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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 12:07:35 -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/t1356109768sst6qlu473kg94v.htm/, Retrieved Thu, 31 Oct 2024 23:15:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203970, Retrieved Thu, 31 Oct 2024 23:15:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Testing Variance - Critical Value (Region)] [] [2011-10-13 20:48:25] [db02340d173e1867f482a5214ce3fc15]
- RMPD    [Multiple Regression] [Correctanalysis] [2012-12-21 17:07:35] [4289cf790da1cc09a0cb8798de13fde3] [Current]
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Dataseries X:
4	1	2	0	0	0	1
4	0	1	0	0	0	0
4	0	1	0	0	0	0
4	0	1	0	0	0	0
4	0	1	0	0	0	0
4	1	1	0	0	1	1
4	0	1	0	0	0	0
4	0	2	0	0	0	0
4	0	1	0	0	0	1
4	1	1	0	0	0	0
4	1	2	0	0	0	0
4	0	1	0	0	0	0
4	0	1	1	0	1	0
4	1	2	0	0	0	0
4	0	1	1	0	1	1
4	0	2	1	0	1	1
4	1	2	1	1	1	0
4	1	2	0	0	0	0
4	0	1	0	0	0	1
4	0	2	1	1	1	1
4	1	1	0	0	1	0
4	1	1	1	0	1	1
4	0	1	0	0	1	1
4	1	1	0	0	1	1
4	0	2	1	0	0	1
4	0	1	1	0	1	0
4	1	1	0	0	0	1
4	0	1	1	0	0	0
4	0	1	0	0	0	1
4	0	1	0	0	1	0
4	0	1	0	0	0	0
4	1	1	0	0	0	0
4	1	1	0	0	1	0
4	0	2	0	0	0	1
4	0	1	0	0	0	0
4	0	1	0	0	0	0
4	1	2	1	0	1	0
4	0	1	1	0	0	1
4	0	1	0	0	1	1
4	0	2	0	0	1	0
4	0	1	1	1	1	1
4	0	1	1	0	0	1
4	1	1	0	0	1	1
4	1	2	0	0	0	0
4	0	1	0	0	1	0
4	0	1	0	0	1	1
4	0	1	0	0	0	0
4	0	1	0	0	0	1
4	0	1	0	0	1	1
4	0	1	0	0	0	0
4	0	2	1	0	0	0
4	1	2	1	1	1	0
4	0	1	0	0	0	1
4	0	1	1	1	0	0
4	0	1	0	0	0	0
4	0	2	1	0	0	1
4	0	1	1	0	1	1
4	0	1	0	0	0	1
4	0	1	0	0	0	1
4	1	2	1	1	1	1
4	1	2	0	0	0	1
4	0	1	1	0	1	0
4	0	1	0	0	0	0
4	1	2	0	0	0	1
4	0	1	0	0	0	0
4	0	1	0	0	0	0
4	0	2	1	1	1	0
4	1	1	0	0	0	0
4	0	1	0	0	0	1
4	0	1	1	0	0	0
4	0	1	0	0	0	0
4	0	1	0	0	0	1
4	0	1	1	0	0	1
4	1	1	1	0	0	0
4	0	1	0	0	0	1
4	0	2	0	0	1	1
4	0	1	0	0	0	1
4	0	1	1	0	1	1
4	0	2	1	1	0	1
4	0	2	0	0	1	0
4	0	1	0	0	0	0
4	1	1	1	0	0	1
4	0	1	0	0	0	0
4	0	1	1	1	0	0
4	0	1	0	0	1	1
4	1	1	0	0	0	0
2	1	4	0	0	0	1
2	1	3	1	0	0	1
2	0	4	0	0	0	0
2	0	4	0	0	0	1
2	0	4	0	0	1	0
2	1	3	0	0	0	0
2	1	4	0	0	1	0
2	0	4	0	0	0	0
2	0	3	0	0	0	0
2	0	4	0	0	0	1
2	1	3	0	0	0	0
2	0	4	0	0	0	0
2	1	4	0	0	0	0
2	0	4	0	0	0	1
2	1	4	0	0	0	1
2	0	4	0	0	0	0
2	0	4	0	0	0	0
2	0	4	0	0	0	0
2	0	3	1	0	0	0
2	0	4	0	0	0	0
2	0	4	0	0	0	0
2	1	3	1	0	0	0
2	0	4	0	0	0	0
2	1	4	0	0	0	0
2	1	3	1	0	1	0
2	0	3	0	0	0	0
2	0	4	1	0	0	0
2	1	3	1	0	0	0
2	1	4	0	0	0	0
2	0	4	0	0	0	0
2	1	4	0	0	0	1
2	1	4	0	0	0	0
2	0	4	0	0	0	0
2	0	4	0	0	0	1
2	1	4	0	0	0	0
2	0	4	0	0	0	0
2	1	3	1	0	0	0
2	0	4	1	0	1	1
2	0	4	0	0	0	1
2	0	3	0	0	0	0
2	0	4	0	0	1	0
2	0	4	0	0	0	1
2	0	4	0	0	0	0
2	0	4	0	0	0	1
2	1	4	0	0	0	0
2	1	4	0	0	0	1
2	1	4	1	0	0	0
2	0	4	0	0	0	0
2	0	4	0	0	0	0
2	0	4	0	0	0	0
2	1	4	1	0	1	1
2	1	3	1	0	1	1
2	0	3	0	0	0	0
2	0	4	0	0	0	0
2	0	4	1	1	0	1
2	0	3	1	0	0	1
2	1	4	0	0	0	0
2	0	4	0	0	1	1
2	0	4	0	0	1	0
2	0	3	0	0	0	1
2	0	3	1	0	0	0
2	0	3	0	0	0	0
2	1	4	0	0	0	0
2	0	4	0	0	1	1
2	0	4	0	0	0	1
2	1	4	1	1	0	0
2	1	4	1	1	1	0
2	1	4	1	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203970&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203970&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203970&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Correctanalysis[t] = -1.0290876638248 + 0.213517605028194Weeks[t] -0.00869016317841548Uselimit[t] + 0.156816646607144T[t] + 0.263676267751805used[t] + 0.0403960440657074Useful[t] -0.0356946105921077Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Correctanalysis[t] =  -1.0290876638248 +  0.213517605028194Weeks[t] -0.00869016317841548Uselimit[t] +  0.156816646607144T[t] +  0.263676267751805used[t] +  0.0403960440657074Useful[t] -0.0356946105921077Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203970&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Correctanalysis[t] =  -1.0290876638248 +  0.213517605028194Weeks[t] -0.00869016317841548Uselimit[t] +  0.156816646607144T[t] +  0.263676267751805used[t] +  0.0403960440657074Useful[t] -0.0356946105921077Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203970&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203970&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] = -1.0290876638248 + 0.213517605028194Weeks[t] -0.00869016317841548Uselimit[t] + 0.156816646607144T[t] + 0.263676267751805used[t] + 0.0403960440657074Useful[t] -0.0356946105921077Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.02908766382480.275556-3.73460.0002680.000134
Weeks0.2135176050281940.0572823.72750.0002750.000138
Uselimit-0.008690163178415480.040854-0.21270.8318470.415924
T0.1568166466071440.0432083.62930.0003910.000196
used0.2636762677518050.0430076.131100
Useful0.04039604406570740.045360.89060.3746170.187309
Outcome-0.03569461059210770.039365-0.90680.3660230.183011

\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) & -1.0290876638248 & 0.275556 & -3.7346 & 0.000268 & 0.000134 \tabularnewline
Weeks & 0.213517605028194 & 0.057282 & 3.7275 & 0.000275 & 0.000138 \tabularnewline
Uselimit & -0.00869016317841548 & 0.040854 & -0.2127 & 0.831847 & 0.415924 \tabularnewline
T & 0.156816646607144 & 0.043208 & 3.6293 & 0.000391 & 0.000196 \tabularnewline
used & 0.263676267751805 & 0.043007 & 6.1311 & 0 & 0 \tabularnewline
Useful & 0.0403960440657074 & 0.04536 & 0.8906 & 0.374617 & 0.187309 \tabularnewline
Outcome & -0.0356946105921077 & 0.039365 & -0.9068 & 0.366023 & 0.183011 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203970&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]-1.0290876638248[/C][C]0.275556[/C][C]-3.7346[/C][C]0.000268[/C][C]0.000134[/C][/ROW]
[ROW][C]Weeks[/C][C]0.213517605028194[/C][C]0.057282[/C][C]3.7275[/C][C]0.000275[/C][C]0.000138[/C][/ROW]
[ROW][C]Uselimit[/C][C]-0.00869016317841548[/C][C]0.040854[/C][C]-0.2127[/C][C]0.831847[/C][C]0.415924[/C][/ROW]
[ROW][C]T[/C][C]0.156816646607144[/C][C]0.043208[/C][C]3.6293[/C][C]0.000391[/C][C]0.000196[/C][/ROW]
[ROW][C]used[/C][C]0.263676267751805[/C][C]0.043007[/C][C]6.1311[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Useful[/C][C]0.0403960440657074[/C][C]0.04536[/C][C]0.8906[/C][C]0.374617[/C][C]0.187309[/C][/ROW]
[ROW][C]Outcome[/C][C]-0.0356946105921077[/C][C]0.039365[/C][C]-0.9068[/C][C]0.366023[/C][C]0.183011[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203970&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203970&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)-1.02908766382480.275556-3.73460.0002680.000134
Weeks0.2135176050281940.0572823.72750.0002750.000138
Uselimit-0.008690163178415480.040854-0.21270.8318470.415924
T0.1568166466071440.0432083.62930.0003910.000196
used0.2636762677518050.0430076.131100
Useful0.04039604406570740.045360.89060.3746170.187309
Outcome-0.03569461059210770.039365-0.90680.3660230.183011







Multiple Linear Regression - Regression Statistics
Multiple R0.532932958766495
R-squared0.28401753853961
Adjusted R-squared0.254793764602451
F-TEST (value)9.7187152881194
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value5.25346932622739e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.232149076014747
Sum Squared Residuals7.92229944369159

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.532932958766495 \tabularnewline
R-squared & 0.28401753853961 \tabularnewline
Adjusted R-squared & 0.254793764602451 \tabularnewline
F-TEST (value) & 9.7187152881194 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 5.25346932622739e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.232149076014747 \tabularnewline
Sum Squared Residuals & 7.92229944369159 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203970&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.532932958766495[/C][/ROW]
[ROW][C]R-squared[/C][C]0.28401753853961[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.254793764602451[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.7187152881194[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]5.25346932622739e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.232149076014747[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.92229944369159[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203970&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203970&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 R0.532932958766495
R-squared0.28401753853961
Adjusted R-squared0.254793764602451
F-TEST (value)9.7187152881194
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value5.25346932622739e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.232149076014747
Sum Squared Residuals7.92229944369159







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0942312757317402-0.0942312757317402
20-0.01820059710488170.0182005971048817
30-0.01820059710488140.0182005971048814
40-0.01820059710488150.0182005971048815
50-0.01820059710488130.0182005971048813
60-0.02218932680969720.0221893268096972
70-0.01820059710488130.0182005971048813
800.138616049502263-0.138616049502263
90-0.0538952076969890.053895207696989
100-0.02689076028329680.0268907602832968
1100.129925886323847-0.129925886323847
120-0.01820059710488130.0182005971048813
1300.285871714712631-0.285871714712631
1400.129925886323847-0.129925886323847
1500.250177104120523-0.250177104120523
1600.406993750727667-0.406993750727667
1710.433998198141360.56600180185864
1800.129925886323847-0.129925886323847
190-0.0538952076969890.053895207696989
2010.4069937507276670.593006249272333
2100.0135052837824106-0.0135052837824106
2200.241486940942108-0.241486940942108
230-0.01349916363128160.0134991636312816
240-0.02218932680969710.0221893268096971
2500.36659770666196-0.36659770666196
2600.285871714712631-0.285871714712631
270-0.06258537087540450.0625853708754045
2800.245475670646924-0.245475670646924
290-0.0538952076969890.053895207696989
3000.0221954469608261-0.0221954469608261
310-0.01820059710488130.0182005971048813
320-0.02689076028329680.0268907602832968
3300.0135052837824106-0.0135052837824106
3400.102921438910155-0.102921438910155
