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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 06:07:47 -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/23/t13246384991ixv6jk5j7xwkme.htm/, Retrieved Fri, 15 Aug 2025 07:15:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160277, Retrieved Fri, 15 Aug 2025 07:15:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact303

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
-  M D  [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 10:32:18] [d946de7cca328fbcf207448a112523ab]
-         [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:25:47] [3635fb7041b1998c5a1332cf9de22bce]
-    D      [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:46:24] [3635fb7041b1998c5a1332cf9de22bce]
- R PD        [Multiple Regression] [Paper, MR poging 2] [2010-12-19 20:57:32] [3635fb7041b1998c5a1332cf9de22bce]
- R  D          [Multiple Regression] [] [2011-12-23 10:56:39] [74be16979710d4c4e7c6647856088456]
-   P               [Multiple Regression] [] [2011-12-23 11:07:47] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
631.923	9.911	58608
654.294	8.915	46865
671.833	9.452	51378
586.840	9.112	46235
600.969	8.472	47206
625.568	8.230	45382
558.110	8.384	41227
630.577	8.625	33795
628.654	8.221	31295
603.184	8.649	42625
656.255	8.625	33625
600.730	10.443	21538
670.326	10.357	56421
678.423	8.586	53152
641.502	8.892	53536
625.311	8.329	52408
628.177	8.101	41454
589.767	7.922	38271
582.471	8.120	35306
636.248	7.838	26414
599.885	7.735	31917
621.694	8.406	38030
637.406	8.209	27534
595.994	9.451	18387
696.308	10.041	50556
674.201	9.411	43901
648.861	10.405	48572
649.605	8.467	43899
672.392	8.464	37532
598.396	8.102	40357
613.177	7.627	35489
638.104	7.513	29027
615.632	7.510	34485
634.465	8.291	42598
638.686	8.064	30306
604.243	9.383	26451
706.669	9.706	47460
677.185	8.579	50104
644.328	9.474	61465
664.825	8.318	53726
605.707	8.213	39477
600.136	8.059	43895
612.166	9.111	31481
599.659	7.708	29896
634.210	7.680	33842
618.234	8.014	39120
613.576	8.007	33702
627.200	8.718	25094
668.973	9.486	51442
651.479	9.113	45594
619.661	9.025	52518
644.260	8.476	48564
579.936	7.952	41745
601.752	7.759	49585
595.376	7.835	32747
588.902	7.600	33379
634.341	7.651	35645
594.305	8.319	37034
606.200	8.812	35681
610.926	8.630	20972

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160277&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160277&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160277&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
WERKLOZEN[t] = + 477.412730911941 + 12.6518644068873OVERLIJDENS[t] + 0.00103154326563818INSCHRIJVINGEN[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WERKLOZEN[t] =  +  477.412730911941 +  12.6518644068873OVERLIJDENS[t] +  0.00103154326563818INSCHRIJVINGEN[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160277&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WERKLOZEN[t] =  +  477.412730911941 +  12.6518644068873OVERLIJDENS[t] +  0.00103154326563818INSCHRIJVINGEN[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160277&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160277&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
WERKLOZEN[t] = + 477.412730911941 + 12.6518644068873OVERLIJDENS[t] + 0.00103154326563818INSCHRIJVINGEN[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)477.41273091194140.03675611.924400
OVERLIJDENS12.65186440688734.9397982.56120.0131020.006551
INSCHRIJVINGEN0.001031543265638180.0003762.74110.0081650.004083

\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) & 477.412730911941 & 40.036756 & 11.9244 & 0 & 0 \tabularnewline
OVERLIJDENS & 12.6518644068873 & 4.939798 & 2.5612 & 0.013102 & 0.006551 \tabularnewline
INSCHRIJVINGEN & 0.00103154326563818 & 0.000376 & 2.7411 & 0.008165 & 0.004083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160277&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]477.412730911941[/C][C]40.036756[/C][C]11.9244[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]OVERLIJDENS[/C][C]12.6518644068873[/C][C]4.939798[/C][C]2.5612[/C][C]0.013102[/C][C]0.006551[/C][/ROW]
[ROW][C]INSCHRIJVINGEN[/C][C]0.00103154326563818[/C][C]0.000376[/C][C]2.7411[/C][C]0.008165[/C][C]0.004083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160277&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160277&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)477.41273091194140.03675611.924400
