FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 17:11:10 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323814291n7x765p8lp0l2ew.htm/, Retrieved Thu, 02 May 2024 22:38:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154754, Retrieved Thu, 02 May 2024 22:38:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-12-13 16:45:00] [a1957df0bc37aec4aa3c994e6a08412c]
-   PD      [Multiple Regression] [] [2011-12-13 22:11:10] [fdaf10f0fcbe7b8f79ecbd42ec74e6ad] [Current]
-   PD        [Multiple Regression] [] [2011-12-19 12:16:46] [a1957df0bc37aec4aa3c994e6a08412c]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2981,85	2819,19	11394,84	10539,51	10407	44,23
3080,58	2892,56	11545,71	10723,78	10463	45,85
3106,22	2866,08	11809,38	10682,06	10556	53,38
3119,31	2817,41	11395,64	10283,19	10646	53,26
3061,26	2934,75	11082,38	10377,18	10702	51,8
3097,31	3036,54	11402,75	10486,64	11353	55,3
3161,69	3139,5	11716,87	10545,38	11346	57,81
3257,16	3114,31	12204,98	10554,27	11451	63,96
3277,01	3261,3	12986,62	10532,54	11964	63,77
3295,32	3201,79	13392,79	10324,31	12574	59,15
3363,99	3264,53	14368,05	10695,25	13031	56,12
3494,17	3349,1	15650,83	10827,81	13812	57,42
3667,03	3446,17	16102,64	10872,48	14544	63,52
3813,06	3469,48	16187,64	10971,19	14931	61,71
3917,96	3507,13	16311,54	11145,65	14886	63,01
3895,51	3536,2	17232,97	11234,68	16005	68,18
3801,06	3359,05	16397,83	11333,88	17064	72,03
3570,12	3378,85	14990,31	10997,97	15168	69,75
3701,61	3449,15	15147,55	11036,89	16050	74,41
3862,27	3522,89	15786,78	11257,35	15839	74,33
3970,1	3551,04	15934,09	11533,59	15137	64,24
4138,52	3669,15	16519,44	11963,12	14954	60,03
4199,75	3602	16101,07	12185,15	15648	59,44
4290,89	3697,22	16775,08	12377,62	15305	62,5
4443,91	3760,9	17286,32	12512,89	15579	55,04
4502,64	3665,08	17741,23	12631,48	16348	58,34
4356,98	3708,8	17128,37	12268,53	15928	61,92
4591,27	3858,21	17460,53	12754,8	16171	67,65
4696,96	3933,16	17611,14	13407,75	15937	67,68
4621,4	3946,98	18001,37	13480,21	15713	70,3
4562,84	3794,29	17974,77	13673,28	15594	75,26
4202,52	3765,56	16460,95	13239,71	15683	71,44
4296,49	3820,33	16235,39	13557,69	16438	76,36
4435,23	3885,12	16903,36	13901,28	17032	81,71
4105,18	3752,67	15543,76	13200,58	17696	92,6
4116,68	3683,79	15532,18	13406,97	17745	90,6
3844,49	3240,75	13731,31	12538,12	19394	92,23
3720,98	3188,82	13547,84	12419,57	20148	94,09
3674,4	3017,98	12602,93	12193,88	20108	102,79
3857,62	3237,2	13357,7	12656,63	18584	109,65
3801,06	3182,53	13995,33	12812,48	18441	124,05
3504,37	2906,42	14084,6	12056,67	18391	132,69
3032,6	2881,35	13168,91	11322,38	19178	135,81
3047,03	2915,64	12989,35	11530,75	18079	116,07
2962,34	2635,13	12123,53	11114,08	18483	101,42
2197,82	2331,43	9117,03	9181,73	19644	75,73
2014,45	2159,04	8531,45	8614,55	19195	55,48
1862,83	NA	8460,94	8595,56	19650	43,8
1905,41	1983,48	8331,49	8396,2	20830	45,29
1810,99	1770,41	7694,78	7690,5	23595	44,01
1670,07	1815,99	7764,58	7235,47	22937	47,48
1864,44	2026,97	8767,96	7992,12	21814	51,07
2052,02	2124,81	9304,43	8398,37	21928	57,84
2029,6	2098,28	9810,31	8593	21777	69,04
2070,83	2291,39	9691,12	8679,75	21383	65,61
2293,41	2401,57	10430,35	9374,63	21467	72,87
2443,27	2453,89	10302,87	9634,97	22052	68,41
2513,17	2409,53	10066,24	9857,34	22680	73,25
2466,92	2432,45	9633,83	10238,83	24320	77,43
2502,66	2585,34	10169,02	10433,44	24977	75,28
2539,91	2478,51	10661,62	10471,24	25204	77,33
2482,6	2470,18	10175,13	10214,51	25739	74,31
2626,15	2629,16	10671,49	10677,52	26434	79,7
2656,32	2541,22	11139,77	11052,15	27525	85,47
2446,66	2397,18	10103,98	10500,19	30695	77,98
2467,38	2359,66	9786,05	10159,27	32436	75,69
2462,32	2476,2	9456,84	10222,24	30160	75,2
2504,58	2449,57	9268,24	10350,4	30236	77,21
2579,39	2482,18	9346,72	10598,07	31293	77,85
2649,24	2542,76	9455,09	11044,49	31077	83,53
2636,87	2477,63	9797,18	11198,31	32226	85,99
2613,94	2586,46	10254,46	11465,26	33865	91,77
2634,01	2654,47	10449,53	11802,37	32810	96,59
2711,94	2713,48	10622,27	12190	32242	103,57
2646,43	2582,9	9852,45	12081,48	32700	114,46
2717,79	2661,37	9644,62	12434,93	32819	122,54
2701,54	2631,87	9650,78	12579,99	33947	115,08
2572,98	2561,37	9541,53	12097,31	34148	113,93
2488,92	2510,85	9996,68	12512,33	35261	116,29
2204,91	2238,24	9072,94	11326,62	39506	110,12
2123,99	2159,7	8695,42	11175,45	41591	110,86
2149,1	2318	8733,56	11515,93	39148	108,53

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154754&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154754&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154754&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 time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
BEL20[t] = -916.670775482802 + 0.302833501075295DJEuropeStoxx[t] + 0.114332538998678Nikkei[t] + 0.201776311503969DowJones[t] -0.0132477040586341Goudprijs[t] -2.82917485910548Brent[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL20[t] =  -916.670775482802 +  0.302833501075295DJEuropeStoxx[t] +  0.114332538998678Nikkei[t] +  0.201776311503969DowJones[t] -0.0132477040586341Goudprijs[t] -2.82917485910548Brent[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154754&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL20[t] =  -916.670775482802 +  0.302833501075295DJEuropeStoxx[t] +  0.114332538998678Nikkei[t] +  0.201776311503969DowJones[t] -0.0132477040586341Goudprijs[t] -2.82917485910548Brent[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154754&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154754&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
BEL20[t] = -916.670775482802 + 0.302833501075295DJEuropeStoxx[t] + 0.114332538998678Nikkei[t] + 0.201776311503969DowJones[t] -0.0132477040586341Goudprijs[t] -2.82917485910548Brent[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-916.670775482802116.03824-7.899700
DJEuropeStoxx0.3028335010752950.135342.23760.0282180.014109
Nikkei0.1143325389986780.0167866.811300
DowJones0.2017763115039690.0302836.66300
Goudprijs-0.01324770405863410.00328-4.03910.0001286.4e-05
Brent-2.829174859105481.017892-2.77940.0068790.003439

