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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 computationTue, 18 Dec 2012 13:38:09 -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/18/t1355855989210u7qq5wdrfbmi.htm/, Retrieved Thu, 28 Mar 2024 13:16:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201557, Retrieved Thu, 28 Mar 2024 13:16:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-12-18 18:38:09] [564f08b1e01e129faa0d56ace254d273] [Current]
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Dataseries X:
1	0	0	0	0	1
1	1	1	0	0	1
0	0	0	0	0	0
0	0	0	0	0	1
0	0	0	0	1	0
1	1	0	0	0	0
1	0	0	0	1	0
0	0	0	0	0	0
0	1	0	0	0	0
0	0	0	0	0	1
1	1	0	0	0	0
0	0	0	0	0	0
1	0	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	1	1	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	1	1	0	0	0
0	0	0	0	0	0
1	0	0	0	0	0
1	1	1	0	1	0
0	1	0	0	0	0
0	0	1	0	0	0
1	1	1	0	0	0
1	0	0	0	0	0
0	0	0	0	0	0
1	0	0	0	0	1
1	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
0	0	0	0	0	0
1	1	1	0	0	0
0	0	1	0	1	1
0	0	0	0	0	1
0	1	0	0	0	0
0	0	0	0	1	0
0	0	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
1	0	0	0	0	1
1	0	1	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	0	1	0	1	1
1	1	1	0	1	1
0	1	0	0	0	0
0	0	0	0	0	0
0	0	1	1	0	1
0	1	1	0	0	1
1	0	0	0	0	0
0	0	0	0	1	1
0	0	0	0	1	0
0	1	0	0	0	1
0	1	1	0	0	0
0	1	0	0	0	0
1	0	0	0	0	0
0	0	0	0	1	1
0	0	0	0	0	1
1	0	1	1	0	0
1	0	1	1	1	0
1	0	1	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.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=201557&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.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=201557&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201557&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 time8 seconds
R Server'George Udny Yule' @ yule.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
CorrectAnalysis[t] = + 0.0298690286867837 + 0.000580407821848932UseLimit[t] -0.148729141139891T20[t] + 0.234595538741405Used[t] -0.0103240313718055Useful[t] -0.0186832842002494`Outcome\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  +  0.0298690286867837 +  0.000580407821848932UseLimit[t] -0.148729141139891T20[t] +  0.234595538741405Used[t] -0.0103240313718055Useful[t] -0.0186832842002494`Outcome\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201557&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  +  0.0298690286867837 +  0.000580407821848932UseLimit[t] -0.148729141139891T20[t] +  0.234595538741405Used[t] -0.0103240313718055Useful[t] -0.0186832842002494`Outcome\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201557&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201557&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = + 0.0298690286867837 + 0.000580407821848932UseLimit[t] -0.148729141139891T20[t] + 0.234595538741405Used[t] -0.0103240313718055Useful[t] -0.0186832842002494`Outcome\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.02986902868678370.0368580.81040.420820.21041
UseLimit0.0005804078218489320.0501240.01160.9907980.495399
T20-0.1487291411398910.058375-2.54780.0133330.006666
Used0.2345955387414050.0621133.77690.0003580.000179
Useful-0.01032403137180550.065177-0.15840.8746570.437328
`Outcome\r`-0.01868328420024940.050816-0.36770.7143760.357188

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.0298690286867837 & 0.036858 & 0.8104 & 0.42082 & 0.21041 \tabularnewline
UseLimit & 0.000580407821848932 & 0.050124 & 0.0116 & 0.990798 & 0.495399 \tabularnewline
T20 & -0.148729141139891 & 0.058375 & -2.5478 & 0.013333 & 0.006666 \tabularnewline
Used & 0.234595538741405 & 0.062113 & 3.7769 & 0.000358 & 0.000179 \tabularnewline
Useful & -0.0103240313718055 & 0.065177 & -0.1584 & 0.874657 & 0.437328 \tabularnewline
`Outcome\r` & -0.0186832842002494 & 0.050816 & -0.3677 & 0.714376 & 0.357188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201557&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.0298690286867837[/C][C]0.036858[/C][C]0.8104[/C][C]0.42082[/C][C]0.21041[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.000580407821848932[/C][C]0.050124[/C][C]0.0116[/C][C]0.990798[/C][C]0.495399[/C][/ROW]
