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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 17:50:54 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356130273066ll5luzaccuxw.htm/, Retrieved Fri, 29 Mar 2024 05:25:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204373, Retrieved Fri, 29 Mar 2024 05:25:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Boxplot and Trimmed Means] [Reddy Moores Boxp...] [2010-10-12 16:37:57] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R P   [Boxplot and Trimmed Means] [Reddy-Moores Plac...] [2010-10-13 09:46:26] [98fd0e87c3eb04e0cc2efde01dbafab6]
- RMPD    [Notched Boxplots] [] [2010-10-15 11:13:23] [b98453cac15ba1066b407e146608df68]
- R  D      [Notched Boxplots] [Notched Boxplots ] [2012-12-11 13:08:43] [46762b18b00d15214a19b2ee3ead9dc9]
- RMPD        [Histogram] [histogram] [2012-12-21 19:28:34] [46762b18b00d15214a19b2ee3ead9dc9]
- R             [Histogram] [his] [2012-12-21 19:44:59] [46762b18b00d15214a19b2ee3ead9dc9]
- RM D              [Multiple Regression] [multiple] [2012-12-21 22:50:54] [9f1ef512d1eac2da3e1af89c6a547aff] [Current]
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Dataseries X:
4	1	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	1	0
4	0	0
4	1	0
4	1	1
4	1	0
4	0	0
4	1	1
4	0	0
4	0	0
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	1	0
4	0	1
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	1	0
4	1	1
4	0	0
4	0	1
4	0	0
4	1	0
4	0	0
4	0	0
4	0	0
4	1	1
4	1	0
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	1	1
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	0	0
4	1	0
4	0	0
4	0	0
4	1	1
4	1	0
4	0	0
4	0	0
4	0	0
4	0	1
4	0	0
4	0	0
2	0	0
2	1	0
2	0	0
2	0	0
2	0	0
2	1	0
2	0	0
2	0	0
2	1	0
2	0	0
2	1	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	1	0
2	0	0
2	0	0
2	1	0
2	0	0
2	0	0
2	1	0
2	1	0
2	0	0
2	1	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	1	0
2	0	0
2	0	0
2	1	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	0	0
2	1	0
2	1	0
2	0	0
2	0	1
2	1	0
2	0	0
2	0	0
2	0	0
2	1	0
2	1	0
2	1	0
2	0	0
2	0	0
2	0	0
2	0	1
2	0	1
2	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204373&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204373&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204373&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0387535096537141 + 0.0294290963291622Weeks[t] + 0.0960518562168533`T40:t20`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0387535096537141 +  0.0294290963291622Weeks[t] +  0.0960518562168533`T40:t20`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204373&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0387535096537141 +  0.0294290963291622Weeks[t] +  0.0960518562168533`T40:t20`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204373&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204373&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0387535096537141 + 0.0294290963291622Weeks[t] + 0.0960518562168533`T40:t20`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03875350965371410.071408-0.54270.5881320.294066
Weeks0.02942909632916220.0215551.36530.1741930.087097
`T40:t20`0.09605185621685330.0488211.96740.0509650.025482

\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) & -0.0387535096537141 & 0.071408 & -0.5427 & 0.588132 & 0.294066 \tabularnewline
Weeks & 0.0294290963291622 & 0.021555 & 1.3653 & 0.174193 & 0.087097 \tabularnewline
`T40:t20` & 0.0960518562168533 & 0.048821 & 1.9674 & 0.050965 & 0.025482 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204373&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]-0.0387535096537141[/C][C]0.071408[/C][C]-0.5427[/C][C]0.588132[/C][C]0.294066[/C][/ROW]
[ROW][C]Weeks[/C][C]0.0294290963291622[/C][C]0.021555[/C][C]1.3653[/C][C]0.174193[/C][C]0.087097[/C][/ROW]
[ROW][C]`T40:t20`[/C][C]0.0960518562168533[/C][C]0.048821[/C][C]1.9674[/C][C]0.050965[/C][C]0.025482[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204373&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204373&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03875350965371410.071408-0.54270.5881320.294066
Weeks0.02942909632916220.0215551.36530.1741930.087097
`T40:t20`0.09605185621685330.0488211.96740.0509650.025482







Multiple Linear Regression - Regression Statistics
Multiple R0.193016006112974
R-squared0.0372551786158038
Adjusted R-squared0.0245035915776024
F-TEST (value)2.92161112998676
F-TEST (DF numerator)2
F-TEST (DF denominator)151
p-value0.0568977296709904
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.265608247756508
Sum Squared Residuals10.6527089327186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.193016006112974 \tabularnewline
R-squared & 0.0372551786158038 \tabularnewline
Adjusted R-squared & 0.0245035915776024 \tabularnewline
F-TEST (value) & 2.92161112998676 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0.0568977296709904 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.265608247756508 \tabularnewline
Sum Squared Residuals & 10.6527089327186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204373&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.193016006112974[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0372551786158038[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0245035915776024[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.92161112998676[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]0.0568977296709904[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.265608247756508[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.6527089327186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204373&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204373&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.193016006112974
R-squared0.0372551786158038
Adjusted R-squared0.0245035915776024
F-TEST (value)2.92161112998676
F-TEST (DF numerator)2
F-TEST (DF denominator)151
p-value0.0568977296709904
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.265608247756508
Sum Squared Residuals10.6527089327186







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.175014731879788-0.175014731879788
200.0789628756629345-0.0789628756629345
300.0789628756629347-0.0789628756629347
400.0789628756629346-0.0789628756629346
500.0789628756629346-0.0789628756629346
600.0789628756629346-0.0789628756629346
700.0789628756629346-0.0789628756629346
800.175014731879788-0.175014731879788
900.0789628756629346-0.0789628756629346
1000.0789628756629346-0.0789628756629346
1100.175014731879788-0.175014731879788
1200.0789628756629346-0.0789628756629346
1300.0789628756629346-0.0789628756629346
1400.175014731879788-0.175014731879788
1500.0789628756629346-0.0789628756629346
1600.175014731879788-0.175014731879788
1710.1750147318797880.824985268120212
1800.175014731879788-0.175014731879788
1900.0789628756629346-0.0789628756629346
2010.1750147318797880.824985268120212
2100.0789628756629346-0.0789628756629346
2200.0789628756629346-0.0789628756629346
2300.0789628756629346-0.0789628756629346
2400.0789628756629346-0.0789628756629346
2500.175014731879788-0.175014731879788
2600.0789628756629346-0.0789628756629346
2700.0789628756629346-0.0789628756629346
2800.0789628756629346-0.0789628756629346
2900.0789628756629346-0.0789628756629346
3000.0789628756629346-0.0789628756629346
3100.0789628756629346-0.0789628756629346
3200.0789628756629346-0.0789628756629346
3300.0789628756629346-0.0789628756629346
3400.175014731879788-0.175014731879788
3500.0789628756629346-0.0789628756629346
3600.0789628756629346-0.0789628756629346
3700.175014731879788-0.175014731879788
3800.0789628756629346-0.0789628756629346
3900.0789628756629346-0.0789628756629346
4000.175014731879788-0.175014731879788
4110.07896287566293460.921037124337065
4200.0789628756629346-0.0789628756629346
4300.0789628756629346-0.0789628756629346
4400.175014731879788-0.175014731879788
4500.0789628756629346-0.0789628756629346
4600.0789628756629346-0.0789628756629346
4700.0789628756629346-0.0789628756629346
4800.0789628756629346-0.0789628756629346
