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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 computationMon, 12 Dec 2011 14:30:53 -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/12/t1323718376vjsec2my5qgk3im.htm/, Retrieved Fri, 03 May 2024 05:45:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154166, Retrieved Fri, 03 May 2024 05:45:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [Workshop 10 - data] [2011-12-12 12:00:19] [8b13b85c94b9a060d82f72930775ea89]
-    D    [Kendall tau Correlation Matrix] [WS10 - Pearson Co...] [2011-12-12 16:56:49] [8b13b85c94b9a060d82f72930775ea89]
- RMP         [Multiple Regression] [WS10 - MR] [2011-12-12 19:30:53] [240aada53705cc48eae3e230739818e0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	26	21	21	23	17	23	4
1	1	20	16	15	24	17	20	4
1	1	19	19	18	22	18	20	6
1	2	19	18	11	20	21	21	8
1	1	20	16	8	24	20	24	8
1	1	25	23	19	27	28	22	4
1	2	25	17	4	28	19	23	4
1	1	22	12	20	27	22	20	8
1	1	26	19	16	24	16	25	5
1	1	22	16	14	23	18	23	4
1	2	17	19	10	24	25	27	4
1	2	22	20	13	27	17	27	4
1	1	19	13	14	27	14	22	4
1	1	24	20	8	28	11	24	4
1	1	26	27	23	27	27	25	4
1	2	21	17	11	23	20	22	8
1	1	13	8	9	24	22	28	4
1	2	26	25	24	28	22	28	4
1	2	20	26	5	27	21	27	4
1	1	22	13	15	25	23	25	8
1	2	14	19	5	19	17	16	4
1	1	21	15	19	24	24	28	7
1	1	7	5	6	20	14	21	4
1	2	23	16	13	28	17	24	4
1	1	17	14	11	26	23	27	5
1	1	25	24	17	23	24	14	4
1	1	25	24	17	23	24	14	4
1	1	19	9	5	20	8	27	4
1	2	20	19	9	11	22	20	4
1	1	23	19	15	24	23	21	4
1	2	22	25	17	25	25	22	4
1	1	22	19	17	23	21	21	4
1	1	21	18	20	18	24	12	15
1	2	15	15	12	20	15	20	10
1	2	20	12	7	20	22	24	4
1	2	22	21	16	24	21	19	8
1	1	18	12	7	23	25	28	4
1	2	20	15	14	25	16	23	4
1	2	28	28	24	28	28	27	4
1	1	22	25	15	26	23	22	4
1	1	18	19	15	26	21	27	7
1	1	23	20	10	23	21	26	4
1	1	20	24	14	22	26	22	6
1	2	25	26	18	24	22	21	5
1	2	26	25	12	21	21	19	4
1	1	15	12	9	20	18	24	16
1	2	17	12	9	22	12	19	5
1	2	23	15	8	20	25	26	12
1	1	21	17	18	25	17	22	6
1	2	13	14	10	20	24	28	9
1	1	18	16	17	22	15	21	9
1	1	19	11	14	23	13	23	4
1	1	22	20	16	25	26	28	5
1	1	16	11	10	23	16	10	4
1	2	24	22	19	23	24	24	4
1	1	18	20	10	22	21	21	5
1	1	20	19	14	24	20	21	4
1	1	24	17	10	25	14	24	4
1	2	14	21	4	21	25	24	4
1	2	22	23	19	12	25	25	5
1	1	24	18	9	17	20	25	4
1	1	18	17	12	20	22	23	6
1	1	21	27	16	23	20	21	4
1	2	23	25	11	23	26	16	4
1	1	17	19	18	20	18	17	18
1	2	22	22	11	28	22	25	4
1	2	24	24	24	24	24	24	6
1	2	21	20	17	24	17	23	4
1	1	22	19	18	24	24	25	4
1	1	16	11	9	24	20	23	5
1	1	21	22	19	28	19	28	4
1	2	23	22	18	25	20	26	4
1	2	22	16	12	21	15	22	5
1	1	24	20	23	25	23	19	10
1	1	24	24	22	25	26	26	5
1	1	16	16	14	18	22	18	8
1	1	16	16	14	17	20	18	8
1	2	21	22	16	26	24	25	5
1	2	26	24	23	28	26	27	4
1	2	15	16	7	21	21	12	4
1	2	25	27	10	27	25	15	4
1	1	18	11	12	22	13	21	5
1	0	23	21	12	21	20	23	4
1	1	20	20	12	25	22	22	4
1	2	17	20	17	22	23	21	8
1	2	25	27	21	23	28	24	4
1	1	24	20	16	26	22	27	5
1	1	17	12	11	19	20	22	14
1	1	19	8	14	25	6	28	8
1	1	20	21	13	21	21	26	8
1	1	15	18	9	13	20	10	4
1	2	27	24	19	24	18	19	4
1	1	22	16	13	25	23	22	6
1	1	23	18	19	26	20	21	4
1	1	16	20	13	25	24	24	7
1	1	19	20	13	25	22	25	7
1	2	25	19	13	22	21	21	4
1	1	19	17	14	21	18	20	6
1	2	19	16	12	23	21	21	4
0	2	26	26	22	25	23	24	7
0	1	21	15	11	24	23	23	4
0	2	20	22	5	21	15	18	4
0	1	24	17	18	21	21	24	8
0	1	22	23	19	25	24	24	4
0	2	20	21	14	22	23	19	4
0	1	18	19	15	20	21	20	10
0	2	18	14	12	20	21	18	8
0	1	24	17	19	23	20	20	6
0	1	24	12	15	28	11	27	4
0	1	22	24	17	23	22	23	4
0	1	23	18	8	28	27	26	4
0	1	22	20	10	24	25	23	5
0	1	20	16	12	18	18	17	4
0	1	18	20	12	20	20	21	6
0	1	25	22	20	28	24	25	4
0	2	18	12	12	21	10	23	5
0	1	16	16	12	21	27	27	7
0	1	20	17	14	25	21	24	8
0	2	19	22	6	19	21	20	5
0	1	15	12	10	18	18	27	8
0	1	19	14	18	21	15	21	10
0	1	19	23	18	22	24	24	8
0	1	16	15	7	24	22	21	5
0	1	17	17	18	15	14	15	12
0	1	28	28	9	28	28	25	4
0	2	23	20	17	26	18	25	5
0	1	25	23	22	23	26	22	4
0	1	20	13	11	26	17	24	6
0	2	17	18	15	20	19	21	4
0	2	23	23	17	22	22	22	4
0	1	16	19	15	20	18	23	7
0	2	23	23	22	23	24	22	7
0	2	11	12	9	22	15	20	10
0	2	18	16	13	24	18	23	4
0	2	24	23	20	23	26	25	5
0	1	23	13	14	22	11	23	8
0	1	21	22	14	26	26	22	11
0	2	16	18	12	23	21	25	7
0	2	24	23	20	27	23	26	4
0	1	23	20	20	23	23	22	8
0	1	18	10	8	21	15	24	6
0	1	20	17	17	26	22	24	7
0	1	9	18	9	23	26	25	5
0	2	24	15	18	21	16	20	4
0	1	25	23	22	27	20	26	8
0	1	20	17	10	19	18	21	4
0	2	21	17	13	23	22	26	8
0	2	25	22	15	25	16	21	6
0	2	22	20	18	23	19	22	4
0	2	21	20	18	22	20	16	9
0	1	21	19	12	22	19	26	5
0	1	22	18	12	25	23	28	6
0	1	27	22	20	25	24	18	4
0	2	24	20	12	28	25	25	4
0	2	24	22	16	28	21	23	4
0	2	21	18	16	20	21	21	5
0	1	18	16	18	25	23	20	6
0	1	16	16	16	19	27	25	16
0	1	22	16	13	25	23	22	6
0	1	20	16	17	22	18	21	6
0	2	18	17	13	18	16	16	4
0	1	20	18	17	20	16	18	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154166&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154166&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154166&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Population[t] = + 0.676944536146999 -0.0172747685728744Gender[t] + 0.0014227577348154I1[t] + 0.00215816490645729I2[t] -0.0110848800931062I3[t] + 0.00757535980486066E1[t] + 0.00112700689373423E2[t] -0.0038749521929556E3[t] -0.0111883810004106`A `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Population[t] =  +  0.676944536146999 -0.0172747685728744Gender[t] +  0.0014227577348154I1[t] +  0.00215816490645729I2[t] -0.0110848800931062I3[t] +  0.00757535980486066E1[t] +  0.00112700689373423E2[t] -0.0038749521929556E3[t] -0.0111883810004106`A
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154166&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Population[t] =  +  0.676944536146999 -0.0172747685728744Gender[t] +  0.0014227577348154I1[t] +  0.00215816490645729I2[t] -0.0110848800931062I3[t] +  0.00757535980486066E1[t] +  0.00112700689373423E2[t] -0.0038749521929556E3[t] -0.0111883810004106`A
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154166&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154166&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
Population[t] = + 0.676944536146999 -0.0172747685728744Gender[t] + 0.0014227577348154I1[t] + 0.00215816490645729I2[t] -0.0110848800931062I3[t] + 0.00757535980486066E1[t] + 0.00112700689373423E2[t] -0.0038749521929556E3[t] -0.0111883810004106`A `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6769445361469990.4285781.57950.1162840.058142
Gender-0.01727476857287440.082881-0.20840.8351720.417586
I10.00142275773481540.0159880.0890.9292090.464605
I20.002158164906457290.0142320.15160.8796710.439835
I3-0.01108488009310620.010854-1.02130.3087180.154359
E10.007575359804860660.0155070.48850.6258830.312941
E20.001127006893734230.0118990.09470.9246680.462334
E3-0.00387495219295560.012191-0.31790.7510310.375516
`A `-0.01118838100041060.017117-0.65360.514330.257165

\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) & 0.676944536146999 & 0.428578 & 1.5795 & 0.116284 & 0.058142 \tabularnewline
Gender & -0.0172747685728744 & 0.082881 & -0.2084 & 0.835172 & 0.417586 \tabularnewline
I1 & 0.0014227577348154 & 0.015988 & 0.089 & 0.929209 & 0.464605 \tabularnewline
I2 & 0.00215816490645729 & 0.014232 & 0.1516 & 0.879671 & 0.439835 \tabularnewline
