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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 09:06:59 -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/t1356098869qjepc6zpkerkpli.htm/, Retrieved Fri, 29 Mar 2024 07:18:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203687, Retrieved Fri, 29 Mar 2024 07:18:21 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Skewness and Kurtosis Test] [skewness] [2012-12-12 22:28:02] [a87a0df67d47b041e4d219678c935cd7]
- RMPD  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [chi-kwadraat] [2012-12-16 22:04:34] [754b40392725f52c97d5cd2ee03f2d3f]
- RMPD    [Multiple Regression] [] [2012-12-19 21:50:23] [754b40392725f52c97d5cd2ee03f2d3f]
- R PD        [Multiple Regression] [] [2012-12-21 14:06:59] [9556601f32d45cd6b13539aa40ba329c] [Current]
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Dataseries X:
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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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=203687&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]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=203687&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203687&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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0120098995385728 -0.0127358831515425UseLimit[t] + 0.237333074487545Used[t] + 0.0493474167869582Useful[t] -0.0305819634101805Outcome[t] + 0.158634174536241T40[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0120098995385728 -0.0127358831515425UseLimit[t] +  0.237333074487545Used[t] +  0.0493474167869582Useful[t] -0.0305819634101805Outcome[t] +  0.158634174536241T40[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203687&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0120098995385728 -0.0127358831515425UseLimit[t] +  0.237333074487545Used[t] +  0.0493474167869582Useful[t] -0.0305819634101805Outcome[t] +  0.158634174536241T40[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203687&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203687&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] = -0.0120098995385728 -0.0127358831515425UseLimit[t] + 0.237333074487545Used[t] + 0.0493474167869582Useful[t] -0.0305819634101805Outcome[t] + 0.158634174536241T40[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01200989953857280.030558-0.3930.6948750.347437
UseLimit-0.01273588315154250.041285-0.30850.7581440.379072
Used0.2373330744875450.0438195.416200
Useful0.04934741678695820.0453311.08860.2780980.139049
Outcome-0.03058196341018050.039692-0.77050.4422470.221124
T400.1586341745362410.054932.88790.004460.00223

\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) & -0.0120098995385728 & 0.030558 & -0.393 & 0.694875 & 0.347437 \tabularnewline
UseLimit & -0.0127358831515425 & 0.041285 & -0.3085 & 0.758144 & 0.379072 \tabularnewline
Used & 0.237333074487545 & 0.043819 & 5.4162 & 0 & 0 \tabularnewline
Useful & 0.0493474167869582 & 0.045331 & 1.0886 & 0.278098 & 0.139049 \tabularnewline
Outcome & -0.0305819634101805 & 0.039692 & -0.7705 & 0.442247 & 0.221124 \tabularnewline
T40 & 0.158634174536241 & 0.05493 & 2.8879 & 0.00446 & 0.00223 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203687&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]-0.0120098995385728[/C][C]0.030558[/C][C]-0.393[/C][C]0.694875[/C][C]0.347437[/C][/ROW]
[ROW][C]UseLimit[/C][C]-0.0127358831515425[/C][C]0.041285[/C][C]-0.3085[/C][C]0.758144[/C][C]0.379072[/C][/ROW]
[ROW][C]Used[/C][C]0.237333074487545[/C][C]0.043819[/C][C]5.4162[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Useful[/C][C]0.0493474167869582[/C][C]0.045331[/C][C]1.0886[/C][C]0.278098[/C][C]0.139049[/C][/ROW]
[ROW][C]Outcome[/C][C]-0.0305819634101805[/C][C]0.039692[/C][C]-0.7705[/C][C]0.442247[/C][C]0.221124[/C][/ROW]
[ROW][C]T40[/C][C]0.158634174536241[/C][C]0.05493[/C][C]2.8879[/C][C]0.00446[/C][C]0.00223[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203687&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203687&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)-0.01200989953857280.030558-0.3930.6948750.347437
UseLimit-0.01273588315154250.041285-0.30850.7581440.379072
Used0.2373330744875450.0438195.416200
Useful0.04934741678695820.0453311.08860.2780980.139049
Outcome-0.03058196341018050.039692-0.77050.4422470.221124
T400.1586341745362410.054932.88790.004460.00223







Multiple Linear Regression - Regression Statistics
Multiple R0.507465960155506
R-squared0.25752170071655
Adjusted R-squared0.232437974389406
F-TEST (value)10.2664850253083
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value1.83115576035675e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.235605519034018
Sum Squared Residuals8.2154741686948

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.507465960155506 \tabularnewline
R-squared & 0.25752170071655 \tabularnewline
Adjusted R-squared & 0.232437974389406 \tabularnewline
F-TEST (value) & 10.2664850253083 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 1.83115576035675e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.235605519034018 \tabularnewline
Sum Squared Residuals & 8.2154741686948 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203687&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.507465960155506[/C][/ROW]
[ROW][C]R-squared[/C][C]0.25752170071655[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.232437974389406[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.2664850253083[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/C][/ROW]
[ROW][C]p-value[/C][C]1.83115576035675e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.235605519034018[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.2154741686948[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203687&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203687&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 R0.507465960155506
R-squared0.25752170071655
Adjusted R-squared0.232437974389406
F-TEST (value)10.2664850253083
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value1.83115576035675e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.235605519034018
Sum Squared Residuals8.2154741686948







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.103306428435946-0.103306428435946
20-0.01200989953857270.0120098995385727
30-0.01200989953857320.0120098995385732
40-0.01200989953857280.0120098995385728
50-0.01200989953857290.0120098995385729
60-0.005980329313337630.00598032931333763
70-0.01200989953857280.0120098995385728
800.146624274997669-0.146624274997669
90-0.04259186294875330.0425918629487533
100-0.02474578269011530.0247457826901153
1100.133888391846126-0.133888391846126
120-0.01200989953857280.0120098995385728
1300.27467059173593-0.27467059173593
1400.133888391846126-0.133888391846126
1500.24408862832575-0.24408862832575
1600.402722802861991-0.402722802861991
1710.4205688831206290.579431116879371
1800.133888391846126-0.133888391846126
190-0.04259186294875330.0425918629487533
2010.4027228028619910.597277197138009
2100.0246016340968428-0.0246016340968428
2200.231352745174207-0.231352745174207
2300.00675555383820483-0.00675555383820483
240-0.005980329313337660.00598032931333766
2500.353375386075033-0.353375386075033
2600.27467059173593-0.27467059173593
270-0.05532774610029580.0553277461002958
2800.225323174948972-0.225323174948972
290-0.04259186294875330.0425918629487533
3000.0373375172483853-0.0373375172483853
310-0.01200989953857280.0120098995385728
320-0.02474578269011530.0247457826901153
3300.0246016340968428-0.0246016340968428
3400.116042311587488-0.116042311587488
350-0.01200989953857280.0120098995385728
360-0.01200989953857280.0120098995385728
3700.420568883120629-0.420568883120629
3800.194741211538791-0.194741211538791
3900.00675555383820483-0.00675555383820483
4000.195971691784627-0.195971691784627
4110.244088628325750.75591137167425
4200.194741211538791-0.194741211538791
430-0.005980329313337660.00598032931333766
4400.133888391846126-0.133888391846126
4500.0373375172483853-0.0373375172483853
4600.00675555383820483-0.00675555383820483
470-0.01200989953857280.0120098995385728
480-0.04259186294875330.0425918629487533
4900.00675555383820483-0.00675555383820483
500-0.01200989953857280.0120098995385728
5100.383957349485213-0.383957349485213
5210.4205688831206290.579431116879371
530-0.04259186294875330.0425918629487533
5410.2253231749489720.774676825051028
550-0.01200989953857280.0120098995385728
5600.353375386075033-0.353375386075033
5700.24408862832575-0.24408862832575
580-0.04259186294875330.0425918629487533
590-0.04259186294875330.0425918629487533
6010.3899869197104490.610013080289551
6100.103306428435946-0.103306428435946
6200.27467059173593-0.27467059173593
630-0.01200989953857280.0120098995385728
6400.103306428435946-0.103306428435946
650-0.01200989953857280.0120098995385728
660-0.01200989953857280.0120098995385728
6710.4333047662721720.566695233727828
680-0.02474578269011530.0247457826901153
690-0.04259186294875330.0425918629487533
7000.225323174948972-0.225323174948972
710-0.01200989953857280.0120098995385728
720-0.04259186294875330.0425918629487533
7300.194741211538791-0.194741211538791
7400.212587291797429-0.212587291797429
750-0.04259186294875330.0425918629487533
7600.165389728374446-0.165389728374446
770-0.04259186294875330.0425918629487533
