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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 13:15:17 -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/Nov/22/t1321985789ssgkqsmpejz5x4x.htm/, Retrieved Thu, 31 Oct 2024 23:12:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146353, Retrieved Thu, 31 Oct 2024 23:12:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [ws7 Tutorial Popu...] [2010-11-22 11:00:33] [afe9379cca749d06b3d6872e02cc47ed]
- R  D      [Multiple Regression] [ws7-4] [2011-11-22 18:15:17] [47995d3a8fac585eeb070a274b466f8c] [Current]
-    D        [Multiple Regression] [ws7-5] [2011-11-22 19:09:17] [f7a862281046b7153543b12c78921b36]
-    D        [Multiple Regression] [ws7-5] [2011-11-22 19:16:13] [f7a862281046b7153543b12c78921b36]
-               [Multiple Regression] [ws7-5] [2011-11-22 19:42:43] [f7a862281046b7153543b12c78921b36]
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Dataseries X:
11	14	3	2	3	3	3	7	6
11	8	5	6	0	7	7	2	7
11	12	6	6	0	6	8	3	8
11	7	6	6	6	6	9	8	8
11	10	7	8	5	5	5	7	9
11	9	3	1	0	7	7	7	8
11	16	8	9	8	8	8	9	8
11	7	4	4	0	2	3	2	7
11	14	7	7	0	4	8	4	7
11	6	4	4	9	9	4	4	4
11	16	6	6	6	6	6	6	6
11	11	6	5	6	6	4	4	7
11	17	7	7	5	5	8	9	5
11	12	4	5	4	4	8	8	8
11	7	6	6	0	2	2	7	5
11	13	5	5	0	4	9	4	4
11	9	0	2	2	2	2	2	9
11	15	9	9	6	6	8	8	8
11	7	4	4	0	4	8	4	4
11	9	4	4	4	4	4	4	6
11	7	2	5	5	5	5	2	6
11	14	7	7	7	7	7	9	7
11	15	5	5	5	5	3	3	3
11	7	9	9	4	4	4	4	4
11	13	6	6	6	6	6	6	6
11	17	6	6	6	6	6	6	6
11	15	7	3	0	7	9	7	7
11	14	3	3	1	2	2	2	5
11	14	6	5	0	6	6	6	8
11	8	6	5	4	4	4	4	6
11	8	4	4	4	4	8	2	4
11	12	7	7	7	7	3	9	9
11	14	7	6	7	7	7	7	7
11	8	7	7	0	4	4	4	4
11	11	4	4	4	4	4	4	6
11	16	5	5	5	5	8	7	8
11	11	6	6	0	6	6	6	6
11	8	5	5	5	5	5	5	5
11	14	6	0	1	6	6	6	6
11	16	6	6	2	2	9	2	6
11	14	6	5	0	6	4	2	4
11	5	3	3	9	9	7	7	7
11	8	3	3	3	3	3	3	9
11	10	3	3	0	4	4	4	8
11	8	6	7	6	6	6	6	6
11	13	7	7	1	5	8	5	6
11	15	5	1	5	5	5	7	5
11	6	5	5	0	4	4	4	7
11	12	5	5	0	2	2	2	5
11	14	6	6	0	6	9	6	8
11	5	6	2	6	6	6	9	6
11	15	6	6	7	7	8	8	8
11	11	5	5	0	5	5	5	5
11	8	4	2	4	4	4	4	4
11	13	7	7	5	5	5	2	5
11	14	5	5	1	5	9	9	6
12	12	3	3	4	4	4	4	4
12	16	6	6	9	9	8	6	6
12	10	2	2	2	2	2	2	9
12	15	8	8	8	8	8	8	7
12	8	3	5	3	3	3	3	3
12	16	0	2	1	6	3	3	6
12	19	6	6	0	6	6	7	6
12	14	8	2	6	6	6	2	6
12	7	4	1	0	5	5	9	5
12	13	5	5	0	5	5	5	5
12	15	6	6	6	6	4	4	5
12	7	5	2	2	2	9	2	9
12	13	6	6	1	6	6	6	8
12	4	2	2	5	5	5	5	5
12	14	6	6	5	5	5	5	6
12	13	5	5	5	5	3	9	7
12	11	5	0	5	5	8	2	5
12	14	6	2	6	6	9	6	6
12	12	4	4	6	6	6	6	6
12	15	6	1	0	9	6	6	6
12	14	5	5	0	5	5	5	6
12	13	5	5	1	5	3	3	9
12	7	4	2	7	7	4	2	7
12	5	2	2	2	2	9	2	9
12	7	7	7	4	4	4	4	4
12	13	5	5	0	6	8	8	8
12	13	6	2	5	5	5	5	5
12	11	5	5	5	5	5	9	8
12	6	3	3	3	3	8	2	9
12	12	6	6	0	6	6	6	6
12	8	4	1	4	4	9	4	4
12	11	5	5	9	9	5	5	7
12	12	7	7	0	8	8	8	8
12	9	4	2	4	4	3	3	9
12	12	6	6	2	2	2	2	9
12	13	8	8	7	7	7	7	7
12	16	7	7	7	7	7	7	8
12	16	6	6	6	6	4	9	4
12	11	7	7	0	5	5	5	6
12	8	4	4	5	5	9	5	7
12	4	0	5	6	6	6	2	6
12	7	3	2	0	3	3	3	7
12	14	5	5	5	5	5	5	5
12	11	6	2	9	9	2	2	9
12	17	5	5	0	7	7	7	7
12	15	7	7	7	7	7	7	7
12	14	6	5	1	6	6	6	6
12	5	8	8	3	3	8	3	6
12	4	7	2	7	7	9	3	9
12	19	8	8	8	8	8	2	9
12	11	3	3	0	3	3	3	8
12	15	8	2	5	5	5	5	8
12	10	3	3	3	3	3	3	3
12	9	4	5	0	4	4	4	6
12	12	2	2	5	5	5	5	5
12	15	7	2	7	7	9	7	7
12	7	6	6	0	6	6	6	6
12	13	2	2	0	7	7	7	7
12	14	7	7	0	9	7	2	7
12	14	6	6	6	6	6	6	6
12	14	6	2	0	6	3	9	8
12	8	6	2	6	6	9	4	9
12	15	6	5	6	6	6	6	6
12	15	6	6	2	2	2	2	9
12	9	4	4	5	5	5	2	5
12	16	5	5	0	5	5	5	6
12	9	7	7	4	4	9	4	4
12	15	6	6	0	7	7	7	7
12	15	6	6	6	6	6	6	6
12	6	5	5	5	5	8	7	8
12	8	8	2	8	8	8	8	8
12	15	6	6	6	6	6	6	9
12	10	0	3	5	5	3	3	8
12	9	4	2	0	4	4	4	4
12	14	8	8	8	8	9	8	6
12	12	6	6	0	6	6	9	6
12	8	4	4	9	9	4	2	7
12	11	6	6	5	5	5	5	9
12	13	2	5	0	6	6	6	8
12	9	4	4	0	4	4	4	4
12	15	6	2	0	6	6	6	6
12	13	3	3	3	3	3	3	9
12	15	6	6	6	6	6	6	6
12	14	5	5	0	5	5	5	5
12	16	4	4	4	4	9	8	8
12	12	6	6	6	6	6	6	6
12	14	1	1	0	5	9	5	6
12	10	4	5	4	4	3	3	6
12	10	4	2	7	7	7	2	7
12	4	6	6	0	6	6	6	7
12	8	5	5	5	5	5	5	9
12	17	9	2	6	6	6	6	6
12	16	6	6	6	6	9	6	6
12	12	8	8	8	8	8	9	6
12	12	7	7	2	2	4	4	4
12	15	7	7	7	7	7	7	7
12	9	0	9	0	4	4	4	8
12	13	6	2	0	6	8	7	7
12	14	6	6	5	5	5	5	9
12	11	5	5	0	2	9	2	6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146353&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146353&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146353&T=0

As an alternative you can also use a 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 time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Schoolprestaties[t] = -0.932937855709447 + 0.64172811762034Maand[t] + 0.453349710665393Sport[t] + 0.103879056243291Goingout[t] -0.139197663928534Relation[t] + 0.26787509202272Family[t] -0.0758261422591809Friends[t] + 0.331473918150349Coach[t] + 0.000743014312468364Job[t] + 0.000131362897986379t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Schoolprestaties[t] =  -0.932937855709447 +  0.64172811762034Maand[t] +  0.453349710665393Sport[t] +  0.103879056243291Goingout[t] -0.139197663928534Relation[t] +  0.26787509202272Family[t] -0.0758261422591809Friends[t] +  0.331473918150349Coach[t] +  0.000743014312468364Job[t] +  0.000131362897986379t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146353&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Schoolprestaties[t] =  -0.932937855709447 +  0.64172811762034Maand[t] +  0.453349710665393Sport[t] +  0.103879056243291Goingout[t] -0.139197663928534Relation[t] +  0.26787509202272Family[t] -0.0758261422591809Friends[t] +  0.331473918150349Coach[t] +  0.000743014312468364Job[t] +  0.000131362897986379t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146353&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146353&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Schoolprestaties[t] = -0.932937855709447 + 0.64172811762034Maand[t] + 0.453349710665393Sport[t] + 0.103879056243291Goingout[t] -0.139197663928534Relation[t] + 0.26787509202272Family[t] -0.0758261422591809Friends[t] + 0.331473918150349Coach[t] + 0.000743014312468364Job[t] + 0.000131362897986379t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.93293785570944710.987058-0.08490.9324470.466224
Maand0.641728117620340.9901770.64810.5179420.258971
Sport0.4533497106653930.1769792.56160.0114330.005716
Goingout0.1038790562432910.1450670.71610.4750890.237544
Relation-0.1391976639285340.10082-1.38070.1694940.084747
Family0.267875092022720.1913261.40010.1636050.081803
Friends-0.07582614225918090.138882-0.5460.5859160.292958
Coach0.3314739181503490.1398682.36990.01910.00955
Job0.0007430143124683640.1684110.00440.9964860.498243
t0.0001313628979863790.0104170.01260.9899560.494978

\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) & -0.932937855709447 & 10.987058 & -0.0849 & 0.932447 & 0.466224 \tabularnewline
Maand & 0.64172811762034 & 0.990177 & 0.6481 & 0.517942 & 0.258971 \tabularnewline
Sport & 0.453349710665393 & 0.176979 & 2.5616 & 0.011433 & 0.005716 \tabularnewline
Goingout & 0.103879056243291 & 0.145067 & 0.7161 & 0.475089 & 0.237544 \tabularnewline
Relation & -0.139197663928534 & 0.10082 & -1.3807 & 0.169494 & 0.084747 \tabularnewline
Family & 0.26787509202272 & 0.191326 & 1.4001 & 0.163605 & 0.081803 \tabularnewline
Friends & -0.0758261422591809 & 0.138882 & -0.546 & 0.585916 & 0.292958 \tabularnewline
Coach & 0.331473918150349 & 0.139868 & 2.3699 & 0.0191 & 0.00955 \tabularnewline
Job & 0.000743014312468364 & 0.168411 & 0.0044 & 0.996486 & 0.498243 \tabularnewline
t & 0.000131362897986379 & 0.010417 & 0.0126 & 0.989956 & 0.494978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146353&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]-0.932937855709447[/C][C]10.987058[/C][C]-0.0849[/C][C]0.932447[/C][C]0.466224[/C][/ROW]
[ROW][C]Maand[/C][C]0.64172811762034[/C][C]0.990177[/C][C]0.6481[/C][C]0.517942[/C][C]0.258971[/C][/ROW]
[ROW][C]Sport[/C][C]0.453349710665393[/C][C]0.176979[/C][C]2.5616[/C][C]0.011433[/C][C]0.005716[/C][/ROW]
[ROW][C]Goingout[/C][C]0.103879056243291[/C][C]0.145067[/C][C]0.7161[/C][C]0.475089[/C][C]0.237544[/C][/ROW]
[ROW][C]Relation[/C][C]-0.139197663928534[/C][C]0.10082[/C][C]-1.3807[/C][C]0.169494[/C][C]0.084747[/C][/ROW]
[ROW][C]Family[/C][C]0.26787509202272[/C][C]0.191326[/C][C]1.4001[/C][C]0.163605[/C][C]0.081803[/C][/ROW]
[ROW][C]Friends[/C][C]-0.0758261422591809[/C][C]0.138882[/C][C]-0.546[/C][C]0.585916[/C][C]0.292958[/C][/ROW]
[ROW][C]Coach[/C][C]0.331473918150349[/C][C]0.139868[/C][C]2.3699[/C][C]0.0191[/C][C]0.00955[/C][/ROW]
[ROW][C]Job[/C][C]0.000743014312468364[/C][C]0.168411[/C][C]0.0044[/C][C]0.996486[/C][C]0.498243[/C][/ROW]
[ROW][C]t[/C][C]0.000131362897986379[/C][C]0.010417[/C][C]0.0126[/C][C]0.989956[/C][C]0.494978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146353&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146353&T=2

As an alternative you can also use a 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)-0.93293785570944710.987058-0.08490.9324470.466224
Maand0.641728117620340.9901770.64810.5179420.258971
Sport0.4533497106653930.1769792.56160.0114330.005716
Goingout0.1038790562432910.1450670.71610.4750890.237544
Relation-0.1391976639285340.10082-1.38070.1694940.084747
Family0.267875092022720.1913261.40010.1636050.081803
Friends-0.07582614225918090.138882-0.5460.5859160.292958
Coach0.3314739181503490.1398682.36990.01910.00955
Job0.0007430143124683640.1684110.00440.9964860.498243
t0.0001313628979863790.0104170.01260.9899560.494978







Multiple Linear Regression - Regression Statistics
Multiple R0.440351185270751
R-squared0.193909166369355
Adjusted R-squared0.14421863552911
F-TEST (value)3.90233638261528
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0.000183304668953554
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.23181653749078
Sum Squared Residuals1524.91716727184

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.440351185270751 \tabularnewline
R-squared & 0.193909166369355 \tabularnewline
Adjusted R-squared & 0.14421863552911 \tabularnewline
F-TEST (value) & 3.90233638261528 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0.000183304668953554 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.23181653749078 \tabularnewline
Sum Squared Residuals & 1524.91716727184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146353&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.440351185270751[/C][/ROW]
[ROW][C]R-squared[/C][C]0.193909166369355[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.14421863552911[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.90233638261528[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0.000183304668953554[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.23181653749078[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1524.91716727184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146353&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146353&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.440351185270751
