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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 computationThu, 20 Dec 2012 18:24:06 -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/20/t1356045903rqsm3kzhqnutok8.htm/, Retrieved Thu, 31 Oct 2024 23:21:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203213, Retrieved Thu, 31 Oct 2024 23:21:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact163
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-12-20 23:24:06] [14762276d088da8d9efe369819cbb035] [Current]
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Dataseries X:
4	5	8	0	11	14	16	18
4	6	7	0	11	14	16	17
4	6	7	0	11	14	16	17
4	6	7	0	11	14	16	17
4	6	7	0	11	14	16	17
4	5	7	0	11	14	15	18
4	6	7	0	11	14	16	17
4	6	8	0	11	14	16	17
4	6	7	0	11	14	16	18
4	5	7	0	11	14	16	17
4	5	8	0	11	14	16	17
4	6	7	0	11	14	16	17
4	6	7	0	12	14	15	17
4	5	8	0	11	14	16	17
4	6	7	0	12	14	15	18
4	6	8	0	12	14	15	18
4	5	8	0	12	13	15	17
4	5	8	0	11	14	16	17
4	6	7	0	11	14	16	18
4	6	8	0	12	13	15	18
4	5	7	0	11	14	15	17
4	5	7	0	12	14	15	18
4	6	7	0	11	14	15	18
4	5	7	0	11	14	15	18
4	6	8	0	12	14	16	18
4	6	7	0	12	14	15	17
4	5	7	0	11	14	16	18
4	6	7	0	12	14	16	17
4	6	7	0	11	14	16	18
4	6	7	0	11	14	15	17
4	6	7	0	11	14	16	17
4	5	7	0	11	14	16	17
4	5	7	0	11	14	15	17
4	6	8	0	11	14	16	18
4	6	7	0	11	14	16	17
4	6	7	0	11	14	16	17
4	5	8	0	12	14	15	17
4	6	7	0	12	14	16	18
4	6	7	0	11	14	15	18
4	6	8	0	11	14	15	17
4	6	7	0	12	13	15	18
4	6	7	0	12	14	16	18
4	5	7	0	11	14	15	18
4	5	8	0	11	14	16	17
4	6	7	0	11	14	15	17
4	6	7	0	11	14	15	18
4	6	7	0	11	14	16	17
4	6	7	0	11	14	16	18
4	6	7	0	11	14	15	18
4	6	7	0	11	14	16	17
4	6	8	0	12	14	16	17
4	5	8	0	12	13	15	17
4	6	7	0	11	14	16	18
4	6	7	0	12	13	16	17
4	6	7	0	11	14	16	17
4	6	8	0	12	14	16	18
4	6	7	0	12	14	15	18
4	6	7	0	11	14	16	18
4	6	7	0	11	14	16	18
4	5	8	0	12	13	15	18
4	5	8	0	11	14	16	18
4	6	7	0	12	14	15	17
4	6	7	0	11	14	16	17
4	5	8	0	11	14	16	18
4	6	7	0	11	14	16	17
4	6	7	0	11	14	16	17
4	6	8	0	12	13	15	17
4	5	7	0	11	14	16	17
4	6	7	0	11	14	16	18
4	6	7	0	12	14	16	17
4	6	7	0	11	14	16	17
4	6	7	0	11	14	16	18
4	6	7	0	12	14	16	18
4	5	7	0	12	14	16	17
4	6	7	0	11	14	16	18
4	6	8	0	11	14	15	18
4	6	7	0	11	14	16	18
4	6	7	0	12	14	15	18
4	6	8	0	12	13	16	18
4	6	8	0	11	14	15	17
4	6	7	0	11	14	16	17
4	5	7	0	12	14	16	18
4	6	7	0	11	14	16	17
4	6	7	0	12	13	16	17
4	6	7	0	11	14	15	18
4	5	7	0	11	14	16	17
2	5	0	9	11	14	16	18
2	5	0	10	12	14	16	18
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	18
2	6	0	9	11	14	15	17
2	5	0	10	11	14	16	17
2	5	0	9	11	14	15	17
2	6	0	9	11	14	16	17
2	6	0	10	11	14	16	17
2	6	0	9	11	14	16	18
2	5	0	10	11	14	16	17
2	6	0	9	11	14	16	17
2	5	0	9	11	14	16	17
2	6	0	9	11	14	16	18
2	5	0	9	11	14	16	18
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	6	0	10	12	14	16	17
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	5	0	10	12	14	16	17
2	6	0	9	11	14	16	17
2	5	0	9	11	14	16	17
2	5	0	10	12	14	15	17
2	6	0	10	11	14	16	17
2	6	0	9	12	14	16	17
2	5	0	10	12	14	16	17
2	5	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	5	0	9	11	14	16	18
2	5	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	18
2	5	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	5	0	10	12	14	16	17
2	6	0	9	12	14	15	18
2	6	0	9	11	14	16	18
2	6	0	10	11	14	16	17
2	6	0	9	11	14	15	17
2	6	0	9	11	14	16	18
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	18
2	5	0	9	11	14	16	17
2	5	0	9	11	14	16	18
2	5	0	9	12	14	16	17
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	6	0	9	11	14	16	17
2	5	0	9	12	14	15	18
2	5	0	10	12	14	15	18
2	6	0	10	11	14	16	17
2	6	0	9	11	14	16	17
2	6	0	9	12	13	16	18
2	6	0	10	12	14	16	18
2	5	0	9	11	14	16	17
2	6	0	9	11	14	15	18
2	6	0	9	11	14	15	17
2	6	0	10	11	14	16	18
2	6	0	10	12	14	16	17
2	6	0	10	11	14	16	17
2	5	0	9	11	14	16	17
2	6	0	9	11	14	15	18
2	6	0	9	11	14	16	18
2	5	0	9	12	13	16	17
2	5	0	9	12	13	15	17
2	5	0	9	12	14	16	17




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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=203213&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]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=203213&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203213&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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Weeks[t] = + 2.14356365765267 + 0.00691809520606492Uselimit[t] + 0.0872694099260552T40[t] -0.147467826531057T20[t] + 0.0316759870773371Used[t] + 0.0585909257182569CorrectAnalysis[t] + 0.0083120029122766Useful[t] -0.00701187238829388`Outcome\r\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Weeks[t] =  +  2.14356365765267 +  0.00691809520606492Uselimit[t] +  0.0872694099260552T40[t] -0.147467826531057T20[t] +  0.0316759870773371Used[t] +  0.0585909257182569CorrectAnalysis[t] +  0.0083120029122766Useful[t] -0.00701187238829388`Outcome\r\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203213&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Weeks[t] =  +  2.14356365765267 +  0.00691809520606492Uselimit[t] +  0.0872694099260552T40[t] -0.147467826531057T20[t] +  0.0316759870773371Used[t] +  0.0585909257182569CorrectAnalysis[t] +  0.0083120029122766Useful[t] -0.00701187238829388`Outcome\r\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203213&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203213&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
Weeks[t] = + 2.14356365765267 + 0.00691809520606492Uselimit[t] + 0.0872694099260552T40[t] -0.147467826531057T20[t] + 0.0316759870773371Used[t] + 0.0585909257182569CorrectAnalysis[t] + 0.0083120029122766Useful[t] -0.00701187238829388`Outcome\r\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.143563657652670.3787715.659300
Uselimit0.006918095206064920.0088710.77980.4367610.218381
T400.08726940992605520.0106048.230200
T20-0.1474678265310570.008351-17.659300
Used0.03167598707733710.010353.06050.0026310.001315
CorrectAnalysis0.05859092571825690.0170523.43610.0007690.000384
Useful0.00831200291227660.0097620.85150.3958820.197941
`Outcome\r\r`-0.007011872388293880.008473-0.82760.4092750.204637

\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) & 2.14356365765267 & 0.378771 & 5.6593 & 0 & 0 \tabularnewline
Uselimit & 0.00691809520606492 & 0.008871 & 0.7798 & 0.436761 & 0.218381 \tabularnewline
T40 & 0.0872694099260552 & 0.010604 & 8.2302 & 0 & 0 \tabularnewline
T20 & -0.147467826531057 & 0.008351 & -17.6593 & 0 & 0 \tabularnewline
Used & 0.0316759870773371 & 0.01035 & 3.0605 & 0.002631 & 0.001315 \tabularnewline
CorrectAnalysis & 0.0585909257182569 & 0.017052 & 3.4361 & 0.000769 & 0.000384 \tabularnewline
Useful & 0.0083120029122766 & 0.009762 & 0.8515 & 0.395882 & 0.197941 \tabularnewline
`Outcome\r\r` & -0.00701187238829388 & 0.008473 & -0.8276 & 0.409275 & 0.204637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203213&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]2.14356365765267[/C][C]0.378771[/C][C]5.6593[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Uselimit[/C][C]0.00691809520606492[/C][C]0.008871[/C][C]0.7798[/C][C]0.436761[/C][C]0.218381[/C][/ROW]