7800.24408862832575-0.24408862832575
7910.3533753860750330.646624613924967
8000.195971691784627-0.195971691784627
810-0.01200989953857280.0120098995385728
8200.182005328387249-0.182005328387249
830-0.01200989953857280.0120098995385728
8410.2253231749489720.774676825051028
8500.00675555383820483-0.00675555383820483
860-0.02474578269011530.0247457826901153
870-0.05532774610029580.0553277461002958
8800.182005328387249-0.182005328387249
890-0.01200989953857280.0120098995385728
900-0.04259186294875330.0425918629487533
9100.0373375172483853-0.0373375172483853
920-0.02474578269011530.0247457826901153
9300.0246016340968428-0.0246016340968428
940-0.01200989953857280.0120098995385728
950-0.01200989953857280.0120098995385728
960-0.04259186294875330.0425918629487533
970-0.02474578269011530.0247457826901153
980-0.01200989953857280.0120098995385728
990-0.02474578269011530.0247457826901153
1000-0.04259186294875330.0425918629487533
1010-0.05532774610029580.0553277461002958
1020-0.01200989953857280.0120098995385728
1030-0.01200989953857280.0120098995385728
1040-0.01200989953857280.0120098995385728
10500.225323174948972-0.225323174948972
1060-0.01200989953857280.0120098995385728
1070-0.01200989953857280.0120098995385728
10800.212587291797429-0.212587291797429
1090-0.01200989953857280.0120098995385728
1100-0.02474578269011530.0247457826901153
11100.261934708584388-0.261934708584388
1120-0.01200989953857280.0120098995385728
11300.225323174948972-0.225323174948972
11400.212587291797429-0.212587291797429
1150-0.02474578269011530.0247457826901153
1160-0.01200989953857280.0120098995385728
1170-0.05532774610029580.0553277461002958
1180-0.02474578269011530.0247457826901153
1190-0.01200989953857280.0120098995385728
1200-0.04259186294875330.0425918629487533
1210-0.02474578269011530.0247457826901153
1220-0.01200989953857280.0120098995385728
12300.212587291797429-0.212587291797429
12400.24408862832575-0.24408862832575
1250-0.04259186294875330.0425918629487533
1260-0.01200989953857280.0120098995385728
12700.0373375172483853-0.0373375172483853
1280-0.04259186294875330.0425918629487533
1290-0.01200989953857280.0120098995385728
1300-0.04259186294875330.0425918629487533
1310-0.02474578269011530.0247457826901153
1320-0.05532774610029580.0553277461002958
13300.212587291797429-0.212587291797429
1340-0.01200989953857280.0120098995385728
1350-0.01200989953857280.0120098995385728
1360-0.01200989953857280.0120098995385728
13700.231352745174207-0.231352745174207
13800.231352745174207-0.231352745174207
1390-0.01200989953857280.0120098995385728
1400-0.01200989953857280.0120098995385728
14110.1947412115387910.805258788461209
14200.194741211538791-0.194741211538791
1430-0.02474578269011530.0247457826901153
14400.00675555383820483-0.00675555383820483
14500.0373375172483853-0.0373375172483853
1460-0.04259186294875330.0425918629487533
14700.225323174948972-0.225323174948972
1480-0.01200989953857280.0120098995385728
1490-0.02474578269011530.0247457826901153
15000.00675555383820483-0.00675555383820483
1510-0.04259186294875330.0425918629487533
15210.212587291797430.78741270820257
15310.2619347085843880.738065291415612
15400.212587291797429-0.212587291797429

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.103306428435946 & -0.103306428435946 \tabularnewline
2 & 0 & -0.0120098995385727 & 0.0120098995385727 \tabularnewline
3 & 0 & -0.0120098995385732 & 0.0120098995385732 \tabularnewline
4 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
5 & 0 & -0.0120098995385729 & 0.0120098995385729 \tabularnewline
6 & 0 & -0.00598032931333763 & 0.00598032931333763 \tabularnewline
7 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
8 & 0 & 0.146624274997669 & -0.146624274997669 \tabularnewline
9 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
10 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
11 & 0 & 0.133888391846126 & -0.133888391846126 \tabularnewline
12 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
13 & 0 & 0.27467059173593 & -0.27467059173593 \tabularnewline
14 & 0 & 0.133888391846126 & -0.133888391846126 \tabularnewline
15 & 0 & 0.24408862832575 & -0.24408862832575 \tabularnewline
16 & 0 & 0.402722802861991 & -0.402722802861991 \tabularnewline
17 & 1 & 0.420568883120629 & 0.579431116879371 \tabularnewline
18 & 0 & 0.133888391846126 & -0.133888391846126 \tabularnewline
19 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
20 & 1 & 0.402722802861991 & 0.597277197138009 \tabularnewline
21 & 0 & 0.0246016340968428 & -0.0246016340968428 \tabularnewline
22 & 0 & 0.231352745174207 & -0.231352745174207 \tabularnewline
23 & 0 & 0.00675555383820483 & -0.00675555383820483 \tabularnewline
24 & 0 & -0.00598032931333766 & 0.00598032931333766 \tabularnewline
25 & 0 & 0.353375386075033 & -0.353375386075033 \tabularnewline
26 & 0 & 0.27467059173593 & -0.27467059173593 \tabularnewline
27 & 0 & -0.0553277461002958 & 0.0553277461002958 \tabularnewline
28 & 0 & 0.225323174948972 & -0.225323174948972 \tabularnewline
29 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
30 & 0 & 0.0373375172483853 & -0.0373375172483853 \tabularnewline
31 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
32 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
33 & 0 & 0.0246016340968428 & -0.0246016340968428 \tabularnewline
34 & 0 & 0.116042311587488 & -0.116042311587488 \tabularnewline
35 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
36 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
37 & 0 & 0.420568883120629 & -0.420568883120629 \tabularnewline
38 & 0 & 0.194741211538791 & -0.194741211538791 \tabularnewline
39 & 0 & 0.00675555383820483 & -0.00675555383820483 \tabularnewline
40 & 0 & 0.195971691784627 & -0.195971691784627 \tabularnewline
41 & 1 & 0.24408862832575 & 0.75591137167425 \tabularnewline
42 & 0 & 0.194741211538791 & -0.194741211538791 \tabularnewline
43 & 0 & -0.00598032931333766 & 0.00598032931333766 \tabularnewline
44 & 0 & 0.133888391846126 & -0.133888391846126 \tabularnewline
45 & 0 & 0.0373375172483853 & -0.0373375172483853 \tabularnewline
46 & 0 & 0.00675555383820483 & -0.00675555383820483 \tabularnewline
47 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
48 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
49 & 0 & 0.00675555383820483 & -0.00675555383820483 \tabularnewline
50 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
51 & 0 & 0.383957349485213 & -0.383957349485213 \tabularnewline
52 & 1 & 0.420568883120629 & 0.579431116879371 \tabularnewline
53 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
54 & 1 & 0.225323174948972 & 0.774676825051028 \tabularnewline
55 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
56 & 0 & 0.353375386075033 & -0.353375386075033 \tabularnewline
57 & 0 & 0.24408862832575 & -0.24408862832575 \tabularnewline
58 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
59 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
60 & 1 & 0.389986919710449 & 0.610013080289551 \tabularnewline
61 & 0 & 0.103306428435946 & -0.103306428435946 \tabularnewline
62 & 0 & 0.27467059173593 & -0.27467059173593 \tabularnewline
63 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
64 & 0 & 0.103306428435946 & -0.103306428435946 \tabularnewline
65 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
66 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
67 & 1 & 0.433304766272172 & 0.566695233727828 \tabularnewline
68 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
69 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
70 & 0 & 0.225323174948972 & -0.225323174948972 \tabularnewline
71 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
72 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
73 & 0 & 0.194741211538791 & -0.194741211538791 \tabularnewline
74 & 0 & 0.212587291797429 & -0.212587291797429 \tabularnewline
75 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
76 & 0 & 0.165389728374446 & -0.165389728374446 \tabularnewline
77 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
78 & 0 & 0.24408862832575 & -0.24408862832575 \tabularnewline
79 & 1 & 0.353375386075033 & 0.646624613924967 \tabularnewline
80 & 0 & 0.195971691784627 & -0.195971691784627 \tabularnewline
81 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
82 & 0 & 0.182005328387249 & -0.182005328387249 \tabularnewline
83 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
84 & 1 & 0.225323174948972 & 0.774676825051028 \tabularnewline
85 & 0 & 0.00675555383820483 & -0.00675555383820483 \tabularnewline
86 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
87 & 0 & -0.0553277461002958 & 0.0553277461002958 \tabularnewline
88 & 0 & 0.182005328387249 & -0.182005328387249 \tabularnewline
89 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
90 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
91 & 0 & 0.0373375172483853 & -0.0373375172483853 \tabularnewline
92 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
93 & 0 & 0.0246016340968428 & -0.0246016340968428 \tabularnewline
94 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
95 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
96 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
97 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
98 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
99 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
100 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
