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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 21 Nov 2009 05:55:31 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/21/t1258808472lyqpg4d861kbhz4.htm/, Retrieved Sun, 28 Apr 2024 09:15:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58535, Retrieved Sun, 28 Apr 2024 09:15:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-21 12:55:31] [71596e6a53ccce532e52aaf6113616ef] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3.75	0	3.51	3.37	3.21	3
4.11	0	3.75	3.51	3.37	3.21
4.25	0	4.11	3.75	3.51	3.37
4.25	0	4.25	4.11	3.75	3.51
4.5	0	4.25	4.25	4.11	3.75
4.7	0	4.5	4.25	4.25	4.11
4.75	0	4.7	4.5	4.25	4.25
4.75	0	4.75	4.7	4.5	4.25
4.75	0	4.75	4.75	4.7	4.5
4.75	0	4.75	4.75	4.75	4.7
4.75	0	4.75	4.75	4.75	4.75
4.75	0	4.75	4.75	4.75	4.75
4.58	0	4.75	4.75	4.75	4.75
4.5	0	4.58	4.75	4.75	4.75
4.5	0	4.5	4.58	4.75	4.75
4.49	0	4.5	4.5	4.58	4.75
4.03	0	4.49	4.5	4.5	4.58
3.75	0	4.03	4.49	4.5	4.5
3.39	0	3.75	4.03	4.49	4.5
3.25	0	3.39	3.75	4.03	4.49
3.25	0	3.25	3.39	3.75	4.03
3.25	0	3.25	3.25	3.39	3.75
3.25	0	3.25	3.25	3.25	3.39
3.25	0	3.25	3.25	3.25	3.25
3.25	0	3.25	3.25	3.25	3.25
3.25	0	3.25	3.25	3.25	3.25
3.25	0	3.25	3.25	3.25	3.25
3.25	0	3.25	3.25	3.25	3.25
3.25	0	3.25	3.25	3.25	3.25
3.25	0	3.25	3.25	3.25	3.25
3.25	0	3.25	3.25	3.25	3.25
2.85	0	3.25	3.25	3.25	3.25
2.75	0	2.85	3.25	3.25	3.25
2.75	0	2.75	2.85	3.25	3.25
2.55	0	2.75	2.75	2.85	3.25
2.5	0	2.55	2.75	2.75	2.85
2.5	0	2.5	2.55	2.75	2.75
2.1	0	2.5	2.5	2.55	2.75
2	0	2.1	2.5	2.5	2.55
2	0	2	2.1	2.5	2.5
2	0	2	2	2.1	2.5
2	0	2	2	2	2.1
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2	0	2	2	2	2
2.21	0	2	2	2	2
2.25	0	2.21	2	2	2
2.25	0	2.25	2.21	2	2
2.45	0	2.25	2.25	2.21	2
2.5	0	2.45	2.25	2.25	2.21
2.5	0	2.5	2.45	2.25	2.25
2.64	0	2.5	2.5	2.45	2.25
2.75	0	2.64	2.5	2.5	2.45
2.93	0	2.75	2.64	2.5	2.5
3	0	2.93	2.75	2.64	2.5
3.17	0	3	2.93	2.75	2.64
3.25	0	3.17	3	2.93	2.75
3.39	0	3.25	3.17	3	2.93
3.5	0	3.39	3.25	3.17	3
3.5	0	3.5	3.39	3.25	3.17
3.65	0	3.5	3.5	3.39	3.25
3.75	0	3.65	3.5	3.5	3.39
3.75	0	3.75	3.65	3.5	3.5
3.9	0	3.75	3.75	3.65	3.5
4	0	3.9	3.75	3.75	3.65
4	0	4	3.9	3.75	3.75
4	0	4	4	3.9	3.75
4	0	4	4	4	3.9
4	0	4	4	4	4
4	0	4	4	4	4
4	0	4	4	4	4
4	0	4	4	4	4
4	0	4	4	4	4
4	0	4	4	4	4
4	0	4	4	4	4
4	0	4	4	4	4
4.18	0	4	4	4	4
4.25	0	4.18	4	4	4
4.25	0	4.25	4.18	4	4
3.97	1	4.25	4.25	4.18	4
3.42	1	3.97	4.25	4.25	4.18
2.75	1	3.42	3.97	4.25	4.25
2.31	1	2.75	3.42	3.97	4.25
2	1	2.31	2.75	3.42	3.97
1.66	1	2	2.31	2.75	3.42
1.31	1	1.66	2	2.31	2.75
1.09	1	1.31	1.66	2	2.31
1	1	1.09	1.31	1.66	2
1	1	1	1.09	1.31	1.66
1	1	1	1	1.09	1.31
1	1	1	1	1	1.09
1	1	1	1	1	1

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58535&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58535&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58535&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'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y(t)[t] = + 0.103460639622265 -0.119452797409169`X(t)`[t] + 1.52190634840442`Y(t-1)`[t] -0.619397555123749`Y(t-2)`[t] + 0.254793166385373`Y(t-3)`[t] -0.190018800639385`Y(t-4)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y(t)[t] =  +  0.103460639622265 -0.119452797409169`X(t)`[t] +  1.52190634840442`Y(t-1)`[t] -0.619397555123749`Y(t-2)`[t] +  0.254793166385373`Y(t-3)`[t] -0.190018800639385`Y(t-4)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58535&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y(t)[t] =  +  0.103460639622265 -0.119452797409169`X(t)`[t] +  1.52190634840442`Y(t-1)`[t] -0.619397555123749`Y(t-2)`[t] +  0.254793166385373`Y(t-3)`[t] -0.190018800639385`Y(t-4)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58535&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58535&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
Y(t)[t] = + 0.103460639622265 -0.119452797409169`X(t)`[t] + 1.52190634840442`Y(t-1)`[t] -0.619397555123749`Y(t-2)`[t] + 0.254793166385373`Y(t-3)`[t] -0.190018800639385`Y(t-4)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1034606396222650.0352362.93630.004060.00203
`X(t)`-0.1194527974091690.039381-3.03330.003030.001515
`Y(t-1)`1.521906348404420.09408816.175300
`Y(t-2)`-0.6193975551237490.17246-3.59150.0004960.000248
`Y(t-3)`0.2547931663853730.1718651.48250.1411140.070557
`Y(t-4)`-0.1900188006393850.091765-2.07070.040770.020385

\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) & 0.103460639622265 & 0.035236 & 2.9363 & 0.00406 & 0.00203 \tabularnewline
`X(t)` & -0.119452797409169 & 0.039381 & -3.0333 & 0.00303 & 0.001515 \tabularnewline
`Y(t-1)` & 1.52190634840442 & 0.094088 & 16.1753 & 0 & 0 \tabularnewline
`Y(t-2)` & -0.619397555123749 & 0.17246 & -3.5915 & 0.000496 & 0.000248 \tabularnewline
`Y(t-3)` & 0.254793166385373 & 0.171865 & 1.4825 & 0.141114 & 0.070557 \tabularnewline
`Y(t-4)` & -0.190018800639385 & 0.091765 & -2.0707 & 0.04077 & 0.020385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58535&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]0.103460639622265[/C][C]0.035236[/C][C]2.9363[/C][C]0.00406[/C][C]0.00203[/C][/ROW]
[ROW][C]`X(t)`[/C][C]-0.119452797409169[/C][C]0.039381[/C][C]-3.0333[/C][C]0.00303[/C][C]0.001515[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]1.52190634840442[/C][C]0.094088[/C][C]16.1753[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y(t-2)`[/C][C]-0.619397555123749[/C][C]0.17246[/C][C]-3.5915[/C][C]0.000496[/C][C]0.000248[/C][/ROW]
[ROW][C]`Y(t-3)`[/C][C]0.254793166385373[/C][C]0.171865[/C][C]1.4825[/C][C]0.141114[/C][C]0.070557[/C][/ROW]
[ROW][C]`Y(t-4)`[/C][C]-0.190018800639385[/C][C]0.091765[/C][C]-2.0707[/C][C]0.04077[/C][C]0.020385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58535&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58535&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)0.1034606396222650.0352362.93630.004060.00203
`X(t)`-0.1194527974091690.039381-3.03330.003030.001515
`Y(t-1)`1.521906348404420.09408816.175300
`Y(t-2)`-0.6193975551237490.17246-3.59150.0004960.000248
`Y(t-3)`0.2547931663853730.1718651.48250.1411140.070557
`Y(t-4)`-0.1900188006393850.091765-2.07070.040770.020385






Multiple Linear Regression - Regression Statistics
Multiple R0.994995919607588
R-squared0.99001688003575
Adjusted R-squared0.989554698555923
F-TEST (value)2142.05225273751
F-TEST (DF numerator)5
F-TEST (DF denominator)108
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.107112126367783
Sum Squared Residuals1.23908482242302

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.994995919607588 \tabularnewline
R-squared & 0.99001688003575 \tabularnewline
Adjusted R-squared & 0.989554698555923 \tabularnewline
F-TEST (value) & 2142.05225273751 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.107112126367783 \tabularnewline
Sum Squared Residuals & 1.23908482242302 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58535&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.994995919607588[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99001688003575[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.989554698555923[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2142.05225273751[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/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]0.107112126367783[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.23908482242302[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58535&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58535&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 R0.994995919607588
R-squared0.99001688003575
Adjusted R-squared0.989554698555923