[ROW][C]T40[/C][C]0.0872694099260552[/C][C]0.010604[/C][C]8.2302[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]T20[/C][C]-0.147467826531057[/C][C]0.008351[/C][C]-17.6593[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Used[/C][C]0.0316759870773371[/C][C]0.01035[/C][C]3.0605[/C][C]0.002631[/C][C]0.001315[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]0.0585909257182569[/C][C]0.017052[/C][C]3.4361[/C][C]0.000769[/C][C]0.000384[/C][/ROW]
[ROW][C]Useful[/C][C]0.0083120029122766[/C][C]0.009762[/C][C]0.8515[/C][C]0.395882[/C][C]0.197941[/C][/ROW]
[ROW][C]`Outcome\r\r`[/C][C]-0.00701187238829388[/C][C]0.008473[/C][C]-0.8276[/C][C]0.409275[/C][C]0.204637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203213&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203213&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)2.143563657652670.3787715.659300
Uselimit0.006918095206064920.0088710.77980.4367610.218381
T400.08726940992605520.0106048.230200
T20-0.1474678265310570.008351-17.659300
Used0.03167598707733710.010353.06050.0026310.001315
CorrectAnalysis0.05859092571825690.0170523.43610.0007690.000384
Useful0.00831200291227660.0097620.85150.3958820.197941
`Outcome\r\r`-0.007011872388293880.008473-0.82760.4092750.204637







Multiple Linear Regression - Regression Statistics
Multiple R0.998802740696495
R-squared0.99760691482283
Adjusted R-squared0.997492177862281
F-TEST (value)8694.73019022995
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0498971761014772
Sum Squared Residuals0.363500314703667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998802740696495 \tabularnewline
R-squared & 0.99760691482283 \tabularnewline
Adjusted R-squared & 0.997492177862281 \tabularnewline
F-TEST (value) & 8694.73019022995 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0498971761014772 \tabularnewline
Sum Squared Residuals & 0.363500314703667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203213&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998802740696495[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99760691482283[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997492177862281[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8694.73019022995[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0498971761014772[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.363500314703667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203213&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203213&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 R0.998802740696495
R-squared0.99760691482283
Adjusted R-squared0.997492177862281
F-TEST (value)8694.73019022995
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0498971761014772
Sum Squared Residuals0.363500314703667







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
144.05179657460487-0.0517965746048727
243.978457132273180.021542867726821
343.978457132273180.0215428677268202
443.978457132273180.0215428677268212
543.978457132273180.0215428677268211
643.956215161766540.0437848382334565
743.978457132273180.0215428677268211
844.06572654219923-0.065726542199234
943.971445259884880.028554740115115
1043.971539037067110.028460962932886
1144.05880844699317-0.0588084469931692
1243.978457132273180.0215428677268211
1344.00182111643824-0.00182111643823935
1444.05880844699317-0.0588084469931692
1543.994809244049950.00519075595005453
1644.082078653976-0.0820786539760007
1744.02358150543997-0.0235815054399728
1844.05880844699317-0.0588084469931692
1943.971445259884880.028554740115115
2044.02348772825774-0.0234877282577437
2143.963227034154840.0367729658451626
2243.987891148843880.0121088511561194
2343.963133256972610.0368667430273916
2443.956215161766540.0437848382334565
2544.09039065688828-0.0903906568882773
2644.00182111643824-0.00182111643823935
2743.964527164678820.0354728353211799
2844.01013311935052-0.0101331193505159
2943.971445259884880.028554740115115
3043.97014512936090.0298548706390977
3143.978457132273180.0215428677268211
3243.971539037067110.028460962932886
3343.963227034154840.0367729658451626
3444.05871466981094-0.0587146698109402
3543.978457132273180.0215428677268211
3643.978457132273180.0215428677268211
3744.08217243115823-0.0821724311582297
3844.00312124696222-0.00312124696222207
3943.963133256972610.0368667430273916
4044.05741453928696-0.0574145392869575
4143.936218318331690.0637816816683115
4244.00312124696222-0.00312124696222207
4343.956215161766540.0437848382334565
4444.05880844699317-0.0588084469931692
4543.97014512936090.0298548706390977
4643.963133256972610.0368667430273916
4743.978457132273180.0215428677268211
4843.971445259884880.028554740115115
4943.963133256972610.0368667430273916
5043.978457132273180.0215428677268211
5144.09740252927657-0.0974025292765711
5244.02358150543997-0.0235815054399728
5343.971445259884880.028554740115115
5443.951542193632260.048457806367741
5543.978457132273180.0215428677268211
5644.09039065688828-0.0903906568882773
5743.994809244049950.00519075595005453
5843.971445259884880.028554740115115
5943.971445259884880.028554740115115
6044.01656963305168-0.0165696330516789
6144.05179657460488-0.0517965746048753
6244.00182111643824-0.00182111643823935
6343.978457132273180.0215428677268211
6444.05179657460488-0.0517965746048753
6543.978457132273180.0215428677268211
6643.978457132273180.0215428677268211
6744.03049960064604-0.0304996006460376
6843.971539037067110.028460962932886
6943.971445259884880.028554740115115
7044.01013311935052-0.0101331193505159
7143.978457132273180.0215428677268211
7243.971445259884880.028554740115115
7344.00312124696222-0.00312124696222207
7444.00321502414445-0.00321502414445106
7543.971445259884880.028554740115115
7644.05040266689866-0.0504026668986636
7743.971445259884880.028554740115115
7843.994809244049950.00519075595005453
7944.03179973117002-0.0317997311700203
8044.05741453928696-0.0574145392869575
8143.978457132273180.0215428677268211
8243.996203151756160.00379684824384282
8343.978457132273180.0215428677268211
8443.951542193632260.048457806367741
8543.963133256972610.0368667430273916
8643.971539037067110.028460962932886
8722.02643085641692-0.026430856416917
8821.91063901696320.0893609830368034
8922.04036082401128-0.0403608240112758
9022.03334895162298-0.0333489516229819
9122.032048821099-0.0320488210989992
9221.885974902274150.114025097725847
9322.02513072589293-0.0251307258929342
9422.04036082401128-0.0403608240112758
9521.892892997480220.107107002519782
9622.03334895162298-0.0333489516229819
9721.885974902274150.114025097725847
9822.04036082401128-0.0403608240112758
9922.03344272880521-0.0334427288052108
10022.03334895162298-0.0333489516229819
10122.02643085641692-0.026430856416917
10222.04036082401128-0.0403608240112758
10322.04036082401128-0.0403608240112758
10422.04036082401128-0.0403608240112758
