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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 19:06:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290539131vb7v5v2t1cyw285.htm/, Retrieved Thu, 25 Apr 2024 20:44:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99593, Retrieved Thu, 25 Apr 2024 20:44:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [] [2010-11-23 07:59:52] [b1e5ec7263cdefe98ec76e1a1363de05]
-   PD      [Multiple Regression] [W7 p1] [2010-11-23 19:06:32] [cda497ce08bc921f0aec22acd67c882b] [Current]
-   PD        [Multiple Regression] [Meervoudige linea...] [2010-12-22 18:52:01] [b1e5ec7263cdefe98ec76e1a1363de05]
-    D          [Multiple Regression] [Meervoudige linea...] [2010-12-22 18:58:15] [b1e5ec7263cdefe98ec76e1a1363de05]
-   PD            [Multiple Regression] [Meervoudige linea...] [2010-12-22 19:11:38] [b1e5ec7263cdefe98ec76e1a1363de05]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3,50	2,20	3,00	3,43	4,33	2,67
2,75	1,40	2,00	3,57	3,83	2,78
1,50	3,40	2,00	4,29	4,17	1,89
3,00	2,00	2,00	2,71	3,83	2,00
2,00	2,40	2,25	3,14	3,17	2,00
2,50	2,40	1,75	3,14	4,83	1,78
2,50	2,20	1,00	3,57	4,17	2,22
2,75	2,20	2,75	3,29	3,50	1,78
4,00	2,40	1,75	2,43	3,67	2,00
2,75	2,60	1,75	3,00	4,17	1,89
3,25	2,80	3,00	2,71	4,00	2,56
3,00	3,20	2,50	2,71	3,00	3,33
2,00	2,20	2,50	2,14	3,67	2,56
3,00	2,00	2,00	2,29	2,50	2,00
2,75	2,20	2,00	3,29	3,67	1,67
1,00	3,00	1,00	3,86	4,67	1,33
2,25	1,80	2,25	3,14	3,33	2,33
2,00	2,20	2,00	2,00	2,00	1,67
2,00	3,40	1,75	3,14	4,00	2,22
3,50	3,40	2,75	3,29	3,33	3,44
3,75	2,20	2,25	3,29	3,50	3,00
4,00	3,60	2,75	3,00	3,33	3,78
2,25	2,80	3,25	2,71	3,50	2,33
3,50	2,00	2,00	2,57	3,83	3,44
2,75	2,20	2,00	2,86	4,67	2,11
2,00	3,00	2,25	3,29	4,00	1,78
2,25	3,00	1,50	3,57	4,00	2,22
2,25	2,60	2,25	2,71	4,00	2,33
2,25	3,20	2,25	3,43	3,83	2,44
2,25	2,60	1,50	3,14	3,83	1,89
2,50	1,80	1,50	3,57	4,83	2,67
4,00	3,60	4,00	3,71	4,00	2,78
2,75	3,60	1,25	4,14	3,00	2,89
2,00	2,40	1,75	4,57	4,17	2,78
2,25	3,40	2,25	3,57	3,50	1,89
4,00	1,80	1,50	4,14	4,33	3,56
2,75	1,80	1,50	4,00	3,67	3,67
4,00	2,40	1,25	2,43	3,67	1,44
3,00	3,60	3,00	4,00	3,67	3,56
3,00	2,40	1,75	4,14	3,83	2,78
3,50	3,60	2,50	3,71	5,00	3,22
2,25	2,80	2,25	3,57	3,83	2,44
2,50	3,00	2,00	2,00	2,83	2,00
2,25	3,20	1,25	3,57	3,83	1,89
2,50	2,00	2,00	3,71	3,83	2,22
3,00	2,20	2,00	2,86	4,17	1,67
3,50	2,80	2,50	2,57	4,00	2,22
3,50	1,80	1,50	4,57	4,00	3,67
2,50	2,40	2,00	3,57	3,83	3,22
3,50	3,40	1,75	3,57	3,50	2,56
4,00	1,00	1,00	3,29	4,00	2,89
2,25	2,40	2,00	3,00	4,00	2,00
2,50	2,40	2,00	2,86	4,67	2,22
1,50	1,20	1,00	2,14	2,67	1,22
2,00	4,80	5,00	4,29	3,33	3,11
3,25	2,40	2,00	3,43	4,83	2,89
2,50	2,40	2,00	3,71	4,50	2,44
2,00	2,80	1,50	3,43	3,67	1,89
1,75	1,40	1,00	3,14	4,67	1,33
3,75	2,60	2,00	2,00	2,67	1,56
2,25	2,40	2,25	3,43	4,17	1,89
2,50	2,60	1,50	3,43	4,00	2,33
3,00	2,80	1,75	3,43	4,67	2,11
3,25	1,60	2,25	3,43	4,00	2,00
2,50	2,20	1,25	2,71	3,83	1,11
2,75	1,80	1,25	4,43	5,00	3,22
2,00	2,20	2,00	3,14	4,00	3,44
2,25	2,60	2,00	3,86	3,50	2,11
3,25	2,00	1,50	2,71	4,17	1,00
2,75	2,20	2,00	3,57	4,17	2,22
2,00	2,40	1,75	2,86	3,67	3,11
2,25	1,80	1,75	3,00	3,83	2,11
2,25	3,00	2,25	3,86	4,33	3,33
3,75	3,60	2,75	3,29	3,83	3,22
2,25	3,00	1,50	3,57	4,17	2,89
2,50	2,40	2,00	2,86	3,50	2,56
3,50	2,60	1,50	3,00	4,17	1,44
3,00	2,80	2,25	3,14	4,00	2,33
3,00	2,00	2,00	3,29	4,83	2,11
2,75	2,60	1,50	3,57	3,67	3,11
3,50	2,60	2,50	3,57	4,50	2,56
1,50	2,20	2,00	2,43	4,33	2,00
3,00	2,60	2,00	2,71	3,67	2,33
2,00	3,20	2,50	3,57	4,00	2,22
3,50	1,60	1,25	2,71	4,50	2,56
2,75	3,20	1,75	2,86	4,00	2,33
2,50	2,20	1,25	3,71	4,00	2,33
3,50	1,80	2,00	3,29	4,83	1,67
3,00	3,20	3,50	3,86	3,67	3,11
2,50	2,40	1,75	2,43	3,50	2,11
3,50	2,80	2,00	2,43	4,00	2,89
1,25	1,60	1,50	2,71	4,00	1,11
2,75	1,80	1,25	2,43	3,83	1,78
2,50	3,00	1,50	3,14	3,33	2,44
2,25	2,20	2,50	3,00	4,50	2,11
2,50	4,20	3,00	4,57	4,33	3,44
4,00	2,80	2,25	3,00	4,17	3,44
3,25	3,60	3,00	3,00	3,50	3,22
2,25	2,40	1,75	2,57	3,50	2,11
2,50	2,60	2,00	2,57	3,17	2,44
2,50	3,00	2,50	3,29	3,50	2,56
1,75	2,40	1,50	2,71	3,50	1,67
2,25	3,80	2,50	2,86	2,67	2,22
2,00	3,00	2,50	3,00	3,67	2,00
3,50	2,20	2,50	2,86	4,83	2,56
3,50	2,20	1,25	2,43	2,50	2,78
2,00	2,00	1,75	2,57	2,83	2,33
2,25	2,60	2,50	2,71	2,50	2,67
3,50	3,00	2,75	3,14	3,50	2,78
3,50	2,40	1,50	2,14	3,50	1,89
2,00	2,40	1,75	2,00	3,17	1,44
2,00	3,20	3,00	2,57	4,00	3,11
2,00	1,80	2,75	3,43	3,33	2,33
1,75	3,60	2,75	5,00	2,83	2,78
1,50	1,60	2,75	4,14	3,83	1,00
2,00	2,60	1,25	3,00	4,00	1,78
1,50	3,40	2,00	3,57	2,33	2,11
2,75	1,80	1,50	2,86	3,17	1,89
3,50	3,00	2,25	3,14	4,00	2,78
2,75	1,60	1,00	1,86	2,17	2,22
2,75	1,40	1,00	3,71	3,67	3,22
2,75	2,40	1,75	2,43	2,67	1,56
3,50	2,80	2,75	3,57	3,17	2,44
2,00	1,20	1,50	2,86	4,17	1,67
5,00	1,60	1,75	2,71	4,17	2,11
2,75	3,40	2,00	3,00	3,83	2,22
2,00	2,00	1,00	3,14	4,00	1,67
2,75	2,20	2,00	3,43	4,33	2,22
2,50	2,80	2,25	3,00	4,33	2,00
3,50	2,20	2,00	3,71	4,17	3,67
2,75	2,60	2,75	3,43	3,00	2,44
2,25	2,40	2,00	2,29	3,50	1,78
2,25	2,20	1,25	3,29	4,33	1,89
2,00	1,80	1,00	2,57	3,83	1,78
2,50	2,40	2,00	2,29	3,83	2,33
3,25	4,00	2,50	3,71	3,67	2,89
3,25	2,40	1,50	2,71	3,33	2,00
3,00	2,60	2,25	3,00	2,17	2,00
2,00	2,40	2,25	3,00	4,00	1,89
3,25	2,40	3,25	3,14	2,50	2,44
3,50	1,80	2,25	3,29	2,33	3,33
3,00	3,00	2,50	4,14	3,67	3,33
3,50	4,80	5,00	3,00	1,67	2,67
3,75	1,40	1,25	3,00	4,00	2,33
3,25	3,40	2,75	3,29	3,67	2,33
4,00	2,20	1,50	3,86	4,00	3,22
2,25	3,40	2,25	3,57	3,17	3,44
2,25	2,20	1,75	3,00	3,33	2,22
2,25	2,40	2,25	1,43	2,17	1,78
2,00	2,80	2,50	2,86	3,33	2,44
1,75	2,20	2,25	3,71	3,67	2,22
4,00	3,20	2,00	3,43	4,00	3,11
2,75	4,20	1,75	4,14	4,83	4,22
2,25	2,80	1,50	2,71	2,00	2,44
2,75	4,00	3,25	3,43	3,33	2,22
2,25	2,60	1,50	2,71	3,50	1,89
3,50	2,20	2,00	3,43	4,00	3,11
3,25	3,00	2,50	3,14	3,67	2,44
4,00	3,80	4,00	2,43	3,33	3,44

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99593&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99593&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99593&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Y[t] = -0.216966647663763 + 0.360374452098849X1[t] + 0.139026563992538X2[t] + 0.0835584328678894X3[t] + 0.440407549873858X4[t] -0.0776309220351274X5[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -0.216966647663763 +  0.360374452098849X1[t] +  0.139026563992538X2[t] +  0.0835584328678894X3[t] +  0.440407549873858X4[t] -0.0776309220351274X5[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99593&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -0.216966647663763 +  0.360374452098849X1[t] +  0.139026563992538X2[t] +  0.0835584328678894X3[t] +  0.440407549873858X4[t] -0.0776309220351274X5[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99593&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99593&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = -0.216966647663763 + 0.360374452098849X1[t] + 0.139026563992538X2[t] + 0.0835584328678894X3[t] + 0.440407549873858X4[t] -0.0776309220351274X5[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2169666476637630.339069-0.63990.5232020.261601
X10.3603744520988490.0578896.225200
X20.1390265639925380.0737021.88630.0611450.030572
X30.08355843286788940.0747411.1180.2653330.132666
X40.4404075498738580.0744855.912700
X5-0.07763092203512740.068675-1.13040.2600740.130037

\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) & -0.216966647663763 & 0.339069 & -0.6399 & 0.523202 & 0.261601 \tabularnewline
X1 & 0.360374452098849 & 0.057889 & 6.2252 & 0 & 0 \tabularnewline
X2 & 0.139026563992538 & 0.073702 & 1.8863 & 0.061145 & 0.030572 \tabularnewline
X3 & 0.0835584328678894 & 0.074741 & 1.118 & 0.265333 & 0.132666 \tabularnewline
X4 & 0.440407549873858 & 0.074485 & 5.9127 & 0 & 0 \tabularnewline
X5 & -0.0776309220351274 & 0.068675 & -1.1304 & 0.260074 & 0.130037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99593&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]-0.216966647663763[/C][C]0.339069[/C][C]-0.6399[/C][C]0.523202[/C][C]0.261601[/C][/ROW]
[ROW][C]X1[/C][C]0.360374452098849[/C][C]0.057889[/C][C]6.2252[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X2[/C][C]0.139026563992538[/C][C]0.073702[/C][C]1.8863[/C][C]0.061145[/C][C]0.030572[/C][/ROW]
[ROW][C]X3[/C][C]0.0835584328678894[/C][C]0.074741[/C][C]1.118[/C][C]0.265333[/C][C]0.132666[/C][/ROW]
[ROW][C]X4[/C][C]0.440407549873858[/C][C]0.074485[/C][C]5.9127[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X5[/C][C]-0.0776309220351274[/C][C]0.068675[/C][C]-1.1304[/C][C]0.260074[/C][C]0.130037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99593&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99593&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2169666476637630.339069-0.63990.5232020.261601
X10.3603744520988490.0578896.225200
X20.1390265639925380.0737021.88630.0611450.030572
X30.08355843286788940.0747411.1180.2653330.132666
X40.4404075498738580.0744855.912700
X5-0.07763092203512740.068675-1.13040.2600740.130037






Multiple Linear Regression - Regression Statistics
Multiple R0.638298402197689
R-squared0.407424850248123
Adjusted R-squared0.388059649275839
F-TEST (value)21.0390199839005
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value5.55111512312578e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.497192288158996
Sum Squared Residuals37.8216262249311

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.638298402197689 \tabularnewline
R-squared & 0.407424850248123 \tabularnewline
Adjusted R-squared & 0.388059649275839 \tabularnewline
F-TEST (value) & 21.0390199839005 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 5.55111512312578e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.497192288158996 \tabularnewline
Sum Squared Residuals & 37.8216262249311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99593&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.638298402197689[/C][/ROW]
[ROW][C]R-squared[/C][C]0.407424850248123[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.388059649275839[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.0390199839005[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]5.55111512312578e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.497192288158996[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37.8216262249311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99593&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99593&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.638298402197689
R-squared0.407424850248123
Adjusted R-squared0.388059649275839
F-TEST (value)21.0390199839005
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value5.55111512312578e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.497192288158996
Sum Squared Residuals37.8216262249311






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.672.77533367772469-0.105333677724692
22.782.410745672588540.36925432741146
31.892.52902965786729-0.639029657867289
422.20550473111726-0.205504731117258
522.16224216782134-0.16224216782134
61.782.17178284685851-0.391782846858507
72.222.32192036439803-0.101920364398025