[ROW][C]T20[/C][C]-0.148729141139891[/C][C]0.058375[/C][C]-2.5478[/C][C]0.013333[/C][C]0.006666[/C][/ROW]
[ROW][C]Used[/C][C]0.234595538741405[/C][C]0.062113[/C][C]3.7769[/C][C]0.000358[/C][C]0.000179[/C][/ROW]
[ROW][C]Useful[/C][C]-0.0103240313718055[/C][C]0.065177[/C][C]-0.1584[/C][C]0.874657[/C][C]0.437328[/C][/ROW]
[ROW][C]`Outcome\r`[/C][C]-0.0186832842002494[/C][C]0.050816[/C][C]-0.3677[/C][C]0.714376[/C][C]0.357188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201557&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201557&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.02986902868678370.0368580.81040.420820.21041
UseLimit0.0005804078218489320.0501240.01160.9907980.495399
T20-0.1487291411398910.058375-2.54780.0133330.006666
Used0.2345955387414050.0621133.77690.0003580.000179
Useful-0.01032403137180550.065177-0.15840.8746570.437328
`Outcome\r`-0.01868328420024940.050816-0.36770.7143760.357188







Multiple Linear Regression - Regression Statistics
Multiple R0.469900206318933
R-squared0.220806203898575
Adjusted R-squared0.157967994535557
F-TEST (value)3.5138844046776
F-TEST (DF numerator)5
F-TEST (DF denominator)62
p-value0.00734121515282071
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.189840884037973
Sum Squared Residuals2.23445279764379

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.469900206318933 \tabularnewline
R-squared & 0.220806203898575 \tabularnewline
Adjusted R-squared & 0.157967994535557 \tabularnewline
F-TEST (value) & 3.5138844046776 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 62 \tabularnewline
p-value & 0.00734121515282071 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.189840884037973 \tabularnewline
Sum Squared Residuals & 2.23445279764379 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201557&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.469900206318933[/C][/ROW]
[ROW][C]R-squared[/C][C]0.220806203898575[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.157967994535557[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.5138844046776[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]62[/C][/ROW]
[ROW][C]p-value[/C][C]0.00734121515282071[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.189840884037973[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.23445279764379[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201557&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201557&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 R0.469900206318933
R-squared0.220806203898575
Adjusted R-squared0.157967994535557
F-TEST (value)3.5138844046776
F-TEST (DF numerator)5
F-TEST (DF denominator)62
p-value0.00734121515282071
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.189840884037973
Sum Squared Residuals2.23445279764379







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0117661523083833-0.0117661523083833
200.0976325499098969-0.0976325499098969
300.0298690286867838-0.0298690286867838
400.0111857444865342-0.0111857444865342
500.0195449973149782-0.0195449973149782
60-0.1182797046312590.118279704631259
700.0201254051368271-0.0201254051368271
800.0298690286867837-0.0298690286867837
90-0.1188601124531070.118860112453107
1000.0111857444865343-0.0111857444865343
110-0.1182797046312590.118279704631259
1200.0298690286867837-0.0298690286867837
1300.0304494365086326-0.0304494365086326
1400.0111857444865343-0.0111857444865343
1500.0117661523083832-0.0117661523083832
1600.0298690286867837-0.0298690286867837
1700.0298690286867837-0.0298690286867837
1800.0298690286867837-0.0298690286867837
1900.115735426288297-0.115735426288297
2000.0298690286867837-0.0298690286867837
2100.0298690286867837-0.0298690286867837
2200.116315834110146-0.116315834110146
2300.0298690286867837-0.0298690286867837
2400.0304494365086326-0.0304494365086326
2500.105991802738341-0.105991802738341
260-0.1188601124531070.118860112453107
2700.264464567428189-0.264464567428189
2800.116315834110146-0.116315834110146
2900.0304494365086326-0.0304494365086326
3000.0298690286867837-0.0298690286867837
3100.0117661523083832-0.0117661523083832
3200.0304494365086326-0.0304494365086326
3300.0298690286867837-0.0298690286867837