10521.924568984557560.0754310154424446
10622.04036082401128-0.0403608240112758
10722.04036082401128-0.0403608240112758
10821.917650889351490.0823491106485096
10922.04036082401128-0.0403608240112758
11022.03344272880521-0.0334427288052108
11121.909338886439210.0906611135607862
11221.892892997480220.107107002519782
11322.07203681108861-0.0720368110886129
11421.917650889351490.0823491106485096
11522.03344272880521-0.0334427288052108
11622.04036082401128-0.0403608240112758
11722.02643085641692-0.026430856416917
11822.03344272880521-0.0334427288052108
11922.04036082401128-0.0403608240112758
12022.03334895162298-0.0333489516229819
12122.03344272880521-0.0334427288052108
12222.04036082401128-0.0403608240112758
12321.917650889351490.0823491106485096
12422.05671293578804-0.0567129357880424
12522.03334895162298-0.0333489516229819
12621.892892997480220.107107002519782
12722.032048821099-0.0320488210989992
12822.03334895162298-0.0333489516229819
12922.04036082401128-0.0403608240112758
13022.03334895162298-0.0333489516229819
13122.03344272880521-0.0334427288052108
13222.02643085641692-0.026430856416917
13322.06511871588255-0.0651187158825479
13422.04036082401128-0.0403608240112758
13522.04036082401128-0.0403608240112758
13622.04036082401128-0.0403608240112758
13722.04979484058198-0.0497948405819774
13821.902327014050920.09767298594908
13921.892892997480220.107107002519782
14022.04036082401128-0.0403608240112758
14122.00643401298206-0.00643401298206208
14221.917557112169260.0824428878307385
14322.03344272880521-0.0334427288052108
14422.02503694871071-0.0250369487107053
14522.032048821099-0.0320488210989992
14621.885881125091920.114118874908076
14721.924568984557560.0754310154424446
14821.892892997480220.107107002519782
14922.03344272880521-0.0334427288052108
15022.02503694871071-0.0250369487107053
15122.03334895162298-0.0333489516229819
15222.00652779016429-0.006527790164291
15321.998215787252010.0017842127479856
15422.06511871588255-0.0651187158825479

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 4.05179657460487 & -0.0517965746048727 \tabularnewline
2 & 4 & 3.97845713227318 & 0.021542867726821 \tabularnewline
3 & 4 & 3.97845713227318 & 0.0215428677268202 \tabularnewline
4 & 4 & 3.97845713227318 & 0.0215428677268212 \tabularnewline
5 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
6 & 4 & 3.95621516176654 & 0.0437848382334565 \tabularnewline
7 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
8 & 4 & 4.06572654219923 & -0.065726542199234 \tabularnewline
9 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
10 & 4 & 3.97153903706711 & 0.028460962932886 \tabularnewline
11 & 4 & 4.05880844699317 & -0.0588084469931692 \tabularnewline
12 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
13 & 4 & 4.00182111643824 & -0.00182111643823935 \tabularnewline
14 & 4 & 4.05880844699317 & -0.0588084469931692 \tabularnewline
15 & 4 & 3.99480924404995 & 0.00519075595005453 \tabularnewline
16 & 4 & 4.082078653976 & -0.0820786539760007 \tabularnewline
17 & 4 & 4.02358150543997 & -0.0235815054399728 \tabularnewline
18 & 4 & 4.05880844699317 & -0.0588084469931692 \tabularnewline
19 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
20 & 4 & 4.02348772825774 & -0.0234877282577437 \tabularnewline
21 & 4 & 3.96322703415484 & 0.0367729658451626 \tabularnewline
22 & 4 & 3.98789114884388 & 0.0121088511561194 \tabularnewline
23 & 4 & 3.96313325697261 & 0.0368667430273916 \tabularnewline
24 & 4 & 3.95621516176654 & 0.0437848382334565 \tabularnewline
25 & 4 & 4.09039065688828 & -0.0903906568882773 \tabularnewline
26 & 4 & 4.00182111643824 & -0.00182111643823935 \tabularnewline
27 & 4 & 3.96452716467882 & 0.0354728353211799 \tabularnewline
28 & 4 & 4.01013311935052 & -0.0101331193505159 \tabularnewline
29 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
30 & 4 & 3.9701451293609 & 0.0298548706390977 \tabularnewline
31 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
32 & 4 & 3.97153903706711 & 0.028460962932886 \tabularnewline
33 & 4 & 3.96322703415484 & 0.0367729658451626 \tabularnewline
34 & 4 & 4.05871466981094 & -0.0587146698109402 \tabularnewline
35 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
36 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
37 & 4 & 4.08217243115823 & -0.0821724311582297 \tabularnewline
38 & 4 & 4.00312124696222 & -0.00312124696222207 \tabularnewline
39 & 4 & 3.96313325697261 & 0.0368667430273916 \tabularnewline
40 & 4 & 4.05741453928696 & -0.0574145392869575 \tabularnewline
41 & 4 & 3.93621831833169 & 0.0637816816683115 \tabularnewline
42 & 4 & 4.00312124696222 & -0.00312124696222207 \tabularnewline
43 & 4 & 3.95621516176654 & 0.0437848382334565 \tabularnewline
44 & 4 & 4.05880844699317 & -0.0588084469931692 \tabularnewline
45 & 4 & 3.9701451293609 & 0.0298548706390977 \tabularnewline
46 & 4 & 3.96313325697261 & 0.0368667430273916 \tabularnewline
47 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
48 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
49 & 4 & 3.96313325697261 & 0.0368667430273916 \tabularnewline
50 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
51 & 4 & 4.09740252927657 & -0.0974025292765711 \tabularnewline
52 & 4 & 4.02358150543997 & -0.0235815054399728 \tabularnewline
53 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
54 & 4 & 3.95154219363226 & 0.048457806367741 \tabularnewline
55 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
56 & 4 & 4.09039065688828 & -0.0903906568882773 \tabularnewline
57 & 4 & 3.99480924404995 & 0.00519075595005453 \tabularnewline
58 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
59 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
60 & 4 & 4.01656963305168 & -0.0165696330516789 \tabularnewline
61 & 4 & 4.05179657460488 & -0.0517965746048753 \tabularnewline
62 & 4 & 4.00182111643824 & -0.00182111643823935 \tabularnewline
63 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
64 & 4 & 4.05179657460488 & -0.0517965746048753 \tabularnewline
65 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
66 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
67 & 4 & 4.03049960064604 & -0.0304996006460376 \tabularnewline
68 & 4 & 3.97153903706711 & 0.028460962932886 \tabularnewline
69 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
70 & 4 & 4.01013311935052 & -0.0101331193505159 \tabularnewline
71 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
72 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
73 & 4 & 4.00312124696222 & -0.00312124696222207 \tabularnewline
74 & 4 & 4.00321502414445 & -0.00321502414445106 \tabularnewline
75 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
76 & 4 & 4.05040266689866 & -0.0504026668986636 \tabularnewline
77 & 4 & 3.97144525988488 & 0.028554740115115 \tabularnewline
78 & 4 & 3.99480924404995 & 0.00519075595005453 \tabularnewline
79 & 4 & 4.03179973117002 & -0.0317997311700203 \tabularnewline
