5.3541638007725e-07[/C][C]2.67708190038625e-07[/C][/ROW]
[ROW][C]142[/C][C]0.999998777746246[/C][C]2.44450750808191e-06[/C][C]1.22225375404095e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999997387339385[/C][C]5.22532123064641e-06[/C][C]2.61266061532321e-06[/C][/ROW]
[ROW][C]144[/C][C]0.999991035166893[/C][C]1.79296662132601e-05[/C][C]8.96483310663005e-06[/C][/ROW]
[ROW][C]145[/C][C]0.999978075955544[/C][C]4.38480889128657e-05[/C][C]2.19240444564329e-05[/C][/ROW]
[ROW][C]146[/C][C]0.999981299021832[/C][C]3.74019563356824e-05[/C][C]1.87009781678412e-05[/C][/ROW]
[ROW][C]147[/C][C]0.99991752758197[/C][C]0.000164944836060923[/C][C]8.24724180304616e-05[/C][/ROW]
[ROW][C]148[/C][C]0.999886595531148[/C][C]0.000226808937703659[/C][C]0.00011340446885183[/C][/ROW]
[ROW][C]149[/C][C]0.999939092214705[/C][C]0.000121815570589053[/C][C]6.09077852945267e-05[/C][/ROW]
[ROW][C]150[/C][C]0.999273281351517[/C][C]0.00145343729696659[/C][C]0.000726718648483293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105504&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105504&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
260.2333656745542590.4667313491085180.766634325445741
270.3696428602917970.7392857205835950.630357139708203
280.5317505549841650.936498890031670.468249445015835
290.4065651092164890.8131302184329770.593434890783511
300.299606489852270.5992129797045390.70039351014773
310.221994876168250.4439897523365010.77800512383175
320.1696274562833520.3392549125667040.830372543716648
330.134928940759120.269857881518240.86507105924088
340.1138373366974720.2276746733949440.886162663302528
350.085756338294770.171512676589540.91424366170523
360.05950296489856130.1190059297971230.940497035101439
370.03774675162472980.07549350324945960.96225324837527
380.03417163559428760.06834327118857530.965828364405712
390.0795977360774480.1591954721548960.920402263922552
400.1455358276923970.2910716553847940.854464172307603
410.115682353508440.231364707016880.88431764649156
420.1477996485846670.2955992971693330.852200351415333
430.1115116863328340.2230233726656680.888488313667166
440.1126823414152420.2253646828304830.887317658584758
450.1053035865567760.2106071731135520.894696413443224
460.07944123620579520.158882472411590.920558763794205
470.1034984268532540.2069968537065080.896501573146746
480.1096598593413160.2193197186826320.890340140658684
490.125159426558060.2503188531161190.87484057344194
500.09705589110952360.1941117822190470.902944108890476
510.07581583306622610.1516316661324520.924184166933774
520.06990344241630610.1398068848326120.930096557583694
530.06469816121147310.1293963224229460.935301838788527
540.09823993197562590.1964798639512520.901760068024374
550.0850143123281480.1700286246562960.914985687671852
560.06554700014056590.1310940002811320.934452999859434
570.05434109222524390.1086821844504880.945658907774756
580.04615368438940020.09230736877880040.9538463156106
590.04794145933533570.09588291867067130.952058540664664
600.06498581483378620.1299716296675720.935014185166214
610.0895555765045750.179111153009150.910444423495425
620.12394129476290.2478825895257990.8760587052371
630.19848402131720.3969680426344010.8015159786828
640.240696363206110.481392726412220.75930363679389
650.2723491107690390.5446982215380770.727650889230961
660.2480948040531260.4961896081062530.751905195946874
670.2097793548704070.4195587097408140.790220645129593
680.2179656699547080.4359313399094150.782034330045292
690.1957073422561940.3914146845123880.804292657743806
700.1620331923954410.3240663847908810.83796680760456
710.1556581676303510.3113163352607020.84434183236965
720.1536456077326680.3072912154653360.846354392267332
730.1425359683304490.2850719366608980.857464031669551
740.1352408273428090.2704816546856170.864759172657191
750.1148981209005970.2297962418011940.885101879099403
760.1404774562123420.2809549124246840.859522543787658
770.1542389744439090.3084779488878190.84576102555609
780.1688473817889030.3376947635778070.831152618211097
790.265767405346090.5315348106921810.73423259465391
800.5183857908987490.9632284182025020.481614209101251
810.6976149119825350.604770176034930.302385088017465
820.7628293026615340.4743413946769320.237170697338466
830.773424121718920.4531517565621610.22657587828108
840.7877804359720330.4244391280559350.212219564027967
850.8057810608255890.3884378783488220.194218939174411
860.8010532540114530.3978934919770940.198946745988547
870.8046014442278760.3907971115442490.195398555772124