350-0.01820059710488130.0182005971048813
360-0.01820059710488130.0182005971048813
3700.43399819814136-0.43399819814136
3800.209781060054816-0.209781060054816
390-0.01349916363128160.0134991636312816
4000.17901209356797-0.17901209356797
4110.2501771041205230.749822895879477
4200.209781060054816-0.209781060054816
430-0.02218932680969710.0221893268096971
4400.129925886323847-0.129925886323847
4500.0221954469608261-0.0221954469608261
460-0.01349916363128160.0134991636312816
470-0.01820059710488130.0182005971048813
480-0.0538952076969890.053895207696989
490-0.01349916363128160.0134991636312816
500-0.01820059710488130.0182005971048813
5100.402292317254067-0.402292317254067
5210.433998198141360.56600180185864
530-0.0538952076969890.053895207696989
5410.2454756706469240.754524329353076
550-0.01820059710488130.0182005971048813
5600.36659770666196-0.36659770666196
5700.250177104120523-0.250177104120523
580-0.0538952076969890.053895207696989
590-0.0538952076969890.053895207696989
6010.3983035875492520.601696412450748
6100.0942312757317396-0.0942312757317396
6200.285871714712631-0.285871714712631
630-0.01820059710488130.0182005971048813
6400.0942312757317396-0.0942312757317396
650-0.01820059710488130.0182005971048813
660-0.01820059710488130.0182005971048813
6710.4426883613197750.557311638680225
680-0.02689076028329680.0268907602832968
690-0.0538952076969890.053895207696989
7000.245475670646924-0.245475670646924
710-0.01820059710488130.0182005971048813
720-0.0538952076969890.053895207696989
7300.209781060054816-0.209781060054816
7400.236785507468508-0.236785507468508
750-0.0538952076969890.053895207696989
7600.143317482975862-0.143317482975862
770-0.0538952076969890.053895207696989
7800.250177104120523-0.250177104120523
7910.366597706661960.63340229333804
8000.17901209356797-0.17901209356797
810-0.01820059710488130.0182005971048813
8200.2010908968764-0.2010908968764
830-0.01820059710488130.0182005971048813
8410.2454756706469240.754524329353076
850-0.01349916363128160.0134991636312816
860-0.02689076028329680.0268907602832968
870-0.01917064111036070.0191706411103607
8800.0876889800343001-0.0876889800343001
8900.0252141326601625-0.0252141326601625
900-0.01048047793194520.0104804779319452
9100.0656101767258699-0.0656101767258699
920-0.1402926771253970.140292677125397
9300.0569200135474544-0.0569200135474544
9400.0252141326601625-0.0252141326601625
950-0.1316025139469820.131602513946982
960-0.01048047793194520.0104804779319452
970-0.1402926771253970.140292677125397
9800.0252141326601625-0.0252141326601625
9900.016523969481747-0.016523969481747
1000-0.01048047793194520.0104804779319452
1010-0.01917064111036070.0191706411103607
10200.0252141326601625-0.0252141326601625
10300.0252141326601625-0.0252141326601625
10400.0252141326601625-0.0252141326601625
10500.132073753804823-0.132073753804823
10600.0252141326601625-0.0252141326601625
10700.0252141326601625-0.0252141326601625
10800.123383590626408-0.123383590626408
10900.0252141326601625-0.0252141326601625
11000.016523969481747-0.016523969481747
11100.163779634692115-0.163779634692115
1120-0.1316025139469820.131602513946982
11300.288890400411967-0.288890400411967
11400.123383590626408-0.123383590626408
11500.016523969481747-0.016523969481747
11600.0252141326601625-0.0252141326601625
1170-0.01917064111036070.0191706411103607
11800.016523969481747-0.016523969481747
11900.0252141326601625-0.0252141326601625
1200-0.01048047793194520.0104804779319452
12100.016523969481747-0.016523969481747
12200.0252141326601625-0.0252141326601625
12300.123383590626408-0.123383590626408
12400.293591833885567-0.293591833885567
1250-0.01048047793194520.0104804779319452
1260-0.1316025139469820.131602513946982
12700.0656101767258699-0.0656101767258699
1280-0.01048047793194520.0104804779319452
12900.0252141326601625-0.0252141326601625
1300-0.01048047793194520.0104804779319452
13100.016523969481747-0.016523969481747
1320-0.01917064111036070.0191706411103607
13300.280200237233552-0.280200237233552
13400.0252141326601625-0.0252141326601625
13500.0252141326601625-0.0252141326601625
13600.0252141326601625-0.0252141326601625
13700.284901670707152-0.284901670707152
13800.128085024100008-0.128085024100008
1390-0.1316025139469820.131602513946982
14000.0252141326601625-0.0252141326601625
14110.253195789819860.74680421018014
14200.0963791432127156-0.0963791432127156
14300.016523969481747-0.016523969481747
14400.0299155661337622-0.0299155661337622
14500.0656101767258699-0.0656101767258699
1460-0.1672971245390890.167297124539089
14700.132073753804823-0.132073753804823
1480-0.1316025139469820.131602513946982
14900.016523969481747-0.016523969481747
15000.0299155661337622-0.0299155661337622
1510-0.01048047793194520.0104804779319452
15210.2802002372335520.719799762766448
15310.3205962812992590.679403718700741
15400.280200237233552-0.280200237233552

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0942312757317402 & -0.0942312757317402 \tabularnewline
2 & 0 & -0.0182005971048817 & 0.0182005971048817 \tabularnewline
3 & 0 & -0.0182005971048814 & 0.0182005971048814 \tabularnewline
4 & 0 & -0.0182005971048815 & 0.0182005971048815 \tabularnewline
5 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
6 & 0 & -0.0221893268096972 & 0.0221893268096972 \tabularnewline
7 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
8 & 0 & 0.138616049502263 & -0.138616049502263 \tabularnewline
9 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
10 & 0 & -0.0268907602832968 & 0.0268907602832968 \tabularnewline
11 & 0 & 0.129925886323847 & -0.129925886323847 \tabularnewline
12 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
13 & 0 & 0.285871714712631 & -0.285871714712631 \tabularnewline
14 & 0 & 0.129925886323847 & -0.129925886323847 \tabularnewline
15 & 0 & 0.250177104120523 & -0.250177104120523 \tabularnewline
16 & 0 & 0.406993750727667 & -0.406993750727667 \tabularnewline
17 & 1 & 0.43399819814136 & 0.56600180185864 \tabularnewline
18 & 0 & 0.129925886323847 & -0.129925886323847 \tabularnewline
19 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
20 & 1 & 0.406993750727667 & 0.593006249272333 \tabularnewline
21 & 0 & 0.0135052837824106 & -0.0135052837824106 \tabularnewline
22 & 0 & 0.241486940942108 & -0.241486940942108 \tabularnewline
23 & 0 & -0.0134991636312816 & 0.0134991636312816 \tabularnewline
24 & 0 & -0.0221893268096971 & 0.0221893268096971 \tabularnewline
25 & 0 & 0.36659770666196 & -0.36659770666196 \tabularnewline
26 & 0 & 0.285871714712631 & -0.285871714712631 \tabularnewline
27 & 0 & -0.0625853708754045 & 0.0625853708754045 \tabularnewline
28 & 0 & 0.245475670646924 & -0.245475670646924 \tabularnewline
29 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
30 & 0 & 0.0221954469608261 & -0.0221954469608261 \tabularnewline
31 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
32 & 0 & -0.0268907602832968 & 0.0268907602832968 \tabularnewline
33 & 0 & 0.0135052837824106 & -0.0135052837824106 \tabularnewline
34 & 0 & 0.102921438910155 & -0.102921438910155 \tabularnewline
35 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
36 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
37 & 0 & 0.43399819814136 & -0.43399819814136 \tabularnewline
38 & 0 & 0.209781060054816 & -0.209781060054816 \tabularnewline
39 & 0 & -0.0134991636312816 & 0.0134991636312816 \tabularnewline
40 & 0 & 0.17901209356797 & -0.17901209356797 \tabularnewline
41 & 1 & 0.250177104120523 & 0.749822895879477 \tabularnewline
42 & 0 & 0.209781060054816 & -0.209781060054816 \tabularnewline
43 & 0 & -0.0221893268096971 & 0.0221893268096971 \tabularnewline
44 & 0 & 0.129925886323847 & -0.129925886323847 \tabularnewline
45 & 0 & 0.0221954469608261 & -0.0221954469608261 \tabularnewline
46 & 0 & -0.0134991636312816 & 0.0134991636312816 \tabularnewline
47 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
48 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
49 & 0 & -0.0134991636312816 & 0.0134991636312816 \tabularnewline
50 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
51 & 0 & 0.402292317254067 & -0.402292317254067 \tabularnewline
52 & 1 & 0.43399819814136 & 0.56600180185864 \tabularnewline
53 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
54 & 1 & 0.245475670646924 & 0.754524329353076 \tabularnewline
55 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
56 & 0 & 0.36659770666196 & -0.36659770666196 \tabularnewline
57 & 0 & 0.250177104120523 & -0.250177104120523 \tabularnewline
58 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
59 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
60 & 1 & 0.398303587549252 & 0.601696412450748 \tabularnewline
61 & 0 & 0.0942312757317396 & -0.0942312757317396 \tabularnewline
62 & 0 & 0.285871714712631 & -0.285871714712631 \tabularnewline
63 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
64 & 0 & 0.0942312757317396 & -0.0942312757317396 \tabularnewline
65 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
66 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
67 & 1 & 0.442688361319775 & 0.557311638680225 \tabularnewline
68 & 0 & -0.0268907602832968 & 0.0268907602832968 \tabularnewline
69 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
70 & 0 & 0.245475670646924 & -0.245475670646924 \tabularnewline
71 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
72 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
73 & 0 & 0.209781060054816 & -0.209781060054816 \tabularnewline
74 & 0 & 0.236785507468508 & -0.236785507468508 \tabularnewline
75 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
76 & 0 & 0.143317482975862 & -0.143317482975862 \tabularnewline
77 & 0 & -0.053895207696989 & 0.053895207696989 \tabularnewline
78 & 0 & 0.250177104120523 & -0.250177104120523 \tabularnewline
79 & 1 & 0.36659770666196 & 0.63340229333804 \tabularnewline