81.782.4869398387404-0.7069398387404
922.48970703415709-0.48970703415709
101.892.27926112424257-0.389261124242571
112.562.477180771457920.0828192285420817
123.332.47854948963140.851450510368596
132.561.676103452348380.883896547651618
1422.12378268647696-0.123782686476957
151.672.41107375734351-0.741073757343511
161.331.98148266588964-0.651482665889637
172.332.156498894924910.173501105075092
181.671.70231081873076-0.03231081873076
192.222.195055850090780.0249441499092237
203.442.937249811351550.502750188648446
2132.80553507440530.194464925594695
223.783.017524160736070.762475839263932
232.332.17651138859360.153488611406395
243.442.324034900184341.11596509981566
252.112.14406758886262-0.0340675888626247
261.782.24728557340878-0.467285573408785
272.222.39802447574726-0.17802447574726
282.332.026332181909640.303667818090356
292.442.44003881296032-3.88129603165992e-05
301.892.16623586045046-0.276235860450458
312.672.256852546691770.413147453308229
322.783.38264884446783-0.602648844467834
332.892.96940125743846-0.0794012574384616
342.782.672614825671880.107385174328116
351.892.55511938701276-0.665119387012756
363.563.087261989285710.472738010714292
373.672.626373275722991.04362672427701
381.442.44792781772315-1.00792781772315
393.563.092052353236110.467947646763895
402.782.87000854481692-0.0900085448169176
413.222.999493047081450.220506952918553
422.442.44608524434564-0.00608524434564125
4321.929285630685060.0707143693149406
441.892.41813743707477-0.528137437074767
452.222.46572505494169-0.245725054941691
461.672.2729766629049-0.6029766629049
472.222.46383811106635-0.243838111066346
483.673.122068213953630.547931786046366
493.222.459678623556370.760321376443634
502.562.96380823570237-0.403808235702373
512.892.585533308536550.304466691463455
5222.10535545035758-0.105355450357583
532.222.081779288636420.13822071136358
541.221.30918293503971-0.0891829350397122
553.113.21973934661944-0.109739346619443
562.892.590671483613040.299328516386964
572.442.46932296277517-0.0293229627751708
581.892.24408669721329-0.354086697213292
591.331.71222756666654-0.382227566666535
601.562.33656401773723-0.776564017737226
611.892.30242304827434-0.412423048274343
622.332.37085040619262-0.0408504061926172
632.112.54771983549399-0.437719835493987
6422.56477350592513-0.564773505925133
651.111.99045399321542-0.880453993215423
663.222.691609787645060.528390212354943
673.442.04911358151671.3908864184833
682.112.55072671706517-0.440726717065172
6912.22742461421608-1.22742461421608
702.222.49557241029063-0.275572410290627
713.111.958333376405151.15166662359485
722.112.014247160491060.0957528395089405
733.332.562793285590.767206714409996
743.223.016333276157210.20366672384279
752.892.384827219001290.505172780998712
762.562.172607467417520.387392532582481
771.442.52865235509974-1.08865235509974
782.332.51379358022805-0.183793580228048
792.112.38331018800897-0.273310188008968
803.112.548219280471260.561780719528738
812.562.83762488712413-0.277624887124132
8221.530618790785250.469381209214753
832.332.30134161703840.0286583829615991
842.222.41929460838894-0.199294608388945
852.562.215399789155210.344600210844785
862.332.314217262401730.0157827375982743
872.332.41766428634331-0.0876642863433097
881.672.53569210125988-0.865692101259885
893.113.016563887090690.093436112909305
902.111.962342612754790.147657387245212
912.892.360401837650060.529598162349939
921.111.46426234116734-0.354262341167339
931.781.90162286667844-0.12162286667844
942.442.350755560089750.0892444399102509
952.112.080513892975460.0294861070245435
963.443.195076960467120.244923039532881
973.442.799313718598590.640686281401414
983.222.754935673132930.465064326867069
992.111.933906056712420.176093943287585
1002.442.09831279502420.3416872049758
1012.562.487177868692750.0728221313072546
1021.671.79448627942836-0.124486279428358
1032.222.38336392570546-0.16336392570546
10422.16607519643393-0.16607519643393
1052.562.443706696845090.116293303154914
1062.782.330763457656310.449236542343687
1072.331.840214535854220.489785464145777
1082.672.163668173179310.506331826820692
1092.782.80238079652749-0.022380796527489
1101.892.17410926717325-0.284109267173246
1111.441.6183983445312-0.178398344531196
1123.112.020666274949031.08933372505097
1132.332.235902687797560.094097312202441
1142.783.12631220427894-0.346312204278936
11512.3017840483425-1.3017840483425
1161.781.98039832548046-0.200398325480461
1172.112.35477711850275-0.244777118502746
1181.892.16312412988436-0.273124129884356
1192.782.721786119075980.0582138809240194
1202.221.730762972813170.489237027186827
1213.222.401265244228610.818734755771389
1221.562.11686989106866-0.556869891068656
1232.442.98956893444633-0.549568934446332
1241.671.73179643037957-0.0617964303795683
1252.112.82335888800903-0.713358888009026
1262.222.43776649714552-0.217766497145517
1271.671.93774983585031-0.267749835850306
1282.222.42149440578267-0.201494405782667
12922.24633109292469-0.246331092924691
1303.672.82751030634710.842489693652895
1312.442.64302298233732-0.203022982337319
1321.781.83148155096471-0.0514815509647076
1331.892.11698129809999-0.226981298099985
1341.781.672109476369670.107890523630329
1352.331.895956959717830.434043040282172
1362.893.06825918596047-0.178259185960469
13722.3482452143226-0.348245214322604
13822.56639579777148-0.566395797771483
1391.892.03615144554984-0.146151445549843
1402.442.74828138357633-0.308281383576326
1413.332.750659014564680.579340985435324
1423.333.028514255388980.301485744611022
1432.673.32104261600875-0.651042616008752
1442.332.4442217398624-0.114221739862402
1452.332.8207616848349-0.490761684834899
1463.223.045176705189640.174823294810365
1473.442.580737591284350.859262408715652
1482.222.108673247105640.111326752894362
1491.781.576869792596880.203130207403119
1502.442.103007340145030.336992659854974
1512.222.25656008440865-0.0365600844086538
1523.113.036607239170360.0733927608296406
1534.222.952531824943651.26746817505635
1542.442.146730514127490.29326948587251
1552.222.85382118408923-0.633821184089225
1561.892.00247881827629-0.112478818276291
1573.112.71739344912840.392606550871604
1582.442.67820031853983-0.238200318539832
1593.442.898745211191340.541254788808662

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.67 & 2.77533367772469 & -0.105333677724692 \tabularnewline
2 & 2.78 & 2.41074567258854 & 0.36925432741146 \tabularnewline
3 & 1.89 & 2.52902965786729 & -0.639029657867289 \tabularnewline
4 & 2 & 2.20550473111726 & -0.205504731117258 \tabularnewline
5 & 2 & 2.16224216782134 & -0.16224216782134 \tabularnewline
6 & 1.78 & 2.17178284685851 & -0.391782846858507 \tabularnewline
7 & 2.22 & 2.32192036439803 & -0.101920364398025 \tabularnewline
8 & 1.78 & 2.4869398387404 & -0.7069398387404 \tabularnewline
9 & 2 & 2.48970703415709 & -0.48970703415709 \tabularnewline
10 & 1.89 & 2.27926112424257 & -0.389261124242571 \tabularnewline
11 & 2.56 & 2.47718077145792 & 0.0828192285420817 \tabularnewline
12 & 3.33 & 2.4785494896314 & 0.851450510368596 \tabularnewline
13 & 2.56 & 1.67610345234838 & 0.883896547651618 \tabularnewline
14 & 2 & 2.12378268647696 & -0.123782686476957 \tabularnewline
15 & 1.67 & 2.41107375734351 & -0.741073757343511 \tabularnewline
16 & 1.33 & 1.98148266588964 & -0.651482665889637 \tabularnewline
17 & 2.33 & 2.15649889492491 & 0.173501105075092 \tabularnewline
18 & 1.67 & 1.70231081873076 & -0.03231081873076 \tabularnewline
19 & 2.22 & 2.19505585009078 & 0.0249441499092237 \tabularnewline
20 & 3.44 & 2.93724981135155 & 0.502750188648446 \tabularnewline
21 & 3 & 2.8055350744053 & 0.194464925594695 \tabularnewline
22 & 3.78 & 3.01752416073607 & 0.762475839263932 \tabularnewline
23 & 2.33 & 2.1765113885936 & 0.153488611406395 \tabularnewline
24 & 3.44 & 2.32403490018434 & 1.11596509981566 \tabularnewline
25 & 2.11 & 2.14406758886262 & -0.0340675888626247 \tabularnewline
26 & 1.78 & 2.24728557340878 & -0.467285573408785 \tabularnewline
27 & 2.22 & 2.39802447574726 & -0.17802447574726 \tabularnewline
28 & 2.33 & 2.02633218190964 & 0.303667818090356 \tabularnewline
29 & 2.44 & 2.44003881296032 & -3.88129603165992e-05 \tabularnewline
30 & 1.89 & 2.16623586045046 & -0.276235860450458 \tabularnewline
31 & 2.67 & 2.25685254669177 & 0.413147453308229 \tabularnewline
32 & 2.78 & 3.38264884446783 & -0.602648844467834 \tabularnewline
33 & 2.89 & 2.96940125743846 & -0.0794012574384616 \tabularnewline
34 & 2.78 & 2.67261482567188 & 0.107385174328116 \tabularnewline
35 & 1.89 & 2.55511938701276 & -0.665119387012756 \tabularnewline
36 & 3.56 & 3.08726198928571 & 0.472738010714292 \tabularnewline
37 & 3.67 & 2.62637327572299 & 1.04362672427701 \tabularnewline
38 & 1.44 & 2.44792781772315 & -1.00792781772315 \tabularnewline
39 & 3.56 & 3.09205235323611 & 0.467947646763895 \tabularnewline
40 & 2.78 & 2.87000854481692 & -0.0900085448169176 \tabularnewline
41 & 3.22 & 2.99949304708145 & 0.220506952918553 \tabularnewline
42 & 2.44 & 2.44608524434564 & -0.00608524434564125 \tabularnewline
43 & 2 & 1.92928563068506 & 0.0707143693149406 \tabularnewline
44 & 1.89 & 2.41813743707477 & -0.528137437074767 \tabularnewline
45 & 2.22 & 2.46572505494169 & -0.245725054941691 \tabularnewline
46 & 1.67 & 2.2729766629049 & -0.6029766629049 \tabularnewline
47 & 2.22 & 2.46383811106635 & -0.243838111066346 \tabularnewline
48 & 3.67 & 3.12206821395363 & 0.547931786046366 \tabularnewline
49 & 3.22 & 2.45967862355637 & 0.760321376443634 \tabularnewline
50 & 2.56 & 2.96380823570237 & -0.403808235702373 \tabularnewline
51 & 2.89 & 2.58553330853655 & 0.304466691463455 \tabularnewline
52 & 2 & 2.10535545035758 & -0.105355450357583 \tabularnewline
53 & 2.22 & 2.08177928863642 & 0.13822071136358 \tabularnewline
54 & 1.22 & 1.30918293503971 & -0.0891829350397122 \tabularnewline
55 & 3.11 & 3.21973934661944 & -0.109739346619443 \tabularnewline
56 & 2.89 & 2.59067148361304 & 0.299328516386964 \tabularnewline
57 & 2.44 & 2.46932296277517 & -0.0293229627751708 \tabularnewline
58 & 1.89 & 2.24408669721329 & -0.354086697213292 \tabularnewline
59 & 1.33 & 1.71222756666654 & -0.382227566666535 \tabularnewline
60 & 1.56 & 2.33656401773723 & -0.776564017737226 \tabularnewline
61 & 1.89 & 2.30242304827434 & -0.412423048274343 \tabularnewline
62 & 2.33 & 2.37085040619262 & -0.0408504061926172 \tabularnewline
63 & 2.11 & 2.54771983549399 & -0.437719835493987 \tabularnewline
64 & 2 & 2.56477350592513 & -0.564773505925133 \tabularnewline
65 & 1.11 & 1.99045399321542 & -0.880453993215423 \tabularnewline
66 & 3.22 & 2.69160978764506 & 0.528390212354943 \tabularnewline
67 & 3.44 & 2.0491135815167 & 1.3908864184833 \tabularnewline
68 & 2.11 & 2.55072671706517 & -0.440726717065172 \tabularnewline
69 & 1 & 2.22742461421608 & -1.22742461421608 \tabularnewline
70 & 2.22 & 2.49557241029063 & -0.275572410290627 \tabularnewline
71 & 3.11 & 1.95833337640515 & 1.15166662359485 \tabularnewline
72 & 2.11 & 2.01424716049106 & 0.0957528395089405 \tabularnewline
73 & 3.33 & 2.56279328559 & 0.767206714409996 \tabularnewline