OVERLIJDENS12.65186440688734.9397982.56120.0131020.006551
INSCHRIJVINGEN0.001031543265638180.0003762.74110.0081650.004083






Multiple Linear Regression - Regression Statistics
Multiple R0.52089604026947
R-squared0.271332684768414
Adjusted R-squared0.245765410549761
F-TEST (value)10.6124994963471
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0.000120804955369258
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26.9689457429041
Sum Squared Residuals41457.4699655711

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.52089604026947 \tabularnewline
R-squared & 0.271332684768414 \tabularnewline
Adjusted R-squared & 0.245765410549761 \tabularnewline
F-TEST (value) & 10.6124994963471 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0.000120804955369258 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 26.9689457429041 \tabularnewline
Sum Squared Residuals & 41457.4699655711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160277&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.52089604026947[/C][/ROW]
[ROW][C]R-squared[/C][C]0.271332684768414[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.245765410549761[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.6124994963471[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0.000120804955369258[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]26.9689457429041[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]41457.4699655711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160277&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160277&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 R0.52089604026947
R-squared0.271332684768414
Adjusted R-squared0.245765410549761
F-TEST (value)10.6124994963471
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0.000120804955369258
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26.9689457429041
Sum Squared Residuals41457.4699655711






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1631.923663.262046761124-31.3390467611236
2654.294638.54737724347515.7466227565248
3671.833649.99678318779921.8362168122011
4586.84640.38992227428-53.54992227428
5600.969633.294357564807-32.3253575648067
6625.568628.351071461816-2.78307146181601
7558.11626.01339631175-67.90339631175
8630.577621.3960660835879.1809339164131
9628.654613.70585469910914.948145300891
10603.184630.808237864937-27.6242378649373
11656.255621.22070372842835.0342962715716
12600.73631.753529768381-31.0235297683809
13670.326666.6487931646453.67720683535483
14678.423640.87022636467737.5527736353235
15641.502645.137809487189-3.63580948718915
16625.311636.851229022472-11.5402290224716
17628.177622.6670790059015.50992099409926
18589.767617.118993062542-27.3519930625415
19582.471616.565536432488-34.0945364324881
20636.248603.82522795169132.4227720483089
21599.885608.198668508589-8.31366850858868
22621.694622.993893508456-1.29989350845631
23637.406609.67439810416127.7316018958388
24595.994615.952487446723-19.9584874467227
25696.308656.60080275910139.7071972408991
26674.201641.7652077499432.4357922500602
27648.861659.159499564182-10.2984995641817
28649.605629.81978466330719.7852153366931
29672.392623.21399309776849.1780069022321
30598.396621.548127907903-23.1521279079026
31613.177610.5169396975042.66006030249561
32638.104602.40879457256535.6952054274347
33615.632608.0010021231987.63099787680209
34634.465626.2510187390998.21398126090061
35638.686610.69931569751127.9866843024885
36604.243623.410525561161-19.1675255611606
37706.669649.16877023237857.5002297676222
38677.185637.63751944016339.5474805598368
39644.328660.680301125243-16.3523011252427
40664.825638.07163253810726.753367461893
41605.707622.044726783305-16.3377267833054
42600.136624.653697812234-24.5176978122343
43612.166625.157881068647-12.9918810686473
44599.659605.772319229748-6.11331922974796
45634.21609.48853675256324.7214632474367
46618.234619.158744820502-0.924744820501995
47613.576613.4812803564260.0947196435738547
48627.2613.5972315191113.6027684808904
49668.973650.49296534663418.4800346533661
50651.479639.74135490541311.7376450945873
51619.661645.770396408886-26.1093964088855
52644.26634.7458007771719.51419922282902
53579.936621.082130299575-41.1461302995752
54601.752626.727619671649-24.9756196716494
55595.376610.320035859757-14.9440358597571
56588.902607.998783068022-19.0967830680218
57634.341610.98150519270923.3594948072908
58594.305620.865764212481-26.5607642124815