F-TEST (value)2142.05225273751
F-TEST (DF numerator)5
F-TEST (DF denominator)108
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.107112126367783
Sum Squared Residuals1.23908482242302






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13.753.605811823933640.144188176066364
24.113.885216648320760.224783351679238
34.254.2897155557083-0.0397155557082959
44.254.31434705248335-0.0643470524833468
54.54.273752422511310.226247577488695
64.74.621493284676180.0785067153238155
74.754.744422533486620.00557746651338359
84.754.76033663147843-0.0103366314784305
94.754.732820686839470.0171793131605282
104.754.707556585030860.0424434149691367
114.754.698055644998890.0519443550011059
124.754.698055644998890.0519443550011059
134.584.69805564499889-0.118055644998894
144.54.439331565770140.0606684342298568
154.54.422876642268830.0771233577311728
164.494.429113608393210.0608863916067867
174.034.42581428770703-0.395814287707035
183.753.747132847043390.00286715295660920
193.393.60337401318322-0.213374013183224
203.253.113614374661410.136385625338595
213.253.139597167435550.110402832564452
223.253.187792549433170.0622074505668336
233.253.220528274369390.0294717256306073
243.253.247130906458910.00286909354109334
253.253.247130906458910.00286909354109334
263.253.247130906458910.00286909354109334
273.253.247130906458910.00286909354109334
283.253.247130906458910.00286909354109334
293.253.247130906458910.00286909354109334
303.253.247130906458910.00286909354109334
313.253.247130906458910.00286909354109334
322.853.24713090645891-0.397130906458907
332.752.638368367097140.111631632902860
342.752.733936754306200.0160632456938030
352.552.69395924326442-0.143959243264422
362.52.440106177200760.0598938227992451
372.52.50689225086922-0.00689225086922279
382.12.48690349534834-0.386903495348335
3921.903405057795180.0965949422048237
4022.00847438503620-0.00847438503620295
4121.968496873994430.0315031260055718
4222.01902507761164-0.0190250776116448
4322.03802695767558-0.0380269576755834
4422.03802695767558-0.0380269576755834
4522.03802695767558-0.0380269576755834
4622.03802695767558-0.0380269576755834
4722.03802695767558-0.0380269576755834
4822.03802695767558-0.0380269576755834
4922.03802695767558-0.0380269576755834
5022.03802695767558-0.0380269576755834
5122.03802695767558-0.0380269576755834
5222.03802695767558-0.0380269576755834
5322.03802695767558-0.0380269576755834
5422.03802695767558-0.0380269576755834
5522.03802695767558-0.0380269576755834
5622.03802695767558-0.0380269576755834
5722.03802695767558-0.0380269576755834
5822.03802695767558-0.0380269576755834
5922.03802695767558-0.0380269576755834
6022.03802695767558-0.0380269576755834
6122.03802695767558-0.0380269576755834
6222.03802695767558-0.0380269576755834
6322.03802695767558-0.0380269576755834
6422.03802695767558-0.0380269576755834
6522.03802695767558-0.0380269576755834
6622.03802695767558-0.0380269576755834
6722.03802695767558-0.0380269576755834
682.212.038026957675580.171973042324417
692.252.35762729084051-0.107627290840511
702.252.28843005820070-0.0384300582007008
712.452.317160720936680.132839279063321
722.52.59182976913871-0.0918297691387076
732.52.5364448235086-0.0364448235086029
742.642.556433579029490.0835664209705096
752.752.74423636599750.00576363400249928
762.932.815429466572690.114570533427308
7733.05690992151583-0.0569099215158281
783.173.053376422194740.116623577805260
793.253.29370337444386-0.0437033744438632
803.393.293790435477070.0962095645229338
813.53.487319042084540.0126809579154582
823.53.55609333989384-0.0560933398938371
833.653.508429148073030.141570851926973
843.753.738139716546570.0118602834534337
853.753.77651865004811-0.0265186500481137
863.93.752797869493540.147202130506455
8743.978060318296840.0219396817031628
8844.01833943980478-0.0183394398047782
8943.994618659250210.0053813407497906
9043.991595155792840.00840484420716092
9143.97259327572890.0274067242710995
9243.97259327572890.0274067242710995
9343.97259327572890.0274067242710995
9443.97259327572890.0274067242710995
9543.97259327572890.0274067242710995
9643.97259327572890.0274067242710995
9743.97259327572890.0274067242710995
9843.97259327572890.0274067242710995
994.183.97259327572890.207406724271099
1004.254.24653641844170.00346358155830441
1014.254.241578302907730.0084216970922696
1023.974.12463044658926-0.154630446589265
1033.423.68212880656791-0.262128806567915
1042.753.00521031433538-0.255210314335377
1052.312.254859629634570.0551403703654259
10621.913286220936610.0867137790633864
1071.661.647829096059150.0121709039408454
1081.311.33759778290884-0.0275977829088377
1091.091.020148120411230.0698518795887705
11010.8743940196827520.125605980317248
11110.8491186944360890.150881305563911
11210.9153165580162290.0846834419837712
11310.934189309182210.0658106908177902
11410.9512910012397550.0487089987602455

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.75 & 3.60581182393364 & 0.144188176066364 \tabularnewline
2 & 4.11 & 3.88521664832076 & 0.224783351679238 \tabularnewline
3 & 4.25 & 4.2897155557083 & -0.0397155557082959 \tabularnewline
4 & 4.25 & 4.31434705248335 & -0.0643470524833468 \tabularnewline
5 & 4.5 & 4.27375242251131 & 0.226247577488695 \tabularnewline
6 & 4.7 & 4.62149328467618 & 0.0785067153238155 \tabularnewline
7 & 4.75 & 4.74442253348662 & 0.00557746651338359 \tabularnewline
8 & 4.75 & 4.76033663147843 & -0.0103366314784305 \tabularnewline
9 & 4.75 & 4.73282068683947 & 0.0171793131605282 \tabularnewline
10 & 4.75 & 4.70755658503086 & 0.0424434149691367 \tabularnewline
11 & 4.75 & 4.69805564499889 & 0.0519443550011059 \tabularnewline
12 & 4.75 & 4.69805564499889 & 0.0519443550011059 \tabularnewline
13 & 4.58 & 4.69805564499889 & -0.118055644998894 \tabularnewline
14 & 4.5 & 4.43933156577014 & 0.0606684342298568 \tabularnewline
15 & 4.5 & 4.42287664226883 & 0.0771233577311728 \tabularnewline
16 & 4.49 & 4.42911360839321 & 0.0608863916067867 \tabularnewline
17 & 4.03 & 4.42581428770703 & -0.395814287707035 \tabularnewline
18 & 3.75 & 3.74713284704339 & 0.00286715295660920 \tabularnewline
19 & 3.39 & 3.60337401318322 & -0.213374013183224 \tabularnewline
20 & 3.25 & 3.11361437466141 & 0.136385625338595 \tabularnewline
21 & 3.25 & 3.13959716743555 & 0.110402832564452 \tabularnewline
22 & 3.25 & 3.18779254943317 & 0.0622074505668336 \tabularnewline
23 & 3.25 & 3.22052827436939 & 0.0294717256306073 \tabularnewline
24 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
25 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
26 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
27 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
28 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
29 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
30 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
31 & 3.25 & 3.24713090645891 & 0.00286909354109334 \tabularnewline
32 & 2.85 & 3.24713090645891 & -0.397130906458907 \tabularnewline
33 & 2.75 & 2.63836836709714 & 0.111631632902860 \tabularnewline
34 & 2.75 & 2.73393675430620 & 0.0160632456938030 \tabularnewline
35 & 2.55 & 2.69395924326442 & -0.143959243264422 \tabularnewline
36 & 2.5 & 2.44010617720076 & 0.0598938227992451 \tabularnewline
37 & 2.5 & 2.50689225086922 & -0.00689225086922279 \tabularnewline
38 & 2.1 & 2.48690349534834 & -0.386903495348335 \tabularnewline
39 & 2 & 1.90340505779518 & 0.0965949422048237 \tabularnewline
40 & 2 & 2.00847438503620 & -0.00847438503620295 \tabularnewline
41 & 2 & 1.96849687399443 & 0.0315031260055718 \tabularnewline
42 & 2 & 2.01902507761164 & -0.0190250776116448 \tabularnewline
