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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 01 Dec 2010 21:09:21 +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/01/t1291237757oajlwq929qg3thn.htm/, Retrieved Sun, 05 May 2024 13:44:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104184, Retrieved Sun, 05 May 2024 13:44:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-12-01 21:09:21] [c474a97a96075919a678ad3d2290b00b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2502,66	10169,02	10433,44	24977	-7,9	-15	2,85	0,3
2466,92	9633,83	10238,83	24320	-8,8	-10	2,98	-0,1
2513,17	10066,24	9857,34	22680	-14,2	-12	3,06	-1
2443,27	10302,87	9634,97	22052	-17,8	-11	3,08	-1,2
2293,41	10430,35	9374,63	21467	-18,2	-11	3,3	-0,8
2070,83	9691,12	8679,75	21383	-22,8	-17	3,47	-1,7
2029,6	9810,31	8593	21777	-23,6	-18	3,72	-1,1
2052,02	9304,43	8398,37	21928	-27,6	-19	3,67	-0,4
1864,44	8767,96	7992,12	21814	-29,4	-22	3,82	0,6
1670,07	7764,58	7235,47	22937	-31,8	-24	3,85	0,6
1810,99	7694,78	7690,5	23595	-31,4	-24	3,9	1,9
1905,41	8331,49	8396,2	20830	-27,6	-20	3,99	2,3
1862,83	8460,94	8595,56	19650	-28,8	-25	4,35	2,6
2014,45	8531,45	8614,55	19195	-21,9	-22	4,98	3,1
2197,82	9117,03	9181,73	19644	-13,9	-17	5,46	4,7
2962,34	12123,53	11114,08	18483	-8	-9	5,19	5,5
3047,03	12989,35	11530,75	18079	-2,8	-11	5,03	5,4
3032,6	13168,91	11322,38	19178	-3,3	-13	5,38	5,9
3504,37	14084,6	12056,67	18391	-1,3	-11	5,37	5,8
3801,06	13995,33	12812,48	18441	0,5	-9	4,87	5,2
3857,62	13357,7	12656,63	18584	-1,9	-7	4,7	4,2
3674,4	12602,93	12193,88	20108	2	-3	4,4	4,4
3720,98	13547,84	12419,57	20148	1,7	-3	4,37	3,6
3844,49	13731,31	12538,12	19394	1,9	-6	4,54	3,5
4116,68	15532,18	13406,97	17745	0,1	-4	4,8	3,1
4105,18	15543,76	13200,58	17696	2,4	-8	4,56	2,9
4435,23	16903,36	13901,28	17032	2,3	-1	4,61	2,2
4296,49	16235,39	13557,69	16438	4,7	-2	4,58	1,5
4202,52	16460,95	13239,71	15683	5	-2	4,61	1,1
4562,84	17974,77	13673,28	15594	7,2	-1	4,77	1,4
4621,4	18001,37	13480,21	15713	8,5	1	4,76	1,3
4696,96	17611,14	13407,75	15937	6,8	2	4,5	1,3
4591,27	17460,53	12754,8	16171	5,8	2	4,37	1,8
4356,98	17128,37	12268,53	15928	3,7	-1	4,15	1,8
4502,64	17741,23	12631,48	16348	4,8	1	4,24	1,8
4443,91	17286,32	12512,89	15579	6,1	-1	4,22	1,7
4290,89	16775,08	12377,62	15305	6,9	-8	4,01	1,6
4199,75	16101,07	12185,15	15648	5,7	1	3,93	1,5
4138,52	16519,44	11963,12	14954	6,9	2	3,97	1,2
3970,1	15934,09	11533,59	15137	5,5	-2	3,92	1,2
3862,27	15786,78	11257,35	15839	6,5	-2	3,99	1,6
3701,61	15147,55	11036,89	16050	7,7	-2	4,1	1,6
3570,12	14990,31	10997,97	15168	6,3	-2	4,04	1,9
3801,06	16397,83	11333,88	17064	5,5	-6	3,97	2,2
3895,51	17232,97	11234,68	16005	5,3	-4	3,9	2
3917,96	16311,54	11145,65	14886	3,3	-5	3,66	1,7
3813,06	16187,64	10971,19	14931	2,2	-2	3,44	2,4
3667,03	16102,64	10872,48	14544	0,6	-1	3,27	2,6
3494,17	15650,83	10827,81	13812	0,2	-5	3,24	2,9
3363,99	14368,05	10695,25	13031	-0,7	-9	3,27	2,6
3295,32	13392,79	10324,31	12574	-1,7	-8	2,99	2,5
3277,01	12986,62	10532,54	11964	-3,7	-14	2,77	3,2
3257,16	12204,98	10554,27	11451	-7,6	-10	2,9	3,1
3161,69	11716,87	10545,38	11346	-8,2	-11	2,87	3,1
3097,31	11402,75	10486,64	11353	-7,5	-11	2,84	2,9
3061,26	11082,38	10377,18	10702	-8	-11	3,02	2,5
3119,31	11395,64	10283,19	10646	-6,9	-5	3,19	2,8
3106,22	11809,38	10682,06	10556	-4,2	-2	3,39	3,1
3080,58	11545,71	10723,78	10463	-3,6	-3	3,28	2,6
2981,85	11394,84	10539,51	10407	-1,8	-6	3,28	2,3
2921,44	11068,05	10673,38	10625	-3,2	-6	3,33	2,3
2849,27	10973	10411,75	10872	-1,3	-7	3,51	2,6
2756,76	11028,93	10001,6	10805	0,6	-6	3,65	2,9
2645,64	11079,42	10204,59	10653	1,2	-2	3,76	2
2497,84	10989,34	10032,8	10574	0,4	-2	3,67	2,2
2448,05	11383,89	10152,09	10431	3	-4	3,87	2,4
2454,62	11527,72	10364,91	10383	-0,4	0	3,99	2,3
2407,6	11037,54	10092,96	10296	0	-6	3,9	2,6
2472,81	11950,95	10418,4	10872	-1,3	-4	3,74	1,9
2408,64	11441,08	10323,73	10635	-3,1	-3	3,55	1,1
2440,25	10631,92	10601,61	10297	-4	-1	3,67	1,3
2350,44	10892,76	10540,05	10570	-4,9	-3	3,6	1,6
2196,72	10295,98	10124,63	10662	-4,6	-6	3,82	1,7
2174,56	10205,29	9762,12	10709	-5,4	-6	3,91	1,9
2120,88	10717,13	9682,35	10413	-8,1	-15	3,79	1,6
2093,48	10637,44	9492,49	10846	-9,4	-5	3,73	1,8
2061,41	9884,59	9284,73	10371	-12,6	-11	3,77	1,8
1969,6	9676,31	9154,34	9924	-15,7	-13	3,47	1,5
1959,67	8895,71	9098,03	9828	-17,3	-10	3,18	1,6
1910,43	8145,82	8623,36	9897	-14,4	-9	3,44	1
1833,42	7905,84	8334,59	9721	-16,2	-11	3,81	1,5
1635,25	8169,75	7977,64	10171	-14,9	-18	3,6	1,8
1765,9	8538,47	7916,13	10738	-11	-13	3,42	1,7
1946,81	8570,73	8474,21	10812	-11,5	-9	3,73	1,2
1995,37	8692,94	8526,63	10511	-9,6	-8	4,04	1,4
2042	8721,14	8641,21	10244	-8,8	-4	4,22	1,1
1940,49	8792,5	8048,1	10368	-9,7	-3	4,3	1,3
2065,81	9354,01	8160,67	10457	-8,4	-3	4,28	1,3
2214,95	9751,2	8685,4	10186	-8,4	-3	4,56	1,3
2304,98	10352,27	8616,49	10166	-6,8	-1	4,79	1,3
2555,28	10965,88	9492,44	10827	-5,3	0	4,93	0,9
2799,43	11717,46	10080,48	10997	-5,1	1	5,12	1,3
2811,7	11384,49	10179,35	10940	-6,5	0	5,13	1,8
2735,7	11448,79	10500,98	10756	-7,3	2	5,15	2,7
2745,88	9981,65	9892,56	10893	-10,8	1	4,92	2,6
2720,25	10300,79	9923,81	10236	-10,9	-1	4,79	2,9
2638,53	10496,2	9978,53	9960	-13,4	-8	4,68	2,2
2659,81	10511,22	9721,84	10018	-15,5	-18	4,42	2,1
2641,65	10438,9	9220,75	10063	-15,4	-14	4,53	2,3
2604,42	9996,83	9042,56	10002	-11,9	-4	4,71	2,3
2892,63	11576,21	10314,68	9728	-8	0	4,83	2,7
2915,02	12151,11	10444,5	10002	-7,7	4	5,04	2,6
2845,26	12974,89	10767,2	10177	-6,4	4	5,06	2,9
2794,83	13975,55	10546,82	9948	-5,6	3	5,14	3,1
2848,96	13411,84	10213,97	9394	-5,7	3	5,06	2,8
2833,18	12708,47	10052,6	9308	-0,1	7	5,04	2,1
2995,55	13266,27	10777,22	9155	1,9	8	5,19	2,3
2987,1	13720,95	10682,74	9103	3,6	13	5,22	2,2
3013,24	14452,93	10666,71	9732	5	15	5,4	2,5
3110,52	14760,87	10665,78	9984	4,7	14	5,7	3,1
3045,78	15311,7	10433,56	10137	5,1	14	5,61	3
3032,93	16153,34	10967,87	10070	6,6	10	5,66	3,4
3142,95	16329,89	11014,51	9753	6	16	5,65	2,9
3012,61	16973,38	10654,41	9647	6,2	13	5,63	2,8
2897,06	16969,28	10582,92	9694	8,6	15	5,5	2,7
2863,36	17039,97	10580,27	9736	7,4	13	5,61	2,2
2882,6	19598,93	10947,43	9490	8,6	12	5,3	2,1
2767,63	19834,71	10483,39	9536	9,2	13	5,38	2,2
2803,47	19685,53	10539,68	9787	7,7	11	5,5	1,9
3030,29	18941,6	11281,26	9026	6,4	9	5,35	1,8
3210,52	18409,96	11251,2	9023	8,6	8	4,99	1,9
3249,57	18470,97	10817,9	9113	6,4	8	4,93	1,5
2999,93	17677,9	10394,48	9315	6	5	5,16	1,3
3181,96	17544,22	10714,03	8086	2,6	3	4,87	1,2
3053,05	17671	10935,47	7786	0,1	-2	4,73	0,9
3092,71	18033,25	11052,23	7984	0	0	4,4	0,7
3165,26	17135,96	10704,02	8099	-0,9	-8	3,99	0,7
3173,95	16505,21	10853,87	8388	-0,1	2	3,67	0,8
3280,37	16666,97	10443,5	8490	-1,4	2	3,65	1,2
3288,18	15418,03	9753,63	8444	-7,1	2	3,75	1,2
3411,13	14153,22	9327,78	8241	-6,2	3	3,67	1
3484,74	13830,14	9349,44	7977	-5,6	6	3,68	1
3361,13	14295,79	9018,68		-7,6	1	3,85	0,6
3230,66	14525,87	9005,73		-7,3	1	4,02	0,6
3006,84	13486,9	8164,47		-7,8	-4	3,99	0,9
3149,9	14144,81	7895,51		-3,7	1	4,12	0,8
3403,13	15243,98	8478,52		-0,3	2	4,47	0,4
3564,95	16370,17	9097,14		2	3	4,69	1
3327,7	15231,29	8872,96		2	5	4,77	1,6
3141,12	15514,27	9081,69		3,1	5	4,92	1,9
3064,42	15941,29	9037,44		2,7	3	4,84	1,5
2880,4	16840,31	8709,47		2,4	2	4,77	1
2661,39	16797,69	8323,61		2	3	4,88	0,7
2504,67	15929,69	7808,33		4,1	-1	5	0,4
2450,41	15917,07	7909,82		5,2	-9	5,3	1,1
2354,32	16135,96	7683,23		6	-5	5,5	1,4
2401,33	17274,75	7875,82		5,1	-1	5,44	1,3
2394,36	18233,45	7855,04		3,6	-9	5,36	1,6
2409,36	19081,79	7948,43		0,8	-8	5,32	1,8
2525,56	20152,53	7990,65		1,8	-12	5,26	1,9
2346,9	20482,48	7603,88		0,1	-13	5,42	1,7
2250,27	20021,79	7242,41		-2	-16	5,37	1,6
2152,18	18200,34	6657,9		-4,1	-21	5,32	1,3
2154,87	18255,96	6901,12		-3	-21	5,11	1,5
2097,76	18555,87	6921,03		-3,1	-16	4,82	2
1989,31	18223,28	6707,03		-3,9	-15	4,89	2,3
1877,1	20090,77	6435,87		-4,8	-8	5,05	2,5
1852,13	21021,37	6323,43		-5,1	-8	5,23	2,4
1795,65	21108,13	5996,21		-6,2	-9	5,3	2,5
1751,01	20824,57	5804,8		-6,6	-9	5,69	2
1745,74	20870,31	5685,5		-7,8	-11	5,86	1,9
1703,45	21597,85	5496,26		-10,5	-12	5,96	1,9
1748,09	22204,88	5671,51		-10,8	-13	6,09	1,8
1734,1	21761,31	5600,81		-12,3	-13	6,11	1,9
1711,74	21837,95	5580,18		-13	-12	6,37	2
1690,6	20394,71	5610,95		-12,8	-15	6,45	2
1665,5	20675,4	5518,24		-15,1	-18	6,26	1,9
1631,59	20407,27	5174,59		-14	-16	6,07	2
1538,09	19417,95	5136,72		-12,4	-16	6,4	1,5
1452,46	18111,66	4935,8		-11,8	-20	6,72	1,5
1429,12	17961,87	4761,28		-9,9	-16	6,99	1,2
1471,16	18128,65	4744,66		-9,9	-12	6,94	1,2
1475,57	17410,71	4637,58		-9,4	-12	7,14	1,3
1464,65	16188,69	4684,76		-9,2	-6	7,35	1,2
1433,75	15039,44	4510,76		-7	-5	7,48	1,3
1451,04	16373,21	4392,16		-5,1	-7	7,74	1,4
1365,41	16322,08	4230,64		-2,2	-4	8,1	1,7
1299,88	16433,75	4064,33		0,4	-7	8,29	1,7
1349,03	18065,03	3953,66		4,3	-8	8,26	1,8
1368,43	19036,23	3872,33		3,7	-7	8,41	1,9

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time24 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 24 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104184&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]24 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104184&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104184&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 time24 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -1721.83682309609 + 0.177196980378819Nikkei[t] + 0.101752847975346DJ_Indust[t] + 0.0867712556300353Goudprijs[t] + 0.0560764634016684Conjunct_Seizoenzuiver[t] + 0.0579872921663899Cons_vertrouw[t] + 0.100295528669206Rend_oblig_EUR[t] + 1.01027190454475Alg_consumptie_index_BE[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL_20[t] =  -1721.83682309609 +  0.177196980378819Nikkei[t] +  0.101752847975346DJ_Indust[t] +  0.0867712556300353Goudprijs[t] +  0.0560764634016684Conjunct_Seizoenzuiver[t] +  0.0579872921663899Cons_vertrouw[t] +  0.100295528669206Rend_oblig_EUR[t] +  1.01027190454475Alg_consumptie_index_BE[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104184&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL_20[t] =  -1721.83682309609 +  0.177196980378819Nikkei[t] +  0.101752847975346DJ_Indust[t] +  0.0867712556300353Goudprijs[t] +  0.0560764634016684Conjunct_Seizoenzuiver[t] +  0.0579872921663899Cons_vertrouw[t] +  0.100295528669206Rend_oblig_EUR[t] +  1.01027190454475Alg_consumptie_index_BE[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104184&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104184&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] = -1721.83682309609 + 0.177196980378819Nikkei[t] + 0.101752847975346DJ_Indust[t] + 0.0867712556300353Goudprijs[t] + 0.0560764634016684Conjunct_Seizoenzuiver[t] + 0.0579872921663899Cons_vertrouw[t] + 0.100295528669206Rend_oblig_EUR[t] + 1.01027190454475Alg_consumptie_index_BE[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1721.83682309609331.951655-5.1871e-060
Nikkei0.1771969803788190.01258714.077300
DJ_Indust0.1017528479753460.0219464.63647e-063e-06
Goudprijs0.08677125563003530.0100358.647200
Conjunct_Seizoenzuiver0.05607646340166840.0221062.53670.012080.00604
Cons_vertrouw0.05798729216638990.0209662.76580.0062990.00315
Rend_oblig_EUR0.1002955286692060.0207574.8323e-061e-06
Alg_consumptie_index_BE1.010271904544750.02166746.626600

\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) & -1721.83682309609 & 331.951655 & -5.187 & 1e-06 & 0 \tabularnewline
Nikkei & 0.177196980378819 & 0.012587 & 14.0773 & 0 & 0 \tabularnewline
DJ_Indust & 0.101752847975346 & 0.021946 & 4.6364 & 7e-06 & 3e-06 \tabularnewline
Goudprijs & 0.0867712556300353 & 0.010035 & 8.6472 & 0 & 0 \tabularnewline
Conjunct_Seizoenzuiver & 0.0560764634016684 & 0.022106 & 2.5367 & 0.01208 & 0.00604 \tabularnewline
Cons_vertrouw & 0.0579872921663899 & 0.020966 & 2.7658 & 0.006299 & 0.00315 \tabularnewline
Rend_oblig_EUR & 0.100295528669206 & 0.020757 & 4.832 & 3e-06 & 1e-06 \tabularnewline
Alg_consumptie_index_BE & 1.01027190454475 & 0.021667 & 46.6266 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104184&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]-1721.83682309609[/C][C]331.951655[/C][C]-5.187[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.177196980378819[/C][C]0.012587[/C][C]14.0773[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJ_Indust[/C][C]0.101752847975346[/C][C]0.021946[/C][C]4.6364[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Goudprijs[/C][C]0.0867712556300353[/C][C]0.010035[/C][C]8.6472[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Conjunct_Seizoenzuiver[/C][C]0.0560764634016684[/C][C]0.022106[/C][C]2.5367[/C][C]0.01208[/C][C]0.00604[/C][/ROW]
[ROW][C]Cons_vertrouw[/C][C]0.0579872921663899[/C][C]0.020966[/C][C]2.7658[/C][C]0.006299[/C][C]0.00315[/C][/ROW]