4900.0789628756629346-0.0789628756629346
5000.0789628756629346-0.0789628756629346
5100.175014731879788-0.175014731879788
5210.1750147318797880.824985268120212
5300.0789628756629346-0.0789628756629346
5410.07896287566293460.921037124337065
5500.0789628756629346-0.0789628756629346
5600.175014731879788-0.175014731879788
5700.0789628756629346-0.0789628756629346
5800.0789628756629346-0.0789628756629346
5900.0789628756629346-0.0789628756629346
6010.1750147318797880.824985268120212
6100.175014731879788-0.175014731879788
6200.0789628756629346-0.0789628756629346
6300.0789628756629346-0.0789628756629346
6400.175014731879788-0.175014731879788
6500.0789628756629346-0.0789628756629346
6600.0789628756629346-0.0789628756629346
6710.1750147318797880.824985268120212
6800.0789628756629346-0.0789628756629346
6900.0789628756629346-0.0789628756629346
7000.0789628756629346-0.0789628756629346
7100.0789628756629346-0.0789628756629346
7200.0789628756629346-0.0789628756629346
7300.0789628756629346-0.0789628756629346
7400.0789628756629346-0.0789628756629346
7500.0789628756629346-0.0789628756629346
7600.175014731879788-0.175014731879788
7700.0789628756629346-0.0789628756629346
7800.0789628756629346-0.0789628756629346
7910.1750147318797880.824985268120212
8000.175014731879788-0.175014731879788
8100.0789628756629346-0.0789628756629346
8200.0789628756629346-0.0789628756629346
8300.0789628756629346-0.0789628756629346
8410.07896287566293460.921037124337065
8500.0789628756629346-0.0789628756629346
8600.0789628756629346-0.0789628756629346
8700.0201046830046102-0.0201046830046102
8800.116156539221463-0.116156539221463
8900.0201046830046102-0.0201046830046102
9000.0201046830046102-0.0201046830046102
9100.0201046830046102-0.0201046830046102
9200.116156539221463-0.116156539221463
9300.0201046830046102-0.0201046830046102
9400.0201046830046102-0.0201046830046102
9500.116156539221463-0.116156539221463
9600.0201046830046102-0.0201046830046102
9700.116156539221463-0.116156539221463
9800.0201046830046102-0.0201046830046102
9900.0201046830046102-0.0201046830046102
10000.0201046830046102-0.0201046830046102
10100.0201046830046102-0.0201046830046102
10200.0201046830046102-0.0201046830046102
10300.0201046830046102-0.0201046830046102
10400.0201046830046102-0.0201046830046102
10500.116156539221463-0.116156539221463
10600.0201046830046102-0.0201046830046102
10700.0201046830046102-0.0201046830046102
10800.116156539221463-0.116156539221463
10900.0201046830046102-0.0201046830046102
11000.0201046830046102-0.0201046830046102
11100.116156539221463-0.116156539221463
11200.116156539221463-0.116156539221463
11300.0201046830046102-0.0201046830046102
11400.116156539221463-0.116156539221463
11500.0201046830046102-0.0201046830046102
11600.0201046830046102-0.0201046830046102
11700.0201046830046102-0.0201046830046102
11800.0201046830046102-0.0201046830046102
11900.0201046830046102-0.0201046830046102
12000.0201046830046102-0.0201046830046102
12100.0201046830046102-0.0201046830046102
12200.0201046830046102-0.0201046830046102
12300.116156539221463-0.116156539221463
12400.0201046830046102-0.0201046830046102
12500.0201046830046102-0.0201046830046102
12600.116156539221463-0.116156539221463
12700.0201046830046102-0.0201046830046102
12800.0201046830046102-0.0201046830046102
12900.0201046830046102-0.0201046830046102
13000.0201046830046102-0.0201046830046102
13100.0201046830046102-0.0201046830046102
13200.0201046830046102-0.0201046830046102
13300.0201046830046102-0.0201046830046102
13400.0201046830046102-0.0201046830046102
13500.0201046830046102-0.0201046830046102
13600.0201046830046102-0.0201046830046102
13700.0201046830046102-0.0201046830046102
13800.116156539221463-0.116156539221463
13900.116156539221463-0.116156539221463
14000.0201046830046102-0.0201046830046102
14110.02010468300461020.97989531699539
14200.116156539221463-0.116156539221463
14300.0201046830046102-0.0201046830046102
14400.0201046830046102-0.0201046830046102
14500.0201046830046102-0.0201046830046102
14600.116156539221463-0.116156539221463
14700.116156539221463-0.116156539221463
14800.116156539221463-0.116156539221463
14900.0201046830046102-0.0201046830046102
15000.0201046830046102-0.0201046830046102
15100.0201046830046102-0.0201046830046102
15210.02010468300461020.97989531699539
15310.02010468300461020.97989531699539
15400.0201046830046102-0.0201046830046102

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
2 & 0 & 0.0789628756629345 & -0.0789628756629345 \tabularnewline
3 & 0 & 0.0789628756629347 & -0.0789628756629347 \tabularnewline
4 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
5 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
6 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
7 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
8 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
9 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
10 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
11 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
12 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
13 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
14 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
15 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
16 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
17 & 1 & 0.175014731879788 & 0.824985268120212 \tabularnewline
18 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
19 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
20 & 1 & 0.175014731879788 & 0.824985268120212 \tabularnewline
21 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
22 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
23 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
24 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
25 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
26 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
27 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
28 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
29 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
30 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
31 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
32 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
33 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
34 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
35 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
36 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
37 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
38 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
39 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
40 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
41 & 1 & 0.0789628756629346 & 0.921037124337065 \tabularnewline
42 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
43 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
44 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
45 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
46 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
47 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
48 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
49 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
50 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
51 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
52 & 1 & 0.175014731879788 & 0.824985268120212 \tabularnewline
53 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
54 & 1 & 0.0789628756629346 & 0.921037124337065 \tabularnewline
55 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
56 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
57 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
58 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
59 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
60 & 1 & 0.175014731879788 & 0.824985268120212 \tabularnewline
61 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
62 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
63 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
64 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