R-squared0.193909166369355
Adjusted R-squared0.14421863552911
F-TEST (value)3.90233638261528
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0.000183304668953554
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.23181653749078
Sum Squared Residuals1524.91716727184







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11410.17733941592733.82266058407267
2811.0288486395297-3.02884863952973
31211.4708454112740.529154588725974
4712.2173342390934-5.21733423909338
51012.722469662248-2.72246966224799
6911.2613919936387-2.26139199363865
71614.10071983444591.8992801655541
879.3324581026888-2.3324581026888
91411.82384307536312.17615692463693
10610.5399601483911-4.53996014839112
111611.78129834123124.21870165876881
121111.166998110416-0.166998110416014
131713.05213886146193.94786113853812
141211.026540676570.97345932343003
15712.1795448811781-5.17954488117806
161310.6322498966352.36775010336498
1797.111400256649571.88859974335043
181513.96668576265041.03331423734958
19710.1512413606795-3.15124136067948
2099.89937266552499-0.899372665524988
2178.48658711286979-1.48658711286979
221413.38798815461620.612011845383757
231510.32779613039324.67220386960676
24712.6845559230354-5.68455592303541
251311.7831374218031.216862578197
261711.7832687847015.21673121529902
271513.13291227081371.86708772918631
28148.712999043445555.28700095655445
291412.51645582934781.48354417065221
30810.9112647720789-2.91126477207893
3188.93307922344048-0.933079223440478
321213.6940923812578-1.69409238125777
331412.62260625395011.3773937460499
34812.128202673912-4.12820267391205
35119.901343108994781.09865689100522
361611.27998388093494.7200161190651
371112.61989976015-1.61989976015004
38810.8425481542703-2.84254815427031
391411.85769048455772.14230951544227
40169.71702405351716.2829759464829
411411.34081673879072.65918326120928
42510.7561073648289-5.75610736482891
4388.96306908422204-0.963069084222041
44109.903573292507050.0964267074929509
45811.889643736006-3.88964373600602
461312.28811183452060.711888165479367
471511.09116203167973.90883796832028
48611.0178132636039-5.0178132636039
49129.969412862049172.03058713795083
501412.39561507967131.60438492032875
51512.3654583866285-7.36545838662853
521512.42814322855012.57185677144991
531111.5405069173828-0.540506917382778
5489.694594862945-1.694594862945
551310.96481710290242.0351828970976
561412.42503746002531.57496253997465
57129.98724641483722.0127535851628
581612.66358051482113.33641948517892
59108.665345037316121.33465496268388
601514.31331419695340.686685803046594
6189.81046176061791-1.81046176061791
62169.223156469652486.77684353034752
631913.59651723126845.40348276873163
641411.5952762162012.40472378379897
65712.6408411267418-5.64084112674179
661312.18394275267690.816057247323059
671511.9183442150443.08165578495604
6879.49579343957991-2.49579343957991
691313.1281198552023-0.128119855202344
7049.81679358390016-5.81679358390017
711412.04658302874541.95341697125464
721312.96777659616690.0322234038331152
73119.747578510875131.25242148912487
741411.7883076696742.21169233032604
751211.31697615050530.683023849494712
761513.55098102564361.44901897435645
771412.18613075886731.81386924113274
781311.53799794899181.46200205100822
79710.0649205439343-3.06492054393427
8058.13732066235957-3.13732066235957
81712.2193141920236-5.21931419202361
821313.2230920216784-0.223092021678354
831311.63190014423561.36809985576444
841112.8184436807368-1.81844368073683
8568.89970981411155-2.89970981411155
861213.2680646597717-1.26806465977171
8789.8576481886597-1.8576481886597
881112.0070401577917-1.00704015779165
891214.8742192798271-2.87421927982707
90910.089119340564-1.08911934056403
911210.89846371768641.10153628231358
921313.9331926057036-0.933192605703615
931613.37683821600542.62316178399461
941613.57851758972892.42148241027113
951113.3029528248484-2.30295282484838
96810.6328480126534-2.63284801265339
9748.28445067524131-4.28445067524131
9879.92425006814901-2.92425006814901
991411.49228940866782.50771059133218
1001111.3848717554544-0.384871755454441
1011713.23707221855923.76292778144082
1021513.37727746777481.6227225322252
1031413.02722110886570.972778891134347
104512.0175944189297-7.01759441892966
105411.3822143467575-7.38221434675748
1061912.33199940998366.66800059001638
1071110.03005440478660.969945595213358
1081512.54411268095342.45588731904659
109109.609009067234670.390990932765329
110911.2135931559215-2.21359315592153
111129.822179462717612.17782053728239
1121512.70754353101982.29245646898016
113713.2716114580173-6.27161145801734
1141311.5670936355071.43290636449305
1151413.23174942624210.768250573757949
1161412.43681956314011.5631804368599
1171414.0800068944896-0.0800068944896496
118811.1333688438221-3.13336884382207
1191512.33333459559082.66666540440923
1201510.9022732417284.09772675827197
12199.94352887106379-0.94352887106379
1221612.19204208927663.80795791072335
123911.8457007224431-2.84570072244314
1241513.79732233212151.20267766787845
1251512.4380018292222.56199817077803
126611.933534659374-5.93353465937401
127813.6995841879712-5.69958418797121
1281512.44062496085332.55937503914666
129108.512657120948941.48734287905106
130910.9030972165264-1.90309721652645
1311414.2460718061388-0.24607180613879
1321214.2685291075301-2.26852910753013
133810.5371271091005-2.53712710910049
1341112.0570879342559-1.0570879342559
1351311.35870957149311.64129042850688
136911.1116435064009-2.11164350640095
1371512.85924794259592.14075205740415
138139.617276677151093.38272332284891
1391512.43984090979382.56015909020622
1401412.19366360712791.80633639287207
1411611.50524668373214.49475331626789
1421212.4402349984877-0.440234998487743
143149.66258107346294.3374189265371
1441010.4056210628478-0.405621062847761
145109.846112068423830.153887931576171
146413.2766894479634-9.27668944796336
147811.501566885021-3.50156688502104
1481713.38555608289873.61444391710133
1491612.21367611199613.78632388800389
1501214.6558677616101-2.65586776161006
1511211.97115473869430.0288452613057146
1521513.38384561267411.61615438732589
15399.82284517147147-0.822845171471475
1541313.0420457598061-0.0420457598060739
1551412.05984655511361.94015344488638
1561110.09515682825230.904843171747749

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 10.1773394159273 & 3.82266058407267 \tabularnewline
2 & 8 & 11.0288486395297 & -3.02884863952973 \tabularnewline
3 & 12 & 11.470845411274 & 0.529154588725974 \tabularnewline
4 & 7 & 12.2173342390934 & -5.21733423909338 \tabularnewline
5 & 10 & 12.722469662248 & -2.72246966224799 \tabularnewline
6 & 9 & 11.2613919936387 & -2.26139199363865 \tabularnewline
7 & 16 & 14.1007198344459 & 1.8992801655541 \tabularnewline
8 & 7 & 9.3324581026888 & -2.3324581026888 \tabularnewline
9 & 14 & 11.8238430753631 & 2.17615692463693 \tabularnewline
10 & 6 & 10.5399601483911 & -4.53996014839112 \tabularnewline
11 & 16 & 11.7812983412312 & 4.21870165876881 \tabularnewline
12 & 11 & 11.166998110416 & -0.166998110416014 \tabularnewline
13 & 17 & 13.0521388614619 & 3.94786113853812 \tabularnewline
14 & 12 & 11.02654067657 & 0.97345932343003 \tabularnewline
15 & 7 & 12.1795448811781 & -5.17954488117806 \tabularnewline
16 & 13 & 10.632249896635 & 2.36775010336498 \tabularnewline
17 & 9 & 7.11140025664957 & 1.88859974335043 \tabularnewline
18 & 15 & 13.9666857626504 & 1.03331423734958 \tabularnewline
19 & 7 & 10.1512413606795 & -3.15124136067948 \tabularnewline
20 & 9 & 9.89937266552499 & -0.899372665524988 \tabularnewline
21 & 7 & 8.48658711286979 & -1.48658711286979 \tabularnewline
22 & 14 & 13.3879881546162 & 0.612011845383757 \tabularnewline
23 & 15 & 10.3277961303932 & 4.67220386960676 \tabularnewline
24 & 7 & 12.6845559230354 & -5.68455592303541 \tabularnewline
25 & 13 & 11.783137421803 & 1.216862578197 \tabularnewline
26 & 17 & 11.783268784701 & 5.21673121529902 \tabularnewline
27 & 15 & 13.1329122708137 & 1.86708772918631 \tabularnewline
28 & 14 & 8.71299904344555 & 5.28700095655445 \tabularnewline
29 & 14 & 12.5164558293478 & 1.48354417065221 \tabularnewline
30 & 8 & 10.9112647720789 & -2.91126477207893 \tabularnewline
31 & 8 & 8.93307922344048 & -0.933079223440478 \tabularnewline
32 & 12 & 13.6940923812578 & -1.69409238125777 \tabularnewline
33 & 14 & 12.6226062539501 & 1.3773937460499 \tabularnewline
34 & 8 & 12.128202673912 & -4.12820267391205 \tabularnewline
35 & 11 & 9.90134310899478 & 1.09865689100522 \tabularnewline
36 & 16 & 11.2799838809349 & 4.7200161190651 \tabularnewline
37 & 11 & 12.61989976015 & -1.61989976015004 \tabularnewline
38 & 8 & 10.8425481542703 & -2.84254815427031 \tabularnewline
39 & 14 & 11.8576904845577 & 2.14230951544227 \tabularnewline
40 & 16 & 9.7170240535171 & 6.2829759464829 \tabularnewline
41 & 14 & 11.3408167387907 & 2.65918326120928 \tabularnewline
42 & 5 & 10.7561073648289 & -5.75610736482891 \tabularnewline
43 & 8 & 8.96306908422204 & -0.963069084222041 \tabularnewline
44 & 10 & 9.90357329250705 & 0.0964267074929509 \tabularnewline
45 & 8 & 11.889643736006 & -3.88964373600602 \tabularnewline
46 & 13 & 12.2881118345206 & 0.711888165479367 \tabularnewline
47 & 15 & 11.0911620316797 & 3.90883796832028 \tabularnewline
48 & 6 & 11.0178132636039 & -5.0178132636039 \tabularnewline
49 & 12 & 9.96941286204917 & 2.03058713795083 \tabularnewline
50 & 14 & 12.3956150796713 & 1.60438492032875 \tabularnewline
51 & 5 & 12.3654583866285 & -7.36545838662853 \tabularnewline
52 & 15 & 12.4281432285501 & 2.57185677144991 \tabularnewline
53 & 11 & 11.5405069173828 & -0.540506917382778 \tabularnewline
54 & 8 & 9.694594862945 & -1.694594862945 \tabularnewline
55 & 13 & 10.9648171029024 & 2.0351828970976 \tabularnewline
56 & 14 & 12.4250374600253 & 1.57496253997465 \tabularnewline
57 & 12 & 9.9872464148372 & 2.0127535851628 \tabularnewline
58 & 16 & 12.6635805148211 & 3.33641948517892 \tabularnewline
59 & 10 & 8.66534503731612 & 1.33465496268388 \tabularnewline
60 & 15 & 14.3133141969534 & 0.686685803046594 \tabularnewline
61 & 8 & 9.81046176061791 & -1.81046176061791 \tabularnewline
62 & 16 & 9.22315646965248 & 6.77684353034752 \tabularnewline
63 & 19 & 13.5965172312684 & 5.40348276873163 \tabularnewline
64 & 14 & 11.595276216201 & 2.40472378379897 \tabularnewline
65 & 7 & 12.6408411267418 & -5.64084112674179 \tabularnewline
66 & 13 & 12.1839427526769 & 0.816057247323059 \tabularnewline
67 & 15 & 11.918344215044 & 3.08165578495604 \tabularnewline
