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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 16 Dec 2012 10:28:08 -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/16/t135567177243l53btra689nxl.htm/, Retrieved Wed, 24 Apr 2024 23:09:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200426, Retrieved Wed, 24 Apr 2024 23:09:33 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [DEEL MR T20] [2012-12-16 15:28:08] [5821104a6123eb5bf529ba8614395dc8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
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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
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0	0	0	0	0	0
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0	1	1	0	0	1
1	0	0	0	0	0
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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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.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=200426&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]'Gertrude Mary Cox' @ cox.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=200426&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200426&T=0

As an alternative you can also use a QR Code:  
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'Gertrude Mary Cox' @ cox.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] = + 0.0342091785256804 + 0.00703473203162012UseLimit[t] -0.165833400963344T20[t] + 0.258868076514766Used[t] -0.0221823235624419Useful[t] -0.0260285290801734`Outcome\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  +  0.0342091785256804 +  0.00703473203162012UseLimit[t] -0.165833400963344T20[t] +  0.258868076514766Used[t] -0.0221823235624419Useful[t] -0.0260285290801734`Outcome\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200426&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  +  0.0342091785256804 +  0.00703473203162012UseLimit[t] -0.165833400963344T20[t] +  0.258868076514766Used[t] -0.0221823235624419Useful[t] -0.0260285290801734`Outcome\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200426&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200426&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = + 0.0342091785256804 + 0.00703473203162012UseLimit[t] -0.165833400963344T20[t] + 0.258868076514766Used[t] -0.0221823235624419Useful[t] -0.0260285290801734`Outcome\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.03420917852568040.0366060.93450.3537170.176858
UseLimit0.007034732031620120.0498110.14120.8881550.444078
T20-0.1658334009633440.058908-2.81510.0065560.003278
Used0.2588680765147660.0635874.07110.0001376.8e-05
Useful-0.02218232356244190.065011-0.34120.734120.36706
`Outcome\r`-0.02602852908017340.050549-0.51490.6084690.304234

\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) & 0.0342091785256804 & 0.036606 & 0.9345 & 0.353717 & 0.176858 \tabularnewline
UseLimit & 0.00703473203162012 & 0.049811 & 0.1412 & 0.888155 & 0.444078 \tabularnewline
T20 & -0.165833400963344 & 0.058908 & -2.8151 & 0.006556 & 0.003278 \tabularnewline
Used & 0.258868076514766 & 0.063587 & 4.0711 & 0.000137 & 6.8e-05 \tabularnewline
Useful & -0.0221823235624419 & 0.065011 & -0.3412 & 0.73412 & 0.36706 \tabularnewline
`Outcome\r` & -0.0260285290801734 & 0.050549 & -0.5149 & 0.608469 & 0.304234 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200426&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]0.0342091785256804[/C][C]0.036606[/C][C]0.9345[/C][C]0.353717[/C][C]0.176858[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.00703473203162012[/C][C]0.049811[/C][C]0.1412[/C][C]0.888155[/C][C]0.444078[/C][/ROW]
[ROW][C]T20[/C][C]-0.165833400963344[/C][C]0.058908[/C][C]-2.8151[/C][C]0.006556[/C][C]0.003278[/C][/ROW]
[ROW][C]Used[/C][C]0.258868076514766[/C][C]0.063587[/C][C]4.0711[/C][C]0.000137[/C][C]6.8e-05[/C][/ROW]
[ROW][C]Useful[/C][C]-0.0221823235624419[/C][C]0.065011[/C][C]-0.3412[/C][C]0.73412[/C][C]0.36706[/C][/ROW]
[ROW][C]`Outcome\r`[/C][C]-0.0260285290801734[/C][C]0.050549[/C][C]-0.5149[/C][C]0.608469[/C][C]0.304234[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200426&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200426&T=2

As an alternative you can also use a QR Code:  
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)0.03420917852568040.0366060.93450.3537170.176858
UseLimit0.007034732031620120.0498110.14120.8881550.444078
T20-0.1658334009633440.058908-2.81510.0065560.003278
Used0.2588680765147660.0635874.07110.0001376.8e-05
Useful-0.02218232356244190.065011-0.34120.734120.36706
`Outcome\r`-0.02602852908017340.050549-0.51490.6084690.304234