3400.0111857444865343-0.0111857444865343
3500.0304494365086326-0.0304494365086326
3600.0298690286867837-0.0298690286867837
3700.116315834110146-0.116315834110146
3800.235457251856134-0.235457251856134
3900.0111857444865343-0.0111857444865343
400-0.1188601124531070.118860112453107
4100.0195449973149782-0.0195449973149782
4200.0111857444865343-0.0111857444865343
4300.0298690286867837-0.0298690286867837
4400.0111857444865343-0.0111857444865343
4500.0304494365086326-0.0304494365086326
4600.0117661523083832-0.0117661523083832
4700.265044975250038-0.265044975250038
4800.0298690286867837-0.0298690286867837
4900.0298690286867837-0.0298690286867837
5000.0298690286867837-0.0298690286867837
5100.236037659677983-0.236037659677983
5200.0873085185380914-0.0873085185380914
530-0.1188601124531070.118860112453107
5400.0298690286867837-0.0298690286867837
5510.2457812832279390.754218716772061
5600.097052142088048-0.097052142088048
5700.0304494365086326-0.0304494365086326
5800.000861713114728814-0.000861713114728814
5900.0195449973149782-0.0195449973149782
600-0.1375433966533570.137543396653357
6100.115735426288297-0.115735426288297
620-0.1188601124531070.118860112453107
6300.0304494365086326-0.0304494365086326
6400.000861713114728814-0.000861713114728814
6500.0111857444865343-0.0111857444865343
6610.2650449752500380.734955024749962
6710.2547209438782320.745279056121768
6800.265044975250038-0.265044975250038

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0117661523083833 & -0.0117661523083833 \tabularnewline
2 & 0 & 0.0976325499098969 & -0.0976325499098969 \tabularnewline
3 & 0 & 0.0298690286867838 & -0.0298690286867838 \tabularnewline
4 & 0 & 0.0111857444865342 & -0.0111857444865342 \tabularnewline
5 & 0 & 0.0195449973149782 & -0.0195449973149782 \tabularnewline
6 & 0 & -0.118279704631259 & 0.118279704631259 \tabularnewline
7 & 0 & 0.0201254051368271 & -0.0201254051368271 \tabularnewline
8 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
9 & 0 & -0.118860112453107 & 0.118860112453107 \tabularnewline
10 & 0 & 0.0111857444865343 & -0.0111857444865343 \tabularnewline
11 & 0 & -0.118279704631259 & 0.118279704631259 \tabularnewline
12 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
13 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
14 & 0 & 0.0111857444865343 & -0.0111857444865343 \tabularnewline
15 & 0 & 0.0117661523083832 & -0.0117661523083832 \tabularnewline
16 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
17 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
18 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
19 & 0 & 0.115735426288297 & -0.115735426288297 \tabularnewline
20 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
21 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
22 & 0 & 0.116315834110146 & -0.116315834110146 \tabularnewline
23 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
24 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
25 & 0 & 0.105991802738341 & -0.105991802738341 \tabularnewline
26 & 0 & -0.118860112453107 & 0.118860112453107 \tabularnewline
27 & 0 & 0.264464567428189 & -0.264464567428189 \tabularnewline
28 & 0 & 0.116315834110146 & -0.116315834110146 \tabularnewline
29 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
30 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
31 & 0 & 0.0117661523083832 & -0.0117661523083832 \tabularnewline
32 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
33 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
34 & 0 & 0.0111857444865343 & -0.0111857444865343 \tabularnewline
35 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
36 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
37 & 0 & 0.116315834110146 & -0.116315834110146 \tabularnewline
38 & 0 & 0.235457251856134 & -0.235457251856134 \tabularnewline
39 & 0 & 0.0111857444865343 & -0.0111857444865343 \tabularnewline
40 & 0 & -0.118860112453107 & 0.118860112453107 \tabularnewline
41 & 0 & 0.0195449973149782 & -0.0195449973149782 \tabularnewline
42 & 0 & 0.0111857444865343 & -0.0111857444865343 \tabularnewline
43 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
44 & 0 & 0.0111857444865343 & -0.0111857444865343 \tabularnewline