I3 & -0.0110848800931062 & 0.010854 & -1.0213 & 0.308718 & 0.154359 \tabularnewline
E1 & 0.00757535980486066 & 0.015507 & 0.4885 & 0.625883 & 0.312941 \tabularnewline
E2 & 0.00112700689373423 & 0.011899 & 0.0947 & 0.924668 & 0.462334 \tabularnewline
E3 & -0.0038749521929556 & 0.012191 & -0.3179 & 0.751031 & 0.375516 \tabularnewline
`A
` & -0.0111883810004106 & 0.017117 & -0.6536 & 0.51433 & 0.257165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154166&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]0.676944536146999[/C][C]0.428578[/C][C]1.5795[/C][C]0.116284[/C][C]0.058142[/C][/ROW]
[ROW][C]Gender[/C][C]-0.0172747685728744[/C][C]0.082881[/C][C]-0.2084[/C][C]0.835172[/C][C]0.417586[/C][/ROW]
[ROW][C]I1[/C][C]0.0014227577348154[/C][C]0.015988[/C][C]0.089[/C][C]0.929209[/C][C]0.464605[/C][/ROW]
[ROW][C]I2[/C][C]0.00215816490645729[/C][C]0.014232[/C][C]0.1516[/C][C]0.879671[/C][C]0.439835[/C][/ROW]
[ROW][C]I3[/C][C]-0.0110848800931062[/C][C]0.010854[/C][C]-1.0213[/C][C]0.308718[/C][C]0.154359[/C][/ROW]
[ROW][C]E1[/C][C]0.00757535980486066[/C][C]0.015507[/C][C]0.4885[/C][C]0.625883[/C][C]0.312941[/C][/ROW]
[ROW][C]E2[/C][C]0.00112700689373423[/C][C]0.011899[/C][C]0.0947[/C][C]0.924668[/C][C]0.462334[/C][/ROW]
[ROW][C]E3[/C][C]-0.0038749521929556[/C][C]0.012191[/C][C]-0.3179[/C][C]0.751031[/C][C]0.375516[/C][/ROW]
[ROW][C]`A
`[/C][C]-0.0111883810004106[/C][C]0.017117[/C][C]-0.6536[/C][C]0.51433[/C][C]0.257165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154166&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154166&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)0.6769445361469990.4285781.57950.1162840.058142
Gender-0.01727476857287440.082881-0.20840.8351720.417586
I10.00142275773481540.0159880.0890.9292090.464605
I20.002158164906457290.0142320.15160.8796710.439835
I3-0.01108488009310620.010854-1.02130.3087180.154359
E10.007575359804860660.0155070.48850.6258830.312941
E20.001127006893734230.0118990.09470.9246680.462334
E3-0.00387495219295560.012191-0.31790.7510310.375516
`A `-0.01118838100041060.017117-0.65360.514330.257165






Multiple Linear Regression - Regression Statistics
Multiple R0.126534107560573
R-squared0.0160108803761506
Adjusted R-squared-0.0354395311074494
F-TEST (value)0.311190521406309
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0.960905894700176
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.497599342106503
Sum Squared Residuals37.8835811055182

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.126534107560573 \tabularnewline
R-squared & 0.0160108803761506 \tabularnewline
Adjusted R-squared & -0.0354395311074494 \tabularnewline
F-TEST (value) & 0.311190521406309 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 0.960905894700176 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.497599342106503 \tabularnewline
Sum Squared Residuals & 37.8835811055182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154166&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.126534107560573[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0160108803761506[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0354395311074494[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.311190521406309[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]0.960905894700176[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.497599342106503[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37.8835811055182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154166&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154166&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 R0.126534107560573
R-squared0.0160108803761506
Adjusted R-squared-0.0354395311074494
F-TEST (value)0.311190521406309
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0.960905894700176
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.497599342106503
Sum Squared Residuals37.8835811055182






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.5687154180253570.431284581974643
210.635097544026540.36490245597346
310.570494166014970.42950583398503
410.5906339800650860.409366019934914
510.6558193925860210.344180607413979
610.6403522175331410.359647782466859
710.7699590064864120.230040993513588
810.5574935892864220.442506410713578
910.6073335562027760.392666443797224
1010.6309547300992840.369045269900716
1110.6573447872333070.342655212766693
1210.6470721247992310.352927875200769
1310.6498803260129270.350119673987073
1410.7350549843285890.264945015671411
1510.593316251048760.40668374895124
1610.6090454509561520.390954549043848
1710.6490176181148380.350982381885162
1810.5509557413271870.449044258672813
1910.750362667088130.24963733291187
2010.5816776809776450.418322319022355
2110.7042325344427260.295767465557274
2210.5333469041937370.466653095806263
2310.6550683882472650.344931611752735
2410.6590624392919450.340937560708055
2510.6544521760026310.345547823997369
2610.6608702933750760.339129706624924
2710.6608702933750760.339129706624924
2810.6618472662638460.338152733736154
2910.5979619077371570.402038092262843
3010.6487274011198080.351272598880192
3110.6267635254640230.373236474535977
3210.6153055096064510.384694490393549
3310.4557765470748430.544223452925157
3410.5421197231430760.457880276856924
3510.6577029430500920.342297056949908
3610.5840036911287270.415996308871273
3710.6827394874772980.317260512522702
3810.6215729869725750.378427013027425
3910.5709127450715510.429087254928449
4010.6715294002405020.328470599759498
4110.5976954621090190.402304537890981
4210.6771058319346890.322894168065311
4310.6283134194184590.371686580581541
4410.6038352741883020.396164725811698
4510.6646943466533030.335305653346697
4610.5069255631828130.493074436817187
4710.643331940296180.35666805970382
4810.5634848991728110.436515100827189
4910.5828725198066230.417127480193377
5010.5496176284535740.450382371546426
5110.5328606777784990.467139322221501
5210.610260597893880.38973940210612
5310.6110212623333080.388978737666692
5410.7040872442514840.295912755748516
5510.5769371551387030.423062844861297
5610.6706030634201190.329396936579881
5710.6521629873272650.347837012672735
5810.6870656706896260.312934329310374
5910.7128009015646970.287199098435303
6010.4789845204224310.521015479577569
6110.6425929264197530.357407073580247
6210.6089968104122260.391003189587774
6310.6411059443226660.358894055677334
6410.7039215641992220.296078435800778
6510.4298622155095580.570137784490442
6610.6945185134578010.305481486542199
6710.5110276822901270.488972317709873
6810.5940835760492310.405916423950769
6910.5996772012275860.400322798772414
7010.6656927522155550.334307247784445
7110.6066856062909410.393314393709059
7210.5894920651458670.410507934854133
7310.6100045526142430.389995447385757
7410.511824261204540.48817573879546
7510.5637400612560420.436259938743958
7610.5636706492041610.436329350795839
7710.5538412756118320.446158724388168
7810.6135862684347910.386413731565209
7910.5682654362817290.431734563718271
8010.7121695932284380.287830406771562
8110.7552176740935380.244782325906462
8210.6199937639259170.380006236074083
8310.669716135303220.33028386469678
8410.6824453338193090.317554666180691
8510.5430002472469230.456999752753077