\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) & -916.670775482802 & 116.03824 & -7.8997 & 0 & 0 \tabularnewline
DJEuropeStoxx & 0.302833501075295 & 0.13534 & 2.2376 & 0.028218 & 0.014109 \tabularnewline
Nikkei & 0.114332538998678 & 0.016786 & 6.8113 & 0 & 0 \tabularnewline
DowJones & 0.201776311503969 & 0.030283 & 6.663 & 0 & 0 \tabularnewline
Goudprijs & -0.0132477040586341 & 0.00328 & -4.0391 & 0.000128 & 6.4e-05 \tabularnewline
Brent & -2.82917485910548 & 1.017892 & -2.7794 & 0.006879 & 0.003439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154754&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]-916.670775482802[/C][C]116.03824[/C][C]-7.8997[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DJEuropeStoxx[/C][C]0.302833501075295[/C][C]0.13534[/C][C]2.2376[/C][C]0.028218[/C][C]0.014109[/C][/ROW]
[ROW][C]Nikkei[/C][C]0.114332538998678[/C][C]0.016786[/C][C]6.8113[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DowJones[/C][C]0.201776311503969[/C][C]0.030283[/C][C]6.663[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Goudprijs[/C][C]-0.0132477040586341[/C][C]0.00328[/C][C]-4.0391[/C][C]0.000128[/C][C]6.4e-05[/C][/ROW]
[ROW][C]Brent[/C][C]-2.82917485910548[/C][C]1.017892[/C][C]-2.7794[/C][C]0.006879[/C][C]0.003439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154754&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154754&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)-916.670775482802116.03824-7.899700
DJEuropeStoxx0.3028335010752950.135342.23760.0282180.014109
Nikkei0.1143325389986780.0167866.811300
DowJones0.2017763115039690.0302836.66300
Goudprijs-0.01324770405863410.00328-4.03910.0001286.4e-05
Brent-2.829174859105481.017892-2.77940.0068790.003439






Multiple Linear Regression - Regression Statistics
Multiple R0.990984766767812
R-squared0.982050807965855
Adjusted R-squared0.980854195163579
F-TEST (value)820.692212299317
F-TEST (DF numerator)5
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation113.622511590482
Sum Squared Residuals968255.635509686

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990984766767812 \tabularnewline
R-squared & 0.982050807965855 \tabularnewline
Adjusted R-squared & 0.980854195163579 \tabularnewline
F-TEST (value) & 820.692212299317 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 75 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 113.622511590482 \tabularnewline
Sum Squared Residuals & 968255.635509686 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154754&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990984766767812[/C][/ROW]
[ROW][C]R-squared[/C][C]0.982050807965855[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.980854195163579[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]820.692212299317[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]75[/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]113.622511590482[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]968255.635509686[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154754&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154754&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 R0.990984766767812
R-squared0.982050807965855
Adjusted R-squared0.980854195163579
F-TEST (value)820.692212299317
F-TEST (DF numerator)5
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation113.622511590482
Sum Squared Residuals968255.635509686






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12981.853103.49558380012-121.645583800115
23080.583174.82001415455-94.2400141545457
33106.223165.99321272139-59.7732127213907
43119.313022.6150517869796.6949482130298
53061.263044.6874030216916.5725969783123
63097.313115.70160832333-18.3916083233332
73161.693187.63932831411-25.9493283141093
83257.163218.8211651322838.3388348677173
93277.013342.05841903044-65.0484190304367
103295.323333.44905177537-38.1290517753677
113363.993541.31788167426-177.327881674259
123494.173726.31508890326-232.145088903257
133667.033789.42578312105-122.395783121047
143813.063826.11438257885-13.0543825788469
153917.963883.8019801470534.1580198529481
163895.513986.46791158287-90.9579115828742
173801.063832.43184854357-31.3718485435739
183570.123641.29210135008-71.1721013500772
193701.613663.5436491284138.0663508715899
203862.273806.4645855810955.8054144189067
213970.13925.4166258236544.6833741763499
224138.524129.112977418399.40702258160685
234199.754098.22010447372101.529895526283
244290.894238.8397591550252.0502408449767
254443.914361.3456189351982.5643810648144
264502.644388.74401950316113.895980496843
274356.984254.1149377681102.865062231897
284591.274416.02539028368175.244609716325
294696.964590.70731498832106.252685011676
304621.44649.68421977649-28.2842197764946
314562.844626.90404890383-64.0640489038285
324202.524367.26900515274-164.749005152744
334296.494398.30562317106-101.815623171061
344435.234540.62001294328-105.390012943283
354105.184164.07234452183-58.8923445218332
364116.684188.5430273168-71.8630273168042
373844.493646.7072662306197.782733769397
383720.983570.83291576273150.147084237267
393674.43341.44007215859332.959927841415
403857.623588.2753523247269.344647675299
413801.063637.2224435198163.837556480199
423504.373387.52731171676116.842688283241
433032.63107.8268167803-75.2268167802969
443047.033210.13269533683-163.102695336829
452962.342978.21467456621-15.8746745662108
462197.822209.90182397377-12.0818239737674
472014.452039.54103019694-25.0910301969426
481862.831884.04508431151-21.2150843115145
491905.411708.32157840073197.088421599267
501810.991683.69061808769127.299381912313
511670.071684.40589313818-14.3358931381824
521864.441843.4689745696720.9710254303303
532052.022112.85871501368-60.8387150136834
542029.62126.48935727188-96.8893572718843
552070.832181.5813019442-110.7513019442
562293.412312.96909658316-19.5590965831611
572443.272375.2971276944567.9728723055512
582513.172492.372997859120.7970021408957
592466.922554.51951574755-87.599515747551
602502.662575.7981288308-73.1381288307999
612539.912561.86844282009-21.9584428200907
622482.62534.87105505655-52.2710550565486
632626.152719.97339384801-93.8233938480116
642656.322665.58160071478-9.26160071478444