74 & 3.22 & 3.01633327615721 & 0.20366672384279 \tabularnewline
75 & 2.89 & 2.38482721900129 & 0.505172780998712 \tabularnewline
76 & 2.56 & 2.17260746741752 & 0.387392532582481 \tabularnewline
77 & 1.44 & 2.52865235509974 & -1.08865235509974 \tabularnewline
78 & 2.33 & 2.51379358022805 & -0.183793580228048 \tabularnewline
79 & 2.11 & 2.38331018800897 & -0.273310188008968 \tabularnewline
80 & 3.11 & 2.54821928047126 & 0.561780719528738 \tabularnewline
81 & 2.56 & 2.83762488712413 & -0.277624887124132 \tabularnewline
82 & 2 & 1.53061879078525 & 0.469381209214753 \tabularnewline
83 & 2.33 & 2.3013416170384 & 0.0286583829615991 \tabularnewline
84 & 2.22 & 2.41929460838894 & -0.199294608388945 \tabularnewline
85 & 2.56 & 2.21539978915521 & 0.344600210844785 \tabularnewline
86 & 2.33 & 2.31421726240173 & 0.0157827375982743 \tabularnewline
87 & 2.33 & 2.41766428634331 & -0.0876642863433097 \tabularnewline
88 & 1.67 & 2.53569210125988 & -0.865692101259885 \tabularnewline
89 & 3.11 & 3.01656388709069 & 0.093436112909305 \tabularnewline
90 & 2.11 & 1.96234261275479 & 0.147657387245212 \tabularnewline
91 & 2.89 & 2.36040183765006 & 0.529598162349939 \tabularnewline
92 & 1.11 & 1.46426234116734 & -0.354262341167339 \tabularnewline
93 & 1.78 & 1.90162286667844 & -0.12162286667844 \tabularnewline
94 & 2.44 & 2.35075556008975 & 0.0892444399102509 \tabularnewline
95 & 2.11 & 2.08051389297546 & 0.0294861070245435 \tabularnewline
96 & 3.44 & 3.19507696046712 & 0.244923039532881 \tabularnewline
97 & 3.44 & 2.79931371859859 & 0.640686281401414 \tabularnewline
98 & 3.22 & 2.75493567313293 & 0.465064326867069 \tabularnewline
99 & 2.11 & 1.93390605671242 & 0.176093943287585 \tabularnewline
100 & 2.44 & 2.0983127950242 & 0.3416872049758 \tabularnewline
101 & 2.56 & 2.48717786869275 & 0.0728221313072546 \tabularnewline
102 & 1.67 & 1.79448627942836 & -0.124486279428358 \tabularnewline
103 & 2.22 & 2.38336392570546 & -0.16336392570546 \tabularnewline
104 & 2 & 2.16607519643393 & -0.16607519643393 \tabularnewline
105 & 2.56 & 2.44370669684509 & 0.116293303154914 \tabularnewline
106 & 2.78 & 2.33076345765631 & 0.449236542343687 \tabularnewline
107 & 2.33 & 1.84021453585422 & 0.489785464145777 \tabularnewline
108 & 2.67 & 2.16366817317931 & 0.506331826820692 \tabularnewline
109 & 2.78 & 2.80238079652749 & -0.022380796527489 \tabularnewline
110 & 1.89 & 2.17410926717325 & -0.284109267173246 \tabularnewline
111 & 1.44 & 1.6183983445312 & -0.178398344531196 \tabularnewline
112 & 3.11 & 2.02066627494903 & 1.08933372505097 \tabularnewline
113 & 2.33 & 2.23590268779756 & 0.094097312202441 \tabularnewline
114 & 2.78 & 3.12631220427894 & -0.346312204278936 \tabularnewline
115 & 1 & 2.3017840483425 & -1.3017840483425 \tabularnewline
116 & 1.78 & 1.98039832548046 & -0.200398325480461 \tabularnewline
117 & 2.11 & 2.35477711850275 & -0.244777118502746 \tabularnewline
118 & 1.89 & 2.16312412988436 & -0.273124129884356 \tabularnewline
119 & 2.78 & 2.72178611907598 & 0.0582138809240194 \tabularnewline
120 & 2.22 & 1.73076297281317 & 0.489237027186827 \tabularnewline
121 & 3.22 & 2.40126524422861 & 0.818734755771389 \tabularnewline
122 & 1.56 & 2.11686989106866 & -0.556869891068656 \tabularnewline
123 & 2.44 & 2.98956893444633 & -0.549568934446332 \tabularnewline
124 & 1.67 & 1.73179643037957 & -0.0617964303795683 \tabularnewline
125 & 2.11 & 2.82335888800903 & -0.713358888009026 \tabularnewline
126 & 2.22 & 2.43776649714552 & -0.217766497145517 \tabularnewline
127 & 1.67 & 1.93774983585031 & -0.267749835850306 \tabularnewline
128 & 2.22 & 2.42149440578267 & -0.201494405782667 \tabularnewline
129 & 2 & 2.24633109292469 & -0.246331092924691 \tabularnewline
130 & 3.67 & 2.8275103063471 & 0.842489693652895 \tabularnewline
131 & 2.44 & 2.64302298233732 & -0.203022982337319 \tabularnewline
132 & 1.78 & 1.83148155096471 & -0.0514815509647076 \tabularnewline
133 & 1.89 & 2.11698129809999 & -0.226981298099985 \tabularnewline
134 & 1.78 & 1.67210947636967 & 0.107890523630329 \tabularnewline
135 & 2.33 & 1.89595695971783 & 0.434043040282172 \tabularnewline
136 & 2.89 & 3.06825918596047 & -0.178259185960469 \tabularnewline
137 & 2 & 2.3482452143226 & -0.348245214322604 \tabularnewline
138 & 2 & 2.56639579777148 & -0.566395797771483 \tabularnewline
139 & 1.89 & 2.03615144554984 & -0.146151445549843 \tabularnewline
140 & 2.44 & 2.74828138357633 & -0.308281383576326 \tabularnewline
141 & 3.33 & 2.75065901456468 & 0.579340985435324 \tabularnewline
142 & 3.33 & 3.02851425538898 & 0.301485744611022 \tabularnewline
143 & 2.67 & 3.32104261600875 & -0.651042616008752 \tabularnewline
144 & 2.33 & 2.4442217398624 & -0.114221739862402 \tabularnewline
145 & 2.33 & 2.8207616848349 & -0.490761684834899 \tabularnewline
146 & 3.22 & 3.04517670518964 & 0.174823294810365 \tabularnewline
147 & 3.44 & 2.58073759128435 & 0.859262408715652 \tabularnewline
148 & 2.22 & 2.10867324710564 & 0.111326752894362 \tabularnewline
149 & 1.78 & 1.57686979259688 & 0.203130207403119 \tabularnewline
150 & 2.44 & 2.10300734014503 & 0.336992659854974 \tabularnewline
151 & 2.22 & 2.25656008440865 & -0.0365600844086538 \tabularnewline
152 & 3.11 & 3.03660723917036 & 0.0733927608296406 \tabularnewline
153 & 4.22 & 2.95253182494365 & 1.26746817505635 \tabularnewline
154 & 2.44 & 2.14673051412749 & 0.29326948587251 \tabularnewline
155 & 2.22 & 2.85382118408923 & -0.633821184089225 \tabularnewline
156 & 1.89 & 2.00247881827629 & -0.112478818276291 \tabularnewline
157 & 3.11 & 2.7173934491284 & 0.392606550871604 \tabularnewline
158 & 2.44 & 2.67820031853983 & -0.238200318539832 \tabularnewline
159 & 3.44 & 2.89874521119134 & 0.541254788808662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99593&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]2.67[/C][C]2.77533367772469[/C][C]-0.105333677724692[/C][/ROW]
[ROW][C]2[/C][C]2.78[/C][C]2.41074567258854[/C][C]0.36925432741146[/C][/ROW]
[ROW][C]3[/C][C]1.89[/C][C]2.52902965786729[/C][C]-0.639029657867289[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]2.20550473111726[/C][C]-0.205504731117258[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]2.16224216782134[/C][C]-0.16224216782134[/C][/ROW]
[ROW][C]6[/C][C]1.78[/C][C]2.17178284685851[/C][C]-0.391782846858507[/C][/ROW]
[ROW][C]7[/C][C]2.22[/C][C]2.32192036439803[/C][C]-0.101920364398025[/C][/ROW]
[ROW][C]8[/C][C]1.78[/C][C]2.4869398387404[/C][C]-0.7069398387404[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]2.48970703415709[/C][C]-0.48970703415709[/C][/ROW]
[ROW][C]10[/C][C]1.89[/C][C]2.27926112424257[/C][C]-0.389261124242571[/C][/ROW]
[ROW][C]11[/C][C]2.56[/C][C]2.47718077145792[/C][C]0.0828192285420817[/C][/ROW]
[ROW][C]12[/C][C]3.33[/C][C]2.4785494896314[/C][C]0.851450510368596[/C][/ROW]
[ROW][C]13[/C][C]2.56[/C][C]1.67610345234838[/C][C]0.883896547651618[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]2.12378268647696[/C][C]-0.123782686476957[/C][/ROW]
[ROW][C]15[/C][C]1.67[/C][C]2.41107375734351[/C][C]-0.741073757343511[/C][/ROW]
[ROW][C]16[/C][C]1.33[/C][C]1.98148266588964[/C][C]-0.651482665889637[/C][/ROW]
[ROW][C]17[/C][C]2.33[/C][C]2.15649889492491[/C][C]0.173501105075092[/C][/ROW]
[ROW][C]18[/C][C]1.67[/C][C]1.70231081873076[/C][C]-0.03231081873076[/C][/ROW]
[ROW][C]19[/C][C]2.22[/C][C]2.19505585009078[/C][C]0.0249441499092237[/C][/ROW]
[ROW][C]20[/C][C]3.44[/C][C]2.93724981135155[/C][C]0.502750188648446[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.8055350744053[/C][C]0.194464925594695[/C][/ROW]
[ROW][C]22[/C][C]3.78[/C][C]3.01752416073607[/C][C]0.762475839263932[/C][/ROW]
[ROW][C]23[/C][C]2.33[/C][C]2.1765113885936[/C][C]0.153488611406395[/C][/ROW]
[ROW][C]24[/C][C]3.44[/C][C]2.32403490018434[/C][C]1.11596509981566[/C][/ROW]
[ROW][C]25[/C][C]2.11[/C][C]2.14406758886262[/C][C]-0.0340675888626247[/C][/ROW]
[ROW][C]26[/C][C]1.78[/C][C]2.24728557340878[/C][C]-0.467285573408785[/C][/ROW]
[ROW][C]27[/C][C]2.22[/C][C]2.39802447574726[/C][C]-0.17802447574726[/C][/ROW]
[ROW][C]28[/C][C]2.33[/C][C]2.02633218190964[/C][C]0.303667818090356[/C][/ROW]
[ROW][C]29[/C][C]2.44[/C][C]2.44003881296032[/C][C]-3.88129603165992e-05[/C][/ROW]
[ROW][C]30[/C][C]1.89[/C][C]2.16623586045046[/C][C]-0.276235860450458[/C][/ROW]
[ROW][C]31[/C][C]2.67[/C][C]2.25685254669177[/C][C]0.413147453308229[/C][/ROW]
[ROW][C]32[/C][C]2.78[/C][C]3.38264884446783[/C][C]-0.602648844467834[/C][/ROW]
[ROW][C]33[/C][C]2.89[/C][C]2.96940125743846[/C][C]-0.0794012574384616[/C][/ROW]
[ROW][C]34[/C][C]2.78[/C][C]2.67261482567188[/C][C]0.107385174328116[/C][/ROW]
[ROW][C]35[/C][C]1.89[/C][C]2.55511938701276[/C][C]-0.665119387012756[/C][/ROW]
[ROW][C]36[/C][C]3.56[/C][C]3.08726198928571[/C][C]0.472738010714292[/C][/ROW]
[ROW][C]37[/C][C]3.67[/C][C]2.62637327572299[/C][C]1.04362672427701[/C][/ROW]
[ROW][C]38[/C][C]1.44[/C][C]2.44792781772315[/C][C]-1.00792781772315[/C][/ROW]
[ROW][C]39[/C][C]3.56[/C][C]3.09205235323611[/C][C]0.467947646763895[/C][/ROW]
[ROW][C]40[/C][C]2.78[/C][C]2.87000854481692[/C][C]-0.0900085448169176[/C][/ROW]
[ROW][C]41[/C][C]3.22[/C][C]2.99949304708145[/C][C]0.220506952918553[/C][/ROW]
[ROW][C]42[/C][C]2.44[/C][C]2.44608524434564[/C][C]-0.00608524434564125[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.92928563068506[/C][C]0.0707143693149406[/C][/ROW]
[ROW][C]44[/C][C]1.89[/C][C]2.41813743707477[/C][C]-0.528137437074767[/C][/ROW]
[ROW][C]45[/C][C]2.22[/C][C]2.46572505494169[/C][C]-0.245725054941691[/C][/ROW]
[ROW][C]46[/C][C]1.67[/C][C]2.2729766629049[/C][C]-0.6029766629049[/C][/ROW]
[ROW][C]47[/C][C]2.22[/C][C]2.46383811106635[/C][C]-0.243838111066346[/C][/ROW]
[ROW][C]48[/C][C]3.67[/C][C]3.12206821395363[/C][C]0.547931786046366[/C][/ROW]
[ROW][C]49[/C][C]3.22[/C][C]2.45967862355637[/C][C]0.760321376443634[/C][/ROW]
[ROW][C]50[/C][C]2.56[/C][C]2.96380823570237[/C][C]-0.403808235702373[/C][/ROW]
[ROW][C]51[/C][C]2.89[/C][C]2.58553330853655[/C][C]0.304466691463455[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]2.10535545035758[/C][C]-0.105355450357583[/C][/ROW]
[ROW][C]53[/C][C]2.22[/C][C]2.08177928863642[/C][C]0.13822071136358[/C][/ROW]
[ROW][C]54[/C][C]1.22[/C][C]1.30918293503971[/C][C]-0.0891829350397122[/C][/ROW]
[ROW][C]55[/C][C]3.11[/C][C]3.21973934661944[/C][C]-0.109739346619443[/C][/ROW]
[ROW][C]56[/C][C]2.89[/C][C]2.59067148361304[/C][C]0.299328516386964[/C][/ROW]
[ROW][C]57[/C][C]2.44[/C][C]2.46932296277517[/C][C]-0.0293229627751708[/C][/ROW]
[ROW][C]58[/C][C]1.89[/C][C]2.24408669721329[/C][C]-0.354086697213292[/C][/ROW]
[ROW][C]59[/C][C]1.33[/C][C]1.71222756666654[/C][C]-0.382227566666535[/C][/ROW]
[ROW][C]60[/C][C]1.56[/C][C]2.33656401773723[/C][C]-0.776564017737226[/C][/ROW]
[ROW][C]61[/C][C]1.89[/C][C]2.30242304827434[/C][C]-0.412423048274343[/C][/ROW]
[ROW][C]62[/C][C]2.33[/C][C]2.37085040619262[/C][C]-0.0408504061926172[/C][/ROW]
[ROW][C]63[/C][C]2.11[/C][C]2.54771983549399[/C][C]-0.437719835493987[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]2.56477350592513[/C][C]-0.564773505925133[/C][/ROW]