80 & 4 & 4.05741453928696 & -0.0574145392869575 \tabularnewline
81 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
82 & 4 & 3.99620315175616 & 0.00379684824384282 \tabularnewline
83 & 4 & 3.97845713227318 & 0.0215428677268211 \tabularnewline
84 & 4 & 3.95154219363226 & 0.048457806367741 \tabularnewline
85 & 4 & 3.96313325697261 & 0.0368667430273916 \tabularnewline
86 & 4 & 3.97153903706711 & 0.028460962932886 \tabularnewline
87 & 2 & 2.02643085641692 & -0.026430856416917 \tabularnewline
88 & 2 & 1.9106390169632 & 0.0893609830368034 \tabularnewline
89 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
90 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
91 & 2 & 2.032048821099 & -0.0320488210989992 \tabularnewline
92 & 2 & 1.88597490227415 & 0.114025097725847 \tabularnewline
93 & 2 & 2.02513072589293 & -0.0251307258929342 \tabularnewline
94 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
95 & 2 & 1.89289299748022 & 0.107107002519782 \tabularnewline
96 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
97 & 2 & 1.88597490227415 & 0.114025097725847 \tabularnewline
98 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
99 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
100 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
101 & 2 & 2.02643085641692 & -0.026430856416917 \tabularnewline
102 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
103 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
104 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
105 & 2 & 1.92456898455756 & 0.0754310154424446 \tabularnewline
106 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
107 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
108 & 2 & 1.91765088935149 & 0.0823491106485096 \tabularnewline
109 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
110 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
111 & 2 & 1.90933888643921 & 0.0906611135607862 \tabularnewline
112 & 2 & 1.89289299748022 & 0.107107002519782 \tabularnewline
113 & 2 & 2.07203681108861 & -0.0720368110886129 \tabularnewline
114 & 2 & 1.91765088935149 & 0.0823491106485096 \tabularnewline
115 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
116 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
117 & 2 & 2.02643085641692 & -0.026430856416917 \tabularnewline
118 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
119 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
120 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
121 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
122 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
123 & 2 & 1.91765088935149 & 0.0823491106485096 \tabularnewline
124 & 2 & 2.05671293578804 & -0.0567129357880424 \tabularnewline
125 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
126 & 2 & 1.89289299748022 & 0.107107002519782 \tabularnewline
127 & 2 & 2.032048821099 & -0.0320488210989992 \tabularnewline
128 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
129 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
130 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
131 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
132 & 2 & 2.02643085641692 & -0.026430856416917 \tabularnewline
133 & 2 & 2.06511871588255 & -0.0651187158825479 \tabularnewline
134 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
135 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
136 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
137 & 2 & 2.04979484058198 & -0.0497948405819774 \tabularnewline
138 & 2 & 1.90232701405092 & 0.09767298594908 \tabularnewline
139 & 2 & 1.89289299748022 & 0.107107002519782 \tabularnewline
140 & 2 & 2.04036082401128 & -0.0403608240112758 \tabularnewline
141 & 2 & 2.00643401298206 & -0.00643401298206208 \tabularnewline
142 & 2 & 1.91755711216926 & 0.0824428878307385 \tabularnewline
143 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
144 & 2 & 2.02503694871071 & -0.0250369487107053 \tabularnewline
145 & 2 & 2.032048821099 & -0.0320488210989992 \tabularnewline
146 & 2 & 1.88588112509192 & 0.114118874908076 \tabularnewline
147 & 2 & 1.92456898455756 & 0.0754310154424446 \tabularnewline
148 & 2 & 1.89289299748022 & 0.107107002519782 \tabularnewline
149 & 2 & 2.03344272880521 & -0.0334427288052108 \tabularnewline
150 & 2 & 2.02503694871071 & -0.0250369487107053 \tabularnewline
151 & 2 & 2.03334895162298 & -0.0333489516229819 \tabularnewline
152 & 2 & 2.00652779016429 & -0.006527790164291 \tabularnewline
153 & 2 & 1.99821578725201 & 0.0017842127479856 \tabularnewline
154 & 2 & 2.06511871588255 & -0.0651187158825479 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203213&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]4[/C][C]4.05179657460487[/C][C]-0.0517965746048727[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]3.97845713227318[/C][C]0.021542867726821[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268202[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268212[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]3.95621516176654[/C][C]0.0437848382334565[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.06572654219923[/C][C]-0.065726542199234[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.97153903706711[/C][C]0.028460962932886[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]4.05880844699317[/C][C]-0.0588084469931692[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]4.00182111643824[/C][C]-0.00182111643823935[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]4.05880844699317[/C][C]-0.0588084469931692[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]3.99480924404995[/C][C]0.00519075595005453[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]4.082078653976[/C][C]-0.0820786539760007[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.02358150543997[/C][C]-0.0235815054399728[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]4.05880844699317[/C][C]-0.0588084469931692[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]4.02348772825774[/C][C]-0.0234877282577437[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.96322703415484[/C][C]0.0367729658451626[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]3.98789114884388[/C][C]0.0121088511561194[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]3.96313325697261[/C][C]0.0368667430273916[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]3.95621516176654[/C][C]0.0437848382334565[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]4.09039065688828[/C][C]-0.0903906568882773[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]4.00182111643824[/C][C]-0.00182111643823935[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.96452716467882[/C][C]0.0354728353211799[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]4.01013311935052[/C][C]-0.0101331193505159[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.9701451293609[/C][C]0.0298548706390977