8610.5714890047945290.428510995205471
8710.6208090622258180.379190937774182
8810.5124066388355720.487593361164428
8910.546919489561670.45308051043833
9010.5818368397460180.418163160253982
9110.6576109504813060.342389049518694
9210.6057098375632970.394290162436703
9310.6443235544629170.355676445537083
9410.6139994147694450.386000585230555
9510.6266083891872660.373391610812734
9610.6247476964112880.375252303588712
9710.6390631748055870.360936825194413
9810.6029419967696190.397058003230381
9910.642712373575290.35728762642471
10000.5356192596696-0.5356192596696
10100.673838842010862-0.673838842010862
10200.724390377007332-0.724390377007332
10300.531220714979831-0.531220714979831
10400.608675292758125-0.608675292758125
10500.635184754024698-0.635184754024698
10600.545802825629312-0.545802825629312
10700.581118539190202-0.581118539190202
10800.572036118375355-0.572036118375355
10900.628570647846321-0.628570647846321
11000.619473436646561-0.619473436646561
11100.739598102694696-0.739598102694696
11200.688202937158128-0.688202937158128
11300.635651888949296-0.635651888949296
11400.62096719573004-0.62096719573004
11500.618551648184634-0.618551648184634
11600.586170875387525-0.586170875387525
11700.590515334537437-0.590515334537437
11800.600170643632436-0.600170643632436
11900.684555775545773-0.684555775545773
12000.558572154904404-0.558572154904404
12100.500118484802022-0.500118484802022
12200.548012296230561-0.548012296230561
12300.706499090200975-0.706499090200975
12400.458041249485778-0.458041249485778
12500.762210619426929-0.762210619426929
12600.594268532635996-0.594268532635996
12700.574542124246911-0.574542124246911
12800.650236718516671-0.650236718516671
12900.585948454437204-0.585948454437204
13000.597762853290139-0.597762853290139
13100.561516575900844-0.561516575900844
13200.518602683415706-0.518602683415706
13300.578359557373482-0.578359557373482
13400.626649170485115-0.626649170485115
13500.555201120546157-0.555201120546157
13600.565685061052085-0.565685061052085
13700.59977938155775-0.59977938155775
13800.593695478410704-0.593695478410704
13900.589434967891851-0.589434967891851
14000.539257329561276-0.539257329561276
14100.634040535795215-0.634040535795215
14200.586806751052124-0.586806751052124
14300.662277379588681-0.662277379588681
14400.564247914898835-0.564247914898835
14500.537828189330484-0.537828189330484
14600.652055365075004-0.652055365075004
14700.573629895785586-0.573629895785586
14800.618082192283836-0.618082192283836
14900.582975059866504-0.582975059866504
15000.542411757376243-0.542411757376243
15100.628914636779649-0.628914636779649
15200.636475051211204-0.636475051211204
15300.625795749590373-0.625795749590373
15400.685343839702614-0.685343839702614
15500.648562525954078-0.648562525954078
15600.571620238070418-0.571620238070418
15700.590958027444036-0.590958027444036
15800.438079569937507-0.438079569937507
15900.644323554462917-0.644323554462917
16000.572652356930564-0.572652356930564
16100.608225828125629-0.608225828125629
16200.593565571925977-0.593565571925977

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.568715418025357 & 0.431284581974643 \tabularnewline
2 & 1 & 0.63509754402654 & 0.36490245597346 \tabularnewline
3 & 1 & 0.57049416601497 & 0.42950583398503 \tabularnewline
4 & 1 & 0.590633980065086 & 0.409366019934914 \tabularnewline
5 & 1 & 0.655819392586021 & 0.344180607413979 \tabularnewline
6 & 1 & 0.640352217533141 & 0.359647782466859 \tabularnewline
7 & 1 & 0.769959006486412 & 0.230040993513588 \tabularnewline
8 & 1 & 0.557493589286422 & 0.442506410713578 \tabularnewline
9 & 1 & 0.607333556202776 & 0.392666443797224 \tabularnewline
10 & 1 & 0.630954730099284 & 0.369045269900716 \tabularnewline
11 & 1 & 0.657344787233307 & 0.342655212766693 \tabularnewline
12 & 1 & 0.647072124799231 & 0.352927875200769 \tabularnewline
13 & 1 & 0.649880326012927 & 0.350119673987073 \tabularnewline
14 & 1 & 0.735054984328589 & 0.264945015671411 \tabularnewline
15 & 1 & 0.59331625104876 & 0.40668374895124 \tabularnewline
16 & 1 & 0.609045450956152 & 0.390954549043848 \tabularnewline
17 & 1 & 0.649017618114838 & 0.350982381885162 \tabularnewline
18 & 1 & 0.550955741327187 & 0.449044258672813 \tabularnewline
19 & 1 & 0.75036266708813 & 0.24963733291187 \tabularnewline
20 & 1 & 0.581677680977645 & 0.418322319022355 \tabularnewline
21 & 1 & 0.704232534442726 & 0.295767465557274 \tabularnewline
22 & 1 & 0.533346904193737 & 0.466653095806263 \tabularnewline
23 & 1 & 0.655068388247265 & 0.344931611752735 \tabularnewline
24 & 1 & 0.659062439291945 & 0.340937560708055 \tabularnewline
25 & 1 & 0.654452176002631 & 0.345547823997369 \tabularnewline
26 & 1 & 0.660870293375076 & 0.339129706624924 \tabularnewline
27 & 1 & 0.660870293375076 & 0.339129706624924 \tabularnewline
28 & 1 & 0.661847266263846 & 0.338152733736154 \tabularnewline
29 & 1 & 0.597961907737157 & 0.402038092262843 \tabularnewline
30 & 1 & 0.648727401119808 & 0.351272598880192 \tabularnewline
31 & 1 & 0.626763525464023 & 0.373236474535977 \tabularnewline
32 & 1 & 0.615305509606451 & 0.384694490393549 \tabularnewline
33 & 1 & 0.455776547074843 & 0.544223452925157 \tabularnewline
34 & 1 & 0.542119723143076 & 0.457880276856924 \tabularnewline
35 & 1 & 0.657702943050092 & 0.342297056949908 \tabularnewline
36 & 1 & 0.584003691128727 & 0.415996308871273 \tabularnewline
37 & 1 & 0.682739487477298 & 0.317260512522702 \tabularnewline
38 & 1 & 0.621572986972575 & 0.378427013027425 \tabularnewline
39 & 1 & 0.570912745071551 & 0.429087254928449 \tabularnewline
40 & 1 & 0.671529400240502 & 0.328470599759498 \tabularnewline
41 & 1 & 0.597695462109019 & 0.402304537890981 \tabularnewline
42 & 1 & 0.677105831934689 & 0.322894168065311 \tabularnewline
43 & 1 & 0.628313419418459 & 0.371686580581541 \tabularnewline
44 & 1 & 0.603835274188302 & 0.396164725811698 \tabularnewline
45 & 1 & 0.664694346653303 & 0.335305653346697 \tabularnewline
46 & 1 & 0.506925563182813 & 0.493074436817187 \tabularnewline
47 & 1 & 0.64333194029618 & 0.35666805970382 \tabularnewline
48 & 1 & 0.563484899172811 & 0.436515100827189 \tabularnewline
49 & 1 & 0.582872519806623 & 0.417127480193377 \tabularnewline
50 & 1 & 0.549617628453574 & 0.450382371546426 \tabularnewline
51 & 1 & 0.532860677778499 & 0.467139322221501 \tabularnewline
52 & 1 & 0.61026059789388 & 0.38973940210612 \tabularnewline
53 & 1 & 0.611021262333308 & 0.388978737666692 \tabularnewline
54 & 1 & 0.704087244251484 & 0.295912755748516 \tabularnewline
55 & 1 & 0.576937155138703 & 0.423062844861297 \tabularnewline
56 & 1 & 0.670603063420119 & 0.329396936579881 \tabularnewline
57 & 1 & 0.652162987327265 & 0.347837012672735 \tabularnewline
58 & 1 & 0.687065670689626 & 0.312934329310374 \tabularnewline
59 & 1 & 0.712800901564697 & 0.287199098435303 \tabularnewline
60 & 1 & 0.478984520422431 & 0.521015479577569 \tabularnewline
61 & 1 & 0.642592926419753 & 0.357407073580247 \tabularnewline
62 & 1 & 0.608996810412226 & 0.391003189587774 \tabularnewline
63 & 1 & 0.641105944322666 & 0.358894055677334 \tabularnewline
64 & 1 & 0.703921564199222 & 0.296078435800778 \tabularnewline