652446.662302.11452117393144.545478826074
662467.382369.791246679397.5887533206966
672462.322312.00985879761150.310141202392
682504.582332.46852089867172.111479101326
692579.392445.03316350365134.356836496349
702649.242555.4974859634493.7425140365552
712636.872667.09540803704-30.2254080370382
722613.942735.35448015971-121.414480159715
732634.012781.10609486183-147.096094861831
742711.942738.21269312053-26.2726931205332
752646.432648.22593402634-1.79593402634453
762717.792763.03854020241-45.2485402024085
772701.542744.70532102644-43.1653210264443
782572.982799.264285193-226.284285192997
792488.922533.01661972863-44.0966197286299
802204.912202.761679063352.1483209366501
812123.992256.68778229881-132.697782298809
822149.1NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2981.85 & 3103.49558380012 & -121.645583800115 \tabularnewline
2 & 3080.58 & 3174.82001415455 & -94.2400141545457 \tabularnewline
3 & 3106.22 & 3165.99321272139 & -59.7732127213907 \tabularnewline
4 & 3119.31 & 3022.61505178697 & 96.6949482130298 \tabularnewline
5 & 3061.26 & 3044.68740302169 & 16.5725969783123 \tabularnewline
6 & 3097.31 & 3115.70160832333 & -18.3916083233332 \tabularnewline
7 & 3161.69 & 3187.63932831411 & -25.9493283141093 \tabularnewline
8 & 3257.16 & 3218.82116513228 & 38.3388348677173 \tabularnewline
9 & 3277.01 & 3342.05841903044 & -65.0484190304367 \tabularnewline
10 & 3295.32 & 3333.44905177537 & -38.1290517753677 \tabularnewline
11 & 3363.99 & 3541.31788167426 & -177.327881674259 \tabularnewline
12 & 3494.17 & 3726.31508890326 & -232.145088903257 \tabularnewline
13 & 3667.03 & 3789.42578312105 & -122.395783121047 \tabularnewline
14 & 3813.06 & 3826.11438257885 & -13.0543825788469 \tabularnewline
15 & 3917.96 & 3883.80198014705 & 34.1580198529481 \tabularnewline
16 & 3895.51 & 3986.46791158287 & -90.9579115828742 \tabularnewline
17 & 3801.06 & 3832.43184854357 & -31.3718485435739 \tabularnewline
18 & 3570.12 & 3641.29210135008 & -71.1721013500772 \tabularnewline
19 & 3701.61 & 3663.54364912841 & 38.0663508715899 \tabularnewline
20 & 3862.27 & 3806.46458558109 & 55.8054144189067 \tabularnewline
21 & 3970.1 & 3925.41662582365 & 44.6833741763499 \tabularnewline
22 & 4138.52 & 4129.11297741839 & 9.40702258160685 \tabularnewline
23 & 4199.75 & 4098.22010447372 & 101.529895526283 \tabularnewline
24 & 4290.89 & 4238.83975915502 & 52.0502408449767 \tabularnewline
25 & 4443.91 & 4361.34561893519 & 82.5643810648144 \tabularnewline
26 & 4502.64 & 4388.74401950316 & 113.895980496843 \tabularnewline
27 & 4356.98 & 4254.1149377681 & 102.865062231897 \tabularnewline
28 & 4591.27 & 4416.02539028368 & 175.244609716325 \tabularnewline
29 & 4696.96 & 4590.70731498832 & 106.252685011676 \tabularnewline
30 & 4621.4 & 4649.68421977649 & -28.2842197764946 \tabularnewline
31 & 4562.84 & 4626.90404890383 & -64.0640489038285 \tabularnewline
32 & 4202.52 & 4367.26900515274 & -164.749005152744 \tabularnewline
33 & 4296.49 & 4398.30562317106 & -101.815623171061 \tabularnewline
34 & 4435.23 & 4540.62001294328 & -105.390012943283 \tabularnewline
35 & 4105.18 & 4164.07234452183 & -58.8923445218332 \tabularnewline
36 & 4116.68 & 4188.5430273168 & -71.8630273168042 \tabularnewline
37 & 3844.49 & 3646.7072662306 & 197.782733769397 \tabularnewline
38 & 3720.98 & 3570.83291576273 & 150.147084237267 \tabularnewline
39 & 3674.4 & 3341.44007215859 & 332.959927841415 \tabularnewline
40 & 3857.62 & 3588.2753523247 & 269.344647675299 \tabularnewline
41 & 3801.06 & 3637.2224435198 & 163.837556480199 \tabularnewline
42 & 3504.37 & 3387.52731171676 & 116.842688283241 \tabularnewline
43 & 3032.6 & 3107.8268167803 & -75.2268167802969 \tabularnewline
44 & 3047.03 & 3210.13269533683 & -163.102695336829 \tabularnewline
45 & 2962.34 & 2978.21467456621 & -15.8746745662108 \tabularnewline
46 & 2197.82 & 2209.90182397377 & -12.0818239737674 \tabularnewline
47 & 2014.45 & 2039.54103019694 & -25.0910301969426 \tabularnewline
48 & 1862.83 & 1884.04508431151 & -21.2150843115145 \tabularnewline
49 & 1905.41 & 1708.32157840073 & 197.088421599267 \tabularnewline
50 & 1810.99 & 1683.69061808769 & 127.299381912313 \tabularnewline
51 & 1670.07 & 1684.40589313818 & -14.3358931381824 \tabularnewline
52 & 1864.44 & 1843.46897456967 & 20.9710254303303 \tabularnewline
53 & 2052.02 & 2112.85871501368 & -60.8387150136834 \tabularnewline
54 & 2029.6 & 2126.48935727188 & -96.8893572718843 \tabularnewline
55 & 2070.83 & 2181.5813019442 & -110.7513019442 \tabularnewline
56 & 2293.41 & 2312.96909658316 & -19.5590965831611 \tabularnewline
57 & 2443.27 & 2375.29712769445 & 67.9728723055512 \tabularnewline
58 & 2513.17 & 2492.3729978591 & 20.7970021408957 \tabularnewline
59 & 2466.92 & 2554.51951574755 & -87.599515747551 \tabularnewline
60 & 2502.66 & 2575.7981288308 & -73.1381288307999 \tabularnewline
61 & 2539.91 & 2561.86844282009 & -21.9584428200907 \tabularnewline
62 & 2482.6 & 2534.87105505655 & -52.2710550565486 \tabularnewline
63 & 2626.15 & 2719.97339384801 & -93.8233938480116 \tabularnewline
64 & 2656.32 & 2665.58160071478 & -9.26160071478444 \tabularnewline
65 & 2446.66 & 2302.11452117393 & 144.545478826074 \tabularnewline
66 & 2467.38 & 2369.7912466793 & 97.5887533206966 \tabularnewline
67 & 2462.32 & 2312.00985879761 & 150.310141202392 \tabularnewline
68 & 2504.58 & 2332.46852089867 & 172.111479101326 \tabularnewline
69 & 2579.39 & 2445.03316350365 & 134.356836496349 \tabularnewline
70 & 2649.24 & 2555.49748596344 & 93.7425140365552 \tabularnewline
71 & 2636.87 & 2667.09540803704 & -30.2254080370382 \tabularnewline
72 & 2613.94 & 2735.35448015971 & -121.414480159715 \tabularnewline
73 & 2634.01 & 2781.10609486183 & -147.096094861831 \tabularnewline
74 & 2711.94 & 2738.21269312053 & -26.2726931205332 \tabularnewline
75 & 2646.43 & 2648.22593402634 & -1.79593402634453 \tabularnewline
76 & 2717.79 & 2763.03854020241 & -45.2485402024085 \tabularnewline
77 & 2701.54 & 2744.70532102644 & -43.1653210264443 \tabularnewline
78 & 2572.98 & 2799.264285193 & -226.284285192997 \tabularnewline
79 & 2488.92 & 2533.01661972863 & -44.0966197286299 \tabularnewline