43 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
44 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
45 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
46 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
47 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
48 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
49 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
50 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
51 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
52 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
53 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
54 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
55 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
56 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
57 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
58 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
59 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
60 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
61 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
62 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
63 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
64 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
65 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
66 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
67 & 2 & 2.03802695767558 & -0.0380269576755834 \tabularnewline
68 & 2.21 & 2.03802695767558 & 0.171973042324417 \tabularnewline
69 & 2.25 & 2.35762729084051 & -0.107627290840511 \tabularnewline
70 & 2.25 & 2.28843005820070 & -0.0384300582007008 \tabularnewline
71 & 2.45 & 2.31716072093668 & 0.132839279063321 \tabularnewline
72 & 2.5 & 2.59182976913871 & -0.0918297691387076 \tabularnewline
73 & 2.5 & 2.5364448235086 & -0.0364448235086029 \tabularnewline
74 & 2.64 & 2.55643357902949 & 0.0835664209705096 \tabularnewline
75 & 2.75 & 2.7442363659975 & 0.00576363400249928 \tabularnewline
76 & 2.93 & 2.81542946657269 & 0.114570533427308 \tabularnewline
77 & 3 & 3.05690992151583 & -0.0569099215158281 \tabularnewline
78 & 3.17 & 3.05337642219474 & 0.116623577805260 \tabularnewline
79 & 3.25 & 3.29370337444386 & -0.0437033744438632 \tabularnewline
80 & 3.39 & 3.29379043547707 & 0.0962095645229338 \tabularnewline
81 & 3.5 & 3.48731904208454 & 0.0126809579154582 \tabularnewline
82 & 3.5 & 3.55609333989384 & -0.0560933398938371 \tabularnewline
83 & 3.65 & 3.50842914807303 & 0.141570851926973 \tabularnewline
84 & 3.75 & 3.73813971654657 & 0.0118602834534337 \tabularnewline
85 & 3.75 & 3.77651865004811 & -0.0265186500481137 \tabularnewline
86 & 3.9 & 3.75279786949354 & 0.147202130506455 \tabularnewline
87 & 4 & 3.97806031829684 & 0.0219396817031628 \tabularnewline
88 & 4 & 4.01833943980478 & -0.0183394398047782 \tabularnewline
89 & 4 & 3.99461865925021 & 0.0053813407497906 \tabularnewline
90 & 4 & 3.99159515579284 & 0.00840484420716092 \tabularnewline
91 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
92 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
93 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
94 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
95 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
96 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
97 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
98 & 4 & 3.9725932757289 & 0.0274067242710995 \tabularnewline
99 & 4.18 & 3.9725932757289 & 0.207406724271099 \tabularnewline
100 & 4.25 & 4.2465364184417 & 0.00346358155830441 \tabularnewline
101 & 4.25 & 4.24157830290773 & 0.0084216970922696 \tabularnewline
102 & 3.97 & 4.12463044658926 & -0.154630446589265 \tabularnewline
103 & 3.42 & 3.68212880656791 & -0.262128806567915 \tabularnewline
104 & 2.75 & 3.00521031433538 & -0.255210314335377 \tabularnewline
105 & 2.31 & 2.25485962963457 & 0.0551403703654259 \tabularnewline
106 & 2 & 1.91328622093661 & 0.0867137790633864 \tabularnewline
107 & 1.66 & 1.64782909605915 & 0.0121709039408454 \tabularnewline
108 & 1.31 & 1.33759778290884 & -0.0275977829088377 \tabularnewline
109 & 1.09 & 1.02014812041123 & 0.0698518795887705 \tabularnewline
110 & 1 & 0.874394019682752 & 0.125605980317248 \tabularnewline
111 & 1 & 0.849118694436089 & 0.150881305563911 \tabularnewline
112 & 1 & 0.915316558016229 & 0.0846834419837712 \tabularnewline
113 & 1 & 0.93418930918221 & 0.0658106908177902 \tabularnewline
114 & 1 & 0.951291001239755 & 0.0487089987602455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58535&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]3.75[/C][C]3.60581182393364[/C][C]0.144188176066364[/C][/ROW]
[ROW][C]2[/C][C]4.11[/C][C]3.88521664832076[/C][C]0.224783351679238[/C][/ROW]
[ROW][C]3[/C][C]4.25[/C][C]4.2897155557083[/C][C]-0.0397155557082959[/C][/ROW]
[ROW][C]4[/C][C]4.25[/C][C]4.31434705248335[/C][C]-0.0643470524833468[/C][/ROW]
[ROW][C]5[/C][C]4.5[/C][C]4.27375242251131[/C][C]0.226247577488695[/C][/ROW]
[ROW][C]6[/C][C]4.7[/C][C]4.62149328467618[/C][C]0.0785067153238155[/C][/ROW]
[ROW][C]7[/C][C]4.75[/C][C]4.74442253348662[/C][C]0.00557746651338359[/C][/ROW]
[ROW][C]8[/C][C]4.75[/C][C]4.76033663147843[/C][C]-0.0103366314784305[/C][/ROW]
[ROW][C]9[/C][C]4.75[/C][C]4.73282068683947[/C][C]0.0171793131605282[/C][/ROW]
[ROW][C]10[/C][C]4.75[/C][C]4.70755658503086[/C][C]0.0424434149691367[/C][/ROW]
[ROW][C]11[/C][C]4.75[/C][C]4.69805564499889[/C][C]0.0519443550011059[/C][/ROW]
[ROW][C]12[/C][C]4.75[/C][C]4.69805564499889[/C][C]0.0519443550011059[/C][/ROW]
[ROW][C]13[/C][C]4.58[/C][C]4.69805564499889[/C][C]-0.118055644998894[/C][/ROW]
[ROW][C]14[/C][C]4.5[/C][C]4.43933156577014[/C][C]0.0606684342298568[/C][/ROW]
[ROW][C]15[/C][C]4.5[/C][C]4.42287664226883[/C][C]0.0771233577311728[/C][/ROW]
[ROW][C]16[/C][C]4.49[/C][C]4.42911360839321[/C][C]0.0608863916067867[/C][/ROW]
[ROW][C]17[/C][C]4.03[/C][C]4.42581428770703[/C][C]-0.395814287707035[/C][/ROW]
[ROW][C]18[/C][C]3.75[/C][C]3.74713284704339[/C][C]0.00286715295660920[/C][/ROW]
[ROW][C]19[/C][C]3.39[/C][C]3.60337401318322[/C][C]-0.213374013183224[/C][/ROW]
[ROW][C]20[/C][C]3.25[/C][C]3.11361437466141[/C][C]0.136385625338595[/C][/ROW]
[ROW][C]21[/C][C]3.25[/C][C]3.13959716743555[/C][C]0.110402832564452[/C][/ROW]
[ROW][C]22[/C][C]3.25[/C][C]3.18779254943317[/C][C]0.0622074505668336[/C][/ROW]
[ROW][C]23[/C][C]3.25[/C][C]3.22052827436939[/C][C]0.0294717256306073[/C][/ROW]
[ROW][C]24[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]25[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]26[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]27[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]28[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]29[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]30[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]31[/C][C]3.25[/C][C]3.24713090645891[/C][C]0.00286909354109334[/C][/ROW]
[ROW][C]32[/C][C]2.85[/C][C]3.24713090645891[/C][C]-0.397130906458907[/C][/ROW]
[ROW][C]33[/C][C]2.75[/C][C]2.63836836709714[/C][C]0.111631632902860[/C][/ROW]
[ROW][C]34[/C][C]2.75[/C][C]2.73393675430620[/C][C]0.0160632456938030[/C][/ROW]
[ROW][C]35[/C][C]2.55[/C][C]2.69395924326442[/C][C]-0.143959243264422[/C][/ROW]
[ROW][C]36[/C][C]2.5[/C][C]2.44010617720076[/C][C]0.0598938227992451[/C][/ROW]