101 & 0 & -0.0553277461002958 & 0.0553277461002958 \tabularnewline
102 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
103 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
104 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
105 & 0 & 0.225323174948972 & -0.225323174948972 \tabularnewline
106 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
107 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
108 & 0 & 0.212587291797429 & -0.212587291797429 \tabularnewline
109 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
110 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
111 & 0 & 0.261934708584388 & -0.261934708584388 \tabularnewline
112 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
113 & 0 & 0.225323174948972 & -0.225323174948972 \tabularnewline
114 & 0 & 0.212587291797429 & -0.212587291797429 \tabularnewline
115 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
116 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
117 & 0 & -0.0553277461002958 & 0.0553277461002958 \tabularnewline
118 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
119 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
120 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
121 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
122 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
123 & 0 & 0.212587291797429 & -0.212587291797429 \tabularnewline
124 & 0 & 0.24408862832575 & -0.24408862832575 \tabularnewline
125 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
126 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
127 & 0 & 0.0373375172483853 & -0.0373375172483853 \tabularnewline
128 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
129 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
130 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
131 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
132 & 0 & -0.0553277461002958 & 0.0553277461002958 \tabularnewline
133 & 0 & 0.212587291797429 & -0.212587291797429 \tabularnewline
134 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
135 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
136 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
137 & 0 & 0.231352745174207 & -0.231352745174207 \tabularnewline
138 & 0 & 0.231352745174207 & -0.231352745174207 \tabularnewline
139 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
140 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
141 & 1 & 0.194741211538791 & 0.805258788461209 \tabularnewline
142 & 0 & 0.194741211538791 & -0.194741211538791 \tabularnewline
143 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
144 & 0 & 0.00675555383820483 & -0.00675555383820483 \tabularnewline
145 & 0 & 0.0373375172483853 & -0.0373375172483853 \tabularnewline
146 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
147 & 0 & 0.225323174948972 & -0.225323174948972 \tabularnewline
148 & 0 & -0.0120098995385728 & 0.0120098995385728 \tabularnewline
149 & 0 & -0.0247457826901153 & 0.0247457826901153 \tabularnewline
150 & 0 & 0.00675555383820483 & -0.00675555383820483 \tabularnewline
151 & 0 & -0.0425918629487533 & 0.0425918629487533 \tabularnewline
152 & 1 & 0.21258729179743 & 0.78741270820257 \tabularnewline
153 & 1 & 0.261934708584388 & 0.738065291415612 \tabularnewline
154 & 0 & 0.212587291797429 & -0.212587291797429 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203687&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]0.103306428435946[/C][C]-0.103306428435946[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.0120098995385727[/C][C]0.0120098995385727[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0120098995385732[/C][C]0.0120098995385732[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0120098995385729[/C][C]0.0120098995385729[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.00598032931333763[/C][C]0.00598032931333763[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.146624274997669[/C][C]-0.146624274997669[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.133888391846126[/C][C]-0.133888391846126[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.27467059173593[/C][C]-0.27467059173593[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.133888391846126[/C][C]-0.133888391846126[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.24408862832575[/C][C]-0.24408862832575[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.402722802861991[/C][C]-0.402722802861991[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.420568883120629[/C][C]0.579431116879371[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.133888391846126[/C][C]-0.133888391846126[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.402722802861991[/C][C]0.597277197138009[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0246016340968428[/C][C]-0.0246016340968428[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.231352745174207[/C][C]-0.231352745174207[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.00675555383820483[/C][C]-0.00675555383820483[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]-0.00598032931333766[/C][C]0.00598032931333766[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.353375386075033[/C][C]-0.353375386075033[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.27467059173593[/C][C]-0.27467059173593[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0553277461002958[/C][C]0.0553277461002958[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.225323174948972[/C][C]-0.225323174948972[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0373375172483853[/C][C]-0.0373375172483853[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0246016340968428[/C][C]-0.0246016340968428[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.116042311587488[/C][C]-0.116042311587488[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.420568883120629[/C][C]-0.420568883120629[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.194741211538791[/C][C]-0.194741211538791[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.00675555383820483[/C][C]-0.00675555383820483[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.195971691784627[/C][C]-0.195971691784627[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.24408862832575[/C][C]0.75591137167425[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.194741211538791[/C][C]-0.194741211538791[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]-0.00598032931333766[/C][C]0.00598032931333766[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.133888391846126[/C][C]-0.133888391846126[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0373375172483853[/C][C]-0.0373375172483853[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.00675555383820483[/C][C]-0.00675555383820483[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.00675555383820483[/C][C]-0.00675555383820483[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.383957349485213[/C][C]-0.383957349485213[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.420568883120629[/C][C]0.579431116879371[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.225323174948972[/C][C]0.774676825051028[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.353375386075033[/C][C]-0.353375386075033[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.24408862832575[/C][C]-0.24408862832575[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.389986919710449[/C][C]0.610013080289551[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.103306428435946[/C][C]-0.103306428435946[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.27467059173593[/C][C]-0.27467059173593[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.103306428435946[/C][C]-0.103306428435946[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.433304766272172[/C][C]0.566695233727828[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.225323174948972[/C][C]-0.225323174948972[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.194741211538791[/C][C]-0.194741211538791[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.212587291797429[/C][C]-0.212587291797429[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.165389728374446[/C][C]-0.165389728374446[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.24408862832575[/C][C]-0.24408862832575[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.353375386075033[/C][C]0.646624613924967[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.195971691784627[/C][C]-0.195971691784627[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.182005328387249[/C][C]-0.182005328387249[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.225323174948972[/C][C]0.774676825051028[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.00675555383820483[/C][C]-0.00675555383820483[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]-0.0553277461002958[/C][C]0.0553277461002958[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.182005328387249[/C][C]-0.182005328387249[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0373375172483853[/C][C]-0.0373375172483853[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0246016340968428[/C][C]-0.0246016340968428[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]-0.0553277461002958[/C][C]0.0553277461002958[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.225323174948972[/C][C]-0.225323174948972[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.212587291797429[/C][C]-0.212587291