59606.2625.707455326668-19.5074553266684
60610.926608.2318461103432.69415388965708

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 631.923 & 663.262046761124 & -31.3390467611236 \tabularnewline
2 & 654.294 & 638.547377243475 & 15.7466227565248 \tabularnewline
3 & 671.833 & 649.996783187799 & 21.8362168122011 \tabularnewline
4 & 586.84 & 640.38992227428 & -53.54992227428 \tabularnewline
5 & 600.969 & 633.294357564807 & -32.3253575648067 \tabularnewline
6 & 625.568 & 628.351071461816 & -2.78307146181601 \tabularnewline
7 & 558.11 & 626.01339631175 & -67.90339631175 \tabularnewline
8 & 630.577 & 621.396066083587 & 9.1809339164131 \tabularnewline
9 & 628.654 & 613.705854699109 & 14.948145300891 \tabularnewline
10 & 603.184 & 630.808237864937 & -27.6242378649373 \tabularnewline
11 & 656.255 & 621.220703728428 & 35.0342962715716 \tabularnewline
12 & 600.73 & 631.753529768381 & -31.0235297683809 \tabularnewline
13 & 670.326 & 666.648793164645 & 3.67720683535483 \tabularnewline
14 & 678.423 & 640.870226364677 & 37.5527736353235 \tabularnewline
15 & 641.502 & 645.137809487189 & -3.63580948718915 \tabularnewline
16 & 625.311 & 636.851229022472 & -11.5402290224716 \tabularnewline
17 & 628.177 & 622.667079005901 & 5.50992099409926 \tabularnewline
18 & 589.767 & 617.118993062542 & -27.3519930625415 \tabularnewline
19 & 582.471 & 616.565536432488 & -34.0945364324881 \tabularnewline
20 & 636.248 & 603.825227951691 & 32.4227720483089 \tabularnewline
21 & 599.885 & 608.198668508589 & -8.31366850858868 \tabularnewline
22 & 621.694 & 622.993893508456 & -1.29989350845631 \tabularnewline
23 & 637.406 & 609.674398104161 & 27.7316018958388 \tabularnewline
24 & 595.994 & 615.952487446723 & -19.9584874467227 \tabularnewline
25 & 696.308 & 656.600802759101 & 39.7071972408991 \tabularnewline
26 & 674.201 & 641.76520774994 & 32.4357922500602 \tabularnewline
27 & 648.861 & 659.159499564182 & -10.2984995641817 \tabularnewline
28 & 649.605 & 629.819784663307 & 19.7852153366931 \tabularnewline
29 & 672.392 & 623.213993097768 & 49.1780069022321 \tabularnewline
30 & 598.396 & 621.548127907903 & -23.1521279079026 \tabularnewline
31 & 613.177 & 610.516939697504 & 2.66006030249561 \tabularnewline
32 & 638.104 & 602.408794572565 & 35.6952054274347 \tabularnewline
33 & 615.632 & 608.001002123198 & 7.63099787680209 \tabularnewline
34 & 634.465 & 626.251018739099 & 8.21398126090061 \tabularnewline
35 & 638.686 & 610.699315697511 & 27.9866843024885 \tabularnewline
36 & 604.243 & 623.410525561161 & -19.1675255611606 \tabularnewline
37 & 706.669 & 649.168770232378 & 57.5002297676222 \tabularnewline
38 & 677.185 & 637.637519440163 & 39.5474805598368 \tabularnewline
39 & 644.328 & 660.680301125243 & -16.3523011252427 \tabularnewline
40 & 664.825 & 638.071632538107 & 26.753367461893 \tabularnewline
41 & 605.707 & 622.044726783305 & -16.3377267833054 \tabularnewline
42 & 600.136 & 624.653697812234 & -24.5176978122343 \tabularnewline
43 & 612.166 & 625.157881068647 & -12.9918810686473 \tabularnewline
44 & 599.659 & 605.772319229748 & -6.11331922974796 \tabularnewline
45 & 634.21 & 609.488536752563 & 24.7214632474367 \tabularnewline
46 & 618.234 & 619.158744820502 & -0.924744820501995 \tabularnewline
47 & 613.576 & 613.481280356426 & 0.0947196435738547 \tabularnewline
48 & 627.2 & 613.59723151911 & 13.6027684808904 \tabularnewline
49 & 668.973 & 650.492965346634 & 18.4800346533661 \tabularnewline
50 & 651.479 & 639.741354905413 & 11.7376450945873 \tabularnewline
51 & 619.661 & 645.770396408886 & -26.1093964088855 \tabularnewline
52 & 644.26 & 634.745800777171 & 9.51419922282902 \tabularnewline
53 & 579.936 & 621.082130299575 & -41.1461302995752 \tabularnewline
54 & 601.752 & 626.727619671649 & -24.9756196716494 \tabularnewline
55 & 595.376 & 610.320035859757 & -14.9440358597571 \tabularnewline
56 & 588.902 & 607.998783068022 & -19.0967830680218 \tabularnewline
57 & 634.341 & 610.981505192709 & 23.3594948072908 \tabularnewline
58 & 594.305 & 620.865764212481 & -26.5607642124815 \tabularnewline
59 & 606.2 & 625.707455326668 & -19.5074553266684 \tabularnewline
60 & 610.926 & 608.231846110343 & 2.69415388965708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160277&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]631.923[/C][C]663.262046761124[/C][C]-31.3390467611236[/C][/ROW]
[ROW][C]2[/C][C]654.294[/C][C]638.547377243475[/C][C]15.7466227565248[/C][/ROW]