80 & 2204.91 & 2202.76167906335 & 2.1483209366501 \tabularnewline
81 & 2123.99 & 2256.68778229881 & -132.697782298809 \tabularnewline
82 & 2149.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154754&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]2981.85[/C][C]3103.49558380012[/C][C]-121.645583800115[/C][/ROW]
[ROW][C]2[/C][C]3080.58[/C][C]3174.82001415455[/C][C]-94.2400141545457[/C][/ROW]
[ROW][C]3[/C][C]3106.22[/C][C]3165.99321272139[/C][C]-59.7732127213907[/C][/ROW]
[ROW][C]4[/C][C]3119.31[/C][C]3022.61505178697[/C][C]96.6949482130298[/C][/ROW]
[ROW][C]5[/C][C]3061.26[/C][C]3044.68740302169[/C][C]16.5725969783123[/C][/ROW]
[ROW][C]6[/C][C]3097.31[/C][C]3115.70160832333[/C][C]-18.3916083233332[/C][/ROW]
[ROW][C]7[/C][C]3161.69[/C][C]3187.63932831411[/C][C]-25.9493283141093[/C][/ROW]
[ROW][C]8[/C][C]3257.16[/C][C]3218.82116513228[/C][C]38.3388348677173[/C][/ROW]
[ROW][C]9[/C][C]3277.01[/C][C]3342.05841903044[/C][C]-65.0484190304367[/C][/ROW]
[ROW][C]10[/C][C]3295.32[/C][C]3333.44905177537[/C][C]-38.1290517753677[/C][/ROW]
[ROW][C]11[/C][C]3363.99[/C][C]3541.31788167426[/C][C]-177.327881674259[/C][/ROW]
[ROW][C]12[/C][C]3494.17[/C][C]3726.31508890326[/C][C]-232.145088903257[/C][/ROW]
[ROW][C]13[/C][C]3667.03[/C][C]3789.42578312105[/C][C]-122.395783121047[/C][/ROW]
[ROW][C]14[/C][C]3813.06[/C][C]3826.11438257885[/C][C]-13.0543825788469[/C][/ROW]
[ROW][C]15[/C][C]3917.96[/C][C]3883.80198014705[/C][C]34.1580198529481[/C][/ROW]
[ROW][C]16[/C][C]3895.51[/C][C]3986.46791158287[/C][C]-90.9579115828742[/C][/ROW]
[ROW][C]17[/C][C]3801.06[/C][C]3832.43184854357[/C][C]-31.3718485435739[/C][/ROW]
[ROW][C]18[/C][C]3570.12[/C][C]3641.29210135008[/C][C]-71.1721013500772[/C][/ROW]
[ROW][C]19[/C][C]3701.61[/C][C]3663.54364912841[/C][C]38.0663508715899[/C][/ROW]
[ROW][C]20[/C][C]3862.27[/C][C]3806.46458558109[/C][C]55.8054144189067[/C][/ROW]
[ROW][C]21[/C][C]3970.1[/C][C]3925.41662582365[/C][C]44.6833741763499[/C][/ROW]
[ROW][C]22[/C][C]4138.52[/C][C]4129.11297741839[/C][C]9.40702258160685[/C][/ROW]
[ROW][C]23[/C][C]4199.75[/C][C]4098.22010447372[/C][C]101.529895526283[/C][/ROW]
[ROW][C]24[/C][C]4290.89[/C][C]4238.83975915502[/C][C]52.0502408449767[/C][/ROW]
[ROW][C]25[/C][C]4443.91[/C][C]4361.34561893519[/C][C]82.5643810648144[/C][/ROW]
[ROW][C]26[/C][C]4502.64[/C][C]4388.74401950316[/C][C]113.895980496843[/C][/ROW]
[ROW][C]27[/C][C]4356.98[/C][C]4254.1149377681[/C][C]102.865062231897[/C][/ROW]
[ROW][C]28[/C][C]4591.27[/C][C]4416.02539028368[/C][C]175.244609716325[/C][/ROW]
[ROW][C]29[/C][C]4696.96[/C][C]4590.70731498832[/C][C]106.252685011676[/C][/ROW]
[ROW][C]30[/C][C]4621.4[/C][C]4649.68421977649[/C][C]-28.2842197764946[/C][/ROW]
[ROW][C]31[/C][C]4562.84[/C][C]4626.90404890383[/C][C]-64.0640489038285[/C][/ROW]
[ROW][C]32[/C][C]4202.52[/C][C]4367.26900515274[/C][C]-164.749005152744[/C][/ROW]
[ROW][C]33[/C][C]4296.49[/C][C]4398.30562317106[/C][C]-101.815623171061[/C][/ROW]
[ROW][C]34[/C][C]4435.23[/C][C]4540.62001294328[/C][C]-105.390012943283[/C][/ROW]
[ROW][C]35[/C][C]4105.18[/C][C]4164.07234452183[/C][C]-58.8923445218332[/C][/ROW]
[ROW][C]36[/C][C]4116.68[/C][C]4188.5430273168[/C][C]-71.8630273168042[/C][/ROW]
[ROW][C]37[/C][C]3844.49[/C][C]3646.7072662306[/C][C]197.782733769397[/C][/ROW]
[ROW][C]38[/C][C]3720.98[/C][C]3570.83291576273[/C][C]150.147084237267[/C][/ROW]
[ROW][C]39[/C][C]3674.4[/C][C]3341.44007215859[/C][C]332.959927841415[/C][/ROW]
[ROW][C]40[/C][C]3857.62[/C][C]3588.2753523247[/C][C]269.344647675299[/C][/ROW]
[ROW][C]41[/C][C]3801.06[/C][C]3637.2224435198[/C][C]163.837556480199[/C][/ROW]
[ROW][C]42[/C][C]3504.37[/C][C]3387.52731171676[/C][C]116.842688283241[/C][/ROW]
[ROW][C]43[/C][C]3032.6[/C][C]3107.8268167803[/C][C]-75.2268167802969[/C][/ROW]
[ROW][C]44[/C][C]3047.03[/C][C]3210.13269533683[/C][C]-163.102695336829[/C][/ROW]
[ROW][C]45[/C][C]2962.34[/C][C]2978.21467456621[/C][C]-15.8746745662108[/C][/ROW]
[ROW][C]46[/C][C]2197.82[/C][C]2209.90182397377[/C][C]-12.0818239737674[/C][/ROW]
[ROW][C]47[/C][C]2014.45[/C][C]2039.54103019694[/C][C]-25.0910301969426[/C][/ROW]
[ROW][C]48[/C][C]1862.83[/C][C]1884.04508431151[/C][C]-21.2150843115145[/C][/ROW]
[ROW][C]49[/C][C]1905.41[/C][C]1708.32157840073[/C][C]197.088421599267[/C][/ROW]
[ROW][C]50[/C][C]1810.99[/C][C]1683.69061808769[/C][C]127.299381912313[/C][/ROW]
[ROW][C]51[/C][C]1670.07[/C][C]1684.40589313818[/C][C]-14.3358931381824[/C][/ROW]
[ROW][C]52[/C][C]1864.44[/C][C]1843.46897456967[/C][C]20.9710254303303[/C][/ROW]
[ROW][C]53[/C][C]2052.02[/C][C]2112.85871501368[/C][C]-60.8387150136834[/C][/ROW]
[ROW][C]54[/C][C]2029.6[/C][C]2126.48935727188[/C][C]-96.8893572718843[/C][/ROW]
[ROW][C]55[/C][C]2070.83[/C][C]2181.5813019442[/C][C]-110.7513019442[/C][/ROW]
[ROW][C]56[/C][C]2293.41[/C][C]2312.96909658316[/C][C]-19.5590965831611[/C][/ROW]
[ROW][C]57[/C][C]2443.27[/C][C]2375.29712769445[/C][C]67.9728723055512[/C][/ROW]
[ROW][C]58[/C][C]2513.17[/C][C]2492.3729978591[/C][C]20.7970021408957[/C][/ROW]
[ROW][C]59[/C][C]2466.92[/C][C]2554.51951574755[/C][C]-87.599515747551[/C][/ROW]
[ROW][C]60[/C][C]2502.66[/C][C]2575.7981288308[/C][C]-73.1381288307999[/C][/ROW]
[ROW][C]61[/C][C]2539.91[/C][C]2561.86844282009[/C][C]-21.9584428200907[/C][/ROW]
[ROW][C]62[/C][C]2482.6[/C][C]2534.87105505655[/C][C]-52.2710550565486[/C][/ROW]
[ROW][C]63[/C][C]2626.15[/C][C]2719.97339384801[/C][C]-93.8233938480116[/C][/ROW]
[ROW][C]64[/C][C]2656.32[/C][C]2665.58160071478[/C][C]-9.26160071478444[/C][/ROW]
[ROW][C]65[/C][C]2446.66[/C][C]2302.11452117393[/C][C]144.545478826074[/C][/ROW]
[ROW][C]66[/C][C]2467.38[/C][C]2369.7912466793[/C][C]97.5887533206966[/C][/ROW]
[ROW][C]67[/C][C]2462.32[/C][C]2312.00985879761[/C][C]150.310141202392[/C][/ROW]
[ROW][C]68[/C][C]2504.58[/C][C]2332.46852089867[/C][C]172.111479101326[/C][/ROW]
[ROW][C]69[/C][C]2579.39[/C][C]2445.03316350365[/C][C]134.356836496349[/C][/ROW]
[ROW][C]70[/C][C]2649.24[/C][C]2555.49748596344[/C][C]93.7425140365552[/C][/ROW]
[ROW][C]71[/C][C]2636.87[/C][C]2667.09540803704[/C][C]-30.2254080370382[/C][/ROW]
[ROW][C]72[/C][C]2613.94[/C][C]2735.35448015971[/C][C]-121.414480159715[/C][/ROW]