65 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
66 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
67 & 1 & 0.175014731879788 & 0.824985268120212 \tabularnewline
68 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
69 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
70 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
71 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
72 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
73 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
74 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
75 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
76 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
77 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
78 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
79 & 1 & 0.175014731879788 & 0.824985268120212 \tabularnewline
80 & 0 & 0.175014731879788 & -0.175014731879788 \tabularnewline
81 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
82 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
83 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
84 & 1 & 0.0789628756629346 & 0.921037124337065 \tabularnewline
85 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
86 & 0 & 0.0789628756629346 & -0.0789628756629346 \tabularnewline
87 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
88 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
89 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
90 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
91 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
92 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
93 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
94 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
95 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
96 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
97 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
98 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
99 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
100 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
101 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
102 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
103 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
104 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
105 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
106 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
107 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
108 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
109 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
110 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
111 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
112 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
113 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
114 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
115 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
116 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
117 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
118 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
119 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
120 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
121 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
122 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
123 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
124 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
125 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
126 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
127 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
128 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
129 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
130 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
131 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
132 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
133 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
134 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
135 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
136 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
137 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
138 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
139 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
140 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
141 & 1 & 0.0201046830046102 & 0.97989531699539 \tabularnewline
142 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
143 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
144 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
145 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
146 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
147 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
148 & 0 & 0.116156539221463 & -0.116156539221463 \tabularnewline
149 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
150 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
151 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
152 & 1 & 0.0201046830046102 & 0.97989531699539 \tabularnewline
153 & 1 & 0.0201046830046102 & 0.97989531699539 \tabularnewline
154 & 0 & 0.0201046830046102 & -0.0201046830046102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204373&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]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.0789628756629345[/C][C]-0.0789628756629345[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0789628756629347[/C][C]-0.0789628756629347[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.175014731879788[/C][C]0.824985268120212[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.175014731879788[/C][C]0.824985268120212[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.0789628756629346[/C][C]0.921037124337065[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.175014731879788[/C][C]0.824985268120212[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.0789628756629346[/C][C]0.921037124337065[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.175014731879788[/C][C]0.824985268120212[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.175014731879788[/C][C]0.824985268120212[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.175014731879788[/C][C]0.824985268120212[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.175014731879788[/C][C]-0.175014731879788[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.0789628756629346[/C][C]0.921037124337065[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.0789628756629346[/C][C]-0.0789628756629346[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.0201046830046102[/C][C]0.97989531699539[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.116156539221463[/C][C]-0.116156539221463[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.0201046830046102[/C][C]0.97989531699539[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.0201046830046102[/C][C]0.97989531699539[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.0201046830046102[/C][C]-0.0201046830046102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204373&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204373&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.175014731879788-0.175014731879788
200.0789628756629345-0.0789628756629345
300.0789628756629347-0.0789628756629347
400.0789628756629346-0.0789628756629346
500.0789628756629346-0.0789628756629346
600.0789628756629346-0.0789628756629346
700.0789628756629346-0.0789628756629346
800.175014731879788-0.175014731879788
900.0789628756629346-0.0789628756629346
1000.0789628756629346-0.0789628756629346
1100.175014731879788-0.175014731879788
1200.0789628756629346-0.0789628756629346
1300.0789628756629346-0.0789628756629346
1400.175014731879788-0.175014731879788
1500.0789628756629346-0.0789628756629346
1600.175014731879788-0.175014731879788
1710.1750147318797880.824985268120212
1800.175014731879788-0.175014731879788
1900.0789628756629346-0.0789628756629346
2010.1750147318797880.824985268120212