[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.97153903706711[/C][C]0.028460962932886[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.96322703415484[/C][C]0.0367729658451626[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.05871466981094[/C][C]-0.0587146698109402[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]4.08217243115823[/C][C]-0.0821724311582297[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.00312124696222[/C][C]-0.00312124696222207[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.96313325697261[/C][C]0.0368667430273916[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.05741453928696[/C][C]-0.0574145392869575[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]3.93621831833169[/C][C]0.0637816816683115[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.00312124696222[/C][C]-0.00312124696222207[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]3.95621516176654[/C][C]0.0437848382334565[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]4.05880844699317[/C][C]-0.0588084469931692[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]3.9701451293609[/C][C]0.0298548706390977[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]3.96313325697261[/C][C]0.0368667430273916[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]3.96313325697261[/C][C]0.0368667430273916[/C][/ROW]
[ROW][C]50[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]4.09740252927657[/C][C]-0.0974025292765711[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]4.02358150543997[/C][C]-0.0235815054399728[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.95154219363226[/C][C]0.048457806367741[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]4.09039065688828[/C][C]-0.0903906568882773[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]3.99480924404995[/C][C]0.00519075595005453[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]4.01656963305168[/C][C]-0.0165696330516789[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]4.05179657460488[/C][C]-0.0517965746048753[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]4.00182111643824[/C][C]-0.00182111643823935[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]4.05179657460488[/C][C]-0.0517965746048753[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]4.03049960064604[/C][C]-0.0304996006460376[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]3.97153903706711[/C][C]0.028460962932886[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]4.01013311935052[/C][C]-0.0101331193505159[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]4.00312124696222[/C][C]-0.00312124696222207[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]4.00321502414445[/C][C]-0.00321502414445106[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]4.05040266689866[/C][C]-0.0504026668986636[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]3.97144525988488[/C][C]0.028554740115115[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]3.99480924404995[/C][C]0.00519075595005453[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]4.03179973117002[/C][C]-0.0317997311700203[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]4.05741453928696[/C][C]-0.0574145392869575[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]3.99620315175616[/C][C]0.00379684824384282[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.97845713227318[/C][C]0.0215428677268211[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]3.95154219363226[/C][C]0.048457806367741[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]3.96313325697261[/C][C]0.0368667430273916[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]3.97153903706711[/C][C]0.028460962932886[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]2.02643085641692[/C][C]-0.026430856416917[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.9106390169632[/C][C]0.0893609830368034[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]2.032048821099[/C][C]-0.0320488210989992[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.88597490227415[/C][C]0.114025097725847[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]2.02513072589293[/C][C]-0.0251307258929342[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]1.89289299748022[/C][C]0.107107002519782[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.88597490227415[/C][C]0.114025097725847[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]2.02643085641692[/C][C]-0.026430856416917[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.92456898455756[/C][C]0.0754310154424446[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]108[/C][C]2[/C][C]1.91765088935149[/C][C]0.0823491106485096[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]1.90933888643921[/C][C]0.0906611135607862[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]1.89289299748022[/C][C]0.107107002519782[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]2.07203681108861[/C][C]-0.0720368110886129[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]1.91765088935149[/C][C]0.0823491106485096[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]2.02643085641692[/C][C]-0.026430856416917[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.91765088935149[/C][C]0.0823491106485096[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]2.05671293578804[/C][C]-0.0567129357880424[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.89289299748022[/C][C]0.107107002519782[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]2.032048821099[/C][C]-0.0320488210989992[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]2.02643085641692[/C][C]-0.026430856416917[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]2.06511871588255[/C][C]-0.0651187158825479[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]2.04979484058198[/C][C]-0.0497948405819774[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.90232701405092[/C][C]0.09767298594908[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.89289299748022[/C][C]0.107107002519782[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]2.04036082401128[/C][C]-0.0403608240112758[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]2.00643401298206[/C][C]-0.00643401298206208[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.91755711216926[/C][C]0.0824428878307385[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]2.02503694871071[/C][C]-0.0250369487107053[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]2.032048821099[/C][C]-0.0320488210989992[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.88588112509192[/C][C]0.114118874908076[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.92456898455756[/C][C]0.0754310154424446[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.89289299748022[/C][C]0.107107002519782[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]2.03344272880521[/C][C]-0.0334427288052108[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]2.02503694871071[/C][C]-0.0250369487107053[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]2.03334895162298[/C][C]-0.0333489516229819[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]2.00652779016429[/C][C]-0.006527790164291[/C][/ROW]