[ROW][C]37[/C][C]2.5[/C][C]2.50689225086922[/C][C]-0.00689225086922279[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.48690349534834[/C][C]-0.386903495348335[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.90340505779518[/C][C]0.0965949422048237[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]2.00847438503620[/C][C]-0.00847438503620295[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]1.96849687399443[/C][C]0.0315031260055718[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]2.01902507761164[/C][C]-0.0190250776116448[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]2.03802695767558[/C][C]-0.0380269576755834[/C][/ROW]
[ROW][C]68[/C][C]2.21[/C][C]2.03802695767558[/C][C]0.171973042324417[/C][/ROW]
[ROW][C]69[/C][C]2.25[/C][C]2.35762729084051[/C][C]-0.107627290840511[/C][/ROW]
[ROW][C]70[/C][C]2.25[/C][C]2.28843005820070[/C][C]-0.0384300582007008[/C][/ROW]
[ROW][C]71[/C][C]2.45[/C][C]2.31716072093668[/C][C]0.132839279063321[/C][/ROW]
[ROW][C]72[/C][C]2.5[/C][C]2.59182976913871[/C][C]-0.0918297691387076[/C][/ROW]
[ROW][C]73[/C][C]2.5[/C][C]2.5364448235086[/C][C]-0.0364448235086029[/C][/ROW]
[ROW][C]74[/C][C]2.64[/C][C]2.55643357902949[/C][C]0.0835664209705096[/C][/ROW]
[ROW][C]75[/C][C]2.75[/C][C]2.7442363659975[/C][C]0.00576363400249928[/C][/ROW]
[ROW][C]76[/C][C]2.93[/C][C]2.81542946657269[/C][C]0.114570533427308[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]3.05690992151583[/C][C]-0.0569099215158281[/C][/ROW]
[ROW][C]78[/C][C]3.17[/C][C]3.05337642219474[/C][C]0.116623577805260[/C][/ROW]
[ROW][C]79[/C][C]3.25[/C][C]3.29370337444386[/C][C]-0.0437033744438632[/C][/ROW]
[ROW][C]80[/C][C]3.39[/C][C]3.29379043547707[/C][C]0.0962095645229338[/C][/ROW]
[ROW][C]81[/C][C]3.5[/C][C]3.48731904208454[/C][C]0.0126809579154582[/C][/ROW]
[ROW][C]82[/C][C]3.5[/C][C]3.55609333989384[/C][C]-0.0560933398938371[/C][/ROW]
[ROW][C]83[/C][C]3.65[/C][C]3.50842914807303[/C][C]0.141570851926973[/C][/ROW]
[ROW][C]84[/C][C]3.75[/C][C]3.73813971654657[/C][C]0.0118602834534337[/C][/ROW]
[ROW][C]85[/C][C]3.75[/C][C]3.77651865004811[/C][C]-0.0265186500481137[/C][/ROW]
[ROW][C]86[/C][C]3.9[/C][C]3.75279786949354[/C][C]0.147202130506455[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.97806031829684[/C][C]0.0219396817031628[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.01833943980478[/C][C]-0.0183394398047782[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]3.99461865925021[/C][C]0.0053813407497906[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]3.99159515579284[/C][C]0.00840484420716092[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]3.9725932757289[/C][C]0.0274067242710995[/C][/ROW]
[ROW][C]99[/C][C]4.18[/C][C]3.9725932757289[/C][C]0.207406724271099[/C][/ROW]
[ROW][C]100[/C][C]4.25[/C][C]4.2465364184417[/C][C]0.00346358155830441[/C][/ROW]
[ROW][C]101[/C][C]4.25[/C][C]4.24157830290773[/C][C]0.0084216970922696[/C][/ROW]
[ROW][C]102[/C][C]3.97[/C][C]4.12463044658926[/C][C]-0.154630446589265[/C][/ROW]
[ROW][C]103[/C][C]3.42[/C][C]3.68212880656791[/C][C]-0.262128806567915[/C][/ROW]
[ROW][C]104[/C][C]2.75[/C][C]3.00521031433538[/C][C]-0.255210314335377[/C][/ROW]
[ROW][C]105[/C][C]2.31[/C][C]2.25485962963457[/C][C]0.0551403703654259[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]1.91328622093661[/C][C]0.0867137790633864[/C][/ROW]
[ROW][C]107[/C][C]1.66[/C][C]1.64782909605915[/C][C]0.0121709039408454[/C][/ROW]
[ROW][C]108[/C][C]1.31[/C][C]1.33759778290884[/C][C]-0.0275977829088377[/C][/ROW]
[ROW][C]109[/C][C]1.09[/C][C]1.02014812041123[/C][C]0.0698518795887705[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0.874394019682752[/C][C]0.125605980317248[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0.849118694436089[/C][C]0.150881305563911[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0.915316558016229[/C][C]0.0846834419837712[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0.93418930918221[/C][C]0.0658106908177902[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.951291001239755[/C][C]0.0487089987602455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58535&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58535&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
13.753.605811823933640.144188176066364
24.113.885216648320760.224783351679238
34.254.2897155557083-0.0397155557082959
44.254.31434705248335-0.0643470524833468
54.54.273752422511310.226247577488695
64.74.621493284676180.0785067153238155
74.754.744422533486620.00557746651338359
84.754.76033663147843-0.0103366314784305
94.754.732820686839470.0171793131605282
104.754.707556585030860.0424434149691367
114.754.698055644998890.0519443550011059
124.754.698055644998890.0519443550011059
134.584.69805564499889-0.118055644998894
144.54.439331565770140.0606684342298568
154.54.422876642268830.0771233577311728
164.494.429113608393210.0608863916067867
174.034.42581428770703-0.395814287707035
183.753.747132847043390.00286715295660920
193.393.60337401318322-0.213374013183224
203.253.113614374661410.136385625338595
213.253.139597167435550.110402832564452
223.253.187792549433170.0622074505668336
233.253.220528274369390.0294717256306073
243.253.247130906458910.00286909354109334
253.253.247130906458910.00286909354109334
263.253.247130906458910.00286909354109334
273.253.247130906458910.00286909354109334
283.253.247130906458910.00286909354109334
293.253.247130906458910.00286909354109334
303.253.247130906458910.00286909354109334
313.253.247130906458910.00286909354109334
322.853.24713090645891-0.397130906458907
332.752.638368367097140.111631632902860
342.752.733936754306200.0160632456938030
352.552.69395924326442-0.143959243264422
362.52.440106177200760.0598938227992451
372.52.50689225086922-0.00689225086922279
382.12.48690349534834-0.386903495348335
3921.903405057795180.0965949422048237
4022.00847438503620-0.00847438503620295
4121.968496873994430.0315031260055718
4222.01902507761164-0.0190250776116448
4322.03802695767558-0.0380269576755834
4422.03802695767558-0.0380269576755834
4522.03802695767558-0.0380269576755834
4622.03802695767558-0.0380269576755834
4722.03802695767558-0.0380269576755834
4822.03802695767558-0.0380269576755834
4922.03802695767558-0.0380269576755834
5022.03802695767558-0.0380269576755834
5122.03802695767558-0.0380269576755834
5222.03802695767558-0.0380269576755834
5322.03802695767558-0.0380269576755834
5422.03802695767558-0.0380269576755834
5522.03802695767558-0.0380269576755834
5622.03802695767558-0.0380269576755834
5722.03802695767558-0.0380269576755834
5822.03802695767558-0.0380269576755834
5922.03802695767558-0.0380269576755834
6022.03802695767558-0.0380269576755834
6122.03802695767558-0.0380269576755834
6222.03802695767558-0.0380269576755834
6322.03802695767558-0.0380269576755834
6422.03802695767558-0.0380269576755834
6522.03802695767558-0.0380269576755834
6622.03802695767558-0.0380269576755834
6722.03802695767558-0.0380269576755834
682.212.038026957675580.171973042324417
692.252.35762729084051-0.107627290840511
702.252.28843005820070-0.0384300582007008
712.452.317160720936680.132839279063321
722.52.59182976913871-0.0918297691387076
732.52.5364448235086-0.0364448235086029
742.642.556433579029490.0835664209705096
752.752.74423636599750.00576363400249928
762.932.815429466572690.114570533427308
7733.05690992151583-0.0569099215158281
783.173.053376422194740.116623577805260
793.253.29370337444386-0.0437033744438632
803.393.293790435477070.0962095645229338
813.53.487319042084540.0126809579154582
823.53.55609333989384-0.0560933398938371
833.653.508429148073030.141570851926973
843.753.738139716546570.0118602834534337
853.753.77651865004811-0.0265186500481137
863.93.752797869493540.147202130506455
8743.978060318296840.0219396817031628
8844.01833943980478-0.0183394398047782
8943.994618659250210.0053813407497906