2100.0789628756629346-0.0789628756629346
2200.0789628756629346-0.0789628756629346
2300.0789628756629346-0.0789628756629346
2400.0789628756629346-0.0789628756629346
2500.175014731879788-0.175014731879788
2600.0789628756629346-0.0789628756629346
2700.0789628756629346-0.0789628756629346
2800.0789628756629346-0.0789628756629346
2900.0789628756629346-0.0789628756629346
3000.0789628756629346-0.0789628756629346
3100.0789628756629346-0.0789628756629346
3200.0789628756629346-0.0789628756629346
3300.0789628756629346-0.0789628756629346
3400.175014731879788-0.175014731879788
3500.0789628756629346-0.0789628756629346
3600.0789628756629346-0.0789628756629346
3700.175014731879788-0.175014731879788
3800.0789628756629346-0.0789628756629346
3900.0789628756629346-0.0789628756629346
4000.175014731879788-0.175014731879788
4110.07896287566293460.921037124337065
4200.0789628756629346-0.0789628756629346
4300.0789628756629346-0.0789628756629346
4400.175014731879788-0.175014731879788
4500.0789628756629346-0.0789628756629346
4600.0789628756629346-0.0789628756629346
4700.0789628756629346-0.0789628756629346
4800.0789628756629346-0.0789628756629346
4900.0789628756629346-0.0789628756629346
5000.0789628756629346-0.0789628756629346
5100.175014731879788-0.175014731879788
5210.1750147318797880.824985268120212
5300.0789628756629346-0.0789628756629346
5410.07896287566293460.921037124337065
5500.0789628756629346-0.0789628756629346
5600.175014731879788-0.175014731879788
5700.0789628756629346-0.0789628756629346
5800.0789628756629346-0.0789628756629346
5900.0789628756629346-0.0789628756629346
6010.1750147318797880.824985268120212
6100.175014731879788-0.175014731879788
6200.0789628756629346-0.0789628756629346
6300.0789628756629346-0.0789628756629346
6400.175014731879788-0.175014731879788
6500.0789628756629346-0.0789628756629346
6600.0789628756629346-0.0789628756629346
6710.1750147318797880.824985268120212
6800.0789628756629346-0.0789628756629346
6900.0789628756629346-0.0789628756629346
7000.0789628756629346-0.0789628756629346
7100.0789628756629346-0.0789628756629346
7200.0789628756629346-0.0789628756629346
7300.0789628756629346-0.0789628756629346
7400.0789628756629346-0.0789628756629346
7500.0789628756629346-0.0789628756629346
7600.175014731879788-0.175014731879788
7700.0789628756629346-0.0789628756629346
7800.0789628756629346-0.0789628756629346
7910.1750147318797880.824985268120212
8000.175014731879788-0.175014731879788
8100.0789628756629346-0.0789628756629346
8200.0789628756629346-0.0789628756629346
8300.0789628756629346-0.0789628756629346
8410.07896287566293460.921037124337065
8500.0789628756629346-0.0789628756629346
8600.0789628756629346-0.0789628756629346
8700.0201046830046102-0.0201046830046102
8800.116156539221463-0.116156539221463
8900.0201046830046102-0.0201046830046102
9000.0201046830046102-0.0201046830046102
9100.0201046830046102-0.0201046830046102
9200.116156539221463-0.116156539221463
9300.0201046830046102-0.0201046830046102
9400.0201046830046102-0.0201046830046102
9500.116156539221463-0.116156539221463
9600.0201046830046102-0.0201046830046102
9700.116156539221463-0.116156539221463
9800.0201046830046102-0.0201046830046102
9900.0201046830046102-0.0201046830046102
10000.0201046830046102-0.0201046830046102
10100.0201046830046102-0.0201046830046102
10200.0201046830046102-0.0201046830046102
10300.0201046830046102-0.0201046830046102
10400.0201046830046102-0.0201046830046102
10500.116156539221463-0.116156539221463
10600.0201046830046102-0.0201046830046102
10700.0201046830046102-0.0201046830046102
10800.116156539221463-0.116156539221463
10900.0201046830046102-0.0201046830046102
11000.0201046830046102-0.0201046830046102
11100.116156539221463-0.116156539221463
11200.116156539221463-0.116156539221463
11300.0201046830046102-0.0201046830046102
11400.116156539221463-0.116156539221463
11500.0201046830046102-0.0201046830046102
11600.0201046830046102-0.0201046830046102
11700.0201046830046102-0.0201046830046102
11800.0201046830046102-0.0201046830046102
11900.0201046830046102-0.0201046830046102
12000.0201046830046102-0.0201046830046102
12100.0201046830046102-0.0201046830046102
12200.0201046830046102-0.0201046830046102
12300.116156539221463-0.116156539221463
12400.0201046830046102-0.0201046830046102
12500.0201046830046102-0.0201046830046102
12600.116156539221463-0.116156539221463
12700.0201046830046102-0.0201046830046102
12800.0201046830046102-0.0201046830046102
12900.0201046830046102-0.0201046830046102
13000.0201046830046102-0.0201046830046102
13100.0201046830046102-0.0201046830046102
13200.0201046830046102-0.0201046830046102
13300.0201046830046102-0.0201046830046102
13400.0201046830046102-0.0201046830046102
13500.0201046830046102-0.0201046830046102
13600.0201046830046102-0.0201046830046102
13700.0201046830046102-0.0201046830046102
13800.116156539221463-0.116156539221463
13900.116156539221463-0.116156539221463
14000.0201046830046102-0.0201046830046102
14110.02010468300461020.97989531699539
14200.116156539221463-0.116156539221463
14300.0201046830046102-0.0201046830046102
14400.0201046830046102-0.0201046830046102
14500.0201046830046102-0.0201046830046102
14600.116156539221463-0.116156539221463
14700.116156539221463-0.116156539221463
14800.116156539221463-0.116156539221463
14900.0201046830046102-0.0201046830046102
15000.0201046830046102-0.0201046830046102
15100.0201046830046102-0.0201046830046102
15210.02010468300461020.97989531699539
15310.02010468300461020.97989531699539
15400.0201046830046102-0.0201046830046102







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.352700197504370.7054003950087390.64729980249563
180.3059357021783130.6118714043566260.694064297821687
190.2401960772232080.4803921544464160.759803922776792
200.7866362961414540.4267274077170910.213363703858546
210.7322162336497820.5355675327004370.267783766350218
220.6726666947016090.6546666105967820.327333305298391
230.609447523951560.781104952096880.39055247604844
240.5442409555461340.9115180889077310.455759044453866
250.5251046823444350.9497906353111310.474895317655565
260.4608926961312390.9217853922624780.539107303868761
270.3985398751369340.7970797502738670.601460124863066
280.3394482292661840.6788964585323670.660551770733816
290.2847438060193620.5694876120387240.715256193980638
300.2352314911799980.4704629823599960.764768508820002
310.1913842932985770.3827685865971530.808615706701423
320.1533627075677060.3067254151354120.846637292432294
330.1210568195226780.2421136390453560.878943180477322
340.1115761347035650.2231522694071290.888423865296435
350.08658497481334950.1731699496266990.913415025186651
360.06623400652700780.1324680130540160.933765993472992
370.05895720540611650.1179144108122330.941042794593883
380.04432005203324680.08864010406649350.955679947966753
390.03286950037445880.06573900074891760.967130499625541
400.02833568322964660.05667136645929320.971664316770353
410.4603617256082270.9207234512164550.539638274391772
420.410640040604280.8212800812085590.58935995939572
430.3627078098314330.7254156196628660.637292190168567
440.3346155749427840.6692311498855690.665384425057216
450.2910906456460730.5821812912921460.708909354353927
460.250748234717580.501496469435160.74925176528242
470.213891863342730.427783726685460.78610813665727
480.1806913440503180.3613826881006360.819308655949682
490.1511917993251090.3023835986502180.848808200674891
500.1253285300889320.2506570601778640.874671469911068
510.1114723591436080.2229447182872170.888527640856392
520.451906778993960.903813557987920.54809322100604
530.4075524218163480.8151048436326970.592447578183652
540.8663841268619580.2672317462760830.133615873138042
550.8410124332291340.3179751335417320.158987566770866
560.8254417361084270.3491165277831470.174558263891573
570.7960013651841770.4079972696316460.203998634815823
580.7640890032886340.4718219934227330.235910996711366
590.7299557167869630.5400885664260750.270044283213037
600.9404177078399570.1191645843200860.059582292160043
610.9332721986267170.1334556027465650.0667278013732825
620.9183695622673230.1632608754653540.081630437732677