[ROW][C]153[/C][C]2[/C][C]1.99821578725201[/C][C]0.0017842127479856[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]2.06511871588255[/C][C]-0.0651187158825479[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203213&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203213&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
144.05179657460487-0.0517965746048727
243.978457132273180.021542867726821
343.978457132273180.0215428677268202
443.978457132273180.0215428677268212
543.978457132273180.0215428677268211
643.956215161766540.0437848382334565
743.978457132273180.0215428677268211
844.06572654219923-0.065726542199234
943.971445259884880.028554740115115
1043.971539037067110.028460962932886
1144.05880844699317-0.0588084469931692
1243.978457132273180.0215428677268211
1344.00182111643824-0.00182111643823935
1444.05880844699317-0.0588084469931692
1543.994809244049950.00519075595005453
1644.082078653976-0.0820786539760007
1744.02358150543997-0.0235815054399728
1844.05880844699317-0.0588084469931692
1943.971445259884880.028554740115115
2044.02348772825774-0.0234877282577437
2143.963227034154840.0367729658451626
2243.987891148843880.0121088511561194
2343.963133256972610.0368667430273916
2443.956215161766540.0437848382334565
2544.09039065688828-0.0903906568882773
2644.00182111643824-0.00182111643823935
2743.964527164678820.0354728353211799
2844.01013311935052-0.0101331193505159
2943.971445259884880.028554740115115
3043.97014512936090.0298548706390977
3143.978457132273180.0215428677268211
3243.971539037067110.028460962932886
3343.963227034154840.0367729658451626
3444.05871466981094-0.0587146698109402
3543.978457132273180.0215428677268211
3643.978457132273180.0215428677268211
3744.08217243115823-0.0821724311582297
3844.00312124696222-0.00312124696222207
3943.963133256972610.0368667430273916
4044.05741453928696-0.0574145392869575
4143.936218318331690.0637816816683115
4244.00312124696222-0.00312124696222207
4343.956215161766540.0437848382334565
4444.05880844699317-0.0588084469931692
4543.97014512936090.0298548706390977
4643.963133256972610.0368667430273916
4743.978457132273180.0215428677268211
4843.971445259884880.028554740115115
4943.963133256972610.0368667430273916
5043.978457132273180.0215428677268211
5144.09740252927657-0.0974025292765711
5244.02358150543997-0.0235815054399728
5343.971445259884880.028554740115115
5443.951542193632260.048457806367741
5543.978457132273180.0215428677268211
5644.09039065688828-0.0903906568882773
5743.994809244049950.00519075595005453
5843.971445259884880.028554740115115
5943.971445259884880.028554740115115
6044.01656963305168-0.0165696330516789
6144.05179657460488-0.0517965746048753
6244.00182111643824-0.00182111643823935
6343.978457132273180.0215428677268211
6444.05179657460488-0.0517965746048753
6543.978457132273180.0215428677268211
6643.978457132273180.0215428677268211
6744.03049960064604-0.0304996006460376
6843.971539037067110.028460962932886
6943.971445259884880.028554740115115
7044.01013311935052-0.0101331193505159
7143.978457132273180.0215428677268211
7243.971445259884880.028554740115115
7344.00312124696222-0.00312124696222207
7444.00321502414445-0.00321502414445106
7543.971445259884880.028554740115115
7644.05040266689866-0.0504026668986636
7743.971445259884880.028554740115115
7843.994809244049950.00519075595005453
7944.03179973117002-0.0317997311700203
8044.05741453928696-0.0574145392869575
8143.978457132273180.0215428677268211
8243.996203151756160.00379684824384282
8343.978457132273180.0215428677268211
8443.951542193632260.048457806367741
8543.963133256972610.0368667430273916
8643.971539037067110.028460962932886
8722.02643085641692-0.026430856416917
8821.91063901696320.0893609830368034
8922.04036082401128-0.0403608240112758
9022.03334895162298-0.0333489516229819
9122.032048821099-0.0320488210989992
9221.885974902274150.114025097725847
9322.02513072589293-0.0251307258929342
9422.04036082401128-0.0403608240112758
9521.892892997480220.107107002519782
9622.03334895162298-0.0333489516229819
9721.885974902274150.114025097725847
9822.04036082401128-0.0403608240112758
9922.03344272880521-0.0334427288052108
10022.03334895162298-0.0333489516229819
10122.02643085641692-0.026430856416917
10222.04036082401128-0.0403608240112758
10322.04036082401128-0.0403608240112758
10422.04036082401128-0.0403608240112758
10521.924568984557560.0754310154424446
10622.04036082401128-0.0403608240112758
10722.04036082401128-0.0403608240112758
10821.917650889351490.0823491106485096
10922.04036082401128-0.0403608240112758
11022.03344272880521-0.0334427288052108
11121.909338886439210.0906611135607862
11221.892892997480220.107107002519782
11322.07203681108861-0.0720368110886129
11421.917650889351490.0823491106485096
11522.03344272880521-0.0334427288052108
11622.04036082401128-0.0403608240112758
11722.02643085641692-0.026430856416917
11822.03344272880521-0.0334427288052108
11922.04036082401128-0.0403608240112758
12022.03334895162298-0.0333489516229819
12122.03344272880521-0.0334427288052108
12222.04036082401128-0.0403608240112758
12321.917650889351490.0823491106485096
12422.05671293578804-0.0567129357880424
12522.03334895162298-0.0333489516229819
12621.892892997480220.107107002519782
12722.032048821099-0.0320488210989992
12822.03334895162298-0.0333489516229819
12922.04036082401128-0.0403608240112758
13022.03334895162298-0.0333489516229819
13122.03344272880521-0.0334427288052108
13222.02643085641692-0.026430856416917
13322.06511871588255-0.0651187158825479
13422.04036082401128-0.0403608240112758
13522.04036082401128-0.0403608240112758
13622.04036082401128-0.0403608240112758
13722.04979484058198-0.0497948405819774
13821.902327014050920.09767298594908
13921.892892997480220.107107002519782
14022.04036082401128-0.0403608240112758
14122.00643401298206-0.00643401298206208
14221.917557112169260.0824428878307385
14322.03344272880521-0.0334427288052108
14422.02503694871071-0.0250369487107053
14522.032048821099-0.0320488210989992
14621.885881125091920.114118874908076
14721.924568984557560.0754310154424446
14821.892892997480220.107107002519782
14922.03344272880521-0.0334427288052108
15022.02503694871071-0.0250369487107053
15122.03334895162298-0.0333489516229819
15222.00652779016429-0.006527790164291
15321.998215787252010.0017842127479856
15422.06511871588255-0.0651187158825479







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
118.91451204988924e-461.78290240997785e-451
121.46610128453191e-562.93220256906382e-561
135.91051827904491e-811.18210365580898e-801
148.34450190884771e-841.66890038176954e-831
151.490285723565e-972.98057144713001e-971
16001
171.13666093284665e-1352.27332186569329e-1351