[ROW][C]Rend_oblig_EUR[/C][C]0.100295528669206[/C][C]0.020757[/C][C]4.832[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Alg_consumptie_index_BE[/C][C]1.01027190454475[/C][C]0.021667[/C][C]46.6266[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104184&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104184&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)-1721.83682309609331.951655-5.1871e-060
Nikkei0.1771969803788190.01258714.077300
DJ_Indust0.1017528479753460.0219464.63647e-063e-06
Goudprijs0.08677125563003530.0100358.647200
Conjunct_Seizoenzuiver0.05607646340166840.0221062.53670.012080.00604
Cons_vertrouw0.05798729216638990.0209662.76580.0062990.00315
Rend_oblig_EUR0.1002955286692060.0207574.8323e-061e-06
Alg_consumptie_index_BE1.010271904544750.02166746.626600






Multiple Linear Regression - Regression Statistics
Multiple R0.979163444725556
R-squared0.958761051486818
Adjusted R-squared0.957082722186863
F-TEST (value)571.259198962014
F-TEST (DF numerator)7
F-TEST (DF denominator)172
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation658.324428868461
Sum Squared Residuals74543261.2269374

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.979163444725556 \tabularnewline
R-squared & 0.958761051486818 \tabularnewline
Adjusted R-squared & 0.957082722186863 \tabularnewline
F-TEST (value) & 571.259198962014 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 172 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 658.324428868461 \tabularnewline
Sum Squared Residuals & 74543261.2269374 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104184&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.979163444725556[/C][/ROW]
[ROW][C]R-squared[/C][C]0.958761051486818[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.957082722186863[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]571.259198962014[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]172[/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]658.324428868461[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74543261.2269374[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104184&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104184&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 R0.979163444725556
R-squared0.958761051486818
Adjusted R-squared0.957082722186863
F-TEST (value)571.259198962014
F-TEST (DF numerator)7
F-TEST (DF denominator)172
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation658.324428868461
Sum Squared Residuals74543261.2269374






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12502.663308.27681075174-805.616810751737
22466.923136.48031943005-669.56031943005
32513.173030.65950394978-517.489503949776
42443.272995.12655963047-551.856559630465
52293.412940.86785289644-647.457852896442
62070.832730.38565465812-659.555654658125
72029.62777.39496646785-747.794966467847
82052.022681.47274324348-629.452743243482
91864.442535.93327827107-671.493278271071
101670.072378.34265051992-708.27265051992
111810.992470.71118474596-659.721184745964
121905.412416.27691221111-510.866912211106
131862.832357.09238719484-494.262387194838
142014.452333.16712316204-318.717123162039
152197.822536.00573009389-338.185730093889
162962.343166.20502680379-203.865026803794
173047.033327.02603679705-279.996036797052
183032.63433.39912216993-400.799122169931
193504.373602.20984305739-97.839843057386
203801.063667.19645274011133.863547259889
213857.623549.71451926705307.905480732948
223674.43501.74743066435172.652569335646
233720.983694.7900305503426.1899694496553
243844.493673.69091038848170.799089611522
254116.683937.75880289858178.921197101421
264105.183914.22908350123190.950916498769
274435.234168.52633270433266.703667295668
284296.493963.02707586156333.462924138443
294202.523904.74368119899297.776318801011
304562.844209.88283894925352.957161050747
314621.44205.36347952972416.036520470283
324696.964148.21623224025548.743767759745
334591.274075.82956782747515.440432172531
344356.983946.09325885582410.886741144179
354502.644128.25200907971374.387990920295
364443.913968.70325663175475.206743368254
374290.893840.09050146871450.799498531295
384199.753731.18167779553468.568322204468
394138.523722.33035153144416.189648468561
403970.13590.46586706228379.634132937718
413862.273597.63540052248264.63459947752
423701.613480.32040109246221.289598907545
433570.123372.18403637968197.935963620321
443801.063820.30968058539-19.2496805853900
453895.513866.2050087713329.3049912286678
463917.963596.27701131841321.682988681591
473813.063561.27261317868251.787386821323
483667.033502.73963938577164.290360614234
493494.173354.66410579204139.505894207956
503363.993045.52016443580318.469835564197
513295.322795.18117299094500.138827009063
523277.012691.69165370713585.31834629287
533257.162510.81010340180746.349896598203
543161.692414.20827861337747.481721386631
553097.312353.01178991373744.298210086275
563061.262228.20314535687833.05685464313
573119.312270.01867060783849.291329392168
583106.222376.75740373977729.462596260232
593080.582325.67076809267754.909231907328
602981.852274.95176623748706.898233762519
612921.442249.50986023296671.930139767035
622849.272227.84788252761621.422117472392
632756.762190.6925604339566.067439566099
642645.642206.47219856875439.167801431252
652497.842166.32341024037331.516589759626
662448.052236.21822423588211.831775764125
672454.622279.14678422572175.473215774282
682407.62157.03614394307250.563856056934
692472.812401.3041654430271.5058345569777
702408.642279.88878733909128.751212660911
712440.252135.73407129982304.515928700178
722350.442199.50839661043150.931603389568
732196.722059.43952334129137.280476658711
742174.562010.72757309357163.832426906429
752120.882066.6345500657054.2454499343046
762093.482071.4698908389422.0101091610560
772061.411875.18726942639186.222730573611
781969.61785.60339538811183.996604611885
791959.671633.37987011663326.290129883370
801910.431457.83034152113452.599658478869
811833.421370.97703234852462.442967651481
821635.251420.41648125661214.833518743390
831765.91529.08259040764236.817409592363
841946.811597.73613390794349.073866092058
851995.371599.00479179369396.365208206309
8620421592.48444467135449.515555328647
871940.491555.75582172496384.73417827504
882065.811674.50155151698391.308448483022
892214.951774.78826454403440.161735455968
902304.981872.77760457112432.202395428884
912555.282127.74568545629427.534314543714
922799.432336.00161764704463.428382352961
932811.72282.48432616534529.215673834655
942735.72311.62131350036424.078686499638
952745.882001.24937988741744.630620112585
962720.252004.13954667777716.110453322229
972638.532019.12033285591619.409667144086
982659.811999.87088825824659.939111741762
992641.651940.42401825270701.225981747304
1002604.421839.4603563012764.959643698798
1012892.632225.85502369686666.77497630314
1022915.022364.87925346168550.140746538316
1032845.262559.24918261941286.010817380593
1042794.832694.46515473095100.364845269051
1052848.962512.32102099397336.638979006026
1062833.182363.64057696292469.53942303708
1072995.552523.32443813590472.225561864096
1082987.12590.05389492909397.046105070906
1093013.242773.22217866465240.017821335352
1103110.522850.12138464279260.398615357206
1113045.782937.28612989675108.493870103255
1123032.933135.23737560196-102.307375601958
1133142.953143.56890625009-0.618906250085992
1143012.613211.4886578165-198.878657816500
1152897.063207.70258059712-310.642580597116
1162863.363222.92601304489-359.566013044888
1172882.63692.25703039228-809.657030392284
1182767.633691.01130461346-923.38130461346
1192803.473691.59317666886-888.12317666886
1203030.293568.89103301624-538.601033016236
1213210.523471.49732771806-260.977327718062
1223249.573445.49452475694-195.924524756938
1232999.933279.03313940428-279.103139404279
1243181.963180.781949014781.17805098522014
1253053.053199.00050558914-145.950505589143
1263092.713292.12669790834-199.416697908342
1273165.263107.1214662670458.1385337329572
1283173.953036.37269475295137.577305247046
1293280.373032.45963359848247.910366401525
1303288.182736.65391564390551.526084356104
1313411.132451.48576577379959.644234226211
1323484.742373.741931263641110.99806873636
1333361.134992.53544646112-1631.40544646112
13414525.8713800.5642910627725.30570893727
1358164.477854.69800871457309.771991285429
136-3.7203.676193718251-207.376193718251
137237.0622970173114-35.0622970173114
1384.69441.582150173878-436.892150173878
1391.61204.28770132970-1202.68770132970
1403064.424933.31769407148-1868.89769407148
14116840.3117059.3579682700-219.047968270041
1428323.617910.7376582313412.872341768698
1434.1137.500560354516-133.400560354516
144-9-170.570761580749161.570761580749
1455.5469.044618110266-463.544618110266
1461.31241.16769730366-1239.86769730366
1472409.365019.7862754446-2610.4262754446
14820152.5320621.8869219128-469.356921912819
1497603.887732.81016086258-128.930160862581
150-2115.703833649995-117.703833649995
151-21-131.390742902412110.390742902412
1525.11493.190072963279-488.080072963279
15321068.63651896766-1066.63651896766
1541877.14363.88841956572-2486.78841956572
15521021.3720902.9215809088118.448419091161
1565996.216331.32279622364-335.112796223638
157-6.6147.778716482023-154.378716482023
158-11-56.126709360322245.1267093603222
1595.96705.378980115892-699.418980115892
1601.81286.79379199926-1284.99379199926
1611711.744422.3158898304-2710.5758898304
16220394.7120325.098174668369.611825331747
1635518.245643.41799848654-125.177998486536
164-14-8.9731285585259-5.02687144147409
165-16-314.055764718157298.055764718157
1666.72254.29875746318-247.57875746318
1671.2795.847532504622-794.647532504622
1681475.573313.92597894979-1838.35597894979
16916188.6914445.03590204081743.65409795921
1704510.764440.7134638482370.0465361517735
171-5.1-277.035892321974271.935892321974
172-4-457.242041467639453.242041467639
1738.29212.756489720136-204.466489720136
1741.8796.206968175441-794.406968175441
1752502.663308.27681075172-805.616810751721
1762466.923136.48031943004-669.560319430045
1772513.173030.65950394978-517.489503949779
1782443.272995.12655963046-551.856559630464
1792293.412940.86785289645-647.457852896448
1802070.832730.38565465812-659.55565465812

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2502.66 & 3308.27681075174 & -805.616810751737 \tabularnewline
2 & 2466.92 & 3136.48031943005 & -669.56031943005 \tabularnewline
3 & 2513.17 & 3030.65950394978 & -517.489503949776 \tabularnewline
4 & 2443.27 & 2995.12655963047 & -551.856559630465 \tabularnewline
5 & 2293.41 & 2940.86785289644 & -647.457852896442 \tabularnewline
6 & 2070.83 & 2730.38565465812 & -659.555654658125 \tabularnewline
7 & 2029.6 & 2777.39496646785 & -747.794966467847 \tabularnewline
8 & 2052.02 & 2681.47274324348 & -629.452743243482 \tabularnewline
9 & 1864.44 & 2535.93327827107 & -671.493278271071 \tabularnewline
10 & 1670.07 & 2378.34265051992 & -708.27265051992 \tabularnewline
11 & 1810.99 & 2470.71118474596 & -659.721184745964 \tabularnewline
12 & 1905.41 & 2416.27691221111 & -510.866912211106 \tabularnewline
13 & 1862.83 & 2357.09238719484 & -494.262387194838 \tabularnewline
14 & 2014.45 & 2333.16712316204 & -318.717123162039 \tabularnewline
15 & 2197.82 & 2536.00573009389 & -338.185730093889 \tabularnewline
16 & 2962.34 & 3166.20502680379 & -203.865026803794 \tabularnewline
17 & 3047.03 & 3327.02603679705 & -279.996036797052 \tabularnewline
18 & 3032.6 & 3433.39912216993 & -400.799122169931 \tabularnewline
19 & 3504.37 & 3602.20984305739 & -97.839843057386 \tabularnewline
20 & 3801.06 & 3667.19645274011 & 133.863547259889 \tabularnewline
21 & 3857.62 & 3549.71451926705 & 307.905480732948 \tabularnewline
22 & 3674.4 & 3501.74743066435 & 172.652569335646 \tabularnewline
23 & 3720.98 & 3694.79003055034 & 26.1899694496553 \tabularnewline
24 & 3844.49 & 3673.69091038848 & 170.799089611522 \tabularnewline
25 & 4116.68 & 3937.75880289858 & 178.921197101421 \tabularnewline
26 & 4105.18 & 3914.22908350123 & 190.950916498769 \tabularnewline
27 & 4435.23 & 4168.52633270433 & 266.703667295668 \tabularnewline
28 & 4296.49 & 3963.02707586156 & 333.462924138443 \tabularnewline
29 & 4202.52 & 3904.74368119899 & 297.776318801011 \tabularnewline
30 & 4562.84 & 4209.88283894925 & 352.957161050747 \tabularnewline
31 & 4621.4 & 4205.36347952972 & 416.036520470283 \tabularnewline
32 & 4696.96 & 4148.21623224025 & 548.743767759745 \tabularnewline
33 & 4591.27 & 4075.82956782747 & 515.440432172531 \tabularnewline
34 & 4356.98 & 3946.09325885582 & 410.886741144179 \tabularnewline
35 & 4502.64 & 4128.25200907971 & 374.387990920295 \tabularnewline
36 & 4443.91 & 3968.70325663175 & 475.206743368254 \tabularnewline
37 & 4290.89 & 3840.09050146871 & 450.799498531295 \tabularnewline
38 & 4199.75 & 3731.18167779553 & 468.568322204468 \tabularnewline