68 & 7 & 9.49579343957991 & -2.49579343957991 \tabularnewline
69 & 13 & 13.1281198552023 & -0.128119855202344 \tabularnewline
70 & 4 & 9.81679358390016 & -5.81679358390017 \tabularnewline
71 & 14 & 12.0465830287454 & 1.95341697125464 \tabularnewline
72 & 13 & 12.9677765961669 & 0.0322234038331152 \tabularnewline
73 & 11 & 9.74757851087513 & 1.25242148912487 \tabularnewline
74 & 14 & 11.788307669674 & 2.21169233032604 \tabularnewline
75 & 12 & 11.3169761505053 & 0.683023849494712 \tabularnewline
76 & 15 & 13.5509810256436 & 1.44901897435645 \tabularnewline
77 & 14 & 12.1861307588673 & 1.81386924113274 \tabularnewline
78 & 13 & 11.5379979489918 & 1.46200205100822 \tabularnewline
79 & 7 & 10.0649205439343 & -3.06492054393427 \tabularnewline
80 & 5 & 8.13732066235957 & -3.13732066235957 \tabularnewline
81 & 7 & 12.2193141920236 & -5.21931419202361 \tabularnewline
82 & 13 & 13.2230920216784 & -0.223092021678354 \tabularnewline
83 & 13 & 11.6319001442356 & 1.36809985576444 \tabularnewline
84 & 11 & 12.8184436807368 & -1.81844368073683 \tabularnewline
85 & 6 & 8.89970981411155 & -2.89970981411155 \tabularnewline
86 & 12 & 13.2680646597717 & -1.26806465977171 \tabularnewline
87 & 8 & 9.8576481886597 & -1.8576481886597 \tabularnewline
88 & 11 & 12.0070401577917 & -1.00704015779165 \tabularnewline
89 & 12 & 14.8742192798271 & -2.87421927982707 \tabularnewline
90 & 9 & 10.089119340564 & -1.08911934056403 \tabularnewline
91 & 12 & 10.8984637176864 & 1.10153628231358 \tabularnewline
92 & 13 & 13.9331926057036 & -0.933192605703615 \tabularnewline
93 & 16 & 13.3768382160054 & 2.62316178399461 \tabularnewline
94 & 16 & 13.5785175897289 & 2.42148241027113 \tabularnewline
95 & 11 & 13.3029528248484 & -2.30295282484838 \tabularnewline
96 & 8 & 10.6328480126534 & -2.63284801265339 \tabularnewline
97 & 4 & 8.28445067524131 & -4.28445067524131 \tabularnewline
98 & 7 & 9.92425006814901 & -2.92425006814901 \tabularnewline
99 & 14 & 11.4922894086678 & 2.50771059133218 \tabularnewline
100 & 11 & 11.3848717554544 & -0.384871755454441 \tabularnewline
101 & 17 & 13.2370722185592 & 3.76292778144082 \tabularnewline
102 & 15 & 13.3772774677748 & 1.6227225322252 \tabularnewline
103 & 14 & 13.0272211088657 & 0.972778891134347 \tabularnewline
104 & 5 & 12.0175944189297 & -7.01759441892966 \tabularnewline
105 & 4 & 11.3822143467575 & -7.38221434675748 \tabularnewline
106 & 19 & 12.3319994099836 & 6.66800059001638 \tabularnewline
107 & 11 & 10.0300544047866 & 0.969945595213358 \tabularnewline
108 & 15 & 12.5441126809534 & 2.45588731904659 \tabularnewline
109 & 10 & 9.60900906723467 & 0.390990932765329 \tabularnewline
110 & 9 & 11.2135931559215 & -2.21359315592153 \tabularnewline
111 & 12 & 9.82217946271761 & 2.17782053728239 \tabularnewline
112 & 15 & 12.7075435310198 & 2.29245646898016 \tabularnewline
113 & 7 & 13.2716114580173 & -6.27161145801734 \tabularnewline
114 & 13 & 11.567093635507 & 1.43290636449305 \tabularnewline
115 & 14 & 13.2317494262421 & 0.768250573757949 \tabularnewline
116 & 14 & 12.4368195631401 & 1.5631804368599 \tabularnewline
117 & 14 & 14.0800068944896 & -0.0800068944896496 \tabularnewline
118 & 8 & 11.1333688438221 & -3.13336884382207 \tabularnewline
119 & 15 & 12.3333345955908 & 2.66666540440923 \tabularnewline
120 & 15 & 10.902273241728 & 4.09772675827197 \tabularnewline
121 & 9 & 9.94352887106379 & -0.94352887106379 \tabularnewline
122 & 16 & 12.1920420892766 & 3.80795791072335 \tabularnewline
123 & 9 & 11.8457007224431 & -2.84570072244314 \tabularnewline
124 & 15 & 13.7973223321215 & 1.20267766787845 \tabularnewline
125 & 15 & 12.438001829222 & 2.56199817077803 \tabularnewline
126 & 6 & 11.933534659374 & -5.93353465937401 \tabularnewline
127 & 8 & 13.6995841879712 & -5.69958418797121 \tabularnewline
128 & 15 & 12.4406249608533 & 2.55937503914666 \tabularnewline
129 & 10 & 8.51265712094894 & 1.48734287905106 \tabularnewline
130 & 9 & 10.9030972165264 & -1.90309721652645 \tabularnewline
131 & 14 & 14.2460718061388 & -0.24607180613879 \tabularnewline
132 & 12 & 14.2685291075301 & -2.26852910753013 \tabularnewline
133 & 8 & 10.5371271091005 & -2.53712710910049 \tabularnewline
134 & 11 & 12.0570879342559 & -1.0570879342559 \tabularnewline
135 & 13 & 11.3587095714931 & 1.64129042850688 \tabularnewline
136 & 9 & 11.1116435064009 & -2.11164350640095 \tabularnewline
137 & 15 & 12.8592479425959 & 2.14075205740415 \tabularnewline
138 & 13 & 9.61727667715109 & 3.38272332284891 \tabularnewline
139 & 15 & 12.4398409097938 & 2.56015909020622 \tabularnewline
140 & 14 & 12.1936636071279 & 1.80633639287207 \tabularnewline
141 & 16 & 11.5052466837321 & 4.49475331626789 \tabularnewline
142 & 12 & 12.4402349984877 & -0.440234998487743 \tabularnewline
143 & 14 & 9.6625810734629 & 4.3374189265371 \tabularnewline
144 & 10 & 10.4056210628478 & -0.405621062847761 \tabularnewline
145 & 10 & 9.84611206842383 & 0.153887931576171 \tabularnewline
146 & 4 & 13.2766894479634 & -9.27668944796336 \tabularnewline
147 & 8 & 11.501566885021 & -3.50156688502104 \tabularnewline
148 & 17 & 13.3855560828987 & 3.61444391710133 \tabularnewline
149 & 16 & 12.2136761119961 & 3.78632388800389 \tabularnewline
150 & 12 & 14.6558677616101 & -2.65586776161006 \tabularnewline
151 & 12 & 11.9711547386943 & 0.0288452613057146 \tabularnewline
152 & 15 & 13.3838456126741 & 1.61615438732589 \tabularnewline
153 & 9 & 9.82284517147147 & -0.822845171471475 \tabularnewline
154 & 13 & 13.0420457598061 & -0.0420457598060739 \tabularnewline
155 & 14 & 12.0598465551136 & 1.94015344488638 \tabularnewline
156 & 11 & 10.0951568282523 & 0.904843171747749 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146353&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]14[/C][C]10.1773394159273[/C][C]3.82266058407267[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.0288486395297[/C][C]-3.02884863952973[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]11.470845411274[/C][C]0.529154588725974[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]12.2173342390934[/C][C]-5.21733423909338[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]12.722469662248[/C][C]-2.72246966224799[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]11.2613919936387[/C][C]-2.26139199363865[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]14.1007198344459[/C][C]1.8992801655541[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]9.3324581026888[/C][C]-2.3324581026888[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]11.8238430753631[/C][C]2.17615692463693[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]10.5399601483911[/C][C]-4.53996014839112[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]11.7812983412312[/C][C]4.21870165876881[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.166998110416[/C][C]-0.166998110416014[/C][/ROW]
[ROW][C]13[/C][C]17[/C][C]13.0521388614619[/C][C]3.94786113853812[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.02654067657[/C][C]0.97345932343003[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]12.1795448811781[/C][C]-5.17954488117806[/C][/ROW]
[ROW][C]16[/C][C]13[/C][C]10.632249896635[/C][C]2.36775010336498[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]7.11140025664957[/C][C]1.88859974335043[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]13.9666857626504[/C][C]1.03331423734958[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]10.1512413606795[/C][C]-3.15124136067948[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]9.89937266552499[/C][C]-0.899372665524988[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]8.48658711286979[/C][C]-1.48658711286979[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]13.3879881546162[/C][C]0.612011845383757[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]10.3277961303932[/C][C]4.67220386960676[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]12.6845559230354[/C][C]-5.68455592303541[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]11.783137421803[/C][C]1.216862578197[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]11.783268784701[/C][C]5.21673121529902[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]13.1329122708137[/C][C]1.86708772918631[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]8.71299904344555[/C][C]5.28700095655445[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.5164558293478[/C][C]1.48354417065221[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]10.9112647720789[/C][C]-2.91126477207893[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]8.93307922344048[/C][C]-0.933079223440478[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.6940923812578[/C][C]-1.69409238125777[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]12.6226062539501[/C][C]1.3773937460499[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]12.128202673912[/C][C]-4.12820267391205[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]9.90134310899478[/C][C]1.09865689100522[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]11.2799838809349[/C][C]4.7200161190651[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]12.61989976015[/C][C]-1.61989976015004[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]10.8425481542703[/C][C]-2.84254815427031[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]11.8576904845577[/C][C]2.14230951544227[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]9.7170240535171[/C][C]6.2829759464829[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]11.3408167387907[/C][C]2.65918326120928[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]10.7561073648289[/C][C]-5.75610736482891[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]8.96306908422204[/C][C]-0.963069084222041[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]9.90357329250705[/C][C]0.0964267074929509[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]11.889643736006[/C][C]-3.88964373600602[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.2881118345206[/C][C]0.711888165479367[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]11.0911620316797[/C][C]3.90883796832028[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]11.0178132636039[/C][C]-5.0178132636039[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]9.96941286204917[/C][C]2.03058713795083[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]12.3956150796713[/C][C]1.60438492032875[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]12.3654583866285[/C][C]-7.36545838662853[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]12.4281432285501[/C][C]2.57185677144991[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.5405069173828[/C][C]-0.