182.01572717666455e-1404.0314543533291e-1401
194.33446927744519e-1538.66893855489037e-1531
207.68497037040315e-1761.53699407408063e-1751
215.73721645844938e-2071.14744329168988e-2061
221.49066387677907e-1972.98132775355814e-1971
236.9849123970139e-2081.39698247940278e-2071
241.01212683974462e-2242.02425367948924e-2241
251.20773110720919e-2412.41546221441838e-2411
263.09052883358848e-2816.18105766717696e-2811
272.45053446414041e-2684.90106892828081e-2681
281.65693982740206e-2773.31387965480412e-2771
292.47443230454028e-2964.94886460908057e-2961
303.1406928684659e-3116.28138573693181e-3111
313.23502277914783e-3176.47004555829567e-3171
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
8019.84778823764973e-814.92389411882487e-81
810.9999865515920042.68968159923314e-051.34484079961657e-05
820.999994110790741.17784185196614e-055.88920925983071e-06
8311.76860226441068e-448.8430113220534e-45
842.09007139446932e-164.18014278893863e-161
8514.80944255334761e-782.40472127667381e-78
8611.72556705773829e-188.62783528869143e-19
877.39654526325061e-201.47930905265012e-191
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
12313.95252516672997e-3231.97626258336499e-323
12416.52775321623496e-3163.26387660811748e-316
12513.90756809023467e-3011.95378404511733e-301
12611.5052637034234e-2827.52631851711701e-283
12711.22781609509348e-2726.13908047546742e-273
12815.74019652693714e-2852.87009826346857e-285
12912.50350694457748e-2451.25175347228874e-245
13018.13513662891946e-2284.06756831445973e-228
13111.05620997250836e-2115.28104986254182e-212
13211.34876001880076e-2006.74380009400379e-201
13313.54540970738559e-2091.7727048536928e-209
13418.09481675483133e-1784.04740837741567e-178
13515.71364069231766e-1552.85682034615883e-155
13611.81952913608771e-1429.09764568043853e-143
13711.32998912058844e-1386.6499456029422e-139
138100
13915.5078516624098e-992.7539258312049e-99
14011.59895731260546e-857.99478656302731e-86
14119.99636551547207e-824.99818275773603e-82
14216.21920524076175e-573.10960262038087e-57
14316.47139151207899e-463.23569575603949e-46

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 8.91451204988924e-46 & 1.78290240997785e-45 & 1 \tabularnewline
12 & 1.46610128453191e-56 & 2.93220256906382e-56 & 1 \tabularnewline
13 & 5.91051827904491e-81 & 1.18210365580898e-80 & 1 \tabularnewline
14 & 8.34450190884771e-84 & 1.66890038176954e-83 & 1 \tabularnewline
15 & 1.490285723565e-97 & 2.98057144713001e-97 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 1.13666093284665e-135 & 2.27332186569329e-135 & 1 \tabularnewline
18 & 2.01572717666455e-140 & 4.0314543533291e-140 & 1 \tabularnewline
19 & 4.33446927744519e-153 & 8.66893855489037e-153 & 1 \tabularnewline
20 & 7.68497037040315e-176 & 1.53699407408063e-175 & 1 \tabularnewline
21 & 5.73721645844938e-207 & 1.14744329168988e-206 & 1 \tabularnewline
22 & 1.49066387677907e-197 & 2.98132775355814e-197 & 1 \tabularnewline
23 & 6.9849123970139e-208 & 1.39698247940278e-207 & 1 \tabularnewline
24 & 1.01212683974462e-224 & 2.02425367948924e-224 & 1 \tabularnewline
25 & 1.20773110720919e-241 & 2.41546221441838e-241 & 1 \tabularnewline
26 & 3.09052883358848e-281 & 6.18105766717696e-281 & 1 \tabularnewline
27 & 2.45053446414041e-268 & 4.90106892828081e-268 & 1 \tabularnewline
28 & 1.65693982740206e-277 & 3.31387965480412e-277 & 1 \tabularnewline
29 & 2.47443230454028e-296 & 4.94886460908057e-296 & 1 \tabularnewline
30 & 3.1406928684659e-311 & 6.28138573693181e-311 & 1 \tabularnewline
31 & 3.23502277914783e-317 & 6.47004555829567e-317 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 1 & 9.84778823764973e-81 & 4.92389411882487e-81 \tabularnewline
81 & 0.999986551592004 & 2.68968159923314e-05 & 1.34484079961657e-05 \tabularnewline
82 & 0.99999411079074 & 1.17784185196614e-05 & 5.88920925983071e-06 \tabularnewline
83 & 1 & 1.76860226441068e-44 & 8.8430113220534e-45 \tabularnewline
84 & 2.09007139446932e-16 & 4.18014278893863e-16 & 1 \tabularnewline
85 & 1 & 4.80944255334761e-78 & 2.40472127667381e-78 \tabularnewline
86 & 1 & 1.72556705773829e-18 & 8.62783528869143e-19 \tabularnewline
87 & 7.39654526325061e-20 & 1.47930905265012e-19 & 1 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 3.95252516672997e-323 & 1.97626258336499e-323 \tabularnewline
124 & 1 & 6.52775321623496e-316 & 3.26387660811748e-316 \tabularnewline
125 & 1 & 3.90756809023467e-301 & 1.95378404511733e-301 \tabularnewline
126 & 1 & 1.5052637034234e-282 & 7.52631851711701e-283 \tabularnewline
127 & 1 & 1.22781609509348e-272 & 6.13908047546742e-273 \tabularnewline
128 & 1 & 5.74019652693714e-285 & 2.87009826346857e-285 \tabularnewline
129 & 1 & 2.50350694457748e-245 & 1.25175347228874e-245 \tabularnewline
130 & 1 & 8.13513662891946e-228 & 4.06756831445973e-228 \tabularnewline
131 & 1 & 1.05620997250836e-211 & 5.28104986254182e-212 \tabularnewline
132 & 1 & 1.34876001880076e-200 & 6.74380009400379e-201 \tabularnewline
133 & 1 & 3.54540970738559e-209 & 1.7727048536928e-209 \tabularnewline
134 & 1 & 8.09481675483133e-178 & 4.04740837741567e-178 \tabularnewline
135 & 1 & 5.71364069231766e-155 & 2.85682034615883e-155 \tabularnewline
136 & 1 & 1.81952913608771e-142 & 9.09764568043853e-143 \tabularnewline
137 & 1 & 1.32998912058844e-138 & 6.6499456029422e-139 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 5.5078516624098e-99 & 2.7539258312049e-99 \tabularnewline
140 & 1 & 1.59895731260546e-85 & 7.99478656302731e-86 \tabularnewline
141 & 1 & 9.99636551547207e-82 & 4.99818275773603e-82 \tabularnewline
142 & 1 & 6.21920524076175e-57 & 3.10960262038087e-57 \tabularnewline
143 & 1 & 6.47139151207899e-46 & 3.23569575603949e-46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203213&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]11[/C][C]8.91451204988924e-46[/C][C]1.78290240997785e-45[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]1.46610128453191e-56[/C][C]2.93220256906382e-56[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]5.91051827904491e-81[/C][C]1.18210365580898e-80[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]8.34450190884771e-84[/C][C]1.66890038176954e-83[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]1.490285723565e-97[/C][C]2.98057144713001e-97[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.13666093284665e-135[/C][C]2.27332186569329e-135[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]2.01572717666455e-140[/C][C]4.0314543533291e-140[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]4.33446927744519e-153[/C][C]8.66893855489037e-153[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]7.68497037040315e-176[/C][C]1.53699407408063e-175[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]5.73721645844938e-207[/C][C]1.14744329168988e-206[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]1.49066387677907e-197[/C][C]2.98132775355814e-197[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]6.9849123970139e-208[/C][C]1.39698247940278e-207[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]1.01212683974462e-224[/C][C]2.02425367948924e-224[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.20773110720919e-241[/C][C]2.41546221441838e-241[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]3.09052883358848e-281[/C][C]6.18105766717696e-281[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]2.45053446414041e-268[/C][C]4.90106892828081e-268[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.65693982740206e-277[/C][C]3.31387965480412e-277[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]2.47443230454028e-296[/C][C]4.94886460908057e-296[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]3.1406928684659e-311[/C][C]6.28138573693181e-311[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]3.23502277914783e-317[/C][C]6.47004555829567e-317[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]9.84778823764973e-81[/C][C]4.92389411882487e-81[/C][/ROW]