[ROW][C]3[/C][C]671.833[/C][C]649.996783187799[/C][C]21.8362168122011[/C][/ROW]
[ROW][C]4[/C][C]586.84[/C][C]640.38992227428[/C][C]-53.54992227428[/C][/ROW]
[ROW][C]5[/C][C]600.969[/C][C]633.294357564807[/C][C]-32.3253575648067[/C][/ROW]
[ROW][C]6[/C][C]625.568[/C][C]628.351071461816[/C][C]-2.78307146181601[/C][/ROW]
[ROW][C]7[/C][C]558.11[/C][C]626.01339631175[/C][C]-67.90339631175[/C][/ROW]
[ROW][C]8[/C][C]630.577[/C][C]621.396066083587[/C][C]9.1809339164131[/C][/ROW]
[ROW][C]9[/C][C]628.654[/C][C]613.705854699109[/C][C]14.948145300891[/C][/ROW]
[ROW][C]10[/C][C]603.184[/C][C]630.808237864937[/C][C]-27.6242378649373[/C][/ROW]
[ROW][C]11[/C][C]656.255[/C][C]621.220703728428[/C][C]35.0342962715716[/C][/ROW]
[ROW][C]12[/C][C]600.73[/C][C]631.753529768381[/C][C]-31.0235297683809[/C][/ROW]
[ROW][C]13[/C][C]670.326[/C][C]666.648793164645[/C][C]3.67720683535483[/C][/ROW]
[ROW][C]14[/C][C]678.423[/C][C]640.870226364677[/C][C]37.5527736353235[/C][/ROW]
[ROW][C]15[/C][C]641.502[/C][C]645.137809487189[/C][C]-3.63580948718915[/C][/ROW]
[ROW][C]16[/C][C]625.311[/C][C]636.851229022472[/C][C]-11.5402290224716[/C][/ROW]
[ROW][C]17[/C][C]628.177[/C][C]622.667079005901[/C][C]5.50992099409926[/C][/ROW]
[ROW][C]18[/C][C]589.767[/C][C]617.118993062542[/C][C]-27.3519930625415[/C][/ROW]
[ROW][C]19[/C][C]582.471[/C][C]616.565536432488[/C][C]-34.0945364324881[/C][/ROW]
[ROW][C]20[/C][C]636.248[/C][C]603.825227951691[/C][C]32.4227720483089[/C][/ROW]
[ROW][C]21[/C][C]599.885[/C][C]608.198668508589[/C][C]-8.31366850858868[/C][/ROW]
[ROW][C]22[/C][C]621.694[/C][C]622.993893508456[/C][C]-1.29989350845631[/C][/ROW]
[ROW][C]23[/C][C]637.406[/C][C]609.674398104161[/C][C]27.7316018958388[/C][/ROW]
[ROW][C]24[/C][C]595.994[/C][C]615.952487446723[/C][C]-19.9584874467227[/C][/ROW]
[ROW][C]25[/C][C]696.308[/C][C]656.600802759101[/C][C]39.7071972408991[/C][/ROW]
[ROW][C]26[/C][C]674.201[/C][C]641.76520774994[/C][C]32.4357922500602[/C][/ROW]
[ROW][C]27[/C][C]648.861[/C][C]659.159499564182[/C][C]-10.2984995641817[/C][/ROW]
[ROW][C]28[/C][C]649.605[/C][C]629.819784663307[/C][C]19.7852153366931[/C][/ROW]
[ROW][C]29[/C][C]672.392[/C][C]623.213993097768[/C][C]49.1780069022321[/C][/ROW]
[ROW][C]30[/C][C]598.396[/C][C]621.548127907903[/C][C]-23.1521279079026[/C][/ROW]
[ROW][C]31[/C][C]613.177[/C][C]610.516939697504[/C][C]2.66006030249561[/C][/ROW]
[ROW][C]32[/C][C]638.104[/C][C]602.408794572565[/C][C]35.6952054274347[/C][/ROW]
[ROW][C]33[/C][C]615.632[/C][C]608.001002123198[/C][C]7.63099787680209[/C][/ROW]
[ROW][C]34[/C][C]634.465[/C][C]626.251018739099[/C][C]8.21398126090061[/C][/ROW]
[ROW][C]35[/C][C]638.686[/C][C]610.699315697511[/C][C]27.9866843024885[/C][/ROW]
[ROW][C]36[/C][C]604.243[/C][C]623.410525561161[/C][C]-19.1675255611606[/C][/ROW]
[ROW][C]37[/C][C]706.669[/C][C]649.168770232378[/C][C]57.5002297676222[/C][/ROW]
[ROW][C]38[/C][C]677.185[/C][C]637.637519440163[/C][C]39.5474805598368[/C][/ROW]
[ROW][C]39[/C][C]644.328[/C][C]660.680301125243[/C][C]-16.3523011252427[/C][/ROW]
[ROW][C]40[/C][C]664.825[/C][C]638.071632538107[/C][C]26.753367461893[/C][/ROW]
[ROW][C]41[/C][C]605.707[/C][C]622.044726783305[/C][C]-16.3377267833054[/C][/ROW]
[ROW][C]42[/C][C]600.136[/C][C]624.653697812234[/C][C]-24.5176978122343[/C][/ROW]
[ROW][C]43[/C][C]612.166[/C][C]625.157881068647[/C][C]-12.9918810686473[/C][/ROW]
[ROW][C]44[/C][C]599.659[/C][C]605.772319229748[/C][C]-6.11331922974796[/C][/ROW]
[ROW][C]45[/C][C]634.21[/C][C]609.488536752563[/C][C]24.7214632474367[/C][/ROW]
[ROW][C]46[/C][C]618.234[/C][C]619.158744820502[/C][C]-0.924744820501995[/C][/ROW]
[ROW][C]47[/C][C]613.576[/C][C]613.481280356426[/C][C]0.0947196435738547[/C][/ROW]
[ROW][C]48[/C][C]627.2[/C][C]613.59723151911[/C][C]13.6027684808904[/C][/ROW]
[ROW][C]49[/C][C]668.973[/C][C]650.492965346634[/C][C]18.4800346533661[/C][/ROW]
[ROW][C]50[/C][C]651.479[/C][C]639.741354905413[/C][C]11.7376450945873[/C][/ROW]
[ROW][C]51[/C][C]619.661[/C][C]645.770396408886[/C][C]-26.1093964088855[/C][/ROW]
[ROW][C]52[/C][C]644.26[/C][C]634.745800777171[/C][C]9.51419922282902[/C][/ROW]
[ROW][C]53[/C][C]579.936[/C][C]621.082130299575[/C][C]-41.1461302995752[/C][/ROW]
[ROW][C]54[/C][C]601.752[/C][C]626.727619671649[/C][C]-24.9756196716494[/C][/ROW]
[ROW][C]55[/C][C]595.376[/C][C]610.320035859757[/C][C]-14.9440358597571[/C][/ROW]
[ROW][C]56[/C][C]588.902[/C][C]607.998783068022[/C][C]-19.0967830680218[/C][/ROW]
[ROW][C]57[/C][C]634.341[/C][C]610.981505192709[/C][C]23.3594948072908[/C][/ROW]