797429[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.261934708584388[/C][C]-0.261934708584388[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.225323174948972[/C][C]-0.225323174948972[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.212587291797429[/C][C]-0.212587291797429[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]-0.0553277461002958[/C][C]0.0553277461002958[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.212587291797429[/C][C]-0.212587291797429[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.24408862832575[/C][C]-0.24408862832575[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0373375172483853[/C][C]-0.0373375172483853[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]-0.0553277461002958[/C][C]0.0553277461002958[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.212587291797429[/C][C]-0.212587291797429[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.231352745174207[/C][C]-0.231352745174207[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.231352745174207[/C][C]-0.231352745174207[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.194741211538791[/C][C]0.805258788461209[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.194741211538791[/C][C]-0.194741211538791[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.00675555383820483[/C][C]-0.00675555383820483[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0373375172483853[/C][C]-0.0373375172483853[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.225323174948972[/C][C]-0.225323174948972[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]-0.0120098995385728[/C][C]0.0120098995385728[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.0247457826901153[/C][C]0.0247457826901153[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.00675555383820483[/C][C]-0.00675555383820483[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-0.0425918629487533[/C][C]0.0425918629487533[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.21258729179743[/C][C]0.78741270820257[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.261934708584388[/C][C]0.738065291415612[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.212587291797429[/C][C]-0.212587291797429[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203687&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203687&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
100.103306428435946-0.103306428435946
20-0.01200989953857270.0120098995385727
30-0.01200989953857320.0120098995385732
40-0.01200989953857280.0120098995385728
50-0.01200989953857290.0120098995385729
60-0.005980329313337630.00598032931333763
70-0.01200989953857280.0120098995385728
800.146624274997669-0.146624274997669
90-0.04259186294875330.0425918629487533
100-0.02474578269011530.0247457826901153
1100.133888391846126-0.133888391846126
120-0.01200989953857280.0120098995385728
1300.27467059173593-0.27467059173593
1400.133888391846126-0.133888391846126
1500.24408862832575-0.24408862832575
1600.402722802861991-0.402722802861991
1710.4205688831206290.579431116879371
1800.133888391846126-0.133888391846126
190-0.04259186294875330.0425918629487533
2010.4027228028619910.597277197138009
2100.0246016340968428-0.0246016340968428
2200.231352745174207-0.231352745174207
2300.00675555383820483-0.00675555383820483
240-0.005980329313337660.00598032931333766
2500.353375386075033-0.353375386075033
2600.27467059173593-0.27467059173593
270-0.05532774610029580.0553277461002958
2800.225323174948972-0.225323174948972
290-0.04259186294875330.0425918629487533
3000.0373375172483853-0.0373375172483853
310-0.01200989953857280.0120098995385728
320-0.02474578269011530.0247457826901153
3300.0246016340968428-0.0246016340968428
3400.116042311587488-0.116042311587488
350-0.01200989953857280.0120098995385728
360-0.01200989953857280.0120098995385728
3700.420568883120629-0.420568883120629
3800.194741211538791-0.194741211538791
3900.00675555383820483-0.00675555383820483
4000.195971691784627-0.195971691784627
4110.244088628325750.75591137167425
4200.194741211538791-0.194741211538791
430-0.005980329313337660.00598032931333766
4400.133888391846126-0.133888391846126
4500.0373375172483853-0.0373375172483853
4600.00675555383820483-0.00675555383820483
470-0.01200989953857280.0120098995385728
480-0.04259186294875330.0425918629487533
4900.00675555383820483-0.00675555383820483
500-0.01200989953857280.0120098995385728
5100.383957349485213-0.383957349485213
5210.4205688831206290.579431116879371
530-0.04259186294875330.0425918629487533
5410.2253231749489720.774676825051028
550-0.01200989953857280.0120098995385728
5600.353375386075033-0.353375386075033
5700.24408862832575-0.24408862832575
580-0.04259186294875330.0425918629487533
590-0.04259186294875330.0425918629487533
6010.3899869197104490.610013080289551
6100.103306428435946-0.103306428435946
6200.27467059173593-0.27467059173593
630-0.01200989953857280.0120098995385728
6400.103306428435946-0.103306428435946
650-0.01200989953857280.0120098995385728
660-0.01200989953857280.0120098995385728
6710.4333047662721720.566695233727828
680-0.02474578269011530.0247457826901153
690-0.04259186294875330.0425918629487533
7000.225323174948972-0.225323174948972
710-0.01200989953857280.0120098995385728
720-0.04259186294875330.0425918629487533
7300.194741211538791-0.194741211538791
7400.212587291797429-0.212587291797429
750-0.04259186294875330.0425918629487533
7600.165389728374446-0.165389728374446
770-0.04259186294875330.0425918629487533
7800.24408862832575-0.24408862832575
7910.3533753860750330.646624613924967
8000.195971691784627-0.195971691784627
810-0.01200989953857280.0120098995385728
8200.182005328387249-0.182005328387249
830-0.01200989953857280.0120098995385728
8410.2253231749489720.774676825051028
8500.00675555383820483-0.00675555383820483
860-0.02474578269011530.0247457826901153
870-0.05532774610029580.0553277461002958
8800.182005328387249-0.182005328387249
890-0.01200989953857280.0120098995385728
900-0.04259186294875330.0425918629487533
9100.0373375172483853-0.0373375172483853
920-0.02474578269011530.0247457826901153
9300.0246016340968428-0.0246016340968428
940-0.01200989953857280.0120098995385728
950-0.01200989953857280.0120098995385728
960-0.04259186294875330.0425918629487533
970-0.02474578269011530.0247457826901153
980-0.01200989953857280.0120098995385728
990-0.02474578269011530.0247457826901153
1000-0.04259186294875330.0425918629487533
1010-0.05532774610029580.0553277461002958
1020-0.01200989953857280.0120098995385728
1030-0.01200989953857280.0120098995385728
1040-0.01200989953857280.0120098995385728
10500.225323174948972-0.225323174948972
1060-0.01200989953857280.0120098995385728
1070-0.01200989953857280.0120098995385728
10800.212587291797429-0.212587291797429
1090-0.01200989953857280.0120098995385728
1100-0.02474578269011530.0247457826901153
11100.261934708584388-0.261934708584388
1120-0.01200989953857280.0120098995385728
11300.225323174948972-0.225323174948972
11400.212587291797429-0.212587291797429
1150-0.02474578269011530.0247457826901153
1160-0.01200989953857280.0120098995385728
1170-0.05532774610029580.0553277461002958
1180-0.02474578269011530.0247457826901153
1190-0.01200989953857280.0120098995385728
1200-0.04259186294875330.0425918629487533
1210-0.02474578269011530.0247457826901153
1220-0.01200989953857280.0120098995385728
12300.212587291797429-0.212587291797429
12400.24408862832575-0.24408862832575
1250-0.04259186294875330.0425918629487533
1260-0.01200989953857280.0120098995385728
12700.0373375172483853-0.0373375172483853
1280-0.04259186294875330.0425918629487533
1290-0.01200989953857280.0120098995385728
1300-0.04259186294875330.0425918629487533
1310-0.02474578269011530.0247457826901153
1320-0.05532774610029580.0553277461002958
13300.212587291797429-0.212587291797429
1340-0.01200989953857280.0120098995385728
1350-0.01200989953857280.0120098995385728
1360-0.01200989953857280.0120098995385728
13700.231352745174207-0.231352745174207
13800.231352745174207-0.231352745174207
1390-0.01200989953857280.0120098995385728
1400-0.01200989953857280.0120098995385728
14110.1947412115387910.805258788461209
14200.194741211538791-0.194741211538791
1430-0.02474578269011530.0247457826901153
14400.00675555383820483-0.00675555383820483
14500.0373375172483853-0.0373375172483853
1460-0.04259186294875330.0425918629487533
14700.225323174948972-0.225323174948972
1480-0.01200989953857280.0120098995385728
1490-0.02474578269011530.0247457826901153
15000.00675555383820483-0.00675555383820483
1510-0.04259186294875330.0425918629487533
15210.212587291797430.78741270820257
15310.2619347085843880.738065291415612
15400.212587291797429-0.212587291797429







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9001
10001
11001
12001
13001
14001
15001
16001
170.3551299835175040.7102599670350090.644870016482496
180.3131552940077140.6263105880154270.686844705992286
190.28440060031360.5688012006272010.7155993996864
200.8024570427591370.3950859144817260.197542957240863
210.7456116271902790.5087767456194420.254388372809721
220.732454192065720.535091615868560.26754580793428
230.6685031726898960.6629936546202090.331496827310104
240.6016875627382820.7966248745234370.398312437261718
250.6062879631002710.7874240737994570.393712036899729
260.6094704289283360.7810591421433280.390529571071664
270.5678687624063020.8642624751873970.432131237593698
280.5129674849979090.9740650300041830.487032515002091