Multiple Linear Regression - Regression Statistics
Multiple R0.498022418485476
R-squared0.248026329314123
Adjusted R-squared0.18638914319233
F-TEST (value)4.02397229529639
F-TEST (DF numerator)5
F-TEST (DF denominator)61
p-value0.00320542835937931
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.187953151795345
Sum Squared Residuals2.15490962345804

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.498022418485476 \tabularnewline
R-squared & 0.248026329314123 \tabularnewline
Adjusted R-squared & 0.18638914319233 \tabularnewline
F-TEST (value) & 4.02397229529639 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 61 \tabularnewline
p-value & 0.00320542835937931 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.187953151795345 \tabularnewline
Sum Squared Residuals & 2.15490962345804 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200426&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.498022418485476[/C][/ROW]
[ROW][C]R-squared[/C][C]0.248026329314123[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.18638914319233[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.02397229529639[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]61[/C][/ROW]
[ROW][C]p-value[/C][C]0.00320542835937931[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.187953151795345[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.15490962345804[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200426&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200426&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.498022418485476
R-squared0.248026329314123
Adjusted R-squared0.18638914319233
F-TEST (value)4.02397229529639
F-TEST (DF numerator)5
F-TEST (DF denominator)61
p-value0.00320542835937931
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.187953151795345
Sum Squared Residuals2.15490962345804






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0152153814771272-0.0152153814771272
200.108250057028549-0.108250057028549
300.0342091785256803-0.0342091785256803
400.00818064944550692-0.00818064944550692
500.0120268549632385-0.0120268549632385
60-0.1245894904060430.124589490406043
700.0190615869948587-0.0190615869948587
800.0342091785256804-0.0342091785256804
90-0.1316242224376630.131624222437663
1000.00818064944550704-0.00818064944550704
110-0.1245894904060430.124589490406043
1200.0342091785256804-0.0342091785256804
1300.0412439105573005-0.0412439105573005
1400.00818064944550704-0.00818064944550704
1500.0152153814771272-0.0152153814771272
1600.0342091785256804-0.0342091785256804
1700.0342091785256804-0.0342091785256804
1800.0342091785256804-0.0342091785256804
1900.127243854077103-0.127243854077103
2000.0342091785256804-0.0342091785256804
2100.0342091785256804-0.0342091785256804
2200.134278586108723-0.134278586108723
2300.0342091785256804-0.0342091785256804
2400.0412439105573005-0.0412439105573005
2500.112096262546281-0.112096262546281
260-0.1316242224376630.131624222437663
2700.293077255040446-0.293077255040446
2800.134278586108723-0.134278586108723
2900.0412439105573005-0.0412439105573005
3000.0342091785256804-0.0342091785256804
3100.0152153814771272-0.0152153814771272
3200.0412439105573005-0.0412439105573005
3300.0342091785256804-0.0342091785256804
3400.00818064944550704-0.00818064944550704
3500.0412439105573005-0.0412439105573005
3600.0342091785256804-0.0342091785256804
3700.134278586108723-0.134278586108723
3800.244866402397831-0.244866402397831
3900.00818064944550704-0.00818064944550704
400-0.1316242224376630.131624222437663
4100.0120268549632385-0.0120268549632385
4200.00818064944550704-0.00818064944550704
4300.0342091785256804-0.0342091785256804
4400.00818064944550704-0.00818064944550704
4500.0412439105573005-0.0412439105573005
4600.0152153814771272-0.0152153814771272
4700.300111987072067-0.300111987072067
4800.0342091785256804-0.0342091785256804
4900.0342091785256804-0.0342091785256804
5000.0342091785256804-0.0342091785256804
5100.251901134429451-0.251901134429451
5200.0860677334661075-0.0860677334661075
530-0.1316242224376630.131624222437663
5400.0342091785256804-0.0342091785256804
5510.2670487259602730.732951274039727
5600.101215324996929-0.101215324996929
5700.0412439105573005-0.0412439105573005
580-0.01400167411693480.0140016741169348
5900.0120268549632385-0.0120268549632385
600-0.1576527515178370.157652751517837
6100.127243854077103-0.127243854077103
620-0.1316242224376630.131624222437663
6300.0412439105573005-0.0412439105573005
640-0.01400167411693480.0140016741169348
6500.00818064944550704-0.00818064944550704
6610.3001119870720660.699888012927934
6710.2779296635096250.722070336490375