630.9012765110989490.1974469778021010.0987234889010507
640.8909851387172520.2180297225654950.109014861282747
650.8705824171501920.2588351656996160.129417582849808
660.8480454871484530.3039090257030950.151954512851547
670.9777747849793570.04445043004128610.022225215020643
680.9715988758457850.05680224830843060.0284011241542153
690.9641660836129650.07166783277407040.0358339163870352
700.9553648721625360.0892702556749290.0446351278374645
710.9451209082537520.1097581834924970.0548790917462483
720.9334168740245790.1331662519508420.0665831259754208
730.9203175760375080.1593648479249850.0796824239624924
740.9060024787067190.1879950425865620.0939975212932811
750.8908097009639230.2183805980721530.109190299036077
760.8827773951777170.2344452096445650.117222604822283
770.8679275018524070.2641449962951850.132072498147593
780.8545504792022760.2908990415954490.145449520797724
790.9806389651355750.03872206972885090.0193610348644254
800.9776380886597680.0447238226804640.022361911340232
810.9731020761102840.05379584777943180.0268979238897159
820.9692100331564690.06157993368706270.0307899668435313
830.9678669143029310.06426617139413770.0321330856970689
840.9991981451632240.001603709673551420.000801854836775711
850.9988023372532170.002395325493566110.00119766274678305
860.9982330557533390.003533888493322020.00176694424666101
870.9974551725277780.005089654944444460.00254482747222223
880.9964533217483560.007093356503287950.00354667825164398
890.9950634952939310.009873009412138130.00493650470606907
900.9931688685375680.01366226292486470.00683113146243235
910.9906374520372170.01872509592556690.00936254796278346
920.987468490475940.025063019048120.01253150952406
930.9832409138574670.03351817228506590.0167590861425329
940.9778167096644540.04436658067109110.0221832903355455
950.9711147497620170.05777050047596650.0288852502379833
960.9626419634406760.07471607311864760.0373580365593238
970.9522007811492280.09559843770154440.0477992188507722
980.939559491512010.120881016975980.0604405084879901
990.9243731151435640.1512537697128720.0756268848564359
1000.9063749178918890.1872501642162230.0936250821081113
1010.8853325890614240.2293348218771530.114667410938576
1020.861065406964120.2778691860717590.138934593035879
1030.833461328811250.3330773423775010.16653867118875
1040.8024927066202520.3950145867594950.197507293379748
1050.767963788124190.464072423751620.23203621187581
1060.7304846027574090.5390307944851820.269515397242591
1070.6901336565366810.6197326869266380.309866343463319
1080.6461804491721770.7076391016556460.353819550827823
1090.6011662673320880.7976674653358230.398833732667912
1100.5547065565380.8905868869240.445293443462
1110.5053852037841150.9892295924317690.494614796215885
1120.4553166276848610.9106332553697220.544683372315139
1130.4081132754855790.8162265509711570.591886724514421
1140.3592847700574170.7185695401148350.640715229942582
1150.315107740687240.6302154813744790.68489225931276
1160.2733771018197440.5467542036394880.726622898180256
1170.2345515831681990.4691031663363970.765448416831801
1180.1989758739127220.3979517478254440.801024126087278
1190.1668731758116510.3337463516233010.833126824188349
1200.1383454149297050.2766908298594090.861654585070295
1210.113380536648050.22676107329610.88661946335195
1220.09186572698774880.1837314539754980.908134273012251
1230.07122294468437270.1424458893687450.928777055315627
1240.05612574260633390.1122514852126680.943874257393666
1250.04372900595450750.0874580119090150.956270994045493
1260.03207843577354740.06415687154709480.967921564226453
1270.02426765524080640.04853531048161280.975732344759194
1280.01816447630609320.03632895261218640.981835523693907
1290.01346685852906130.02693371705812260.986533141470939
1300.009903733018957820.01980746603791560.990096266981042
1310.007239209403982070.01447841880796410.992760790596018
1320.005273782350235720.01054756470047140.994726217649764
1330.003843204120906690.007686408241813370.996156795879093
1340.002815742284135130.005631484568270250.997184257715865
1350.002088526203011870.004177052406023740.997911473796988
1360.001583646680180580.003167293360361160.998416353319819
1370.001244691644921250.00248938328984250.998755308355079
1380.0006917068767706910.001383413753541380.999308293123229
1390.0003678639943851450.000735727988770290.999632136005615
1400.0002891306320302090.0005782612640604190.99971086936797
1410.008899495488601660.01779899097720330.991100504511398
1420.004980414173928210.009960828347856410.995019585826072
1430.003490924453757060.006981848907514120.996509075546243
1440.002570919723788380.005141839447576760.997429080276212
1450.002095090051972350.00419018010394470.997904909948028
1460.0009126178422342540.001825235684468510.999087382157766
1470.0003509947514206430.0007019895028412850.999649005248579
1480.0001144819813067060.0002289639626134110.999885518018693

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0 & 0 & 1 \tabularnewline
7 & 0 & 0 & 1 \tabularnewline
8 & 0 & 0 & 1 \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.35270019750437 & 0.705400395008739 & 0.64729980249563 \tabularnewline
18 & 0.305935702178313 & 0.611871404356626 & 0.694064297821687 \tabularnewline
19 & 0.240196077223208 & 0.480392154446416 & 0.759803922776792 \tabularnewline
20 & 0.786636296141454 & 0.426727407717091 & 0.213363703858546 \tabularnewline
21 & 0.732216233649782 & 0.535567532700437 & 0.267783766350218 \tabularnewline
22 & 0.672666694701609 & 0.654666610596782 & 0.327333305298391 \tabularnewline
23 & 0.60944752395156 & 0.78110495209688 & 0.39055247604844 \tabularnewline
24 & 0.544240955546134 & 0.911518088907731 & 0.455759044453866 \tabularnewline
25 & 0.525104682344435 & 0.949790635311131 & 0.474895317655565 \tabularnewline
26 & 0.460892696131239 & 0.921785392262478 & 0.539107303868761 \tabularnewline
27 & 0.398539875136934 & 0.797079750273867 & 0.601460124863066 \tabularnewline
28 & 0.339448229266184 & 0.678896458532367 & 0.660551770733816 \tabularnewline
29 & 0.284743806019362 & 0.569487612038724 & 0.715256193980638 \tabularnewline
30 & 0.235231491179998 & 0.470462982359996 & 0.764768508820002 \tabularnewline
31 & 0.191384293298577 & 0.382768586597153 & 0.808615706701423 \tabularnewline
32 & 0.153362707567706 & 0.306725415135412 & 0.846637292432294 \tabularnewline
33 & 0.121056819522678 & 0.242113639045356 & 0.878943180477322 \tabularnewline
34 & 0.111576134703565 & 0.223152269407129 & 0.888423865296435 \tabularnewline
35 & 0.0865849748133495 & 0.173169949626699 & 0.913415025186651 \tabularnewline
36 & 0.0662340065270078 & 0.132468013054016 & 0.933765993472992 \tabularnewline
37 & 0.0589572054061165 & 0.117914410812233 & 0.941042794593883 \tabularnewline
38 & 0.0443200520332468 & 0.0886401040664935 & 0.955679947966753 \tabularnewline
39 & 0.0328695003744588 & 0.0657390007489176 & 0.967130499625541 \tabularnewline
40 & 0.0283356832296466 & 0.0566713664592932 & 0.971664316770353 \tabularnewline
41 & 0.460361725608227 & 0.920723451216455 & 0.539638274391772 \tabularnewline
42 & 0.41064004060428 & 0.821280081208559 & 0.58935995939572 \tabularnewline
43 & 0.362707809831433 & 0.725415619662866 & 0.637292190168567 \tabularnewline
44 & 0.334615574942784 & 0.669231149885569 & 0.665384425057216 \tabularnewline
45 & 0.291090645646073 & 0.582181291292146 & 0.708909354353927 \tabularnewline
46 & 0.25074823471758 & 0.50149646943516 & 0.74925176528242 \tabularnewline
47 & 0.21389186334273 & 0.42778372668546 & 0.78610813665727 \tabularnewline
48 & 0.180691344050318 & 0.361382688100636 & 0.819308655949682 \tabularnewline
49 & 0.151191799325109 & 0.302383598650218 & 0.848808200674891 \tabularnewline
50 & 0.125328530088932 & 0.250657060177864 & 0.874671469911068 \tabularnewline
51 & 0.111472359143608 & 0.222944718287217 & 0.888527640856392 \tabularnewline
52 & 0.45190677899396 & 0.90381355798792 & 0.54809322100604 \tabularnewline
53 & 0.407552421816348 & 0.815104843632697 & 0.592447578183652 \tabularnewline
54 & 0.866384126861958 & 0.267231746276083 & 0.133615873138042 \tabularnewline