9043.991595155792840.00840484420716092
9143.97259327572890.0274067242710995
9243.97259327572890.0274067242710995
9343.97259327572890.0274067242710995
9443.97259327572890.0274067242710995
9543.97259327572890.0274067242710995
9643.97259327572890.0274067242710995
9743.97259327572890.0274067242710995
9843.97259327572890.0274067242710995
994.183.97259327572890.207406724271099
1004.254.24653641844170.00346358155830441
1014.254.241578302907730.0084216970922696
1023.974.12463044658926-0.154630446589265
1033.423.68212880656791-0.262128806567915
1042.753.00521031433538-0.255210314335377
1052.312.254859629634570.0551403703654259
10621.913286220936610.0867137790633864
1071.661.647829096059150.0121709039408454
1081.311.33759778290884-0.0275977829088377
1091.091.020148120411230.0698518795887705
11010.8743940196827520.125605980317248
11110.8491186944360890.150881305563911
11210.9153165580162290.0846834419837712
11310.934189309182210.0658106908177902
11410.9512910012397550.0487089987602455






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5961928136110310.8076143727779370.403807186388969
100.4643812208337990.9287624416675970.535618779166201
110.3219264756498170.6438529512996330.678073524350183
120.2096482000708970.4192964001417940.790351799929103
130.364514946783910.729029893567820.63548505321609
140.2645652255474940.5291304510949870.735434774452506
150.2041205141761330.4082410283522650.795879485823867
160.1451659918078630.2903319836157250.854834008192137
170.958199015967840.08360196806432050.0418009840321602
180.9359319832179850.128136033564030.064068016782015
190.9919789734475120.01604205310497580.00802102655248791
200.9961582113360970.007683577327805030.00384178866390252
210.9945625918497040.01087481630059280.00543740815029642
220.9913770234798370.01724595304032680.0086229765201634
230.987723916389020.02455216722195840.0122760836109792
240.984128752039060.03174249592187820.0158712479609391
250.9786753020982630.04264939580347390.0213246979017370
260.9709876025367330.0580247949265350.0290123974632675
270.9605671138813050.07886577223738980.0394328861186949
280.9468528070008160.1062943859983670.0531471929991837
290.9292688737239690.1414622525520630.0707311262760313
300.9072749041400370.1854501917199250.0927250958599626
310.8804200281655330.2391599436689340.119579971834467
320.998571837083650.002856325832700260.00142816291635013
330.998433090267150.003133819465699210.00156690973284961
340.9974796929948390.005040614010322320.00252030700516116
350.9980898014152980.00382039716940340.0019101985847017
360.9972524888918910.005495022216217010.00274751110810851
370.995749436397070.008501127205861020.00425056360293051
380.9999911646424951.76707150100390e-058.83535750501948e-06
390.9999917677760441.64644479127333e-058.23222395636666e-06
400.9999845567858353.0886428330954e-051.5443214165477e-05
410.9999783968620474.3206275906652e-052.1603137953326e-05
420.999961974134857.60517302979685e-053.80258651489842e-05
430.9999361054028340.00012778919433196.389459716595e-05
440.9998941668283470.0002116663433062290.000105833171653114
450.9998274754459360.0003450491081285980.000172524554064299
460.9997234768141570.0005530463716860310.000276523185843016
470.9995644832655530.00087103346889420.0004355167344471
480.9993262372970760.001347525405848160.00067376270292408
490.9989763995126380.002047200974723840.00102360048736192
500.9984731365174970.003053726965004980.00152686348250249
510.9977640769036530.004471846192693290.00223592309634665
520.996786006837240.00642798632551850.00321399316275925
530.9954657803727920.009068439254415780.00453421962720789
540.99372300931420.01255398137159960.00627699068579978
550.9914751569427740.01704968611445130.00852484305722563
560.9886456736636830.02270865267263350.0113543263363168
570.98517576919650.02964846160699990.0148242308035000
580.9810403109862740.03791937802745180.0189596890137259
590.9762681739912320.04746365201753630.0237318260087682
600.9709671408705860.0580657182588270.0290328591294135
610.965353124268670.06929375146266050.0346468757313303
620.9597828878663880.0804342242672240.040217112133612
630.9547880599544760.0904238800910480.045211940045524
640.9511046179055780.09779076418884450.0488953820944222
650.949682384823080.1006352303538410.0503176151769204
660.9516339473191370.0967321053617260.048366052680863
670.95802038317530.08395923364940.0419796168247
680.9641023314641420.0717953370717160.035897668535858
690.9853401264988570.0293197470022860.014659873501143
700.9876897314437440.02462053711251270.0123102685562563
710.9857558163748640.02848836725027180.0142441836251359
720.9955585917920180.00888281641596420.0044414082079821
730.9972451260163660.005509747967268160.00275487398363408
740.9958159274026920.008368145194616040.00418407259730802
750.9964382067748120.007123586450375240.00356179322518762
760.9953221868299890.009355626340022270.00467781317001113
770.9977819652147240.004436069570551960.00221803478527598
780.9969812653424510.006037469315097350.00301873465754868
790.9984792479814460.003041504037107720.00152075201855386
800.9977716577840450.00445668443191080.0022283422159554
810.9973156305239560.005368738952088840.00268436947604442
820.9982925437611420.003414912477715470.00170745623885773
830.9984952400492710.003009519901457130.00150475995072856
840.9985689618405430.002862076318914150.00143103815945707
850.9979816193803930.004036761239213820.00201838061960691
860.9990908337515360.001818332496928600.000909166248464302
870.998913225685160.002173548629682810.00108677431484141
880.9979995360621830.004000927875633440.00200046393781672
890.996275715114240.007448569771518250.00372428488575913
900.994220030294130.01155993941173930.00577996970586964
910.9899347448128150.02013051037436960.0100652551871848
920.9830131555631420.03397368887371690.0169868444368585
930.972263665456950.05547266908609940.0277363345430497
940.9562804240129920.08743915197401570.0437195759870078
950.9336817881613960.1326364236772080.066318211838604
960.9036813881817570.1926372236364860.0963186118182428
970.8673887790652670.2652224418694660.132611220934733
980.8312744552615930.3374510894768140.168725544738407
990.9401901719196750.119619656160650.059809828080325
1000.9700727814573260.05985443708534830.0299272185426741
1010.9400161196841020.1199677606317970.0599838803158983
1020.9136242988991430.1727514022017130.0863757011008567
1030.9126025331522740.1747949336954530.0873974668477265
1040.935777979094240.1284440418115210.0642220209057607
1050.9822250926778040.03554981464439140.0177749073221957

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.596192813611031 & 0.807614372777937 & 0.403807186388969 \tabularnewline
10 & 0.464381220833799 & 0.928762441667597 & 0.535618779166201 \tabularnewline
11 & 0.321926475649817 & 0.643852951299633 & 0.678073524350183 \tabularnewline
12 & 0.209648200070897 & 0.419296400141794 & 0.790351799929103 \tabularnewline
13 & 0.36451494678391 & 0.72902989356782 & 0.63548505321609 \tabularnewline
14 & 0.264565225547494 & 0.529130451094987 & 0.735434774452506 \tabularnewline
15 & 0.204120514176133 & 0.408241028352265 & 0.795879485823867 \tabularnewline
16 & 0.145165991807863 & 0.290331983615725 & 0.854834008192137 \tabularnewline
17 & 0.95819901596784 & 0.0836019680643205 & 0.0418009840321602 \tabularnewline
18 & 0.935931983217985 & 0.12813603356403 & 0.064068016782015 \tabularnewline
19 & 0.991978973447512 & 0.0160420531049758 & 0.00802102655248791 \tabularnewline
20 & 0.996158211336097 & 0.00768357732780503 & 0.00384178866390252 \tabularnewline