[ROW][C]73[/C][C]2634.01[/C][C]2781.10609486183[/C][C]-147.096094861831[/C][/ROW]
[ROW][C]74[/C][C]2711.94[/C][C]2738.21269312053[/C][C]-26.2726931205332[/C][/ROW]
[ROW][C]75[/C][C]2646.43[/C][C]2648.22593402634[/C][C]-1.79593402634453[/C][/ROW]
[ROW][C]76[/C][C]2717.79[/C][C]2763.03854020241[/C][C]-45.2485402024085[/C][/ROW]
[ROW][C]77[/C][C]2701.54[/C][C]2744.70532102644[/C][C]-43.1653210264443[/C][/ROW]
[ROW][C]78[/C][C]2572.98[/C][C]2799.264285193[/C][C]-226.284285192997[/C][/ROW]
[ROW][C]79[/C][C]2488.92[/C][C]2533.01661972863[/C][C]-44.0966197286299[/C][/ROW]
[ROW][C]80[/C][C]2204.91[/C][C]2202.76167906335[/C][C]2.1483209366501[/C][/ROW]
[ROW][C]81[/C][C]2123.99[/C][C]2256.68778229881[/C][C]-132.697782298809[/C][/ROW]
[ROW][C]82[/C][C]2149.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154754&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154754&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
12981.853103.49558380012-121.645583800115
23080.583174.82001415455-94.2400141545457
33106.223165.99321272139-59.7732127213907
43119.313022.6150517869796.6949482130298
53061.263044.6874030216916.5725969783123
63097.313115.70160832333-18.3916083233332
73161.693187.63932831411-25.9493283141093
83257.163218.8211651322838.3388348677173
93277.013342.05841903044-65.0484190304367
103295.323333.44905177537-38.1290517753677
113363.993541.31788167426-177.327881674259
123494.173726.31508890326-232.145088903257
133667.033789.42578312105-122.395783121047
143813.063826.11438257885-13.0543825788469
153917.963883.8019801470534.1580198529481
163895.513986.46791158287-90.9579115828742
173801.063832.43184854357-31.3718485435739
183570.123641.29210135008-71.1721013500772
193701.613663.5436491284138.0663508715899
203862.273806.4645855810955.8054144189067
213970.13925.4166258236544.6833741763499
224138.524129.112977418399.40702258160685
234199.754098.22010447372101.529895526283
244290.894238.8397591550252.0502408449767
254443.914361.3456189351982.5643810648144
264502.644388.74401950316113.895980496843
274356.984254.1149377681102.865062231897
284591.274416.02539028368175.244609716325
294696.964590.70731498832106.252685011676
304621.44649.68421977649-28.2842197764946
314562.844626.90404890383-64.0640489038285
324202.524367.26900515274-164.749005152744
334296.494398.30562317106-101.815623171061
344435.234540.62001294328-105.390012943283
354105.184164.07234452183-58.8923445218332
364116.684188.5430273168-71.8630273168042
373844.493646.7072662306197.782733769397
383720.983570.83291576273150.147084237267
393674.43341.44007215859332.959927841415
403857.623588.2753523247269.344647675299
413801.063637.2224435198163.837556480199
423504.373387.52731171676116.842688283241
433032.63107.8268167803-75.2268167802969
443047.033210.13269533683-163.102695336829
452962.342978.21467456621-15.8746745662108
462197.822209.90182397377-12.0818239737674
472014.452039.54103019694-25.0910301969426
481862.831884.04508431151-21.2150843115145
491905.411708.32157840073197.088421599267
501810.991683.69061808769127.299381912313
511670.071684.40589313818-14.3358931381824
521864.441843.4689745696720.9710254303303
532052.022112.85871501368-60.8387150136834
542029.62126.48935727188-96.8893572718843
552070.832181.5813019442-110.7513019442
562293.412312.96909658316-19.5590965831611
572443.272375.2971276944567.9728723055512
582513.172492.372997859120.7970021408957
592466.922554.51951574755-87.599515747551
602502.662575.7981288308-73.1381288307999
612539.912561.86844282009-21.9584428200907
622482.62534.87105505655-52.2710550565486
632626.152719.97339384801-93.8233938480116
642656.322665.58160071478-9.26160071478444
652446.662302.11452117393144.545478826074
662467.382369.791246679397.5887533206966
672462.322312.00985879761150.310141202392
682504.582332.46852089867172.111479101326
692579.392445.03316350365134.356836496349
702649.242555.4974859634493.7425140365552
712636.872667.09540803704-30.2254080370382
722613.942735.35448015971-121.414480159715
732634.012781.10609486183-147.096094861831
742711.942738.21269312053-26.2726931205332
752646.432648.22593402634-1.79593402634453
762717.792763.03854020241-45.2485402024085
772701.542744.70532102644-43.1653210264443
782572.982799.264285193-226.284285192997
792488.922533.01661972863-44.0966197286299
802204.912202.761679063352.1483209366501
812123.992256.68778229881-132.697782298809
822149.1NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.03431111523567550.0686222304713510.965688884764324
100.01246425896196350.0249285179239270.987535741038036
110.004087829070713610.008175658141427230.995912170929286
120.001324192096954810.002648384193909620.998675807903045
130.001264019563575560.002528039127151120.998735980436424
140.005949897580418280.01189979516083660.994050102419582
150.0128912222328120.02578244446562390.987108777767188
160.04914238731817240.09828477463634470.950857612681828
170.08163891172654630.1632778234530930.918361088273454
180.06921450930503540.1384290186100710.930785490694965
190.04237850501602060.08475701003204120.957621494983979
200.02904540709018120.05809081418036240.970954592909819
210.02589804753998350.0517960950799670.974101952460017
220.01550101886689530.03100203773379050.984498981133105
230.01014746597632860.02029493195265720.989852534023671
240.005685759056638890.01137151811327780.994314240943361
250.003573269936639830.007146539873279660.99642673006336
260.0026175080142570.0052350160285140.997382491985743
270.001605727410042530.003211454820085060.998394272589957
280.001183576653808610.002367153307617230.998816423346191
290.002000702094623970.004001404189247940.997999297905376
300.00426444687709370.008528893754187390.995735553122906
310.003249585251937040.006499170503874080.996750414748063
320.0354739181543950.070947836308790.964526081845605
330.06361384480052020.127227689601040.93638615519948
340.06992352467528270.1398470493505650.930076475324717
350.06236235848179450.1247247169635890.937637641518206
360.08297413388315150.1659482677663030.917025866116849
370.09666246087633590.1933249217526720.903337539123664
380.07062818912269730.1412563782453950.929371810877303