65 & 1 & 0.429862215509558 & 0.570137784490442 \tabularnewline
66 & 1 & 0.694518513457801 & 0.305481486542199 \tabularnewline
67 & 1 & 0.511027682290127 & 0.488972317709873 \tabularnewline
68 & 1 & 0.594083576049231 & 0.405916423950769 \tabularnewline
69 & 1 & 0.599677201227586 & 0.400322798772414 \tabularnewline
70 & 1 & 0.665692752215555 & 0.334307247784445 \tabularnewline
71 & 1 & 0.606685606290941 & 0.393314393709059 \tabularnewline
72 & 1 & 0.589492065145867 & 0.410507934854133 \tabularnewline
73 & 1 & 0.610004552614243 & 0.389995447385757 \tabularnewline
74 & 1 & 0.51182426120454 & 0.48817573879546 \tabularnewline
75 & 1 & 0.563740061256042 & 0.436259938743958 \tabularnewline
76 & 1 & 0.563670649204161 & 0.436329350795839 \tabularnewline
77 & 1 & 0.553841275611832 & 0.446158724388168 \tabularnewline
78 & 1 & 0.613586268434791 & 0.386413731565209 \tabularnewline
79 & 1 & 0.568265436281729 & 0.431734563718271 \tabularnewline
80 & 1 & 0.712169593228438 & 0.287830406771562 \tabularnewline
81 & 1 & 0.755217674093538 & 0.244782325906462 \tabularnewline
82 & 1 & 0.619993763925917 & 0.380006236074083 \tabularnewline
83 & 1 & 0.66971613530322 & 0.33028386469678 \tabularnewline
84 & 1 & 0.682445333819309 & 0.317554666180691 \tabularnewline
85 & 1 & 0.543000247246923 & 0.456999752753077 \tabularnewline
86 & 1 & 0.571489004794529 & 0.428510995205471 \tabularnewline
87 & 1 & 0.620809062225818 & 0.379190937774182 \tabularnewline
88 & 1 & 0.512406638835572 & 0.487593361164428 \tabularnewline
89 & 1 & 0.54691948956167 & 0.45308051043833 \tabularnewline
90 & 1 & 0.581836839746018 & 0.418163160253982 \tabularnewline
91 & 1 & 0.657610950481306 & 0.342389049518694 \tabularnewline
92 & 1 & 0.605709837563297 & 0.394290162436703 \tabularnewline
93 & 1 & 0.644323554462917 & 0.355676445537083 \tabularnewline
94 & 1 & 0.613999414769445 & 0.386000585230555 \tabularnewline
95 & 1 & 0.626608389187266 & 0.373391610812734 \tabularnewline
96 & 1 & 0.624747696411288 & 0.375252303588712 \tabularnewline
97 & 1 & 0.639063174805587 & 0.360936825194413 \tabularnewline
98 & 1 & 0.602941996769619 & 0.397058003230381 \tabularnewline
99 & 1 & 0.64271237357529 & 0.35728762642471 \tabularnewline
100 & 0 & 0.5356192596696 & -0.5356192596696 \tabularnewline
101 & 0 & 0.673838842010862 & -0.673838842010862 \tabularnewline
102 & 0 & 0.724390377007332 & -0.724390377007332 \tabularnewline
103 & 0 & 0.531220714979831 & -0.531220714979831 \tabularnewline
104 & 0 & 0.608675292758125 & -0.608675292758125 \tabularnewline
105 & 0 & 0.635184754024698 & -0.635184754024698 \tabularnewline
106 & 0 & 0.545802825629312 & -0.545802825629312 \tabularnewline
107 & 0 & 0.581118539190202 & -0.581118539190202 \tabularnewline
108 & 0 & 0.572036118375355 & -0.572036118375355 \tabularnewline
109 & 0 & 0.628570647846321 & -0.628570647846321 \tabularnewline
110 & 0 & 0.619473436646561 & -0.619473436646561 \tabularnewline
111 & 0 & 0.739598102694696 & -0.739598102694696 \tabularnewline
112 & 0 & 0.688202937158128 & -0.688202937158128 \tabularnewline
113 & 0 & 0.635651888949296 & -0.635651888949296 \tabularnewline
114 & 0 & 0.62096719573004 & -0.62096719573004 \tabularnewline
115 & 0 & 0.618551648184634 & -0.618551648184634 \tabularnewline
116 & 0 & 0.586170875387525 & -0.586170875387525 \tabularnewline
117 & 0 & 0.590515334537437 & -0.590515334537437 \tabularnewline
118 & 0 & 0.600170643632436 & -0.600170643632436 \tabularnewline
119 & 0 & 0.684555775545773 & -0.684555775545773 \tabularnewline
120 & 0 & 0.558572154904404 & -0.558572154904404 \tabularnewline
121 & 0 & 0.500118484802022 & -0.500118484802022 \tabularnewline
122 & 0 & 0.548012296230561 & -0.548012296230561 \tabularnewline
123 & 0 & 0.706499090200975 & -0.706499090200975 \tabularnewline
124 & 0 & 0.458041249485778 & -0.458041249485778 \tabularnewline
125 & 0 & 0.762210619426929 & -0.762210619426929 \tabularnewline
126 & 0 & 0.594268532635996 & -0.594268532635996 \tabularnewline
127 & 0 & 0.574542124246911 & -0.574542124246911 \tabularnewline
128 & 0 & 0.650236718516671 & -0.650236718516671 \tabularnewline
129 & 0 & 0.585948454437204 & -0.585948454437204 \tabularnewline
130 & 0 & 0.597762853290139 & -0.597762853290139 \tabularnewline
131 & 0 & 0.561516575900844 & -0.561516575900844 \tabularnewline
132 & 0 & 0.518602683415706 & -0.518602683415706 \tabularnewline
133 & 0 & 0.578359557373482 & -0.578359557373482 \tabularnewline
134 & 0 & 0.626649170485115 & -0.626649170485115 \tabularnewline
135 & 0 & 0.555201120546157 & -0.555201120546157 \tabularnewline
136 & 0 & 0.565685061052085 & -0.565685061052085 \tabularnewline
137 & 0 & 0.59977938155775 & -0.59977938155775 \tabularnewline
138 & 0 & 0.593695478410704 & -0.593695478410704 \tabularnewline
139 & 0 & 0.589434967891851 & -0.589434967891851 \tabularnewline
140 & 0 & 0.539257329561276 & -0.539257329561276 \tabularnewline
141 & 0 & 0.634040535795215 & -0.634040535795215 \tabularnewline
142 & 0 & 0.586806751052124 & -0.586806751052124 \tabularnewline
143 & 0 & 0.662277379588681 & -0.662277379588681 \tabularnewline
144 & 0 & 0.564247914898835 & -0.564247914898835 \tabularnewline
145 & 0 & 0.537828189330484 & -0.537828189330484 \tabularnewline
146 & 0 & 0.652055365075004 & -0.652055365075004 \tabularnewline
147 & 0 & 0.573629895785586 & -0.573629895785586 \tabularnewline
148 & 0 & 0.618082192283836 & -0.618082192283836 \tabularnewline
149 & 0 & 0.582975059866504 & -0.582975059866504 \tabularnewline
150 & 0 & 0.542411757376243 & -0.542411757376243 \tabularnewline
151 & 0 & 0.628914636779649 & -0.628914636779649 \tabularnewline
152 & 0 & 0.636475051211204 & -0.636475051211204 \tabularnewline
153 & 0 & 0.625795749590373 & -0.625795749590373 \tabularnewline
154 & 0 & 0.685343839702614 & -0.685343839702614 \tabularnewline
155 & 0 & 0.648562525954078 & -0.648562525954078 \tabularnewline
156 & 0 & 0.571620238070418 & -0.571620238070418 \tabularnewline
157 & 0 & 0.590958027444036 & -0.590958027444036 \tabularnewline
158 & 0 & 0.438079569937507 & -0.438079569937507 \tabularnewline
159 & 0 & 0.644323554462917 & -0.644323554462917 \tabularnewline
160 & 0 & 0.572652356930564 & -0.572652356930564 \tabularnewline
161 & 0 & 0.608225828125629 & -0.608225828125629 \tabularnewline