21 & 0.994562591849704 & 0.0108748163005928 & 0.00543740815029642 \tabularnewline
22 & 0.991377023479837 & 0.0172459530403268 & 0.0086229765201634 \tabularnewline
23 & 0.98772391638902 & 0.0245521672219584 & 0.0122760836109792 \tabularnewline
24 & 0.98412875203906 & 0.0317424959218782 & 0.0158712479609391 \tabularnewline
25 & 0.978675302098263 & 0.0426493958034739 & 0.0213246979017370 \tabularnewline
26 & 0.970987602536733 & 0.058024794926535 & 0.0290123974632675 \tabularnewline
27 & 0.960567113881305 & 0.0788657722373898 & 0.0394328861186949 \tabularnewline
28 & 0.946852807000816 & 0.106294385998367 & 0.0531471929991837 \tabularnewline
29 & 0.929268873723969 & 0.141462252552063 & 0.0707311262760313 \tabularnewline
30 & 0.907274904140037 & 0.185450191719925 & 0.0927250958599626 \tabularnewline
31 & 0.880420028165533 & 0.239159943668934 & 0.119579971834467 \tabularnewline
32 & 0.99857183708365 & 0.00285632583270026 & 0.00142816291635013 \tabularnewline
33 & 0.99843309026715 & 0.00313381946569921 & 0.00156690973284961 \tabularnewline
34 & 0.997479692994839 & 0.00504061401032232 & 0.00252030700516116 \tabularnewline
35 & 0.998089801415298 & 0.0038203971694034 & 0.0019101985847017 \tabularnewline
36 & 0.997252488891891 & 0.00549502221621701 & 0.00274751110810851 \tabularnewline
37 & 0.99574943639707 & 0.00850112720586102 & 0.00425056360293051 \tabularnewline
38 & 0.999991164642495 & 1.76707150100390e-05 & 8.83535750501948e-06 \tabularnewline
39 & 0.999991767776044 & 1.64644479127333e-05 & 8.23222395636666e-06 \tabularnewline
40 & 0.999984556785835 & 3.0886428330954e-05 & 1.5443214165477e-05 \tabularnewline
41 & 0.999978396862047 & 4.3206275906652e-05 & 2.1603137953326e-05 \tabularnewline
42 & 0.99996197413485 & 7.60517302979685e-05 & 3.80258651489842e-05 \tabularnewline
43 & 0.999936105402834 & 0.0001277891943319 & 6.389459716595e-05 \tabularnewline
44 & 0.999894166828347 & 0.000211666343306229 & 0.000105833171653114 \tabularnewline
45 & 0.999827475445936 & 0.000345049108128598 & 0.000172524554064299 \tabularnewline
46 & 0.999723476814157 & 0.000553046371686031 & 0.000276523185843016 \tabularnewline
47 & 0.999564483265553 & 0.0008710334688942 & 0.0004355167344471 \tabularnewline
48 & 0.999326237297076 & 0.00134752540584816 & 0.00067376270292408 \tabularnewline
49 & 0.998976399512638 & 0.00204720097472384 & 0.00102360048736192 \tabularnewline
50 & 0.998473136517497 & 0.00305372696500498 & 0.00152686348250249 \tabularnewline
51 & 0.997764076903653 & 0.00447184619269329 & 0.00223592309634665 \tabularnewline
52 & 0.99678600683724 & 0.0064279863255185 & 0.00321399316275925 \tabularnewline
53 & 0.995465780372792 & 0.00906843925441578 & 0.00453421962720789 \tabularnewline
54 & 0.9937230093142 & 0.0125539813715996 & 0.00627699068579978 \tabularnewline
55 & 0.991475156942774 & 0.0170496861144513 & 0.00852484305722563 \tabularnewline
56 & 0.988645673663683 & 0.0227086526726335 & 0.0113543263363168 \tabularnewline
57 & 0.9851757691965 & 0.0296484616069999 & 0.0148242308035000 \tabularnewline
58 & 0.981040310986274 & 0.0379193780274518 & 0.0189596890137259 \tabularnewline
59 & 0.976268173991232 & 0.0474636520175363 & 0.0237318260087682 \tabularnewline
60 & 0.970967140870586 & 0.058065718258827 & 0.0290328591294135 \tabularnewline
61 & 0.96535312426867 & 0.0692937514626605 & 0.0346468757313303 \tabularnewline
62 & 0.959782887866388 & 0.080434224267224 & 0.040217112133612 \tabularnewline
63 & 0.954788059954476 & 0.090423880091048 & 0.045211940045524 \tabularnewline
64 & 0.951104617905578 & 0.0977907641888445 & 0.0488953820944222 \tabularnewline
65 & 0.94968238482308 & 0.100635230353841 & 0.0503176151769204 \tabularnewline
66 & 0.951633947319137 & 0.096732105361726 & 0.048366052680863 \tabularnewline
67 & 0.9580203831753 & 0.0839592336494 & 0.0419796168247 \tabularnewline
68 & 0.964102331464142 & 0.071795337071716 & 0.035897668535858 \tabularnewline
69 & 0.985340126498857 & 0.029319747002286 & 0.014659873501143 \tabularnewline
70 & 0.987689731443744 & 0.0246205371125127 & 0.0123102685562563 \tabularnewline
71 & 0.985755816374864 & 0.0284883672502718 & 0.0142441836251359 \tabularnewline
72 & 0.995558591792018 & 0.0088828164159642 & 0.0044414082079821 \tabularnewline
73 & 0.997245126016366 & 0.00550974796726816 & 0.00275487398363408 \tabularnewline
74 & 0.995815927402692 & 0.00836814519461604 & 0.00418407259730802 \tabularnewline
75 & 0.996438206774812 & 0.00712358645037524 & 0.00356179322518762 \tabularnewline
76 & 0.995322186829989 & 0.00935562634002227 & 0.00467781317001113 \tabularnewline
77 & 0.997781965214724 & 0.00443606957055196 & 0.00221803478527598 \tabularnewline
78 & 0.996981265342451 & 0.00603746931509735 & 0.00301873465754868 \tabularnewline
79 & 0.998479247981446 & 0.00304150403710772 & 0.00152075201855386 \tabularnewline
80 & 0.997771657784045 & 0.0044566844319108 & 0.0022283422159554 \tabularnewline
81 & 0.997315630523956 & 0.00536873895208884 & 0.00268436947604442 \tabularnewline
82 & 0.998292543761142 & 0.00341491247771547 & 0.00170745623885773 \tabularnewline
83 & 0.998495240049271 & 0.00300951990145713 & 0.00150475995072856 \tabularnewline
84 & 0.998568961840543 & 0.00286207631891415 & 0.00143103815945707 \tabularnewline
85 & 0.997981619380393 & 0.00403676123921382 & 0.00201838061960691 \tabularnewline
86 & 0.999090833751536 & 0.00181833249692860 & 0.000909166248464302 \tabularnewline
87 & 0.99891322568516 & 0.00217354862968281 & 0.00108677431484141 \tabularnewline
88 & 0.997999536062183 & 0.00400092787563344 & 0.00200046393781672 \tabularnewline
89 & 0.99627571511424 & 0.00744856977151825 & 0.00372428488575913 \tabularnewline
90 & 0.99422003029413 & 0.0115599394117393 & 0.00577996970586964 \tabularnewline
91 & 0.989934744812815 & 0.0201305103743696 & 0.0100652551871848 \tabularnewline
92 & 0.983013155563142 & 0.0339736888737169 & 0.0169868444368585 \tabularnewline
93 & 0.97226366545695 & 0.0554726690860994 & 0.0277363345430497 \tabularnewline
94 & 0.956280424012992 & 0.0874391519740157 & 0.0437195759870078 \tabularnewline
95 & 0.933681788161396 & 0.132636423677208 & 0.066318211838604 \tabularnewline
96 & 0.903681388181757 & 0.192637223636486 & 0.0963186118182428 \tabularnewline
97 & 0.867388779065267 & 0.265222441869466 & 0.132611220934733 \tabularnewline
98 & 0.831274455261593 & 0.337451089476814 & 0.168725544738407 \tabularnewline
99 & 0.940190171919675 & 0.11961965616065 & 0.059809828080325 \tabularnewline
100 & 0.970072781457326 & 0.0598544370853483 & 0.0299272185426741 \tabularnewline
101 & 0.940016119684102 & 0.119967760631797 & 0.0599838803158983 \tabularnewline
102 & 0.913624298899143 & 0.172751402201713 & 0.0863757011008567 \tabularnewline
103 & 0.912602533152274 & 0.174794933695453 & 0.0873974668477265 \tabularnewline
104 & 0.93577797909424 & 0.128444041811521 & 0.0642220209057607 \tabularnewline
105 & 0.982225092677804 & 0.0355498146443914 & 0.0177749073221957 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58535&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]0.596192813611031[/C][C]0.807614372777937[/C][C]0.403807186388969[/C][/ROW]
[ROW][C]10[/C][C]0.464381220833799[/C][C]0.928762441667597[/C][C]0.535618779166201[/C][/ROW]
[ROW][C]11[/C][C]0.321926475649817[/C][C]0.643852951299633[/C][C]0.678073524350183[/C][/ROW]
[ROW][C]12[/C][C]0.209648200070897[/C][C]0.419296400141794[/C][C]0.790351799929103[/C][/ROW]
[ROW][C]13[/C][C]0.36451494678391[/C][C]0.72902989356782[/C][C]0.63548505321609[/C][/ROW]
[ROW][C]14[/C][C]0.264565225547494[/C][C]0.529130451094987[/C][C]0.735434774452506[/C][/ROW]