[ROW][C]65[/C][C]1.11[/C][C]1.99045399321542[/C][C]-0.880453993215423[/C][/ROW]
[ROW][C]66[/C][C]3.22[/C][C]2.69160978764506[/C][C]0.528390212354943[/C][/ROW]
[ROW][C]67[/C][C]3.44[/C][C]2.0491135815167[/C][C]1.3908864184833[/C][/ROW]
[ROW][C]68[/C][C]2.11[/C][C]2.55072671706517[/C][C]-0.440726717065172[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]2.22742461421608[/C][C]-1.22742461421608[/C][/ROW]
[ROW][C]70[/C][C]2.22[/C][C]2.49557241029063[/C][C]-0.275572410290627[/C][/ROW]
[ROW][C]71[/C][C]3.11[/C][C]1.95833337640515[/C][C]1.15166662359485[/C][/ROW]
[ROW][C]72[/C][C]2.11[/C][C]2.01424716049106[/C][C]0.0957528395089405[/C][/ROW]
[ROW][C]73[/C][C]3.33[/C][C]2.56279328559[/C][C]0.767206714409996[/C][/ROW]
[ROW][C]74[/C][C]3.22[/C][C]3.01633327615721[/C][C]0.20366672384279[/C][/ROW]
[ROW][C]75[/C][C]2.89[/C][C]2.38482721900129[/C][C]0.505172780998712[/C][/ROW]
[ROW][C]76[/C][C]2.56[/C][C]2.17260746741752[/C][C]0.387392532582481[/C][/ROW]
[ROW][C]77[/C][C]1.44[/C][C]2.52865235509974[/C][C]-1.08865235509974[/C][/ROW]
[ROW][C]78[/C][C]2.33[/C][C]2.51379358022805[/C][C]-0.183793580228048[/C][/ROW]
[ROW][C]79[/C][C]2.11[/C][C]2.38331018800897[/C][C]-0.273310188008968[/C][/ROW]
[ROW][C]80[/C][C]3.11[/C][C]2.54821928047126[/C][C]0.561780719528738[/C][/ROW]
[ROW][C]81[/C][C]2.56[/C][C]2.83762488712413[/C][C]-0.277624887124132[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]1.53061879078525[/C][C]0.469381209214753[/C][/ROW]
[ROW][C]83[/C][C]2.33[/C][C]2.3013416170384[/C][C]0.0286583829615991[/C][/ROW]
[ROW][C]84[/C][C]2.22[/C][C]2.41929460838894[/C][C]-0.199294608388945[/C][/ROW]
[ROW][C]85[/C][C]2.56[/C][C]2.21539978915521[/C][C]0.344600210844785[/C][/ROW]
[ROW][C]86[/C][C]2.33[/C][C]2.31421726240173[/C][C]0.0157827375982743[/C][/ROW]
[ROW][C]87[/C][C]2.33[/C][C]2.41766428634331[/C][C]-0.0876642863433097[/C][/ROW]
[ROW][C]88[/C][C]1.67[/C][C]2.53569210125988[/C][C]-0.865692101259885[/C][/ROW]
[ROW][C]89[/C][C]3.11[/C][C]3.01656388709069[/C][C]0.093436112909305[/C][/ROW]
[ROW][C]90[/C][C]2.11[/C][C]1.96234261275479[/C][C]0.147657387245212[/C][/ROW]
[ROW][C]91[/C][C]2.89[/C][C]2.36040183765006[/C][C]0.529598162349939[/C][/ROW]
[ROW][C]92[/C][C]1.11[/C][C]1.46426234116734[/C][C]-0.354262341167339[/C][/ROW]
[ROW][C]93[/C][C]1.78[/C][C]1.90162286667844[/C][C]-0.12162286667844[/C][/ROW]
[ROW][C]94[/C][C]2.44[/C][C]2.35075556008975[/C][C]0.0892444399102509[/C][/ROW]
[ROW][C]95[/C][C]2.11[/C][C]2.08051389297546[/C][C]0.0294861070245435[/C][/ROW]
[ROW][C]96[/C][C]3.44[/C][C]3.19507696046712[/C][C]0.244923039532881[/C][/ROW]
[ROW][C]97[/C][C]3.44[/C][C]2.79931371859859[/C][C]0.640686281401414[/C][/ROW]
[ROW][C]98[/C][C]3.22[/C][C]2.75493567313293[/C][C]0.465064326867069[/C][/ROW]
[ROW][C]99[/C][C]2.11[/C][C]1.93390605671242[/C][C]0.176093943287585[/C][/ROW]
[ROW][C]100[/C][C]2.44[/C][C]2.0983127950242[/C][C]0.3416872049758[/C][/ROW]
[ROW][C]101[/C][C]2.56[/C][C]2.48717786869275[/C][C]0.0728221313072546[/C][/ROW]
[ROW][C]102[/C][C]1.67[/C][C]1.79448627942836[/C][C]-0.124486279428358[/C][/ROW]
[ROW][C]103[/C][C]2.22[/C][C]2.38336392570546[/C][C]-0.16336392570546[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]2.16607519643393[/C][C]-0.16607519643393[/C][/ROW]
[ROW][C]105[/C][C]2.56[/C][C]2.44370669684509[/C][C]0.116293303154914[/C][/ROW]
[ROW][C]106[/C][C]2.78[/C][C]2.33076345765631[/C][C]0.449236542343687[/C][/ROW]
[ROW][C]107[/C][C]2.33[/C][C]1.84021453585422[/C][C]0.489785464145777[/C][/ROW]
[ROW][C]108[/C][C]2.67[/C][C]2.16366817317931[/C][C]0.506331826820692[/C][/ROW]
[ROW][C]109[/C][C]2.78[/C][C]2.80238079652749[/C][C]-0.022380796527489[/C][/ROW]
[ROW][C]110[/C][C]1.89[/C][C]2.17410926717325[/C][C]-0.284109267173246[/C][/ROW]
[ROW][C]111[/C][C]1.44[/C][C]1.6183983445312[/C][C]-0.178398344531196[/C][/ROW]
[ROW][C]112[/C][C]3.11[/C][C]2.02066627494903[/C][C]1.08933372505097[/C][/ROW]
[ROW][C]113[/C][C]2.33[/C][C]2.23590268779756[/C][C]0.094097312202441[/C][/ROW]
[ROW][C]114[/C][C]2.78[/C][C]3.12631220427894[/C][C]-0.346312204278936[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]2.3017840483425[/C][C]-1.3017840483425[/C][/ROW]
[ROW][C]116[/C][C]1.78[/C][C]1.98039832548046[/C][C]-0.200398325480461[/C][/ROW]
[ROW][C]117[/C][C]2.11[/C][C]2.35477711850275[/C][C]-0.244777118502746[/C][/ROW]
[ROW][C]118[/C][C]1.89[/C][C]2.16312412988436[/C][C]-0.273124129884356[/C][/ROW]
[ROW][C]119[/C][C]2.78[/C][C]2.72178611907598[/C][C]0.0582138809240194[/C][/ROW]
[ROW][C]120[/C][C]2.22[/C][C]1.73076297281317[/C][C]0.489237027186827[/C][/ROW]
[ROW][C]121[/C][C]3.22[/C][C]2.40126524422861[/C][C]0.818734755771389[/C][/ROW]
[ROW][C]122[/C][C]1.56[/C][C]2.11686989106866[/C][C]-0.556869891068656[/C][/ROW]
[ROW][C]123[/C][C]2.44[/C][C]2.98956893444633[/C][C]-0.549568934446332[/C][/ROW]
[ROW][C]124[/C][C]1.67[/C][C]1.73179643037957[/C][C]-0.0617964303795683[/C][/ROW]
[ROW][C]125[/C][C]2.11[/C][C]2.82335888800903[/C][C]-0.713358888009026[/C][/ROW]
[ROW][C]126[/C][C]2.22[/C][C]2.43776649714552[/C][C]-0.217766497145517[/C][/ROW]
[ROW][C]127[/C][C]1.67[/C][C]1.93774983585031[/C][C]-0.267749835850306[/C][/ROW]
[ROW][C]128[/C][C]2.22[/C][C]2.42149440578267[/C][C]-0.201494405782667[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]2.24633109292469[/C][C]-0.246331092924691[/C][/ROW]
[ROW][C]130[/C][C]3.67[/C][C]2.8275103063471[/C][C]0.842489693652895[/C][/ROW]
[ROW][C]131[/C][C]2.44[/C][C]2.64302298233732[/C][C]-0.203022982337319[/C][/ROW]
[ROW][C]132[/C][C]1.78[/C][C]1.83148155096471[/C][C]-0.0514815509647076[/C][/ROW]
[ROW][C]133[/C][C]1.89[/C][C]2.11698129809999[/C][C]-0.226981298099985[/C][/ROW]
[ROW][C]134[/C][C]1.78[/C][C]1.67210947636967[/C][C]0.107890523630329[/C][/ROW]
[ROW][C]135[/C][C]2.33[/C][C]1.89595695971783[/C][C]0.434043040282172[/C][/ROW]
[ROW][C]136[/C][C]2.89[/C][C]3.06825918596047[/C][C]-0.178259185960469[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]2.3482452143226[/C][C]-0.348245214322604[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]2.56639579777148[/C][C]-0.566395797771483[/C][/ROW]
[ROW][C]139[/C][C]1.89[/C][C]2.03615144554984[/C][C]-0.146151445549843[/C][/ROW]
[ROW][C]140[/C][C]2.44[/C][C]2.74828138357633[/C][C]-0.308281383576326[/C][/ROW]
[ROW][C]141[/C][C]3.33[/C][C]2.75065901456468[/C][C]0.579340985435324[/C][/ROW]
[ROW][C]142[/C][C]3.33[/C][C]3.02851425538898[/C][C]0.301485744611022[/C][/ROW]
[ROW][C]143[/C][C]2.67[/C][C]3.32104261600875[/C][C]-0.651042616008752[/C][/ROW]
[ROW][C]144[/C][C]2.33[/C][C]2.4442217398624[/C][C]-0.114221739862402[/C][/ROW]
[ROW][C]145[/C][C]2.33[/C][C]2.8207616848349[/C][C]-0.490761684834899[/C][/ROW]
[ROW][C]146[/C][C]3.22[/C][C]3.04517670518964[/C][C]0.174823294810365[/C][/ROW]
[ROW][C]147[/C][C]3.44[/C][C]2.58073759128435[/C][C]0.859262408715652[/C][/ROW]
[ROW][C]148[/C][C]2.22[/C][C]2.10867324710564[/C][C]0.111326752894362[/C][/ROW]
[ROW][C]149[/C][C]1.78[/C][C]1.57686979259688[/C][C]0.203130207403119[/C][/ROW]
[ROW][C]150[/C][C]2.44[/C][C]2.10300734014503[/C][C]0.336992659854974[/C][/ROW]
[ROW][C]151[/C][C]2.22[/C][C]2.25656008440865[/C][C]-0.0365600844086538[/C][/ROW]
[ROW][C]152[/C][C]3.11[/C][C]3.03660723917036[/C][C]0.0733927608296406[/C][/ROW]
[ROW][C]153[/C][C]4.22[/C][C]2.95253182494365[/C][C]1.26746817505635[/C][/ROW]
[ROW][C]154[/C][C]2.44[/C][C]2.14673051412749[/C][C]0.29326948587251[/C][/ROW]
[ROW][C]155[/C][C]2.22[/C][C]2.85382118408923[/C][C]-0.633821184089225[/C][/ROW]
[ROW][C]156[/C][C]1.89[/C][C]2.00247881827629[/C][C]-0.112478818276291[/C][/ROW]
[ROW][C]157[/C][C]3.11[/C][C]2.7173934491284[/C][C]0.392606550871604[/C][/ROW]
[ROW][C]158[/C][C]2.44[/C][C]2.67820031853983[/C][C]-0.238200318539832[/C][/ROW]
[ROW][C]159[/C][C]3.44[/C][C]2.89874521119134[/C][C]0.541254788808662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99593&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99593&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.672.77533367772469-0.105333677724692
22.782.410745672588540.36925432741146
31.892.52902965786729-0.639029657867289
422.20550473111726-0.205504731117258
522.16224216782134-0.16224216782134
61.782.17178284685851-0.391782846858507
72.222.32192036439803-0.101920364398025
81.782.4869398387404-0.7069398387404
922.48970703415709-0.48970703415709
101.892.27926112424257-0.389261124242571
112.562.477180771457920.0828192285420817
123.332.47854948963140.851450510368596
132.561.676103452348380.883896547651618
1422.12378268647696-0.123782686476957
151.672.41107375734351-0.741073757343511
161.331.98148266588964-0.651482665889637
172.332.156498894924910.173501105075092
181.671.70231081873076-0.03231081873076
192.222.195055850090780.0249441499092237
203.442.937249811351550.502750188648446
2132.80553507440530.194464925594695
223.783.017524160736070.762475839263932
232.332.17651138859360.153488611406395
243.442.324034900184341.11596509981566
252.112.14406758886262-0.0340675888626247
261.782.24728557340878-0.467285573408785
272.222.39802447574726-0.17802447574726
282.332.026332181909640.303667818090356
292.442.44003881296032-3.88129603165992e-05
301.892.16623586045046-0.276235860450458
312.672.256852546691770.413147453308229
322.783.38264884446783-0.602648844467834
332.892.96940125743846-0.0794012574384616
342.782.672614825671880.107385174328116
351.892.55511938701276-0.665119387012756
363.563.087261989285710.472738010714292
373.672.626373275722991.04362672427701
381.442.44792781772315-1.00792781772315
393.563.092052353236110.467947646763895
402.782.87000854481692-0.0900085448169176
413.222.999493047081450.220506952918553
422.442.44608524434564-0.00608524434564125
4321.929285630685060.0707143693149406
441.892.41813743707477-0.528137437074767
452.222.46572505494169-0.245725054941691
461.672.2729766629049-0.6029766629049
472.222.46383811106635-0.243838111066346
483.673.122068213953630.547931786046366
493.222.459678623556370.760321376443634
502.562.96380823570237-0.403808235702373
512.892.585533308536550.304466691463455
5222.10535545035758-0.105355450357583
532.222.081779288636420.13822071136358
541.221.30918293503971-0.0891829350397122
553.113.21973934661944-0.109739346619443
562.892.590671483613040.299328516386964
572.442.46932296277517-0.0293229627751708
581.892.24408669721329-0.354086697213292
591.331.71222756666654-0.382227566666535
601.562.33656401773723-0.776564017737226
611.892.30242304827434-0.412423048274343
622.332.37085040619262-0.0408504061926172
632.112.54771983549399-0.437719835493987
6422.56477350592513-0.564773505925133
651.111.99045399321542-0.880453993215423
663.222.691609787645060.528390212354943
673.442.04911358151671.3908864184833
682.112.55072671706517-0.440726717065172
6912.22742461421608-1.22742461421608
702.222.49557241029063-0.275572410290627
713.111.958333376405151.15166662359485
722.112.014247160491060.0957528395089405
733.332.562793285590.767206714409996
743.223.016333276157210.20366672384279
752.892.384827219001290.505172780998712
762.562.172607467417520.387392532582481
771.442.52865235509974-1.08865235509974
782.332.51379358022805-0.183793580228048
792.112.38331018800897-0.273310188008968