80 & 0 & 0.17901209356797 & -0.17901209356797 \tabularnewline
81 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
82 & 0 & 0.2010908968764 & -0.2010908968764 \tabularnewline
83 & 0 & -0.0182005971048813 & 0.0182005971048813 \tabularnewline
84 & 1 & 0.245475670646924 & 0.754524329353076 \tabularnewline
85 & 0 & -0.0134991636312816 & 0.0134991636312816 \tabularnewline
86 & 0 & -0.0268907602832968 & 0.0268907602832968 \tabularnewline
87 & 0 & -0.0191706411103607 & 0.0191706411103607 \tabularnewline
88 & 0 & 0.0876889800343001 & -0.0876889800343001 \tabularnewline
89 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
90 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
91 & 0 & 0.0656101767258699 & -0.0656101767258699 \tabularnewline
92 & 0 & -0.140292677125397 & 0.140292677125397 \tabularnewline
93 & 0 & 0.0569200135474544 & -0.0569200135474544 \tabularnewline
94 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
95 & 0 & -0.131602513946982 & 0.131602513946982 \tabularnewline
96 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
97 & 0 & -0.140292677125397 & 0.140292677125397 \tabularnewline
98 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
99 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
100 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
101 & 0 & -0.0191706411103607 & 0.0191706411103607 \tabularnewline
102 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
103 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
104 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
105 & 0 & 0.132073753804823 & -0.132073753804823 \tabularnewline
106 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
107 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
108 & 0 & 0.123383590626408 & -0.123383590626408 \tabularnewline
109 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
110 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
111 & 0 & 0.163779634692115 & -0.163779634692115 \tabularnewline
112 & 0 & -0.131602513946982 & 0.131602513946982 \tabularnewline
113 & 0 & 0.288890400411967 & -0.288890400411967 \tabularnewline
114 & 0 & 0.123383590626408 & -0.123383590626408 \tabularnewline
115 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
116 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
117 & 0 & -0.0191706411103607 & 0.0191706411103607 \tabularnewline
118 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
119 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
120 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
121 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
122 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
123 & 0 & 0.123383590626408 & -0.123383590626408 \tabularnewline
124 & 0 & 0.293591833885567 & -0.293591833885567 \tabularnewline
125 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
126 & 0 & -0.131602513946982 & 0.131602513946982 \tabularnewline
127 & 0 & 0.0656101767258699 & -0.0656101767258699 \tabularnewline
128 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
129 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
130 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
131 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
132 & 0 & -0.0191706411103607 & 0.0191706411103607 \tabularnewline
133 & 0 & 0.280200237233552 & -0.280200237233552 \tabularnewline
134 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
135 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
136 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
137 & 0 & 0.284901670707152 & -0.284901670707152 \tabularnewline
138 & 0 & 0.128085024100008 & -0.128085024100008 \tabularnewline
139 & 0 & -0.131602513946982 & 0.131602513946982 \tabularnewline
140 & 0 & 0.0252141326601625 & -0.0252141326601625 \tabularnewline
141 & 1 & 0.25319578981986 & 0.74680421018014 \tabularnewline
142 & 0 & 0.0963791432127156 & -0.0963791432127156 \tabularnewline
143 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
144 & 0 & 0.0299155661337622 & -0.0299155661337622 \tabularnewline
145 & 0 & 0.0656101767258699 & -0.0656101767258699 \tabularnewline
146 & 0 & -0.167297124539089 & 0.167297124539089 \tabularnewline
147 & 0 & 0.132073753804823 & -0.132073753804823 \tabularnewline
148 & 0 & -0.131602513946982 & 0.131602513946982 \tabularnewline
149 & 0 & 0.016523969481747 & -0.016523969481747 \tabularnewline
150 & 0 & 0.0299155661337622 & -0.0299155661337622 \tabularnewline
151 & 0 & -0.0104804779319452 & 0.0104804779319452 \tabularnewline
152 & 1 & 0.280200237233552 & 0.719799762766448 \tabularnewline
153 & 1 & 0.320596281299259 & 0.679403718700741 \tabularnewline
154 & 0 & 0.280200237233552 & -0.280200237233552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203970&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]0.0942312757317402[/C][C]-0.0942312757317402[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.0182005971048817[/C][C]0.0182005971048817[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0182005971048814[/C][C]0.0182005971048814[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0182005971048815[/C][C]0.0182005971048815[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.0221893268096972[/C][C]0.0221893268096972[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.138616049502263[/C][C]-0.138616049502263[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0268907602832968[/C][C]0.0268907602832968[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.129925886323847[/C][C]-0.129925886323847[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.285871714712631[/C][C]-0.285871714712631[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.129925886323847[/C][C]-0.129925886323847[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.250177104120523[/C][C]-0.250177104120523[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.406993750727667[/C][C]-0.406993750727667[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.43399819814136[/C][C]0.56600180185864[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.129925886323847[/C][C]-0.129925886323847[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.406993750727667[/C][C]0.593006249272333[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0135052837824106[/C][C]-0.0135052837824106[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.241486940942108[/C][C]-0.241486940942108[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]-0.0134991636312816[/C][C]0.0134991636312816[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]-0.0221893268096971[/C][C]0.0221893268096971[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.36659770666196[/C][C]-0.36659770666196[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.285871714712631[/C][C]-0.285871714712631[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0625853708754045[/C][C]0.0625853708754045[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.245475670646924[/C][C]-0.245475670646924[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0221954469608261[/C][C]-0.0221954469608261[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.0268907602832968[/C][C]0.0268907602832968[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0135052837824106[/C][C]-0.0135052837824106[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.102921438910155[/C][C]-0.102921438910155[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.43399819814136[/C][C]-0.43399819814136[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.209781060054816[/C][C]-0.209781060054816[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]-0.0134991636312816[/C][C]0.0134991636312816[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.17901209356797[/C][C]-0.17901209356797[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.250177104120523[/C][C]0.749822895879477[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.209781060054816[/C][C]-0.209781060054816[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]-0.0221893268096971[/C][C]0.0221893268096971[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.129925886323847[/C][C]-0.129925886323847[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0221954469608261[/C][C]-0.0221954469608261[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]-0.0134991636312816[/C][C]0.0134991636312816[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]-0.0134991636312816[/C][C]0.0134991636312816[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.402292317254067[/C][C]-0.402292317254067[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.43399819814136[/C][C]0.56600180185864[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.245475670646924[/C][C]0.754524329353076[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.36659770666196[/C][C]-0.36659770666196[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.250177104120523[/C][C]-0.250177104120523[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.398303587549252[/C][C]0.601696412450748[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.0942312757317396[/C][C]-0.0942312757317396[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.285871714712631[/C][C]-0.285871714712631[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.0942312757317396[/C][C]-0.0942312757317396[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.442688361319775[/C][C]0.557311638680225[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]-0.0268907602832968[/C][C]0.0268907602832968[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.245475670646924[/C][C]-0.245475670646924[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.209781060054816[/C][C]-0.209781060054816[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.236785507468508[/C][C]-0.236785507468508[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.143317482975862[/C][C]-0.143317482975862[