390.2116372535294430.4232745070588860.788362746470557
400.4494100710572780.8988201421145560.550589928942722
410.6040561894526320.7918876210947370.395943810547368
420.829571629375720.3408567412485590.170428370624279
430.8810172353912530.2379655292174940.118982764608747
440.919156709191720.161686581616560.0808432908082801
450.9895149375390840.02097012492183130.0104850624609156
460.9908633257874390.01827334842512160.00913667421256079
470.9914247472870890.01715050542582260.0085752527129113
480.995291399008720.009417201982559260.00470860099127963
490.9934723824522860.01305523509542860.00652761754771432
500.9892819572103590.0214360855792820.010718042789641
510.9922550139874170.01548997202516510.00774498601258257
520.98824199302370.02351601395260.0117580069763
530.9837010798518030.03259784029639360.0162989201481968
540.9889596379016920.02208072419661640.0110403620983082
550.9883580571775280.02328388564494350.0116419428224717
560.983350120887770.03329975822445920.0166498791122296
570.9774035398877130.0451929202245750.0225964601122875
580.9658667659383570.06826646812328550.0341332340616428
590.9883506047487770.02329879050244670.0116493952512234
600.9833078972166860.03338420556662870.0166921027833143
610.9854580467143580.0290839065712850.0145419532856425
620.9876099777770160.02478004444596780.0123900222229839
630.9804495303287910.0391009393424180.019550469671209
640.9770827538030380.04583449239392330.0229172461969616
650.9662249864619030.0675500270761930.0337750135380965
660.9703726712685350.05925465746292940.0296273287314647
670.9776180404501930.0447639190996140.022381959549807
680.9594399094173940.08112018116521120.0405600905826056
690.9262772013035250.1474455973929510.0737227986964754
700.8803195874250280.2393608251499440.119680412574972
710.852152057956060.2956958840878810.14784794204394
720.7376469230318940.5247061539362110.262353076968106
730.7515981999912320.4968036000175360.248401800008768

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0343111152356755 & 0.068622230471351 & 0.965688884764324 \tabularnewline
10 & 0.0124642589619635 & 0.024928517923927 & 0.987535741038036 \tabularnewline
11 & 0.00408782907071361 & 0.00817565814142723 & 0.995912170929286 \tabularnewline
12 & 0.00132419209695481 & 0.00264838419390962 & 0.998675807903045 \tabularnewline
13 & 0.00126401956357556 & 0.00252803912715112 & 0.998735980436424 \tabularnewline
14 & 0.00594989758041828 & 0.0118997951608366 & 0.994050102419582 \tabularnewline
15 & 0.012891222232812 & 0.0257824444656239 & 0.987108777767188 \tabularnewline
16 & 0.0491423873181724 & 0.0982847746363447 & 0.950857612681828 \tabularnewline
17 & 0.0816389117265463 & 0.163277823453093 & 0.918361088273454 \tabularnewline
18 & 0.0692145093050354 & 0.138429018610071 & 0.930785490694965 \tabularnewline
19 & 0.0423785050160206 & 0.0847570100320412 & 0.957621494983979 \tabularnewline
20 & 0.0290454070901812 & 0.0580908141803624 & 0.970954592909819 \tabularnewline
21 & 0.0258980475399835 & 0.051796095079967 & 0.974101952460017 \tabularnewline
22 & 0.0155010188668953 & 0.0310020377337905 & 0.984498981133105 \tabularnewline
23 & 0.0101474659763286 & 0.0202949319526572 & 0.989852534023671 \tabularnewline
24 & 0.00568575905663889 & 0.0113715181132778 & 0.994314240943361 \tabularnewline
25 & 0.00357326993663983 & 0.00714653987327966 & 0.99642673006336 \tabularnewline
26 & 0.002617508014257 & 0.005235016028514 & 0.997382491985743 \tabularnewline
27 & 0.00160572741004253 & 0.00321145482008506 & 0.998394272589957 \tabularnewline
28 & 0.00118357665380861 & 0.00236715330761723 & 0.998816423346191 \tabularnewline
29 & 0.00200070209462397 & 0.00400140418924794 & 0.997999297905376 \tabularnewline
30 & 0.0042644468770937 & 0.00852889375418739 & 0.995735553122906 \tabularnewline
31 & 0.00324958525193704 & 0.00649917050387408 & 0.996750414748063 \tabularnewline
32 & 0.035473918154395 & 0.07094783630879 & 0.964526081845605 \tabularnewline
33 & 0.0636138448005202 & 0.12722768960104 & 0.93638615519948 \tabularnewline
34 & 0.0699235246752827 & 0.139847049350565 & 0.930076475324717 \tabularnewline
35 & 0.0623623584817945 & 0.124724716963589 & 0.937637641518206 \tabularnewline
36 & 0.0829741338831515 & 0.165948267766303 & 0.917025866116849 \tabularnewline
37 & 0.0966624608763359 & 0.193324921752672 & 0.903337539123664 \tabularnewline
38 & 0.0706281891226973 & 0.141256378245395 & 0.929371810877303 \tabularnewline
39 & 0.211637253529443 & 0.423274507058886 & 0.788362746470557 \tabularnewline
40 & 0.449410071057278 & 0.898820142114556 & 0.550589928942722 \tabularnewline
41 & 0.604056189452632 & 0.791887621094737 & 0.395943810547368 \tabularnewline
42 & 0.82957162937572 & 0.340856741248559 & 0.170428370624279 \tabularnewline
43 & 0.881017235391253 & 0.237965529217494 & 0.118982764608747 \tabularnewline
44 & 0.91915670919172 & 0.16168658161656 & 0.0808432908082801 \tabularnewline
45 & 0.989514937539084 & 0.0209701249218313 & 0.0104850624609156 \tabularnewline
46 & 0.990863325787439 & 0.0182733484251216 & 0.00913667421256079 \tabularnewline
47 & 0.991424747287089 & 0.0171505054258226 & 0.0085752527129113 \tabularnewline
48 & 0.99529139900872 & 0.00941720198255926 & 0.00470860099127963 \tabularnewline
49 & 0.993472382452286 & 0.0130552350954286 & 0.00652761754771432 \tabularnewline
50 & 0.989281957210359 & 0.021436085579282 & 0.010718042789641 \tabularnewline
51 & 0.992255013987417 & 0.0154899720251651 & 0.00774498601258257 \tabularnewline
52 & 0.9882419930237 & 0.0235160139526 & 0.0117580069763 \tabularnewline
53 & 0.983701079851803 & 0.0325978402963936 & 0.0162989201481968 \tabularnewline
54 & 0.988959637901692 & 0.0220807241966164 & 0.0110403620983082 \tabularnewline
55 & 0.988358057177528 & 0.0232838856449435 & 0.0116419428224717 \tabularnewline
56 & 0.98335012088777 & 0.0332997582244592 & 0.0166498791122296 \tabularnewline
57 & 0.977403539887713 & 0.045192920224575 & 0.0225964601122875 \tabularnewline
58 & 0.965866765938357 & 0.0682664681232855 & 0.0341332340616428 \tabularnewline
59 & 0.988350604748777 & 0.0232987905024467 & 0.0116493952512234 \tabularnewline