803.112.548219280471260.561780719528738
812.562.83762488712413-0.277624887124132
8221.530618790785250.469381209214753
832.332.30134161703840.0286583829615991
842.222.41929460838894-0.199294608388945
852.562.215399789155210.344600210844785
862.332.314217262401730.0157827375982743
872.332.41766428634331-0.0876642863433097
881.672.53569210125988-0.865692101259885
893.113.016563887090690.093436112909305
902.111.962342612754790.147657387245212
912.892.360401837650060.529598162349939
921.111.46426234116734-0.354262341167339
931.781.90162286667844-0.12162286667844
942.442.350755560089750.0892444399102509
952.112.080513892975460.0294861070245435
963.443.195076960467120.244923039532881
973.442.799313718598590.640686281401414
983.222.754935673132930.465064326867069
992.111.933906056712420.176093943287585
1002.442.09831279502420.3416872049758
1012.562.487177868692750.0728221313072546
1021.671.79448627942836-0.124486279428358
1032.222.38336392570546-0.16336392570546
10422.16607519643393-0.16607519643393
1052.562.443706696845090.116293303154914
1062.782.330763457656310.449236542343687
1072.331.840214535854220.489785464145777
1082.672.163668173179310.506331826820692
1092.782.80238079652749-0.022380796527489
1101.892.17410926717325-0.284109267173246
1111.441.6183983445312-0.178398344531196
1123.112.020666274949031.08933372505097
1132.332.235902687797560.094097312202441
1142.783.12631220427894-0.346312204278936
11512.3017840483425-1.3017840483425
1161.781.98039832548046-0.200398325480461
1172.112.35477711850275-0.244777118502746
1181.892.16312412988436-0.273124129884356
1192.782.721786119075980.0582138809240194
1202.221.730762972813170.489237027186827
1213.222.401265244228610.818734755771389
1221.562.11686989106866-0.556869891068656
1232.442.98956893444633-0.549568934446332
1241.671.73179643037957-0.0617964303795683
1252.112.82335888800903-0.713358888009026
1262.222.43776649714552-0.217766497145517
1271.671.93774983585031-0.267749835850306
1282.222.42149440578267-0.201494405782667
12922.24633109292469-0.246331092924691
1303.672.82751030634710.842489693652895
1312.442.64302298233732-0.203022982337319
1321.781.83148155096471-0.0514815509647076
1331.892.11698129809999-0.226981298099985
1341.781.672109476369670.107890523630329
1352.331.895956959717830.434043040282172
1362.893.06825918596047-0.178259185960469
13722.3482452143226-0.348245214322604
13822.56639579777148-0.566395797771483
1391.892.03615144554984-0.146151445549843
1402.442.74828138357633-0.308281383576326
1413.332.750659014564680.579340985435324
1423.333.028514255388980.301485744611022
1432.673.32104261600875-0.651042616008752
1442.332.4442217398624-0.114221739862402
1452.332.8207616848349-0.490761684834899
1463.223.045176705189640.174823294810365
1473.442.580737591284350.859262408715652
1482.222.108673247105640.111326752894362
1491.781.576869792596880.203130207403119
1502.442.103007340145030.336992659854974
1512.222.25656008440865-0.0365600844086538
1523.113.036607239170360.0733927608296406
1534.222.952531824943651.26746817505635
1542.442.146730514127490.29326948587251
1552.222.85382118408923-0.633821184089225
1561.892.00247881827629-0.112478818276291
1573.112.71739344912840.392606550871604
1582.442.67820031853983-0.238200318539832
1593.442.898745211191340.541254788808662






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2139145335043650.427829067008730.786085466495635
100.1036743392292570.2073486784585150.896325660770743
110.230408437800710.460816875601420.76959156219929
120.6524067601158060.6951864797683880.347593239884194
130.6668130095868930.6663739808262140.333186990413107
140.6417071441097550.7165857117804890.358292855890244
150.6459328711978080.7081342576043850.354067128802192
160.5810133472778220.8379733054443570.418986652722178
170.4959523543459270.9919047086918540.504047645654073
180.46511198919090.93022397838180.5348880108091
190.4011697783279270.8023395566558540.598830221672073
200.4350092611138670.8700185222277330.564990738886133
210.3948136979980860.7896273959961730.605186302001914
220.4000090692036890.8000181384073780.599990930796311
230.3386599432275180.6773198864550360.661340056772482
240.6001138459289220.7997723081421560.399886154071078
250.5302119890858080.9395760218283840.469788010914192
260.4999586932147140.9999173864294270.500041306785286
270.4382063846532510.8764127693065020.561793615346749
280.3912367572172970.7824735144345950.608763242782703
290.3310863240999120.6621726481998250.668913675900088
300.2791698728966380.5583397457932750.720830127103362
310.3406353985137160.6812707970274320.659364601486284
320.4007343645236010.8014687290472030.599265635476399
330.3574668525914060.7149337051828120.642533147408594
340.3688889556619060.7377779113238120.631111044338094
350.3831668283675790.7663336567351580.616833171632421
360.3749604869949010.7499209739898020.625039513005099
370.5558462728618470.8883074542763060.444153727138153
380.7612257694457850.477548461108430.238774230554215
390.758167104155810.483665791688380.24183289584419
400.716459933422390.5670801331552210.28354006657761
410.6916744081273640.6166511837452720.308325591872636
420.6424496882175420.7151006235649170.357550311782458
430.594006505071890.811986989856220.40599349492811
440.5804115553370720.8391768893258560.419588444662928
450.5462171641374370.9075656717251260.453782835862563
460.5678013867907910.8643972264184190.432198613209209
470.5270047219777110.9459905560445770.472995278022289
480.5180980662504460.963803867499110.481901933749554
490.5822876853748290.8354246292503420.417712314625171
500.5606588523571210.8786822952857580.439341147642879
510.5252440885792450.949511822841510.474755911420755
520.4759275937562660.9518551875125320.524072406243734
530.4375251391555150.875050278311030.562474860844485
540.3906885185793170.7813770371586350.609311481420683
550.3479797867122390.6959595734244780.652020213287761
560.3208090867909880.6416181735819760.679190913209012
570.2777014808643410.5554029617286820.722298519135659
580.2538538140708540.5077076281417090.746146185929146
590.2342464423771290.4684928847542570.765753557622871
600.2923915878012940.5847831756025880.707608412198706
610.2805815787596640.5611631575193270.719418421240337
620.2431573320065690.4863146640131380.756842667993431
630.2306702076019770.4613404152039530.769329792398023
640.2650751745206260.5301503490412530.734924825479374
650.34169735193650.6833947038730010.6583026480635
660.3440157142285780.6880314284571570.655984285771422
670.6760600569715440.6478798860569120.323939943028456
680.6692845505469070.6614308989061860.330715449453093
690.8451534768966790.3096930462066430.154846523103321
700.825586885103560.348826229792880.17441311489644
710.9309381680186340.1381236639627320.0690618319813658
720.9147005182284730.1705989635430530.0852994817715266
730.9374713723010130.1250572553979740.062528627698987
740.9255046078496630.1489907843006740.074495392150337
750.9274751374025380.1450497251949240.072524862597462
760.921359797147130.157280405705740.07864020285287
770.9706182195399360.05876356092012770.0293817804600638
780.963390980743840.073218038512320.03660901925616
790.956154910237770.08769017952446050.0438450897622303
800.9588097798578240.08238044028435220.0411902201421761
810.9511044656683240.0977910686633530.0488955343316765
820.950797589334120.09840482133176130.0492024106658806
830.9381351751808620.1237296496382760.0618648248191382
840.925547089416610.148905821166780.07445291058339
850.917502269402110.164995461195780.0824977305978902
860.9024384093409730.1951231813180530.0975615906590265
870.88179374735650.2364125052870010.118206252643501
880.9251324011609530.1497351976780940.0748675988390469
890.909419444068850.1811611118622990.0905805559311496
900.8903452376142640.2193095247714720.109654762385736
910.8922521836066770.2154956327866460.107747816393323
920.8817276295692690.2365447408614630.118272370430731
930.8606806197780830.2786387604438340.139319380221917
940.8342446504921110.3315106990157780.165755349507889
950.802606898697660.394786202604680.19739310130234
960.7741384511390770.4517230977218470.225861548860923
970.7925421467286340.4149157065427310.207457853271366
980.7872543183547970.4254913632904060.212745681645203
990.7534725590309360.4930548819381280.246527440969064
1000.7296962313615590.5406075372768820.270303768638441
1010.688553203860680.6228935922786410.31144679613932
1020.6505828256909190.6988343486181620.349417174309081
1030.6129122062884030.7741755874231950.387087793711597
1040.5711419825736870.8577160348526260.428858017426313
1050.5258418177759810.9483163644480370.474158182224019
1060.5064553717668220.9870892564663570.493544628233179
1070.5028453042741430.9943093914517150.497154695725857
1080.5157227137332460.9685545725335090.484277286266754
1090.4656124270719620.9312248541439240.534387572928038
1100.442151441177730.884302882355460.55784855882227
1110.4028620590655490.8057241181310980.597137940934451
1120.6270164168351660.7459671663296690.372983583164835
1130.6187050795290670.7625898409418660.381294920470933
1140.590199767328620.8196004653427610.40980023267138
1150.7908563943659810.4182872112680370.209143605634019
1160.7688629424127380.4622741151745230.231137057587262
1170.7581785383920750.4836429232158490.241821461607924
1180.731647392705790.5367052145884190.26835260729421
1190.684243214364670.6315135712706610.31575678563533
1200.692626136192880.6147477276142410.307373863807121
1210.7440604391031570.5118791217936870.255939560896843
1220.7484840826974250.503031834605150.251515917302575
1230.7548003979204940.4903992041590120.245199602079506
1240.7056464121971170.5887071756057650.294353587802883
1250.7486948913273120.5026102173453770.251305108672688
1260.7221837547247370.5556324905505260.277816245275263
1270.7113732173554950.577253565289010.288626782644505
1280.6779551711776940.6440896576446120.322044828822306
1290.6526460373194320.6947079253611360.347353962680568
1300.7250387458982420.5499225082035170.274961254101758
1310.6721753812388350.6556492375223290.327824618761165
1320.6116059603846880.7767880792306240.388394039615312
1330.6112742154610970.7774515690778060.388725784538903
1340.5520560160632160.8958879678735680.447943983936784
1350.5109578697536590.9780842604926810.489042130246341
1360.4828055714946610.9656111429893220.517194428505339
1370.4881656189591380.9763312379182770.511834381040862
1380.5317948752359590.9364102495280820.468205124764041
1390.464212791066080.928425582132160.53578720893392
1400.39008034808410.78016069616820.6099196519159
1410.5015022008919130.9969955982161750.498497799108087
1420.4488329314058560.8976658628117130.551167068594144
1430.378358130760960.756716261521920.62164186923904
1440.2985800282534680.5971600565069370.701419971746532
1450.3569862353710260.7139724707420530.643013764628974
1460.2643825845755630.5287651691511260.735617415424437
1470.3415868928939460.6831737857878930.658413107106054
1480.2357393109400210.4714786218800430.764260689059979
1490.1467654360858820.2935308721717640.853234563914118