880.7942319897448880.4115360205102240.205768010255112
890.7781536054012460.4436927891975080.221846394598754
900.8157760681657520.3684478636684960.184223931834248
910.8005974568751470.3988050862497050.199402543124853
920.780059360587980.4398812788240390.219940639412019
930.7602645350734680.4794709298530630.239735464926532
940.7241062366732450.5517875266535090.275893763326755
950.6919673856703440.6160652286593120.308032614329656
960.6802216320987980.6395567358024040.319778367901202
970.7207927245516020.5584145508967970.279207275448398
980.7278280952057060.5443438095885880.272171904794294
990.7896860401767880.4206279196464230.210313959823212
1000.8769809814994090.2460380370011830.123019018500591
1010.8615579112528370.2768841774943270.138442088747163
1020.8370496665252850.3259006669494290.162950333474715
1030.8148114658097750.3703770683804490.185188534190225
1040.8233049215691250.3533901568617490.176695078430875
1050.7897676880712990.4204646238574020.210232311928701
1060.7873966619145960.4252066761708070.212603338085404
1070.7884084115722240.4231831768555520.211591588427776
1080.8020634007296950.3958731985406090.197936599270305
1090.7977798412924040.4044403174151910.202220158707596
1100.838014774170130.3239704516597410.161985225829871
1110.8364074041699140.3271851916601730.163592595830086
1120.8316078134980420.3367843730039150.168392186501958
1130.815311133734910.3693777325301810.184688866265091
1140.8552077000700540.2895845998598920.144792299929946
1150.8460417175137040.3079165649725920.153958282486296
1160.8279275421266580.3441449157466840.172072457873342
1170.8303351415150730.3393297169698550.169664858484927
1180.8004780298812520.3990439402374950.199521970118748
1190.8783948006874030.2432103986251950.121605199312597
1200.8993841971430040.2012316057139920.100615802856996
1210.9674698040705560.06506039185888870.0325301959294444
1220.9592420521449340.08151589571013140.0407579478550657
1230.9449078446403330.1101843107193330.0550921553596666
1240.9421404505321750.115719098935650.0578595494678251
1250.9364278662206960.1271442675586080.0635721337793038
1260.9376817683362650.124636463327470.0623182316637352
1270.9450339368159950.1099321263680090.0549660631840046
1280.9445252179021050.1109495641957910.0554747820978953
1290.9249374384393580.1501251231212840.0750625615606418
1300.9024222772777970.1951554454444060.097577722722203
1310.930693808304920.1386123833901590.0693061916950793
1320.9744303881096050.05113922378079080.0255696118903954
1330.996928429398620.006143141202760620.00307157060138031
1340.999975462565034.90748699407833e-052.45374349703917e-05
1350.9999998396190393.2076192275549e-071.60380961377745e-07
1360.9999999972984295.40314262823092e-092.70157131411546e-09
1370.9999999925440351.4911930388853e-087.45596519442648e-09
1380.99999997600584.7988400345572e-082.3994200172786e-08
1390.9999999415406361.16918728134318e-075.84593640671591e-08
1400.9999999056956351.8860873044122e-079.43043652206102e-08
1410.999999732291815.3541638007725e-072.67708190038625e-07
1420.9999987777462462.44450750808191e-061.22225375404095e-06
1430.9999973873393855.22532123064641e-062.61266061532321e-06
1440.9999910351668931.79296662132601e-058.96483310663005e-06
1450.9999780759555444.38480889128657e-052.19240444564329e-05
1460.9999812990218323.74019563356824e-051.87009781678412e-05
1470.999917527581970.0001649448360609238.24724180304616e-05
1480.9998865955311480.0002268089377036590.00011340446885183
1490.9999390922147050.0001218155705890536.09077852945267e-05
1500.9992732813515170.001453437296966590.000726718648483293






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.144NOK
5% type I error level180.144NOK
10% type I error level250.2NOK

\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 & 18 & 0.144 & NOK \tabularnewline
5% type I error level & 18 & 0.144 & NOK \tabularnewline
10% type I error level & 25 & 0.2 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105504&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]18[/C][C]0.144[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.144[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.2[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105504&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105504&T=6

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


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 level180.144NOK
5% type I error level180.144NOK
10% type I error level250.2NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}