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.053895207696989[/C][C]0.053895207696989[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.250177104120523[/C][C]-0.250177104120523[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.36659770666196[/C][C]0.63340229333804[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.17901209356797[/C][C]-0.17901209356797[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.2010908968764[/C][C]-0.2010908968764[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.0182005971048813[/C][C]0.0182005971048813[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.245475670646924[/C][C]0.754524329353076[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.0134991636312816[/C][C]0.0134991636312816[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]-0.0268907602832968[/C][C]0.0268907602832968[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]-0.0191706411103607[/C][C]0.0191706411103607[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.0876889800343001[/C][C]-0.0876889800343001[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0656101767258699[/C][C]-0.0656101767258699[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]-0.140292677125397[/C][C]0.140292677125397[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0569200135474544[/C][C]-0.0569200135474544[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-0.131602513946982[/C][C]0.131602513946982[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]-0.140292677125397[/C][C]0.140292677125397[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]-0.0191706411103607[/C][C]0.0191706411103607[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.132073753804823[/C][C]-0.132073753804823[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.123383590626408[/C][C]-0.123383590626408[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.163779634692115[/C][C]-0.163779634692115[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]-0.131602513946982[/C][C]0.131602513946982[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.288890400411967[/C][C]-0.288890400411967[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.123383590626408[/C][C]-0.123383590626408[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]-0.0191706411103607[/C][C]0.0191706411103607[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.123383590626408[/C][C]-0.123383590626408[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.293591833885567[/C][C]-0.293591833885567[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]-0.131602513946982[/C][C]0.131602513946982[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0656101767258699[/C][C]-0.0656101767258699[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]-0.0191706411103607[/C][C]0.0191706411103607[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.280200237233552[/C][C]-0.280200237233552[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.284901670707152[/C][C]-0.284901670707152[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.128085024100008[/C][C]-0.128085024100008[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-0.131602513946982[/C][C]0.131602513946982[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.0252141326601625[/C][C]-0.0252141326601625[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.25319578981986[/C][C]0.74680421018014[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.0963791432127156[/C][C]-0.0963791432127156[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.0299155661337622[/C][C]-0.0299155661337622[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0656101767258699[/C][C]-0.0656101767258699[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-0.167297124539089[/C][C]0.167297124539089[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.132073753804823[/C][C]-0.132073753804823[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]-0.131602513946982[/C][C]0.131602513946982[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.016523969481747[/C][C]-0.016523969481747[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.0299155661337622[/C][C]-0.0299155661337622[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-0.0104804779319452[/C][C]0.0104804779319452[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.280200237233552[/C][C]0.719799762766448[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.320596281299259[/C][C]0.679403718700741[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.280200237233552[/C][C]-0.280200237233552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203970&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203970&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
100.0942312757317402-0.0942312757317402
20-0.01820059710488170.0182005971048817
30-0.01820059710488140.0182005971048814
40-0.01820059710488150.0182005971048815
50-0.01820059710488130.0182005971048813
60-0.02218932680969720.0221893268096972
70-0.01820059710488130.0182005971048813
800.138616049502263-0.138616049502263
90-0.0538952076969890.053895207696989
100-0.02689076028329680.0268907602832968
1100.129925886323847-0.129925886323847
120-0.01820059710488130.0182005971048813
1300.285871714712631-0.285871714712631
1400.129925886323847-0.129925886323847
1500.250177104120523-0.250177104120523
1600.406993750727667-0.406993750727667
1710.433998198141360.56600180185864
1800.129925886323847-0.129925886323847
190-0.0538952076969890.053895207696989
2010.4069937507276670.593006249272333
2100.0135052837824106-0.0135052837824106
2200.241486940942108-0.241486940942108
230-0.01349916363128160.0134991636312816
240-0.02218932680969710.0221893268096971
2500.36659770666196-0.36659770666196
2600.285871714712631-0.285871714712631
270-0.06258537087540450.0625853708754045
2800.245475670646924-0.245475670646924
290-0.0538952076969890.053895207696989
3000.0221954469608261-0.0221954469608261
310-0.01820059710488130.0182005971048813
320-0.02689076028329680.0268907602832968
3300.0135052837824106-0.0135052837824106
3400.102921438910155-0.102921438910155
350-0.01820059710488130.0182005971048813
360-0.01820059710488130.0182005971048813
3700.43399819814136-0.43399819814136
3800.209781060054816-0.209781060054816
390-0.01349916363128160.0134991636312816
4000.17901209356797-0.17901209356797
4110.2501771041205230.749822895879477
4200.209781060054816-0.209781060054816
430-0.02218932680969710.0221893268096971
4400.129925886323847-0.129925886323847
4500.0221954469608261-0.0221954469608261
460-0.01349916363128160.0134991636312816
470-0.01820059710488130.0182005971048813
480-0.0538952076969890.053895207696989
490-0.01349916363128160.0134991636312816
500-0.01820059710488130.0182005971048813
5100.402292317254067-0.402292317254067
5210.433998198141360.56600180185864
530-0.0538952076969890.053895207696989
5410.2454756706469240.754524329353076
550-0.01820059710488130.0182005971048813
5600.36659770666196-0.36659770666196
5700.250177104120523-0.250177104120523
580-0.0538952076969890.053895207696989
590-0.0538952076969890.053895207696989
6010.3983035875492520.601696412450748
6100.0942312757317396-0.0942312757317396
6200.285871714712631-0.285871714712631
630-0.01820059710488130.0182005971048813
6400.0942312757317396-0.0942312757317396
650-0.01820059710488130.0182005971048813
660-0.01820059710488130.0182005971048813
6710.4426883613197750.557311638680225
680-0.02689076028329680.0268907602832968
690-0.0538952076969890.053895207696989
7000.245475670646924-0.245475670646924
710-0.01820059710488130.0182005971048813
720-0.0538952076969890.053895207696989
7300.209781060054816-0.209781060054816
7400.236785507468508-0.236785507468508
750-0.0538952076969890.053895207696989
7600.143317482975862-0.143317482975862
770-0.0538952076969890.053895207696989
7800.250177104120523-0.250177104120523
7910.366597706661960.63340229333804
8000.17901209356797-0.17901209356797
810-0.01820059710488130.0182005971048813
8200.2010908968764-0.2010908968764
830-0.01820059710488130.0182005971048813
8410.2454756706469240.754524329353076
850-0.01349916363128160.0134991636312816
860-0.02689076028329680.0268907602832968
870-0.01917064111036070.0191706411103607
8800.0876889800343001-0.0876889800343001
8900.0252141326601625-0.0252141326601625
900-0.01048047793194520.0104804779319452
9100.0656101767258699-0.0656101767258699
920-0.1402926771253970.140292677125397
9300.0569200135474544-0.0569200135474544
9400.0252141326601625-0.0252141326601625
950-0.1316025139469820.131602513946982
960-0.01048047793194520.0104804779319452
970-0.1402926771253970.140292677125397
9800.0252141326601625-0.0252141326601625
9900.016523969481747-0.016523969481747
1000-0.01048047793194520.0104804779319452
1010-0.01917064111036070.0191706411103607
10200.0252141326601625-0.0252141326601625
10300.0252141326601625-0.0252141326601625
10400.0252141326601625-0.0252141326601625
10500.132073753804823-0.132073753804823
10600.0252141326601625-0.0252141326601625
10700.0252141326601625-0.0252141326601625
10800.123383590626408-0.123383590626408
10900.0252141326601625-0.0252141326601625
11000.016523969481747-0.016523969481747
11100.163779634692115-0.163779634692115
1120-0.1316025139469820.131602513946982
11300.288890400411967-0.288890400411967
11400.123383590626408-0.123383590626408
11500.016523969481747-0.016523969481747
11600.0252141326601625-0.0252141326601625
1170-0.01917064111036070.0191706411103607
11800.016523969481747-0.016523969481747
11900.0252141326601625-0.0252141326601625
1200-0.01048047793194520.0104804779319452
12100.016523969481747-0.016523969481747
12200.0252141326601625-0.0252141326601625
12300.123383590626408-0.123383590626408
12400.293591833885567-0.293591833885567
1250-0.01048047793194520.0104804779319452
1260-0.1316025139469820.131602513946982
12700.0656101767258699-0.0656101767258699
1280-0.01048047793194520.0104804779319452