1500.09219834241016970.1843966848203390.90780165758983

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.213914533504365 & 0.42782906700873 & 0.786085466495635 \tabularnewline
10 & 0.103674339229257 & 0.207348678458515 & 0.896325660770743 \tabularnewline
11 & 0.23040843780071 & 0.46081687560142 & 0.76959156219929 \tabularnewline
12 & 0.652406760115806 & 0.695186479768388 & 0.347593239884194 \tabularnewline
13 & 0.666813009586893 & 0.666373980826214 & 0.333186990413107 \tabularnewline
14 & 0.641707144109755 & 0.716585711780489 & 0.358292855890244 \tabularnewline
15 & 0.645932871197808 & 0.708134257604385 & 0.354067128802192 \tabularnewline
16 & 0.581013347277822 & 0.837973305444357 & 0.418986652722178 \tabularnewline
17 & 0.495952354345927 & 0.991904708691854 & 0.504047645654073 \tabularnewline
18 & 0.4651119891909 & 0.9302239783818 & 0.5348880108091 \tabularnewline
19 & 0.401169778327927 & 0.802339556655854 & 0.598830221672073 \tabularnewline
20 & 0.435009261113867 & 0.870018522227733 & 0.564990738886133 \tabularnewline
21 & 0.394813697998086 & 0.789627395996173 & 0.605186302001914 \tabularnewline
22 & 0.400009069203689 & 0.800018138407378 & 0.599990930796311 \tabularnewline
23 & 0.338659943227518 & 0.677319886455036 & 0.661340056772482 \tabularnewline
24 & 0.600113845928922 & 0.799772308142156 & 0.399886154071078 \tabularnewline
25 & 0.530211989085808 & 0.939576021828384 & 0.469788010914192 \tabularnewline
26 & 0.499958693214714 & 0.999917386429427 & 0.500041306785286 \tabularnewline
27 & 0.438206384653251 & 0.876412769306502 & 0.561793615346749 \tabularnewline
28 & 0.391236757217297 & 0.782473514434595 & 0.608763242782703 \tabularnewline
29 & 0.331086324099912 & 0.662172648199825 & 0.668913675900088 \tabularnewline
30 & 0.279169872896638 & 0.558339745793275 & 0.720830127103362 \tabularnewline
31 & 0.340635398513716 & 0.681270797027432 & 0.659364601486284 \tabularnewline
32 & 0.400734364523601 & 0.801468729047203 & 0.599265635476399 \tabularnewline
33 & 0.357466852591406 & 0.714933705182812 & 0.642533147408594 \tabularnewline
34 & 0.368888955661906 & 0.737777911323812 & 0.631111044338094 \tabularnewline
35 & 0.383166828367579 & 0.766333656735158 & 0.616833171632421 \tabularnewline
36 & 0.374960486994901 & 0.749920973989802 & 0.625039513005099 \tabularnewline
37 & 0.555846272861847 & 0.888307454276306 & 0.444153727138153 \tabularnewline
38 & 0.761225769445785 & 0.47754846110843 & 0.238774230554215 \tabularnewline
39 & 0.75816710415581 & 0.48366579168838 & 0.24183289584419 \tabularnewline
40 & 0.71645993342239 & 0.567080133155221 & 0.28354006657761 \tabularnewline
41 & 0.691674408127364 & 0.616651183745272 & 0.308325591872636 \tabularnewline
42 & 0.642449688217542 & 0.715100623564917 & 0.357550311782458 \tabularnewline
43 & 0.59400650507189 & 0.81198698985622 & 0.40599349492811 \tabularnewline
44 & 0.580411555337072 & 0.839176889325856 & 0.419588444662928 \tabularnewline
45 & 0.546217164137437 & 0.907565671725126 & 0.453782835862563 \tabularnewline
46 & 0.567801386790791 & 0.864397226418419 & 0.432198613209209 \tabularnewline
47 & 0.527004721977711 & 0.945990556044577 & 0.472995278022289 \tabularnewline
48 & 0.518098066250446 & 0.96380386749911 & 0.481901933749554 \tabularnewline
49 & 0.582287685374829 & 0.835424629250342 & 0.417712314625171 \tabularnewline
50 & 0.560658852357121 & 0.878682295285758 & 0.439341147642879 \tabularnewline
51 & 0.525244088579245 & 0.94951182284151 & 0.474755911420755 \tabularnewline
52 & 0.475927593756266 & 0.951855187512532 & 0.524072406243734 \tabularnewline
53 & 0.437525139155515 & 0.87505027831103 & 0.562474860844485 \tabularnewline
54 & 0.390688518579317 & 0.781377037158635 & 0.609311481420683 \tabularnewline
55 & 0.347979786712239 & 0.695959573424478 & 0.652020213287761 \tabularnewline
56 & 0.320809086790988 & 0.641618173581976 & 0.679190913209012 \tabularnewline
57 & 0.277701480864341 & 0.555402961728682 & 0.722298519135659 \tabularnewline
58 & 0.253853814070854 & 0.507707628141709 & 0.746146185929146 \tabularnewline
59 & 0.234246442377129 & 0.468492884754257 & 0.765753557622871 \tabularnewline
60 & 0.292391587801294 & 0.584783175602588 & 0.707608412198706 \tabularnewline
61 & 0.280581578759664 & 0.561163157519327 & 0.719418421240337 \tabularnewline
62 & 0.243157332006569 & 0.486314664013138 & 0.756842667993431 \tabularnewline
63 & 0.230670207601977 & 0.461340415203953 & 0.769329792398023 \tabularnewline
64 & 0.265075174520626 & 0.530150349041253 & 0.734924825479374 \tabularnewline
65 & 0.3416973519365 & 0.683394703873001 & 0.6583026480635 \tabularnewline
66 & 0.344015714228578 & 0.688031428457157 & 0.655984285771422 \tabularnewline
67 & 0.676060056971544 & 0.647879886056912 & 0.323939943028456 \tabularnewline
68 & 0.669284550546907 & 0.661430898906186 & 0.330715449453093 \tabularnewline
69 & 0.845153476896679 & 0.309693046206643 & 0.154846523103321 \tabularnewline
70 & 0.82558688510356 & 0.34882622979288 & 0.17441311489644 \tabularnewline
71 & 0.930938168018634 & 0.138123663962732 & 0.0690618319813658 \tabularnewline
72 & 0.914700518228473 & 0.170598963543053 & 0.0852994817715266 \tabularnewline
73 & 0.937471372301013 & 0.125057255397974 & 0.062528627698987 \tabularnewline
74 & 0.925504607849663 & 0.148990784300674 & 0.074495392150337 \tabularnewline
75 & 0.927475137402538 & 0.145049725194924 & 0.072524862597462 \tabularnewline
76 & 0.92135979714713 & 0.15728040570574 & 0.07864020285287 \tabularnewline
77 & 0.970618219539936 & 0.0587635609201277 & 0.0293817804600638 \tabularnewline
78 & 0.96339098074384 & 0.07321803851232 & 0.03660901925616 \tabularnewline
79 & 0.95615491023777 & 0.0876901795244605 & 0.0438450897622303 \tabularnewline
80 & 0.958809779857824 & 0.0823804402843522 & 0.0411902201421761 \tabularnewline
81 & 0.951104465668324 & 0.097791068663353 & 0.0488955343316765 \tabularnewline
82 & 0.95079758933412 & 0.0984048213317613 & 0.0492024106658806 \tabularnewline
83 & 0.938135175180862 & 0.123729649638276 & 0.0618648248191382 \tabularnewline
84 & 0.92554708941661 & 0.14890582116678 & 0.07445291058339 \tabularnewline
85 & 0.91750226940211 & 0.16499546119578 & 0.0824977305978902 \tabularnewline
86 & 0.902438409340973 & 0.195123181318053 & 0.0975615906590265 \tabularnewline
87 & 0.8817937473565 & 0.236412505287001 & 0.118206252643501 \tabularnewline
88 & 0.925132401160953 & 0.149735197678094 & 0.0748675988390469 \tabularnewline
89 & 0.90941944406885 & 0.181161111862299 & 0.0905805559311496 \tabularnewline
90 & 0.890345237614264 & 0.219309524771472 & 0.109654762385736 \tabularnewline
91 & 0.892252183606677 & 0.215495632786646 & 0.107747816393323 \tabularnewline
92 & 0.881727629569269 & 0.236544740861463 & 0.118272370430731 \tabularnewline
93 & 0.860680619778083 & 0.278638760443834 & 0.139319380221917 \tabularnewline
94 & 0.834244650492111 & 0.331510699015778 & 0.165755349507889 \tabularnewline
95 & 0.80260689869766 & 0.39478620260468 & 0.19739310130234 \tabularnewline
96 & 0.774138451139077 & 0.451723097721847 & 0.225861548860923 \tabularnewline
97 & 0.792542146728634 & 0.414915706542731 & 0.207457853271366 \tabularnewline
98 & 0.787254318354797 & 0.425491363290406 & 0.212745681645203 \tabularnewline
99 & 0.753472559030936 & 0.493054881938128 & 0.246527440969064 \tabularnewline
100 & 0.729696231361559 & 0.540607537276882 & 0.270303768638441 \tabularnewline
101 & 0.68855320386068 & 0.622893592278641 & 0.31144679613932 \tabularnewline
102 & 0.650582825690919 & 0.698834348618162 & 0.349417174309081 \tabularnewline
103 & 0.612912206288403 & 0.774175587423195 & 0.387087793711597 \tabularnewline
104 & 0.571141982573687 & 0.857716034852626 & 0.428858017426313 \tabularnewline
105 & 0.525841817775981 & 0.948316364448037 & 0.474158182224019 \tabularnewline
106 & 0.506455371766822 & 0.987089256466357 & 0.493544628233179 \tabularnewline
107 & 0.502845304274143 & 0.994309391451715 & 0.497154695725857 \tabularnewline
108 & 0.515722713733246 & 0.968554572533509 & 0.484277286266754 \tabularnewline
109 & 0.465612427071962 & 0.931224854143924 & 0.534387572928038 \tabularnewline
110 & 0.44215144117773 & 0.88430288235546 & 0.55784855882227 \tabularnewline
111 & 0.402862059065549 & 0.805724118131098 & 0.597137940934451 \tabularnewline
112 & 0.627016416835166 & 0.745967166329669 & 0.372983583164835 \tabularnewline
113 & 0.618705079529067 & 0.762589840941866 & 0.381294920470933 \tabularnewline
114 & 0.59019976732862 & 0.819600465342761 & 0.40980023267138 \tabularnewline
115 & 0.790856394365981 & 0.418287211268037 & 0.209143605634019 \tabularnewline
116 & 0.768862942412738 & 0.462274115174523 & 0.231137057587262 \tabularnewline
117 & 0.758178538392075 & 0.483642923215849 & 0.241821461607924 \tabularnewline
118 & 0.73164739270579 & 0.536705214588419 & 0.26835260729421 \tabularnewline
119 & 0.68424321436467 & 0.631513571270661 & 0.31575678563533 \tabularnewline
120 & 0.69262613619288 & 0.614747727614241 & 0.307373863807121 \tabularnewline
121 & 0.744060439103157 & 0.511879121793687 & 0.255939560896843 \tabularnewline
122 & 0.748484082697425 & 0.50303183460515 & 0.251515917302575 \tabularnewline
123 & 0.754800397920494 & 0.490399204159012 & 0.245199602079506 \tabularnewline
124 & 0.705646412197117 & 0.588707175605765 & 0.294353587802883 \tabularnewline
125 & 0.748694891327312 & 0.502610217345377 & 0.251305108672688 \tabularnewline
126 & 0.722183754724737 & 0.555632490550526 & 0.277816245275263 \tabularnewline
127 & 0.711373217355495 & 0.57725356528901 & 0.288626782644505 \tabularnewline
128 & 0.677955171177694 & 0.644089657644612 & 0.322044828822306 \tabularnewline
129 & 0.652646037319432 & 0.694707925361136 & 0.347353962680568 \tabularnewline
130 & 0.725038745898242 & 0.549922508203517 & 0.274961254101758 \tabularnewline
131 & 0.672175381238835 & 0.655649237522329 & 0.327824618761165 \tabularnewline
132 & 0.611605960384688 & 0.776788079230624 & 0.388394039615312 \tabularnewline
133 & 0.611274215461097 & 0.777451569077806 & 0.388725784538903 \tabularnewline
134 & 0.552056016063216 & 0.895887967873568 & 0.447943983936784 \tabularnewline
135 & 0.510957869753659 & 0.978084260492681 & 0.489042130246341 \tabularnewline
136 & 0.482805571494661 & 0.965611142989322 & 0.517194428505339 \tabularnewline
137 & 0.488165618959138 & 0.976331237918277 & 0.511834381040862 \tabularnewline
138 & 0.531794875235959 & 0.936410249528082 & 0.468205124764041 \tabularnewline
139 & 0.46421279106608 & 0.92842558213216 & 0.53578720893392 \tabularnewline
140 & 0.3900803480841 & 0.7801606961682 & 0.6099196519159 \tabularnewline
141 & 0.501502200891913 & 0.996995598216175 & 0.498497799108087 \tabularnewline
142 & 0.448832931405856 & 0.897665862811713 & 0.551167068594144 \tabularnewline
143 & 0.37835813076096 & 0.75671626152192 & 0.62164186923904 \tabularnewline
144 & 0.298580028253468 & 0.597160056506937 & 0.701419971746532 \tabularnewline
145 & 0.356986235371026 & 0.713972470742053 & 0.643013764628974 \tabularnewline
146 & 0.264382584575563 & 0.528765169151126 & 0.735617415424437 \tabularnewline