60 & 0.983307897216686 & 0.0333842055666287 & 0.0166921027833143 \tabularnewline
61 & 0.985458046714358 & 0.029083906571285 & 0.0145419532856425 \tabularnewline
62 & 0.987609977777016 & 0.0247800444459678 & 0.0123900222229839 \tabularnewline
63 & 0.980449530328791 & 0.039100939342418 & 0.019550469671209 \tabularnewline
64 & 0.977082753803038 & 0.0458344923939233 & 0.0229172461969616 \tabularnewline
65 & 0.966224986461903 & 0.067550027076193 & 0.0337750135380965 \tabularnewline
66 & 0.970372671268535 & 0.0592546574629294 & 0.0296273287314647 \tabularnewline
67 & 0.977618040450193 & 0.044763919099614 & 0.022381959549807 \tabularnewline
68 & 0.959439909417394 & 0.0811201811652112 & 0.0405600905826056 \tabularnewline
69 & 0.926277201303525 & 0.147445597392951 & 0.0737227986964754 \tabularnewline
70 & 0.880319587425028 & 0.239360825149944 & 0.119680412574972 \tabularnewline
71 & 0.85215205795606 & 0.295695884087881 & 0.14784794204394 \tabularnewline
72 & 0.737646923031894 & 0.524706153936211 & 0.262353076968106 \tabularnewline
73 & 0.751598199991232 & 0.496803600017536 & 0.248401800008768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154754&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]9[/C][C]0.0343111152356755[/C][C]0.068622230471351[/C][C]0.965688884764324[/C][/ROW]
[ROW][C]10[/C][C]0.0124642589619635[/C][C]0.024928517923927[/C][C]0.987535741038036[/C][/ROW]
[ROW][C]11[/C][C]0.00408782907071361[/C][C]0.00817565814142723[/C][C]0.995912170929286[/C][/ROW]
[ROW][C]12[/C][C]0.00132419209695481[/C][C]0.00264838419390962[/C][C]0.998675807903045[/C][/ROW]
[ROW][C]13[/C][C]0.00126401956357556[/C][C]0.00252803912715112[/C][C]0.998735980436424[/C][/ROW]
[ROW][C]14[/C][C]0.00594989758041828[/C][C]0.0118997951608366[/C][C]0.994050102419582[/C][/ROW]
[ROW][C]15[/C][C]0.012891222232812[/C][C]0.0257824444656239[/C][C]0.987108777767188[/C][/ROW]
[ROW][C]16[/C][C]0.0491423873181724[/C][C]0.0982847746363447[/C][C]0.950857612681828[/C][/ROW]
[ROW][C]17[/C][C]0.0816389117265463[/C][C]0.163277823453093[/C][C]0.918361088273454[/C][/ROW]
[ROW][C]18[/C][C]0.0692145093050354[/C][C]0.138429018610071[/C][C]0.930785490694965[/C][/ROW]
[ROW][C]19[/C][C]0.0423785050160206[/C][C]0.0847570100320412[/C][C]0.957621494983979[/C][/ROW]
[ROW][C]20[/C][C]0.0290454070901812[/C][C]0.0580908141803624[/C][C]0.970954592909819[/C][/ROW]
[ROW][C]21[/C][C]0.0258980475399835[/C][C]0.051796095079967[/C][C]0.974101952460017[/C][/ROW]
[ROW][C]22[/C][C]0.0155010188668953[/C][C]0.0310020377337905[/C][C]0.984498981133105[/C][/ROW]
[ROW][C]23[/C][C]0.0101474659763286[/C][C]0.0202949319526572[/C][C]0.989852534023671[/C][/ROW]
[ROW][C]24[/C][C]0.00568575905663889[/C][C]0.0113715181132778[/C][C]0.994314240943361[/C][/ROW]
[ROW][C]25[/C][C]0.00357326993663983[/C][C]0.00714653987327966[/C][C]0.99642673006336[/C][/ROW]
[ROW][C]26[/C][C]0.002617508014257[/C][C]0.005235016028514[/C][C]0.997382491985743[/C][/ROW]
[ROW][C]27[/C][C]0.00160572741004253[/C][C]0.00321145482008506[/C][C]0.998394272589957[/C][/ROW]
[ROW][C]28[/C][C]0.00118357665380861[/C][C]0.00236715330761723[/C][C]0.998816423346191[/C][/ROW]
[ROW][C]29[/C][C]0.00200070209462397[/C][C]0.00400140418924794[/C][C]0.997999297905376[/C][/ROW]
[ROW][C]30[/C][C]0.0042644468770937[/C][C]0.00852889375418739[/C][C]0.995735553122906[/C][/ROW]
[ROW][C]31[/C][C]0.00324958525193704[/C][C]0.00649917050387408[/C][C]0.996750414748063[/C][/ROW]
[ROW][C]32[/C][C]0.035473918154395[/C][C]0.07094783630879[/C][C]0.964526081845605[/C][/ROW]
[ROW][C]33[/C][C]0.0636138448005202[/C][C]0.12722768960104[/C][C]0.93638615519948[/C][/ROW]
[ROW][C]34[/C][C]0.0699235246752827[/C][C]0.139847049350565[/C][C]0.930076475324717[/C][/ROW]
[ROW][C]35[/C][C]0.0623623584817945[/C][C]0.124724716963589[/C][C]0.937637641518206[/C][/ROW]
[ROW][C]36[/C][C]0.0829741338831515[/C][C]0.165948267766303[/C][C]0.917025866116849[/C][/ROW]
[ROW][C]37[/C][C]0.0966624608763359[/C][C]0.193324921752672[/C][C]0.903337539123664[/C][/ROW]
[ROW][C]38[/C][C]0.0706281891226973[/C][C]0.141256378245395[/C][C]0.929371810877303[/C][/ROW]
[ROW][C]39[/C][C]0.211637253529443[/C][C]0.423274507058886[/C][C]0.788362746470557[/C][/ROW]
[ROW][C]40[/C][C]0.449410071057278[/C][C]0.898820142114556[/C][C]0.550589928942722[/C][/ROW]
[ROW][C]41[/C][C]0.604056189452632[/C][C]0.791887621094737[/C][C]0.395943810547368[/C][/ROW]
[ROW][C]42[/C][C]0.82957162937572[/C][C]0.340856741248559[/C][C]0.170428370624279[/C][/ROW]
[ROW][C]43[/C][C]0.881017235391253[/C][C]0.237965529217494[/C][C]0.118982764608747[/C][/ROW]
[ROW][C]44[/C][C]0.91915670919172[/C][C]0.16168658161656[/C][C]0.0808432908082801[/C][/ROW]
[ROW][C]45[/C][C]0.989514937539084[/C][C]0.0209701249218313[/C][C]0.0104850624609156[/C][/ROW]
[ROW][C]46[/C][C]0.990863325787439[/C][C]0.0182733484251216[/C][C]0.00913667421256079[/C][/ROW]
[ROW][C]47[/C][C]0.991424747287089[/C][C]0.0171505054258226[/C][C]0.0085752527129113[/C][/ROW]
[ROW][C]48[/C][C]0.99529139900872[/C][C]0.00941720198255926[/C][C]0.00470860099127963[/C][/ROW]
[ROW][C]49[/C][C]0.993472382452286[/C][C]0.0130552350954286[/C][C]0.00652761754771432[/C][/ROW]
[ROW][C]50[/C][C]0.989281957210359[/C][C]0.021436085579282[/C][C]0.010718042789641[/C][/ROW]
[ROW][C]51[/C][C]0.992255013987417[/C][C]0.0154899720251651[/C][C]0.00774498601258257[/C][/ROW]
[ROW][C]52[/C][C]0.9882419930237[/C][C]0.0235160139526[/C][C]0.0117580069763[/C][/ROW]
[ROW][C]53[/C][C]0.983701079851803[/C][C]0.0325978402963936[/C][C]0.0162989201481968[/C][/ROW]
[ROW][C]54[/C][C]0.988959637901692[/C][C]0.0220807241966164[/C][C]0.0110403620983082[/C][/ROW]
[ROW][C]55[/C][C]0.988358057177528[/C][C]0.0232838856449435[/C][C]0.0116419428224717[/C][/ROW]
[ROW][C]56[/C][C]0.98335012088777[/C][C]0.0332997582244592[/C][C]0.0166498791122296[/C][/ROW]
[ROW][C]57[/C][C]0.977403539887713[/C][C]0.045192920224575[/C][C]0.0225964601122875[/C][/ROW]
[ROW][C]58[/C][C]0.965866765938357[/C][C]0.0682664681232855[/C][C]0.0341332340616428[/C][/ROW]
[ROW][C]59[/C][C]0.988350604748777[/C][C]0.0232987905024467[/C][C]0.0116493952512234[/C][/ROW]
[ROW][C]60[/C][C]0.983307897216686[/C][C]0.0333842055666287[/C][C]0.0166921027833143[/C][/ROW]