[ROW][C]15[/C][C]0.204120514176133[/C][C]0.408241028352265[/C][C]0.795879485823867[/C][/ROW]
[ROW][C]16[/C][C]0.145165991807863[/C][C]0.290331983615725[/C][C]0.854834008192137[/C][/ROW]
[ROW][C]17[/C][C]0.95819901596784[/C][C]0.0836019680643205[/C][C]0.0418009840321602[/C][/ROW]
[ROW][C]18[/C][C]0.935931983217985[/C][C]0.12813603356403[/C][C]0.064068016782015[/C][/ROW]
[ROW][C]19[/C][C]0.991978973447512[/C][C]0.0160420531049758[/C][C]0.00802102655248791[/C][/ROW]
[ROW][C]20[/C][C]0.996158211336097[/C][C]0.00768357732780503[/C][C]0.00384178866390252[/C][/ROW]
[ROW][C]21[/C][C]0.994562591849704[/C][C]0.0108748163005928[/C][C]0.00543740815029642[/C][/ROW]
[ROW][C]22[/C][C]0.991377023479837[/C][C]0.0172459530403268[/C][C]0.0086229765201634[/C][/ROW]
[ROW][C]23[/C][C]0.98772391638902[/C][C]0.0245521672219584[/C][C]0.0122760836109792[/C][/ROW]
[ROW][C]24[/C][C]0.98412875203906[/C][C]0.0317424959218782[/C][C]0.0158712479609391[/C][/ROW]
[ROW][C]25[/C][C]0.978675302098263[/C][C]0.0426493958034739[/C][C]0.0213246979017370[/C][/ROW]
[ROW][C]26[/C][C]0.970987602536733[/C][C]0.058024794926535[/C][C]0.0290123974632675[/C][/ROW]
[ROW][C]27[/C][C]0.960567113881305[/C][C]0.0788657722373898[/C][C]0.0394328861186949[/C][/ROW]
[ROW][C]28[/C][C]0.946852807000816[/C][C]0.106294385998367[/C][C]0.0531471929991837[/C][/ROW]
[ROW][C]29[/C][C]0.929268873723969[/C][C]0.141462252552063[/C][C]0.0707311262760313[/C][/ROW]
[ROW][C]30[/C][C]0.907274904140037[/C][C]0.185450191719925[/C][C]0.0927250958599626[/C][/ROW]
[ROW][C]31[/C][C]0.880420028165533[/C][C]0.239159943668934[/C][C]0.119579971834467[/C][/ROW]
[ROW][C]32[/C][C]0.99857183708365[/C][C]0.00285632583270026[/C][C]0.00142816291635013[/C][/ROW]
[ROW][C]33[/C][C]0.99843309026715[/C][C]0.00313381946569921[/C][C]0.00156690973284961[/C][/ROW]
[ROW][C]34[/C][C]0.997479692994839[/C][C]0.00504061401032232[/C][C]0.00252030700516116[/C][/ROW]
[ROW][C]35[/C][C]0.998089801415298[/C][C]0.0038203971694034[/C][C]0.0019101985847017[/C][/ROW]
[ROW][C]36[/C][C]0.997252488891891[/C][C]0.00549502221621701[/C][C]0.00274751110810851[/C][/ROW]
[ROW][C]37[/C][C]0.99574943639707[/C][C]0.00850112720586102[/C][C]0.00425056360293051[/C][/ROW]
[ROW][C]38[/C][C]0.999991164642495[/C][C]1.76707150100390e-05[/C][C]8.83535750501948e-06[/C][/ROW]
[ROW][C]39[/C][C]0.999991767776044[/C][C]1.64644479127333e-05[/C][C]8.23222395636666e-06[/C][/ROW]
[ROW][C]40[/C][C]0.999984556785835[/C][C]3.0886428330954e-05[/C][C]1.5443214165477e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999978396862047[/C][C]4.3206275906652e-05[/C][C]2.1603137953326e-05[/C][/ROW]
[ROW][C]42[/C][C]0.99996197413485[/C][C]7.60517302979685e-05[/C][C]3.80258651489842e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999936105402834[/C][C]0.0001277891943319[/C][C]6.389459716595e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999894166828347[/C][C]0.000211666343306229[/C][C]0.000105833171653114[/C][/ROW]
[ROW][C]45[/C][C]0.999827475445936[/C][C]0.000345049108128598[/C][C]0.000172524554064299[/C][/ROW]
[ROW][C]46[/C][C]0.999723476814157[/C][C]0.000553046371686031[/C][C]0.000276523185843016[/C][/ROW]
[ROW][C]47[/C][C]0.999564483265553[/C][C]0.0008710334688942[/C][C]0.0004355167344471[/C][/ROW]
[ROW][C]48[/C][C]0.999326237297076[/C][C]0.00134752540584816[/C][C]0.00067376270292408[/C][/ROW]
[ROW][C]49[/C][C]0.998976399512638[/C][C]0.00204720097472384[/C][C]0.00102360048736192[/C][/ROW]
[ROW][C]50[/C][C]0.998473136517497[/C][C]0.00305372696500498[/C][C]0.00152686348250249[/C][/ROW]
[ROW][C]51[/C][C]0.997764076903653[/C][C]0.00447184619269329[/C][C]0.00223592309634665[/C][/ROW]
[ROW][C]52[/C][C]0.99678600683724[/C][C]0.0064279863255185[/C][C]0.00321399316275925[/C][/ROW]
[ROW][C]53[/C][C]0.995465780372792[/C][C]0.00906843925441578[/C][C]0.00453421962720789[/C][/ROW]
[ROW][C]54[/C][C]0.9937230093142[/C][C]0.0125539813715996[/C][C]0.00627699068579978[/C][/ROW]
[ROW][C]55[/C][C]0.991475156942774[/C][C]0.0170496861144513[/C][C]0.00852484305722563[/C][/ROW]
[ROW][C]56[/C][C]0.988645673663683[/C][C]0.0227086526726335[/C][C]0.0113543263363168[/C][/ROW]
[ROW][C]57[/C][C]0.9851757691965[/C][C]0.0296484616069999[/C][C]0.0148242308035000[/C][/ROW]
[ROW][C]58[/C][C]0.981040310986274[/C][C]0.0379193780274518[/C][C]0.0189596890137259[/C][/ROW]
[ROW][C]59[/C][C]0.976268173991232[/C][C]0.0474636520175363[/C][C]0.0237318260087682[/C][/ROW]
[ROW][C]60[/C][C]0.970967140870586[/C][C]0.058065718258827[/C][C]0.0290328591294135[/C][/ROW]
[ROW][C]61[/C][C]0.96535312426867[/C][C]0.0692937514626605[/C][C]0.0346468757313303[/C][/ROW]
[ROW][C]62[/C][C]0.959782887866388[/C][C]0.080434224267224[/C][C]0.040217112133612[/C][/ROW]
[ROW][C]63[/C][C]0.954788059954476[/C][C]0.090423880091048[/C][C]0.045211940045524[/C][/ROW]
[ROW][C]64[/C][C]0.951104617905578[/C][C]0.0977907641888445[/C][C]0.0488953820944222[/C][/ROW]
[ROW][C]65[/C][C]0.94968238482308[/C][C]0.100635230353841[/C][C]0.0503176151769204[/C][/ROW]
[ROW][C]66[/C][C]0.951633947319137[/C][C]0.096732105361726[/C][C]0.048366052680863[/C][/ROW]
[ROW][C]67[/C][C]0.9580203831753[/C][C]0.0839592336494[/C][C]0.0419796168247[/C][/ROW]
[ROW][C]68[/C][C]0.964102331464142[/C][C]0.071795337071716[/C][C]0.035897668535858[/C][/ROW]
[ROW][C]69[/C][C]0.985340126498857[/C][C]0.029319747002286[/C][C]0.014659873501143[/C][/ROW]
[ROW][C]70[/C][C]0.987689731443744[/C][C]0.0246205371125127[/C][C]0.0123102685562563[/C][/ROW]
[ROW][C]71[/C][C]0.985755816374864[/C][C]0.0284883672502718[/C][C]0.0142441836251359[/C][/ROW]
[ROW][C]72[/C][C]0.995558591792018[/C][C]0.0088828164159642[/C][C]0.0044414082079821[/C][/ROW]
[ROW][C]73[/C][C]0.997245126016366[/C][C]0.00550974796726816[/C][C]0.00275487398363408[/C][/ROW]
[ROW][C]74[/C][C]0.995815927402692[/C][C]0.00836814519461604[/C][C]0.00418407259730802[/C][/ROW]
[ROW][C]75[/C][C]0.996438206774812[/C][C]0.00712358645037524[/C][C]0.00356179322518762[/C][/ROW]
[ROW][C]76[/C][C]0.995322186829989[/C][C]0.00935562634002227[/C][C]0.00467781317001113[/C][/ROW]
[ROW][C]77[/C][C]0.997781965214724[/C][C]0.00443606957055196[/C][C]0.00221803478527598[/C][/ROW]
[ROW][C]78[/C][C]0.996981265342451[/C][C]0.00603746931509735[/C][C]0.00301873465754868[/C][/ROW]
[ROW][C]79[/C][C]0.998479247981446[/C][C]0.00304150403710772[/C][C]0.00152075201855386[/C][/ROW]
[ROW][C]80[/C][C]0.997771657784045[/C][C]0.0044566844319108[/C][C]0.0022283422159554[/C][/ROW]
[ROW][C]81[/C][C]0.997315630523956[/C][C]0.00536873895208884[/C][C]0.00268436947604442[/C][/ROW]
[ROW][C]82[/C][C]0.998292543761142[/C][C]0.00341491247771547[/C][C]0.00170745623885773[/C][/ROW]
[ROW][C]83[/C][C]0.998495240049271[/C][C]0.00300951990145713[/C][C]0.00150475995072856[/C][/ROW]
[ROW][C]84[/C][C]0.998568961840543[/C][C]0.00286207631891415[/C][C]0.00143103815945707[/C][/ROW]
[ROW][C]85[/C][C]0.997981619380393[/C][C]0.00403676123921382[/C][C]0.00201838061960691[/C][/ROW]
[ROW][C]86[/C][C]0.999090833751536[/C][C]0.00181833249692860[/C][C]0.000909166248464302[/C][/ROW]
[ROW][C]87[/C][C]0.99891322568516[/C][C]0.00217354862968281[/C][C]0.00108677431484141[/C][/ROW]
[ROW][C]88[/C][C]0.997999536062183[/C][C]0.00400092787563344[/C][C]0.00200046393781672[/C][/ROW]
[ROW][C]89[/C][C]0.99627571511424[/C][C]0.00744856977151825[/C][C]0.00372428488575913[/C][/ROW]
[ROW][C]90[/C][C]0.99422003029413[/C][C]0.0115599394117393[/C][C]0.00577996970586964[/C][/ROW]
[ROW][C]91[/C][C]0.989934744812815[/C][C]0.0201305103743696[/C][C]0.0100652551871848[/C][/ROW]
[ROW][C]92[/C][C]0.983013155563142[/C][C]0.0339736888737169[/C][C]0.0169868444368585[/C][/ROW]