45 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
46 & 0 & 0.0117661523083832 & -0.0117661523083832 \tabularnewline
47 & 0 & 0.265044975250038 & -0.265044975250038 \tabularnewline
48 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
49 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
50 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
51 & 0 & 0.236037659677983 & -0.236037659677983 \tabularnewline
52 & 0 & 0.0873085185380914 & -0.0873085185380914 \tabularnewline
53 & 0 & -0.118860112453107 & 0.118860112453107 \tabularnewline
54 & 0 & 0.0298690286867837 & -0.0298690286867837 \tabularnewline
55 & 1 & 0.245781283227939 & 0.754218716772061 \tabularnewline
56 & 0 & 0.097052142088048 & -0.097052142088048 \tabularnewline
57 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
58 & 0 & 0.000861713114728814 & -0.000861713114728814 \tabularnewline
59 & 0 & 0.0195449973149782 & -0.0195449973149782 \tabularnewline
60 & 0 & -0.137543396653357 & 0.137543396653357 \tabularnewline
61 & 0 & 0.115735426288297 & -0.115735426288297 \tabularnewline
62 & 0 & -0.118860112453107 & 0.118860112453107 \tabularnewline
63 & 0 & 0.0304494365086326 & -0.0304494365086326 \tabularnewline
64 & 0 & 0.000861713114728814 & -0.000861713114728814 \tabularnewline
65 & 0 & 0.0111857444865343 & -0.0111857444865343 \tabularnewline
66 & 1 & 0.265044975250038 & 0.734955024749962 \tabularnewline
67 & 1 & 0.254720943878232 & 0.745279056121768 \tabularnewline
68 & 0 & 0.265044975250038 & -0.265044975250038 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201557&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]0.0117661523083833[/C][C]-0.0117661523083833[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.0976325499098969[/C][C]-0.0976325499098969[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0298690286867838[/C][C]-0.0298690286867838[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0111857444865342[/C][C]-0.0111857444865342[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.0195449973149782[/C][C]-0.0195449973149782[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.118279704631259[/C][C]0.118279704631259[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0201254051368271[/C][C]-0.0201254051368271[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.118860112453107[/C][C]0.118860112453107[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.0111857444865343[/C][C]-0.0111857444865343[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]-0.118279704631259[/C][C]0.118279704631259[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.0111857444865343[/C][C]-0.0111857444865343[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.0117661523083832[/C][C]-0.0117661523083832[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.115735426288297[/C][C]-0.115735426288297[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.116315834110146[/C][C]-0.116315834110146[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.105991802738341[/C][C]-0.105991802738341[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]-0.118860112453107[/C][C]0.118860112453107[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.264464567428189[/C][C]-0.264464567428189[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.116315834110146[/C][C]-0.116315834110146[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0117661523083832[/C][C]-0.0117661523083832[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.0111857444865343[/C][C]-0.0111857444865343[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.116315834110146[/C][C]-0.116315834110146[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.235457251856134[/C][C]-0.235457251856134[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0111857444865343[/C][C]-0.0111857444865343[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]-0.118860112453107[/C][C]0.118860112453107[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.0195449973149782[/C][C]-0.0195449973149782[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.0111857444865343[/C][C]-0.0111857444865343[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.0111857444865343[/C][