[ROW][C]58[/C][C]594.305[/C][C]620.865764212481[/C][C]-26.5607642124815[/C][/ROW]
[ROW][C]59[/C][C]606.2[/C][C]625.707455326668[/C][C]-19.5074553266684[/C][/ROW]
[ROW][C]60[/C][C]610.926[/C][C]608.231846110343[/C][C]2.69415388965708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160277&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160277&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
1631.923663.262046761124-31.3390467611236
2654.294638.54737724347515.7466227565248
3671.833649.99678318779921.8362168122011
4586.84640.38992227428-53.54992227428
5600.969633.294357564807-32.3253575648067
6625.568628.351071461816-2.78307146181601
7558.11626.01339631175-67.90339631175
8630.577621.3960660835879.1809339164131
9628.654613.70585469910914.948145300891
10603.184630.808237864937-27.6242378649373
11656.255621.22070372842835.0342962715716
12600.73631.753529768381-31.0235297683809
13670.326666.6487931646453.67720683535483
14678.423640.87022636467737.5527736353235
15641.502645.137809487189-3.63580948718915
16625.311636.851229022472-11.5402290224716
17628.177622.6670790059015.50992099409926
18589.767617.118993062542-27.3519930625415
19582.471616.565536432488-34.0945364324881
20636.248603.82522795169132.4227720483089
21599.885608.198668508589-8.31366850858868
22621.694622.993893508456-1.29989350845631
23637.406609.67439810416127.7316018958388
24595.994615.952487446723-19.9584874467227
25696.308656.60080275910139.7071972408991
26674.201641.7652077499432.4357922500602
27648.861659.159499564182-10.2984995641817
28649.605629.81978466330719.7852153366931
29672.392623.21399309776849.1780069022321
30598.396621.548127907903-23.1521279079026
31613.177610.5169396975042.66006030249561
32638.104602.40879457256535.6952054274347
33615.632608.0010021231987.63099787680209
34634.465626.2510187390998.21398126090061
35638.686610.69931569751127.9866843024885
36604.243623.410525561161-19.1675255611606
37706.669649.16877023237857.5002297676222
38677.185637.63751944016339.5474805598368
39644.328660.680301125243-16.3523011252427
40664.825638.07163253810726.753367461893
41605.707622.044726783305-16.3377267833054
42600.136624.653697812234-24.5176978122343
43612.166625.157881068647-12.9918810686473
44599.659605.772319229748-6.11331922974796
45634.21609.48853675256324.7214632474367
46618.234619.158744820502-0.924744820501995
47613.576613.4812803564260.0947196435738547
48627.2613.5972315191113.6027684808904
49668.973650.49296534663418.4800346533661
50651.479639.74135490541311.7376450945873
51619.661645.770396408886-26.1093964088855
52644.26634.7458007771719.51419922282902
53579.936621.082130299575-41.1461302995752
54601.752626.727619671649-24.9756196716494
55595.376610.320035859757-14.9440358597571
56588.902607.998783068022-19.0967830680218
57634.341610.98150519270923.3594948072908
58594.305620.865764212481-26.5607642124815
59606.2625.707455326668-19.5074553266684
60610.926608.2318461103432.69415388965708






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.8937609723947370.2124780552105260.106239027605263
70.9504549850959960.09909002980800720.0495450149040036
80.9499124702015870.1001750595968270.0500875297984134
90.9264029641644810.1471940716710390.0735970358355195
100.8986131420176040.2027737159647910.101386857982396
110.8958813871172590.2082372257654820.104118612882741
120.9435548393535480.1128903212929050.0564451606464524
130.9315840845641810.1368318308716370.0684159154358186
140.9581058710720720.0837882578558560.041894128927928
150.9342489870968070.1315020258063870.0657510129031935
160.9058583990596190.1882832018807610.0941416009403806
170.8668891642835860.2662216714328290.133110835716414
180.8567000455890710.2865999088218580.143299954410929
190.863376776812530.2732464463749410.13662322318747
200.8931045766815590.2137908466368820.106895423318441
210.8534456093688210.2931087812623580.146554390631179
220.8035519884816470.3928960230367070.196448011518353
230.8082706088483470.3834587823033050.191729391151653
240.780993803475670.438012393048660.21900619652433
250.845724441656190.308551116687620.15427555834381
260.8603753516532560.2792492966934880.139624648346744
270.8252753905827360.3494492188345280.174724609417264
280.7990174250076050.4019651499847890.200982574992395
290.8933657966051910.2132684067896190.106634203394809
300.8826654705655850.234669058868830.117334529434415
310.8400476028100850.3199047943798290.159952397189915
320.8772494418474330.2455011163051340.122750558152567