162 & 0 & 0.593565571925977 & -0.593565571925977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154166&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]1[/C][C]0.568715418025357[/C][C]0.431284581974643[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.63509754402654[/C][C]0.36490245597346[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.57049416601497[/C][C]0.42950583398503[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]0.590633980065086[/C][C]0.409366019934914[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.655819392586021[/C][C]0.344180607413979[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.640352217533141[/C][C]0.359647782466859[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.769959006486412[/C][C]0.230040993513588[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.557493589286422[/C][C]0.442506410713578[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.607333556202776[/C][C]0.392666443797224[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.630954730099284[/C][C]0.369045269900716[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.657344787233307[/C][C]0.342655212766693[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]0.647072124799231[/C][C]0.352927875200769[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.649880326012927[/C][C]0.350119673987073[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.735054984328589[/C][C]0.264945015671411[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.59331625104876[/C][C]0.40668374895124[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.609045450956152[/C][C]0.390954549043848[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.649017618114838[/C][C]0.350982381885162[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.550955741327187[/C][C]0.449044258672813[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.75036266708813[/C][C]0.24963733291187[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.581677680977645[/C][C]0.418322319022355[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.704232534442726[/C][C]0.295767465557274[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.533346904193737[/C][C]0.466653095806263[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.655068388247265[/C][C]0.344931611752735[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.659062439291945[/C][C]0.340937560708055[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.654452176002631[/C][C]0.345547823997369[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.660870293375076[/C][C]0.339129706624924[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.660870293375076[/C][C]0.339129706624924[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.661847266263846[/C][C]0.338152733736154[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.597961907737157[/C][C]0.402038092262843[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.648727401119808[/C][C]0.351272598880192[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.626763525464023[/C][C]0.373236474535977[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.615305509606451[/C][C]0.384694490393549[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.455776547074843[/C][C]0.544223452925157[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.542119723143076[/C][C]0.457880276856924[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]0.657702943050092[/C][C]0.342297056949908[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]0.584003691128727[/C][C]0.415996308871273[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.682739487477298[/C][C]0.317260512522702[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.621572986972575[/C][C]0.378427013027425[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.570912745071551[/C][C]0.429087254928449[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.671529400240502[/C][C]0.328470599759498[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.597695462109019[/C][C]0.402304537890981[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.677105831934689[/C][C]0.322894168065311[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.628313419418459[/C][C]0.371686580581541[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.603835274188302[/C][C]0.396164725811698[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]0.664694346653303[/C][C]0.335305653346697[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.506925563182813[/C][C]0.493074436817187[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]0.64333194029618[/C][C]0.35666805970382[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.563484899172811[/C][C]0.436515100827189[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.582872519806623[/C][C]0.417127480193377[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]0.549617628453574[/C][C]0.450382371546426[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.532860677778499[/C][C]0.467139322221501[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.61026059789388[/C][C]0.38973940210612[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.611021262333308[/C][C]0.388978737666692[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.704087244251484[/C][C]0.295912755748516[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.576937155138703[/C][C]0.423062844861297[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.670603063420119[/C][C]0.329396936579881[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.652162987327265[/C][C]0.347837012672735[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.687065670689626[/C][C]0.312934329310374[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.712800901564697[/C][C]0.287199098435303[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.478984520422431[/C][C]0.521015479577569[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.642592926419753[/C][C]0.357407073580247[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.608996810412226[/C][C]0.391003189587774[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.641105944322666[/C][C]0.358894055677334[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.703921564199222[/C][C]0.296078435800778[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0.429862215509558[/C][C]0.570137784490442[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.694518513457801[/C][C]0.305481486542199[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.511027682290127[/C][C]0.488972317709873[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.594083576049231[/C][C]0.405916423950769[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.599677201227586[/C][C]0.400322798772414[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0.665692752215555[/C][C]0.334307247784445[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.606685606290941[/C][C]0.393314393709059[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.589492065145867[/C][C]0.410507934854133[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.610004552614243[/C][C]0.389995447385757[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.51182426120454[/C][C]0.48817573879546[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.563740061256042[/C][C]0.436259938743958[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.563670649204161[/C][C]0.436329350795839[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.553841275611832[/C][C]0.446158724388168[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.613586268434791[/C][C]0.386413731565209[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.568265436281729[/C][C]0.431734563718271[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.712169593228438[/C][C]0.287830406771562[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]0.755217674093538[/C][C]0.244782325906462[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.619993763925917[/C][C]0.380006236074083[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.66971613530322[/C][C]0.33028386469678[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.682445333819309[/C][C]0.317554666180691[