540506917382778[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]9.694594862945[/C][C]-1.694594862945[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]10.9648171029024[/C][C]2.0351828970976[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.4250374600253[/C][C]1.57496253997465[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]9.9872464148372[/C][C]2.0127535851628[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]12.6635805148211[/C][C]3.33641948517892[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]8.66534503731612[/C][C]1.33465496268388[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.3133141969534[/C][C]0.686685803046594[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]9.81046176061791[/C][C]-1.81046176061791[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]9.22315646965248[/C][C]6.77684353034752[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]13.5965172312684[/C][C]5.40348276873163[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]11.595276216201[/C][C]2.40472378379897[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]12.6408411267418[/C][C]-5.64084112674179[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.1839427526769[/C][C]0.816057247323059[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]11.918344215044[/C][C]3.08165578495604[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]9.49579343957991[/C][C]-2.49579343957991[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.1281198552023[/C][C]-0.128119855202344[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]9.81679358390016[/C][C]-5.81679358390017[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]12.0465830287454[/C][C]1.95341697125464[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.9677765961669[/C][C]0.0322234038331152[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]9.74757851087513[/C][C]1.25242148912487[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]11.788307669674[/C][C]2.21169233032604[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.3169761505053[/C][C]0.683023849494712[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]13.5509810256436[/C][C]1.44901897435645[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]12.1861307588673[/C][C]1.81386924113274[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.5379979489918[/C][C]1.46200205100822[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]10.0649205439343[/C][C]-3.06492054393427[/C][/ROW]
[ROW][C]80[/C][C]5[/C][C]8.13732066235957[/C][C]-3.13732066235957[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]12.2193141920236[/C][C]-5.21931419202361[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.2230920216784[/C][C]-0.223092021678354[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]11.6319001442356[/C][C]1.36809985576444[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]12.8184436807368[/C][C]-1.81844368073683[/C][/ROW]
[ROW][C]85[/C][C]6[/C][C]8.89970981411155[/C][C]-2.89970981411155[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]13.2680646597717[/C][C]-1.26806465977171[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]9.8576481886597[/C][C]-1.8576481886597[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]12.0070401577917[/C][C]-1.00704015779165[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]14.8742192798271[/C][C]-2.87421927982707[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]10.089119340564[/C][C]-1.08911934056403[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.8984637176864[/C][C]1.10153628231358[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.9331926057036[/C][C]-0.933192605703615[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]13.3768382160054[/C][C]2.62316178399461[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.5785175897289[/C][C]2.42148241027113[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]13.3029528248484[/C][C]-2.30295282484838[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]10.6328480126534[/C][C]-2.63284801265339[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]8.28445067524131[/C][C]-4.28445067524131[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]9.92425006814901[/C][C]-2.92425006814901[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.4922894086678[/C][C]2.50771059133218[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]11.3848717554544[/C][C]-0.384871755454441[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]13.2370722185592[/C][C]3.76292778144082[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]13.3772774677748[/C][C]1.6227225322252[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]13.0272211088657[/C][C]0.972778891134347[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]12.0175944189297[/C][C]-7.01759441892966[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]11.3822143467575[/C][C]-7.38221434675748[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.3319994099836[/C][C]6.66800059001638[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]10.0300544047866[/C][C]0.969945595213358[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.5441126809534[/C][C]2.45588731904659[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]9.60900906723467[/C][C]0.390990932765329[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]11.2135931559215[/C][C]-2.21359315592153[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]9.82217946271761[/C][C]2.17782053728239[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.7075435310198[/C][C]2.29245646898016[/C][/ROW]
[ROW][C]113[/C][C]7[/C][C]13.2716114580173[/C][C]-6.27161145801734[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]11.567093635507[/C][C]1.43290636449305[/C][/ROW]
[ROW][C]115[/C][C]14[/C][C]13.2317494262421[/C][C]0.768250573757949[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]12.4368195631401[/C][C]1.5631804368599[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.0800068944896[/C][C]-0.0800068944896496[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]11.1333688438221[/C][C]-3.13336884382207[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]12.3333345955908[/C][C]2.66666540440923[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]10.902273241728[/C][C]4.09772675827197[/C][/ROW]
[ROW][C]121[/C][C]9[/C][C]9.94352887106379[/C][C]-0.94352887106379[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]12.1920420892766[/C][C]3.80795791072335[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]11.8457007224431[/C][C]-2.84570072244314[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]13.7973223321215[/C][C]1.20267766787845[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]12.438001829222[/C][C]2.56199817077803[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]11.933534659374[/C][C]-5.93353465937401[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.6995841879712[/C][C]-5.69958418797121[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]12.4406249608533[/C][C]2.55937503914666[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]8.51265712094894[/C][C]1.48734287905106[/C][/ROW]
[ROW][C]130[/C][C]9[/C][C]10.9030972165264[/C][C]-1.90309721652645[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]14.2460718061388[/C][C]-0.24607180613879[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]14.2685291075301[/C][C]-2.26852910753013[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]10.5371271091005[/C][C]-2.53712710910049[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]12.0570879342559[/C][C]-1.0570879342559[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]11.3587095714931[/C][C]1.64129042850688[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]11.1116435064009[/C][C]-2.11164350640095[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]12.8592479425959[/C][C]2.14075205740415[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]9.61727667715109[/C][C]3.38272332284891[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]12.4398409097938[/C][C]2.56015909020622[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]12.1936636071279[/C][C]1.80633639287207[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]11.5052466837321[/C][C]4.49475331626789[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]12.4402349984877[/C][C]-0.440234998487743[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]9.6625810734629[/C][C]4.3374189265371[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]10.4056210628478[/C][C]-0.405621062847761[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]9.84611206842383[/C][C]0.153887931576171[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]13.2766894479634[/C][C]-9.27668944796336[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.501566885021[/C][C]-3.50156688502104[/C][/ROW]
[ROW][C]148[/C][C]17[/C][C]13.3855560828987[/C][C]3.61444391710133[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]12.2136761119961[/C][C]3.78632388800389[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]14.6558677616101[/C][C]-2.65586776161006[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.9711547386943[/C][C]0.0288452613057146[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]13.3838456126741[/C][C]1.61615438732589[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.82284517147147[/C][C]-0.822845171471475[/C][/ROW]
[ROW][C]154[/C][C]13[/C][C]13.0420457598061[/C][C]-0.0420457598060739[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.0598465551136[/C][C]1.94015344488638[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]10.0951568282523[/C][C]0.904843171747749[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146353&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146353&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11410.17733941592733.82266058407267
2811.0288486395297-3.02884863952973
31211.4708454112740.529154588725974
4712.2173342390934-5.21733423909338
51012.722469662248-2.72246966224799
6911.2613919936387-2.26139199363865
71614.10071983444591.8992801655541
879.3324581026888-2.3324581026888
91411.82384307536312.17615692463693
10610.5399601483911-4.53996014839112
111611.78129834123124.21870165876881
121111.166998110416-0.166998110416014
131713.05213886146193.94786113853812
141211.026540676570.97345932343003
15712.1795448811781-5.17954488117806
161310.6322498966352.36775010336498
1797.111400256649571.88859974335043
181513.96668576265041.03331423734958
19710.1512413606795-3.15124136067948
2099.89937266552499-0.899372665524988
2178.48658711286979-1.48658711286979
221413.38798815461620.612011845383757
231510.32779613039324.67220386960676