290.4604264133129360.9208528266258720.539573586687064
300.4040857547691220.8081715095382440.595914245230878
310.3471751416932520.6943502833865040.652824858306748
320.2943534808745910.5887069617491810.705646519125409
330.2462451932650550.4924903865301090.753754806734945
340.2092965124988030.4185930249976050.790703487501197
350.1701059663872920.3402119327745840.829894033612708
360.135989859439780.2719797188795590.86401014056022
370.1947990124356070.3895980248712140.805200987564393
380.1631189266120860.3262378532241720.836881073387914
390.1303150783201540.2606301566403090.869684921679846
400.1268348943846270.2536697887692530.873165105615373
410.6390408544686680.7219182910626640.360959145531332
420.6074225812116410.7851548375767180.392577418788359
430.5562625746040750.887474850791850.443737425395925
440.5183732669071570.9632534661856860.481626733092843
450.4669319143548950.933863828709790.533068085645105
460.4166001873503050.833200374700610.583399812649695
470.367683234791350.7353664695827010.63231676520865
480.3208304836476950.641660967295390.679169516352305
490.2770616176869840.5541232353739680.722938382313016
500.2364145640783650.472829128156730.763585435921635
510.2921552183830440.5843104367660880.707844781616956
520.5665433223325860.8669133553348280.433456677667414
530.5205199649978520.9589600700042960.479480035002148
540.9003924565990620.1992150868018760.0996075434009378
550.8771440534854950.2457118930290110.122855946514505
560.9155419336092160.1689161327815680.0844580663907839
570.9152376958757090.1695246082485820.0847623041242912
580.8962790640550190.2074418718899630.103720935944981
590.874267975688970.251464048622060.12573202431103
600.957251085945220.08549782810956090.0427489140547804
610.9520262973185510.09594740536289780.0479737026814489
620.9548421846604380.09031563067912410.045157815339562
630.9425297857985060.1149404284029880.0574702142014942
640.9409996405868580.1180007188262830.0590003594131415
650.9258815321085890.1482369357828220.0741184678914111
660.907956200476060.1840875990478810.0920437995239404
670.9622233059486060.07555338810278740.0377766940513937
680.9513305331632160.09733893367356750.0486694668367838
690.9390075448304740.1219849103390520.0609924551695258
700.9375987373465030.1248025253069930.0624012626534967
710.9218788651249510.1562422697500980.0781211348750491
720.9041683150720040.1916633698559920.0958316849279959
730.8987105471774060.2025789056451890.101289452822595
740.8944679065691090.2110641868617820.105532093430891
750.8724707197765650.255058560446870.127529280223435
760.8731464151664710.2537071696670590.126853584833529
770.8481922816294610.3036154367410780.151807718370539
780.8462578531755640.3074842936488720.153742146824436
790.9540564558076130.0918870883847740.045943544192387
800.9451611640487510.1096776719024970.0548388359512487
810.9308236368873430.1383527262253150.0691763631126573
820.9241941716517820.1516116566964360.0758058283482179
830.9058161303686970.1883677392626060.094183869631303
840.9948015204909980.01039695901800410.00519847950900204
850.9926607801782250.01467843964355060.00733921982177528
860.9897911554413830.02041768911723390.0102088445586169
870.98609604624830.02780790750340010.0139039537517
880.9844942399757880.03101152004842310.0155057600242116
890.9790240479927780.04195190401444460.0209759520072223
900.9721793656965720.05564126860685530.0278206343034276
910.9633777530296950.07324449394061080.0366222469703054
920.9523430904817780.09531381903644440.0476569095182222
930.9387394552361220.1225210895277560.0612605447638782
940.9221588129789040.1556823740421920.0778411870210961
950.9023108269329990.1953783461340030.0976891730670014
960.8794518560361540.2410962879276920.120548143963846
970.8524679965474410.2950640069051190.147532003452559
980.8214529787283660.3570940425432680.178547021271634
990.7866356696162870.4267286607674270.213364330383713
1000.7486499600906130.5027000798187740.251350039909387
1010.7076113115518490.5847773768963020.292388688448151
1020.662155750812220.675688498375560.33784424918778
1030.6140012687302170.7719974625395670.385998731269783
1040.563806177535460.872387644929080.43619382246454
1050.5499619471376630.9000761057246740.450038052862337
1060.4980401534647910.9960803069295810.501959846535209
1070.4459001753057440.8918003506114890.554099824694256
1080.433699415498740.8673988309974810.56630058450126
1090.3822205753609580.7644411507219150.617779424639042
1100.3327613482671910.6655226965343820.667238651732809
1110.3320351259170870.6640702518341730.667964874082913
1120.2844534465514160.5689068931028310.715546553448584
1130.280230487999920.560460975999840.71976951200008
1140.2824588328241770.5649176656483550.717541167175823
1150.2380593716152840.4761187432305680.761940628384716
1160.1971168114624780.3942336229249560.802883188537522
1170.1614368292620610.3228736585241220.838563170737939
1180.1294544146323440.2589088292646890.870545585367656
1190.1017655805046080.2035311610092170.898234419495392
1200.07875570280534230.1575114056106850.921244297194658
1210.05967807900339550.1193561580067910.940321920996604
1220.04420684482225970.08841368964451940.95579315517774
1230.04792641755225810.09585283510451610.952073582447742
1240.04961177544996320.09922355089992640.950388224550037
1250.03603552377013890.07207104754027780.963964476229861
1260.02542948171688640.05085896343377290.974570518283114
1270.017614133831580.03522826766315990.98238586616842
1280.01188846012882680.02377692025765360.988111539871173
1290.007752124394632950.01550424878926590.992247875605367
1300.004954250786801140.009908501573602280.995045749213199
1310.003041642374255690.006083284748511370.996958357625744
1320.001878612247197150.00375722449439430.998121387752803
1330.002546867002629750.00509373400525950.99745313299737
1340.001465396703495390.002930793406990770.998534603296505
1350.0008114923573732520.00162298471474650.999188507642627
1360.0004316461550754360.0008632923101508720.999568353844925
1370.0005869765294369420.001173953058873880.999413023470563
1380.003438103753024010.006876207506048030.996561896246976
1390.002075421716547890.004150843433095780.997924578283452
1400.001352421437018810.002704842874037620.998647578562981
1410.03962812188777280.07925624377554560.960371878112227
1420.03160424227785430.06320848455570850.968395757722146
1430.01867023813322550.0373404762664510.981329761866774
1440.01004709625200240.02009419250400490.989952903747998
1450.004210643217241750.00842128643448350.995789356782758

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \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 & 0.355129983517504 & 0.710259967035009 & 0.644870016482496 \tabularnewline
18 & 0.313155294007714 & 0.626310588015427 & 0.686844705992286 \tabularnewline
19 & 0.2844006003136 & 0.568801200627201 & 0.7155993996864 \tabularnewline
20 & 0.802457042759137 & 0.395085914481726 & 0.197542957240863 \tabularnewline
21 & 0.745611627190279 & 0.508776745619442 & 0.254388372809721 \tabularnewline
22 & 0.73245419206572 & 0.53509161586856 & 0.26754580793428 \tabularnewline
23 & 0.668503172689896 & 0.662993654620209 & 0.331496827310104 \tabularnewline
24 & 0.601687562738282 & 0.796624874523437 & 0.398312437261718 \tabularnewline
25 & 0.606287963100271 & 0.787424073799457 & 0.393712036899729 \tabularnewline
26 & 0.609470428928336 & 0.781059142143328 & 0.390529571071664 \tabularnewline
27 & 0.567868762406302 & 0.864262475187397 & 0.432131237593698 \tabularnewline
28 & 0.512967484997909 & 0.974065030004183 & 0.487032515002091 \tabularnewline
29 & 0.460426413312936 & 0.920852826625872 & 0.539573586687064 \tabularnewline
30 & 0.404085754769122 & 0.808171509538244 & 0.595914245230878 \tabularnewline
31 & 0.347175141693252 & 0.694350283386504 & 0.652824858306748 \tabularnewline
32 & 0.294353480874591 & 0.588706961749181 & 0.705646519125409 \tabularnewline
33 & 0.246245193265055 & 0.492490386530109 & 0.753754806734945 \tabularnewline
34 & 0.209296512498803 & 0.418593024997605 & 0.790703487501197 \tabularnewline
35 & 0.170105966387292 & 0.340211932774584 & 0.829894033612708 \tabularnewline
36 & 0.13598985943978 & 0.271979718879559 & 0.86401014056022 \tabularnewline
37 & 0.194799012435607 & 0.389598024871214 & 0.805200987564393 \tabularnewline
38 & 0.163118926612086 & 0.326237853224172 & 0.836881073387914 \tabularnewline
39 & 0.130315078320154 & 0.260630156640309 & 0.869684921679846 \tabularnewline
40 & 0.126834894384627 & 0.253669788769253 & 0.873165105615373 \tabularnewline
41 & 0.639040854468668 & 0.721918291062664 & 0.360959145531332 \tabularnewline
42 & 0.607422581211641 & 0.785154837576718 & 0.392577418788359 \tabularnewline
43 & 0.556262574604075 & 0.88747485079185 & 0.443737425395925 \tabularnewline
44 & 0.518373266907157 & 0.963253466185686 & 0.481626733092843 \tabularnewline
45 & 0.466931914354895 & 0.93386382870979 & 0.533068085645105 \tabularnewline
46 & 0.416600187350305 & 0.83320037470061 & 0.583399812649695 \tabularnewline
47 & 0.36768323479135 & 0.735366469582701 & 0.63231676520865 \tabularnewline
48 & 0.320830483647695 & 0.64166096729539 & 0.679169516352305 \tabularnewline
49 & 0.277061617686984 & 0.554123235373968 & 0.722938382313016 \tabularnewline
50 & 0.236414564078365 & 0.47282912815673 & 0.763585435921635 \tabularnewline
51 & 0.292155218383044 & 0.584310436766088 & 0.707844781616956 \tabularnewline
52 & 0.566543322332586 & 0.866913355334828 & 0.433456677667414 \tabularnewline