12900.0252141326601625-0.0252141326601625
1300-0.01048047793194520.0104804779319452
13100.016523969481747-0.016523969481747
1320-0.01917064111036070.0191706411103607
13300.280200237233552-0.280200237233552
13400.0252141326601625-0.0252141326601625
13500.0252141326601625-0.0252141326601625
13600.0252141326601625-0.0252141326601625
13700.284901670707152-0.284901670707152
13800.128085024100008-0.128085024100008
1390-0.1316025139469820.131602513946982
14000.0252141326601625-0.0252141326601625
14110.253195789819860.74680421018014
14200.0963791432127156-0.0963791432127156
14300.016523969481747-0.016523969481747
14400.0299155661337622-0.0299155661337622
14500.0656101767258699-0.0656101767258699
1460-0.1672971245390890.167297124539089
14700.132073753804823-0.132073753804823
1480-0.1316025139469820.131602513946982
14900.016523969481747-0.016523969481747
15000.0299155661337622-0.0299155661337622
1510-0.01048047793194520.0104804779319452
15210.2802002372335520.719799762766448
15310.3205962812992590.679403718700741
15400.280200237233552-0.280200237233552







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
10001
11001
12001
13001
14001
15001
16001
170.4666224635606690.9332449271213390.533377536439331
180.4177459993878590.8354919987757170.582254000612141
190.3831703225491210.7663406450982410.616829677450879
200.8670890044035680.2658219911928650.132910995596432
210.8209910074313570.3580179851372860.179008992568643
220.8100010914611110.3799978170777780.189998908538889
230.7549992076427390.4900015847145210.245000792357261
240.6947879999086930.6104240001826130.305212000091307
250.7005083585768360.5989832828463280.299491641423164
260.7047485073794380.5905029852411250.295251492620563
270.6660794970735770.6678410058528450.333920502926423
280.6171864028152490.7656271943695020.382813597184751
290.5654056864087280.8691886271825450.434594313591272
300.5077242199619810.9845515600760370.492275780038019
310.4473565429832180.8947130859664350.552643457016782
320.3890665597756680.7781331195513360.610933440224332
330.334035136446830.6680702728936590.665964863553171
340.2898323407785090.5796646815570180.710167659221491
350.2419663880413730.4839327760827470.758033611958627
360.1987943226837650.397588645367530.801205677316235
370.2760047162918730.5520094325837470.723995283708127
380.2399128324963040.4798256649926080.760087167503696
390.1974537488730010.3949074977460020.802546251126999
400.1906217622807460.3812435245614920.809378237719254
410.7301852599959860.5396294800080290.269814740004014
420.7066172399397880.5867655201204250.293382760060212
430.6597682071587130.6804635856825740.340231792841287
440.6199629490584470.7600741018831060.380037050941553
450.5696966873938280.8606066252123440.430303312606172
460.518949514174010.9621009716519790.48105048582599
470.4679535923052760.9359071846105520.532046407694724
480.4173443513180620.8346887026361230.582655648681939
490.3687287810683540.7374575621367070.631271218931646
500.3220296867873430.6440593735746860.677970313212657
510.3826113759920760.7652227519841530.617388624007924
520.6714753794982970.6570492410034050.328524620501703
530.6282624343213710.7434751313572580.371737565678629
540.9346986006640920.1306027986718170.0653013993359083
550.9174292374052390.1651415251895210.0825707625947606
560.9403824460196360.1192351079607280.0596175539803638
570.9427537685829080.1144924628341840.0572462314170921
580.928493594819350.1430128103613010.0715064051806504
590.9115524520880220.1768950958239560.0884475479119782
600.9774139487393350.04517210252132940.0225860512606647
610.9714631679939610.05707366401207830.0285368320060391
620.9751877985172110.04962440296557760.0248122014827888
630.9674922056870890.06501558862582240.0325077943129112
640.9601002768168830.07979944636623390.039899723183117
650.9488728535992690.1022542928014630.0511271464007314
660.9352652418766360.1294695162467280.0647347581233642
670.980845582000940.03830883599812040.0191544179990602
680.9745322203357180.05093555932856340.0254677796642817
690.9671331904057240.06573361918855190.032866809594276
700.9692833374292370.06143332514152540.0307166625707627
710.9601898524402060.07962029511958740.0398101475597937
720.9494877930712540.1010244138574930.0505122069287465
730.9508685381151920.09826292376961620.0491314618848081
740.9557257376091340.08854852478173170.0442742623908659
750.9441916257778990.1116167484442020.0558083742221012
760.9375557547262550.124888490547490.062444245273745
770.9228377068222910.1543245863554180.0771622931777088
780.9333569663841980.1332860672316040.0666430336158021
790.9818863017146190.03622739657076130.0181136982853806
800.9810153828625390.03796923427492170.0189846171374609
810.9758736014554360.0482527970891290.0241263985445645
820.9817374560367310.03652508792653870.0182625439632693
830.9800497891806710.0399004216386580.019950210819329
840.9982757936111370.003448412777725880.00172420638886294
850.9974583500125660.00508329997486850.00254164998743425
860.9963034879705190.007393024058962080.00369651202948104
870.994685971475440.0106280570491190.00531402852455948
880.9929643903720930.01407121925581420.0070356096279071
890.9901484922086120.01970301558277580.00985150779138788
900.9863970423442460.02720591531150710.0136029576557535
910.9815229828222840.03695403435543250.0184770171777163
920.9773276488181460.04534470236370820.0226723511818541
930.9698628179291760.06027436414164830.0301371820708242
940.9602699978904210.07946000421915750.0397300021095787
950.952417976042710.09516404791457980.0475820239572899
960.9384975872155520.1230048255688950.0615024127844475
970.9284239598994070.1431520802011860.0715760401005929
980.9095508799230210.1808982401539580.0904491200769791
990.8869045306553040.2261909386893910.113095469344696
1000.8603688795862290.2792622408275410.139631120413771
1010.8296113160044550.3407773679910910.170388683995545
1020.7951831421159430.4096337157681140.204816857884057
1030.7567767772631470.4864464454737050.243223222736853
1040.7146270679651920.5707458640696160.285372932034808
1050.6827504592266580.6344990815466830.317249540773342
1060.6351116490790210.7297767018419580.364888350920979
1070.585148798811860.829702402376280.41485120118814
1080.5457741607015020.9084516785969960.454225839298498
1090.4933047406053170.9866094812106340.506695259394683
1100.4397142950502820.8794285901005630.560285704949718
1110.4014913435099290.8029826870198570.598508656490071
1120.365737537512730.7314750750254610.63426246248727
1130.4170349625072090.8340699250144180.582965037492791
1140.3804142856764760.7608285713529530.619585714323524
1150.3280150147249490.6560300294498980.671984985275051
1160.2806791148501740.5613582297003490.719320885149826
1170.235114078207310.470228156414620.76488592179269
1180.1931234006872360.3862468013744720.806876599312764
1190.1577250585853190.3154501171706390.842274941414681
1200.1250265073113420.2500530146226850.874973492688658
1210.09713203821577740.1942640764315550.902867961784223
1220.0752788692889050.150557738577810.924721130711095
1230.06357656920336630.1271531384067330.936423430796634
1240.08155958046831980.163119160936640.91844041953168
1250.06069291939867820.1213858387973560.939307080601322
1260.04951858407320830.09903716814641660.950481415926792
1270.03586641128024140.07173282256048270.964133588719759
1280.02494319693849310.04988639387698610.975056803061507
1290.01735631624471080.03471263248942150.982643683755289
1300.01146253854244220.02292507708488440.988537461457558
1310.007226842929110290.01445368585822060.99277315707089
1320.004510036427978330.009020072855956660.995489963572022
1330.009036534262885460.01807306852577090.990963465737115
1340.005920795954859820.01184159190971960.99407920404514
1350.003883751093828140.007767502187656290.996116248906172
1360.002620683552474150.00524136710494830.997379316447526
1370.004884304761400770.009768609522801540.995115695238599
1380.004067610219138880.008135220438277750.995932389780861
1390.003375991061050050.00675198212210010.99662400893895
1400.001652063886560460.003304127773120930.99834793611344
1410.02313262164419710.04626524328839420.976867378355803
1420.01872251522451180.03744503044902360.981277484775488
1430.01007660841259530.02015321682519060.989923391587405
1440.00469220623609780.00938441247219560.995307793763902

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \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 & 0.466622463560669 & 0.933244927121339 & 0.533377536439331 \tabularnewline
18 & 0.417745999387859 & 0.835491998775717 & 0.582254000612141 \tabularnewline
19 & 0.383170322549121 & 0.766340645098241 & 0.616829677450879 \tabularnewline
20 & 0.867089004403568 & 0.265821991192865 & 0.132910995596432 \tabularnewline
21 & 0.820991007431357 & 0.358017985137286 & 0.179008992568643 \tabularnewline
22 & 0.810001091461111 & 0.379997817077778 & 0.189998908538889 \tabularnewline
23 & 0.754999207642739 & 0.490001584714521 & 0.245000792357261 \tabularnewline
24 & 0.694787999908693 & 0.610424000182613 & 0.305212000091307 \tabularnewline
25 & 0.700508358576836 & 0.598983282846328 & 0.299491641423164 \tabularnewline
26 & 0.704748507379438 & 0.590502985241125 & 0.295251492620563 \tabularnewline
27 & 0.666079497073577 & 0.667841005852845 & 0.333920502926423 \tabularnewline
28 & 0.617186402815249 & 0.765627194369502 & 0.382813597184751 \tabularnewline
29 & 0.565405686408728 & 0.869188627182545 & 0.434594313591272 \tabularnewline
30 & 0.507724219961981 & 0.984551560076037 & 0.492275780038019 \tabularnewline
31 & 0.447356542983218 & 0.894713085966435 & 0.552643457016782 \tabularnewline
32 & 0.389066559775668 & 0.778133119551336 & 0.610933440224332 \tabularnewline
33 & 0.33403513644683 & 0.668070272893659 & 0.665964863553171 \tabularnewline
34 & 0.289832340778509 & 0.579664681557018 & 0.710167659221491 \tabularnewline