39 & 4138.52 & 3722.33035153144 & 416.189648468561 \tabularnewline
40 & 3970.1 & 3590.46586706228 & 379.634132937718 \tabularnewline
41 & 3862.27 & 3597.63540052248 & 264.63459947752 \tabularnewline
42 & 3701.61 & 3480.32040109246 & 221.289598907545 \tabularnewline
43 & 3570.12 & 3372.18403637968 & 197.935963620321 \tabularnewline
44 & 3801.06 & 3820.30968058539 & -19.2496805853900 \tabularnewline
45 & 3895.51 & 3866.20500877133 & 29.3049912286678 \tabularnewline
46 & 3917.96 & 3596.27701131841 & 321.682988681591 \tabularnewline
47 & 3813.06 & 3561.27261317868 & 251.787386821323 \tabularnewline
48 & 3667.03 & 3502.73963938577 & 164.290360614234 \tabularnewline
49 & 3494.17 & 3354.66410579204 & 139.505894207956 \tabularnewline
50 & 3363.99 & 3045.52016443580 & 318.469835564197 \tabularnewline
51 & 3295.32 & 2795.18117299094 & 500.138827009063 \tabularnewline
52 & 3277.01 & 2691.69165370713 & 585.31834629287 \tabularnewline
53 & 3257.16 & 2510.81010340180 & 746.349896598203 \tabularnewline
54 & 3161.69 & 2414.20827861337 & 747.481721386631 \tabularnewline
55 & 3097.31 & 2353.01178991373 & 744.298210086275 \tabularnewline
56 & 3061.26 & 2228.20314535687 & 833.05685464313 \tabularnewline
57 & 3119.31 & 2270.01867060783 & 849.291329392168 \tabularnewline
58 & 3106.22 & 2376.75740373977 & 729.462596260232 \tabularnewline
59 & 3080.58 & 2325.67076809267 & 754.909231907328 \tabularnewline
60 & 2981.85 & 2274.95176623748 & 706.898233762519 \tabularnewline
61 & 2921.44 & 2249.50986023296 & 671.930139767035 \tabularnewline
62 & 2849.27 & 2227.84788252761 & 621.422117472392 \tabularnewline
63 & 2756.76 & 2190.6925604339 & 566.067439566099 \tabularnewline
64 & 2645.64 & 2206.47219856875 & 439.167801431252 \tabularnewline
65 & 2497.84 & 2166.32341024037 & 331.516589759626 \tabularnewline
66 & 2448.05 & 2236.21822423588 & 211.831775764125 \tabularnewline
67 & 2454.62 & 2279.14678422572 & 175.473215774282 \tabularnewline
68 & 2407.6 & 2157.03614394307 & 250.563856056934 \tabularnewline
69 & 2472.81 & 2401.30416544302 & 71.5058345569777 \tabularnewline
70 & 2408.64 & 2279.88878733909 & 128.751212660911 \tabularnewline
71 & 2440.25 & 2135.73407129982 & 304.515928700178 \tabularnewline
72 & 2350.44 & 2199.50839661043 & 150.931603389568 \tabularnewline
73 & 2196.72 & 2059.43952334129 & 137.280476658711 \tabularnewline
74 & 2174.56 & 2010.72757309357 & 163.832426906429 \tabularnewline
75 & 2120.88 & 2066.63455006570 & 54.2454499343046 \tabularnewline
76 & 2093.48 & 2071.46989083894 & 22.0101091610560 \tabularnewline
77 & 2061.41 & 1875.18726942639 & 186.222730573611 \tabularnewline
78 & 1969.6 & 1785.60339538811 & 183.996604611885 \tabularnewline
79 & 1959.67 & 1633.37987011663 & 326.290129883370 \tabularnewline
80 & 1910.43 & 1457.83034152113 & 452.599658478869 \tabularnewline
81 & 1833.42 & 1370.97703234852 & 462.442967651481 \tabularnewline
82 & 1635.25 & 1420.41648125661 & 214.833518743390 \tabularnewline
83 & 1765.9 & 1529.08259040764 & 236.817409592363 \tabularnewline
84 & 1946.81 & 1597.73613390794 & 349.073866092058 \tabularnewline
85 & 1995.37 & 1599.00479179369 & 396.365208206309 \tabularnewline
86 & 2042 & 1592.48444467135 & 449.515555328647 \tabularnewline
87 & 1940.49 & 1555.75582172496 & 384.73417827504 \tabularnewline
88 & 2065.81 & 1674.50155151698 & 391.308448483022 \tabularnewline
89 & 2214.95 & 1774.78826454403 & 440.161735455968 \tabularnewline
90 & 2304.98 & 1872.77760457112 & 432.202395428884 \tabularnewline
91 & 2555.28 & 2127.74568545629 & 427.534314543714 \tabularnewline
92 & 2799.43 & 2336.00161764704 & 463.428382352961 \tabularnewline
93 & 2811.7 & 2282.48432616534 & 529.215673834655 \tabularnewline
94 & 2735.7 & 2311.62131350036 & 424.078686499638 \tabularnewline
95 & 2745.88 & 2001.24937988741 & 744.630620112585 \tabularnewline
96 & 2720.25 & 2004.13954667777 & 716.110453322229 \tabularnewline
97 & 2638.53 & 2019.12033285591 & 619.409667144086 \tabularnewline
98 & 2659.81 & 1999.87088825824 & 659.939111741762 \tabularnewline
99 & 2641.65 & 1940.42401825270 & 701.225981747304 \tabularnewline
100 & 2604.42 & 1839.4603563012 & 764.959643698798 \tabularnewline
101 & 2892.63 & 2225.85502369686 & 666.77497630314 \tabularnewline
102 & 2915.02 & 2364.87925346168 & 550.140746538316 \tabularnewline
103 & 2845.26 & 2559.24918261941 & 286.010817380593 \tabularnewline
104 & 2794.83 & 2694.46515473095 & 100.364845269051 \tabularnewline
105 & 2848.96 & 2512.32102099397 & 336.638979006026 \tabularnewline
106 & 2833.18 & 2363.64057696292 & 469.53942303708 \tabularnewline
107 & 2995.55 & 2523.32443813590 & 472.225561864096 \tabularnewline
108 & 2987.1 & 2590.05389492909 & 397.046105070906 \tabularnewline
109 & 3013.24 & 2773.22217866465 & 240.017821335352 \tabularnewline
110 & 3110.52 & 2850.12138464279 & 260.398615357206 \tabularnewline
111 & 3045.78 & 2937.28612989675 & 108.493870103255 \tabularnewline
112 & 3032.93 & 3135.23737560196 & -102.307375601958 \tabularnewline
113 & 3142.95 & 3143.56890625009 & -0.618906250085992 \tabularnewline
114 & 3012.61 & 3211.4886578165 & -198.878657816500 \tabularnewline
115 & 2897.06 & 3207.70258059712 & -310.642580597116 \tabularnewline
116 & 2863.36 & 3222.92601304489 & -359.566013044888 \tabularnewline
117 & 2882.6 & 3692.25703039228 & -809.657030392284 \tabularnewline
118 & 2767.63 & 3691.01130461346 & -923.38130461346 \tabularnewline
119 & 2803.47 & 3691.59317666886 & -888.12317666886 \tabularnewline
120 & 3030.29 & 3568.89103301624 & -538.601033016236 \tabularnewline
121 & 3210.52 & 3471.49732771806 & -260.977327718062 \tabularnewline
122 & 3249.57 & 3445.49452475694 & -195.924524756938 \tabularnewline
123 & 2999.93 & 3279.03313940428 & -279.103139404279 \tabularnewline
124 & 3181.96 & 3180.78194901478 & 1.17805098522014 \tabularnewline
125 & 3053.05 & 3199.00050558914 & -145.950505589143 \tabularnewline
126 & 3092.71 & 3292.12669790834 & -199.416697908342 \tabularnewline
127 & 3165.26 & 3107.12146626704 & 58.1385337329572 \tabularnewline
128 & 3173.95 & 3036.37269475295 & 137.577305247046 \tabularnewline
129 & 3280.37 & 3032.45963359848 & 247.910366401525 \tabularnewline
130 & 3288.18 & 2736.65391564390 & 551.526084356104 \tabularnewline
131 & 3411.13 & 2451.48576577379 & 959.644234226211 \tabularnewline
132 & 3484.74 & 2373.74193126364 & 1110.99806873636 \tabularnewline
133 & 3361.13 & 4992.53544646112 & -1631.40544646112 \tabularnewline
134 & 14525.87 & 13800.5642910627 & 725.30570893727 \tabularnewline
135 & 8164.47 & 7854.69800871457 & 309.771991285429 \tabularnewline
136 & -3.7 & 203.676193718251 & -207.376193718251 \tabularnewline
137 & 2 & 37.0622970173114 & -35.0622970173114 \tabularnewline
138 & 4.69 & 441.582150173878 & -436.892150173878 \tabularnewline
139 & 1.6 & 1204.28770132970 & -1202.68770132970 \tabularnewline
140 & 3064.42 & 4933.31769407148 & -1868.89769407148 \tabularnewline
141 & 16840.31 & 17059.3579682700 & -219.047968270041 \tabularnewline
142 & 8323.61 & 7910.7376582313 & 412.872341768698 \tabularnewline
143 & 4.1 & 137.500560354516 & -133.400560354516 \tabularnewline
144 & -9 & -170.570761580749 & 161.570761580749 \tabularnewline
145 & 5.5 & 469.044618110266 & -463.544618110266 \tabularnewline
146 & 1.3 & 1241.16769730366 & -1239.86769730366 \tabularnewline
147 & 2409.36 & 5019.7862754446 & -2610.4262754446 \tabularnewline
148 & 20152.53 & 20621.8869219128 & -469.356921912819 \tabularnewline
149 & 7603.88 & 7732.81016086258 & -128.930160862581 \tabularnewline
150 & -2 & 115.703833649995 & -117.703833649995 \tabularnewline
151 & -21 & -131.390742902412 & 110.390742902412 \tabularnewline
152 & 5.11 & 493.190072963279 & -488.080072963279 \tabularnewline
153 & 2 & 1068.63651896766 & -1066.63651896766 \tabularnewline
154 & 1877.1 & 4363.88841956572 & -2486.78841956572 \tabularnewline
155 & 21021.37 & 20902.9215809088 & 118.448419091161 \tabularnewline
156 & 5996.21 & 6331.32279622364 & -335.112796223638 \tabularnewline
157 & -6.6 & 147.778716482023 & -154.378716482023 \tabularnewline
158 & -11 & -56.1267093603222 & 45.1267093603222 \tabularnewline
159 & 5.96 & 705.378980115892 & -699.418980115892 \tabularnewline
160 & 1.8 & 1286.79379199926 & -1284.99379199926 \tabularnewline
161 & 1711.74 & 4422.3158898304 & -2710.5758898304 \tabularnewline
162 & 20394.71 & 20325.0981746683 & 69.611825331747 \tabularnewline
163 & 5518.24 & 5643.41799848654 & -125.177998486536 \tabularnewline
164 & -14 & -8.9731285585259 & -5.02687144147409 \tabularnewline
165 & -16 & -314.055764718157 & 298.055764718157 \tabularnewline
166 & 6.72 & 254.29875746318 & -247.57875746318 \tabularnewline
167 & 1.2 & 795.847532504622 & -794.647532504622 \tabularnewline
168 & 1475.57 & 3313.92597894979 & -1838.35597894979 \tabularnewline
169 & 16188.69 & 14445.0359020408 & 1743.65409795921 \tabularnewline
170 & 4510.76 & 4440.71346384823 & 70.0465361517735 \tabularnewline
171 & -5.1 & -277.035892321974 & 271.935892321974 \tabularnewline
172 & -4 & -457.242041467639 & 453.242041467639 \tabularnewline
173 & 8.29 & 212.756489720136 & -204.466489720136 \tabularnewline
174 & 1.8 & 796.206968175441 & -794.406968175441 \tabularnewline
175 & 2502.66 & 3308.27681075172 & -805.616810751721 \tabularnewline
176 & 2466.92 & 3136.48031943004 & -669.560319430045 \tabularnewline
177 & 2513.17 & 3030.65950394978 & -517.489503949779 \tabularnewline
178 & 2443.27 & 2995.12655963046 & -551.856559630464 \tabularnewline
179 & 2293.41 & 2940.86785289645 & -647.457852896448 \tabularnewline
180 & 2070.83 & 2730.38565465812 & -659.55565465812 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104184&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]2502.66[/C][C]3308.27681075174[/C][C]-805.616810751737[/C][/ROW]
[ROW][C]2[/C][C]2466.92[/C][C]3136.48031943005[/C][C]-669.56031943005[/C][/ROW]
[ROW][C]3[/C][C]2513.17[/C][C]3030.65950394978[/C][C]-517.489503949776[/C][/ROW]
[ROW][C]4[/C][C]2443.27[/C][C]2995.12655963047[/C][C]-551.856559630465[/C][/ROW]
[ROW][C]5[/C][C]2293.41[/C][C]2940.86785289644[/C][C]-647.457852896442[/C][/ROW]
[ROW][C]6[/C][C]2070.83[/C][C]2730.38565465812[/C][C]-659.555654658125[/C][/ROW]
[ROW][C]7[/C][C]2029.6[/C][C]2777.39496646785[/C][C]-747.794966467847[/C][/ROW]
[ROW][C]8[/C][C]2052.02[/C][C]2681.47274324348[/C][C]-629.452743243482[/C][/ROW]
[ROW][C]9[/C][C]1864.44[/C][C]2535.93327827107[/C][C]-671.493278271071[/C][/ROW]
[ROW][C]10[/C][C]1670.07[/C][C]2378.34265051992[/C][C]-708.27265051992[/C][/ROW]
[ROW][C]11[/C][C]1810.99[/C][C]2470.71118474596[/C][C]-659.721184745964[/C][/ROW]
[ROW][C]12[/C][C]1905.41[/C][C]2416.27691221111[/C][C]-510.866912211106[/C][/ROW]
[ROW][C]13[/C][C]1862.83[/C][C]2357.09238719484[/C][C]-494.262387194838[/C][/ROW]
[ROW][C]14[/C][C]2014.45[/C][C]2333.16712316204[/C][C]-318.717123162039[/C][/ROW]
[ROW][C]15[/C][C]2197.82[/C][C]2536.00573009389[/C][C]-338.185730093889[/C][/ROW]
[ROW][C]16[/C][C]2962.34[/C][C]3166.20502680379[/C][C]-203.865026803794[/C][/ROW]
[ROW][C]17[/C][C]3047.03[/C][C]3327.02603679705[/C][C]-279.996036797052[/C][/ROW]
[ROW][C]18[/C][C]3032.6[/C][C]3433.39912216993[/C][C]-400.799122169931[/C][/ROW]
[ROW][C]19[/C][C]3504.37[/C][C]3602.20984305739[/C][C]-97.839843057386[/C][/ROW]
[ROW][C]20[/C][C]3801.06[/C][C]3667.19645274011[/C][C]133.863547259889[/C][/ROW]
[ROW][C]21[/C][C]3857.62[/C][C]3549.71451926705[/C][C]307.905480732948[/C][/ROW]
[ROW][C]22[/C][C]3674.4[/C][C]3501.74743066435[/C][C]172.652569335646[/C][/ROW]
[ROW][C]23[/C][C]3720.98[/C][C]3694.79003055034[/C][C]26.1899694496553[/C][/ROW]
[ROW][C]24[/C][C]3844.49[/C][C]3673.69091038848[/C][C]170.799089611522[/C][/ROW]
[ROW][C]25[/C][C]4116.68[/C][C]3937.75880289858[/C][C]178.921197101421[/C][/ROW]
[ROW][C]26[/C][C]4105.18[/C][C]3914.22908350123[/C][C]190.950916498769[/C][/ROW]
[ROW][C]27[/C][C]4435.23[/C][C]4168.52633270433[/C][C]266.703667295668[/C][/ROW]
[ROW][C]28[/C][C]4296.49[/C][C]3963.02707586156[/C][C]333.462924138443[/C][/ROW]
[ROW][C]29[/C][C]4202.52[/C][C]3904.74368119899[/C][C]297.776318801011[/C][/ROW]
[ROW][C]30[/C][C]4562.84[/C][C]4209.88283894925[/C][C]352.957161050747[/C][/ROW]
[ROW][C]31[/C][C]4621.4[/C][C]4205.36347952972[/C][C]416.036520470283[/C][/ROW]
[ROW][C]32[/C][C]4696.96[/C][C]4148.21623224025[/C][C]548.743767759745[/C][/ROW]
[ROW][C]33[/C][C]4591.27[/C][C]4075.82956782747[/C][C]515.440432172531[/C][/ROW]
[ROW][C]34[/C][C]4356.98[/C][C]3946.09325885582[/C][C]410.886741144179[/C][/ROW]
[ROW][C]35[/C][C]4502.64[/C][C]4128.25200907971[/C][C]374.387990920295[/C][/ROW]
[ROW][C]36[/C][C]4443.91[/C][C]3968.70325663175[/C][C]475.206743368254[/C][/ROW]
[ROW][C]37[/C][C]4290.89[/C][C]3840.09050146871[/C][C]450.799498531295[/C][/ROW]
[ROW][C]38[/C][C]4199.75[/C][C]3731.18167779553[/C][C]468.568322204468[/C][/ROW]