C]-0.0111857444865343[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0117661523083832[/C][C]-0.0117661523083832[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.265044975250038[/C][C]-0.265044975250038[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.236037659677983[/C][C]-0.236037659677983[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.0873085185380914[/C][C]-0.0873085185380914[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.118860112453107[/C][C]0.118860112453107[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.0298690286867837[/C][C]-0.0298690286867837[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.245781283227939[/C][C]0.754218716772061[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.097052142088048[/C][C]-0.097052142088048[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.000861713114728814[/C][C]-0.000861713114728814[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0195449973149782[/C][C]-0.0195449973149782[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]-0.137543396653357[/C][C]0.137543396653357[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.115735426288297[/C][C]-0.115735426288297[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]-0.118860112453107[/C][C]0.118860112453107[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0304494365086326[/C][C]-0.0304494365086326[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.000861713114728814[/C][C]-0.000861713114728814[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0111857444865343[/C][C]-0.0111857444865343[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.265044975250038[/C][C]0.734955024749962[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.254720943878232[/C][C]0.745279056121768[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.265044975250038[/C][C]-0.265044975250038[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201557&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201557&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
100.0117661523083833-0.0117661523083833
200.0976325499098969-0.0976325499098969
300.0298690286867838-0.0298690286867838
400.0111857444865342-0.0111857444865342
500.0195449973149782-0.0195449973149782
60-0.1182797046312590.118279704631259
700.0201254051368271-0.0201254051368271
800.0298690286867837-0.0298690286867837
90-0.1188601124531070.118860112453107
1000.0111857444865343-0.0111857444865343
110-0.1182797046312590.118279704631259
1200.0298690286867837-0.0298690286867837
1300.0304494365086326-0.0304494365086326
1400.0111857444865343-0.0111857444865343
1500.0117661523083832-0.0117661523083832
1600.0298690286867837-0.0298690286867837
1700.0298690286867837-0.0298690286867837
1800.0298690286867837-0.0298690286867837
1900.115735426288297-0.115735426288297
2000.0298690286867837-0.0298690286867837
2100.0298690286867837-0.0298690286867837
2200.116315834110146-0.116315834110146
2300.0298690286867837-0.0298690286867837
2400.0304494365086326-0.0304494365086326
2500.105991802738341-0.105991802738341
260-0.1188601124531070.118860112453107
2700.264464567428189-0.264464567428189
2800.116315834110146-0.116315834110146
2900.0304494365086326-0.0304494365086326
3000.0298690286867837-0.0298690286867837
3100.0117661523083832-0.0117661523083832
3200.0304494365086326-0.0304494365086326
3300.0298690286867837-0.0298690286867837
3400.0111857444865343-0.0111857444865343
3500.0304494365086326-0.0304494365086326
3600.0298690286867837-0.0298690286867837
3700.116315834110146-0.116315834110146
3800.235457251856134-0.235457251856134
3900.0111857444865343-0.0111857444865343
400-0.1188601124531070.118860112453107
4100.0195449973149782-0.0195449973149782
4200.0111857444865343-0.0111857444865343
4300.0298690286867837-0.0298690286867837
4400.0111857444865343-0.0111857444865343
4500.0304494365086326-0.0304494365086326
4600.0117661523083832-0.0117661523083832
4700.265044975250038-0.265044975250038
4800.0298690286867837-0.0298690286867837
4900.0298690286867837-0.0298690286867837
5000.0298690286867837-0.0298690286867837
5100.236037659677983-0.236037659677983
5200.0873085185380914-0.0873085185380914
530-0.1188601124531070.118860112453107