330.8407054778874880.3185890442250240.159294522112512
340.7933649518191410.4132700963617180.206635048180859
350.8081625059034410.3836749881931170.191837494096559
360.7960577138106740.4078845723786520.203942286189326
370.9255972240886990.1488055518226020.0744027759113008
380.9662303934083460.06753921318330830.0337696065916541
390.9533437508777860.09331249824442730.0466562491222136
400.9712265672776210.05754686544475840.0287734327223792
410.9577017366585310.0845965266829390.0422982633414695
420.9470012292921390.1059975414157220.0529987707078609
430.9311882683770980.1376234632458040.0688117316229021
440.8940022902163830.2119954195672340.105997709783617
450.9262414648802870.1475170702394260.0737585351197132
460.8909707560211460.2180584879577070.109029243978854
470.8417174759601690.3165650480796620.158282524039831
480.7841924485144680.4316151029710640.215807551485532
490.7608548218848060.4782903562303870.239145178115194
500.7559123562844980.4881752874310050.244087643715502
510.6598447403927490.6803105192145020.340155259607251
520.790111161274190.4197776774516210.20988883872581
530.7792283147203940.4415433705592110.220771685279606
540.6261848485206480.7476303029587050.373815151479352

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.893760972394737 & 0.212478055210526 & 0.106239027605263 \tabularnewline
7 & 0.950454985095996 & 0.0990900298080072 & 0.0495450149040036 \tabularnewline
8 & 0.949912470201587 & 0.100175059596827 & 0.0500875297984134 \tabularnewline
9 & 0.926402964164481 & 0.147194071671039 & 0.0735970358355195 \tabularnewline
10 & 0.898613142017604 & 0.202773715964791 & 0.101386857982396 \tabularnewline
11 & 0.895881387117259 & 0.208237225765482 & 0.104118612882741 \tabularnewline
12 & 0.943554839353548 & 0.112890321292905 & 0.0564451606464524 \tabularnewline
13 & 0.931584084564181 & 0.136831830871637 & 0.0684159154358186 \tabularnewline
14 & 0.958105871072072 & 0.083788257855856 & 0.041894128927928 \tabularnewline
15 & 0.934248987096807 & 0.131502025806387 & 0.0657510129031935 \tabularnewline
16 & 0.905858399059619 & 0.188283201880761 & 0.0941416009403806 \tabularnewline
17 & 0.866889164283586 & 0.266221671432829 & 0.133110835716414 \tabularnewline
18 & 0.856700045589071 & 0.286599908821858 & 0.143299954410929 \tabularnewline
19 & 0.86337677681253 & 0.273246446374941 & 0.13662322318747 \tabularnewline
20 & 0.893104576681559 & 0.213790846636882 & 0.106895423318441 \tabularnewline
21 & 0.853445609368821 & 0.293108781262358 & 0.146554390631179 \tabularnewline
22 & 0.803551988481647 & 0.392896023036707 & 0.196448011518353 \tabularnewline
23 & 0.808270608848347 & 0.383458782303305 & 0.191729391151653 \tabularnewline
24 & 0.78099380347567 & 0.43801239304866 & 0.21900619652433 \tabularnewline
25 & 0.84572444165619 & 0.30855111668762 & 0.15427555834381 \tabularnewline
26 & 0.860375351653256 & 0.279249296693488 & 0.139624648346744 \tabularnewline
27 & 0.825275390582736 & 0.349449218834528 & 0.174724609417264 \tabularnewline
28 & 0.799017425007605 & 0.401965149984789 & 0.200982574992395 \tabularnewline
29 & 0.893365796605191 & 0.213268406789619 & 0.106634203394809 \tabularnewline
30 & 0.882665470565585 & 0.23466905886883 & 0.117334529434415 \tabularnewline
31 & 0.840047602810085 & 0.319904794379829 & 0.159952397189915 \tabularnewline
32 & 0.877249441847433 & 0.245501116305134 & 0.122750558152567 \tabularnewline
33 & 0.840705477887488 & 0.318589044225024 & 0.159294522112512 \tabularnewline
34 & 0.793364951819141 & 0.413270096361718 & 0.206635048180859 \tabularnewline
35 & 0.808162505903441 & 0.383674988193117 & 0.191837494096559 \tabularnewline
36 & 0.796057713810674 & 0.407884572378652 & 0.203942286189326 \tabularnewline
37 & 0.925597224088699 & 0.148805551822602 & 0.0744027759113008 \tabularnewline
38 & 0.966230393408346 & 0.0675392131833083 & 0.0337696065916541 \tabularnewline
39 & 0.953343750877786 & 0.0933124982444273 & 0.0466562491222136 \tabularnewline
40 & 0.971226567277621 & 0.0575468654447584 & 0.0287734327223792 \tabularnewline
41 & 0.957701736658531 & 0.084596526682939 & 0.0422982633414695 \tabularnewline
42 & 0.947001229292139 & 0.105997541415722 & 0.0529987707078609 \tabularnewline
43 & 0.931188268377098 & 0.137623463245804 & 0.0688117316229021 \tabularnewline
44 & 0.894002290216383 & 0.211995419567234 & 0.105997709783617 \tabularnewline
45 & 0.926241464880287 & 0.147517070239426 & 0.0737585351197132 \tabularnewline