[ROW][C]81[/C][C]0.999986551592004[/C][C]2.68968159923314e-05[/C][C]1.34484079961657e-05[/C][/ROW]
[ROW][C]82[/C][C]0.99999411079074[/C][C]1.17784185196614e-05[/C][C]5.88920925983071e-06[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.76860226441068e-44[/C][C]8.8430113220534e-45[/C][/ROW]
[ROW][C]84[/C][C]2.09007139446932e-16[/C][C]4.18014278893863e-16[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]4.80944255334761e-78[/C][C]2.40472127667381e-78[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.72556705773829e-18[/C][C]8.62783528869143e-19[/C][/ROW]
[ROW][C]87[/C][C]7.39654526325061e-20[/C][C]1.47930905265012e-19[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]3.95252516672997e-323[/C][C]1.97626258336499e-323[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]6.52775321623496e-316[/C][C]3.26387660811748e-316[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]3.90756809023467e-301[/C][C]1.95378404511733e-301[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]1.5052637034234e-282[/C][C]7.52631851711701e-283[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.22781609509348e-272[/C][C]6.13908047546742e-273[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]5.74019652693714e-285[/C][C]2.87009826346857e-285[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]2.50350694457748e-245[/C][C]1.25175347228874e-245[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]8.13513662891946e-228[/C][C]4.06756831445973e-228[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.05620997250836e-211[/C][C]5.28104986254182e-212[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.34876001880076e-200[/C][C]6.74380009400379e-201[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]3.54540970738559e-209[/C][C]1.7727048536928e-209[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]8.09481675483133e-178[/C][C]4.04740837741567e-178[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]5.71364069231766e-155[/C][C]2.85682034615883e-155[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.81952913608771e-142[/C][C]9.09764568043853e-143[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.32998912058844e-138[/C][C]6.6499456029422e-139[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]5.5078516624098e-99[/C][C]2.7539258312049e-99[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.59895731260546e-85[/C][C]7.99478656302731e-86[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]9.99636551547207e-82[/C][C]4.99818275773603e-82[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]6.21920524076175e-57[/C][C]3.10960262038087e-57[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]6.47139151207899e-46[/C][C]3.23569575603949e-46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203213&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203213&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
118.91451204988924e-461.78290240997785e-451
121.46610128453191e-562.93220256906382e-561
135.91051827904491e-811.18210365580898e-801
148.34450190884771e-841.66890038176954e-831
151.490285723565e-972.98057144713001e-971
16001
171.13666093284665e-1352.27332186569329e-1351
182.01572717666455e-1404.0314543533291e-1401
194.33446927744519e-1538.66893855489037e-1531
207.68497037040315e-1761.53699407408063e-1751
215.73721645844938e-2071.14744329168988e-2061
221.49066387677907e-1972.98132775355814e-1971
236.9849123970139e-2081.39698247940278e-2071
241.01212683974462e-2242.02425367948924e-2241
251.20773110720919e-2412.41546221441838e-2411
263.09052883358848e-2816.18105766717696e-2811
272.45053446414041e-2684.90106892828081e-2681
281.65693982740206e-2773.31387965480412e-2771
292.47443230454028e-2964.94886460908057e-2961
303.1406928684659e-3116.28138573693181e-3111
313.23502277914783e-3176.47004555829567e-3171
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
8019.84778823764973e-814.92389411882487e-81
810.9999865515920042.68968159923314e-051.34484079961657e-05
820.999994110790741.17784185196614e-055.88920925983071e-06
8311.76860226441068e-448.8430113220534e-45
842.09007139446932e-164.18014278893863e-161
8514.80944255334761e-782.40472127667381e-78
8611.72556705773829e-188.62783528869143e-19
877.39654526325061e-201.47930905265012e-191
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
12313.95252516672997e-3231.97626258336499e-323
12416.52775321623496e-3163.26387660811748e-316
12513.90756809023467e-3011.95378404511733e-301
12611.5052637034234e-2827.52631851711701e-283
12711.22781609509348e-2726.13908047546742e-273
12815.74019652693714e-2852.87009826346857e-285
12912.50350694457748e-2451.25175347228874e-245
13018.13513662891946e-2284.06756831445973e-228
13111.05620997250836e-2115.28104986254182e-212
13211.34876001880076e-2006.74380009400379e-201
13313.54540970738559e-2091.7727048536928e-209
13418.09481675483133e-1784.04740837741567e-178
13515.71364069231766e-1552.85682034615883e-155
13611.81952913608771e-1429.09764568043853e-143
13711.32998912058844e-1386.6499456029422e-139
138100
13915.5078516624098e-992.7539258312049e-99
14011.59895731260546e-857.99478656302731e-86
14119.99636551547207e-824.99818275773603e-82
14216.21920524076175e-573.10960262038087e-57
14316.47139151207899e-463.23569575603949e-46







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1331NOK
5% type I error level1331NOK
10% type I error level1331NOK

\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 & 133 & 1 & NOK \tabularnewline
5% type I error level & 133 & 1 & NOK \tabularnewline
10% type I error level & 133 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203213&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]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203213&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203213&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 level1331NOK
5% type I error level1331NOK
10% type I error level1331NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}