35 & 0.241966388041373 & 0.483932776082747 & 0.758033611958627 \tabularnewline
36 & 0.198794322683765 & 0.39758864536753 & 0.801205677316235 \tabularnewline
37 & 0.276004716291873 & 0.552009432583747 & 0.723995283708127 \tabularnewline
38 & 0.239912832496304 & 0.479825664992608 & 0.760087167503696 \tabularnewline
39 & 0.197453748873001 & 0.394907497746002 & 0.802546251126999 \tabularnewline
40 & 0.190621762280746 & 0.381243524561492 & 0.809378237719254 \tabularnewline
41 & 0.730185259995986 & 0.539629480008029 & 0.269814740004014 \tabularnewline
42 & 0.706617239939788 & 0.586765520120425 & 0.293382760060212 \tabularnewline
43 & 0.659768207158713 & 0.680463585682574 & 0.340231792841287 \tabularnewline
44 & 0.619962949058447 & 0.760074101883106 & 0.380037050941553 \tabularnewline
45 & 0.569696687393828 & 0.860606625212344 & 0.430303312606172 \tabularnewline
46 & 0.51894951417401 & 0.962100971651979 & 0.48105048582599 \tabularnewline
47 & 0.467953592305276 & 0.935907184610552 & 0.532046407694724 \tabularnewline
48 & 0.417344351318062 & 0.834688702636123 & 0.582655648681939 \tabularnewline
49 & 0.368728781068354 & 0.737457562136707 & 0.631271218931646 \tabularnewline
50 & 0.322029686787343 & 0.644059373574686 & 0.677970313212657 \tabularnewline
51 & 0.382611375992076 & 0.765222751984153 & 0.617388624007924 \tabularnewline
52 & 0.671475379498297 & 0.657049241003405 & 0.328524620501703 \tabularnewline
53 & 0.628262434321371 & 0.743475131357258 & 0.371737565678629 \tabularnewline
54 & 0.934698600664092 & 0.130602798671817 & 0.0653013993359083 \tabularnewline
55 & 0.917429237405239 & 0.165141525189521 & 0.0825707625947606 \tabularnewline
56 & 0.940382446019636 & 0.119235107960728 & 0.0596175539803638 \tabularnewline
57 & 0.942753768582908 & 0.114492462834184 & 0.0572462314170921 \tabularnewline
58 & 0.92849359481935 & 0.143012810361301 & 0.0715064051806504 \tabularnewline
59 & 0.911552452088022 & 0.176895095823956 & 0.0884475479119782 \tabularnewline
60 & 0.977413948739335 & 0.0451721025213294 & 0.0225860512606647 \tabularnewline
61 & 0.971463167993961 & 0.0570736640120783 & 0.0285368320060391 \tabularnewline
62 & 0.975187798517211 & 0.0496244029655776 & 0.0248122014827888 \tabularnewline
63 & 0.967492205687089 & 0.0650155886258224 & 0.0325077943129112 \tabularnewline
64 & 0.960100276816883 & 0.0797994463662339 & 0.039899723183117 \tabularnewline
65 & 0.948872853599269 & 0.102254292801463 & 0.0511271464007314 \tabularnewline
66 & 0.935265241876636 & 0.129469516246728 & 0.0647347581233642 \tabularnewline
67 & 0.98084558200094 & 0.0383088359981204 & 0.0191544179990602 \tabularnewline
68 & 0.974532220335718 & 0.0509355593285634 & 0.0254677796642817 \tabularnewline
69 & 0.967133190405724 & 0.0657336191885519 & 0.032866809594276 \tabularnewline
70 & 0.969283337429237 & 0.0614333251415254 & 0.0307166625707627 \tabularnewline
71 & 0.960189852440206 & 0.0796202951195874 & 0.0398101475597937 \tabularnewline
72 & 0.949487793071254 & 0.101024413857493 & 0.0505122069287465 \tabularnewline
73 & 0.950868538115192 & 0.0982629237696162 & 0.0491314618848081 \tabularnewline
74 & 0.955725737609134 & 0.0885485247817317 & 0.0442742623908659 \tabularnewline
75 & 0.944191625777899 & 0.111616748444202 & 0.0558083742221012 \tabularnewline
76 & 0.937555754726255 & 0.12488849054749 & 0.062444245273745 \tabularnewline
77 & 0.922837706822291 & 0.154324586355418 & 0.0771622931777088 \tabularnewline
78 & 0.933356966384198 & 0.133286067231604 & 0.0666430336158021 \tabularnewline
79 & 0.981886301714619 & 0.0362273965707613 & 0.0181136982853806 \tabularnewline
80 & 0.981015382862539 & 0.0379692342749217 & 0.0189846171374609 \tabularnewline
81 & 0.975873601455436 & 0.048252797089129 & 0.0241263985445645 \tabularnewline
82 & 0.981737456036731 & 0.0365250879265387 & 0.0182625439632693 \tabularnewline
83 & 0.980049789180671 & 0.039900421638658 & 0.019950210819329 \tabularnewline
84 & 0.998275793611137 & 0.00344841277772588 & 0.00172420638886294 \tabularnewline
85 & 0.997458350012566 & 0.0050832999748685 & 0.00254164998743425 \tabularnewline
86 & 0.996303487970519 & 0.00739302405896208 & 0.00369651202948104 \tabularnewline
87 & 0.99468597147544 & 0.010628057049119 & 0.00531402852455948 \tabularnewline
88 & 0.992964390372093 & 0.0140712192558142 & 0.0070356096279071 \tabularnewline
89 & 0.990148492208612 & 0.0197030155827758 & 0.00985150779138788 \tabularnewline
90 & 0.986397042344246 & 0.0272059153115071 & 0.0136029576557535 \tabularnewline
91 & 0.981522982822284 & 0.0369540343554325 & 0.0184770171777163 \tabularnewline
92 & 0.977327648818146 & 0.0453447023637082 & 0.0226723511818541 \tabularnewline
93 & 0.969862817929176 & 0.0602743641416483 & 0.0301371820708242 \tabularnewline
94 & 0.960269997890421 & 0.0794600042191575 & 0.0397300021095787 \tabularnewline
95 & 0.95241797604271 & 0.0951640479145798 & 0.0475820239572899 \tabularnewline
96 & 0.938497587215552 & 0.123004825568895 & 0.0615024127844475 \tabularnewline
97 & 0.928423959899407 & 0.143152080201186 & 0.0715760401005929 \tabularnewline
98 & 0.909550879923021 & 0.180898240153958 & 0.0904491200769791 \tabularnewline
99 & 0.886904530655304 & 0.226190938689391 & 0.113095469344696 \tabularnewline
100 & 0.860368879586229 & 0.279262240827541 & 0.139631120413771 \tabularnewline
101 & 0.829611316004455 & 0.340777367991091 & 0.170388683995545 \tabularnewline
102 & 0.795183142115943 & 0.409633715768114 & 0.204816857884057 \tabularnewline
103 & 0.756776777263147 & 0.486446445473705 & 0.243223222736853 \tabularnewline
104 & 0.714627067965192 & 0.570745864069616 & 0.285372932034808 \tabularnewline
105 & 0.682750459226658 & 0.634499081546683 & 0.317249540773342 \tabularnewline
106 & 0.635111649079021 & 0.729776701841958 & 0.364888350920979 \tabularnewline
107 & 0.58514879881186 & 0.82970240237628 & 0.41485120118814 \tabularnewline
108 & 0.545774160701502 & 0.908451678596996 & 0.454225839298498 \tabularnewline
109 & 0.493304740605317 & 0.986609481210634 & 0.506695259394683 \tabularnewline
110 & 0.439714295050282 & 0.879428590100563 & 0.560285704949718 \tabularnewline
111 & 0.401491343509929 & 0.802982687019857 & 0.598508656490071 \tabularnewline
112 & 0.36573753751273 & 0.731475075025461 & 0.63426246248727 \tabularnewline
113 & 0.417034962507209 & 0.834069925014418 & 0.582965037492791 \tabularnewline
114 & 0.380414285676476 & 0.760828571352953 & 0.619585714323524 \tabularnewline
115 & 0.328015014724949 & 0.656030029449898 & 0.671984985275051 \tabularnewline
116 & 0.280679114850174 & 0.561358229700349 & 0.719320885149826 \tabularnewline
117 & 0.23511407820731 & 0.47022815641462 & 0.76488592179269 \tabularnewline
118 & 0.193123400687236 & 0.386246801374472 & 0.806876599312764 \tabularnewline
119 & 0.157725058585319 & 0.315450117170639 & 0.842274941414681 \tabularnewline
120 & 0.125026507311342 & 0.250053014622685 & 0.874973492688658 \tabularnewline
121 & 0.0971320382157774 & 0.194264076431555 & 0.902867961784223 \tabularnewline
122 & 0.075278869288905 & 0.15055773857781 & 0.924721130711095 \tabularnewline
123 & 0.0635765692033663 & 0.127153138406733 & 0.936423430796634 \tabularnewline
124 & 0.0815595804683198 & 0.16311916093664 & 0.91844041953168 \tabularnewline
125 & 0.0606929193986782 & 0.121385838797356 & 0.939307080601322 \tabularnewline
126 & 0.0495185840732083 & 0.0990371681464166 & 0.950481415926792 \tabularnewline
127 & 0.0358664112802414 & 0.0717328225604827 & 0.964133588719759 \tabularnewline
128 & 0.0249431969384931 & 0.0498863938769861 & 0.975056803061507 \tabularnewline
129 & 0.0173563162447108 & 0.0347126324894215 & 0.982643683755289 \tabularnewline
130 & 0.0114625385424422 & 0.0229250770848844 & 0.988537461457558 \tabularnewline
131 & 0.00722684292911029 & 0.0144536858582206 & 0.99277315707089 \tabularnewline
132 & 0.00451003642797833 & 0.00902007285595666 & 0.995489963572022 \tabularnewline
133 & 0.00903653426288546 & 0.0180730685257709 & 0.990963465737115 \tabularnewline
134 & 0.00592079595485982 & 0.0118415919097196 & 0.99407920404514 \tabularnewline
135 & 0.00388375109382814 & 0.00776750218765629 & 0.996116248906172 \tabularnewline
136 & 0.00262068355247415 & 0.0052413671049483 & 0.997379316447526 \tabularnewline
137 & 0.00488430476140077 & 0.00976860952280154 & 0.995115695238599 \tabularnewline
138 & 0.00406761021913888 & 0.00813522043827775 & 0.995932389780861 \tabularnewline
139 & 0.00337599106105005 & 0.0067519821221001 & 0.99662400893895 \tabularnewline
140 & 0.00165206388656046 & 0.00330412777312093 & 0.99834793611344 \tabularnewline
141 & 0.0231326216441971 & 0.0462652432883942 & 0.976867378355803 \tabularnewline
142 & 0.0187225152245118 & 0.0374450304490236 & 0.981277484775488 \tabularnewline
143 & 0.0100766084125953 & 0.0201532168251906 & 0.989923391587405 \tabularnewline
144 & 0.0046922062360978 & 0.0093844124721956 & 0.995307793763902 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203970&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]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]0.466622463560669[/C][C]0.933244927121339[/C][C]0.533377536439331[/C][/ROW]
[ROW][C]18[/C][C]0.417745999387859[/C][C]0.835491998775717[/C][C]0.582254000612141[/C][/ROW]
[ROW][C]19[/C][C]0.383170322549121[/C][C]0.766340645098241[/C][C]0.616829677450879[/C][/ROW]
[ROW][C]20[/C][C]0.867089004403568[/C][C]0.265821991192865[/C][C]0.132910995596432[/C][/ROW]
[ROW][C]21[/C][C]0.820991007431357[/C][C]0.358017985137286[/C][C]0.179008992568643[/C][/ROW]
[ROW][C]22[/C][C]0.810001091461111[/C][C]0.379997817077778[/C][C]0.189998908538889[/C][/ROW]
[ROW][C]23[/C][C]0.754999207642739[/C][C]0.490001584714521[/C][C]0.245000792357261[/C][/ROW]
[ROW][C]24[/C][C]0.694787999908693[/C][C]0.610424000182613[/C][C]0.305212000091307[/C][/ROW]
[ROW][C]25[/C][C]0.700508358576836[/C][C]0.598983282846328[/C][C]0.299491641423164[/C][/ROW]