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0152153814771272 & -0.0152153814771272 \tabularnewline
2 & 0 & 0.108250057028549 & -0.108250057028549 \tabularnewline
3 & 0 & 0.0342091785256803 & -0.0342091785256803 \tabularnewline
4 & 0 & 0.00818064944550692 & -0.00818064944550692 \tabularnewline
5 & 0 & 0.0120268549632385 & -0.0120268549632385 \tabularnewline
6 & 0 & -0.124589490406043 & 0.124589490406043 \tabularnewline
7 & 0 & 0.0190615869948587 & -0.0190615869948587 \tabularnewline
8 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
9 & 0 & -0.131624222437663 & 0.131624222437663 \tabularnewline
10 & 0 & 0.00818064944550704 & -0.00818064944550704 \tabularnewline
11 & 0 & -0.124589490406043 & 0.124589490406043 \tabularnewline
12 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
13 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
14 & 0 & 0.00818064944550704 & -0.00818064944550704 \tabularnewline
15 & 0 & 0.0152153814771272 & -0.0152153814771272 \tabularnewline
16 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
17 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
18 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
19 & 0 & 0.127243854077103 & -0.127243854077103 \tabularnewline
20 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
21 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
22 & 0 & 0.134278586108723 & -0.134278586108723 \tabularnewline
23 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
24 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
25 & 0 & 0.112096262546281 & -0.112096262546281 \tabularnewline
26 & 0 & -0.131624222437663 & 0.131624222437663 \tabularnewline
27 & 0 & 0.293077255040446 & -0.293077255040446 \tabularnewline
28 & 0 & 0.134278586108723 & -0.134278586108723 \tabularnewline
29 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
30 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
31 & 0 & 0.0152153814771272 & -0.0152153814771272 \tabularnewline
32 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
33 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
34 & 0 & 0.00818064944550704 & -0.00818064944550704 \tabularnewline
35 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
36 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
37 & 0 & 0.134278586108723 & -0.134278586108723 \tabularnewline
38 & 0 & 0.244866402397831 & -0.244866402397831 \tabularnewline
39 & 0 & 0.00818064944550704 & -0.00818064944550704 \tabularnewline
40 & 0 & -0.131624222437663 & 0.131624222437663 \tabularnewline
41 & 0 & 0.0120268549632385 & -0.0120268549632385 \tabularnewline
42 & 0 & 0.00818064944550704 & -0.00818064944550704 \tabularnewline
43 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
44 & 0 & 0.00818064944550704 & -0.00818064944550704 \tabularnewline
45 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
46 & 0 & 0.0152153814771272 & -0.0152153814771272 \tabularnewline
47 & 0 & 0.300111987072067 & -0.300111987072067 \tabularnewline
48 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
49 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
50 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
51 & 0 & 0.251901134429451 & -0.251901134429451 \tabularnewline
52 & 0 & 0.0860677334661075 & -0.0860677334661075 \tabularnewline
53 & 0 & -0.131624222437663 & 0.131624222437663 \tabularnewline
54 & 0 & 0.0342091785256804 & -0.0342091785256804 \tabularnewline
55 & 1 & 0.267048725960273 & 0.732951274039727 \tabularnewline
56 & 0 & 0.101215324996929 & -0.101215324996929 \tabularnewline
57 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
58 & 0 & -0.0140016741169348 & 0.0140016741169348 \tabularnewline
59 & 0 & 0.0120268549632385 & -0.0120268549632385 \tabularnewline
60 & 0 & -0.157652751517837 & 0.157652751517837 \tabularnewline
61 & 0 & 0.127243854077103 & -0.127243854077103 \tabularnewline
62 & 0 & -0.131624222437663 & 0.131624222437663 \tabularnewline
63 & 0 & 0.0412439105573005 & -0.0412439105573005 \tabularnewline
64 & 0 & -0.0140016741169348 & 0.0140016741169348 \tabularnewline
65 & 0 & 0.00818064944550704 & -0.00818064944550704 \tabularnewline
66 & 1 & 0.300111987072066 & 0.699888012927934 \tabularnewline
67 & 1 & 0.277929663509625 & 0.722070336490375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200426&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]0.0152153814771272[/C][C]-0.0152153814771272[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.108250057028549[/C][C]-0.108250057028549[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0342091785256803[/C][C]-0.0342091785256803[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.00818064944550692[/C][C]-0.00818064944550692[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.0120268549632385[/C][C]-0.0120268549632385[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.124589490406043[/C][C]0.124589490406043[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0190615869948587[/C][C]-0.0190615869948587[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.131624222437663[/C][C]0.131624222437663[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.00818064944550704[/C][C]-0.00818064944550704[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]-0.124589490406043[/C][C]0.124589490406043[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.00818064944550704[/C][C]-0.00818064944550704[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.0152153814771272[/C][C]-0.0152153814771272[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.127243854077103[/C][C]-0.127243854077103[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.134278586108723[/C][C]-0.134278586108723[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.112096262546281[/C][C]-0.112096262546281[