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.543000247246923[/C][C]0.456999752753077[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.571489004794529[/C][C]0.428510995205471[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.620809062225818[/C][C]0.379190937774182[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.512406638835572[/C][C]0.487593361164428[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0.54691948956167[/C][C]0.45308051043833[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.581836839746018[/C][C]0.418163160253982[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0.657610950481306[/C][C]0.342389049518694[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0.605709837563297[/C][C]0.394290162436703[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0.644323554462917[/C][C]0.355676445537083[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0.613999414769445[/C][C]0.386000585230555[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0.626608389187266[/C][C]0.373391610812734[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.624747696411288[/C][C]0.375252303588712[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0.639063174805587[/C][C]0.360936825194413[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0.602941996769619[/C][C]0.397058003230381[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0.64271237357529[/C][C]0.35728762642471[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.5356192596696[/C][C]-0.5356192596696[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.673838842010862[/C][C]-0.673838842010862[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.724390377007332[/C][C]-0.724390377007332[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.531220714979831[/C][C]-0.531220714979831[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.608675292758125[/C][C]-0.608675292758125[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.635184754024698[/C][C]-0.635184754024698[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.545802825629312[/C][C]-0.545802825629312[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.581118539190202[/C][C]-0.581118539190202[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.572036118375355[/C][C]-0.572036118375355[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.628570647846321[/C][C]-0.628570647846321[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.619473436646561[/C][C]-0.619473436646561[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.739598102694696[/C][C]-0.739598102694696[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.688202937158128[/C][C]-0.688202937158128[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.635651888949296[/C][C]-0.635651888949296[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.62096719573004[/C][C]-0.62096719573004[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.618551648184634[/C][C]-0.618551648184634[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.586170875387525[/C][C]-0.586170875387525[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.590515334537437[/C][C]-0.590515334537437[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.600170643632436[/C][C]-0.600170643632436[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.684555775545773[/C][C]-0.684555775545773[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.558572154904404[/C][C]-0.558572154904404[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.500118484802022[/C][C]-0.500118484802022[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.548012296230561[/C][C]-0.548012296230561[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.706499090200975[/C][C]-0.706499090200975[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.458041249485778[/C][C]-0.458041249485778[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.762210619426929[/C][C]-0.762210619426929[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.594268532635996[/C][C]-0.594268532635996[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.574542124246911[/C][C]-0.574542124246911[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.650236718516671[/C][C]-0.650236718516671[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.585948454437204[/C][C]-0.585948454437204[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.597762853290139[/C][C]-0.597762853290139[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.561516575900844[/C][C]-0.561516575900844[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]0.518602683415706[/C][C]-0.518602683415706[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.578359557373482[/C][C]-0.578359557373482[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.626649170485115[/C][C]-0.626649170485115[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.555201120546157[/C][C]-0.555201120546157[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.565685061052085[/C][C]-0.565685061052085[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.59977938155775[/C][C]-0.59977938155775[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.593695478410704[/C][C]-0.593695478410704[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.589434967891851[/C][C]-0.589434967891851[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.539257329561276[/C][C]-0.539257329561276[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]0.634040535795215[/C][C]-0.634040535795215[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.586806751052124[/C][C]-0.586806751052124[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.662277379588681[/C][C]-0.662277379588681[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.564247914898835[/C][C]-0.564247914898835[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.537828189330484[/C][C]-0.537828189330484[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.652055365075004[/C][C]-0.652055365075004[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.573629895785586[/C][C]-0.573629895785586[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.618082192283836[/C][C]-0.618082192283836[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.582975059866504[/C][C]-0.582975059866504[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.542411757376243[/C][C]-0.542411757376243[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.628914636779649[/C][C]-0.628914636779649[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.636475051211204[/C][C]-0.636475051211204[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.625795749590373[/C][C]-0.625795749590373[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.685343839702614[/C][C]-0.685343839702614[/C][/ROW]
[ROW][C]155[/C][C]0[/C][C]0.648562525954078[/C][C]-0.648562525954078[/C][/ROW]
[ROW][C]156[/C][C]0[/C][C]0.571620238070418[/C][C]-0.571620238070418[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]0.590958027444036[/C][C]-0.590958027444036[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]0.438079569937507[/C][C]-0.438079569937507[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]0.644323554462917[/C][C]-0.644323554462917[/C][/ROW]