[ROW][C]61[/C][C]0.985458046714358[/C][C]0.029083906571285[/C][C]0.0145419532856425[/C][/ROW]
[ROW][C]62[/C][C]0.987609977777016[/C][C]0.0247800444459678[/C][C]0.0123900222229839[/C][/ROW]
[ROW][C]63[/C][C]0.980449530328791[/C][C]0.039100939342418[/C][C]0.019550469671209[/C][/ROW]
[ROW][C]64[/C][C]0.977082753803038[/C][C]0.0458344923939233[/C][C]0.0229172461969616[/C][/ROW]
[ROW][C]65[/C][C]0.966224986461903[/C][C]0.067550027076193[/C][C]0.0337750135380965[/C][/ROW]
[ROW][C]66[/C][C]0.970372671268535[/C][C]0.0592546574629294[/C][C]0.0296273287314647[/C][/ROW]
[ROW][C]67[/C][C]0.977618040450193[/C][C]0.044763919099614[/C][C]0.022381959549807[/C][/ROW]
[ROW][C]68[/C][C]0.959439909417394[/C][C]0.0811201811652112[/C][C]0.0405600905826056[/C][/ROW]
[ROW][C]69[/C][C]0.926277201303525[/C][C]0.147445597392951[/C][C]0.0737227986964754[/C][/ROW]
[ROW][C]70[/C][C]0.880319587425028[/C][C]0.239360825149944[/C][C]0.119680412574972[/C][/ROW]
[ROW][C]71[/C][C]0.85215205795606[/C][C]0.295695884087881[/C][C]0.14784794204394[/C][/ROW]
[ROW][C]72[/C][C]0.737646923031894[/C][C]0.524706153936211[/C][C]0.262353076968106[/C][/ROW]
[ROW][C]73[/C][C]0.751598199991232[/C][C]0.496803600017536[/C][C]0.248401800008768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154754&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154754&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
90.03431111523567550.0686222304713510.965688884764324
100.01246425896196350.0249285179239270.987535741038036
110.004087829070713610.008175658141427230.995912170929286
120.001324192096954810.002648384193909620.998675807903045
130.001264019563575560.002528039127151120.998735980436424
140.005949897580418280.01189979516083660.994050102419582
150.0128912222328120.02578244446562390.987108777767188
160.04914238731817240.09828477463634470.950857612681828
170.08163891172654630.1632778234530930.918361088273454
180.06921450930503540.1384290186100710.930785490694965
190.04237850501602060.08475701003204120.957621494983979
200.02904540709018120.05809081418036240.970954592909819
210.02589804753998350.0517960950799670.974101952460017
220.01550101886689530.03100203773379050.984498981133105
230.01014746597632860.02029493195265720.989852534023671
240.005685759056638890.01137151811327780.994314240943361
250.003573269936639830.007146539873279660.99642673006336
260.0026175080142570.0052350160285140.997382491985743
270.001605727410042530.003211454820085060.998394272589957
280.001183576653808610.002367153307617230.998816423346191
290.002000702094623970.004001404189247940.997999297905376
300.00426444687709370.008528893754187390.995735553122906
310.003249585251937040.006499170503874080.996750414748063
320.0354739181543950.070947836308790.964526081845605
330.06361384480052020.127227689601040.93638615519948
340.06992352467528270.1398470493505650.930076475324717
350.06236235848179450.1247247169635890.937637641518206
360.08297413388315150.1659482677663030.917025866116849
370.09666246087633590.1933249217526720.903337539123664
380.07062818912269730.1412563782453950.929371810877303
390.2116372535294430.4232745070588860.788362746470557
400.4494100710572780.8988201421145560.550589928942722
410.6040561894526320.7918876210947370.395943810547368
420.829571629375720.3408567412485590.170428370624279
430.8810172353912530.2379655292174940.118982764608747
440.919156709191720.161686581616560.0808432908082801
450.9895149375390840.02097012492183130.0104850624609156
460.9908633257874390.01827334842512160.00913667421256079
470.9914247472870890.01715050542582260.0085752527129113
480.995291399008720.009417201982559260.00470860099127963
490.9934723824522860.01305523509542860.00652761754771432
500.9892819572103590.0214360855792820.010718042789641
510.9922550139874170.01548997202516510.00774498601258257
520.98824199302370.02351601395260.0117580069763
530.9837010798518030.03259784029639360.0162989201481968
540.9889596379016920.02208072419661640.0110403620983082
550.9883580571775280.02328388564494350.0116419428224717
560.983350120887770.03329975822445920.0166498791122296
570.9774035398877130.0451929202245750.0225964601122875
580.9658667659383570.06826646812328550.0341332340616428
590.9883506047487770.02329879050244670.0116493952512234
600.9833078972166860.03338420556662870.0166921027833143
610.9854580467143580.0290839065712850.0145419532856425
620.9876099777770160.02478004444596780.0123900222229839
630.9804495303287910.0391009393424180.019550469671209
640.9770827538030380.04583449239392330.0229172461969616
650.9662249864619030.0675500270761930.0337750135380965
660.9703726712685350.05925465746292940.0296273287314647
670.9776180404501930.0447639190996140.022381959549807
680.9594399094173940.08112018116521120.0405600905826056
690.9262772013035250.1474455973929510.0737227986964754
700.8803195874250280.2393608251499440.119680412574972
710.852152057956060.2956958840878810.14784794204394
720.7376469230318940.5247061539362110.262353076968106
730.7515981999912320.4968036000175360.248401800008768






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.169230769230769NOK
5% type I error level360.553846153846154NOK
10% type I error level460.707692307692308NOK

\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 & 11 & 0.169230769230769 & NOK \tabularnewline
5% type I error level & 36 & 0.553846153846154 & NOK \tabularnewline
10% type I error level & 46 & 0.707692307692308 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154754&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]11[/C][C]0.169230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.553846153846154[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.707692307692308[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154754&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154754&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 level110.169230769230769NOK
5% type I error level360.553846153846154NOK
10% type I error level460.707692307692308NOK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
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')
}