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]-0.131624222437663[/C][C]0.131624222437663[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.293077255040446[/C][C]-0.293077255040446[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.134278586108723[/C][C]-0.134278586108723[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0152153814771272[/C][C]-0.0152153814771272[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.00818064944550704[/C][C]-0.00818064944550704[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.134278586108723[/C][C]-0.134278586108723[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.244866402397831[/C][C]-0.244866402397831[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.00818064944550704[/C][C]-0.00818064944550704[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]-0.131624222437663[/C][C]0.131624222437663[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.0120268549632385[/C][C]-0.0120268549632385[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.00818064944550704[/C][C]-0.00818064944550704[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.00818064944550704[/C][C]-0.00818064944550704[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0152153814771272[/C][C]-0.0152153814771272[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.300111987072067[/C][C]-0.300111987072067[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.251901134429451[/C][C]-0.251901134429451[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.0860677334661075[/C][C]-0.0860677334661075[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.131624222437663[/C][C]0.131624222437663[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.0342091785256804[/C][C]-0.0342091785256804[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.267048725960273[/C][C]0.732951274039727[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.101215324996929[/C][C]-0.101215324996929[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0140016741169348[/C][C]0.0140016741169348[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0120268549632385[/C][C]-0.0120268549632385[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]-0.157652751517837[/C][C]0.157652751517837[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.127243854077103[/C][C]-0.127243854077103[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]-0.131624222437663[/C][C]0.131624222437663[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0412439105573005[/C][C]-0.0412439105573005[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]-0.0140016741169348[/C][C]0.0140016741169348[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.00818064944550704[/C][C]-0.00818064944550704[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.300111987072066[/C][C]0.699888012927934[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.277929663509625[/C][C]0.722070336490375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200426&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200426&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0152153814771272-0.0152153814771272
200.108250057028549-0.108250057028549
300.0342091785256803-0.0342091785256803
400.00818064944550692-0.00818064944550692
500.0120268549632385-0.0120268549632385
60-0.1245894904060430.124589490406043
700.0190615869948587-0.0190615869948587
800.0342091785256804-0.0342091785256804
90-0.1316242224376630.131624222437663
1000.00818064944550704-0.00818064944550704
110-0.1245894904060430.124589490406043
1200.0342091785256804-0.0342091785256804
1300.0412439105573005-0.0412439105573005
1400.00818064944550704-0.00818064944550704
1500.0152153814771272-0.0152153814771272
1600.0342091785256804-0.0342091785256804
1700.0342091785256804-0.0342091785256804
1800.0342091785256804-0.0342091785256804
1900.127243854077103-0.127243854077103
2000.0342091785256804-0.0342091785256804
2100.0342091785256804-0.0342091785256804
2200.134278586108723-0.134278586108723
2300.0342091785256804-0.0342091785256804
2400.0412439105573005-0.0412439105573005
2500.112096262546281-0.112096262546281
260-0.1316242224376630.131624222437663
2700.293077255040446-0.293077255040446
2800.134278586108723-0.134278586108723
2900.0412439105573005-0.0412439105573005
3000.0342091785256804-0.0342091785256804
3100.0152153814771272-0.0152153814771272
3200.0412439105573005-0.0412439105573005
3300.0342091785256804-0.0342091785256804
3400.00818064944550704-0.00818064944550704
3500.0412439105573005-0.0412439105573005
3600.0342091785256804-0.0342091785256804
3700.134278586108723-0.134278586108723
3800.244866402397831-0.244866402397831
3900.00818064944550704-0.00818064944550704
400-0.1316242224376630.131624222437663
4100.0120268549632385-0.0120268549632385
4200.00818064944550704-0.00818064944550704
4300.0342091785256804-0.0342091785256804
4400.00818064944550704-0.00818064944550704
4500.0412439105573005-0.0412439105573005
4600.0152153814771272-0.0152153814771272
4700.300111987072067-0.300111987072067
4800.0342091785256804-0.0342091785256804
4900.0342091785256804-0.0342091785256804
5000.0342091785256804-0.0342091785256804
5100.251901134429451-0.251901134429451
5200.0860677334661075-0.0860677334661075
530-0.1316242224376630.131624222437663
5400.0342091785256804-0.0342091785256804
5510.2670487259602730.732951274039727
5600.101215324996929-0.101215324996929
5700.0412439105573005-0.0412439105573005
580-0.01400167411693480.0140016741169348
5900.0120268549632385-0.0120268549632385
600-0.1576527515178370.157652751517837
6100.127243854077103-0.127243854077103
620-0.1316242224376630.131624222437663
6300.0412439105573005-0.0412439105573005
640-0.01400167411693480.0140016741169348
6500.00818064944550704-0.00818064944550704
6610.3001119870720660.699888012927934
6710.2779296635096250.722070336490375