24712.6845559230354-5.68455592303541
251311.7831374218031.216862578197
261711.7832687847015.21673121529902
271513.13291227081371.86708772918631
28148.712999043445555.28700095655445
291412.51645582934781.48354417065221
30810.9112647720789-2.91126477207893
3188.93307922344048-0.933079223440478
321213.6940923812578-1.69409238125777
331412.62260625395011.3773937460499
34812.128202673912-4.12820267391205
35119.901343108994781.09865689100522
361611.27998388093494.7200161190651
371112.61989976015-1.61989976015004
38810.8425481542703-2.84254815427031
391411.85769048455772.14230951544227
40169.71702405351716.2829759464829
411411.34081673879072.65918326120928
42510.7561073648289-5.75610736482891
4388.96306908422204-0.963069084222041
44109.903573292507050.0964267074929509
45811.889643736006-3.88964373600602
461312.28811183452060.711888165479367
471511.09116203167973.90883796832028
48611.0178132636039-5.0178132636039
49129.969412862049172.03058713795083
501412.39561507967131.60438492032875
51512.3654583866285-7.36545838662853
521512.42814322855012.57185677144991
531111.5405069173828-0.540506917382778
5489.694594862945-1.694594862945
551310.96481710290242.0351828970976
561412.42503746002531.57496253997465
57129.98724641483722.0127535851628
581612.66358051482113.33641948517892
59108.665345037316121.33465496268388
601514.31331419695340.686685803046594
6189.81046176061791-1.81046176061791
62169.223156469652486.77684353034752
631913.59651723126845.40348276873163
641411.5952762162012.40472378379897
65712.6408411267418-5.64084112674179
661312.18394275267690.816057247323059
671511.9183442150443.08165578495604
6879.49579343957991-2.49579343957991
691313.1281198552023-0.128119855202344
7049.81679358390016-5.81679358390017
711412.04658302874541.95341697125464
721312.96777659616690.0322234038331152
73119.747578510875131.25242148912487
741411.7883076696742.21169233032604
751211.31697615050530.683023849494712
761513.55098102564361.44901897435645
771412.18613075886731.81386924113274
781311.53799794899181.46200205100822
79710.0649205439343-3.06492054393427
8058.13732066235957-3.13732066235957
81712.2193141920236-5.21931419202361
821313.2230920216784-0.223092021678354
831311.63190014423561.36809985576444
841112.8184436807368-1.81844368073683
8568.89970981411155-2.89970981411155
861213.2680646597717-1.26806465977171
8789.8576481886597-1.8576481886597
881112.0070401577917-1.00704015779165
891214.8742192798271-2.87421927982707
90910.089119340564-1.08911934056403
911210.89846371768641.10153628231358
921313.9331926057036-0.933192605703615
931613.37683821600542.62316178399461
941613.57851758972892.42148241027113
951113.3029528248484-2.30295282484838
96810.6328480126534-2.63284801265339
9748.28445067524131-4.28445067524131
9879.92425006814901-2.92425006814901
991411.49228940866782.50771059133218
1001111.3848717554544-0.384871755454441
1011713.23707221855923.76292778144082
1021513.37727746777481.6227225322252
1031413.02722110886570.972778891134347
104512.0175944189297-7.01759441892966
105411.3822143467575-7.38221434675748
1061912.33199940998366.66800059001638
1071110.03005440478660.969945595213358
1081512.54411268095342.45588731904659
109109.609009067234670.390990932765329
110911.2135931559215-2.21359315592153
111129.822179462717612.17782053728239
1121512.70754353101982.29245646898016
113713.2716114580173-6.27161145801734
1141311.5670936355071.43290636449305
1151413.23174942624210.768250573757949
1161412.43681956314011.5631804368599
1171414.0800068944896-0.0800068944896496
118811.1333688438221-3.13336884382207
1191512.33333459559082.66666540440923
1201510.9022732417284.09772675827197
12199.94352887106379-0.94352887106379
1221612.19204208927663.80795791072335
123911.8457007224431-2.84570072244314
1241513.79732233212151.20267766787845
1251512.4380018292222.56199817077803
126611.933534659374-5.93353465937401
127813.6995841879712-5.69958418797121
1281512.44062496085332.55937503914666
129108.512657120948941.48734287905106
130910.9030972165264-1.90309721652645
1311414.2460718061388-0.24607180613879
1321214.2685291075301-2.26852910753013
133810.5371271091005-2.53712710910049
1341112.0570879342559-1.0570879342559
1351311.35870957149311.64129042850688
136911.1116435064009-2.11164350640095
1371512.85924794259592.14075205740415
138139.617276677151093.38272332284891
1391512.43984090979382.56015909020622
1401412.19366360712791.80633639287207
1411611.50524668373214.49475331626789
1421212.4402349984877-0.440234998487743
143149.66258107346294.3374189265371
1441010.4056210628478-0.405621062847761
145109.846112068423830.153887931576171
146413.2766894479634-9.27668944796336
147811.501566885021-3.50156688502104
1481713.38555608289873.61444391710133
1491612.21367611199613.78632388800389
1501214.6558677616101-2.65586776161006
1511211.97115473869430.0288452613057146
1521513.38384561267411.61615438732589
15399.82284517147147-0.822845171471475
1541313.0420457598061-0.0420457598060739
1551412.05984655511361.94015344488638
1561110.09515682825230.904843171747749







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.6598902692591370.6802194614817260.340109730740863
140.7892465962062640.4215068075874720.210753403793736
150.9367430042698520.1265139914602970.0632569957301483
160.8937016207323120.2125967585353760.106298379267688
170.8944589161319160.2110821677361680.105541083868084
180.8406129787600540.3187740424798930.159387021239946
190.8534679967745480.2930640064509040.146532003225452
200.7970174764700760.4059650470598480.202982523529924
210.7308101469489270.5383797061021450.269189853051073
220.6622471426849580.6755057146300840.337752857315042
230.7374198726017790.5251602547964410.262580127398221
240.8253025096784760.3493949806430480.174697490321524
250.7766587801885890.4466824396228210.223341219811411
260.8134023588400030.3731952823199930.186597641159997
270.762614746246080.4747705075078390.23738525375392
280.7960888824986180.4078222350027640.203911117501382
290.7515426759377840.4969146481244320.248457324062216
300.7926695290745440.4146609418509120.207330470925456
310.7816689351924070.4366621296151860.218331064807593
320.7485475267839070.5029049464321860.251452473216093
330.6960293369010380.6079413261979240.303970663098962
340.6891933753441080.6216132493117840.310806624655892
350.6326436623930980.7347126752138030.367356337606902
360.6178031870143130.7643936259713730.382196812985687
370.5628244815570540.8743510368858930.437175518442946
380.5714230658589950.857153868282010.428576934141005
390.5187535847231430.9624928305537140.481246415276857
400.5522578264977810.8954843470044380.447742173502219
410.5805928555180980.8388142889638040.419407144481902
420.7186872212626960.5626255574746080.281312778737304
430.6885268949231810.6229462101536370.311473105076819
440.6375895681753290.7248208636493430.362410431824671
450.6436224940861160.7127550118277690.356377505913884
460.5927867284340260.8144265431319480.407213271565974
470.5712497112536590.8575005774926810.428750288746341
480.612912959370540.7741740812589210.38708704062946
490.5851743484842420.8296513030315150.414825651515758
500.5520342628999030.8959314742001940.447965737100097
510.8061561054998750.387687789000250.193843894500125
520.7903837123560270.4192325752879450.209616287643973
530.7546300020724450.4907399958551110.245369997927555
540.7376604471530940.5246791056938110.262339552846906
550.7013721858902470.5972556282195050.298627814109753
560.6608303621797210.6783392756405590.339169637820279
570.6193116707602330.7613766584795350.380688329239767
580.5915639240979270.8168721518041460.408436075902073
590.5559032236927810.8881935526144390.444096776307219
600.5111545042241960.9776909915516080.488845495775804
610.4834933754722330.9669867509444670.516506624527767
620.6502533838567950.6994932322864090.349746616143205
630.6984977229909550.6030045540180890.301502277009045
640.6748540162196450.6502919675607110.325145983780355
650.7891787346100680.4216425307798630.210821265389932
660.7559643160771920.4880713678456170.244035683922808
670.7461913211671830.5076173576656340.253808678832817
680.7932398739562710.4135202520874590.206760126043729
690.7602051125226620.4795897749546770.239794887477338
700.8440905430318810.3118189139362370.155909456968119
710.82486025222260.3502794955548010.1751397477774
720.7923626042061930.4152747915876140.207637395793807
730.7669166926672170.4661666146655660.233083307332783
740.7501264800872650.4997470398254710.249873519912735
750.7131296569596920.5737406860806150.286870343040308
760.683392805124250.63321438975150.31660719487575
770.660084576039460.679830847921080.33991542396054
780.6289410500848120.7421178998303760.371058949915188
790.6233510689381870.7532978621236270.376648931061813
800.6271956678741310.7456086642517370.372804332125869
810.686287821417290.627424357165420.31371217858271
820.645303934590780.7093921308184390.35469606540922
830.6102012887141750.779597422571650.389798711285825
840.5777347013009560.8445305973980870.422265298699044
850.5572444130135490.8855111739729010.442755586986451
860.5132406012319160.9735187975361690.486759398768085
870.4740237754088150.948047550817630.525976224591185
880.4286076337891970.8572152675783950.571392366210803
890.405204821285470.810409642570940.59479517871453
900.3628479683308210.7256959366616410.637152031669179
910.3251197879240670.6502395758481340.674880212075933
920.2835190337181920.5670380674363840.716480966281808
930.272400283332220.5448005666644390.72759971666778
940.2541310708706910.5082621417413810.745868929129309
950.2264829641638330.4529659283276660.773517035836167
960.2048544319660690.4097088639321370.795145568033931
970.2234257251607630.4468514503215260.776574274839237
980.2151389136071920.4302778272143850.784861086392808
990.2004047746759780.4008095493519570.799595225324022
1000.1683689821406510.3367379642813010.831631017859349
1010.1874104157895620.3748208315791240.812589584210438
1020.166008129094060.3320162581881210.83399187090594
1030.1429760226708440.2859520453416870.857023977329156
1040.2363459799089250.472691959817850.763654020091075
1050.451065432948730.902130865897460.54893456705127
1060.6221431112409620.7557137775180760.377856888759038
1070.5747856558579030.8504286882841930.425214344142097
1080.5474903195630720.9050193608738550.452509680436928
1090.4966892633386380.9933785266772760.503310736661362
1100.4664226923607680.9328453847215360.533577307639232
1110.4256033027327720.8512066054655450.574396697267228
1120.3975375439913480.7950750879826960.602462456008652
1130.5107171671917450.978565665616510.489282832808255
1140.4630763727562030.9261527455124060.536923627243797
1150.4580171953646220.9160343907292450.541982804635378