[ROW][C]39[/C][C]4138.52[/C][C]3722.33035153144[/C][C]416.189648468561[/C][/ROW]
[ROW][C]40[/C][C]3970.1[/C][C]3590.46586706228[/C][C]379.634132937718[/C][/ROW]
[ROW][C]41[/C][C]3862.27[/C][C]3597.63540052248[/C][C]264.63459947752[/C][/ROW]
[ROW][C]42[/C][C]3701.61[/C][C]3480.32040109246[/C][C]221.289598907545[/C][/ROW]
[ROW][C]43[/C][C]3570.12[/C][C]3372.18403637968[/C][C]197.935963620321[/C][/ROW]
[ROW][C]44[/C][C]3801.06[/C][C]3820.30968058539[/C][C]-19.2496805853900[/C][/ROW]
[ROW][C]45[/C][C]3895.51[/C][C]3866.20500877133[/C][C]29.3049912286678[/C][/ROW]
[ROW][C]46[/C][C]3917.96[/C][C]3596.27701131841[/C][C]321.682988681591[/C][/ROW]
[ROW][C]47[/C][C]3813.06[/C][C]3561.27261317868[/C][C]251.787386821323[/C][/ROW]
[ROW][C]48[/C][C]3667.03[/C][C]3502.73963938577[/C][C]164.290360614234[/C][/ROW]
[ROW][C]49[/C][C]3494.17[/C][C]3354.66410579204[/C][C]139.505894207956[/C][/ROW]
[ROW][C]50[/C][C]3363.99[/C][C]3045.52016443580[/C][C]318.469835564197[/C][/ROW]
[ROW][C]51[/C][C]3295.32[/C][C]2795.18117299094[/C][C]500.138827009063[/C][/ROW]
[ROW][C]52[/C][C]3277.01[/C][C]2691.69165370713[/C][C]585.31834629287[/C][/ROW]
[ROW][C]53[/C][C]3257.16[/C][C]2510.81010340180[/C][C]746.349896598203[/C][/ROW]
[ROW][C]54[/C][C]3161.69[/C][C]2414.20827861337[/C][C]747.481721386631[/C][/ROW]
[ROW][C]55[/C][C]3097.31[/C][C]2353.01178991373[/C][C]744.298210086275[/C][/ROW]
[ROW][C]56[/C][C]3061.26[/C][C]2228.20314535687[/C][C]833.05685464313[/C][/ROW]
[ROW][C]57[/C][C]3119.31[/C][C]2270.01867060783[/C][C]849.291329392168[/C][/ROW]
[ROW][C]58[/C][C]3106.22[/C][C]2376.75740373977[/C][C]729.462596260232[/C][/ROW]
[ROW][C]59[/C][C]3080.58[/C][C]2325.67076809267[/C][C]754.909231907328[/C][/ROW]
[ROW][C]60[/C][C]2981.85[/C][C]2274.95176623748[/C][C]706.898233762519[/C][/ROW]
[ROW][C]61[/C][C]2921.44[/C][C]2249.50986023296[/C][C]671.930139767035[/C][/ROW]
[ROW][C]62[/C][C]2849.27[/C][C]2227.84788252761[/C][C]621.422117472392[/C][/ROW]
[ROW][C]63[/C][C]2756.76[/C][C]2190.6925604339[/C][C]566.067439566099[/C][/ROW]
[ROW][C]64[/C][C]2645.64[/C][C]2206.47219856875[/C][C]439.167801431252[/C][/ROW]
[ROW][C]65[/C][C]2497.84[/C][C]2166.32341024037[/C][C]331.516589759626[/C][/ROW]
[ROW][C]66[/C][C]2448.05[/C][C]2236.21822423588[/C][C]211.831775764125[/C][/ROW]
[ROW][C]67[/C][C]2454.62[/C][C]2279.14678422572[/C][C]175.473215774282[/C][/ROW]
[ROW][C]68[/C][C]2407.6[/C][C]2157.03614394307[/C][C]250.563856056934[/C][/ROW]
[ROW][C]69[/C][C]2472.81[/C][C]2401.30416544302[/C][C]71.5058345569777[/C][/ROW]
[ROW][C]70[/C][C]2408.64[/C][C]2279.88878733909[/C][C]128.751212660911[/C][/ROW]
[ROW][C]71[/C][C]2440.25[/C][C]2135.73407129982[/C][C]304.515928700178[/C][/ROW]
[ROW][C]72[/C][C]2350.44[/C][C]2199.50839661043[/C][C]150.931603389568[/C][/ROW]
[ROW][C]73[/C][C]2196.72[/C][C]2059.43952334129[/C][C]137.280476658711[/C][/ROW]
[ROW][C]74[/C][C]2174.56[/C][C]2010.72757309357[/C][C]163.832426906429[/C][/ROW]
[ROW][C]75[/C][C]2120.88[/C][C]2066.63455006570[/C][C]54.2454499343046[/C][/ROW]
[ROW][C]76[/C][C]2093.48[/C][C]2071.46989083894[/C][C]22.0101091610560[/C][/ROW]
[ROW][C]77[/C][C]2061.41[/C][C]1875.18726942639[/C][C]186.222730573611[/C][/ROW]
[ROW][C]78[/C][C]1969.6[/C][C]1785.60339538811[/C][C]183.996604611885[/C][/ROW]
[ROW][C]79[/C][C]1959.67[/C][C]1633.37987011663[/C][C]326.290129883370[/C][/ROW]
[ROW][C]80[/C][C]1910.43[/C][C]1457.83034152113[/C][C]452.599658478869[/C][/ROW]
[ROW][C]81[/C][C]1833.42[/C][C]1370.97703234852[/C][C]462.442967651481[/C][/ROW]
[ROW][C]82[/C][C]1635.25[/C][C]1420.41648125661[/C][C]214.833518743390[/C][/ROW]
[ROW][C]83[/C][C]1765.9[/C][C]1529.08259040764[/C][C]236.817409592363[/C][/ROW]
[ROW][C]84[/C][C]1946.81[/C][C]1597.73613390794[/C][C]349.073866092058[/C][/ROW]
[ROW][C]85[/C][C]1995.37[/C][C]1599.00479179369[/C][C]396.365208206309[/C][/ROW]
[ROW][C]86[/C][C]2042[/C][C]1592.48444467135[/C][C]449.515555328647[/C][/ROW]
[ROW][C]87[/C][C]1940.49[/C][C]1555.75582172496[/C][C]384.73417827504[/C][/ROW]
[ROW][C]88[/C][C]2065.81[/C][C]1674.50155151698[/C][C]391.308448483022[/C][/ROW]
[ROW][C]89[/C][C]2214.95[/C][C]1774.78826454403[/C][C]440.161735455968[/C][/ROW]
[ROW][C]90[/C][C]2304.98[/C][C]1872.77760457112[/C][C]432.202395428884[/C][/ROW]
[ROW][C]91[/C][C]2555.28[/C][C]2127.74568545629[/C][C]427.534314543714[/C][/ROW]
[ROW][C]92[/C][C]2799.43[/C][C]2336.00161764704[/C][C]463.428382352961[/C][/ROW]
[ROW][C]93[/C][C]2811.7[/C][C]2282.48432616534[/C][C]529.215673834655[/C][/ROW]
[ROW][C]94[/C][C]2735.7[/C][C]2311.62131350036[/C][C]424.078686499638[/C][/ROW]
[ROW][C]95[/C][C]2745.88[/C][C]2001.24937988741[/C][C]744.630620112585[/C][/ROW]
[ROW][C]96[/C][C]2720.25[/C][C]2004.13954667777[/C][C]716.110453322229[/C][/ROW]
[ROW][C]97[/C][C]2638.53[/C][C]2019.12033285591[/C][C]619.409667144086[/C][/ROW]
[ROW][C]98[/C][C]2659.81[/C][C]1999.87088825824[/C][C]659.939111741762[/C][/ROW]
[ROW][C]99[/C][C]2641.65[/C][C]1940.42401825270[/C][C]701.225981747304[/C][/ROW]
[ROW][C]100[/C][C]2604.42[/C][C]1839.4603563012[/C][C]764.959643698798[/C][/ROW]
[ROW][C]101[/C][C]2892.63[/C][C]2225.85502369686[/C][C]666.77497630314[/C][/ROW]
[ROW][C]102[/C][C]2915.02[/C][C]2364.87925346168[/C][C]550.140746538316[/C][/ROW]
[ROW][C]103[/C][C]2845.26[/C][C]2559.24918261941[/C][C]286.010817380593[/C][/ROW]
[ROW][C]104[/C][C]2794.83[/C][C]2694.46515473095[/C][C]100.364845269051[/C][/ROW]
[ROW][C]105[/C][C]2848.96[/C][C]2512.32102099397[/C][C]336.638979006026[/C][/ROW]
[ROW][C]106[/C][C]2833.18[/C][C]2363.64057696292[/C][C]469.53942303708[/C][/ROW]
[ROW][C]107[/C][C]2995.55[/C][C]2523.32443813590[/C][C]472.225561864096[/C][/ROW]
[ROW][C]108[/C][C]2987.1[/C][C]2590.05389492909[/C][C]397.046105070906[/C][/ROW]
[ROW][C]109[/C][C]3013.24[/C][C]2773.22217866465[/C][C]240.017821335352[/C][/ROW]
[ROW][C]110[/C][C]3110.52[/C][C]2850.12138464279[/C][C]260.398615357206[/C][/ROW]
[ROW][C]111[/C][C]3045.78[/C][C]2937.28612989675[/C][C]108.493870103255[/C][/ROW]
[ROW][C]112[/C][C]3032.93[/C][C]3135.23737560196[/C][C]-102.307375601958[/C][/ROW]
[ROW][C]113[/C][C]3142.95[/C][C]3143.56890625009[/C][C]-0.618906250085992[/C][/ROW]
[ROW][C]114[/C][C]3012.61[/C][C]3211.4886578165[/C][C]-198.878657816500[/C][/ROW]
[ROW][C]115[/C][C]2897.06[/C][C]3207.70258059712[/C][C]-310.642580597116[/C][/ROW]
[ROW][C]116[/C][C]2863.36[/C][C]3222.92601304489[/C][C]-359.566013044888[/C][/ROW]
[ROW][C]117[/C][C]2882.6[/C][C]3692.25703039228[/C][C]-809.657030392284[/C][/ROW]
[ROW][C]118[/C][C]2767.63[/C][C]3691.01130461346[/C][C]-923.38130461346[/C][/ROW]
[ROW][C]119[/C][C]2803.47[/C][C]3691.59317666886[/C][C]-888.12317666886[/C][/ROW]
[ROW][C]120[/C][C]3030.29[/C][C]3568.89103301624[/C][C]-538.601033016236[/C][/ROW]
[ROW][C]121[/C][C]3210.52[/C][C]3471.49732771806[/C][C]-260.977327718062[/C][/ROW]
[ROW][C]122[/C][C]3249.57[/C][C]3445.49452475694[/C][C]-195.924524756938[/C][/ROW]
[ROW][C]123[/C][C]2999.93[/C][C]3279.03313940428[/C][C]-279.103139404279[/C][/ROW]
[ROW][C]124[/C][C]3181.96[/C][C]3180.78194901478[/C][C]1.17805098522014[/C][/ROW]
[ROW][C]125[/C][C]3053.05[/C][C]3199.00050558914[/C][C]-145.950505589143[/C][/ROW]
[ROW][C]126[/C][C]3092.71[/C][C]3292.12669790834[/C][C]-199.416697908342[/C][/ROW]
[ROW][C]127[/C][C]3165.26[/C][C]3107.12146626704[/C][C]58.1385337329572[/C][/ROW]
[ROW][C]128[/C][C]3173.95[/C][C]3036.37269475295[/C][C]137.577305247046[/C][/ROW]
[ROW][C]129[/C][C]3280.37[/C][C]3032.45963359848[/C][C]247.910366401525[/C][/ROW]
[ROW][C]130[/C][C]3288.18[/C][C]2736.65391564390[/C][C]551.526084356104[/C][/ROW]
[ROW][C]131[/C][C]3411.13[/C][C]2451.48576577379[/C][C]959.644234226211[/C][/ROW]
[ROW][C]132[/C][C]3484.74[/C][C]2373.74193126364[/C][C]1110.99806873636[/C][/ROW]
[ROW][C]133[/C][C]3361.13[/C][C]4992.53544646112[/C][C]-1631.40544646112[/C][/ROW]
[ROW][C]134[/C][C]14525.87[/C][C]13800.5642910627[/C][C]725.30570893727[/C][/ROW]
[ROW][C]135[/C][C]8164.47[/C][C]7854.69800871457[/C][C]309.771991285429[/C][/ROW]
[ROW][C]136[/C][C]-3.7[/C][C]203.676193718251[/C][C]-207.376193718251[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]37.0622970173114[/C][C]-35.0622970173114[/C][/ROW]
[ROW][C]138[/C][C]4.69[/C][C]441.582150173878[/C][C]-436.892150173878[/C][/ROW]
[ROW][C]139[/C][C]1.6[/C][C]1204.28770132970[/C][C]-1202.68770132970[/C][/ROW]
[ROW][C]140[/C][C]3064.42[/C][C]4933.31769407148[/C][C]-1868.89769407148[/C][/ROW]
[ROW][C]141[/C][C]16840.31[/C][C]17059.3579682700[/C][C]-219.047968270041[/C][/ROW]
[ROW][C]142[/C][C]8323.61[/C][C]7910.7376582313[/C][C]412.872341768698[/C][/ROW]
[ROW][C]143[/C][C]4.1[/C][C]137.500560354516[/C][C]-133.400560354516[/C][/ROW]
[ROW][C]144[/C][C]-9[/C][C]-170.570761580749[/C][C]161.570761580749[/C][/ROW]
[ROW][C]145[/C][C]5.5[/C][C]469.044618110266[/C][C]-463.544618110266[/C][/ROW]
[ROW][C]146[/C][C]1.3[/C][C]1241.16769730366[/C][C]-1239.86769730366[/C][/ROW]
[ROW][C]147[/C][C]2409.36[/C][C]5019.7862754446[/C][C]-2610.4262754446[/C][/ROW]
[ROW][C]148[/C][C]20152.53[/C][C]20621.8869219128[/C][C]-469.356921912819[/C][/ROW]
[ROW][C]149[/C][C]7603.88[/C][C]7732.81016086258[/C][C]-128.930160862581[/C][/ROW]
[ROW][C]150[/C][C]-2[/C][C]115.703833649995[/C][C]-117.703833649995[/C][/ROW]
[ROW][C]151[/C][C]-21[/C][C]-131.390742902412[/C][C]110.390742902412[/C][/ROW]
[ROW][C]152[/C][C]5.11[/C][C]493.190072963279[/C][C]-488.080072963279[/C][/ROW]
[ROW][C]153[/C][C]2[/C][C]1068.63651896766[/C][C]-1066.63651896766[/C][/ROW]
[ROW][C]154[/C][C]1877.1[/C][C]4363.88841956572[/C][C]-2486.78841956572[/C][/ROW]
[ROW][C]155[/C][C]21021.37[/C][C]20902.9215809088[/C][C]118.448419091161[/C][/ROW]
[ROW][C]156[/C][C]5996.21[/C][C]6331.32279622364[/C][C]-335.112796223638[/C][/ROW]
[ROW][C]157[/C][C]-6.6[/C][C]147.778716482023[/C][C]-154.378716482023[/C][/ROW]
[ROW][C]158[/C][C]-11[/C][C]-56.1267093603222[/C][C]45.1267093603222[/C][/ROW]
[ROW][C]159[/C][C]5.96[/C][C]705.378980115892[/C][C]-699.418980115892[/C][/ROW]
[ROW][C]160[/C][C]1.8[/C][C]1286.79379199926[/C][C]-1284.99379199926[/C][/ROW]
[ROW][C]161[/C][C]1711.74[/C][C]4422.3158898304[/C][C]-2710.5758898304[/C][/ROW]
[ROW][C]162[/C][C]20394.71[/C][C]20325.0981746683[/C][C]69.611825331747[/C][/ROW]
[ROW][C]163[/C][C]5518.24[/C][C]5643.41799848654[/C][C]-125.177998486536[/C][/ROW]
[ROW][C]164[/C][C]-14[/C][C]-8.9731285585259[/C][C]-5.02687144147409[/C][/ROW]
[ROW][C]165[/C][C]-16[/C][C]-314.055764718157[/C][C]298.055764718157[/C][/ROW]
[ROW][C]166[/C][C]6.72[/C][C]254.29875746318[/C][C]-247.57875746318[/C][/ROW]
[ROW][C]167[/C][C]1.2[/C][C]795.847532504622[/C][C]-794.647532504622[/C][/ROW]
[ROW][C]168[/C][C]1475.57[/C][C]3313.92597894979[/C][C]-1838.35597894979[/C][/ROW]
[ROW][C]169[/C][C]16188.69[/C][C]14445.0359020408[/C][C]1743.65409795921[/C][/ROW]
[ROW][C]170[/C][C]4510.76[/C][C]4440.71346384823[/C][C]70.0465361517735[/C][/ROW]
[ROW][C]171[/C][C]-5.1[/C][C]-277.035892321974[/C][C]271.935892321974[/C][/ROW]
[ROW][C]172[/C][C]-4[/C][C]-457.242041467639[/C][C]453.242041467639[/C][/ROW]
[ROW][C]173[/C][C]8.29[/C][C]212.756489720136[/C][C]-204.466489720136[/C][/ROW]
[ROW][C]174[/C][C]1.8[/C][C]796.206968175441[/C][C]-794.406968175441[/C][/ROW]
[ROW][C]175[/C][C]2502.66[/C][C]3308.27681075172[/C][C]-805.616810751721[/C][/ROW]
[ROW][C]176[/C][C]2466.92[/C][C]3136.48031943004[/C][C]-669.560319430045[/C][/ROW]
[ROW][C]177[/C][C]2513.17[/C][C]3030.65950394978[/C][C]-517.489503949779[/C][/ROW]
[ROW][C]178[/C][C]2443.27[/C][C]2995.12655963046[/C][C]-551.856559630464[/C][/ROW]
[ROW][C]179[/C][C]2293.41[/C][C]2940.86785289645[/C][C]-647.457852896448[/C][/ROW]
[ROW][C]180[/C][C]2070.83[/C][C]2730.38565465812[/C][C]-659.55565465812[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104184&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104184&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
12502.663308.27681075174-805.616810751737
22466.923136.48031943005-669.56031943005
32513.173030.65950394978-517.489503949776
42443.272995.12655963047-551.856559630465
52293.412940.86785289644-647.457852896442
62070.832730.38565465812-659.555654658125
72029.62777.39496646785-747.794966467847
82052.022681.47274324348-629.452743243482
91864.442535.93327827107-671.493278271071
101670.072378.34265051992-708.27265051992
111810.992470.71118474596-659.721184745964
121905.412416.27691221111-510.866912211106
131862.832357.09238719484-494.262387194838
142014.452333.16712316204-318.717123162039
152197.822536.00573009389-338.185730093889
162962.343166.20502680379-203.865026803794
173047.033327.02603679705-279.996036797052
183032.63433.39912216993-400.799122169931
193504.373602.20984305739-97.839843057386
203801.063667.19645274011133.863547259889
213857.623549.71451926705307.905480732948
223674.43501.74743066435172.652569335646
233720.983694.7900305503426.1899694496553
243844.493673.69091038848170.799089611522