5400.0298690286867837-0.0298690286867837
5510.2457812832279390.754218716772061
5600.097052142088048-0.097052142088048
5700.0304494365086326-0.0304494365086326
5800.000861713114728814-0.000861713114728814
5900.0195449973149782-0.0195449973149782
600-0.1375433966533570.137543396653357
6100.115735426288297-0.115735426288297
620-0.1188601124531070.118860112453107
6300.0304494365086326-0.0304494365086326
6400.000861713114728814-0.000861713114728814
6500.0111857444865343-0.0111857444865343
6610.2650449752500380.734955024749962
6710.2547209438782320.745279056121768
6800.265044975250038-0.265044975250038







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9001
10001
11001
12001
13001
14001
15001
16001
17001
18001
19001
20001
21001
22001
23001
24001
25001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
551.45211643141408e-052.90423286282816e-050.999985478835686
561.51989301605345e-053.0397860321069e-050.999984801069839
576.40835870671037e-061.28167174134207e-050.999993591641293
582.39851400635358e-064.79702801270715e-060.999997601485994
595.81398359539641e-071.16279671907928e-060.99999941860164

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0 & 0 & 1 \tabularnewline
18 & 0 & 0 & 1 \tabularnewline
19 & 0 & 0 & 1 \tabularnewline
20 & 0 & 0 & 1 \tabularnewline
21 & 0 & 0 & 1 \tabularnewline
22 & 0 & 0 & 1 \tabularnewline
23 & 0 & 0 & 1 \tabularnewline
24 & 0 & 0 & 1 \tabularnewline
25 & 0 & 0 & 1 \tabularnewline
26 & 0 & 0 & 1 \tabularnewline
27 & 0 & 0 & 1 \tabularnewline
28 & 0 & 0 & 1 \tabularnewline
29 & 0 & 0 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 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 & 1.45211643141408e-05 & 2.90423286282816e-05 & 0.999985478835686 \tabularnewline
56 & 1.51989301605345e-05 & 3.0397860321069e-05 & 0.999984801069839 \tabularnewline
57 & 6.40835870671037e-06 & 1.28167174134207e-05 & 0.999993591641293 \tabularnewline
58 & 2.39851400635358e-06 & 4.79702801270715e-06 & 0.999997601485994 \tabularnewline
59 & 5.81398359539641e-07 & 1.16279671907928e-06 & 0.99999941860164 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201557&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/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]1.45211643141408e-05[/C][C]2.90423286282816e-05[/C][C]0.999985478835686[/C][/ROW]
[ROW][C]56[/C][C]1.51989301605345e-05[/C][C]3.0397860321069e-05[/C][C]0.999984801069839[/C][/ROW]
[ROW][C]57[/C][C]6.40835870671037e-06[/C][C]1.28167174134207e-05[/C][C]0.999993591641293[/C][/ROW]
[ROW][C]58[/C][C]2.39851400635358e-06[/C][C]4.79702801270715e-06[/C][C]0.999997601485994[/C][/ROW]
[ROW][C]59[/C][C]5.81398359539641e-07[/C][C]1.16279671907928e-06[/C][C]0.99999941860164[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201557&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201557&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9001
10001
11001
12001
13001
14001
15001
16001
17001
18001
19001
20001
21001
22001
23001
24001
25001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
551.45211643141408e-052.90423286282816e-050.999985478835686
561.51989301605345e-053.0397860321069e-050.999984801069839
576.40835870671037e-061.28167174134207e-050.999993591641293
582.39851400635358e-064.79702801270715e-060.999997601485994
595.81398359539641e-071.16279671907928e-060.99999941860164







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level511NOK
5% type I error level511NOK
10% type I error level511NOK

\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 & 51 & 1 & NOK \tabularnewline
5% type I error level & 51 & 1 & NOK \tabularnewline
10% type I error level & 51 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201557&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]51[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]51[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]51[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201557&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201557&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 level511NOK
5% type I error level511NOK
10% type I error level511NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}