53 & 0.520519964997852 & 0.958960070004296 & 0.479480035002148 \tabularnewline
54 & 0.900392456599062 & 0.199215086801876 & 0.0996075434009378 \tabularnewline
55 & 0.877144053485495 & 0.245711893029011 & 0.122855946514505 \tabularnewline
56 & 0.915541933609216 & 0.168916132781568 & 0.0844580663907839 \tabularnewline
57 & 0.915237695875709 & 0.169524608248582 & 0.0847623041242912 \tabularnewline
58 & 0.896279064055019 & 0.207441871889963 & 0.103720935944981 \tabularnewline
59 & 0.87426797568897 & 0.25146404862206 & 0.12573202431103 \tabularnewline
60 & 0.95725108594522 & 0.0854978281095609 & 0.0427489140547804 \tabularnewline
61 & 0.952026297318551 & 0.0959474053628978 & 0.0479737026814489 \tabularnewline
62 & 0.954842184660438 & 0.0903156306791241 & 0.045157815339562 \tabularnewline
63 & 0.942529785798506 & 0.114940428402988 & 0.0574702142014942 \tabularnewline
64 & 0.940999640586858 & 0.118000718826283 & 0.0590003594131415 \tabularnewline
65 & 0.925881532108589 & 0.148236935782822 & 0.0741184678914111 \tabularnewline
66 & 0.90795620047606 & 0.184087599047881 & 0.0920437995239404 \tabularnewline
67 & 0.962223305948606 & 0.0755533881027874 & 0.0377766940513937 \tabularnewline
68 & 0.951330533163216 & 0.0973389336735675 & 0.0486694668367838 \tabularnewline
69 & 0.939007544830474 & 0.121984910339052 & 0.0609924551695258 \tabularnewline
70 & 0.937598737346503 & 0.124802525306993 & 0.0624012626534967 \tabularnewline
71 & 0.921878865124951 & 0.156242269750098 & 0.0781211348750491 \tabularnewline
72 & 0.904168315072004 & 0.191663369855992 & 0.0958316849279959 \tabularnewline
73 & 0.898710547177406 & 0.202578905645189 & 0.101289452822595 \tabularnewline
74 & 0.894467906569109 & 0.211064186861782 & 0.105532093430891 \tabularnewline
75 & 0.872470719776565 & 0.25505856044687 & 0.127529280223435 \tabularnewline
76 & 0.873146415166471 & 0.253707169667059 & 0.126853584833529 \tabularnewline
77 & 0.848192281629461 & 0.303615436741078 & 0.151807718370539 \tabularnewline
78 & 0.846257853175564 & 0.307484293648872 & 0.153742146824436 \tabularnewline
79 & 0.954056455807613 & 0.091887088384774 & 0.045943544192387 \tabularnewline
80 & 0.945161164048751 & 0.109677671902497 & 0.0548388359512487 \tabularnewline
81 & 0.930823636887343 & 0.138352726225315 & 0.0691763631126573 \tabularnewline
82 & 0.924194171651782 & 0.151611656696436 & 0.0758058283482179 \tabularnewline
83 & 0.905816130368697 & 0.188367739262606 & 0.094183869631303 \tabularnewline
84 & 0.994801520490998 & 0.0103969590180041 & 0.00519847950900204 \tabularnewline
85 & 0.992660780178225 & 0.0146784396435506 & 0.00733921982177528 \tabularnewline
86 & 0.989791155441383 & 0.0204176891172339 & 0.0102088445586169 \tabularnewline
87 & 0.9860960462483 & 0.0278079075034001 & 0.0139039537517 \tabularnewline
88 & 0.984494239975788 & 0.0310115200484231 & 0.0155057600242116 \tabularnewline
89 & 0.979024047992778 & 0.0419519040144446 & 0.0209759520072223 \tabularnewline
90 & 0.972179365696572 & 0.0556412686068553 & 0.0278206343034276 \tabularnewline
91 & 0.963377753029695 & 0.0732444939406108 & 0.0366222469703054 \tabularnewline
92 & 0.952343090481778 & 0.0953138190364444 & 0.0476569095182222 \tabularnewline
93 & 0.938739455236122 & 0.122521089527756 & 0.0612605447638782 \tabularnewline
94 & 0.922158812978904 & 0.155682374042192 & 0.0778411870210961 \tabularnewline
95 & 0.902310826932999 & 0.195378346134003 & 0.0976891730670014 \tabularnewline
96 & 0.879451856036154 & 0.241096287927692 & 0.120548143963846 \tabularnewline
97 & 0.852467996547441 & 0.295064006905119 & 0.147532003452559 \tabularnewline
98 & 0.821452978728366 & 0.357094042543268 & 0.178547021271634 \tabularnewline
99 & 0.786635669616287 & 0.426728660767427 & 0.213364330383713 \tabularnewline
100 & 0.748649960090613 & 0.502700079818774 & 0.251350039909387 \tabularnewline
101 & 0.707611311551849 & 0.584777376896302 & 0.292388688448151 \tabularnewline
102 & 0.66215575081222 & 0.67568849837556 & 0.33784424918778 \tabularnewline
103 & 0.614001268730217 & 0.771997462539567 & 0.385998731269783 \tabularnewline
104 & 0.56380617753546 & 0.87238764492908 & 0.43619382246454 \tabularnewline
105 & 0.549961947137663 & 0.900076105724674 & 0.450038052862337 \tabularnewline
106 & 0.498040153464791 & 0.996080306929581 & 0.501959846535209 \tabularnewline
107 & 0.445900175305744 & 0.891800350611489 & 0.554099824694256 \tabularnewline
108 & 0.43369941549874 & 0.867398830997481 & 0.56630058450126 \tabularnewline
109 & 0.382220575360958 & 0.764441150721915 & 0.617779424639042 \tabularnewline
110 & 0.332761348267191 & 0.665522696534382 & 0.667238651732809 \tabularnewline
111 & 0.332035125917087 & 0.664070251834173 & 0.667964874082913 \tabularnewline
112 & 0.284453446551416 & 0.568906893102831 & 0.715546553448584 \tabularnewline
113 & 0.28023048799992 & 0.56046097599984 & 0.71976951200008 \tabularnewline
114 & 0.282458832824177 & 0.564917665648355 & 0.717541167175823 \tabularnewline
115 & 0.238059371615284 & 0.476118743230568 & 0.761940628384716 \tabularnewline
116 & 0.197116811462478 & 0.394233622924956 & 0.802883188537522 \tabularnewline
117 & 0.161436829262061 & 0.322873658524122 & 0.838563170737939 \tabularnewline
118 & 0.129454414632344 & 0.258908829264689 & 0.870545585367656 \tabularnewline
119 & 0.101765580504608 & 0.203531161009217 & 0.898234419495392 \tabularnewline
120 & 0.0787557028053423 & 0.157511405610685 & 0.921244297194658 \tabularnewline
121 & 0.0596780790033955 & 0.119356158006791 & 0.940321920996604 \tabularnewline
122 & 0.0442068448222597 & 0.0884136896445194 & 0.95579315517774 \tabularnewline
123 & 0.0479264175522581 & 0.0958528351045161 & 0.952073582447742 \tabularnewline
124 & 0.0496117754499632 & 0.0992235508999264 & 0.950388224550037 \tabularnewline
125 & 0.0360355237701389 & 0.0720710475402778 & 0.963964476229861 \tabularnewline
126 & 0.0254294817168864 & 0.0508589634337729 & 0.974570518283114 \tabularnewline
127 & 0.01761413383158 & 0.0352282676631599 & 0.98238586616842 \tabularnewline
128 & 0.0118884601288268 & 0.0237769202576536 & 0.988111539871173 \tabularnewline
129 & 0.00775212439463295 & 0.0155042487892659 & 0.992247875605367 \tabularnewline
130 & 0.00495425078680114 & 0.00990850157360228 & 0.995045749213199 \tabularnewline
131 & 0.00304164237425569 & 0.00608328474851137 & 0.996958357625744 \tabularnewline
132 & 0.00187861224719715 & 0.0037572244943943 & 0.998121387752803 \tabularnewline
133 & 0.00254686700262975 & 0.0050937340052595 & 0.99745313299737 \tabularnewline
134 & 0.00146539670349539 & 0.00293079340699077 & 0.998534603296505 \tabularnewline
135 & 0.000811492357373252 & 0.0016229847147465 & 0.999188507642627 \tabularnewline
136 & 0.000431646155075436 & 0.000863292310150872 & 0.999568353844925 \tabularnewline
137 & 0.000586976529436942 & 0.00117395305887388 & 0.999413023470563 \tabularnewline
138 & 0.00343810375302401 & 0.00687620750604803 & 0.996561896246976 \tabularnewline
139 & 0.00207542171654789 & 0.00415084343309578 & 0.997924578283452 \tabularnewline
140 & 0.00135242143701881 & 0.00270484287403762 & 0.998647578562981 \tabularnewline
141 & 0.0396281218877728 & 0.0792562437755456 & 0.960371878112227 \tabularnewline
142 & 0.0316042422778543 & 0.0632084845557085 & 0.968395757722146 \tabularnewline
143 & 0.0186702381332255 & 0.037340476266451 & 0.981329761866774 \tabularnewline
144 & 0.0100470962520024 & 0.0200941925040049 & 0.989952903747998 \tabularnewline
145 & 0.00421064321724175 & 0.0084212864344835 & 0.995789356782758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203687&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[/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]0.355129983517504[/C][C]0.710259967035009[/C][C]0.644870016482496[/C][/ROW]
[ROW][C]18[/C][C]0.313155294007714[/C][C]0.626310588015427[/C][C]0.686844705992286[/C][/ROW]
[ROW][C]19[/C][C]0.2844006003136[/C][C]0.568801200627201[/C][C]0.7155993996864[/C][/ROW]
[ROW][C]20[/C][C]0.802457042759137[/C][C]0.395085914481726[/C][C]0.197542957240863[/C][/ROW]
[ROW][C]21[/C][C]0.745611627190279[/C][C]0.508776745619442[/C][C]0.254388372809721[/C][/ROW]
[ROW][C]22[/C][C]0.73245419206572[/C][C]0.53509161586856[/C][C]0.26754580793428[/C][/ROW]
[ROW][C]23[/C][C]0.668503172689896[/C][C]0.662993654620209[/C][C]0.331496827310104[/C][/ROW]
[ROW][C]24[/C][C]0.601687562738282[/C][C]0.796624874523437[/C][C]0.398312437261718[/C][/ROW]
[ROW][C]25[/C][C]0.606287963100271[/C][C]0.787424073799457[/C][C]0.393712036899729[/C][/ROW]
[ROW][C]26[/C][C]0.609470428928336[/C][C]0.781059142143328[/C][C]0.390529571071664[/C][/ROW]
[ROW][C]27[/C][C]0.567868762406302[/C][C]0.864262475187397[/C][C]0.432131237593698[/C][/ROW]
[ROW][C]28[/C][C]0.512967484997909[/C][C]0.974065030004183[/C][C]0.487032515002091[/C][/ROW]
[ROW][C]29[/C][C]0.460426413312936[/C][C]0.920852826625872[/C][C]0.539573586687064[/C][/ROW]
[ROW][C]30[/C][C]0.404085754769122[/C][C]0.808171509538244[/C][C]0.595914245230878[/C][/ROW]
[ROW][C]31[/C][C]0.347175141693252[/C][C]0.694350283386504[/C][C]0.652824858306748[/C][/ROW]
[ROW][C]32[/C][C]0.294353480874591[/C][C]0.588706961749181[/C][C]0.705646519125409[/C][/ROW]
[ROW][C]33[/C][C]0.246245193265055[/C][C]0.492490386530109[/C][C]0.753754806734945[/C][/ROW]
[ROW][C]34[/C][C]0.209296512498803[/C][C]0.418593024997605[/C][C]0.790703487501197[/C][/ROW]
[ROW][C]35[/C][C]0.170105966387292[/C][C]0.340211932774584[/C][C]0.829894033612708[/C][/ROW]