254116.683937.75880289858178.921197101421
264105.183914.22908350123190.950916498769
274435.234168.52633270433266.703667295668
284296.493963.02707586156333.462924138443
294202.523904.74368119899297.776318801011
304562.844209.88283894925352.957161050747
314621.44205.36347952972416.036520470283
324696.964148.21623224025548.743767759745
334591.274075.82956782747515.440432172531
344356.983946.09325885582410.886741144179
354502.644128.25200907971374.387990920295
364443.913968.70325663175475.206743368254
374290.893840.09050146871450.799498531295
384199.753731.18167779553468.568322204468
394138.523722.33035153144416.189648468561
403970.13590.46586706228379.634132937718
413862.273597.63540052248264.63459947752
423701.613480.32040109246221.289598907545
433570.123372.18403637968197.935963620321
443801.063820.30968058539-19.2496805853900
453895.513866.2050087713329.3049912286678
463917.963596.27701131841321.682988681591
473813.063561.27261317868251.787386821323
483667.033502.73963938577164.290360614234
493494.173354.66410579204139.505894207956
503363.993045.52016443580318.469835564197
513295.322795.18117299094500.138827009063
523277.012691.69165370713585.31834629287
533257.162510.81010340180746.349896598203
543161.692414.20827861337747.481721386631
553097.312353.01178991373744.298210086275
563061.262228.20314535687833.05685464313
573119.312270.01867060783849.291329392168
583106.222376.75740373977729.462596260232
593080.582325.67076809267754.909231907328
602981.852274.95176623748706.898233762519
612921.442249.50986023296671.930139767035
622849.272227.84788252761621.422117472392
632756.762190.6925604339566.067439566099
642645.642206.47219856875439.167801431252
652497.842166.32341024037331.516589759626
662448.052236.21822423588211.831775764125
672454.622279.14678422572175.473215774282
682407.62157.03614394307250.563856056934
692472.812401.3041654430271.5058345569777
702408.642279.88878733909128.751212660911
712440.252135.73407129982304.515928700178
722350.442199.50839661043150.931603389568
732196.722059.43952334129137.280476658711
742174.562010.72757309357163.832426906429
752120.882066.6345500657054.2454499343046
762093.482071.4698908389422.0101091610560
772061.411875.18726942639186.222730573611
781969.61785.60339538811183.996604611885
791959.671633.37987011663326.290129883370
801910.431457.83034152113452.599658478869
811833.421370.97703234852462.442967651481
821635.251420.41648125661214.833518743390
831765.91529.08259040764236.817409592363
841946.811597.73613390794349.073866092058
851995.371599.00479179369396.365208206309
8620421592.48444467135449.515555328647
871940.491555.75582172496384.73417827504
882065.811674.50155151698391.308448483022
892214.951774.78826454403440.161735455968
902304.981872.77760457112432.202395428884
912555.282127.74568545629427.534314543714
922799.432336.00161764704463.428382352961
932811.72282.48432616534529.215673834655
942735.72311.62131350036424.078686499638
952745.882001.24937988741744.630620112585
962720.252004.13954667777716.110453322229
972638.532019.12033285591619.409667144086
982659.811999.87088825824659.939111741762
992641.651940.42401825270701.225981747304
1002604.421839.4603563012764.959643698798
1012892.632225.85502369686666.77497630314
1022915.022364.87925346168550.140746538316
1032845.262559.24918261941286.010817380593
1042794.832694.46515473095100.364845269051
1052848.962512.32102099397336.638979006026
1062833.182363.64057696292469.53942303708
1072995.552523.32443813590472.225561864096
1082987.12590.05389492909397.046105070906
1093013.242773.22217866465240.017821335352
1103110.522850.12138464279260.398615357206
1113045.782937.28612989675108.493870103255
1123032.933135.23737560196-102.307375601958
1133142.953143.56890625009-0.618906250085992
1143012.613211.4886578165-198.878657816500
1152897.063207.70258059712-310.642580597116
1162863.363222.92601304489-359.566013044888
1172882.63692.25703039228-809.657030392284
1182767.633691.01130461346-923.38130461346
1192803.473691.59317666886-888.12317666886
1203030.293568.89103301624-538.601033016236
1213210.523471.49732771806-260.977327718062
1223249.573445.49452475694-195.924524756938
1232999.933279.03313940428-279.103139404279
1243181.963180.781949014781.17805098522014
1253053.053199.00050558914-145.950505589143
1263092.713292.12669790834-199.416697908342
1273165.263107.1214662670458.1385337329572
1283173.953036.37269475295137.577305247046
1293280.373032.45963359848247.910366401525
1303288.182736.65391564390551.526084356104
1313411.132451.48576577379959.644234226211
1323484.742373.741931263641110.99806873636
1333361.134992.53544646112-1631.40544646112
13414525.8713800.5642910627725.30570893727
1358164.477854.69800871457309.771991285429
136-3.7203.676193718251-207.376193718251
137237.0622970173114-35.0622970173114
1384.69441.582150173878-436.892150173878
1391.61204.28770132970-1202.68770132970
1403064.424933.31769407148-1868.89769407148
14116840.3117059.3579682700-219.047968270041
1428323.617910.7376582313412.872341768698
1434.1137.500560354516-133.400560354516
144-9-170.570761580749161.570761580749
1455.5469.044618110266-463.544618110266
1461.31241.16769730366-1239.86769730366
1472409.365019.7862754446-2610.4262754446
14820152.5320621.8869219128-469.356921912819
1497603.887732.81016086258-128.930160862581
150-2115.703833649995-117.703833649995
151-21-131.390742902412110.390742902412
1525.11493.190072963279-488.080072963279
15321068.63651896766-1066.63651896766
1541877.14363.88841956572-2486.78841956572
15521021.3720902.9215809088118.448419091161
1565996.216331.32279622364-335.112796223638
157-6.6147.778716482023-154.378716482023
158-11-56.126709360322245.1267093603222
1595.96705.378980115892-699.418980115892
1601.81286.79379199926-1284.99379199926
1611711.744422.3158898304-2710.5758898304
16220394.7120325.098174668369.611825331747
1635518.245643.41799848654-125.177998486536
164-14-8.9731285585259-5.02687144147409
165-16-314.055764718157298.055764718157
1666.72254.29875746318-247.57875746318
1671.2795.847532504622-794.647532504622
1681475.573313.92597894979-1838.35597894979
16916188.6914445.03590204081743.65409795921
1704510.764440.7134638482370.0465361517735
171-5.1-277.035892321974271.935892321974
172-4-457.242041467639453.242041467639
1738.29212.756489720136-204.466489720136
1741.8796.206968175441-794.406968175441
1752502.663308.27681075172-805.616810751721
1762466.923136.48031943004-669.560319430045
1772513.173030.65950394978-517.489503949779
1782443.272995.12655963046-551.856559630464
1792293.412940.86785289645-647.457852896448
1802070.832730.38565465812-659.55565465812






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.0005228263092339590.001045652618467920.999477173690766
123.15944634842757e-056.31889269685515e-050.999968405536516
131.72507314555143e-063.45014629110286e-060.999998274926854
146.2716763139989e-061.25433526279978e-050.999993728323686
156.06584306000083e-071.21316861200017e-060.999999393415694
168.89862811798075e-081.77972562359615e-070.999999911013719
171.10766711074340e-082.21533422148681e-080.999999988923329
181.00937683804983e-092.01875367609965e-090.999999998990623
192.07113232648347e-094.14226465296694e-090.999999997928868
202.37894443622177e-094.75788887244353e-090.999999997621056
218.03241466803467e-101.60648293360693e-090.999999999196759
222.81402506458893e-095.62805012917786e-090.999999997185975
234.95345198622942e-109.90690397245885e-100.999999999504655
242.53107632052047e-105.06215264104095e-100.999999999746892
251.42362248258200e-102.84724496516399e-100.999999999857638
268.5434453201894e-111.70868906403788e-100.999999999914566
271.70396356853019e-113.40792713706038e-110.99999999998296
284.499382633877e-128.998765267754e-120.9999999999955
291.57748330049104e-123.15496660098207e-120.999999999998423
307.29592329272364e-131.45918465854473e-120.99999999999927
315.44108075968498e-131.08821615193700e-120.999999999999456
326.53778181642413e-131.30755636328483e-120.999999999999346
335.1768291790363e-131.03536583580726e-120.999999999999482
341.29900204834893e-132.59800409669787e-130.99999999999987
352.67159383321734e-145.34318766643467e-140.999999999999973
366.55885558894393e-151.31177111778879e-140.999999999999993
371.77916987927629e-153.55833975855257e-150.999999999999998
383.63223584052739e-167.26447168105478e-161
399.80005779387519e-171.96001155877504e-161
401.94530431189746e-173.89060862379493e-171
413.79929964377818e-187.59859928755636e-181
427.00291621353972e-191.40058324270794e-181
432.31689629654436e-194.63379259308873e-191
446.53101362008446e-201.30620272401689e-191
452.57245038213323e-205.14490076426646e-201
464.77268397504204e-219.54536795008408e-211
471.43792434510808e-212.87584869021616e-211
481.03068346829208e-212.06136693658415e-211
494.97395380631163e-229.94790761262325e-221
509.3649890022176e-231.87299780044352e-221
512.60619119923298e-235.21238239846595e-231
528.67518011995407e-241.73503602399081e-231
532.49649844307897e-244.99299688615794e-241
545.87110562215808e-251.17422112443162e-241
551.30534398929716e-252.61068797859432e-251
563.10355952433097e-266.20711904866194e-261
577.26801056432223e-271.45360211286445e-261
583.93579212215559e-277.87158424431119e-271
591.49771155516139e-272.99542311032278e-271
604.8889423335518e-289.7778846671036e-281
611.83044332344893e-283.66088664689786e-281
624.89825860135035e-299.7965172027007e-291
631.19412901196022e-292.38825802392045e-291
641.19197459878375e-292.38394919756750e-291
652.35783891370854e-294.71567782741708e-291
668.49261815304705e-291.69852363060941e-281
677.02720619020554e-281.40544123804111e-271
685.00242885778955e-281.00048577155791e-271
693.99056884778608e-277.98113769557217e-271
709.41450360046218e-271.88290072009244e-261
714.99757025484104e-279.99514050968207e-271
728.64244156019448e-271.72848831203890e-261
734.24315675199104e-278.48631350398208e-271
741.20433909429672e-272.40867818859344e-271
758.37852721153428e-281.67570544230686e-271
765.89112487634561e-281.17822497526912e-271
771.5223611669999e-283.0447223339998e-281
785.09029566746982e-291.01805913349396e-281
791.28077477334631e-292.56154954669262e-291
801.00920104199262e-292.01840208398525e-291
811.24430186252918e-292.48860372505836e-291
823.33937217243742e-306.67874434487484e-301
831.10003793231168e-302.20007586462337e-301
846.87850319792859e-311.37570063958572e-301
856.5895980721558e-311.31791961443116e-301
867.89396829358707e-311.57879365871741e-301
876.25829216647165e-311.25165843329433e-301
883.65975601179134e-317.31951202358268e-311
891.93415300748548e-313.86830601497096e-311
909.15688384166924e-321.83137676833385e-311
913.72999750060885e-327.4599950012177e-321
921.39168169517072e-322.78336339034143e-321
936.23881515665973e-331.24776303133195e-321
942.25261660468516e-334.50523320937032e-331
953.50949916076102e-337.01899832152203e-331
962.82162855473594e-335.64325710947188e-331
971.77594137169217e-333.55188274338435e-331
982.96425222806391e-335.92850445612781e-331
996.26982358393165e-331.25396471678633e-321
1001.93860624360509e-323.87721248721017e-321
1011.58849083828129e-323.17698167656258e-321
1021.40601411635527e-322.81202823271054e-321
1035.17981190589277e-321.03596238117855e-311
1046.51362298157517e-311.30272459631503e-301
1058.05718251032566e-311.61143650206513e-301
1066.8538437004469e-311.37076874008938e-301
1077.58386857986049e-311.51677371597210e-301
1089.6063581322565e-311.9212716264513e-301
1091.25413330876228e-302.50826661752457e-301
1101.19522598599097e-302.39045197198195e-301
1111.35106995712570e-302.70213991425141e-301
1129.72214523189985e-301.94442904637997e-291
1132.07798351692806e-294.15596703385612e-291
1148.36409342021507e-291.67281868404301e-281
1153.36734547593583e-286.73469095187167e-281
1161.14941240469585e-272.2988248093917e-271
1172.62766000636865e-255.25532001273729e-251
1184.52917133392812e-249.05834266785623e-241
1191.85939036957645e-233.71878073915289e-231
1204.18599619270075e-238.3719923854015e-231
1213.29634726685878e-236.59269453371756e-231
1221.65489358358826e-233.30978716717652e-231
1237.73543985531887e-241.54708797106377e-231
1246.56377011840348e-241.31275402368070e-231
1258.3529534405788e-241.67059068811576e-231
1261.22922548415184e-232.45845096830367e-231
1272.39841623940851e-234.79683247881701e-231
1289.47084513619317e-231.89416902723863e-221
1291.13013460018570e-212.26026920037140e-211
1304.68033769236859e-199.36067538473718e-191
1311.47446985103194e-132.94893970206387e-130.999999999999853
1327.15868502984278e-061.43173700596856e-050.99999284131497
1335.51463015308344e-061.10292603061669e-050.999994485369847
1340.0003921519448373900.0007843038896747810.999607848055163
1350.0003406142410777990.0006812284821555990.999659385758922
1360.005259575325015630.01051915065003130.994740424674984