1160.4165010488328890.8330020976657780.583498951167111
1170.3606961927345020.7213923854690040.639303807265498
1180.355969175264120.711938350528240.64403082473588
1190.3375978652293370.6751957304586740.662402134770663
1200.3683109108122220.7366218216244440.631689089187778
1210.314724329903640.629448659807280.68527567009636
1220.3861990411022710.7723980822045420.613800958897729
1230.3558559229057290.7117118458114580.644144077094271
1240.3986131599537890.7972263199075790.601386840046211
1250.4129717951714150.825943590342830.587028204828585
1260.6220655408524850.7558689182950290.377934459147515
1270.8509488557783460.2981022884433090.149051144221654
1280.8393798082123420.3212403835753170.160620191787658
1290.7957505078116820.4084989843766360.204249492188318
1300.7967938564747280.4064122870505440.203206143525272
1310.7396302155089520.5207395689820960.260369784491048
1320.6763630056651690.6472739886696620.323636994334831
1330.603815665691110.7923686686177790.39618433430889
1340.5187257271823650.9625485456352710.481274272817635
1350.5046787127801430.9906425744397140.495321287219857
1360.4933032915423310.9866065830846630.506696708457669
1370.492883737888150.98576747577630.50711626211185
1380.4440434535920410.8880869071840810.555956546407959
1390.4223238009117840.8446476018235680.577676199088216
1400.8188095228690150.3623809542619710.181190477130986
1410.7192843429067310.5614313141865390.280715657093269
1420.6279932687887490.7440134624225020.372006731211251
1430.4905450584933510.9810901169867020.509454941506649

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.659890269259137 & 0.680219461481726 & 0.340109730740863 \tabularnewline
14 & 0.789246596206264 & 0.421506807587472 & 0.210753403793736 \tabularnewline
15 & 0.936743004269852 & 0.126513991460297 & 0.0632569957301483 \tabularnewline
16 & 0.893701620732312 & 0.212596758535376 & 0.106298379267688 \tabularnewline
17 & 0.894458916131916 & 0.211082167736168 & 0.105541083868084 \tabularnewline
18 & 0.840612978760054 & 0.318774042479893 & 0.159387021239946 \tabularnewline
19 & 0.853467996774548 & 0.293064006450904 & 0.146532003225452 \tabularnewline
20 & 0.797017476470076 & 0.405965047059848 & 0.202982523529924 \tabularnewline
21 & 0.730810146948927 & 0.538379706102145 & 0.269189853051073 \tabularnewline
22 & 0.662247142684958 & 0.675505714630084 & 0.337752857315042 \tabularnewline
23 & 0.737419872601779 & 0.525160254796441 & 0.262580127398221 \tabularnewline
24 & 0.825302509678476 & 0.349394980643048 & 0.174697490321524 \tabularnewline
25 & 0.776658780188589 & 0.446682439622821 & 0.223341219811411 \tabularnewline
26 & 0.813402358840003 & 0.373195282319993 & 0.186597641159997 \tabularnewline
27 & 0.76261474624608 & 0.474770507507839 & 0.23738525375392 \tabularnewline
28 & 0.796088882498618 & 0.407822235002764 & 0.203911117501382 \tabularnewline
29 & 0.751542675937784 & 0.496914648124432 & 0.248457324062216 \tabularnewline
30 & 0.792669529074544 & 0.414660941850912 & 0.207330470925456 \tabularnewline
31 & 0.781668935192407 & 0.436662129615186 & 0.218331064807593 \tabularnewline
32 & 0.748547526783907 & 0.502904946432186 & 0.251452473216093 \tabularnewline
33 & 0.696029336901038 & 0.607941326197924 & 0.303970663098962 \tabularnewline
34 & 0.689193375344108 & 0.621613249311784 & 0.310806624655892 \tabularnewline
35 & 0.632643662393098 & 0.734712675213803 & 0.367356337606902 \tabularnewline
36 & 0.617803187014313 & 0.764393625971373 & 0.382196812985687 \tabularnewline
37 & 0.562824481557054 & 0.874351036885893 & 0.437175518442946 \tabularnewline
38 & 0.571423065858995 & 0.85715386828201 & 0.428576934141005 \tabularnewline
39 & 0.518753584723143 & 0.962492830553714 & 0.481246415276857 \tabularnewline
40 & 0.552257826497781 & 0.895484347004438 & 0.447742173502219 \tabularnewline
41 & 0.580592855518098 & 0.838814288963804 & 0.419407144481902 \tabularnewline
42 & 0.718687221262696 & 0.562625557474608 & 0.281312778737304 \tabularnewline
43 & 0.688526894923181 & 0.622946210153637 & 0.311473105076819 \tabularnewline
44 & 0.637589568175329 & 0.724820863649343 & 0.362410431824671 \tabularnewline
45 & 0.643622494086116 & 0.712755011827769 & 0.356377505913884 \tabularnewline
46 & 0.592786728434026 & 0.814426543131948 & 0.407213271565974 \tabularnewline
47 & 0.571249711253659 & 0.857500577492681 & 0.428750288746341 \tabularnewline
48 & 0.61291295937054 & 0.774174081258921 & 0.38708704062946 \tabularnewline
49 & 0.585174348484242 & 0.829651303031515 & 0.414825651515758 \tabularnewline
50 & 0.552034262899903 & 0.895931474200194 & 0.447965737100097 \tabularnewline
51 & 0.806156105499875 & 0.38768778900025 & 0.193843894500125 \tabularnewline
52 & 0.790383712356027 & 0.419232575287945 & 0.209616287643973 \tabularnewline
53 & 0.754630002072445 & 0.490739995855111 & 0.245369997927555 \tabularnewline
54 & 0.737660447153094 & 0.524679105693811 & 0.262339552846906 \tabularnewline
55 & 0.701372185890247 & 0.597255628219505 & 0.298627814109753 \tabularnewline
56 & 0.660830362179721 & 0.678339275640559 & 0.339169637820279 \tabularnewline
57 & 0.619311670760233 & 0.761376658479535 & 0.380688329239767 \tabularnewline
58 & 0.591563924097927 & 0.816872151804146 & 0.408436075902073 \tabularnewline
59 & 0.555903223692781 & 0.888193552614439 & 0.444096776307219 \tabularnewline
60 & 0.511154504224196 & 0.977690991551608 & 0.488845495775804 \tabularnewline
61 & 0.483493375472233 & 0.966986750944467 & 0.516506624527767 \tabularnewline
62 & 0.650253383856795 & 0.699493232286409 & 0.349746616143205 \tabularnewline
63 & 0.698497722990955 & 0.603004554018089 & 0.301502277009045 \tabularnewline
64 & 0.674854016219645 & 0.650291967560711 & 0.325145983780355 \tabularnewline
65 & 0.789178734610068 & 0.421642530779863 & 0.210821265389932 \tabularnewline
66 & 0.755964316077192 & 0.488071367845617 & 0.244035683922808 \tabularnewline
67 & 0.746191321167183 & 0.507617357665634 & 0.253808678832817 \tabularnewline
68 & 0.793239873956271 & 0.413520252087459 & 0.206760126043729 \tabularnewline
69 & 0.760205112522662 & 0.479589774954677 & 0.239794887477338 \tabularnewline
70 & 0.844090543031881 & 0.311818913936237 & 0.155909456968119 \tabularnewline
71 & 0.8248602522226 & 0.350279495554801 & 0.1751397477774 \tabularnewline
72 & 0.792362604206193 & 0.415274791587614 & 0.207637395793807 \tabularnewline
73 & 0.766916692667217 & 0.466166614665566 & 0.233083307332783 \tabularnewline
74 & 0.750126480087265 & 0.499747039825471 & 0.249873519912735 \tabularnewline
75 & 0.713129656959692 & 0.573740686080615 & 0.286870343040308 \tabularnewline
76 & 0.68339280512425 & 0.6332143897515 & 0.31660719487575 \tabularnewline
77 & 0.66008457603946 & 0.67983084792108 & 0.33991542396054 \tabularnewline
78 & 0.628941050084812 & 0.742117899830376 & 0.371058949915188 \tabularnewline
79 & 0.623351068938187 & 0.753297862123627 & 0.376648931061813 \tabularnewline
80 & 0.627195667874131 & 0.745608664251737 & 0.372804332125869 \tabularnewline
81 & 0.68628782141729 & 0.62742435716542 & 0.31371217858271 \tabularnewline
82 & 0.64530393459078 & 0.709392130818439 & 0.35469606540922 \tabularnewline
83 & 0.610201288714175 & 0.77959742257165 & 0.389798711285825 \tabularnewline
84 & 0.577734701300956 & 0.844530597398087 & 0.422265298699044 \tabularnewline
85 & 0.557244413013549 & 0.885511173972901 & 0.442755586986451 \tabularnewline
86 & 0.513240601231916 & 0.973518797536169 & 0.486759398768085 \tabularnewline
87 & 0.474023775408815 & 0.94804755081763 & 0.525976224591185 \tabularnewline
88 & 0.428607633789197 & 0.857215267578395 & 0.571392366210803 \tabularnewline
89 & 0.40520482128547 & 0.81040964257094 & 0.59479517871453 \tabularnewline
90 & 0.362847968330821 & 0.725695936661641 & 0.637152031669179 \tabularnewline
91 & 0.325119787924067 & 0.650239575848134 & 0.674880212075933 \tabularnewline
92 & 0.283519033718192 & 0.567038067436384 & 0.716480966281808 \tabularnewline
93 & 0.27240028333222 & 0.544800566664439 & 0.72759971666778 \tabularnewline
94 & 0.254131070870691 & 0.508262141741381 & 0.745868929129309 \tabularnewline
95 & 0.226482964163833 & 0.452965928327666 & 0.773517035836167 \tabularnewline
96 & 0.204854431966069 & 0.409708863932137 & 0.795145568033931 \tabularnewline
97 & 0.223425725160763 & 0.446851450321526 & 0.776574274839237 \tabularnewline
98 & 0.215138913607192 & 0.430277827214385 & 0.784861086392808 \tabularnewline
99 & 0.200404774675978 & 0.400809549351957 & 0.799595225324022 \tabularnewline
100 & 0.168368982140651 & 0.336737964281301 & 0.831631017859349 \tabularnewline
101 & 0.187410415789562 & 0.374820831579124 & 0.812589584210438 \tabularnewline
102 & 0.16600812909406 & 0.332016258188121 & 0.83399187090594 \tabularnewline
103 & 0.142976022670844 & 0.285952045341687 & 0.857023977329156 \tabularnewline
104 & 0.236345979908925 & 0.47269195981785 & 0.763654020091075 \tabularnewline
105 & 0.45106543294873 & 0.90213086589746 & 0.54893456705127 \tabularnewline
106 & 0.622143111240962 & 0.755713777518076 & 0.377856888759038 \tabularnewline
107 & 0.574785655857903 & 0.850428688284193 & 0.425214344142097 \tabularnewline
108 & 0.547490319563072 & 0.905019360873855 & 0.452509680436928 \tabularnewline
109 & 0.496689263338638 & 0.993378526677276 & 0.503310736661362 \tabularnewline
110 & 0.466422692360768 & 0.932845384721536 & 0.533577307639232 \tabularnewline
111 & 0.425603302732772 & 0.851206605465545 & 0.574396697267228 \tabularnewline
112 & 0.397537543991348 & 0.795075087982696 & 0.602462456008652 \tabularnewline
113 & 0.510717167191745 & 0.97856566561651 & 0.489282832808255 \tabularnewline
114 & 0.463076372756203 & 0.926152745512406 & 0.536923627243797 \tabularnewline
115 & 0.458017195364622 & 0.916034390729245 & 0.541982804635378 \tabularnewline
116 & 0.416501048832889 & 0.833002097665778 & 0.583498951167111 \tabularnewline
117 & 0.360696192734502 & 0.721392385469004 & 0.639303807265498 \tabularnewline
118 & 0.35596917526412 & 0.71193835052824 & 0.64403082473588 \tabularnewline
119 & 0.337597865229337 & 0.675195730458674 & 0.662402134770663 \tabularnewline
120 & 0.368310910812222 & 0.736621821624444 & 0.631689089187778 \tabularnewline
121 & 0.31472432990364 & 0.62944865980728 & 0.68527567009636 \tabularnewline
122 & 0.386199041102271 & 0.772398082204542 & 0.613800958897729 \tabularnewline
123 & 0.355855922905729 & 0.711711845811458 & 0.644144077094271 \tabularnewline
124 & 0.398613159953789 & 0.797226319907579 & 0.601386840046211 \tabularnewline
125 & 0.412971795171415 & 0.82594359034283 & 0.587028204828585 \tabularnewline
126 & 0.622065540852485 & 0.755868918295029 & 0.377934459147515 \tabularnewline