[ROW][C]36[/C][C]0.13598985943978[/C][C]0.271979718879559[/C][C]0.86401014056022[/C][/ROW]
[ROW][C]37[/C][C]0.194799012435607[/C][C]0.389598024871214[/C][C]0.805200987564393[/C][/ROW]
[ROW][C]38[/C][C]0.163118926612086[/C][C]0.326237853224172[/C][C]0.836881073387914[/C][/ROW]
[ROW][C]39[/C][C]0.130315078320154[/C][C]0.260630156640309[/C][C]0.869684921679846[/C][/ROW]
[ROW][C]40[/C][C]0.126834894384627[/C][C]0.253669788769253[/C][C]0.873165105615373[/C][/ROW]
[ROW][C]41[/C][C]0.639040854468668[/C][C]0.721918291062664[/C][C]0.360959145531332[/C][/ROW]
[ROW][C]42[/C][C]0.607422581211641[/C][C]0.785154837576718[/C][C]0.392577418788359[/C][/ROW]
[ROW][C]43[/C][C]0.556262574604075[/C][C]0.88747485079185[/C][C]0.443737425395925[/C][/ROW]
[ROW][C]44[/C][C]0.518373266907157[/C][C]0.963253466185686[/C][C]0.481626733092843[/C][/ROW]
[ROW][C]45[/C][C]0.466931914354895[/C][C]0.93386382870979[/C][C]0.533068085645105[/C][/ROW]
[ROW][C]46[/C][C]0.416600187350305[/C][C]0.83320037470061[/C][C]0.583399812649695[/C][/ROW]
[ROW][C]47[/C][C]0.36768323479135[/C][C]0.735366469582701[/C][C]0.63231676520865[/C][/ROW]
[ROW][C]48[/C][C]0.320830483647695[/C][C]0.64166096729539[/C][C]0.679169516352305[/C][/ROW]
[ROW][C]49[/C][C]0.277061617686984[/C][C]0.554123235373968[/C][C]0.722938382313016[/C][/ROW]
[ROW][C]50[/C][C]0.236414564078365[/C][C]0.47282912815673[/C][C]0.763585435921635[/C][/ROW]
[ROW][C]51[/C][C]0.292155218383044[/C][C]0.584310436766088[/C][C]0.707844781616956[/C][/ROW]
[ROW][C]52[/C][C]0.566543322332586[/C][C]0.866913355334828[/C][C]0.433456677667414[/C][/ROW]
[ROW][C]53[/C][C]0.520519964997852[/C][C]0.958960070004296[/C][C]0.479480035002148[/C][/ROW]
[ROW][C]54[/C][C]0.900392456599062[/C][C]0.199215086801876[/C][C]0.0996075434009378[/C][/ROW]
[ROW][C]55[/C][C]0.877144053485495[/C][C]0.245711893029011[/C][C]0.122855946514505[/C][/ROW]
[ROW][C]56[/C][C]0.915541933609216[/C][C]0.168916132781568[/C][C]0.0844580663907839[/C][/ROW]
[ROW][C]57[/C][C]0.915237695875709[/C][C]0.169524608248582[/C][C]0.0847623041242912[/C][/ROW]
[ROW][C]58[/C][C]0.896279064055019[/C][C]0.207441871889963[/C][C]0.103720935944981[/C][/ROW]
[ROW][C]59[/C][C]0.87426797568897[/C][C]0.25146404862206[/C][C]0.12573202431103[/C][/ROW]
[ROW][C]60[/C][C]0.95725108594522[/C][C]0.0854978281095609[/C][C]0.0427489140547804[/C][/ROW]
[ROW][C]61[/C][C]0.952026297318551[/C][C]0.0959474053628978[/C][C]0.0479737026814489[/C][/ROW]
[ROW][C]62[/C][C]0.954842184660438[/C][C]0.0903156306791241[/C][C]0.045157815339562[/C][/ROW]
[ROW][C]63[/C][C]0.942529785798506[/C][C]0.114940428402988[/C][C]0.0574702142014942[/C][/ROW]
[ROW][C]64[/C][C]0.940999640586858[/C][C]0.118000718826283[/C][C]0.0590003594131415[/C][/ROW]
[ROW][C]65[/C][C]0.925881532108589[/C][C]0.148236935782822[/C][C]0.0741184678914111[/C][/ROW]
[ROW][C]66[/C][C]0.90795620047606[/C][C]0.184087599047881[/C][C]0.0920437995239404[/C][/ROW]
[ROW][C]67[/C][C]0.962223305948606[/C][C]0.0755533881027874[/C][C]0.0377766940513937[/C][/ROW]
[ROW][C]68[/C][C]0.951330533163216[/C][C]0.0973389336735675[/C][C]0.0486694668367838[/C][/ROW]
[ROW][C]69[/C][C]0.939007544830474[/C][C]0.121984910339052[/C][C]0.0609924551695258[/C][/ROW]
[ROW][C]70[/C][C]0.937598737346503[/C][C]0.124802525306993[/C][C]0.0624012626534967[/C][/ROW]
[ROW][C]71[/C][C]0.921878865124951[/C][C]0.156242269750098[/C][C]0.0781211348750491[/C][/ROW]
[ROW][C]72[/C][C]0.904168315072004[/C][C]0.191663369855992[/C][C]0.0958316849279959[/C][/ROW]
[ROW][C]73[/C][C]0.898710547177406[/C][C]0.202578905645189[/C][C]0.101289452822595[/C][/ROW]
[ROW][C]74[/C][C]0.894467906569109[/C][C]0.211064186861782[/C][C]0.105532093430891[/C][/ROW]
[ROW][C]75[/C][C]0.872470719776565[/C][C]0.25505856044687[/C][C]0.127529280223435[/C][/ROW]
[ROW][C]76[/C][C]0.873146415166471[/C][C]0.253707169667059[/C][C]0.126853584833529[/C][/ROW]
[ROW][C]77[/C][C]0.848192281629461[/C][C]0.303615436741078[/C][C]0.151807718370539[/C][/ROW]
[ROW][C]78[/C][C]0.846257853175564[/C][C]0.307484293648872[/C][C]0.153742146824436[/C][/ROW]
[ROW][C]79[/C][C]0.954056455807613[/C][C]0.091887088384774[/C][C]0.045943544192387[/C][/ROW]
[ROW][C]80[/C][C]0.945161164048751[/C][C]0.109677671902497[/C][C]0.0548388359512487[/C][/ROW]
[ROW][C]81[/C][C]0.930823636887343[/C][C]0.138352726225315[/C][C]0.0691763631126573[/C][/ROW]
[ROW][C]82[/C][C]0.924194171651782[/C][C]0.151611656696436[/C][C]0.0758058283482179[/C][/ROW]
[ROW][C]83[/C][C]0.905816130368697[/C][C]0.188367739262606[/C][C]0.094183869631303[/C][/ROW]
[ROW][C]84[/C][C]0.994801520490998[/C][C]0.0103969590180041[/C][C]0.00519847950900204[/C][/ROW]
[ROW][C]85[/C][C]0.992660780178225[/C][C]0.0146784396435506[/C][C]0.00733921982177528[/C][/ROW]
[ROW][C]86[/C][C]0.989791155441383[/C][C]0.0204176891172339[/C][C]0.0102088445586169[/C][/ROW]
[ROW][C]87[/C][C]0.9860960462483[/C][C]0.0278079075034001[/C][C]0.0139039537517[/C][/ROW]
[ROW][C]88[/C][C]0.984494239975788[/C][C]0.0310115200484231[/C][C]0.0155057600242116[/C][/ROW]
[ROW][C]89[/C][C]0.979024047992778[/C][C]0.0419519040144446[/C][C]0.0209759520072223[/C][/ROW]
[ROW][C]90[/C][C]0.972179365696572[/C][C]0.0556412686068553[/C][C]0.0278206343034276[/C][/ROW]
[ROW][C]91[/C][C]0.963377753029695[/C][C]0.0732444939406108[/C][C]0.0366222469703054[/C][/ROW]
[ROW][C]92[/C][C]0.952343090481778[/C][C]0.0953138190364444[/C][C]0.0476569095182222[/C][/ROW]
[ROW][C]93[/C][C]0.938739455236122[/C][C]0.122521089527756[/C][C]0.0612605447638782[/C][/ROW]
[ROW][C]94[/C][C]0.922158812978904[/C][C]0.155682374042192[/C][C]0.0778411870210961[/C][/ROW]
[ROW][C]95[/C][C]0.902310826932999[/C][C]0.195378346134003[/C][C]0.0976891730670014[/C][/ROW]
[ROW][C]96[/C][C]0.879451856036154[/C][C]0.241096287927692[/C][C]0.120548143963846[/C][/ROW]
[ROW][C]97[/C][C]0.852467996547441[/C][C]0.295064006905119[/C][C]0.147532003452559[/C][/ROW]
[ROW][C]98[/C][C]0.821452978728366[/C][C]0.357094042543268[/C][C]0.178547021271634[/C][/ROW]
[ROW][C]99[/C][C]0.786635669616287[/C][C]0.426728660767427[/C][C]0.213364330383713[/C][/ROW]
[ROW][C]100[/C][C]0.748649960090613[/C][C]0.502700079818774[/C][C]0.251350039909387[/C][/ROW]
[ROW][C]101[/C][C]0.707611311551849[/C][C]0.584777376896302[/C][C]0.292388688448151[/C][/ROW]
[ROW][C]102[/C][C]0.66215575081222[/C][C]0.67568849837556[/C][C]0.33784424918778[/C][/ROW]
[ROW][C]103[/C][C]0.614001268730217[/C][C]0.771997462539567[/C][C]0.385998731269783[/C][/ROW]
[ROW][C]104[/C][C]0.56380617753546[/C][C]0.87238764492908[/C][C]0.43619382246454[/C][/ROW]
[ROW][C]105[/C][C]0.549961947137663[/C][C]0.900076105724674[/C][C]0.450038052862337[/C][/ROW]
[ROW][C]106[/C][C]0.498040153464791[/C][C]0.996080306929581[/C][C]0.501959846535209[/C][/ROW]
[ROW][C]107[/C][C]0.445900175305744[/C][C]0.891800350611489[/C][C]0.554099824694256[/C][/ROW]
[ROW][C]108[/C][C]0.43369941549874[/C][C]0.867398830997481[/C][C]0.56630058450126[/C][/ROW]
[ROW][C]109[/C][C]0.382220575360958[/C][C]0.764441150721915[/C][C]0.617779424639042[/C][/ROW]
[ROW][C]110[/C][C]0.332761348267191[/C][C]0.665522696534382[/C][C]0.667238651732809[/C][/ROW]
[ROW][C]111[/C][C]0.332035125917087[/C][C]0.664070251834173[/C][C]0.667964874082913[/C][/ROW]
[ROW][C]112[/C][C]0.284453446551416[/C][C]0.568906893102831[/C][C]0.715546553448584[/C][/ROW]
[ROW][C]113[/C][C]0.28023048799992[/C][C]0.56046097599984[/C][C]0.71976951200008[/C][/ROW]
[ROW][C]114[/C][C]0.282458832824177[/C][C]0.564917665648355[/C][C]0.717541167175823[/C][/ROW]
[ROW][C]115[/C][C]0.238059371615284[/C][C]0.476118743230568[/C][C]0.761940628384716[/C][/ROW]
[ROW][C]116[/C][C]0.197116811462478[/C][C]0.394233622924956[/C][C]0.802883188537522[/C][/ROW]
[ROW][C]117[/C][C]0.161436829262061[/C][C]0.322873658524122[/C][C]0.838563170737939[/C][/ROW]
[ROW][C]118[/C][C]0.129454414632344[/C][C]0.258908829264689[/C][C]0.870545585367656[/C][/ROW]
[ROW][C]119[/C][C]0.101765580504608[/C][C]0.203531161009217[/C][C]0.898234419495392[/C][/ROW]
[ROW][C]120[/C][C]0.0787557028053423[/C][C]0.157511405610685[/C][C]0.921244297194658[/C][/ROW]
[ROW][C]121[/C][C]0.0596780790033955[/C][C]0.119356158006791[/C][C]0.940321920996604[/C][/ROW]
[ROW][C]122[/C][C]0.0442068448222597[/C][C]0.0884136896445194[/C][C]0.95579315517774[/C][/ROW]
[ROW][C]123[/C][C]0.0479264175522581[/C][C]0.0958528351045161[/C][C]0.952073582447742[/C][/ROW]
[ROW][C]124[/C][C]0.0496117754499632[/C][C]0.0992235508999264[/C][C]0.950388224550037[/C][/ROW]
[ROW][C]125[/C][C]0.0360355237701389[/C][C]0.0720710475402778[/C][C]0.963964476229861[/C][/ROW]
[ROW][C]126[/C][C]0.0254294817168864[/C][C]0.0508589634337729[/C][C]0.974570518283114[/C][/ROW]
[ROW][C]127[/C][C]0.01761413383158[/C][C]0.0352282676631599[/C][C]0.98238586616842[/C][/ROW]
[ROW][C]128[/C][C]0.0118884601288268[/C][C]0.0237769202576536[/C][C]0.988111539871173[/C][/ROW]
[ROW][C]129[/C][C]0.00775212439463295[/C][C]0.0155042487892659[/C][C]0.992247875605367[/C][/ROW]
[ROW][C]130[/C][C]0.00495425078680114[/C][C]0.00990850157360228[/C][C]0.995045749213199[/C][/ROW]
[ROW][C]131[/C][C]0.00304164237425569[/C][C]0.00608328474851137[/C][C]0.996958357625744[/C][/ROW]
[ROW][C]132[/C][C]0.00187861224719715[/C][C]0.0037572244943943[/C][C]0.998121387752803[/C][/ROW]
[ROW][C]133[/C][C]0.00254686700262975[/C][C]0.0050937340052595[/C][C]0.99745313299737[/C][/ROW]
[ROW][C]134[/C][C]0.00146539670349539[/C][C]0.00293079340699077[/C][C]0.998534603296505[/C][/ROW]