1370.05294615405249750.1058923081049950.947053845947503
1380.04824403611837150.0964880722367430.951755963881628
1390.3906923986111260.7813847972222520.609307601388874
1400.6000853296837580.7998293406324830.399914670316242
1410.5461617827831240.9076764344337530.453838217216876
1420.5186837776710440.9626324446579120.481316222328956
1430.4622672631388230.9245345262776460.537732736861177
1440.4084971860559580.8169943721119160.591502813944042
1450.3999672584830820.7999345169661640.600032741516918
1460.4942692244016330.9885384488032660.505730775598367
1470.7427883741037280.5144232517925430.257211625896272
1480.7856865465176340.4286269069647320.214313453482366
1490.7452319798563620.5095360402872760.254768020143638
1500.6931370140052690.6137259719894620.306862985994731
1510.6328344977673470.7343310044653050.367165502232653
1520.6327321377154620.7345357245690760.367267862284538
1530.6129448739183040.7741102521633910.387055126081696
1540.7240653574385620.5518692851228770.275934642561438
1550.7374514624478230.5250970751043530.262548537552177
1560.6950965737607620.6098068524784770.304903426239238
1570.649923486590650.7001530268187010.350076513409350
1580.5919679087945540.8160641824108930.408032091205446
1590.7163053525787020.5673892948425960.283694647421298
1600.710888727757420.5782225444851610.289111272242581
1610.8046071092491640.3907857815016710.195392890750836
1620.9998245500496770.0003508999006469930.000175449950323497
1630.9997384751136770.0005230497726460960.000261524886323048
1640.999861205352090.0002775892958193940.000138794647909697
1650.9998144619001350.0003710761997297180.000185538099864859
1660.9992987400897250.001402519820549510.000701259910274757
1670.9969648119628470.00607037607430680.0030351880371534
1680.9988468834831680.002306233033664930.00115311651683247
1690.993815053442040.01236989311591950.00618494655795975

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.000522826309233959 & 0.00104565261846792 & 0.999477173690766 \tabularnewline
12 & 3.15944634842757e-05 & 6.31889269685515e-05 & 0.999968405536516 \tabularnewline
13 & 1.72507314555143e-06 & 3.45014629110286e-06 & 0.999998274926854 \tabularnewline
14 & 6.2716763139989e-06 & 1.25433526279978e-05 & 0.999993728323686 \tabularnewline
15 & 6.06584306000083e-07 & 1.21316861200017e-06 & 0.999999393415694 \tabularnewline
16 & 8.89862811798075e-08 & 1.77972562359615e-07 & 0.999999911013719 \tabularnewline
17 & 1.10766711074340e-08 & 2.21533422148681e-08 & 0.999999988923329 \tabularnewline
18 & 1.00937683804983e-09 & 2.01875367609965e-09 & 0.999999998990623 \tabularnewline
19 & 2.07113232648347e-09 & 4.14226465296694e-09 & 0.999999997928868 \tabularnewline
20 & 2.37894443622177e-09 & 4.75788887244353e-09 & 0.999999997621056 \tabularnewline
21 & 8.03241466803467e-10 & 1.60648293360693e-09 & 0.999999999196759 \tabularnewline
22 & 2.81402506458893e-09 & 5.62805012917786e-09 & 0.999999997185975 \tabularnewline
23 & 4.95345198622942e-10 & 9.90690397245885e-10 & 0.999999999504655 \tabularnewline
24 & 2.53107632052047e-10 & 5.06215264104095e-10 & 0.999999999746892 \tabularnewline
25 & 1.42362248258200e-10 & 2.84724496516399e-10 & 0.999999999857638 \tabularnewline
26 & 8.5434453201894e-11 & 1.70868906403788e-10 & 0.999999999914566 \tabularnewline
27 & 1.70396356853019e-11 & 3.40792713706038e-11 & 0.99999999998296 \tabularnewline
28 & 4.499382633877e-12 & 8.998765267754e-12 & 0.9999999999955 \tabularnewline
29 & 1.57748330049104e-12 & 3.15496660098207e-12 & 0.999999999998423 \tabularnewline
30 & 7.29592329272364e-13 & 1.45918465854473e-12 & 0.99999999999927 \tabularnewline
31 & 5.44108075968498e-13 & 1.08821615193700e-12 & 0.999999999999456 \tabularnewline
32 & 6.53778181642413e-13 & 1.30755636328483e-12 & 0.999999999999346 \tabularnewline
33 & 5.1768291790363e-13 & 1.03536583580726e-12 & 0.999999999999482 \tabularnewline
34 & 1.29900204834893e-13 & 2.59800409669787e-13 & 0.99999999999987 \tabularnewline
35 & 2.67159383321734e-14 & 5.34318766643467e-14 & 0.999999999999973 \tabularnewline
36 & 6.55885558894393e-15 & 1.31177111778879e-14 & 0.999999999999993 \tabularnewline
37 & 1.77916987927629e-15 & 3.55833975855257e-15 & 0.999999999999998 \tabularnewline
38 & 3.63223584052739e-16 & 7.26447168105478e-16 & 1 \tabularnewline
39 & 9.80005779387519e-17 & 1.96001155877504e-16 & 1 \tabularnewline
40 & 1.94530431189746e-17 & 3.89060862379493e-17 & 1 \tabularnewline
41 & 3.79929964377818e-18 & 7.59859928755636e-18 & 1 \tabularnewline
42 & 7.00291621353972e-19 & 1.40058324270794e-18 & 1 \tabularnewline
43 & 2.31689629654436e-19 & 4.63379259308873e-19 & 1 \tabularnewline
44 & 6.53101362008446e-20 & 1.30620272401689e-19 & 1 \tabularnewline
45 & 2.57245038213323e-20 & 5.14490076426646e-20 & 1 \tabularnewline
46 & 4.77268397504204e-21 & 9.54536795008408e-21 & 1 \tabularnewline
47 & 1.43792434510808e-21 & 2.87584869021616e-21 & 1 \tabularnewline
48 & 1.03068346829208e-21 & 2.06136693658415e-21 & 1 \tabularnewline
49 & 4.97395380631163e-22 & 9.94790761262325e-22 & 1 \tabularnewline
50 & 9.3649890022176e-23 & 1.87299780044352e-22 & 1 \tabularnewline
51 & 2.60619119923298e-23 & 5.21238239846595e-23 & 1 \tabularnewline
52 & 8.67518011995407e-24 & 1.73503602399081e-23 & 1 \tabularnewline
53 & 2.49649844307897e-24 & 4.99299688615794e-24 & 1 \tabularnewline
54 & 5.87110562215808e-25 & 1.17422112443162e-24 & 1 \tabularnewline
55 & 1.30534398929716e-25 & 2.61068797859432e-25 & 1 \tabularnewline
56 & 3.10355952433097e-26 & 6.20711904866194e-26 & 1 \tabularnewline
57 & 7.26801056432223e-27 & 1.45360211286445e-26 & 1 \tabularnewline
58 & 3.93579212215559e-27 & 7.87158424431119e-27 & 1 \tabularnewline
59 & 1.49771155516139e-27 & 2.99542311032278e-27 & 1 \tabularnewline
60 & 4.8889423335518e-28 & 9.7778846671036e-28 & 1 \tabularnewline
61 & 1.83044332344893e-28 & 3.66088664689786e-28 & 1 \tabularnewline
62 & 4.89825860135035e-29 & 9.7965172027007e-29 & 1 \tabularnewline
63 & 1.19412901196022e-29 & 2.38825802392045e-29 & 1 \tabularnewline
64 & 1.19197459878375e-29 & 2.38394919756750e-29 & 1 \tabularnewline
65 & 2.35783891370854e-29 & 4.71567782741708e-29 & 1 \tabularnewline
66 & 8.49261815304705e-29 & 1.69852363060941e-28 & 1 \tabularnewline
67 & 7.02720619020554e-28 & 1.40544123804111e-27 & 1 \tabularnewline
68 & 5.00242885778955e-28 & 1.00048577155791e-27 & 1 \tabularnewline
69 & 3.99056884778608e-27 & 7.98113769557217e-27 & 1 \tabularnewline
70 & 9.41450360046218e-27 & 1.88290072009244e-26 & 1 \tabularnewline
71 & 4.99757025484104e-27 & 9.99514050968207e-27 & 1 \tabularnewline
72 & 8.64244156019448e-27 & 1.72848831203890e-26 & 1 \tabularnewline
73 & 4.24315675199104e-27 & 8.48631350398208e-27 & 1 \tabularnewline
74 & 1.20433909429672e-27 & 2.40867818859344e-27 & 1 \tabularnewline
75 & 8.37852721153428e-28 & 1.67570544230686e-27 & 1 \tabularnewline
76 & 5.89112487634561e-28 & 1.17822497526912e-27 & 1 \tabularnewline
77 & 1.5223611669999e-28 & 3.0447223339998e-28 & 1 \tabularnewline
78 & 5.09029566746982e-29 & 1.01805913349396e-28 & 1 \tabularnewline
79 & 1.28077477334631e-29 & 2.56154954669262e-29 & 1 \tabularnewline
80 & 1.00920104199262e-29 & 2.01840208398525e-29 & 1 \tabularnewline
81 & 1.24430186252918e-29 & 2.48860372505836e-29 & 1 \tabularnewline
82 & 3.33937217243742e-30 & 6.67874434487484e-30 & 1 \tabularnewline
83 & 1.10003793231168e-30 & 2.20007586462337e-30 & 1 \tabularnewline
84 & 6.87850319792859e-31 & 1.37570063958572e-30 & 1 \tabularnewline
85 & 6.5895980721558e-31 & 1.31791961443116e-30 & 1 \tabularnewline
86 & 7.89396829358707e-31 & 1.57879365871741e-30 & 1 \tabularnewline
87 & 6.25829216647165e-31 & 1.25165843329433e-30 & 1 \tabularnewline
88 & 3.65975601179134e-31 & 7.31951202358268e-31 & 1 \tabularnewline
89 & 1.93415300748548e-31 & 3.86830601497096e-31 & 1 \tabularnewline
90 & 9.15688384166924e-32 & 1.83137676833385e-31 & 1 \tabularnewline
91 & 3.72999750060885e-32 & 7.4599950012177e-32 & 1 \tabularnewline
92 & 1.39168169517072e-32 & 2.78336339034143e-32 & 1 \tabularnewline
93 & 6.23881515665973e-33 & 1.24776303133195e-32 & 1 \tabularnewline
94 & 2.25261660468516e-33 & 4.50523320937032e-33 & 1 \tabularnewline
95 & 3.50949916076102e-33 & 7.01899832152203e-33 & 1 \tabularnewline
96 & 2.82162855473594e-33 & 5.64325710947188e-33 & 1 \tabularnewline
97 & 1.77594137169217e-33 & 3.55188274338435e-33 & 1 \tabularnewline
98 & 2.96425222806391e-33 & 5.92850445612781e-33 & 1 \tabularnewline
99 & 6.26982358393165e-33 & 1.25396471678633e-32 & 1 \tabularnewline
100 & 1.93860624360509e-32 & 3.87721248721017e-32 & 1 \tabularnewline
101 & 1.58849083828129e-32 & 3.17698167656258e-32 & 1 \tabularnewline
102 & 1.40601411635527e-32 & 2.81202823271054e-32 & 1 \tabularnewline
103 & 5.17981190589277e-32 & 1.03596238117855e-31 & 1 \tabularnewline
104 & 6.51362298157517e-31 & 1.30272459631503e-30 & 1 \tabularnewline
105 & 8.05718251032566e-31 & 1.61143650206513e-30 & 1 \tabularnewline
106 & 6.8538437004469e-31 & 1.37076874008938e-30 & 1 \tabularnewline
107 & 7.58386857986049e-31 & 1.51677371597210e-30 & 1 \tabularnewline
108 & 9.6063581322565e-31 & 1.9212716264513e-30 & 1 \tabularnewline
109 & 1.25413330876228e-30 & 2.50826661752457e-30 & 1 \tabularnewline
110 & 1.19522598599097e-30 & 2.39045197198195e-30 & 1 \tabularnewline
111 & 1.35106995712570e-30 & 2.70213991425141e-30 & 1 \tabularnewline
112 & 9.72214523189985e-30 & 1.94442904637997e-29 & 1 \tabularnewline
113 & 2.07798351692806e-29 & 4.15596703385612e-29 & 1 \tabularnewline
114 & 8.36409342021507e-29 & 1.67281868404301e-28 & 1 \tabularnewline
115 & 3.36734547593583e-28 & 6.73469095187167e-28 & 1 \tabularnewline
116 & 1.14941240469585e-27 & 2.2988248093917e-27 & 1 \tabularnewline
117 & 2.62766000636865e-25 & 5.25532001273729e-25 & 1 \tabularnewline
118 & 4.52917133392812e-24 & 9.05834266785623e-24 & 1 \tabularnewline
119 & 1.85939036957645e-23 & 3.71878073915289e-23 & 1 \tabularnewline
120 & 4.18599619270075e-23 & 8.3719923854015e-23 & 1 \tabularnewline
121 & 3.29634726685878e-23 & 6.59269453371756e-23 & 1 \tabularnewline
122 & 1.65489358358826e-23 & 3.30978716717652e-23 & 1 \tabularnewline
123 & 7.73543985531887e-24 & 1.54708797106377e-23 & 1 \tabularnewline
124 & 6.56377011840348e-24 & 1.31275402368070e-23 & 1 \tabularnewline
125 & 8.3529534405788e-24 & 1.67059068811576e-23 & 1 \tabularnewline
126 & 1.22922548415184e-23 & 2.45845096830367e-23 & 1 \tabularnewline
127 & 2.39841623940851e-23 & 4.79683247881701e-23 & 1 \tabularnewline
128 & 9.47084513619317e-23 & 1.89416902723863e-22 & 1 \tabularnewline
129 & 1.13013460018570e-21 & 2.26026920037140e-21 & 1 \tabularnewline
130 & 4.68033769236859e-19 & 9.36067538473718e-19 & 1 \tabularnewline
131 & 1.47446985103194e-13 & 2.94893970206387e-13 & 0.999999999999853 \tabularnewline
132 & 7.15868502984278e-06 & 1.43173700596856e-05 & 0.99999284131497 \tabularnewline
133 & 5.51463015308344e-06 & 1.10292603061669e-05 & 0.999994485369847 \tabularnewline
134 & 0.000392151944837390 & 0.000784303889674781 & 0.999607848055163 \tabularnewline
135 & 0.000340614241077799 & 0.000681228482155599 & 0.999659385758922 \tabularnewline
136 & 0.00525957532501563 & 0.0105191506500313 & 0.994740424674984 \tabularnewline
137 & 0.0529461540524975 & 0.105892308104995 & 0.947053845947503 \tabularnewline
138 & 0.0482440361183715 & 0.096488072236743 & 0.951755963881628 \tabularnewline
139 & 0.390692398611126 & 0.781384797222252 & 0.609307601388874 \tabularnewline
140 & 0.600085329683758 & 0.799829340632483 & 0.399914670316242 \tabularnewline
141 & 0.546161782783124 & 0.907676434433753 & 0.453838217216876 \tabularnewline
142 & 0.518683777671044 & 0.962632444657912 & 0.481316222328956 \tabularnewline
143 & 0.462267263138823 & 0.924534526277646 & 0.537732736861177 \tabularnewline
144 & 0.408497186055958 & 0.816994372111916 & 0.591502813944042 \tabularnewline
145 & 0.399967258483082 & 0.799934516966164 & 0.600032741516918 \tabularnewline
146 & 0.494269224401633 & 0.988538448803266 & 0.505730775598367 \tabularnewline
147 & 0.742788374103728 & 0.514423251792543 & 0.257211625896272 \tabularnewline
148 & 0.785686546517634 & 0.428626906964732 & 0.214313453482366 \tabularnewline
149 & 0.745231979856362 & 0.509536040287276 & 0.254768020143638 \tabularnewline
150 & 0.693137014005269 & 0.613725971989462 & 0.306862985994731 \tabularnewline
151 & 0.632834497767347 & 0.734331004465305 & 0.367165502232653 \tabularnewline
152 & 0.632732137715462 & 0.734535724569076 & 0.367267862284538 \tabularnewline
153 & 0.612944873918304 & 0.774110252163391 & 0.387055126081696 \tabularnewline
154 & 0.724065357438562 & 0.551869285122877 & 0.275934642561438 \tabularnewline
155 & 0.737451462447823 & 0.525097075104353 & 0.262548537552177 \tabularnewline
156 & 0.695096573760762 & 0.609806852478477 & 0.304903426239238 \tabularnewline