[ROW][C]26[/C][C]0.704748507379438[/C][C]0.590502985241125[/C][C]0.295251492620563[/C][/ROW]
[ROW][C]27[/C][C]0.666079497073577[/C][C]0.667841005852845[/C][C]0.333920502926423[/C][/ROW]
[ROW][C]28[/C][C]0.617186402815249[/C][C]0.765627194369502[/C][C]0.382813597184751[/C][/ROW]
[ROW][C]29[/C][C]0.565405686408728[/C][C]0.869188627182545[/C][C]0.434594313591272[/C][/ROW]
[ROW][C]30[/C][C]0.507724219961981[/C][C]0.984551560076037[/C][C]0.492275780038019[/C][/ROW]
[ROW][C]31[/C][C]0.447356542983218[/C][C]0.894713085966435[/C][C]0.552643457016782[/C][/ROW]
[ROW][C]32[/C][C]0.389066559775668[/C][C]0.778133119551336[/C][C]0.610933440224332[/C][/ROW]
[ROW][C]33[/C][C]0.33403513644683[/C][C]0.668070272893659[/C][C]0.665964863553171[/C][/ROW]
[ROW][C]34[/C][C]0.289832340778509[/C][C]0.579664681557018[/C][C]0.710167659221491[/C][/ROW]
[ROW][C]35[/C][C]0.241966388041373[/C][C]0.483932776082747[/C][C]0.758033611958627[/C][/ROW]
[ROW][C]36[/C][C]0.198794322683765[/C][C]0.39758864536753[/C][C]0.801205677316235[/C][/ROW]
[ROW][C]37[/C][C]0.276004716291873[/C][C]0.552009432583747[/C][C]0.723995283708127[/C][/ROW]
[ROW][C]38[/C][C]0.239912832496304[/C][C]0.479825664992608[/C][C]0.760087167503696[/C][/ROW]
[ROW][C]39[/C][C]0.197453748873001[/C][C]0.394907497746002[/C][C]0.802546251126999[/C][/ROW]
[ROW][C]40[/C][C]0.190621762280746[/C][C]0.381243524561492[/C][C]0.809378237719254[/C][/ROW]
[ROW][C]41[/C][C]0.730185259995986[/C][C]0.539629480008029[/C][C]0.269814740004014[/C][/ROW]
[ROW][C]42[/C][C]0.706617239939788[/C][C]0.586765520120425[/C][C]0.293382760060212[/C][/ROW]
[ROW][C]43[/C][C]0.659768207158713[/C][C]0.680463585682574[/C][C]0.340231792841287[/C][/ROW]
[ROW][C]44[/C][C]0.619962949058447[/C][C]0.760074101883106[/C][C]0.380037050941553[/C][/ROW]
[ROW][C]45[/C][C]0.569696687393828[/C][C]0.860606625212344[/C][C]0.430303312606172[/C][/ROW]
[ROW][C]46[/C][C]0.51894951417401[/C][C]0.962100971651979[/C][C]0.48105048582599[/C][/ROW]
[ROW][C]47[/C][C]0.467953592305276[/C][C]0.935907184610552[/C][C]0.532046407694724[/C][/ROW]
[ROW][C]48[/C][C]0.417344351318062[/C][C]0.834688702636123[/C][C]0.582655648681939[/C][/ROW]
[ROW][C]49[/C][C]0.368728781068354[/C][C]0.737457562136707[/C][C]0.631271218931646[/C][/ROW]
[ROW][C]50[/C][C]0.322029686787343[/C][C]0.644059373574686[/C][C]0.677970313212657[/C][/ROW]
[ROW][C]51[/C][C]0.382611375992076[/C][C]0.765222751984153[/C][C]0.617388624007924[/C][/ROW]
[ROW][C]52[/C][C]0.671475379498297[/C][C]0.657049241003405[/C][C]0.328524620501703[/C][/ROW]
[ROW][C]53[/C][C]0.628262434321371[/C][C]0.743475131357258[/C][C]0.371737565678629[/C][/ROW]
[ROW][C]54[/C][C]0.934698600664092[/C][C]0.130602798671817[/C][C]0.0653013993359083[/C][/ROW]
[ROW][C]55[/C][C]0.917429237405239[/C][C]0.165141525189521[/C][C]0.0825707625947606[/C][/ROW]
[ROW][C]56[/C][C]0.940382446019636[/C][C]0.119235107960728[/C][C]0.0596175539803638[/C][/ROW]
[ROW][C]57[/C][C]0.942753768582908[/C][C]0.114492462834184[/C][C]0.0572462314170921[/C][/ROW]
[ROW][C]58[/C][C]0.92849359481935[/C][C]0.143012810361301[/C][C]0.0715064051806504[/C][/ROW]
[ROW][C]59[/C][C]0.911552452088022[/C][C]0.176895095823956[/C][C]0.0884475479119782[/C][/ROW]
[ROW][C]60[/C][C]0.977413948739335[/C][C]0.0451721025213294[/C][C]0.0225860512606647[/C][/ROW]
[ROW][C]61[/C][C]0.971463167993961[/C][C]0.0570736640120783[/C][C]0.0285368320060391[/C][/ROW]
[ROW][C]62[/C][C]0.975187798517211[/C][C]0.0496244029655776[/C][C]0.0248122014827888[/C][/ROW]
[ROW][C]63[/C][C]0.967492205687089[/C][C]0.0650155886258224[/C][C]0.0325077943129112[/C][/ROW]
[ROW][C]64[/C][C]0.960100276816883[/C][C]0.0797994463662339[/C][C]0.039899723183117[/C][/ROW]
[ROW][C]65[/C][C]0.948872853599269[/C][C]0.102254292801463[/C][C]0.0511271464007314[/C][/ROW]
[ROW][C]66[/C][C]0.935265241876636[/C][C]0.129469516246728[/C][C]0.0647347581233642[/C][/ROW]
[ROW][C]67[/C][C]0.98084558200094[/C][C]0.0383088359981204[/C][C]0.0191544179990602[/C][/ROW]
[ROW][C]68[/C][C]0.974532220335718[/C][C]0.0509355593285634[/C][C]0.0254677796642817[/C][/ROW]
[ROW][C]69[/C][C]0.967133190405724[/C][C]0.0657336191885519[/C][C]0.032866809594276[/C][/ROW]
[ROW][C]70[/C][C]0.969283337429237[/C][C]0.0614333251415254[/C][C]0.0307166625707627[/C][/ROW]
[ROW][C]71[/C][C]0.960189852440206[/C][C]0.0796202951195874[/C][C]0.0398101475597937[/C][/ROW]
[ROW][C]72[/C][C]0.949487793071254[/C][C]0.101024413857493[/C][C]0.0505122069287465[/C][/ROW]
[ROW][C]73[/C][C]0.950868538115192[/C][C]0.0982629237696162[/C][C]0.0491314618848081[/C][/ROW]
[ROW][C]74[/C][C]0.955725737609134[/C][C]0.0885485247817317[/C][C]0.0442742623908659[/C][/ROW]
[ROW][C]75[/C][C]0.944191625777899[/C][C]0.111616748444202[/C][C]0.0558083742221012[/C][/ROW]
[ROW][C]76[/C][C]0.937555754726255[/C][C]0.12488849054749[/C][C]0.062444245273745[/C][/ROW]
[ROW][C]77[/C][C]0.922837706822291[/C][C]0.154324586355418[/C][C]0.0771622931777088[/C][/ROW]
[ROW][C]78[/C][C]0.933356966384198[/C][C]0.133286067231604[/C][C]0.0666430336158021[/C][/ROW]
[ROW][C]79[/C][C]0.981886301714619[/C][C]0.0362273965707613[/C][C]0.0181136982853806[/C][/ROW]
[ROW][C]80[/C][C]0.981015382862539[/C][C]0.0379692342749217[/C][C]0.0189846171374609[/C][/ROW]
[ROW][C]81[/C][C]0.975873601455436[/C][C]0.048252797089129[/C][C]0.0241263985445645[/C][/ROW]
[ROW][C]82[/C][C]0.981737456036731[/C][C]0.0365250879265387[/C][C]0.0182625439632693[/C][/ROW]
[ROW][C]83[/C][C]0.980049789180671[/C][C]0.039900421638658[/C][C]0.019950210819329[/C][/ROW]
[ROW][C]84[/C][C]0.998275793611137[/C][C]0.00344841277772588[/C][C]0.00172420638886294[/C][/ROW]
[ROW][C]85[/C][C]0.997458350012566[/C][C]0.0050832999748685[/C][C]0.00254164998743425[/C][/ROW]
[ROW][C]86[/C][C]0.996303487970519[/C][C]0.00739302405896208[/C][C]0.00369651202948104[/C][/ROW]
[ROW][C]87[/C][C]0.99468597147544[/C][C]0.010628057049119[/C][C]0.00531402852455948[/C][/ROW]
[ROW][C]88[/C][C]0.992964390372093[/C][C]0.0140712192558142[/C][C]0.0070356096279071[/C][/ROW]
[ROW][C]89[/C][C]0.990148492208612[/C][C]0.0197030155827758[/C][C]0.00985150779138788[/C][/ROW]
[ROW][C]90[/C][C]0.986397042344246[/C][C]0.0272059153115071[/C][C]0.0136029576557535[/C][/ROW]
[ROW][C]91[/C][C]0.981522982822284[/C][C]0.0369540343554325[/C][C]0.0184770171777163[/C][/ROW]
[ROW][C]92[/C][C]0.977327648818146[/C][C]0.0453447023637082[/C][C]0.0226723511818541[/C][/ROW]
[ROW][C]93[/C][C]0.969862817929176[/C][C]0.0602743641416483[/C][C]0.0301371820708242[/C][/ROW]
[ROW][C]94[/C][C]0.960269997890421[/C][C]0.0794600042191575[/C][C]0.0397300021095787[/C][/ROW]
[ROW][C]95[/C][C]0.95241797604271[/C][C]0.0951640479145798[/C][C]0.0475820239572899[/C][/ROW]
[ROW][C]96[/C][C]0.938497587215552[/C][C]0.123004825568895[/C][C]0.0615024127844475[/C][/ROW]
[ROW][C]97[/C][C]0.928423959899407[/C][C]0.143152080201186[/C][C]0.0715760401005929[/C][/ROW]
[ROW][C]98[/C][C]0.909550879923021[/C][C]0.180898240153958[/C][C]0.0904491200769791[/C][/ROW]
[ROW][C]99[/C][C]0.886904530655304[/C][C]0.226190938689391[/C][C]0.113095469344696[/C][/ROW]
[ROW][C]100[/C][C]0.860368879586229[/C][C]0.279262240827541[/C][C]0.139631120413771[/C][/ROW]
[ROW][C]101[/C][C]0.829611316004455[/C][C]0.340777367991091[/C][C]0.170388683995545[/C][/ROW]
[ROW][C]102[/C][C]0.795183142115943[/C][C]0.409633715768114[/C][C]0.204816857884057[/C][/ROW]
[ROW][C]103[/C][C]0.756776777263147[/C][C]0.486446445473705[/C][C]0.243223222736853[/C][/ROW]
[ROW][C]104[/C][C]0.714627067965192[/C][C]0.570745864069616[/C][C]0.285372932034808[/C][/ROW]
[ROW][C]105[/C][C]0.682750459226658[/C][C]0.634499081546683[/C][C]0.317249540773342[/C][/ROW]
[ROW][C]106[/C][C]0.635111649079021[/C][C]0.729776701841958[/C][C]0.364888350920979[/C][/ROW]
[ROW][C]107[/C][C]0.58514879881186[/C][C]0.82970240237628[/C][C]0.41485120118814[/C][/ROW]
[ROW][C]108[/C][C]0.545774160701502[/C][C]0.908451678596996[/C][C]0.454225839298498[/C][/ROW]
[ROW][C]109[/C][C]0.493304740605317[/C][C]0.986609481210634[/C][C]0.506695259394683[/C][/ROW]
[ROW][C]110[/C][C]0.439714295050282[/C][C]0.879428590100563[/C][C]0.560285704949718[/C][/ROW]
[ROW][C]111[/C][C]0.401491343509929[/C][C]0.802982687019857[/C][C]0.598508656490071[/C][/ROW]
[ROW][C]112[/C][C]0.36573753751273[/C][C]0.731475075025461[/C][C]0.63426246248727[/C][/ROW]
[ROW][C]113[/C][C]0.417034962507209[/C][C]0.834069925014418[/C][C]0.582965037492791[/C][/ROW]
[ROW][C]114[/C][C]0.380414285676476[/C][C]0.760828571352953[/C][C]0.619585714323524[/C][/ROW]
[ROW][C]115[/C][C]0.328015014724949[/C][C]0.656030029449898[/C][C]0.671984985275051[/C][/ROW]
[ROW][C]116[/C][C]0.280679114850174[/C][C]0.561358229700349[/C][C]0.719320885149826[/C][/ROW]
[ROW][C]117[/C][C]0.23511407820731[/C][C]0.47022815641462[/C][C]0.76488592179269[/C][/ROW]
[ROW][C]118[/C][C]0.193123400687236[/C][C]0.386246801374472[/C][C]0.806876599312764[/C][/ROW]
[ROW][C]119[/C][C]0.157725058585319[/C][C]0.315450117170639[/C][C]0.842274941414681[/C][/ROW]
[ROW][C]120[/C][C]0.125026507311342[/C][C]0.250053014622685[/C][C]0.874973492688658[/C][/ROW]
[ROW][C]121[/C][C]0.0971320382157774[/C][C]0.194264076431555[/C][C]0.902867961784223[/C][/ROW]
[ROW][C]122[/C][C]0.075278869288905[/C][C]0.15055773857781[/C][C]0.924721130711095[/C][/ROW]
[ROW][C]123[/C][C]0.0635765692033663[/C][C]0.127153138406733[/C][C]0.936423430796634[/C][/ROW]
[ROW][C]124[/C][C]0.0815595804683198[/C][C]0.16311916093664[/C][C]0.91844041953168[/C][/ROW]
[ROW][C]125[/C][C]0.0606929193986782[/C][C]0.121385838797356[/C][C]0.939307080601322[/C][/ROW]
[ROW][C]126[/C][C]0.0495185840732083[/C][C]0.0990371681464166[/C][C]0.950481415926792[/C][/ROW]
[ROW][C]127[/C][C]0.0358664112802414[/C][C]0.0717328225604827[/C][C]0.964133588719759[/C][/ROW]
[ROW][C]128[/C][C]0.0249431969384931[/C][C]0.0498863938769861[/C][C]0.975056803061507[/C][/ROW]
[ROW][C]129[/C][C]0.0173563162447108[/C][C]0.0347126324894215[/C][C]0.982643683755289[/C][/ROW]