[ROW][C]160[/C][C]0[/C][C]0.572652356930564[/C][C]-0.572652356930564[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]0.608225828125629[/C][C]-0.608225828125629[/C][/ROW]
[ROW][C]162[/C][C]0[/C][C]0.593565571925977[/C][C]-0.593565571925977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154166&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154166&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
110.5687154180253570.431284581974643
210.635097544026540.36490245597346
310.570494166014970.42950583398503
410.5906339800650860.409366019934914
510.6558193925860210.344180607413979
610.6403522175331410.359647782466859
710.7699590064864120.230040993513588
810.5574935892864220.442506410713578
910.6073335562027760.392666443797224
1010.6309547300992840.369045269900716
1110.6573447872333070.342655212766693
1210.6470721247992310.352927875200769
1310.6498803260129270.350119673987073
1410.7350549843285890.264945015671411
1510.593316251048760.40668374895124
1610.6090454509561520.390954549043848
1710.6490176181148380.350982381885162
1810.5509557413271870.449044258672813
1910.750362667088130.24963733291187
2010.5816776809776450.418322319022355
2110.7042325344427260.295767465557274
2210.5333469041937370.466653095806263
2310.6550683882472650.344931611752735
2410.6590624392919450.340937560708055
2510.6544521760026310.345547823997369
2610.6608702933750760.339129706624924
2710.6608702933750760.339129706624924
2810.6618472662638460.338152733736154
2910.5979619077371570.402038092262843
3010.6487274011198080.351272598880192
3110.6267635254640230.373236474535977
3210.6153055096064510.384694490393549
3310.4557765470748430.544223452925157
3410.5421197231430760.457880276856924
3510.6577029430500920.342297056949908
3610.5840036911287270.415996308871273
3710.6827394874772980.317260512522702
3810.6215729869725750.378427013027425
3910.5709127450715510.429087254928449
4010.6715294002405020.328470599759498
4110.5976954621090190.402304537890981
4210.6771058319346890.322894168065311
4310.6283134194184590.371686580581541
4410.6038352741883020.396164725811698
4510.6646943466533030.335305653346697
4610.5069255631828130.493074436817187
4710.643331940296180.35666805970382
4810.5634848991728110.436515100827189
4910.5828725198066230.417127480193377
5010.5496176284535740.450382371546426
5110.5328606777784990.467139322221501
5210.610260597893880.38973940210612
5310.6110212623333080.388978737666692
5410.7040872442514840.295912755748516
5510.5769371551387030.423062844861297
5610.6706030634201190.329396936579881
5710.6521629873272650.347837012672735
5810.6870656706896260.312934329310374
5910.7128009015646970.287199098435303
6010.4789845204224310.521015479577569
6110.6425929264197530.357407073580247
6210.6089968104122260.391003189587774
6310.6411059443226660.358894055677334
6410.7039215641992220.296078435800778
6510.4298622155095580.570137784490442
6610.6945185134578010.305481486542199
6710.5110276822901270.488972317709873
6810.5940835760492310.405916423950769
6910.5996772012275860.400322798772414
7010.6656927522155550.334307247784445
7110.6066856062909410.393314393709059
7210.5894920651458670.410507934854133
7310.6100045526142430.389995447385757
7410.511824261204540.48817573879546
7510.5637400612560420.436259938743958
7610.5636706492041610.436329350795839
7710.5538412756118320.446158724388168
7810.6135862684347910.386413731565209
7910.5682654362817290.431734563718271
8010.7121695932284380.287830406771562
8110.7552176740935380.244782325906462
8210.6199937639259170.380006236074083
8310.669716135303220.33028386469678
8410.6824453338193090.317554666180691
8510.5430002472469230.456999752753077
8610.5714890047945290.428510995205471
8710.6208090622258180.379190937774182
8810.5124066388355720.487593361164428
8910.546919489561670.45308051043833
9010.5818368397460180.418163160253982
9110.6576109504813060.342389049518694
9210.6057098375632970.394290162436703
9310.6443235544629170.355676445537083
9410.6139994147694450.386000585230555
9510.6266083891872660.373391610812734
9610.6247476964112880.375252303588712
9710.6390631748055870.360936825194413
9810.6029419967696190.397058003230381
9910.642712373575290.35728762642471
10000.5356192596696-0.5356192596696
10100.673838842010862-0.673838842010862
10200.724390377007332-0.724390377007332
10300.531220714979831-0.531220714979831
10400.608675292758125-0.608675292758125
10500.635184754024698-0.635184754024698
10600.545802825629312-0.545802825629312
10700.581118539190202-0.581118539190202
10800.572036118375355-0.572036118375355
10900.628570647846321-0.628570647846321
11000.619473436646561-0.619473436646561
11100.739598102694696-0.739598102694696
11200.688202937158128-0.688202937158128
11300.635651888949296-0.635651888949296
11400.62096719573004-0.62096719573004
11500.618551648184634-0.618551648184634
11600.586170875387525-0.586170875387525
11700.590515334537437-0.590515334537437
11800.600170643632436-0.600170643632436
11900.684555775545773-0.684555775545773
12000.558572154904404-0.558572154904404
12100.500118484802022-0.500118484802022
12200.548012296230561-0.548012296230561
12300.706499090200975-0.706499090200975
12400.458041249485778-0.458041249485778
12500.762210619426929-0.762210619426929
12600.594268532635996-0.594268532635996
12700.574542124246911-0.574542124246911
12800.650236718516671-0.650236718516671
12900.585948454437204-0.585948454437204
13000.597762853290139-0.597762853290139
13100.561516575900844-0.561516575900844
13200.518602683415706-0.518602683415706
13300.578359557373482-0.578359557373482
13400.626649170485115-0.626649170485115
13500.555201120546157-0.555201120546157
13600.565685061052085-0.565685061052085
13700.59977938155775-0.59977938155775
13800.593695478410704-0.593695478410704
13900.589434967891851-0.589434967891851
14000.539257329561276-0.539257329561276
14100.634040535795215-0.634040535795215
14200.586806751052124-0.586806751052124
14300.662277379588681-0.662277379588681
14400.564247914898835-0.564247914898835
14500.537828189330484-0.537828189330484
14600.652055365075004-0.652055365075004
14700.573629895785586-0.573629895785586
14800.618082192283836-0.618082192283836
14900.582975059866504-0.582975059866504
15000.542411757376243-0.542411757376243
15100.628914636779649-0.628914636779649
15200.636475051211204-0.636475051211204
15300.625795749590373-0.625795749590373
15400.685343839702614-0.685343839702614
15500.648562525954078-0.648562525954078
15600.571620238070418-0.571620238070418
15700.590958027444036-0.590958027444036
15800.438079569937507-0.438079569937507
15900.644323554462917-0.644323554462917
16000.572652356930564-0.572652356930564
16100.608225828125629-0.608225828125629
16200.593565571925977-0.593565571925977






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
124.65027740287061e-479.30055480574123e-471
137.67237956830601e-711.5344759136612e-701
141.57993981764538e-773.15987963529076e-771
155.56192348445486e-931.11238469689097e-921
16001
173.38963208419215e-1336.7792641683843e-1331
181.42696439275197e-1402.85392878550393e-1401
195.38658511592428e-1551.07731702318486e-1541
201.3497651249326e-1782.6995302498652e-1781
213.70549250177104e-2107.41098500354208e-2101
223.27500088872471e-2046.55000177744941e-2041