157 & 0.64992348659065 & 0.700153026818701 & 0.350076513409350 \tabularnewline
158 & 0.591967908794554 & 0.816064182410893 & 0.408032091205446 \tabularnewline
159 & 0.716305352578702 & 0.567389294842596 & 0.283694647421298 \tabularnewline
160 & 0.71088872775742 & 0.578222544485161 & 0.289111272242581 \tabularnewline
161 & 0.804607109249164 & 0.390785781501671 & 0.195392890750836 \tabularnewline
162 & 0.999824550049677 & 0.000350899900646993 & 0.000175449950323497 \tabularnewline
163 & 0.999738475113677 & 0.000523049772646096 & 0.000261524886323048 \tabularnewline
164 & 0.99986120535209 & 0.000277589295819394 & 0.000138794647909697 \tabularnewline
165 & 0.999814461900135 & 0.000371076199729718 & 0.000185538099864859 \tabularnewline
166 & 0.999298740089725 & 0.00140251982054951 & 0.000701259910274757 \tabularnewline
167 & 0.996964811962847 & 0.0060703760743068 & 0.0030351880371534 \tabularnewline
168 & 0.998846883483168 & 0.00230623303366493 & 0.00115311651683247 \tabularnewline
169 & 0.99381505344204 & 0.0123698931159195 & 0.00618494655795975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104184&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.000522826309233959[/C][C]0.00104565261846792[/C][C]0.999477173690766[/C][/ROW]
[ROW][C]12[/C][C]3.15944634842757e-05[/C][C]6.31889269685515e-05[/C][C]0.999968405536516[/C][/ROW]
[ROW][C]13[/C][C]1.72507314555143e-06[/C][C]3.45014629110286e-06[/C][C]0.999998274926854[/C][/ROW]
[ROW][C]14[/C][C]6.2716763139989e-06[/C][C]1.25433526279978e-05[/C][C]0.999993728323686[/C][/ROW]
[ROW][C]15[/C][C]6.06584306000083e-07[/C][C]1.21316861200017e-06[/C][C]0.999999393415694[/C][/ROW]
[ROW][C]16[/C][C]8.89862811798075e-08[/C][C]1.77972562359615e-07[/C][C]0.999999911013719[/C][/ROW]
[ROW][C]17[/C][C]1.10766711074340e-08[/C][C]2.21533422148681e-08[/C][C]0.999999988923329[/C][/ROW]
[ROW][C]18[/C][C]1.00937683804983e-09[/C][C]2.01875367609965e-09[/C][C]0.999999998990623[/C][/ROW]
[ROW][C]19[/C][C]2.07113232648347e-09[/C][C]4.14226465296694e-09[/C][C]0.999999997928868[/C][/ROW]
[ROW][C]20[/C][C]2.37894443622177e-09[/C][C]4.75788887244353e-09[/C][C]0.999999997621056[/C][/ROW]
[ROW][C]21[/C][C]8.03241466803467e-10[/C][C]1.60648293360693e-09[/C][C]0.999999999196759[/C][/ROW]
[ROW][C]22[/C][C]2.81402506458893e-09[/C][C]5.62805012917786e-09[/C][C]0.999999997185975[/C][/ROW]
[ROW][C]23[/C][C]4.95345198622942e-10[/C][C]9.90690397245885e-10[/C][C]0.999999999504655[/C][/ROW]
[ROW][C]24[/C][C]2.53107632052047e-10[/C][C]5.06215264104095e-10[/C][C]0.999999999746892[/C][/ROW]
[ROW][C]25[/C][C]1.42362248258200e-10[/C][C]2.84724496516399e-10[/C][C]0.999999999857638[/C][/ROW]
[ROW][C]26[/C][C]8.5434453201894e-11[/C][C]1.70868906403788e-10[/C][C]0.999999999914566[/C][/ROW]
[ROW][C]27[/C][C]1.70396356853019e-11[/C][C]3.40792713706038e-11[/C][C]0.99999999998296[/C][/ROW]
[ROW][C]28[/C][C]4.499382633877e-12[/C][C]8.998765267754e-12[/C][C]0.9999999999955[/C][/ROW]
[ROW][C]29[/C][C]1.57748330049104e-12[/C][C]3.15496660098207e-12[/C][C]0.999999999998423[/C][/ROW]
[ROW][C]30[/C][C]7.29592329272364e-13[/C][C]1.45918465854473e-12[/C][C]0.99999999999927[/C][/ROW]
[ROW][C]31[/C][C]5.44108075968498e-13[/C][C]1.08821615193700e-12[/C][C]0.999999999999456[/C][/ROW]
[ROW][C]32[/C][C]6.53778181642413e-13[/C][C]1.30755636328483e-12[/C][C]0.999999999999346[/C][/ROW]
[ROW][C]33[/C][C]5.1768291790363e-13[/C][C]1.03536583580726e-12[/C][C]0.999999999999482[/C][/ROW]
[ROW][C]34[/C][C]1.29900204834893e-13[/C][C]2.59800409669787e-13[/C][C]0.99999999999987[/C][/ROW]
[ROW][C]35[/C][C]2.67159383321734e-14[/C][C]5.34318766643467e-14[/C][C]0.999999999999973[/C][/ROW]
[ROW][C]36[/C][C]6.55885558894393e-15[/C][C]1.31177111778879e-14[/C][C]0.999999999999993[/C][/ROW]
[ROW][C]37[/C][C]1.77916987927629e-15[/C][C]3.55833975855257e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]38[/C][C]3.63223584052739e-16[/C][C]7.26447168105478e-16[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]9.80005779387519e-17[/C][C]1.96001155877504e-16[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]1.94530431189746e-17[/C][C]3.89060862379493e-17[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]3.79929964377818e-18[/C][C]7.59859928755636e-18[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]7.00291621353972e-19[/C][C]1.40058324270794e-18[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]2.31689629654436e-19[/C][C]4.63379259308873e-19[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]6.53101362008446e-20[/C][C]1.30620272401689e-19[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]2.57245038213323e-20[/C][C]5.14490076426646e-20[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]4.77268397504204e-21[/C][C]9.54536795008408e-21[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]1.43792434510808e-21[/C][C]2.87584869021616e-21[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]1.03068346829208e-21[/C][C]2.06136693658415e-21[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]4.97395380631163e-22[/C][C]9.94790761262325e-22[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]9.3649890022176e-23[/C][C]1.87299780044352e-22[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]2.60619119923298e-23[/C][C]5.21238239846595e-23[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]8.67518011995407e-24[/C][C]1.73503602399081e-23[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]2.49649844307897e-24[/C][C]4.99299688615794e-24[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]5.87110562215808e-25[/C][C]1.17422112443162e-24[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]1.30534398929716e-25[/C][C]2.61068797859432e-25[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]3.10355952433097e-26[/C][C]6.20711904866194e-26[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]7.26801056432223e-27[/C][C]1.45360211286445e-26[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]3.93579212215559e-27[/C][C]7.87158424431119e-27[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]1.49771155516139e-27[/C][C]2.99542311032278e-27[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]4.8889423335518e-28[/C][C]9.7778846671036e-28[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]1.83044332344893e-28[/C][C]3.66088664689786e-28[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]4.89825860135035e-29[/C][C]9.7965172027007e-29[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]1.19412901196022e-29[/C][C]2.38825802392045e-29[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]1.19197459878375e-29[/C][C]2.38394919756750e-29[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]2.35783891370854e-29[/C][C]4.71567782741708e-29[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]8.49261815304705e-29[/C][C]1.69852363060941e-28[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]7.02720619020554e-28[/C][C]1.40544123804111e-27[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]5.00242885778955e-28[/C][C]1.00048577155791e-27[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]3.99056884778608e-27[/C][C]7.98113769557217e-27[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]9.41450360046218e-27[/C][C]1.88290072009244e-26[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]4.99757025484104e-27[/C][C]9.99514050968207e-27[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]8.64244156019448e-27[/C][C]1.72848831203890e-26[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]4.24315675199104e-27[/C][C]8.48631350398208e-27[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]1.20433909429672e-27[/C][C]2.40867818859344e-27[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]8.37852721153428e-28[/C][C]1.67570544230686e-27[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]5.89112487634561e-28[/C][C]1.17822497526912e-27[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]1.5223611669999e-28[/C][C]3.0447223339998e-28[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]5.09029566746982e-29[/C][C]1.01805913349396e-28[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]1.28077477334631e-29[/C][C]2.56154954669262e-29[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]1.00920104199262e-29[/C][C]2.01840208398525e-29[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]1.24430186252918e-29[/C][C]2.48860372505836e-29[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]3.33937217243742e-30[/C][C]6.67874434487484e-30[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]1.10003793231168e-30[/C][C]2.20007586462337e-30[/C][C]1[/C][/ROW]
[ROW][C]84[/C][C]6.87850319792859e-31[/C][C]1.37570063958572e-30[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]6.5895980721558e-31[/C][C]1.31791961443116e-30[/C][C]1[/C][/ROW]
[ROW][C]86[/C][C]7.89396829358707e-31[/C][C]1.57879365871741e-30[/C][C]1[/C][/ROW]
[ROW][C]87[/C][C]6.25829216647165e-31[/C][C]1.25165843329433e-30[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]3.65975601179134e-31[/C][C]7.31951202358268e-31[/C][C]1[/C][/ROW]
[ROW][C]89[/C][C]1.93415300748548e-31[/C][C]3.86830601497096e-31[/C][C]1[/C][/ROW]
[ROW][C]90[/C][C]9.15688384166924e-32[/C][C]1.83137676833385e-31[/C][C]1[/C][/ROW]
[ROW][C]91[/C][C]3.72999750060885e-32[/C][C]7.4599950012177e-32[/C][C]1[/C][/ROW]
[ROW][C]92[/C][C]1.39168169517072e-32[/C][C]2.78336339034143e-32[/C][C]1[/C][/ROW]
[ROW][C]93[/C][C]6.23881515665973e-33[/C][C]1.24776303133195e-32[/C][C]1[/C][/ROW]
[ROW][C]94[/C][C]2.25261660468516e-33[/C][C]4.50523320937032e-33[/C][C]1[/C][/ROW]
[ROW][C]95[/C][C]3.50949916076102e-33[/C][C]7.01899832152203e-33[/C][C]1[/C][/ROW]
[ROW][C]96[/C][C]2.82162855473594e-33[/C][C]5.64325710947188e-33[/C][C]1[/C][/ROW]
[ROW][C]97[/C][C]1.77594137169217e-33[/C][C]3.55188274338435e-33[/C][C]1[/C][/ROW]
[ROW][C]98[/C][C]2.96425222806391e-33[/C][C]5.92850445612781e-33[/C][C]1[/C][/ROW]
[ROW][C]99[/C][C]6.26982358393165e-33[/C][C]1.25396471678633e-32[/C][C]1[/C][/ROW]
[ROW][C]100[/C][C]1.93860624360509e-32[/C][C]3.87721248721017e-32[/C][C]1[/C][/ROW]
[ROW][C]101[/C][C]1.58849083828129e-32[/C][C]3.17698167656258e-32[/C][C]1[/C][/ROW]
[ROW][C]102[/C][C]1.40601411635527e-32[/C][C]2.81202823271054e-32[/C][C]1[/C][/ROW]
[ROW][C]103[/C][C]5.17981190589277e-32[/C][C]1.03596238117855e-31[/C][C]1[/C][/ROW]
[ROW][C]104[/C][C]6.51362298157517e-31[/C][C]1.30272459631503e-30[/C][C]1[/C][/ROW]
[ROW][C]105[/C][C]8.05718251032566e-31[/C][C]1.61143650206513e-30[/C][C]1[/C][/ROW]
[ROW][C]106[/C][C]6.8538437004469e-31[/C][C]1.37076874008938e-30[/C][C]1[/C][/ROW]
[ROW][C]107[/C][C]7.58386857986049e-31[/C][C]1.51677371597210e-30[/C][C]1[/C][/ROW]
[ROW][C]108[/C][C]9.6063581322565e-31[/C][C]1.9212716264513e-30[/C][C]1[/C][/ROW]
[ROW][C]109[/C][C]1.25413330876228e-30[/C][C]2.50826661752457e-30[/C][C]1[/C][/ROW]
[ROW][C]110[/C][C]1.19522598599097e-30[/C][C]2.39045197198195e-30[/C][C]1[/C][/ROW]
[ROW][C]111[/C][C]1.35106995712570e-30[/C][C]2.70213991425141e-30[/C][C]1[/C][/ROW]
[ROW][C]112[/C][C]9.72214523189985e-30[/C][C]1.94442904637997e-29[/C][C]1[/C][/ROW]
[ROW][C]113[/C][C]2.07798351692806e-29[/C][C]4.15596703385612e-29[/C][C]1[/C][/ROW]
[ROW][C]114[/C][C]8.36409342021507e-29[/C][C]1.67281868404301e-28[/C][C]1[/C][/ROW]
[ROW][C]115[/C][C]3.36734547593583e-28[/C][C]6.73469095187167e-28[/C][C]1[/C][/ROW]
[ROW][C]116[/C][C]1.14941240469585e-27[/C][C]2.2988248093917e-27[/C][C]1[/C][/ROW]
[ROW][C]117[/C][C]2.62766000636865e-25[/C][C]5.25532001273729e-25[/C][C]1[/C][/ROW]
[ROW][C]118[/C][C]4.52917133392812e-24[/C][C]9.05834266785623e-24[/C][C]1[/C][/ROW]
[ROW][C]119[/C][C]1.85939036957645e-23[/C][C]3.71878073915289e-23[/C][C]1[/C][/ROW]
[ROW][C]120[/C][C]4.18599619270075e-23[/C][C]8.3719923854015e-23[/C][C]1[/C][/ROW]
[ROW][C]121[/C][C]3.29634726685878e-23[/C][C]6.59269453371756e-23[/C][C]1[/C][/ROW]
[ROW][C]122[/C][C]1.65489358358826e-23[/C][C]3.30978716717652e-23[/C][C]1[/C][/ROW]
[ROW][C]123[/C][C]7.73543985531887e-24[/C][C]1.54708797106377e-23[/C][C]1[/C][/ROW]
[ROW][C]124[/C][C]6.56377011840348e-24[/C][C]1.31275402368070e-23[/C][C]1[/C][/ROW]
[ROW][C]125[/C][C]8.3529534405788e-24[/C][C]1.67059068811576e-23[/C][C]1[/C][/ROW]
[ROW][C]126[/C][C]1.22922548415184e-23[/C][C]2.45845096830367e-23[/C][C]1[/C][/ROW]
[ROW][C]127[/C][C]2.39841623940851e-23[/C][C]4.79683247881701e-23[/C][C]1[/C][/ROW]
[ROW][C]128[/C][C]9.47084513619317e-23[/C][C]1.89416902723863e-22[/C][C]1[/C][/ROW]
[ROW][C]129[/C][C]1.13013460018570e-21[/C][C]2.26026920037140e-21[/C][C]1[/C][/ROW]
[ROW][C]130[/C][C]4.68033769236859e-19[/C][C]9.36067538473718e-19[/C][C]1[/C][/ROW]
[ROW][C]131[/C][C]1.47446985103194e-13[/C][C]2.94893970206387e-13[/C][C]0.999999999999853[/C][/ROW]
[ROW][C]132[/C][C]7.15868502984278e-06[/C][C]1.43173700596856e-05[/C][C]0.99999284131497[/C][/ROW]