46 & 0.890970756021146 & 0.218058487957707 & 0.109029243978854 \tabularnewline
47 & 0.841717475960169 & 0.316565048079662 & 0.158282524039831 \tabularnewline
48 & 0.784192448514468 & 0.431615102971064 & 0.215807551485532 \tabularnewline
49 & 0.760854821884806 & 0.478290356230387 & 0.239145178115194 \tabularnewline
50 & 0.755912356284498 & 0.488175287431005 & 0.244087643715502 \tabularnewline
51 & 0.659844740392749 & 0.680310519214502 & 0.340155259607251 \tabularnewline
52 & 0.79011116127419 & 0.419777677451621 & 0.20988883872581 \tabularnewline
53 & 0.779228314720394 & 0.441543370559211 & 0.220771685279606 \tabularnewline
54 & 0.626184848520648 & 0.747630302958705 & 0.373815151479352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160277&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]6[/C][C]0.893760972394737[/C][C]0.212478055210526[/C][C]0.106239027605263[/C][/ROW]
[ROW][C]7[/C][C]0.950454985095996[/C][C]0.0990900298080072[/C][C]0.0495450149040036[/C][/ROW]
[ROW][C]8[/C][C]0.949912470201587[/C][C]0.100175059596827[/C][C]0.0500875297984134[/C][/ROW]
[ROW][C]9[/C][C]0.926402964164481[/C][C]0.147194071671039[/C][C]0.0735970358355195[/C][/ROW]
[ROW][C]10[/C][C]0.898613142017604[/C][C]0.202773715964791[/C][C]0.101386857982396[/C][/ROW]
[ROW][C]11[/C][C]0.895881387117259[/C][C]0.208237225765482[/C][C]0.104118612882741[/C][/ROW]
[ROW][C]12[/C][C]0.943554839353548[/C][C]0.112890321292905[/C][C]0.0564451606464524[/C][/ROW]
[ROW][C]13[/C][C]0.931584084564181[/C][C]0.136831830871637[/C][C]0.0684159154358186[/C][/ROW]
[ROW][C]14[/C][C]0.958105871072072[/C][C]0.083788257855856[/C][C]0.041894128927928[/C][/ROW]
[ROW][C]15[/C][C]0.934248987096807[/C][C]0.131502025806387[/C][C]0.0657510129031935[/C][/ROW]
[ROW][C]16[/C][C]0.905858399059619[/C][C]0.188283201880761[/C][C]0.0941416009403806[/C][/ROW]
[ROW][C]17[/C][C]0.866889164283586[/C][C]0.266221671432829[/C][C]0.133110835716414[/C][/ROW]
[ROW][C]18[/C][C]0.856700045589071[/C][C]0.286599908821858[/C][C]0.143299954410929[/C][/ROW]
[ROW][C]19[/C][C]0.86337677681253[/C][C]0.273246446374941[/C][C]0.13662322318747[/C][/ROW]
[ROW][C]20[/C][C]0.893104576681559[/C][C]0.213790846636882[/C][C]0.106895423318441[/C][/ROW]
[ROW][C]21[/C][C]0.853445609368821[/C][C]0.293108781262358[/C][C]0.146554390631179[/C][/ROW]
[ROW][C]22[/C][C]0.803551988481647[/C][C]0.392896023036707[/C][C]0.196448011518353[/C][/ROW]
[ROW][C]23[/C][C]0.808270608848347[/C][C]0.383458782303305[/C][C]0.191729391151653[/C][/ROW]
[ROW][C]24[/C][C]0.78099380347567[/C][C]0.43801239304866[/C][C]0.21900619652433[/C][/ROW]
[ROW][C]25[/C][C]0.84572444165619[/C][C]0.30855111668762[/C][C]0.15427555834381[/C][/ROW]
[ROW][C]26[/C][C]0.860375351653256[/C][C]0.279249296693488[/C][C]0.139624648346744[/C][/ROW]
[ROW][C]27[/C][C]0.825275390582736[/C][C]0.349449218834528[/C][C]0.174724609417264[/C][/ROW]
[ROW][C]28[/C][C]0.799017425007605[/C][C]0.401965149984789[/C][C]0.200982574992395[/C][/ROW]
[ROW][C]29[/C][C]0.893365796605191[/C][C]0.213268406789619[/C][C]0.106634203394809[/C][/ROW]
[ROW][C]30[/C][C]0.882665470565585[/C][C]0.23466905886883[/C][C]0.117334529434415[/C][/ROW]
[ROW][C]31[/C][C]0.840047602810085[/C][C]0.319904794379829[/C][C]0.159952397189915[/C][/ROW]
[ROW][C]32[/C][C]0.877249441847433[/C][C]0.245501116305134[/C][C]0.122750558152567[/C][/ROW]
[ROW][C]33[/C][C]0.840705477887488[/C][C]0.318589044225024[/C][C]0.159294522112512[/C][/ROW]
[ROW][C]34[/C][C]0.793364951819141[/C][C]0.413270096361718[/C][C]0.206635048180859[/C][/ROW]
[ROW][C]35[/C][C]0.808162505903441[/C][C]0.383674988193117[/C][C]0.191837494096559[/C][/ROW]
[ROW][C]36[/C][C]0.796057713810674[/C][C]0.407884572378652[/C][C]0.203942286189326[/C][/ROW]
[ROW][C]37[/C][C]0.925597224088699[/C][C]0.148805551822602[/C][C]0.0744027759113008[/C][/ROW]
[ROW][C]38[/C][C]0.966230393408346[/C][C]0.0675392131833083[/C][C]0.0337696065916541[/C][/ROW]
[ROW][C]39[/C][C]0.953343750877786[/C][C]0.0933124982444273[/C][C]0.0466562491222136[/C][/ROW]
[ROW][C]40[/C][C]0.971226567277621[/C][C]0.0575468654447584[/C][C]0.0287734327223792[/C][/ROW]
[ROW][C]41[/C][C]0.957701736658531[/C][C]0.084596526682939[/C][C]0.0422982633414695[/C][/ROW]
[ROW][C]42[/C][C]0.947001229292139[/C][C]0.105997541415722[/C][C]0.0529987707078609[/C][/ROW]
[ROW][C]43[/C][C]0.931188268377098[/C][C]0.137623463245804[/C][C]0.0688117316229021[/C][/ROW]
[ROW][C]44[/C][C]0.894002290216383[/C][C]0.211995419567234[/C][C]0.105997709783617[/C][/ROW]