232.20308106989331e-2164.40616213978661e-2161
241.12439939908351e-2342.24879879816702e-2341
252.55437149036452e-2535.10874298072903e-2531
263.32341453822387e-2936.64682907644774e-2931
271.4823544999644e-2832.9647089999288e-2831
284.0172308095469e-2948.03446161909379e-2941
293.71768909685761e-3147.43537819371521e-3141
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83001
84001
85001
86001
87001
88001
89001
90001
91001
92001
93001
94001
95001
96001
97001
98001
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
137100
138100
139100
140100
141100
142100
143100
144100
145100
146100
147100
148100
149100
150100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 4.65027740287061e-47 & 9.30055480574123e-47 & 1 \tabularnewline
13 & 7.67237956830601e-71 & 1.5344759136612e-70 & 1 \tabularnewline
14 & 1.57993981764538e-77 & 3.15987963529076e-77 & 1 \tabularnewline
15 & 5.56192348445486e-93 & 1.11238469689097e-92 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 3.38963208419215e-133 & 6.7792641683843e-133 & 1 \tabularnewline
18 & 1.42696439275197e-140 & 2.85392878550393e-140 & 1 \tabularnewline
19 & 5.38658511592428e-155 & 1.07731702318486e-154 & 1 \tabularnewline
20 & 1.3497651249326e-178 & 2.6995302498652e-178 & 1 \tabularnewline
21 & 3.70549250177104e-210 & 7.41098500354208e-210 & 1 \tabularnewline
22 & 3.27500088872471e-204 & 6.55000177744941e-204 & 1 \tabularnewline
23 & 2.20308106989331e-216 & 4.40616213978661e-216 & 1 \tabularnewline
24 & 1.12439939908351e-234 & 2.24879879816702e-234 & 1 \tabularnewline
25 & 2.55437149036452e-253 & 5.10874298072903e-253 & 1 \tabularnewline
26 & 3.32341453822387e-293 & 6.64682907644774e-293 & 1 \tabularnewline
27 & 1.4823544999644e-283 & 2.9647089999288e-283 & 1 \tabularnewline
28 & 4.0172308095469e-294 & 8.03446161909379e-294 & 1 \tabularnewline
29 & 3.71768909685761e-314 & 7.43537819371521e-314 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 0 & 0 & 1 \tabularnewline
81 & 0 & 0 & 1 \tabularnewline
82 & 0 & 0 & 1 \tabularnewline
83 & 0 & 0 & 1 \tabularnewline
84 & 0 & 0 & 1 \tabularnewline
85 & 0 & 0 & 1 \tabularnewline
86 & 0 & 0 & 1 \tabularnewline
87 & 0 & 0 & 1 \tabularnewline
88 & 0 & 0 & 1 \tabularnewline
89 & 0 & 0 & 1 \tabularnewline
90 & 0 & 0 & 1 \tabularnewline
91 & 0 & 0 & 1 \tabularnewline
92 & 0 & 0 & 1 \tabularnewline
93 & 0 & 0 & 1 \tabularnewline
94 & 0 & 0 & 1 \tabularnewline
95 & 0 & 0 & 1 \tabularnewline
96 & 0 & 0 & 1 \tabularnewline
97 & 0 & 0 & 1 \tabularnewline
98 & 0 & 0 & 1 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 0 & 0 \tabularnewline
130 & 1 & 0 & 0 \tabularnewline
131 & 1 & 0 & 0 \tabularnewline
132 & 1 & 0 & 0 \tabularnewline
133 & 1 & 0 & 0 \tabularnewline
134 & 1 & 0 & 0 \tabularnewline
135 & 1 & 0 & 0 \tabularnewline
136 & 1 & 0 & 0 \tabularnewline
137 & 1 & 0 & 0 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 0 & 0 \tabularnewline
140 & 1 & 0 & 0 \tabularnewline
141 & 1 & 0 & 0 \tabularnewline
142 & 1 & 0 & 0 \tabularnewline
143 & 1 & 0 & 0 \tabularnewline
144 & 1 & 0 & 0 \tabularnewline
145 & 1 & 0 & 0 \tabularnewline
146 & 1 & 0 & 0 \tabularnewline
147 & 1 & 0 & 0 \tabularnewline
148 & 1 & 0 & 0 \tabularnewline
149 & 1 & 0 & 0 \tabularnewline
150 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154166&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]12[/C][C]4.65027740287061e-47[/C][C]9.30055480574123e-47[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]7.67237956830601e-71[/C][C]1.5344759136612e-70[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]1.57993981764538e-77[/C][C]3.15987963529076e-77[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]5.56192348445486e-93[/C][C]1.11238469689097e-92[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]3.38963208419215e-133[/C][C]6.7792641683843e-133[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]1.42696439275197e-140[/C][C]2.85392878550393e-140[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]5.38658511592428e-155[/C][C]1.07731702318486e-154[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.3497651249326e-178[/C][C]2.6995302498652e-178[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]3.70549250177104e-210[/C][C]7.41098500354208e-210[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]3.27500088872471e-204[/C][C]6.55000177744941e-204[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]2.20308106989331e-216[/C][C]4.40616213978661e-216[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]1.12439939908351e-234[/C][C]2.24879879816702e-234[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]2.55437149036452e-253[/C][C]5.10874298072903e-253[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]3.32341453822387e-293[/C][C]6.64682907644774e-293[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.4823544999644e-283[/C][C]2.9647089999288e-283[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]4.0172308095469e-294[/C][C]8.03446161909379e-294[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]3.71768909685761e-314[/C][C]7.43537819371521e-314[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154166&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154166&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
124.65027740287061e-479.30055480574123e-471
137.67237956830601e-711.5344759136612e-701
141.57993981764538e-773.15987963529076e-771
155.56192348445486e-931.11238469689097e-921
16001
173.38963208419215e-1336.7792641683843e-1331
181.42696439275197e-1402.85392878550393e-1401
195.38658511592428e-1551.07731702318486e-1541
201.3497651249326e-1782.6995302498652e-1781
213.70549250177104e-2107.41098500354208e-2101
223.27500088872471e-2046.55000177744941e-2041
232.20308106989331e-2164.40616213978661e-2161
241.12439939908351e-2342.24879879816702e-2341
252.55437149036452e-2535.10874298072903e-2531
263.32341453822387e-2936.64682907644774e-2931
271.4823544999644e-2832.9647089999288e-2831
284.0172308095469e-2948.03446161909379e-2941
293.71768909685761e-3147.43537819371521e-3141
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83001
84001
85001
86001
87001
88001
89001
90001
91001
92001
93001
94001
95001
96001
97001
98001
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
137100
138100
139100
140100
141100
142100
143100
144100
145100
146100
147100
148100
149100
150100






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1391NOK
5% type I error level1391NOK
10% type I error level1391NOK

\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 & 139 & 1 & NOK \tabularnewline
5% type I error level & 139 & 1 & NOK \tabularnewline
10% type I error level & 139 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154166&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]139[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]139[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]139[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154166&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154166&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 level1391NOK
5% type I error level1391NOK
10% type I error level1391NOK


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')
}