[ROW][C]133[/C][C]5.51463015308344e-06[/C][C]1.10292603061669e-05[/C][C]0.999994485369847[/C][/ROW]
[ROW][C]134[/C][C]0.000392151944837390[/C][C]0.000784303889674781[/C][C]0.999607848055163[/C][/ROW]
[ROW][C]135[/C][C]0.000340614241077799[/C][C]0.000681228482155599[/C][C]0.999659385758922[/C][/ROW]
[ROW][C]136[/C][C]0.00525957532501563[/C][C]0.0105191506500313[/C][C]0.994740424674984[/C][/ROW]
[ROW][C]137[/C][C]0.0529461540524975[/C][C]0.105892308104995[/C][C]0.947053845947503[/C][/ROW]
[ROW][C]138[/C][C]0.0482440361183715[/C][C]0.096488072236743[/C][C]0.951755963881628[/C][/ROW]
[ROW][C]139[/C][C]0.390692398611126[/C][C]0.781384797222252[/C][C]0.609307601388874[/C][/ROW]
[ROW][C]140[/C][C]0.600085329683758[/C][C]0.799829340632483[/C][C]0.399914670316242[/C][/ROW]
[ROW][C]141[/C][C]0.546161782783124[/C][C]0.907676434433753[/C][C]0.453838217216876[/C][/ROW]
[ROW][C]142[/C][C]0.518683777671044[/C][C]0.962632444657912[/C][C]0.481316222328956[/C][/ROW]
[ROW][C]143[/C][C]0.462267263138823[/C][C]0.924534526277646[/C][C]0.537732736861177[/C][/ROW]
[ROW][C]144[/C][C]0.408497186055958[/C][C]0.816994372111916[/C][C]0.591502813944042[/C][/ROW]
[ROW][C]145[/C][C]0.399967258483082[/C][C]0.799934516966164[/C][C]0.600032741516918[/C][/ROW]
[ROW][C]146[/C][C]0.494269224401633[/C][C]0.988538448803266[/C][C]0.505730775598367[/C][/ROW]
[ROW][C]147[/C][C]0.742788374103728[/C][C]0.514423251792543[/C][C]0.257211625896272[/C][/ROW]
[ROW][C]148[/C][C]0.785686546517634[/C][C]0.428626906964732[/C][C]0.214313453482366[/C][/ROW]
[ROW][C]149[/C][C]0.745231979856362[/C][C]0.509536040287276[/C][C]0.254768020143638[/C][/ROW]
[ROW][C]150[/C][C]0.693137014005269[/C][C]0.613725971989462[/C][C]0.306862985994731[/C][/ROW]
[ROW][C]151[/C][C]0.632834497767347[/C][C]0.734331004465305[/C][C]0.367165502232653[/C][/ROW]
[ROW][C]152[/C][C]0.632732137715462[/C][C]0.734535724569076[/C][C]0.367267862284538[/C][/ROW]
[ROW][C]153[/C][C]0.612944873918304[/C][C]0.774110252163391[/C][C]0.387055126081696[/C][/ROW]
[ROW][C]154[/C][C]0.724065357438562[/C][C]0.551869285122877[/C][C]0.275934642561438[/C][/ROW]
[ROW][C]155[/C][C]0.737451462447823[/C][C]0.525097075104353[/C][C]0.262548537552177[/C][/ROW]
[ROW][C]156[/C][C]0.695096573760762[/C][C]0.609806852478477[/C][C]0.304903426239238[/C][/ROW]
[ROW][C]157[/C][C]0.64992348659065[/C][C]0.700153026818701[/C][C]0.350076513409350[/C][/ROW]
[ROW][C]158[/C][C]0.591967908794554[/C][C]0.816064182410893[/C][C]0.408032091205446[/C][/ROW]
[ROW][C]159[/C][C]0.716305352578702[/C][C]0.567389294842596[/C][C]0.283694647421298[/C][/ROW]
[ROW][C]160[/C][C]0.71088872775742[/C][C]0.578222544485161[/C][C]0.289111272242581[/C][/ROW]
[ROW][C]161[/C][C]0.804607109249164[/C][C]0.390785781501671[/C][C]0.195392890750836[/C][/ROW]
[ROW][C]162[/C][C]0.999824550049677[/C][C]0.000350899900646993[/C][C]0.000175449950323497[/C][/ROW]
[ROW][C]163[/C][C]0.999738475113677[/C][C]0.000523049772646096[/C][C]0.000261524886323048[/C][/ROW]
[ROW][C]164[/C][C]0.99986120535209[/C][C]0.000277589295819394[/C][C]0.000138794647909697[/C][/ROW]
[ROW][C]165[/C][C]0.999814461900135[/C][C]0.000371076199729718[/C][C]0.000185538099864859[/C][/ROW]
[ROW][C]166[/C][C]0.999298740089725[/C][C]0.00140251982054951[/C][C]0.000701259910274757[/C][/ROW]
[ROW][C]167[/C][C]0.996964811962847[/C][C]0.0060703760743068[/C][C]0.0030351880371534[/C][/ROW]
[ROW][C]168[/C][C]0.998846883483168[/C][C]0.00230623303366493[/C][C]0.00115311651683247[/C][/ROW]
[ROW][C]169[/C][C]0.99381505344204[/C][C]0.0123698931159195[/C][C]0.00618494655795975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104184&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104184&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.0005228263092339590.001045652618467920.999477173690766
123.15944634842757e-056.31889269685515e-050.999968405536516
131.72507314555143e-063.45014629110286e-060.999998274926854
146.2716763139989e-061.25433526279978e-050.999993728323686
156.06584306000083e-071.21316861200017e-060.999999393415694
168.89862811798075e-081.77972562359615e-070.999999911013719
171.10766711074340e-082.21533422148681e-080.999999988923329
181.00937683804983e-092.01875367609965e-090.999999998990623
192.07113232648347e-094.14226465296694e-090.999999997928868
202.37894443622177e-094.75788887244353e-090.999999997621056
218.03241466803467e-101.60648293360693e-090.999999999196759
222.81402506458893e-095.62805012917786e-090.999999997185975
234.95345198622942e-109.90690397245885e-100.999999999504655
242.53107632052047e-105.06215264104095e-100.999999999746892
251.42362248258200e-102.84724496516399e-100.999999999857638
268.5434453201894e-111.70868906403788e-100.999999999914566
271.70396356853019e-113.40792713706038e-110.99999999998296
284.499382633877e-128.998765267754e-120.9999999999955
291.57748330049104e-123.15496660098207e-120.999999999998423
307.29592329272364e-131.45918465854473e-120.99999999999927
315.44108075968498e-131.08821615193700e-120.999999999999456
326.53778181642413e-131.30755636328483e-120.999999999999346
335.1768291790363e-131.03536583580726e-120.999999999999482
341.29900204834893e-132.59800409669787e-130.99999999999987
352.67159383321734e-145.34318766643467e-140.999999999999973
366.55885558894393e-151.31177111778879e-140.999999999999993
371.77916987927629e-153.55833975855257e-150.999999999999998
383.63223584052739e-167.26447168105478e-161
399.80005779387519e-171.96001155877504e-161
401.94530431189746e-173.89060862379493e-171
413.79929964377818e-187.59859928755636e-181
427.00291621353972e-191.40058324270794e-181
432.31689629654436e-194.63379259308873e-191
446.53101362008446e-201.30620272401689e-191
452.57245038213323e-205.14490076426646e-201
464.77268397504204e-219.54536795008408e-211
471.43792434510808e-212.87584869021616e-211
481.03068346829208e-212.06136693658415e-211
494.97395380631163e-229.94790761262325e-221
509.3649890022176e-231.87299780044352e-221
512.60619119923298e-235.21238239846595e-231
528.67518011995407e-241.73503602399081e-231
532.49649844307897e-244.99299688615794e-241
545.87110562215808e-251.17422112443162e-241
551.30534398929716e-252.61068797859432e-251
563.10355952433097e-266.20711904866194e-261
577.26801056432223e-271.45360211286445e-261
583.93579212215559e-277.87158424431119e-271
591.49771155516139e-272.99542311032278e-271
604.8889423335518e-289.7778846671036e-281
611.83044332344893e-283.66088664689786e-281
624.89825860135035e-299.7965172027007e-291
631.19412901196022e-292.38825802392045e-291
641.19197459878375e-292.38394919756750e-291
652.35783891370854e-294.71567782741708e-291
668.49261815304705e-291.69852363060941e-281
677.02720619020554e-281.40544123804111e-271
685.00242885778955e-281.00048577155791e-271
693.99056884778608e-277.98113769557217e-271
709.41450360046218e-271.88290072009244e-261
714.99757025484104e-279.99514050968207e-271
728.64244156019448e-271.72848831203890e-261
734.24315675199104e-278.48631350398208e-271
741.20433909429672e-272.40867818859344e-271
758.37852721153428e-281.67570544230686e-271
765.89112487634561e-281.17822497526912e-271
771.5223611669999e-283.0447223339998e-281
785.09029566746982e-291.01805913349396e-281
791.28077477334631e-292.56154954669262e-291
801.00920104199262e-292.01840208398525e-291
811.24430186252918e-292.48860372505836e-291
823.33937217243742e-306.67874434487484e-301
831.10003793231168e-302.20007586462337e-301
846.87850319792859e-311.37570063958572e-301
856.5895980721558e-311.31791961443116e-301
867.89396829358707e-311.57879365871741e-301
876.25829216647165e-311.25165843329433e-301
883.65975601179134e-317.31951202358268e-311
891.93415300748548e-313.86830601497096e-311
909.15688384166924e-321.83137676833385e-311
913.72999750060885e-327.4599950012177e-321
921.39168169517072e-322.78336339034143e-321
936.23881515665973e-331.24776303133195e-321
942.25261660468516e-334.50523320937032e-331
953.50949916076102e-337.01899832152203e-331
962.82162855473594e-335.64325710947188e-331
971.77594137169217e-333.55188274338435e-331
982.96425222806391e-335.92850445612781e-331
996.26982358393165e-331.25396471678633e-321
1001.93860624360509e-323.87721248721017e-321
1011.58849083828129e-323.17698167656258e-321
1021.40601411635527e-322.81202823271054e-321
1035.17981190589277e-321.03596238117855e-311
1046.51362298157517e-311.30272459631503e-301
1058.05718251032566e-311.61143650206513e-301
1066.8538437004469e-311.37076874008938e-301
1077.58386857986049e-311.51677371597210e-301
1089.6063581322565e-311.9212716264513e-301
1091.25413330876228e-302.50826661752457e-301
1101.19522598599097e-302.39045197198195e-301
1111.35106995712570e-302.70213991425141e-301
1129.72214523189985e-301.94442904637997e-291
1132.07798351692806e-294.15596703385612e-291
1148.36409342021507e-291.67281868404301e-281
1153.36734547593583e-286.73469095187167e-281
1161.14941240469585e-272.2988248093917e-271
1172.62766000636865e-255.25532001273729e-251
1184.52917133392812e-249.05834266785623e-241
1191.85939036957645e-233.71878073915289e-231
1204.18599619270075e-238.3719923854015e-231
1213.29634726685878e-236.59269453371756e-231
1221.65489358358826e-233.30978716717652e-231
1237.73543985531887e-241.54708797106377e-231
1246.56377011840348e-241.31275402368070e-231
1258.3529534405788e-241.67059068811576e-231
1261.22922548415184e-232.45845096830367e-231
1272.39841623940851e-234.79683247881701e-231
1289.47084513619317e-231.89416902723863e-221
1291.13013460018570e-212.26026920037140e-211
1304.68033769236859e-199.36067538473718e-191
1311.47446985103194e-132.94893970206387e-130.999999999999853
1327.15868502984278e-061.43173700596856e-050.99999284131497
1335.51463015308344e-061.10292603061669e-050.999994485369847
1340.0003921519448373900.0007843038896747810.999607848055163
1350.0003406142410777990.0006812284821555990.999659385758922
1360.005259575325015630.01051915065003130.994740424674984
1370.05294615405249750.1058923081049950.947053845947503
1380.04824403611837150.0964880722367430.951755963881628
1390.3906923986111260.7813847972222520.609307601388874
1400.6000853296837580.7998293406324830.399914670316242
1410.5461617827831240.9076764344337530.453838217216876
1420.5186837776710440.9626324446579120.481316222328956
1430.4622672631388230.9245345262776460.537732736861177
1440.4084971860559580.8169943721119160.591502813944042
1450.3999672584830820.7999345169661640.600032741516918
1460.4942692244016330.9885384488032660.505730775598367
1470.7427883741037280.5144232517925430.257211625896272
1480.7856865465176340.4286269069647320.214313453482366
1490.7452319798563620.5095360402872760.254768020143638
1500.6931370140052690.6137259719894620.306862985994731
1510.6328344977673470.7343310044653050.367165502232653
1520.6327321377154620.7345357245690760.367267862284538
1530.6129448739183040.7741102521633910.387055126081696
1540.7240653574385620.5518692851228770.275934642561438
1550.7374514624478230.5250970751043530.262548537552177
1560.6950965737607620.6098068524784770.304903426239238
1570.649923486590650.7001530268187010.350076513409350
1580.5919679087945540.8160641824108930.408032091205446
1590.7163053525787020.5673892948425960.283694647421298
1600.710888727757420.5782225444851610.289111272242581
1610.8046071092491640.3907857815016710.195392890750836
1620.9998245500496770.0003508999006469930.000175449950323497
1630.9997384751136770.0005230497726460960.000261524886323048
1640.999861205352090.0002775892958193940.000138794647909697
1650.9998144619001350.0003710761997297180.000185538099864859
1660.9992987400897250.001402519820549510.000701259910274757
1670.9969648119628470.00607037607430680.0030351880371534
1680.9988468834831680.002306233033664930.00115311651683247
1690.993815053442040.01236989311591950.00618494655795975






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1320.830188679245283NOK
5% type I error level1340.842767295597484NOK
10% type I error level1350.849056603773585NOK

\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 & 132 & 0.830188679245283 & NOK \tabularnewline
5% type I error level & 134 & 0.842767295597484 & NOK \tabularnewline
10% type I error level & 135 & 0.849056603773585 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104184&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]132[/C][C]0.830188679245283[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]134[/C][C]0.842767295597484[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]135[/C][C]0.849056603773585[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104184&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104184&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 level1320.830188679245283NOK
5% type I error level1340.842767295597484NOK
10% type I error level1350.849056603773585NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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