55 & 0.841012433229134 & 0.317975133541732 & 0.158987566770866 \tabularnewline
56 & 0.825441736108427 & 0.349116527783147 & 0.174558263891573 \tabularnewline
57 & 0.796001365184177 & 0.407997269631646 & 0.203998634815823 \tabularnewline
58 & 0.764089003288634 & 0.471821993422733 & 0.235910996711366 \tabularnewline
59 & 0.729955716786963 & 0.540088566426075 & 0.270044283213037 \tabularnewline
60 & 0.940417707839957 & 0.119164584320086 & 0.059582292160043 \tabularnewline
61 & 0.933272198626717 & 0.133455602746565 & 0.0667278013732825 \tabularnewline
62 & 0.918369562267323 & 0.163260875465354 & 0.081630437732677 \tabularnewline
63 & 0.901276511098949 & 0.197446977802101 & 0.0987234889010507 \tabularnewline
64 & 0.890985138717252 & 0.218029722565495 & 0.109014861282747 \tabularnewline
65 & 0.870582417150192 & 0.258835165699616 & 0.129417582849808 \tabularnewline
66 & 0.848045487148453 & 0.303909025703095 & 0.151954512851547 \tabularnewline
67 & 0.977774784979357 & 0.0444504300412861 & 0.022225215020643 \tabularnewline
68 & 0.971598875845785 & 0.0568022483084306 & 0.0284011241542153 \tabularnewline
69 & 0.964166083612965 & 0.0716678327740704 & 0.0358339163870352 \tabularnewline
70 & 0.955364872162536 & 0.089270255674929 & 0.0446351278374645 \tabularnewline
71 & 0.945120908253752 & 0.109758183492497 & 0.0548790917462483 \tabularnewline
72 & 0.933416874024579 & 0.133166251950842 & 0.0665831259754208 \tabularnewline
73 & 0.920317576037508 & 0.159364847924985 & 0.0796824239624924 \tabularnewline
74 & 0.906002478706719 & 0.187995042586562 & 0.0939975212932811 \tabularnewline
75 & 0.890809700963923 & 0.218380598072153 & 0.109190299036077 \tabularnewline
76 & 0.882777395177717 & 0.234445209644565 & 0.117222604822283 \tabularnewline
77 & 0.867927501852407 & 0.264144996295185 & 0.132072498147593 \tabularnewline
78 & 0.854550479202276 & 0.290899041595449 & 0.145449520797724 \tabularnewline
79 & 0.980638965135575 & 0.0387220697288509 & 0.0193610348644254 \tabularnewline
80 & 0.977638088659768 & 0.044723822680464 & 0.022361911340232 \tabularnewline
81 & 0.973102076110284 & 0.0537958477794318 & 0.0268979238897159 \tabularnewline
82 & 0.969210033156469 & 0.0615799336870627 & 0.0307899668435313 \tabularnewline
83 & 0.967866914302931 & 0.0642661713941377 & 0.0321330856970689 \tabularnewline
84 & 0.999198145163224 & 0.00160370967355142 & 0.000801854836775711 \tabularnewline
85 & 0.998802337253217 & 0.00239532549356611 & 0.00119766274678305 \tabularnewline
86 & 0.998233055753339 & 0.00353388849332202 & 0.00176694424666101 \tabularnewline
87 & 0.997455172527778 & 0.00508965494444446 & 0.00254482747222223 \tabularnewline
88 & 0.996453321748356 & 0.00709335650328795 & 0.00354667825164398 \tabularnewline
89 & 0.995063495293931 & 0.00987300941213813 & 0.00493650470606907 \tabularnewline
90 & 0.993168868537568 & 0.0136622629248647 & 0.00683113146243235 \tabularnewline
91 & 0.990637452037217 & 0.0187250959255669 & 0.00936254796278346 \tabularnewline
92 & 0.98746849047594 & 0.02506301904812 & 0.01253150952406 \tabularnewline
93 & 0.983240913857467 & 0.0335181722850659 & 0.0167590861425329 \tabularnewline
94 & 0.977816709664454 & 0.0443665806710911 & 0.0221832903355455 \tabularnewline
95 & 0.971114749762017 & 0.0577705004759665 & 0.0288852502379833 \tabularnewline
96 & 0.962641963440676 & 0.0747160731186476 & 0.0373580365593238 \tabularnewline
97 & 0.952200781149228 & 0.0955984377015444 & 0.0477992188507722 \tabularnewline
98 & 0.93955949151201 & 0.12088101697598 & 0.0604405084879901 \tabularnewline
99 & 0.924373115143564 & 0.151253769712872 & 0.0756268848564359 \tabularnewline
100 & 0.906374917891889 & 0.187250164216223 & 0.0936250821081113 \tabularnewline
101 & 0.885332589061424 & 0.229334821877153 & 0.114667410938576 \tabularnewline
102 & 0.86106540696412 & 0.277869186071759 & 0.138934593035879 \tabularnewline
103 & 0.83346132881125 & 0.333077342377501 & 0.16653867118875 \tabularnewline
104 & 0.802492706620252 & 0.395014586759495 & 0.197507293379748 \tabularnewline
105 & 0.76796378812419 & 0.46407242375162 & 0.23203621187581 \tabularnewline
106 & 0.730484602757409 & 0.539030794485182 & 0.269515397242591 \tabularnewline
107 & 0.690133656536681 & 0.619732686926638 & 0.309866343463319 \tabularnewline
108 & 0.646180449172177 & 0.707639101655646 & 0.353819550827823 \tabularnewline
109 & 0.601166267332088 & 0.797667465335823 & 0.398833732667912 \tabularnewline
110 & 0.554706556538 & 0.890586886924 & 0.445293443462 \tabularnewline
111 & 0.505385203784115 & 0.989229592431769 & 0.494614796215885 \tabularnewline
112 & 0.455316627684861 & 0.910633255369722 & 0.544683372315139 \tabularnewline
113 & 0.408113275485579 & 0.816226550971157 & 0.591886724514421 \tabularnewline
114 & 0.359284770057417 & 0.718569540114835 & 0.640715229942582 \tabularnewline
115 & 0.31510774068724 & 0.630215481374479 & 0.68489225931276 \tabularnewline
116 & 0.273377101819744 & 0.546754203639488 & 0.726622898180256 \tabularnewline
117 & 0.234551583168199 & 0.469103166336397 & 0.765448416831801 \tabularnewline
118 & 0.198975873912722 & 0.397951747825444 & 0.801024126087278 \tabularnewline
119 & 0.166873175811651 & 0.333746351623301 & 0.833126824188349 \tabularnewline
120 & 0.138345414929705 & 0.276690829859409 & 0.861654585070295 \tabularnewline
121 & 0.11338053664805 & 0.2267610732961 & 0.88661946335195 \tabularnewline
122 & 0.0918657269877488 & 0.183731453975498 & 0.908134273012251 \tabularnewline
123 & 0.0712229446843727 & 0.142445889368745 & 0.928777055315627 \tabularnewline
124 & 0.0561257426063339 & 0.112251485212668 & 0.943874257393666 \tabularnewline
125 & 0.0437290059545075 & 0.087458011909015 & 0.956270994045493 \tabularnewline
126 & 0.0320784357735474 & 0.0641568715470948 & 0.967921564226453 \tabularnewline
127 & 0.0242676552408064 & 0.0485353104816128 & 0.975732344759194 \tabularnewline
128 & 0.0181644763060932 & 0.0363289526121864 & 0.981835523693907 \tabularnewline
129 & 0.0134668585290613 & 0.0269337170581226 & 0.986533141470939 \tabularnewline
130 & 0.00990373301895782 & 0.0198074660379156 & 0.990096266981042 \tabularnewline
131 & 0.00723920940398207 & 0.0144784188079641 & 0.992760790596018 \tabularnewline
132 & 0.00527378235023572 & 0.0105475647004714 & 0.994726217649764 \tabularnewline
133 & 0.00384320412090669 & 0.00768640824181337 & 0.996156795879093 \tabularnewline
134 & 0.00281574228413513 & 0.00563148456827025 & 0.997184257715865 \tabularnewline
135 & 0.00208852620301187 & 0.00417705240602374 & 0.997911473796988 \tabularnewline
136 & 0.00158364668018058 & 0.00316729336036116 & 0.998416353319819 \tabularnewline
137 & 0.00124469164492125 & 0.0024893832898425 & 0.998755308355079 \tabularnewline
138 & 0.000691706876770691 & 0.00138341375354138 & 0.999308293123229 \tabularnewline
139 & 0.000367863994385145 & 0.00073572798877029 & 0.999632136005615 \tabularnewline
140 & 0.000289130632030209 & 0.000578261264060419 & 0.99971086936797 \tabularnewline
141 & 0.00889949548860166 & 0.0177989909772033 & 0.991100504511398 \tabularnewline
142 & 0.00498041417392821 & 0.00996082834785641 & 0.995019585826072 \tabularnewline
143 & 0.00349092445375706 & 0.00698184890751412 & 0.996509075546243 \tabularnewline
144 & 0.00257091972378838 & 0.00514183944757676 & 0.997429080276212 \tabularnewline
145 & 0.00209509005197235 & 0.0041901801039447 & 0.997904909948028 \tabularnewline
146 & 0.000912617842234254 & 0.00182523568446851 & 0.999087382157766 \tabularnewline
147 & 0.000350994751420643 & 0.000701989502841285 & 0.999649005248579 \tabularnewline
148 & 0.000114481981306706 & 0.000228963962613411 & 0.999885518018693 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204373&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.35270019750437[/C][C]0.705400395008739[/C][C]0.64729980249563[/C][/ROW]
[ROW][C]18[/C][C]0.305935702178313[/C][C]0.611871404356626[/C][C]0.694064297821687[/C][/ROW]
[ROW][C]19[/C][C]0.240196077223208[/C][C]0.480392154446416[/C][C]0.759803922776792[/C][/ROW]
[ROW][C]20[/C][C]0.786636296141454[/C][C]0.426727407717091[/C][C]0.213363703858546[/C][/ROW]