127 & 0.850948855778346 & 0.298102288443309 & 0.149051144221654 \tabularnewline
128 & 0.839379808212342 & 0.321240383575317 & 0.160620191787658 \tabularnewline
129 & 0.795750507811682 & 0.408498984376636 & 0.204249492188318 \tabularnewline
130 & 0.796793856474728 & 0.406412287050544 & 0.203206143525272 \tabularnewline
131 & 0.739630215508952 & 0.520739568982096 & 0.260369784491048 \tabularnewline
132 & 0.676363005665169 & 0.647273988669662 & 0.323636994334831 \tabularnewline
133 & 0.60381566569111 & 0.792368668617779 & 0.39618433430889 \tabularnewline
134 & 0.518725727182365 & 0.962548545635271 & 0.481274272817635 \tabularnewline
135 & 0.504678712780143 & 0.990642574439714 & 0.495321287219857 \tabularnewline
136 & 0.493303291542331 & 0.986606583084663 & 0.506696708457669 \tabularnewline
137 & 0.49288373788815 & 0.9857674757763 & 0.50711626211185 \tabularnewline
138 & 0.444043453592041 & 0.888086907184081 & 0.555956546407959 \tabularnewline
139 & 0.422323800911784 & 0.844647601823568 & 0.577676199088216 \tabularnewline
140 & 0.818809522869015 & 0.362380954261971 & 0.181190477130986 \tabularnewline
141 & 0.719284342906731 & 0.561431314186539 & 0.280715657093269 \tabularnewline
142 & 0.627993268788749 & 0.744013462422502 & 0.372006731211251 \tabularnewline
143 & 0.490545058493351 & 0.981090116986702 & 0.509454941506649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146353&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]13[/C][C]0.659890269259137[/C][C]0.680219461481726[/C][C]0.340109730740863[/C][/ROW]
[ROW][C]14[/C][C]0.789246596206264[/C][C]0.421506807587472[/C][C]0.210753403793736[/C][/ROW]
[ROW][C]15[/C][C]0.936743004269852[/C][C]0.126513991460297[/C][C]0.0632569957301483[/C][/ROW]
[ROW][C]16[/C][C]0.893701620732312[/C][C]0.212596758535376[/C][C]0.106298379267688[/C][/ROW]
[ROW][C]17[/C][C]0.894458916131916[/C][C]0.211082167736168[/C][C]0.105541083868084[/C][/ROW]
[ROW][C]18[/C][C]0.840612978760054[/C][C]0.318774042479893[/C][C]0.159387021239946[/C][/ROW]
[ROW][C]19[/C][C]0.853467996774548[/C][C]0.293064006450904[/C][C]0.146532003225452[/C][/ROW]
[ROW][C]20[/C][C]0.797017476470076[/C][C]0.405965047059848[/C][C]0.202982523529924[/C][/ROW]
[ROW][C]21[/C][C]0.730810146948927[/C][C]0.538379706102145[/C][C]0.269189853051073[/C][/ROW]
[ROW][C]22[/C][C]0.662247142684958[/C][C]0.675505714630084[/C][C]0.337752857315042[/C][/ROW]
[ROW][C]23[/C][C]0.737419872601779[/C][C]0.525160254796441[/C][C]0.262580127398221[/C][/ROW]
[ROW][C]24[/C][C]0.825302509678476[/C][C]0.349394980643048[/C][C]0.174697490321524[/C][/ROW]
[ROW][C]25[/C][C]0.776658780188589[/C][C]0.446682439622821[/C][C]0.223341219811411[/C][/ROW]
[ROW][C]26[/C][C]0.813402358840003[/C][C]0.373195282319993[/C][C]0.186597641159997[/C][/ROW]
[ROW][C]27[/C][C]0.76261474624608[/C][C]0.474770507507839[/C][C]0.23738525375392[/C][/ROW]
[ROW][C]28[/C][C]0.796088882498618[/C][C]0.407822235002764[/C][C]0.203911117501382[/C][/ROW]
[ROW][C]29[/C][C]0.751542675937784[/C][C]0.496914648124432[/C][C]0.248457324062216[/C][/ROW]
[ROW][C]30[/C][C]0.792669529074544[/C][C]0.414660941850912[/C][C]0.207330470925456[/C][/ROW]
[ROW][C]31[/C][C]0.781668935192407[/C][C]0.436662129615186[/C][C]0.218331064807593[/C][/ROW]
[ROW][C]32[/C][C]0.748547526783907[/C][C]0.502904946432186[/C][C]0.251452473216093[/C][/ROW]
[ROW][C]33[/C][C]0.696029336901038[/C][C]0.607941326197924[/C][C]0.303970663098962[/C][/ROW]
[ROW][C]34[/C][C]0.689193375344108[/C][C]0.621613249311784[/C][C]0.310806624655892[/C][/ROW]
[ROW][C]35[/C][C]0.632643662393098[/C][C]0.734712675213803[/C][C]0.367356337606902[/C][/ROW]
[ROW][C]36[/C][C]0.617803187014313[/C][C]0.764393625971373[/C][C]0.382196812985687[/C][/ROW]
[ROW][C]37[/C][C]0.562824481557054[/C][C]0.874351036885893[/C][C]0.437175518442946[/C][/ROW]
[ROW][C]38[/C][C]0.571423065858995[/C][C]0.85715386828201[/C][C]0.428576934141005[/C][/ROW]
[ROW][C]39[/C][C]0.518753584723143[/C][C]0.962492830553714[/C][C]0.481246415276857[/C][/ROW]
[ROW][C]40[/C][C]0.552257826497781[/C][C]0.895484347004438[/C][C]0.447742173502219[/C][/ROW]
[ROW][C]41[/C][C]0.580592855518098[/C][C]0.838814288963804[/C][C]0.419407144481902[/C][/ROW]
[ROW][C]42[/C][C]0.718687221262696[/C][C]0.562625557474608[/C][C]0.281312778737304[/C][/ROW]
[ROW][C]43[/C][C]0.688526894923181[/C][C]0.622946210153637[/C][C]0.311473105076819[/C][/ROW]
[ROW][C]44[/C][C]0.637589568175329[/C][C]0.724820863649343[/C][C]0.362410431824671[/C][/ROW]
[ROW][C]45[/C][C]0.643622494086116[/C][C]0.712755011827769[/C][C]0.356377505913884[/C][/ROW]
[ROW][C]46[/C][C]0.592786728434026[/C][C]0.814426543131948[/C][C]0.407213271565974[/C][/ROW]
[ROW][C]47[/C][C]0.571249711253659[/C][C]0.857500577492681[/C][C]0.428750288746341[/C][/ROW]
[ROW][C]48[/C][C]0.61291295937054[/C][C]0.774174081258921[/C][C]0.38708704062946[/C][/ROW]
[ROW][C]49[/C][C]0.585174348484242[/C][C]0.829651303031515[/C][C]0.414825651515758[/C][/ROW]
[ROW][C]50[/C][C]0.552034262899903[/C][C]0.895931474200194[/C][C]0.447965737100097[/C][/ROW]
[ROW][C]51[/C][C]0.806156105499875[/C][C]0.38768778900025[/C][C]0.193843894500125[/C][/ROW]
[ROW][C]52[/C][C]0.790383712356027[/C][C]0.419232575287945[/C][C]0.209616287643973[/C][/ROW]
[ROW][C]53[/C][C]0.754630002072445[/C][C]0.490739995855111[/C][C]0.245369997927555[/C][/ROW]
[ROW][C]54[/C][C]0.737660447153094[/C][C]0.524679105693811[/C][C]0.262339552846906[/C][/ROW]
[ROW][C]55[/C][C]0.701372185890247[/C][C]0.597255628219505[/C][C]0.298627814109753[/C][/ROW]
[ROW][C]56[/C][C]0.660830362179721[/C][C]0.678339275640559[/C][C]0.339169637820279[/C][/ROW]
[ROW][C]57[/C][C]0.619311670760233[/C][C]0.761376658479535[/C][C]0.380688329239767[/C][/ROW]
[ROW][C]58[/C][C]0.591563924097927[/C][C]0.816872151804146[/C][C]0.408436075902073[/C][/ROW]
[ROW][C]59[/C][C]0.555903223692781[/C][C]0.888193552614439[/C][C]0.444096776307219[/C][/ROW]
[ROW][C]60[/C][C]0.511154504224196[/C][C]0.977690991551608[/C][C]0.488845495775804[/C][/ROW]
[ROW][C]61[/C][C]0.483493375472233[/C][C]0.966986750944467[/C][C]0.516506624527767[/C][/ROW]
[ROW][C]62[/C][C]0.650253383856795[/C][C]0.699493232286409[/C][C]0.349746616143205[/C][/ROW]
[ROW][C]63[/C][C]0.698497722990955[/C][C]0.603004554018089[/C][C]0.301502277009045[/C][/ROW]
[ROW][C]64[/C][C]0.674854016219645[/C][C]0.650291967560711[/C][C]0.325145983780355[/C][/ROW]
[ROW][C]65[/C][C]0.789178734610068[/C][C]0.421642530779863[/C][C]0.210821265389932[/C][/ROW]
[ROW][C]66[/C][C]0.755964316077192[/C][C]0.488071367845617[/C][C]0.244035683922808[/C][/ROW]
[ROW][C]67[/C][C]0.746191321167183[/C][C]0.507617357665634[/C][C]0.253808678832817[/C][/ROW]
[ROW][C]68[/C][C]0.793239873956271[/C][C]0.413520252087459[/C][C]0.206760126043729[/C][/ROW]
[ROW][C]69[/C][C]0.760205112522662[/C][C]0.479589774954677[/C][C]0.239794887477338[/C][/ROW]
[ROW][C]70[/C][C]0.844090543031881[/C][C]0.311818913936237[/C][C]0.155909456968119[/C][/ROW]
[ROW][C]71[/C][C]0.8248602522226[/C][C]0.350279495554801[/C][C]0.1751397477774[/C][/ROW]
[ROW][C]72[/C][C]0.792362604206193[/C][C]0.415274791587614[/C][C]0.207637395793807[/C][/ROW]
[ROW][C]73[/C][C]0.766916692667217[/C][C]0.466166614665566[/C][C]0.233083307332783[/C][/ROW]
[ROW][C]74[/C][C]0.750126480087265[/C][C]0.499747039825471[/C][C]0.249873519912735[/C][/ROW]
[ROW][C]75[/C][C]0.713129656959692[/C][C]0.573740686080615[/C][C]0.286870343040308[/C][/ROW]
[ROW][C]76[/C][C]0.68339280512425[/C][C]0.6332143897515[/C][C]0.31660719487575[/C][/ROW]
[ROW][C]77[/C][C]0.66008457603946[/C][C]0.67983084792108[/C][C]0.33991542396054[/C][/ROW]
[ROW][C]78[/C][C]0.628941050084812[/C][C]0.742117899830376[/C][C]0.371058949915188[/C][/ROW]
[ROW][C]79[/C][C]0.623351068938187[/C][C]0.753297862123627[/C][C]0.376648931061813[/C][/ROW]
[ROW][C]80[/C][C]0.627195667874131[/C][C]0.745608664251737[/C][C]0.372804332125869[/C][/ROW]
[ROW][C]81[/C][C]0.68628782141729[/C][C]0.62742435716542[/C][C]0.31371217858271[/C][/ROW]
[ROW][C]82[/C][C]0.64530393459078[/C][C]0.709392130818439[/C][C]0.35469606540922[/C][/ROW]
[ROW][C]83[/C][C]0.610201288714175[/C][C]0.77959742257165[/C][C]0.389798711285825[/C][/ROW]
[ROW][C]84[/C][C]0.577734701300956[/C][C]0.844530597398087[/C][C]0.422265298699044[/C][/ROW]
[ROW][C]85[/C][C]0.557244413013549[/C][C]0.885511173972901[/C][C]0.442755586986451[/C][/ROW]
[ROW][C]86[/C][C]0.513240601231916[/C][C]0.973518797536169[/C][C]0.486759398768085[/C][/ROW]
[ROW][C]87[/C][C]0.474023775408815[/C][C]0.94804755081763[/C][C]0.525976224591185[/C][/ROW]
[ROW][C]88[/C][C]0.428607633789197[/C][C]0.857215267578395[/C][C]0.571392366210803[/C][/ROW]
[ROW][C]89[/C][C]0.40520482128547[/C][C]0.81040964257094[/C][C]0.59479517871453[/C][/ROW]
[ROW][C]90[/C][C]0.362847968330821[/C][C]0.725695936661641[/C][C]0.637152031669179[/C][/ROW]
[ROW][C]91[/C][C]0.325119787924067[/C][C]0.650239575848134[/C][C]0.674880212075933[/C][/ROW]
[ROW][C]92[/C][C]0.283519033718192[/C][C]0.567038067436384[/C][C]0.716480966281808[/C][/ROW]
[ROW][C]93[/C][C]0.27240028333222[/C][C]0.544800566664439[/C][C]0.72759971666778[/C][/ROW]
[ROW][C]94[/C][C]0.254131070870691[/C][C]0.508262141741381[/C][C]0.745868929129309[/C][/ROW]
[ROW][C]95[/C][C]0.226482964163833[/C][C]0.452965928327666[/C][C]0.773517035836167[/C][/ROW]
[ROW][C]96[/C][C]0.204854431966069[/C][C]0.409708863932137[/C][C]0.795145568033931[/C][/ROW]
[ROW][C]97[/C][C]0.223425725160763[/C][C]0.446851450321526[/C][C]0.776574274839237[/C][/ROW]
[ROW][C]98[/C][C]0.215138913607192[/C][C]0.430277827214385[/C][C]0.784861086392808[/C][/ROW]
[ROW][C]99[/C][C]0.200404774675978[/C][C]0.400809549351957[/C][C]0.799595225324022[/C][/ROW]
[ROW][C]100[/C][C]0.168368982140651[/C][C]0.336737964281301[/C][C]0.831631017859349[/C][/ROW]
[ROW][C]101[/C][C]0.187410415789562[/C][C]0.374820831579124[/C][C]0.812589584210438[/C][/ROW]
[ROW][C]102[/C][C]0.16600812909406[/C][C]0.332016258188121[/C][C]0.83399187090594[/C][/ROW]
[ROW][C]103[/C][C]0.142976022670844[/C][C]0.285952045341687[/C][C]0.857023977329156[/C][/ROW]
[ROW][C]104[/C][C]0.236345979908925[/C][C]0.47269195981785[/C][C]0.763654020091075[/C][/ROW]
[ROW][C]105[/C][C]0.45106543294873[/C][C]0.90213086589746[/C][C]0.54893456705127[/C][/ROW]
[ROW][C]106[/C][C]0.622143111240962[/C][C]0.755713777518076[/C][C]0.377856888759038[/C][/ROW]
[ROW][C]107[/C][C]0.574785655857903[/C][C]0.850428688284193[/C][C]0.425214344142097[/C][/ROW]
[ROW][C]108[/C][C]0.547490319563072[/C][C]0.905019360873855[/C][C]0.452509680436928[/C][/ROW]
[ROW][C]109[/C][C]0.496689263338638[/C][C]0.993378526677276[/C][C]0.503310736661362[/C][/ROW]
[ROW][C]110[/C][C]0.466422692360768[/C][C]0.932845384721536[/C][C]0.533577307639232[/C][/ROW]