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
550.0008662065728339310.001732413145667860.999133793427166
560.003535325950226590.007070651900453170.996464674049773
570.003690194607895960.007380389215791920.996309805392104
580.00156061185608310.00312122371216620.998439388143917

\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 & 0.000866206572833931 & 0.00173241314566786 & 0.999133793427166 \tabularnewline
56 & 0.00353532595022659 & 0.00707065190045317 & 0.996464674049773 \tabularnewline
57 & 0.00369019460789596 & 0.00738038921579192 & 0.996309805392104 \tabularnewline
58 & 0.0015606118560831 & 0.0031212237121662 & 0.998439388143917 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200426&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]0.000866206572833931[/C][C]0.00173241314566786[/C][C]0.999133793427166[/C][/ROW]
[ROW][C]56[/C][C]0.00353532595022659[/C][C]0.00707065190045317[/C][C]0.996464674049773[/C][/ROW]
[ROW][C]57[/C][C]0.00369019460789596[/C][C]0.00738038921579192[/C][C]0.996309805392104[/C][/ROW]
[ROW][C]58[/C][C]0.0015606118560831[/C][C]0.0031212237121662[/C][C]0.998439388143917[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200426&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200426&T=5

As an alternative you can also use a QR Code:  
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
550.0008662065728339310.001732413145667860.999133793427166
560.003535325950226590.007070651900453170.996464674049773
570.003690194607895960.007380389215791920.996309805392104
580.00156061185608310.00312122371216620.998439388143917






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level501NOK
5% type I error level501NOK
10% type I error level501NOK

\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 & 50 & 1 & NOK \tabularnewline
5% type I error level & 50 & 1 & NOK \tabularnewline
10% type I error level & 50 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200426&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]50[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]50[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200426&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200426&T=6

As an alternative you can also use a QR Code:  
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 level501NOK
5% type I error level501NOK
10% type I error level501NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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')
}