147 & 0.341586892893946 & 0.683173785787893 & 0.658413107106054 \tabularnewline
148 & 0.235739310940021 & 0.471478621880043 & 0.764260689059979 \tabularnewline
149 & 0.146765436085882 & 0.293530872171764 & 0.853234563914118 \tabularnewline
150 & 0.0921983424101697 & 0.184396684820339 & 0.90780165758983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99593&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.213914533504365[/C][C]0.42782906700873[/C][C]0.786085466495635[/C][/ROW]
[ROW][C]10[/C][C]0.103674339229257[/C][C]0.207348678458515[/C][C]0.896325660770743[/C][/ROW]
[ROW][C]11[/C][C]0.23040843780071[/C][C]0.46081687560142[/C][C]0.76959156219929[/C][/ROW]
[ROW][C]12[/C][C]0.652406760115806[/C][C]0.695186479768388[/C][C]0.347593239884194[/C][/ROW]
[ROW][C]13[/C][C]0.666813009586893[/C][C]0.666373980826214[/C][C]0.333186990413107[/C][/ROW]
[ROW][C]14[/C][C]0.641707144109755[/C][C]0.716585711780489[/C][C]0.358292855890244[/C][/ROW]
[ROW][C]15[/C][C]0.645932871197808[/C][C]0.708134257604385[/C][C]0.354067128802192[/C][/ROW]
[ROW][C]16[/C][C]0.581013347277822[/C][C]0.837973305444357[/C][C]0.418986652722178[/C][/ROW]
[ROW][C]17[/C][C]0.495952354345927[/C][C]0.991904708691854[/C][C]0.504047645654073[/C][/ROW]
[ROW][C]18[/C][C]0.4651119891909[/C][C]0.9302239783818[/C][C]0.5348880108091[/C][/ROW]
[ROW][C]19[/C][C]0.401169778327927[/C][C]0.802339556655854[/C][C]0.598830221672073[/C][/ROW]
[ROW][C]20[/C][C]0.435009261113867[/C][C]0.870018522227733[/C][C]0.564990738886133[/C][/ROW]
[ROW][C]21[/C][C]0.394813697998086[/C][C]0.789627395996173[/C][C]0.605186302001914[/C][/ROW]
[ROW][C]22[/C][C]0.400009069203689[/C][C]0.800018138407378[/C][C]0.599990930796311[/C][/ROW]
[ROW][C]23[/C][C]0.338659943227518[/C][C]0.677319886455036[/C][C]0.661340056772482[/C][/ROW]
[ROW][C]24[/C][C]0.600113845928922[/C][C]0.799772308142156[/C][C]0.399886154071078[/C][/ROW]
[ROW][C]25[/C][C]0.530211989085808[/C][C]0.939576021828384[/C][C]0.469788010914192[/C][/ROW]
[ROW][C]26[/C][C]0.499958693214714[/C][C]0.999917386429427[/C][C]0.500041306785286[/C][/ROW]
[ROW][C]27[/C][C]0.438206384653251[/C][C]0.876412769306502[/C][C]0.561793615346749[/C][/ROW]
[ROW][C]28[/C][C]0.391236757217297[/C][C]0.782473514434595[/C][C]0.608763242782703[/C][/ROW]
[ROW][C]29[/C][C]0.331086324099912[/C][C]0.662172648199825[/C][C]0.668913675900088[/C][/ROW]
[ROW][C]30[/C][C]0.279169872896638[/C][C]0.558339745793275[/C][C]0.720830127103362[/C][/ROW]
[ROW][C]31[/C][C]0.340635398513716[/C][C]0.681270797027432[/C][C]0.659364601486284[/C][/ROW]
[ROW][C]32[/C][C]0.400734364523601[/C][C]0.801468729047203[/C][C]0.599265635476399[/C][/ROW]
[ROW][C]33[/C][C]0.357466852591406[/C][C]0.714933705182812[/C][C]0.642533147408594[/C][/ROW]
[ROW][C]34[/C][C]0.368888955661906[/C][C]0.737777911323812[/C][C]0.631111044338094[/C][/ROW]
[ROW][C]35[/C][C]0.383166828367579[/C][C]0.766333656735158[/C][C]0.616833171632421[/C][/ROW]
[ROW][C]36[/C][C]0.374960486994901[/C][C]0.749920973989802[/C][C]0.625039513005099[/C][/ROW]
[ROW][C]37[/C][C]0.555846272861847[/C][C]0.888307454276306[/C][C]0.444153727138153[/C][/ROW]
[ROW][C]38[/C][C]0.761225769445785[/C][C]0.47754846110843[/C][C]0.238774230554215[/C][/ROW]
[ROW][C]39[/C][C]0.75816710415581[/C][C]0.48366579168838[/C][C]0.24183289584419[/C][/ROW]
[ROW][C]40[/C][C]0.71645993342239[/C][C]0.567080133155221[/C][C]0.28354006657761[/C][/ROW]
[ROW][C]41[/C][C]0.691674408127364[/C][C]0.616651183745272[/C][C]0.308325591872636[/C][/ROW]
[ROW][C]42[/C][C]0.642449688217542[/C][C]0.715100623564917[/C][C]0.357550311782458[/C][/ROW]
[ROW][C]43[/C][C]0.59400650507189[/C][C]0.81198698985622[/C][C]0.40599349492811[/C][/ROW]
[ROW][C]44[/C][C]0.580411555337072[/C][C]0.839176889325856[/C][C]0.419588444662928[/C][/ROW]
[ROW][C]45[/C][C]0.546217164137437[/C][C]0.907565671725126[/C][C]0.453782835862563[/C][/ROW]
[ROW][C]46[/C][C]0.567801386790791[/C][C]0.864397226418419[/C][C]0.432198613209209[/C][/ROW]
[ROW][C]47[/C][C]0.527004721977711[/C][C]0.945990556044577[/C][C]0.472995278022289[/C][/ROW]
[ROW][C]48[/C][C]0.518098066250446[/C][C]0.96380386749911[/C][C]0.481901933749554[/C][/ROW]
[ROW][C]49[/C][C]0.582287685374829[/C][C]0.835424629250342[/C][C]0.417712314625171[/C][/ROW]
[ROW][C]50[/C][C]0.560658852357121[/C][C]0.878682295285758[/C][C]0.439341147642879[/C][/ROW]
[ROW][C]51[/C][C]0.525244088579245[/C][C]0.94951182284151[/C][C]0.474755911420755[/C][/ROW]
[ROW][C]52[/C][C]0.475927593756266[/C][C]0.951855187512532[/C][C]0.524072406243734[/C][/ROW]
[ROW][C]53[/C][C]0.437525139155515[/C][C]0.87505027831103[/C][C]0.562474860844485[/C][/ROW]
[ROW][C]54[/C][C]0.390688518579317[/C][C]0.781377037158635[/C][C]0.609311481420683[/C][/ROW]
[ROW][C]55[/C][C]0.347979786712239[/C][C]0.695959573424478[/C][C]0.652020213287761[/C][/ROW]
[ROW][C]56[/C][C]0.320809086790988[/C][C]0.641618173581976[/C][C]0.679190913209012[/C][/ROW]
[ROW][C]57[/C][C]0.277701480864341[/C][C]0.555402961728682[/C][C]0.722298519135659[/C][/ROW]
[ROW][C]58[/C][C]0.253853814070854[/C][C]0.507707628141709[/C][C]0.746146185929146[/C][/ROW]
[ROW][C]59[/C][C]0.234246442377129[/C][C]0.468492884754257[/C][C]0.765753557622871[/C][/ROW]
[ROW][C]60[/C][C]0.292391587801294[/C][C]0.584783175602588[/C][C]0.707608412198706[/C][/ROW]
[ROW][C]61[/C][C]0.280581578759664[/C][C]0.561163157519327[/C][C]0.719418421240337[/C][/ROW]
[ROW][C]62[/C][C]0.243157332006569[/C][C]0.486314664013138[/C][C]0.756842667993431[/C][/ROW]
[ROW][C]63[/C][C]0.230670207601977[/C][C]0.461340415203953[/C][C]0.769329792398023[/C][/ROW]
[ROW][C]64[/C][C]0.265075174520626[/C][C]0.530150349041253[/C][C]0.734924825479374[/C][/ROW]
[ROW][C]65[/C][C]0.3416973519365[/C][C]0.683394703873001[/C][C]0.6583026480635[/C][/ROW]
[ROW][C]66[/C][C]0.344015714228578[/C][C]0.688031428457157[/C][C]0.655984285771422[/C][/ROW]
[ROW][C]67[/C][C]0.676060056971544[/C][C]0.647879886056912[/C][C]0.323939943028456[/C][/ROW]
[ROW][C]68[/C][C]0.669284550546907[/C][C]0.661430898906186[/C][C]0.330715449453093[/C][/ROW]
[ROW][C]69[/C][C]0.845153476896679[/C][C]0.309693046206643[/C][C]0.154846523103321[/C][/ROW]
[ROW][C]70[/C][C]0.82558688510356[/C][C]0.34882622979288[/C][C]0.17441311489644[/C][/ROW]
[ROW][C]71[/C][C]0.930938168018634[/C][C]0.138123663962732[/C][C]0.0690618319813658[/C][/ROW]
[ROW][C]72[/C][C]0.914700518228473[/C][C]0.170598963543053[/C][C]0.0852994817715266[/C][/ROW]
[ROW][C]73[/C][C]0.937471372301013[/C][C]0.125057255397974[/C][C]0.062528627698987[/C][/ROW]
[ROW][C]74[/C][C]0.925504607849663[/C][C]0.148990784300674[/C][C]0.074495392150337[/C][/ROW]
[ROW][C]75[/C][C]0.927475137402538[/C][C]0.145049725194924[/C][C]0.072524862597462[/C][/ROW]
[ROW][C]76[/C][C]0.92135979714713[/C][C]0.15728040570574[/C][C]0.07864020285287[/C][/ROW]
[ROW][C]77[/C][C]0.970618219539936[/C][C]0.0587635609201277[/C][C]0.0293817804600638[/C][/ROW]
[ROW][C]78[/C][C]0.96339098074384[/C][C]0.07321803851232[/C][C]0.03660901925616[/C][/ROW]
[ROW][C]79[/C][C]0.95615491023777[/C][C]0.0876901795244605[/C][C]0.0438450897622303[/C][/ROW]
[ROW][C]80[/C][C]0.958809779857824[/C][C]0.0823804402843522[/C][C]0.0411902201421761[/C][/ROW]
[ROW][C]81[/C][C]0.951104465668324[/C][C]0.097791068663353[/C][C]0.0488955343316765[/C][/ROW]
[ROW][C]82[/C][C]0.95079758933412[/C][C]0.0984048213317613[/C][C]0.0492024106658806[/C][/ROW]
[ROW][C]83[/C][C]0.938135175180862[/C][C]0.123729649638276[/C][C]0.0618648248191382[/C][/ROW]
[ROW][C]84[/C][C]0.92554708941661[/C][C]0.14890582116678[/C][C]0.07445291058339[/C][/ROW]
[ROW][C]85[/C][C]0.91750226940211[/C][C]0.16499546119578[/C][C]0.0824977305978902[/C][/ROW]
[ROW][C]86[/C][C]0.902438409340973[/C][C]0.195123181318053[/C][C]0.0975615906590265[/C][/ROW]
[ROW][C]87[/C][C]0.8817937473565[/C][C]0.236412505287001[/C][C]0.118206252643501[/C][/ROW]
[ROW][C]88[/C][C]0.925132401160953[/C][C]0.149735197678094[/C][C]0.0748675988390469[/C][/ROW]
[ROW][C]89[/C][C]0.90941944406885[/C][C]0.181161111862299[/C][C]0.0905805559311496[/C][/ROW]
[ROW][C]90[/C][C]0.890345237614264[/C][C]0.219309524771472[/C][C]0.109654762385736[/C][/ROW]
[ROW][C]91[/C][C]0.892252183606677[/C][C]0.215495632786646[/C][C]0.107747816393323[/C][/ROW]
[ROW][C]92[/C][C]0.881727629569269[/C][C]0.236544740861463[/C][C]0.118272370430731[/C][/ROW]
[ROW][C]93[/C][C]0.860680619778083[/C][C]0.278638760443834[/C][C]0.139319380221917[/C][/ROW]
[ROW][C]94[/C][C]0.834244650492111[/C][C]0.331510699015778[/C][C]0.165755349507889[/C][/ROW]
[ROW][C]95[/C][C]0.80260689869766[/C][C]0.39478620260468[/C][C]0.19739310130234[/C][/ROW]
[ROW][C]96[/C][C]0.774138451139077[/C][C]0.451723097721847[/C][C]0.225861548860923[/C][/ROW]
[ROW][C]97[/C][C]0.792542146728634[/C][C]0.414915706542731[/C][C]0.207457853271366[/C][/ROW]
[ROW][C]98[/C][C]0.787254318354797[/C][C]0.425491363290406[/C][C]0.212745681645203[/C][/ROW]
[ROW][C]99[/C][C]0.753472559030936[/C][C]0.493054881938128[/C][C]0.246527440969064[/C][/ROW]
[ROW][C]100[/C][C]0.729696231361559[/C][C]0.540607537276882[/C][C]0.270303768638441[/C][/ROW]
[ROW][C]101[/C][C]0.68855320386068[/C][C]0.622893592278641[/C][C]0.31144679613932[/C][/ROW]
[ROW][C]102[/C][C]0.650582825690919[/C][C]0.698834348618162[/C][C]0.349417174309081[/C][/ROW]
[ROW][C]103[/C][C]0.612912206288403[/C][C]0.774175587423195[/C][C]0.387087793711597[/C][/ROW]
[ROW][C]104[/C][C]0.571141982573687[/C][C]0.857716034852626[/C][C]0.428858017426313[/C][/ROW]
[ROW][C]105[/C][C]0.525841817775981[/C][C]0.948316364448037[/C][C]0.474158182224019[/C][/ROW]
[ROW][C]106[/C][C]0.506455371766822[/C][C]0.987089256466357[/C][C]0.493544628233179[/C][/ROW]
[ROW][C]107[/C][C]0.502845304274143[/C][C]0.994309391451715[/C][C]0.497154695725857[/C][/ROW]
[ROW][C]108[/C][C]0.515722713733246[/C][C]0.968554572533509[/C][C]0.484277286266754[/C][/ROW]
[ROW][C]109[/C][C]0.465612427071962[/C][C]0.931224854143924[/C][C]0.534387572928038[/C][/ROW]
[ROW][C]110[/C][C]0.44215144117773[/C][C]0.88430288235546[/C][C]0.55784855882227[/C][/ROW]
[ROW][C]111[/C][C]0.402862059065549[/C][C]0.805724118131098[/C][C]0.597137940934451[/C][/ROW]
[ROW][C]112[/C][C]0.627016416835166[/C][C]0.745967166329669[/C][C]0.372983583164835[/C][/ROW]
[ROW][C]113[/C][C]0.618705079529067[/C][C]0.762589840941866[/C][C]0.381294920470933[/C][/ROW]
[ROW][C]114[/C][C]0.59019976732862[/C][C]0.819600465342761[/C][C]0.40980023267138[/C][/ROW]
[ROW][C]115[/C][C]0.790856394365981[/C][C]0.418287211268037[/C][C]0.209143605634019[/C][/ROW]
[ROW][C]116[/C][C]0.768862942412738[/C][C]0.462274115174523[/C][C]0.231137057587262[/C][/ROW]
[ROW][C]117[/C][C]0.758178538392075[/C][C]0.483642923215849[/C][C]0.241821461607924[/C][/ROW]
[ROW][C]118[/C][C]0.73164739270579[/C][C]0.536705214588419[/C][C]0.26835260729421[/C][/ROW]
[ROW][C]119[/C][C]0.68424321436467[/C][C]0.631513571270661[/C][C]0.31575678563533[/C][/ROW]
[ROW][C]120[/C][C]0.69262613619288[/C][C]0.614747727614241[/C][C]0.307373863807121[/C][/ROW]
[ROW][C]121[/C][C]0.744060439103157[/C][C]0.511879121793687[/C][C]0.255939560896843[/C][/ROW]
[ROW][C]122[/C][C]0.748484082697425[/C][C]0.50303183460515[/C][C]0.251515917302575[/C][/ROW]