[ROW][C]111[/C][C]0.425603302732772[/C][C]0.851206605465545[/C][C]0.574396697267228[/C][/ROW]
[ROW][C]112[/C][C]0.397537543991348[/C][C]0.795075087982696[/C][C]0.602462456008652[/C][/ROW]
[ROW][C]113[/C][C]0.510717167191745[/C][C]0.97856566561651[/C][C]0.489282832808255[/C][/ROW]
[ROW][C]114[/C][C]0.463076372756203[/C][C]0.926152745512406[/C][C]0.536923627243797[/C][/ROW]
[ROW][C]115[/C][C]0.458017195364622[/C][C]0.916034390729245[/C][C]0.541982804635378[/C][/ROW]
[ROW][C]116[/C][C]0.416501048832889[/C][C]0.833002097665778[/C][C]0.583498951167111[/C][/ROW]
[ROW][C]117[/C][C]0.360696192734502[/C][C]0.721392385469004[/C][C]0.639303807265498[/C][/ROW]
[ROW][C]118[/C][C]0.35596917526412[/C][C]0.71193835052824[/C][C]0.64403082473588[/C][/ROW]
[ROW][C]119[/C][C]0.337597865229337[/C][C]0.675195730458674[/C][C]0.662402134770663[/C][/ROW]
[ROW][C]120[/C][C]0.368310910812222[/C][C]0.736621821624444[/C][C]0.631689089187778[/C][/ROW]
[ROW][C]121[/C][C]0.31472432990364[/C][C]0.62944865980728[/C][C]0.68527567009636[/C][/ROW]
[ROW][C]122[/C][C]0.386199041102271[/C][C]0.772398082204542[/C][C]0.613800958897729[/C][/ROW]
[ROW][C]123[/C][C]0.355855922905729[/C][C]0.711711845811458[/C][C]0.644144077094271[/C][/ROW]
[ROW][C]124[/C][C]0.398613159953789[/C][C]0.797226319907579[/C][C]0.601386840046211[/C][/ROW]
[ROW][C]125[/C][C]0.412971795171415[/C][C]0.82594359034283[/C][C]0.587028204828585[/C][/ROW]
[ROW][C]126[/C][C]0.622065540852485[/C][C]0.755868918295029[/C][C]0.377934459147515[/C][/ROW]
[ROW][C]127[/C][C]0.850948855778346[/C][C]0.298102288443309[/C][C]0.149051144221654[/C][/ROW]
[ROW][C]128[/C][C]0.839379808212342[/C][C]0.321240383575317[/C][C]0.160620191787658[/C][/ROW]
[ROW][C]129[/C][C]0.795750507811682[/C][C]0.408498984376636[/C][C]0.204249492188318[/C][/ROW]
[ROW][C]130[/C][C]0.796793856474728[/C][C]0.406412287050544[/C][C]0.203206143525272[/C][/ROW]
[ROW][C]131[/C][C]0.739630215508952[/C][C]0.520739568982096[/C][C]0.260369784491048[/C][/ROW]
[ROW][C]132[/C][C]0.676363005665169[/C][C]0.647273988669662[/C][C]0.323636994334831[/C][/ROW]
[ROW][C]133[/C][C]0.60381566569111[/C][C]0.792368668617779[/C][C]0.39618433430889[/C][/ROW]
[ROW][C]134[/C][C]0.518725727182365[/C][C]0.962548545635271[/C][C]0.481274272817635[/C][/ROW]
[ROW][C]135[/C][C]0.504678712780143[/C][C]0.990642574439714[/C][C]0.495321287219857[/C][/ROW]
[ROW][C]136[/C][C]0.493303291542331[/C][C]0.986606583084663[/C][C]0.506696708457669[/C][/ROW]
[ROW][C]137[/C][C]0.49288373788815[/C][C]0.9857674757763[/C][C]0.50711626211185[/C][/ROW]
[ROW][C]138[/C][C]0.444043453592041[/C][C]0.888086907184081[/C][C]0.555956546407959[/C][/ROW]
[ROW][C]139[/C][C]0.422323800911784[/C][C]0.844647601823568[/C][C]0.577676199088216[/C][/ROW]
[ROW][C]140[/C][C]0.818809522869015[/C][C]0.362380954261971[/C][C]0.181190477130986[/C][/ROW]
[ROW][C]141[/C][C]0.719284342906731[/C][C]0.561431314186539[/C][C]0.280715657093269[/C][/ROW]
[ROW][C]142[/C][C]0.627993268788749[/C][C]0.744013462422502[/C][C]0.372006731211251[/C][/ROW]
[ROW][C]143[/C][C]0.490545058493351[/C][C]0.981090116986702[/C][C]0.509454941506649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146353&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146353&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.6598902692591370.6802194614817260.340109730740863
140.7892465962062640.4215068075874720.210753403793736
150.9367430042698520.1265139914602970.0632569957301483
160.8937016207323120.2125967585353760.106298379267688
170.8944589161319160.2110821677361680.105541083868084
180.8406129787600540.3187740424798930.159387021239946
190.8534679967745480.2930640064509040.146532003225452
200.7970174764700760.4059650470598480.202982523529924
210.7308101469489270.5383797061021450.269189853051073
220.6622471426849580.6755057146300840.337752857315042
230.7374198726017790.5251602547964410.262580127398221
240.8253025096784760.3493949806430480.174697490321524
250.7766587801885890.4466824396228210.223341219811411
260.8134023588400030.3731952823199930.186597641159997
270.762614746246080.4747705075078390.23738525375392
280.7960888824986180.4078222350027640.203911117501382
290.7515426759377840.4969146481244320.248457324062216
300.7926695290745440.4146609418509120.207330470925456
310.7816689351924070.4366621296151860.218331064807593
320.7485475267839070.5029049464321860.251452473216093
330.6960293369010380.6079413261979240.303970663098962
340.6891933753441080.6216132493117840.310806624655892
350.6326436623930980.7347126752138030.367356337606902
360.6178031870143130.7643936259713730.382196812985687
370.5628244815570540.8743510368858930.437175518442946
380.5714230658589950.857153868282010.428576934141005
390.5187535847231430.9624928305537140.481246415276857
400.5522578264977810.8954843470044380.447742173502219
410.5805928555180980.8388142889638040.419407144481902
420.7186872212626960.5626255574746080.281312778737304
430.6885268949231810.6229462101536370.311473105076819
440.6375895681753290.7248208636493430.362410431824671
450.6436224940861160.7127550118277690.356377505913884
460.5927867284340260.8144265431319480.407213271565974
470.5712497112536590.8575005774926810.428750288746341
480.612912959370540.7741740812589210.38708704062946
490.5851743484842420.8296513030315150.414825651515758
500.5520342628999030.8959314742001940.447965737100097
510.8061561054998750.387687789000250.193843894500125
520.7903837123560270.4192325752879450.209616287643973
530.7546300020724450.4907399958551110.245369997927555
540.7376604471530940.5246791056938110.262339552846906
550.7013721858902470.5972556282195050.298627814109753
560.6608303621797210.6783392756405590.339169637820279
570.6193116707602330.7613766584795350.380688329239767
580.5915639240979270.8168721518041460.408436075902073
590.5559032236927810.8881935526144390.444096776307219
600.5111545042241960.9776909915516080.488845495775804
610.4834933754722330.9669867509444670.516506624527767
620.6502533838567950.6994932322864090.349746616143205
630.6984977229909550.6030045540180890.301502277009045
640.6748540162196450.6502919675607110.325145983780355
650.7891787346100680.4216425307798630.210821265389932
660.7559643160771920.4880713678456170.244035683922808
670.7461913211671830.5076173576656340.253808678832817
680.7932398739562710.4135202520874590.206760126043729
690.7602051125226620.4795897749546770.239794887477338
700.8440905430318810.3118189139362370.155909456968119
710.82486025222260.3502794955548010.1751397477774
720.7923626042061930.4152747915876140.207637395793807
730.7669166926672170.4661666146655660.233083307332783
740.7501264800872650.4997470398254710.249873519912735
750.7131296569596920.5737406860806150.286870343040308
760.683392805124250.63321438975150.31660719487575
770.660084576039460.679830847921080.33991542396054
780.6289410500848120.7421178998303760.371058949915188
790.6233510689381870.7532978621236270.376648931061813
800.6271956678741310.7456086642517370.372804332125869
810.686287821417290.627424357165420.31371217858271
820.645303934590780.7093921308184390.35469606540922
830.6102012887141750.779597422571650.389798711285825
840.5777347013009560.8445305973980870.422265298699044
850.5572444130135490.8855111739729010.442755586986451
860.5132406012319160.9735187975361690.486759398768085
870.4740237754088150.948047550817630.525976224591185
880.4286076337891970.8572152675783950.571392366210803
890.405204821285470.810409642570940.59479517871453
900.3628479683308210.7256959366616410.637152031669179
910.3251197879240670.6502395758481340.674880212075933
920.2835190337181920.5670380674363840.716480966281808
930.272400283332220.5448005666644390.72759971666778
940.2541310708706910.5082621417413810.745868929129309
950.2264829641638330.4529659283276660.773517035836167
960.2048544319660690.4097088639321370.795145568033931
970.2234257251607630.4468514503215260.776574274839237
980.2151389136071920.4302778272143850.784861086392808
990.2004047746759780.4008095493519570.799595225324022
1000.1683689821406510.3367379642813010.831631017859349
1010.1874104157895620.3748208315791240.812589584210438
1020.166008129094060.3320162581881210.83399187090594
1030.1429760226708440.2859520453416870.857023977329156
1040.2363459799089250.472691959817850.763654020091075
1050.451065432948730.902130865897460.54893456705127
1060.6221431112409620.7557137775180760.377856888759038
1070.5747856558579030.8504286882841930.425214344142097
1080.5474903195630720.9050193608738550.452509680436928
1090.4966892633386380.9933785266772760.503310736661362
1100.4664226923607680.9328453847215360.533577307639232
1110.4256033027327720.8512066054655450.574396697267228
1120.3975375439913480.7950750879826960.602462456008652
1130.5107171671917450.978565665616510.489282832808255
1140.4630763727562030.9261527455124060.536923627243797
1150.4580171953646220.9160343907292450.541982804635378
1160.4165010488328890.8330020976657780.583498951167111
1170.3606961927345020.7213923854690040.639303807265498
1180.355969175264120.711938350528240.64403082473588
1190.3375978652293370.6751957304586740.662402134770663
1200.3683109108122220.7366218216244440.631689089187778
1210.314724329903640.629448659807280.68527567009636
1220.3861990411022710.7723980822045420.613800958897729
1230.3558559229057290.7117118458114580.644144077094271
1240.3986131599537890.7972263199075790.601386840046211
1250.4129717951714150.825943590342830.587028204828585
1260.6220655408524850.7558689182950290.377934459147515
1270.8509488557783460.2981022884433090.149051144221654
1280.8393798082123420.3212403835753170.160620191787658
1290.7957505078116820.4084989843766360.204249492188318
1300.7967938564747280.4064122870505440.203206143525272
1310.7396302155089520.5207395689820960.260369784491048
1320.6763630056651690.6472739886696620.323636994334831
1330.603815665691110.7923686686177790.39618433430889
1340.5187257271823650.9625485456352710.481274272817635
1350.5046787127801430.9906425744397140.495321287219857
1360.4933032915423310.9866065830846630.506696708457669
1370.492883737888150.98576747577630.50711626211185
1380.4440434535920410.8880869071840810.555956546407959
1390.4223238009117840.8446476018235680.577676199088216
1400.8188095228690150.3623809542619710.181190477130986
1410.7192843429067310.5614313141865390.280715657093269
1420.6279932687887490.7440134624225020.372006731211251
1430.4905450584933510.9810901169867020.509454941506649







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146353&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146353&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146353&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}