[ROW][C]135[/C][C]0.000811492357373252[/C][C]0.0016229847147465[/C][C]0.999188507642627[/C][/ROW]
[ROW][C]136[/C][C]0.000431646155075436[/C][C]0.000863292310150872[/C][C]0.999568353844925[/C][/ROW]
[ROW][C]137[/C][C]0.000586976529436942[/C][C]0.00117395305887388[/C][C]0.999413023470563[/C][/ROW]
[ROW][C]138[/C][C]0.00343810375302401[/C][C]0.00687620750604803[/C][C]0.996561896246976[/C][/ROW]
[ROW][C]139[/C][C]0.00207542171654789[/C][C]0.00415084343309578[/C][C]0.997924578283452[/C][/ROW]
[ROW][C]140[/C][C]0.00135242143701881[/C][C]0.00270484287403762[/C][C]0.998647578562981[/C][/ROW]
[ROW][C]141[/C][C]0.0396281218877728[/C][C]0.0792562437755456[/C][C]0.960371878112227[/C][/ROW]
[ROW][C]142[/C][C]0.0316042422778543[/C][C]0.0632084845557085[/C][C]0.968395757722146[/C][/ROW]
[ROW][C]143[/C][C]0.0186702381332255[/C][C]0.037340476266451[/C][C]0.981329761866774[/C][/ROW]
[ROW][C]144[/C][C]0.0100470962520024[/C][C]0.0200941925040049[/C][C]0.989952903747998[/C][/ROW]
[ROW][C]145[/C][C]0.00421064321724175[/C][C]0.0084212864344835[/C][C]0.995789356782758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203687&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203687&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
9001
10001
11001
12001
13001
14001
15001
16001
170.3551299835175040.7102599670350090.644870016482496
180.3131552940077140.6263105880154270.686844705992286
190.28440060031360.5688012006272010.7155993996864
200.8024570427591370.3950859144817260.197542957240863
210.7456116271902790.5087767456194420.254388372809721
220.732454192065720.535091615868560.26754580793428
230.6685031726898960.6629936546202090.331496827310104
240.6016875627382820.7966248745234370.398312437261718
250.6062879631002710.7874240737994570.393712036899729
260.6094704289283360.7810591421433280.390529571071664
270.5678687624063020.8642624751873970.432131237593698
280.5129674849979090.9740650300041830.487032515002091
290.4604264133129360.9208528266258720.539573586687064
300.4040857547691220.8081715095382440.595914245230878
310.3471751416932520.6943502833865040.652824858306748
320.2943534808745910.5887069617491810.705646519125409
330.2462451932650550.4924903865301090.753754806734945
340.2092965124988030.4185930249976050.790703487501197
350.1701059663872920.3402119327745840.829894033612708
360.135989859439780.2719797188795590.86401014056022
370.1947990124356070.3895980248712140.805200987564393
380.1631189266120860.3262378532241720.836881073387914
390.1303150783201540.2606301566403090.869684921679846
400.1268348943846270.2536697887692530.873165105615373
410.6390408544686680.7219182910626640.360959145531332
420.6074225812116410.7851548375767180.392577418788359
430.5562625746040750.887474850791850.443737425395925
440.5183732669071570.9632534661856860.481626733092843
450.4669319143548950.933863828709790.533068085645105
460.4166001873503050.833200374700610.583399812649695
470.367683234791350.7353664695827010.63231676520865
480.3208304836476950.641660967295390.679169516352305
490.2770616176869840.5541232353739680.722938382313016
500.2364145640783650.472829128156730.763585435921635
510.2921552183830440.5843104367660880.707844781616956
520.5665433223325860.8669133553348280.433456677667414
530.5205199649978520.9589600700042960.479480035002148
540.9003924565990620.1992150868018760.0996075434009378
550.8771440534854950.2457118930290110.122855946514505
560.9155419336092160.1689161327815680.0844580663907839
570.9152376958757090.1695246082485820.0847623041242912
580.8962790640550190.2074418718899630.103720935944981
590.874267975688970.251464048622060.12573202431103
600.957251085945220.08549782810956090.0427489140547804
610.9520262973185510.09594740536289780.0479737026814489
620.9548421846604380.09031563067912410.045157815339562
630.9425297857985060.1149404284029880.0574702142014942
640.9409996405868580.1180007188262830.0590003594131415
650.9258815321085890.1482369357828220.0741184678914111
660.907956200476060.1840875990478810.0920437995239404
670.9622233059486060.07555338810278740.0377766940513937
680.9513305331632160.09733893367356750.0486694668367838
690.9390075448304740.1219849103390520.0609924551695258
700.9375987373465030.1248025253069930.0624012626534967
710.9218788651249510.1562422697500980.0781211348750491
720.9041683150720040.1916633698559920.0958316849279959
730.8987105471774060.2025789056451890.101289452822595
740.8944679065691090.2110641868617820.105532093430891
750.8724707197765650.255058560446870.127529280223435
760.8731464151664710.2537071696670590.126853584833529
770.8481922816294610.3036154367410780.151807718370539
780.8462578531755640.3074842936488720.153742146824436
790.9540564558076130.0918870883847740.045943544192387
800.9451611640487510.1096776719024970.0548388359512487
810.9308236368873430.1383527262253150.0691763631126573
820.9241941716517820.1516116566964360.0758058283482179
830.9058161303686970.1883677392626060.094183869631303
840.9948015204909980.01039695901800410.00519847950900204
850.9926607801782250.01467843964355060.00733921982177528
860.9897911554413830.02041768911723390.0102088445586169
870.98609604624830.02780790750340010.0139039537517
880.9844942399757880.03101152004842310.0155057600242116
890.9790240479927780.04195190401444460.0209759520072223
900.9721793656965720.05564126860685530.0278206343034276
910.9633777530296950.07324449394061080.0366222469703054
920.9523430904817780.09531381903644440.0476569095182222
930.9387394552361220.1225210895277560.0612605447638782
940.9221588129789040.1556823740421920.0778411870210961
950.9023108269329990.1953783461340030.0976891730670014
960.8794518560361540.2410962879276920.120548143963846
970.8524679965474410.2950640069051190.147532003452559
980.8214529787283660.3570940425432680.178547021271634
990.7866356696162870.4267286607674270.213364330383713
1000.7486499600906130.5027000798187740.251350039909387
1010.7076113115518490.5847773768963020.292388688448151
1020.662155750812220.675688498375560.33784424918778
1030.6140012687302170.7719974625395670.385998731269783
1040.563806177535460.872387644929080.43619382246454
1050.5499619471376630.9000761057246740.450038052862337
1060.4980401534647910.9960803069295810.501959846535209
1070.4459001753057440.8918003506114890.554099824694256
1080.433699415498740.8673988309974810.56630058450126
1090.3822205753609580.7644411507219150.617779424639042
1100.3327613482671910.6655226965343820.667238651732809
1110.3320351259170870.6640702518341730.667964874082913
1120.2844534465514160.5689068931028310.715546553448584
1130.280230487999920.560460975999840.71976951200008
1140.2824588328241770.5649176656483550.717541167175823
1150.2380593716152840.4761187432305680.761940628384716
1160.1971168114624780.3942336229249560.802883188537522
1170.1614368292620610.3228736585241220.838563170737939
1180.1294544146323440.2589088292646890.870545585367656
1190.1017655805046080.2035311610092170.898234419495392
1200.07875570280534230.1575114056106850.921244297194658
1210.05967807900339550.1193561580067910.940321920996604
1220.04420684482225970.08841368964451940.95579315517774
1230.04792641755225810.09585283510451610.952073582447742
1240.04961177544996320.09922355089992640.950388224550037
1250.03603552377013890.07207104754027780.963964476229861
1260.02542948171688640.05085896343377290.974570518283114
1270.017614133831580.03522826766315990.98238586616842
1280.01188846012882680.02377692025765360.988111539871173
1290.007752124394632950.01550424878926590.992247875605367
1300.004954250786801140.009908501573602280.995045749213199
1310.003041642374255690.006083284748511370.996958357625744
1320.001878612247197150.00375722449439430.998121387752803
1330.002546867002629750.00509373400525950.99745313299737
1340.001465396703495390.002930793406990770.998534603296505
1350.0008114923573732520.00162298471474650.999188507642627
1360.0004316461550754360.0008632923101508720.999568353844925
1370.0005869765294369420.001173953058873880.999413023470563
1380.003438103753024010.006876207506048030.996561896246976
1390.002075421716547890.004150843433095780.997924578283452
1400.001352421437018810.002704842874037620.998647578562981
1410.03962812188777280.07925624377554560.960371878112227
1420.03160424227785430.06320848455570850.968395757722146
1430.01867023813322550.0373404762664510.981329761866774
1440.01004709625200240.02009419250400490.989952903747998
1450.004210643217241750.00842128643448350.995789356782758







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level200.145985401459854NOK
5% type I error level310.226277372262774NOK
10% type I error level470.343065693430657NOK

\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 & 20 & 0.145985401459854 & NOK \tabularnewline
5% type I error level & 31 & 0.226277372262774 & NOK \tabularnewline
10% type I error level & 47 & 0.343065693430657 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203687&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]20[/C][C]0.145985401459854[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.226277372262774[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.343065693430657[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203687&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203687&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 level200.145985401459854NOK
5% type I error level310.226277372262774NOK
10% type I error level470.343065693430657NOK



Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}