[ROW][C]21[/C][C]0.732216233649782[/C][C]0.535567532700437[/C][C]0.267783766350218[/C][/ROW]
[ROW][C]22[/C][C]0.672666694701609[/C][C]0.654666610596782[/C][C]0.327333305298391[/C][/ROW]
[ROW][C]23[/C][C]0.60944752395156[/C][C]0.78110495209688[/C][C]0.39055247604844[/C][/ROW]
[ROW][C]24[/C][C]0.544240955546134[/C][C]0.911518088907731[/C][C]0.455759044453866[/C][/ROW]
[ROW][C]25[/C][C]0.525104682344435[/C][C]0.949790635311131[/C][C]0.474895317655565[/C][/ROW]
[ROW][C]26[/C][C]0.460892696131239[/C][C]0.921785392262478[/C][C]0.539107303868761[/C][/ROW]
[ROW][C]27[/C][C]0.398539875136934[/C][C]0.797079750273867[/C][C]0.601460124863066[/C][/ROW]
[ROW][C]28[/C][C]0.339448229266184[/C][C]0.678896458532367[/C][C]0.660551770733816[/C][/ROW]
[ROW][C]29[/C][C]0.284743806019362[/C][C]0.569487612038724[/C][C]0.715256193980638[/C][/ROW]
[ROW][C]30[/C][C]0.235231491179998[/C][C]0.470462982359996[/C][C]0.764768508820002[/C][/ROW]
[ROW][C]31[/C][C]0.191384293298577[/C][C]0.382768586597153[/C][C]0.808615706701423[/C][/ROW]
[ROW][C]32[/C][C]0.153362707567706[/C][C]0.306725415135412[/C][C]0.846637292432294[/C][/ROW]
[ROW][C]33[/C][C]0.121056819522678[/C][C]0.242113639045356[/C][C]0.878943180477322[/C][/ROW]
[ROW][C]34[/C][C]0.111576134703565[/C][C]0.223152269407129[/C][C]0.888423865296435[/C][/ROW]
[ROW][C]35[/C][C]0.0865849748133495[/C][C]0.173169949626699[/C][C]0.913415025186651[/C][/ROW]
[ROW][C]36[/C][C]0.0662340065270078[/C][C]0.132468013054016[/C][C]0.933765993472992[/C][/ROW]
[ROW][C]37[/C][C]0.0589572054061165[/C][C]0.117914410812233[/C][C]0.941042794593883[/C][/ROW]
[ROW][C]38[/C][C]0.0443200520332468[/C][C]0.0886401040664935[/C][C]0.955679947966753[/C][/ROW]
[ROW][C]39[/C][C]0.0328695003744588[/C][C]0.0657390007489176[/C][C]0.967130499625541[/C][/ROW]
[ROW][C]40[/C][C]0.0283356832296466[/C][C]0.0566713664592932[/C][C]0.971664316770353[/C][/ROW]
[ROW][C]41[/C][C]0.460361725608227[/C][C]0.920723451216455[/C][C]0.539638274391772[/C][/ROW]
[ROW][C]42[/C][C]0.41064004060428[/C][C]0.821280081208559[/C][C]0.58935995939572[/C][/ROW]
[ROW][C]43[/C][C]0.362707809831433[/C][C]0.725415619662866[/C][C]0.637292190168567[/C][/ROW]
[ROW][C]44[/C][C]0.334615574942784[/C][C]0.669231149885569[/C][C]0.665384425057216[/C][/ROW]
[ROW][C]45[/C][C]0.291090645646073[/C][C]0.582181291292146[/C][C]0.708909354353927[/C][/ROW]
[ROW][C]46[/C][C]0.25074823471758[/C][C]0.50149646943516[/C][C]0.74925176528242[/C][/ROW]
[ROW][C]47[/C][C]0.21389186334273[/C][C]0.42778372668546[/C][C]0.78610813665727[/C][/ROW]
[ROW][C]48[/C][C]0.180691344050318[/C][C]0.361382688100636[/C][C]0.819308655949682[/C][/ROW]
[ROW][C]49[/C][C]0.151191799325109[/C][C]0.302383598650218[/C][C]0.848808200674891[/C][/ROW]
[ROW][C]50[/C][C]0.125328530088932[/C][C]0.250657060177864[/C][C]0.874671469911068[/C][/ROW]
[ROW][C]51[/C][C]0.111472359143608[/C][C]0.222944718287217[/C][C]0.888527640856392[/C][/ROW]
[ROW][C]52[/C][C]0.45190677899396[/C][C]0.90381355798792[/C][C]0.54809322100604[/C][/ROW]
[ROW][C]53[/C][C]0.407552421816348[/C][C]0.815104843632697[/C][C]0.592447578183652[/C][/ROW]
[ROW][C]54[/C][C]0.866384126861958[/C][C]0.267231746276083[/C][C]0.133615873138042[/C][/ROW]
[ROW][C]55[/C][C]0.841012433229134[/C][C]0.317975133541732[/C][C]0.158987566770866[/C][/ROW]
[ROW][C]56[/C][C]0.825441736108427[/C][C]0.349116527783147[/C][C]0.174558263891573[/C][/ROW]
[ROW][C]57[/C][C]0.796001365184177[/C][C]0.407997269631646[/C][C]0.203998634815823[/C][/ROW]
[ROW][C]58[/C][C]0.764089003288634[/C][C]0.471821993422733[/C][C]0.235910996711366[/C][/ROW]
[ROW][C]59[/C][C]0.729955716786963[/C][C]0.540088566426075[/C][C]0.270044283213037[/C][/ROW]
[ROW][C]60[/C][C]0.940417707839957[/C][C]0.119164584320086[/C][C]0.059582292160043[/C][/ROW]
[ROW][C]61[/C][C]0.933272198626717[/C][C]0.133455602746565[/C][C]0.0667278013732825[/C][/ROW]
[ROW][C]62[/C][C]0.918369562267323[/C][C]0.163260875465354[/C][C]0.081630437732677[/C][/ROW]
[ROW][C]63[/C][C]0.901276511098949[/C][C]0.197446977802101[/C][C]0.0987234889010507[/C][/ROW]
[ROW][C]64[/C][C]0.890985138717252[/C][C]0.218029722565495[/C][C]0.109014861282747[/C][/ROW]
[ROW][C]65[/C][C]0.870582417150192[/C][C]0.258835165699616[/C][C]0.129417582849808[/C][/ROW]
[ROW][C]66[/C][C]0.848045487148453[/C][C]0.303909025703095[/C][C]0.151954512851547[/C][/ROW]
[ROW][C]67[/C][C]0.977774784979357[/C][C]0.0444504300412861[/C][C]0.022225215020643[/C][/ROW]
[ROW][C]68[/C][C]0.971598875845785[/C][C]0.0568022483084306[/C][C]0.0284011241542153[/C][/ROW]
[ROW][C]69[/C][C]0.964166083612965[/C][C]0.0716678327740704[/C][C]0.0358339163870352[/C][/ROW]
[ROW][C]70[/C][C]0.955364872162536[/C][C]0.089270255674929[/C][C]0.0446351278374645[/C][/ROW]
[ROW][C]71[/C][C]0.945120908253752[/C][C]0.109758183492497[/C][C]0.0548790917462483[/C][/ROW]
[ROW][C]72[/C][C]0.933416874024579[/C][C]0.133166251950842[/C][C]0.0665831259754208[/C][/ROW]
[ROW][C]73[/C][C]0.920317576037508[/C][C]0.159364847924985[/C][C]0.0796824239624924[/C][/ROW]
[ROW][C]74[/C][C]0.906002478706719[/C][C]0.187995042586562[/C][C]0.0939975212932811[/C][/ROW]
[ROW][C]75[/C][C]0.890809700963923[/C][C]0.218380598072153[/C][C]0.109190299036077[/C][/ROW]
[ROW][C]76[/C][C]0.882777395177717[/C][C]0.234445209644565[/C][C]0.117222604822283[/C][/ROW]
[ROW][C]77[/C][C]0.867927501852407[/C][C]0.264144996295185[/C][C]0.132072498147593[/C][/ROW]
[ROW][C]78[/C][C]0.854550479202276[/C][C]0.290899041595449[/C][C]0.145449520797724[/C][/ROW]
[ROW][C]79[/C][C]0.980638965135575[/C][C]0.0387220697288509[/C][C]0.0193610348644254[/C][/ROW]
[ROW][C]80[/C][C]0.977638088659768[/C][C]0.044723822680464[/C][C]0.022361911340232[/C][/ROW]
[ROW][C]81[/C][C]0.973102076110284[/C][C]0.0537958477794318[/C][C]0.0268979238897159[/C][/ROW]
[ROW][C]82[/C][C]0.969210033156469[/C][C]0.0615799336870627[/C][C]0.0307899668435313[/C][/ROW]
[ROW][C]83[/C][C]0.967866914302931[/C][C]0.0642661713941377[/C][C]0.0321330856970689[/C][/ROW]
[ROW][C]84[/C][C]0.999198145163224[/C][C]0.00160370967355142[/C][C]0.000801854836775711[/C][/ROW]
[ROW][C]85[/C][C]0.998802337253217[/C][C]0.00239532549356611[/C][C]0.00119766274678305[/C][/ROW]
[ROW][C]86[/C][C]0.998233055753339[/C][C]0.00353388849332202[/C][C]0.00176694424666101[/C][/ROW]
[ROW][C]87[/C][C]0.997455172527778[/C][C]0.00508965494444446[/C][C]0.00254482747222223[/C][/ROW]
[ROW][C]88[/C][C]0.996453321748356[/C][C]0.00709335650328795[/C][C]0.00354667825164398[/C][/ROW]
[ROW][C]89[/C][C]0.995063495293931[/C][C]0.00987300941213813[/C][C]0.00493650470606907[/C][/ROW]
[ROW][C]90[/C][C]0.993168868537568[/C][C]0.0136622629248647[/C][C]0.00683113146243235[/C][/ROW]
[ROW][C]91[/C][C]0.990637452037217[/C][C]0.0187250959255669[/C][C]0.00936254796278346[/C][/ROW]
[ROW][C]92[/C][C]0.98746849047594[/C][C]0.02506301904812[/C][C]0.01253150952406[/C][/ROW]
[ROW][C]93[/C][C]0.983240913857467[/C][C]0.0335181722850659[/C][C]0.0167590861425329[/C][/ROW]
[ROW][C]94[/C][C]0.977816709664454[/C][C]0.0443665806710911[/C][C]0.0221832903355455[/C][/ROW]
[ROW][C]95[/C][C]0.971114749762017[/C][C]0.0577705004759665[/C][C]0.0288852502379833[/C][/ROW]
[ROW][C]96[/C][C]0.962641963440676[/C][C]0.0747160731186476[/C][C]0.0373580365593238[/C][/ROW]
[ROW][C]97[/C][C]0.952200781149228[/C][C]0.0955984377015444[/C][C]0.0477992188507722[/C][/ROW]
[ROW][C]98[/C][C]0.93955949151201[/C][C]0.12088101697598[/C][C]0.0604405084879901[/C][/ROW]
[ROW][C]99[/C][C]0.924373115143564[/C][C]0.151253769712872[/C][C]0.0756268848564359[/C][/ROW]
[ROW][C]100[/C][C]0.906374917891889[/C][C]0.187250164216223[/C][C]0.0936250821081113[/C][/ROW]
[ROW][C]101[/C][C]0.885332589061424[/C][C]0.229334821877153[/C][C]0.114667410938576[/C][/ROW]
[ROW][C]102[/C][C]0.86106540696412[/C][C]0.277869186071759[/C][C]0.138934593035879[/C][/ROW]
[ROW][C]103[/C][C]0.83346132881125[/C][C]0.333077342377501[/C][C]0.16653867118875[/C][/ROW]
[ROW][C]104[/C][C]0.802492706620252[/C][C]0.395014586759495[/C][C]0.197507293379748[/C][/ROW]
[ROW][C]105[/C][C]0.76796378812419[/C][C]0.46407242375162[/C][C]0.23203621187581[/C][/ROW]