[ROW][C]130[/C][C]0.0114625385424422[/C][C]0.0229250770848844[/C][C]0.988537461457558[/C][/ROW]
[ROW][C]131[/C][C]0.00722684292911029[/C][C]0.0144536858582206[/C][C]0.99277315707089[/C][/ROW]
[ROW][C]132[/C][C]0.00451003642797833[/C][C]0.00902007285595666[/C][C]0.995489963572022[/C][/ROW]
[ROW][C]133[/C][C]0.00903653426288546[/C][C]0.0180730685257709[/C][C]0.990963465737115[/C][/ROW]
[ROW][C]134[/C][C]0.00592079595485982[/C][C]0.0118415919097196[/C][C]0.99407920404514[/C][/ROW]
[ROW][C]135[/C][C]0.00388375109382814[/C][C]0.00776750218765629[/C][C]0.996116248906172[/C][/ROW]
[ROW][C]136[/C][C]0.00262068355247415[/C][C]0.0052413671049483[/C][C]0.997379316447526[/C][/ROW]
[ROW][C]137[/C][C]0.00488430476140077[/C][C]0.00976860952280154[/C][C]0.995115695238599[/C][/ROW]
[ROW][C]138[/C][C]0.00406761021913888[/C][C]0.00813522043827775[/C][C]0.995932389780861[/C][/ROW]
[ROW][C]139[/C][C]0.00337599106105005[/C][C]0.0067519821221001[/C][C]0.99662400893895[/C][/ROW]
[ROW][C]140[/C][C]0.00165206388656046[/C][C]0.00330412777312093[/C][C]0.99834793611344[/C][/ROW]
[ROW][C]141[/C][C]0.0231326216441971[/C][C]0.0462652432883942[/C][C]0.976867378355803[/C][/ROW]
[ROW][C]142[/C][C]0.0187225152245118[/C][C]0.0374450304490236[/C][C]0.981277484775488[/C][/ROW]
[ROW][C]143[/C][C]0.0100766084125953[/C][C]0.0201532168251906[/C][C]0.989923391587405[/C][/ROW]
[ROW][C]144[/C][C]0.0046922062360978[/C][C]0.0093844124721956[/C][C]0.995307793763902[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203970&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203970&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
10001
11001
12001
13001
14001
15001
16001
170.4666224635606690.9332449271213390.533377536439331
180.4177459993878590.8354919987757170.582254000612141
190.3831703225491210.7663406450982410.616829677450879
200.8670890044035680.2658219911928650.132910995596432
210.8209910074313570.3580179851372860.179008992568643
220.8100010914611110.3799978170777780.189998908538889
230.7549992076427390.4900015847145210.245000792357261
240.6947879999086930.6104240001826130.305212000091307
250.7005083585768360.5989832828463280.299491641423164
260.7047485073794380.5905029852411250.295251492620563
270.6660794970735770.6678410058528450.333920502926423
280.6171864028152490.7656271943695020.382813597184751
290.5654056864087280.8691886271825450.434594313591272
300.5077242199619810.9845515600760370.492275780038019
310.4473565429832180.8947130859664350.552643457016782
320.3890665597756680.7781331195513360.610933440224332
330.334035136446830.6680702728936590.665964863553171
340.2898323407785090.5796646815570180.710167659221491
350.2419663880413730.4839327760827470.758033611958627
360.1987943226837650.397588645367530.801205677316235
370.2760047162918730.5520094325837470.723995283708127
380.2399128324963040.4798256649926080.760087167503696
390.1974537488730010.3949074977460020.802546251126999
400.1906217622807460.3812435245614920.809378237719254
410.7301852599959860.5396294800080290.269814740004014
420.7066172399397880.5867655201204250.293382760060212
430.6597682071587130.6804635856825740.340231792841287
440.6199629490584470.7600741018831060.380037050941553
450.5696966873938280.8606066252123440.430303312606172
460.518949514174010.9621009716519790.48105048582599
470.4679535923052760.9359071846105520.532046407694724
480.4173443513180620.8346887026361230.582655648681939
490.3687287810683540.7374575621367070.631271218931646
500.3220296867873430.6440593735746860.677970313212657
510.3826113759920760.7652227519841530.617388624007924
520.6714753794982970.6570492410034050.328524620501703
530.6282624343213710.7434751313572580.371737565678629
540.9346986006640920.1306027986718170.0653013993359083
550.9174292374052390.1651415251895210.0825707625947606
560.9403824460196360.1192351079607280.0596175539803638
570.9427537685829080.1144924628341840.0572462314170921
580.928493594819350.1430128103613010.0715064051806504
590.9115524520880220.1768950958239560.0884475479119782
600.9774139487393350.04517210252132940.0225860512606647
610.9714631679939610.05707366401207830.0285368320060391
620.9751877985172110.04962440296557760.0248122014827888
630.9674922056870890.06501558862582240.0325077943129112
640.9601002768168830.07979944636623390.039899723183117
650.9488728535992690.1022542928014630.0511271464007314
660.9352652418766360.1294695162467280.0647347581233642
670.980845582000940.03830883599812040.0191544179990602
680.9745322203357180.05093555932856340.0254677796642817
690.9671331904057240.06573361918855190.032866809594276
700.9692833374292370.06143332514152540.0307166625707627
710.9601898524402060.07962029511958740.0398101475597937
720.9494877930712540.1010244138574930.0505122069287465
730.9508685381151920.09826292376961620.0491314618848081
740.9557257376091340.08854852478173170.0442742623908659
750.9441916257778990.1116167484442020.0558083742221012
760.9375557547262550.124888490547490.062444245273745
770.9228377068222910.1543245863554180.0771622931777088
780.9333569663841980.1332860672316040.0666430336158021
790.9818863017146190.03622739657076130.0181136982853806
800.9810153828625390.03796923427492170.0189846171374609
810.9758736014554360.0482527970891290.0241263985445645
820.9817374560367310.03652508792653870.0182625439632693
830.9800497891806710.0399004216386580.019950210819329
840.9982757936111370.003448412777725880.00172420638886294
850.9974583500125660.00508329997486850.00254164998743425
860.9963034879705190.007393024058962080.00369651202948104
870.994685971475440.0106280570491190.00531402852455948
880.9929643903720930.01407121925581420.0070356096279071
890.9901484922086120.01970301558277580.00985150779138788
900.9863970423442460.02720591531150710.0136029576557535
910.9815229828222840.03695403435543250.0184770171777163
920.9773276488181460.04534470236370820.0226723511818541
930.9698628179291760.06027436414164830.0301371820708242
940.9602699978904210.07946000421915750.0397300021095787
950.952417976042710.09516404791457980.0475820239572899
960.9384975872155520.1230048255688950.0615024127844475
970.9284239598994070.1431520802011860.0715760401005929
980.9095508799230210.1808982401539580.0904491200769791
990.8869045306553040.2261909386893910.113095469344696
1000.8603688795862290.2792622408275410.139631120413771
1010.8296113160044550.3407773679910910.170388683995545
1020.7951831421159430.4096337157681140.204816857884057
1030.7567767772631470.4864464454737050.243223222736853
1040.7146270679651920.5707458640696160.285372932034808
1050.6827504592266580.6344990815466830.317249540773342
1060.6351116490790210.7297767018419580.364888350920979
1070.585148798811860.829702402376280.41485120118814
1080.5457741607015020.9084516785969960.454225839298498
1090.4933047406053170.9866094812106340.506695259394683
1100.4397142950502820.8794285901005630.560285704949718
1110.4014913435099290.8029826870198570.598508656490071
1120.365737537512730.7314750750254610.63426246248727
1130.4170349625072090.8340699250144180.582965037492791
1140.3804142856764760.7608285713529530.619585714323524
1150.3280150147249490.6560300294498980.671984985275051
1160.2806791148501740.5613582297003490.719320885149826
1170.235114078207310.470228156414620.76488592179269
1180.1931234006872360.3862468013744720.806876599312764
1190.1577250585853190.3154501171706390.842274941414681
1200.1250265073113420.2500530146226850.874973492688658
1210.09713203821577740.1942640764315550.902867961784223
1220.0752788692889050.150557738577810.924721130711095
1230.06357656920336630.1271531384067330.936423430796634
1240.08155958046831980.163119160936640.91844041953168
1250.06069291939867820.1213858387973560.939307080601322
1260.04951858407320830.09903716814641660.950481415926792
1270.03586641128024140.07173282256048270.964133588719759
1280.02494319693849310.04988639387698610.975056803061507
1290.01735631624471080.03471263248942150.982643683755289
1300.01146253854244220.02292507708488440.988537461457558
1310.007226842929110290.01445368585822060.99277315707089
1320.004510036427978330.009020072855956660.995489963572022
1330.009036534262885460.01807306852577090.990963465737115
1340.005920795954859820.01184159190971960.99407920404514
1350.003883751093828140.007767502187656290.996116248906172
1360.002620683552474150.00524136710494830.997379316447526
1370.004884304761400770.009768609522801540.995115695238599
1380.004067610219138880.008135220438277750.995932389780861
1390.003375991061050050.00675198212210010.99662400893895
1400.001652063886560460.003304127773120930.99834793611344
1410.02313262164419710.04626524328839420.976867378355803
1420.01872251522451180.03744503044902360.981277484775488
1430.01007660841259530.02015321682519060.989923391587405
1440.00469220623609780.00938441247219560.995307793763902







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.133333333333333NOK
5% type I error level410.303703703703704NOK
10% type I error level550.407407407407407NOK

\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 & 18 & 0.133333333333333 & NOK \tabularnewline
5% type I error level & 41 & 0.303703703703704 & NOK \tabularnewline
10% type I error level & 55 & 0.407407407407407 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203970&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]18[/C][C]0.133333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]0.303703703703704[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.407407407407407[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203970&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203970&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 level180.133333333333333NOK
5% type I error level410.303703703703704NOK
10% type I error level550.407407407407407NOK



Parameters (Session):
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')
}