[ROW][C]93[/C][C]0.97226366545695[/C][C]0.0554726690860994[/C][C]0.0277363345430497[/C][/ROW]
[ROW][C]94[/C][C]0.956280424012992[/C][C]0.0874391519740157[/C][C]0.0437195759870078[/C][/ROW]
[ROW][C]95[/C][C]0.933681788161396[/C][C]0.132636423677208[/C][C]0.066318211838604[/C][/ROW]
[ROW][C]96[/C][C]0.903681388181757[/C][C]0.192637223636486[/C][C]0.0963186118182428[/C][/ROW]
[ROW][C]97[/C][C]0.867388779065267[/C][C]0.265222441869466[/C][C]0.132611220934733[/C][/ROW]
[ROW][C]98[/C][C]0.831274455261593[/C][C]0.337451089476814[/C][C]0.168725544738407[/C][/ROW]
[ROW][C]99[/C][C]0.940190171919675[/C][C]0.11961965616065[/C][C]0.059809828080325[/C][/ROW]
[ROW][C]100[/C][C]0.970072781457326[/C][C]0.0598544370853483[/C][C]0.0299272185426741[/C][/ROW]
[ROW][C]101[/C][C]0.940016119684102[/C][C]0.119967760631797[/C][C]0.0599838803158983[/C][/ROW]
[ROW][C]102[/C][C]0.913624298899143[/C][C]0.172751402201713[/C][C]0.0863757011008567[/C][/ROW]
[ROW][C]103[/C][C]0.912602533152274[/C][C]0.174794933695453[/C][C]0.0873974668477265[/C][/ROW]
[ROW][C]104[/C][C]0.93577797909424[/C][C]0.128444041811521[/C][C]0.0642220209057607[/C][/ROW]
[ROW][C]105[/C][C]0.982225092677804[/C][C]0.0355498146443914[/C][C]0.0177749073221957[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58535&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58535&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
90.5961928136110310.8076143727779370.403807186388969
100.4643812208337990.9287624416675970.535618779166201
110.3219264756498170.6438529512996330.678073524350183
120.2096482000708970.4192964001417940.790351799929103
130.364514946783910.729029893567820.63548505321609
140.2645652255474940.5291304510949870.735434774452506
150.2041205141761330.4082410283522650.795879485823867
160.1451659918078630.2903319836157250.854834008192137
170.958199015967840.08360196806432050.0418009840321602
180.9359319832179850.128136033564030.064068016782015
190.9919789734475120.01604205310497580.00802102655248791
200.9961582113360970.007683577327805030.00384178866390252
210.9945625918497040.01087481630059280.00543740815029642
220.9913770234798370.01724595304032680.0086229765201634
230.987723916389020.02455216722195840.0122760836109792
240.984128752039060.03174249592187820.0158712479609391
250.9786753020982630.04264939580347390.0213246979017370
260.9709876025367330.0580247949265350.0290123974632675
270.9605671138813050.07886577223738980.0394328861186949
280.9468528070008160.1062943859983670.0531471929991837
290.9292688737239690.1414622525520630.0707311262760313
300.9072749041400370.1854501917199250.0927250958599626
310.8804200281655330.2391599436689340.119579971834467
320.998571837083650.002856325832700260.00142816291635013
330.998433090267150.003133819465699210.00156690973284961
340.9974796929948390.005040614010322320.00252030700516116
350.9980898014152980.00382039716940340.0019101985847017
360.9972524888918910.005495022216217010.00274751110810851
370.995749436397070.008501127205861020.00425056360293051
380.9999911646424951.76707150100390e-058.83535750501948e-06
390.9999917677760441.64644479127333e-058.23222395636666e-06
400.9999845567858353.0886428330954e-051.5443214165477e-05
410.9999783968620474.3206275906652e-052.1603137953326e-05
420.999961974134857.60517302979685e-053.80258651489842e-05
430.9999361054028340.00012778919433196.389459716595e-05
440.9998941668283470.0002116663433062290.000105833171653114
450.9998274754459360.0003450491081285980.000172524554064299
460.9997234768141570.0005530463716860310.000276523185843016
470.9995644832655530.00087103346889420.0004355167344471
480.9993262372970760.001347525405848160.00067376270292408
490.9989763995126380.002047200974723840.00102360048736192
500.9984731365174970.003053726965004980.00152686348250249
510.9977640769036530.004471846192693290.00223592309634665
520.996786006837240.00642798632551850.00321399316275925
530.9954657803727920.009068439254415780.00453421962720789
540.99372300931420.01255398137159960.00627699068579978
550.9914751569427740.01704968611445130.00852484305722563
560.9886456736636830.02270865267263350.0113543263363168
570.98517576919650.02964846160699990.0148242308035000
580.9810403109862740.03791937802745180.0189596890137259
590.9762681739912320.04746365201753630.0237318260087682
600.9709671408705860.0580657182588270.0290328591294135
610.965353124268670.06929375146266050.0346468757313303
620.9597828878663880.0804342242672240.040217112133612
630.9547880599544760.0904238800910480.045211940045524
640.9511046179055780.09779076418884450.0488953820944222
650.949682384823080.1006352303538410.0503176151769204
660.9516339473191370.0967321053617260.048366052680863
670.95802038317530.08395923364940.0419796168247
680.9641023314641420.0717953370717160.035897668535858
690.9853401264988570.0293197470022860.014659873501143
700.9876897314437440.02462053711251270.0123102685562563
710.9857558163748640.02848836725027180.0142441836251359
720.9955585917920180.00888281641596420.0044414082079821
730.9972451260163660.005509747967268160.00275487398363408
740.9958159274026920.008368145194616040.00418407259730802
750.9964382067748120.007123586450375240.00356179322518762
760.9953221868299890.009355626340022270.00467781317001113
770.9977819652147240.004436069570551960.00221803478527598
780.9969812653424510.006037469315097350.00301873465754868
790.9984792479814460.003041504037107720.00152075201855386
800.9977716577840450.00445668443191080.0022283422159554
810.9973156305239560.005368738952088840.00268436947604442
820.9982925437611420.003414912477715470.00170745623885773
830.9984952400492710.003009519901457130.00150475995072856
840.9985689618405430.002862076318914150.00143103815945707
850.9979816193803930.004036761239213820.00201838061960691
860.9990908337515360.001818332496928600.000909166248464302
870.998913225685160.002173548629682810.00108677431484141
880.9979995360621830.004000927875633440.00200046393781672
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980.8312744552615930.3374510894768140.168725544738407
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1000.9700727814573260.05985443708534830.0299272185426741
1010.9400161196841020.1199677606317970.0599838803158983
1020.9136242988991430.1727514022017130.0863757011008567
1030.9126025331522740.1747949336954530.0873974668477265
1040.935777979094240.1284440418115210.0642220209057607
1050.9822250926778040.03554981464439140.0177749073221957






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level410.422680412371134NOK
5% type I error level600.618556701030928NOK
10% type I error level740.762886597938144NOK

\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 & 41 & 0.422680412371134 & NOK \tabularnewline
5% type I error level & 60 & 0.618556701030928 & NOK \tabularnewline
10% type I error level & 74 & 0.762886597938144 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58535&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]41[/C][C]0.422680412371134[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]60[/C][C]0.618556701030928[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]74[/C][C]0.762886597938144[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58535&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58535&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level410.422680412371134NOK
5% type I error level600.618556701030928NOK
10% type I error level740.762886597938144NOK


Figures (Output of Computation)

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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')
}