[ROW][C]106[/C][C]0.730484602757409[/C][C]0.539030794485182[/C][C]0.269515397242591[/C][/ROW]
[ROW][C]107[/C][C]0.690133656536681[/C][C]0.619732686926638[/C][C]0.309866343463319[/C][/ROW]
[ROW][C]108[/C][C]0.646180449172177[/C][C]0.707639101655646[/C][C]0.353819550827823[/C][/ROW]
[ROW][C]109[/C][C]0.601166267332088[/C][C]0.797667465335823[/C][C]0.398833732667912[/C][/ROW]
[ROW][C]110[/C][C]0.554706556538[/C][C]0.890586886924[/C][C]0.445293443462[/C][/ROW]
[ROW][C]111[/C][C]0.505385203784115[/C][C]0.989229592431769[/C][C]0.494614796215885[/C][/ROW]
[ROW][C]112[/C][C]0.455316627684861[/C][C]0.910633255369722[/C][C]0.544683372315139[/C][/ROW]
[ROW][C]113[/C][C]0.408113275485579[/C][C]0.816226550971157[/C][C]0.591886724514421[/C][/ROW]
[ROW][C]114[/C][C]0.359284770057417[/C][C]0.718569540114835[/C][C]0.640715229942582[/C][/ROW]
[ROW][C]115[/C][C]0.31510774068724[/C][C]0.630215481374479[/C][C]0.68489225931276[/C][/ROW]
[ROW][C]116[/C][C]0.273377101819744[/C][C]0.546754203639488[/C][C]0.726622898180256[/C][/ROW]
[ROW][C]117[/C][C]0.234551583168199[/C][C]0.469103166336397[/C][C]0.765448416831801[/C][/ROW]
[ROW][C]118[/C][C]0.198975873912722[/C][C]0.397951747825444[/C][C]0.801024126087278[/C][/ROW]
[ROW][C]119[/C][C]0.166873175811651[/C][C]0.333746351623301[/C][C]0.833126824188349[/C][/ROW]
[ROW][C]120[/C][C]0.138345414929705[/C][C]0.276690829859409[/C][C]0.861654585070295[/C][/ROW]
[ROW][C]121[/C][C]0.11338053664805[/C][C]0.2267610732961[/C][C]0.88661946335195[/C][/ROW]
[ROW][C]122[/C][C]0.0918657269877488[/C][C]0.183731453975498[/C][C]0.908134273012251[/C][/ROW]
[ROW][C]123[/C][C]0.0712229446843727[/C][C]0.142445889368745[/C][C]0.928777055315627[/C][/ROW]
[ROW][C]124[/C][C]0.0561257426063339[/C][C]0.112251485212668[/C][C]0.943874257393666[/C][/ROW]
[ROW][C]125[/C][C]0.0437290059545075[/C][C]0.087458011909015[/C][C]0.956270994045493[/C][/ROW]
[ROW][C]126[/C][C]0.0320784357735474[/C][C]0.0641568715470948[/C][C]0.967921564226453[/C][/ROW]
[ROW][C]127[/C][C]0.0242676552408064[/C][C]0.0485353104816128[/C][C]0.975732344759194[/C][/ROW]
[ROW][C]128[/C][C]0.0181644763060932[/C][C]0.0363289526121864[/C][C]0.981835523693907[/C][/ROW]
[ROW][C]129[/C][C]0.0134668585290613[/C][C]0.0269337170581226[/C][C]0.986533141470939[/C][/ROW]
[ROW][C]130[/C][C]0.00990373301895782[/C][C]0.0198074660379156[/C][C]0.990096266981042[/C][/ROW]
[ROW][C]131[/C][C]0.00723920940398207[/C][C]0.0144784188079641[/C][C]0.992760790596018[/C][/ROW]
[ROW][C]132[/C][C]0.00527378235023572[/C][C]0.0105475647004714[/C][C]0.994726217649764[/C][/ROW]
[ROW][C]133[/C][C]0.00384320412090669[/C][C]0.00768640824181337[/C][C]0.996156795879093[/C][/ROW]
[ROW][C]134[/C][C]0.00281574228413513[/C][C]0.00563148456827025[/C][C]0.997184257715865[/C][/ROW]
[ROW][C]135[/C][C]0.00208852620301187[/C][C]0.00417705240602374[/C][C]0.997911473796988[/C][/ROW]
[ROW][C]136[/C][C]0.00158364668018058[/C][C]0.00316729336036116[/C][C]0.998416353319819[/C][/ROW]
[ROW][C]137[/C][C]0.00124469164492125[/C][C]0.0024893832898425[/C][C]0.998755308355079[/C][/ROW]
[ROW][C]138[/C][C]0.000691706876770691[/C][C]0.00138341375354138[/C][C]0.999308293123229[/C][/ROW]
[ROW][C]139[/C][C]0.000367863994385145[/C][C]0.00073572798877029[/C][C]0.999632136005615[/C][/ROW]
[ROW][C]140[/C][C]0.000289130632030209[/C][C]0.000578261264060419[/C][C]0.99971086936797[/C][/ROW]
[ROW][C]141[/C][C]0.00889949548860166[/C][C]0.0177989909772033[/C][C]0.991100504511398[/C][/ROW]
[ROW][C]142[/C][C]0.00498041417392821[/C][C]0.00996082834785641[/C][C]0.995019585826072[/C][/ROW]
[ROW][C]143[/C][C]0.00349092445375706[/C][C]0.00698184890751412[/C][C]0.996509075546243[/C][/ROW]
[ROW][C]144[/C][C]0.00257091972378838[/C][C]0.00514183944757676[/C][C]0.997429080276212[/C][/ROW]
[ROW][C]145[/C][C]0.00209509005197235[/C][C]0.0041901801039447[/C][C]0.997904909948028[/C][/ROW]
[ROW][C]146[/C][C]0.000912617842234254[/C][C]0.00182523568446851[/C][C]0.999087382157766[/C][/ROW]
[ROW][C]147[/C][C]0.000350994751420643[/C][C]0.000701989502841285[/C][C]0.999649005248579[/C][/ROW]
[ROW][C]148[/C][C]0.000114481981306706[/C][C]0.000228963962613411[/C][C]0.999885518018693[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204373&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204373&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.352700197504370.7054003950087390.64729980249563
180.3059357021783130.6118714043566260.694064297821687
190.2401960772232080.4803921544464160.759803922776792
200.7866362961414540.4267274077170910.213363703858546
210.7322162336497820.5355675327004370.267783766350218
220.6726666947016090.6546666105967820.327333305298391
230.609447523951560.781104952096880.39055247604844
240.5442409555461340.9115180889077310.455759044453866
250.5251046823444350.9497906353111310.474895317655565
260.4608926961312390.9217853922624780.539107303868761
270.3985398751369340.7970797502738670.601460124863066
280.3394482292661840.6788964585323670.660551770733816
290.2847438060193620.5694876120387240.715256193980638
300.2352314911799980.4704629823599960.764768508820002
310.1913842932985770.3827685865971530.808615706701423
320.1533627075677060.3067254151354120.846637292432294
330.1210568195226780.2421136390453560.878943180477322
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350.08658497481334950.1731699496266990.913415025186651
360.06623400652700780.1324680130540160.933765993472992
370.05895720540611650.1179144108122330.941042794593883
380.04432005203324680.08864010406649350.955679947966753
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400.02833568322964660.05667136645929320.971664316770353
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450.2910906456460730.5821812912921460.708909354353927
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480.1806913440503180.3613826881006360.819308655949682
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600.9404177078399570.1191645843200860.059582292160043
610.9332721986267170.1334556027465650.0667278013732825
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700.9553648721625360.0892702556749290.0446351278374645
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880.9964533217483560.007093356503287950.00354667825164398
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960.9626419634406760.07471607311864760.0373580365593238
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1000.9063749178918890.1872501642162230.0936250821081113
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1080.6461804491721770.7076391016556460.353819550827823
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1100.5547065565380.8905868869240.445293443462
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1120.4553166276848610.9106332553697220.544683372315139
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1470.0003509947514206430.0007019895028412850.999649005248579
1480.0001144819813067060.0002289639626134110.999885518018693







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level320.223776223776224NOK
5% type I error level470.328671328671329NOK
10% type I error level610.426573426573427NOK

\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 & 32 & 0.223776223776224 & NOK \tabularnewline
5% type I error level & 47 & 0.328671328671329 & NOK \tabularnewline
10% type I error level & 61 & 0.426573426573427 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204373&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]32[/C][C]0.223776223776224[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]47[/C][C]0.328671328671329[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]61[/C][C]0.426573426573427[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204373&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204373&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level320.223776223776224NOK
5% type I error level470.328671328671329NOK
10% type I error level610.426573426573427NOK



Parameters (Session):
par1 = 162 ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = 3 ; 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')
}