[ROW][C]123[/C][C]0.754800397920494[/C][C]0.490399204159012[/C][C]0.245199602079506[/C][/ROW]
[ROW][C]124[/C][C]0.705646412197117[/C][C]0.588707175605765[/C][C]0.294353587802883[/C][/ROW]
[ROW][C]125[/C][C]0.748694891327312[/C][C]0.502610217345377[/C][C]0.251305108672688[/C][/ROW]
[ROW][C]126[/C][C]0.722183754724737[/C][C]0.555632490550526[/C][C]0.277816245275263[/C][/ROW]
[ROW][C]127[/C][C]0.711373217355495[/C][C]0.57725356528901[/C][C]0.288626782644505[/C][/ROW]
[ROW][C]128[/C][C]0.677955171177694[/C][C]0.644089657644612[/C][C]0.322044828822306[/C][/ROW]
[ROW][C]129[/C][C]0.652646037319432[/C][C]0.694707925361136[/C][C]0.347353962680568[/C][/ROW]
[ROW][C]130[/C][C]0.725038745898242[/C][C]0.549922508203517[/C][C]0.274961254101758[/C][/ROW]
[ROW][C]131[/C][C]0.672175381238835[/C][C]0.655649237522329[/C][C]0.327824618761165[/C][/ROW]
[ROW][C]132[/C][C]0.611605960384688[/C][C]0.776788079230624[/C][C]0.388394039615312[/C][/ROW]
[ROW][C]133[/C][C]0.611274215461097[/C][C]0.777451569077806[/C][C]0.388725784538903[/C][/ROW]
[ROW][C]134[/C][C]0.552056016063216[/C][C]0.895887967873568[/C][C]0.447943983936784[/C][/ROW]
[ROW][C]135[/C][C]0.510957869753659[/C][C]0.978084260492681[/C][C]0.489042130246341[/C][/ROW]
[ROW][C]136[/C][C]0.482805571494661[/C][C]0.965611142989322[/C][C]0.517194428505339[/C][/ROW]
[ROW][C]137[/C][C]0.488165618959138[/C][C]0.976331237918277[/C][C]0.511834381040862[/C][/ROW]
[ROW][C]138[/C][C]0.531794875235959[/C][C]0.936410249528082[/C][C]0.468205124764041[/C][/ROW]
[ROW][C]139[/C][C]0.46421279106608[/C][C]0.92842558213216[/C][C]0.53578720893392[/C][/ROW]
[ROW][C]140[/C][C]0.3900803480841[/C][C]0.7801606961682[/C][C]0.6099196519159[/C][/ROW]
[ROW][C]141[/C][C]0.501502200891913[/C][C]0.996995598216175[/C][C]0.498497799108087[/C][/ROW]
[ROW][C]142[/C][C]0.448832931405856[/C][C]0.897665862811713[/C][C]0.551167068594144[/C][/ROW]
[ROW][C]143[/C][C]0.37835813076096[/C][C]0.75671626152192[/C][C]0.62164186923904[/C][/ROW]
[ROW][C]144[/C][C]0.298580028253468[/C][C]0.597160056506937[/C][C]0.701419971746532[/C][/ROW]
[ROW][C]145[/C][C]0.356986235371026[/C][C]0.713972470742053[/C][C]0.643013764628974[/C][/ROW]
[ROW][C]146[/C][C]0.264382584575563[/C][C]0.528765169151126[/C][C]0.735617415424437[/C][/ROW]
[ROW][C]147[/C][C]0.341586892893946[/C][C]0.683173785787893[/C][C]0.658413107106054[/C][/ROW]
[ROW][C]148[/C][C]0.235739310940021[/C][C]0.471478621880043[/C][C]0.764260689059979[/C][/ROW]
[ROW][C]149[/C][C]0.146765436085882[/C][C]0.293530872171764[/C][C]0.853234563914118[/C][/ROW]
[ROW][C]150[/C][C]0.0921983424101697[/C][C]0.184396684820339[/C][C]0.90780165758983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99593&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99593&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2139145335043650.427829067008730.786085466495635
100.1036743392292570.2073486784585150.896325660770743
110.230408437800710.460816875601420.76959156219929
120.6524067601158060.6951864797683880.347593239884194
130.6668130095868930.6663739808262140.333186990413107
140.6417071441097550.7165857117804890.358292855890244
150.6459328711978080.7081342576043850.354067128802192
160.5810133472778220.8379733054443570.418986652722178
170.4959523543459270.9919047086918540.504047645654073
180.46511198919090.93022397838180.5348880108091
190.4011697783279270.8023395566558540.598830221672073
200.4350092611138670.8700185222277330.564990738886133
210.3948136979980860.7896273959961730.605186302001914
220.4000090692036890.8000181384073780.599990930796311
230.3386599432275180.6773198864550360.661340056772482
240.6001138459289220.7997723081421560.399886154071078
250.5302119890858080.9395760218283840.469788010914192
260.4999586932147140.9999173864294270.500041306785286
270.4382063846532510.8764127693065020.561793615346749
280.3912367572172970.7824735144345950.608763242782703
290.3310863240999120.6621726481998250.668913675900088
300.2791698728966380.5583397457932750.720830127103362
310.3406353985137160.6812707970274320.659364601486284
320.4007343645236010.8014687290472030.599265635476399
330.3574668525914060.7149337051828120.642533147408594
340.3688889556619060.7377779113238120.631111044338094
350.3831668283675790.7663336567351580.616833171632421
360.3749604869949010.7499209739898020.625039513005099
370.5558462728618470.8883074542763060.444153727138153
380.7612257694457850.477548461108430.238774230554215
390.758167104155810.483665791688380.24183289584419
400.716459933422390.5670801331552210.28354006657761
410.6916744081273640.6166511837452720.308325591872636
420.6424496882175420.7151006235649170.357550311782458
430.594006505071890.811986989856220.40599349492811
440.5804115553370720.8391768893258560.419588444662928
450.5462171641374370.9075656717251260.453782835862563
460.5678013867907910.8643972264184190.432198613209209
470.5270047219777110.9459905560445770.472995278022289
480.5180980662504460.963803867499110.481901933749554
490.5822876853748290.8354246292503420.417712314625171
500.5606588523571210.8786822952857580.439341147642879
510.5252440885792450.949511822841510.474755911420755
520.4759275937562660.9518551875125320.524072406243734
530.4375251391555150.875050278311030.562474860844485
540.3906885185793170.7813770371586350.609311481420683
550.3479797867122390.6959595734244780.652020213287761
560.3208090867909880.6416181735819760.679190913209012
570.2777014808643410.5554029617286820.722298519135659
580.2538538140708540.5077076281417090.746146185929146
590.2342464423771290.4684928847542570.765753557622871
600.2923915878012940.5847831756025880.707608412198706
610.2805815787596640.5611631575193270.719418421240337
620.2431573320065690.4863146640131380.756842667993431
630.2306702076019770.4613404152039530.769329792398023
640.2650751745206260.5301503490412530.734924825479374
650.34169735193650.6833947038730010.6583026480635
660.3440157142285780.6880314284571570.655984285771422
670.6760600569715440.6478798860569120.323939943028456
680.6692845505469070.6614308989061860.330715449453093
690.8451534768966790.3096930462066430.154846523103321
700.825586885103560.348826229792880.17441311489644
710.9309381680186340.1381236639627320.0690618319813658
720.9147005182284730.1705989635430530.0852994817715266
730.9374713723010130.1250572553979740.062528627698987
740.9255046078496630.1489907843006740.074495392150337
750.9274751374025380.1450497251949240.072524862597462
760.921359797147130.157280405705740.07864020285287
770.9706182195399360.05876356092012770.0293817804600638
780.963390980743840.073218038512320.03660901925616
790.956154910237770.08769017952446050.0438450897622303
800.9588097798578240.08238044028435220.0411902201421761
810.9511044656683240.0977910686633530.0488955343316765
820.950797589334120.09840482133176130.0492024106658806
830.9381351751808620.1237296496382760.0618648248191382
840.925547089416610.148905821166780.07445291058339
850.917502269402110.164995461195780.0824977305978902
860.9024384093409730.1951231813180530.0975615906590265
870.88179374735650.2364125052870010.118206252643501
880.9251324011609530.1497351976780940.0748675988390469
890.909419444068850.1811611118622990.0905805559311496
900.8903452376142640.2193095247714720.109654762385736
910.8922521836066770.2154956327866460.107747816393323
920.8817276295692690.2365447408614630.118272370430731
930.8606806197780830.2786387604438340.139319380221917
940.8342446504921110.3315106990157780.165755349507889
950.802606898697660.394786202604680.19739310130234
960.7741384511390770.4517230977218470.225861548860923
970.7925421467286340.4149157065427310.207457853271366
980.7872543183547970.4254913632904060.212745681645203
990.7534725590309360.4930548819381280.246527440969064
1000.7296962313615590.5406075372768820.270303768638441
1010.688553203860680.6228935922786410.31144679613932
1020.6505828256909190.6988343486181620.349417174309081
1030.6129122062884030.7741755874231950.387087793711597
1040.5711419825736870.8577160348526260.428858017426313
1050.5258418177759810.9483163644480370.474158182224019
1060.5064553717668220.9870892564663570.493544628233179
1070.5028453042741430.9943093914517150.497154695725857
1080.5157227137332460.9685545725335090.484277286266754
1090.4656124270719620.9312248541439240.534387572928038
1100.442151441177730.884302882355460.55784855882227
1110.4028620590655490.8057241181310980.597137940934451
1120.6270164168351660.7459671663296690.372983583164835
1130.6187050795290670.7625898409418660.381294920470933
1140.590199767328620.8196004653427610.40980023267138
1150.7908563943659810.4182872112680370.209143605634019
1160.7688629424127380.4622741151745230.231137057587262
1170.7581785383920750.4836429232158490.241821461607924
1180.731647392705790.5367052145884190.26835260729421
1190.684243214364670.6315135712706610.31575678563533
1200.692626136192880.6147477276142410.307373863807121
1210.7440604391031570.5118791217936870.255939560896843
1220.7484840826974250.503031834605150.251515917302575
1230.7548003979204940.4903992041590120.245199602079506
1240.7056464121971170.5887071756057650.294353587802883
1250.7486948913273120.5026102173453770.251305108672688
1260.7221837547247370.5556324905505260.277816245275263
1270.7113732173554950.577253565289010.288626782644505
1280.6779551711776940.6440896576446120.322044828822306
1290.6526460373194320.6947079253611360.347353962680568
1300.7250387458982420.5499225082035170.274961254101758
1310.6721753812388350.6556492375223290.327824618761165
1320.6116059603846880.7767880792306240.388394039615312
1330.6112742154610970.7774515690778060.388725784538903
1340.5520560160632160.8958879678735680.447943983936784
1350.5109578697536590.9780842604926810.489042130246341
1360.4828055714946610.9656111429893220.517194428505339
1370.4881656189591380.9763312379182770.511834381040862
1380.5317948752359590.9364102495280820.468205124764041
1390.464212791066080.928425582132160.53578720893392
1400.39008034808410.78016069616820.6099196519159
1410.5015022008919130.9969955982161750.498497799108087
1420.4488329314058560.8976658628117130.551167068594144
1430.378358130760960.756716261521920.62164186923904
1440.2985800282534680.5971600565069370.701419971746532
1450.3569862353710260.7139724707420530.643013764628974
1460.2643825845755630.5287651691511260.735617415424437
1470.3415868928939460.6831737857878930.658413107106054
1480.2357393109400210.4714786218800430.764260689059979
1490.1467654360858820.2935308721717640.853234563914118
1500.09219834241016970.1843966848203390.90780165758983






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.0422535211267606OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.0422535211267606 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99593&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0422535211267606[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99593&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99593&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.0422535211267606OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}