[ROW][C]45[/C][C]0.926241464880287[/C][C]0.147517070239426[/C][C]0.0737585351197132[/C][/ROW]
[ROW][C]46[/C][C]0.890970756021146[/C][C]0.218058487957707[/C][C]0.109029243978854[/C][/ROW]
[ROW][C]47[/C][C]0.841717475960169[/C][C]0.316565048079662[/C][C]0.158282524039831[/C][/ROW]
[ROW][C]48[/C][C]0.784192448514468[/C][C]0.431615102971064[/C][C]0.215807551485532[/C][/ROW]
[ROW][C]49[/C][C]0.760854821884806[/C][C]0.478290356230387[/C][C]0.239145178115194[/C][/ROW]
[ROW][C]50[/C][C]0.755912356284498[/C][C]0.488175287431005[/C][C]0.244087643715502[/C][/ROW]
[ROW][C]51[/C][C]0.659844740392749[/C][C]0.680310519214502[/C][C]0.340155259607251[/C][/ROW]
[ROW][C]52[/C][C]0.79011116127419[/C][C]0.419777677451621[/C][C]0.20988883872581[/C][/ROW]
[ROW][C]53[/C][C]0.779228314720394[/C][C]0.441543370559211[/C][C]0.220771685279606[/C][/ROW]
[ROW][C]54[/C][C]0.626184848520648[/C][C]0.747630302958705[/C][C]0.373815151479352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160277&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160277&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
60.8937609723947370.2124780552105260.106239027605263
70.9504549850959960.09909002980800720.0495450149040036
80.9499124702015870.1001750595968270.0500875297984134
90.9264029641644810.1471940716710390.0735970358355195
100.8986131420176040.2027737159647910.101386857982396
110.8958813871172590.2082372257654820.104118612882741
120.9435548393535480.1128903212929050.0564451606464524
130.9315840845641810.1368318308716370.0684159154358186
140.9581058710720720.0837882578558560.041894128927928
150.9342489870968070.1315020258063870.0657510129031935
160.9058583990596190.1882832018807610.0941416009403806
170.8668891642835860.2662216714328290.133110835716414
180.8567000455890710.2865999088218580.143299954410929
190.863376776812530.2732464463749410.13662322318747
200.8931045766815590.2137908466368820.106895423318441
210.8534456093688210.2931087812623580.146554390631179
220.8035519884816470.3928960230367070.196448011518353
230.8082706088483470.3834587823033050.191729391151653
240.780993803475670.438012393048660.21900619652433
250.845724441656190.308551116687620.15427555834381
260.8603753516532560.2792492966934880.139624648346744
270.8252753905827360.3494492188345280.174724609417264
280.7990174250076050.4019651499847890.200982574992395
290.8933657966051910.2132684067896190.106634203394809
300.8826654705655850.234669058868830.117334529434415
310.8400476028100850.3199047943798290.159952397189915
320.8772494418474330.2455011163051340.122750558152567
330.8407054778874880.3185890442250240.159294522112512
340.7933649518191410.4132700963617180.206635048180859
350.8081625059034410.3836749881931170.191837494096559
360.7960577138106740.4078845723786520.203942286189326
370.9255972240886990.1488055518226020.0744027759113008
380.9662303934083460.06753921318330830.0337696065916541
390.9533437508777860.09331249824442730.0466562491222136
400.9712265672776210.05754686544475840.0287734327223792
410.9577017366585310.0845965266829390.0422982633414695
420.9470012292921390.1059975414157220.0529987707078609
430.9311882683770980.1376234632458040.0688117316229021
440.8940022902163830.2119954195672340.105997709783617
450.9262414648802870.1475170702394260.0737585351197132
460.8909707560211460.2180584879577070.109029243978854
470.8417174759601690.3165650480796620.158282524039831
480.7841924485144680.4316151029710640.215807551485532
490.7608548218848060.4782903562303870.239145178115194
500.7559123562844980.4881752874310050.244087643715502
510.6598447403927490.6803105192145020.340155259607251
520.790111161274190.4197776774516210.20988883872581
530.7792283147203940.4415433705592110.220771685279606
540.6261848485206480.7476303029587050.373815151479352






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.122448979591837NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.122448979591837 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160277&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.122448979591837[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160277&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160277&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 level00OK
5% type I error level00OK
10% type I error level60.122448979591837NOK


Figures (Output of Computation)

Figure 1
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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')
}