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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 09:09:36 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356099024w2t90k9rabgmuq5.htm/, Retrieved Thu, 31 Oct 2024 23:56:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203689, Retrieved Thu, 31 Oct 2024 23:56:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [chikwadraat t40] [2012-12-21 13:40:35] [885d0a915dae889a27a534b235a2244f]
- RMPD    [Multiple Regression] [deel 5 multiple r...] [2012-12-21 14:09:36] [c070ba665715e84c88fd69c2fb510425] [Current]
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Dataseries X:
0	1
0	0
0	0
0	0
0	0
0	0
0	0
0	1
0	0
0	0
0	1
0	0
0	0
0	1
0	0
0	1
1	1
0	1
0	0
1	1
0	0
0	0
0	0
0	0
0	1
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	1
0	0
0	0
0	1
0	0
0	0
0	1
1	0
0	0
0	0
0	1
0	0
0	0
0	0
0	0
0	0
0	0
0	1
1	1
0	0
1	0
0	0
0	1
0	0
0	0
0	0
1	1
0	1
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
0	1
0	0
0	0
1	1
0	1
0	0
0	0
0	0
1	0
0	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'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.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=203689&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]'Gwilym Jenkins' @ jenkins.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=203689&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203689&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'Gwilym Jenkins' @ jenkins.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] = + 0.0476190476190476 + 0.213250517598344`T40\r\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  +  0.0476190476190476 +  0.213250517598344`T40\r\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203689&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  +  0.0476190476190476 +  0.213250517598344`T40\r\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203689&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203689&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] = + 0.0476190476190476 + 0.213250517598344`T40\r\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.04761904761904760.037121.28280.203080.10154
`T40\r\r`0.2132505175983440.0717792.97090.0038710.001935

\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) & 0.0476190476190476 & 0.03712 & 1.2828 & 0.20308 & 0.10154 \tabularnewline
`T40\r\r` & 0.213250517598344 & 0.071779 & 2.9709 & 0.003871 & 0.001935 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203689&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]0.0476190476190476[/C][C]0.03712[/C][C]1.2828[/C][C]0.20308[/C][C]0.10154[/C][/ROW]
[ROW][C]`T40\r\r`[/C][C]0.213250517598344[/C][C]0.071779[/C][C]2.9709[/C][C]0.003871[/C][C]0.001935[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203689&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203689&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)0.04761904761904760.037121.28280.203080.10154
`T40\r\r`0.2132505175983440.0717792.97090.0038710.001935







Multiple Linear Regression - Regression Statistics
Multiple R0.308359738983709
R-squared0.0950857286261013
Adjusted R-squared0.084312939681174
F-TEST (value)8.82647280218692
F-TEST (DF numerator)1
F-TEST (DF denominator)84
p-value0.00387075225747924
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.294633053980841
Sum Squared Residuals7.29192546583851

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.308359738983709 \tabularnewline
R-squared & 0.0950857286261013 \tabularnewline
Adjusted R-squared & 0.084312939681174 \tabularnewline
F-TEST (value) & 8.82647280218692 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value & 0.00387075225747924 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.294633053980841 \tabularnewline
Sum Squared Residuals & 7.29192546583851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203689&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.308359738983709[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0950857286261013[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.084312939681174[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.82647280218692[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/C][/ROW]
[ROW][C]p-value[/C][C]0.00387075225747924[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.294633053980841[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.29192546583851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203689&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203689&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 R0.308359738983709
R-squared0.0950857286261013
Adjusted R-squared0.084312939681174
F-TEST (value)8.82647280218692
F-TEST (DF numerator)1
F-TEST (DF denominator)84
p-value0.00387075225747924
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.294633053980841
Sum Squared Residuals7.29192546583851







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.260869565217391-0.260869565217391
200.0476190476190475-0.0476190476190475
300.0476190476190476-0.0476190476190476
400.0476190476190476-0.0476190476190476
500.0476190476190476-0.0476190476190476
600.0476190476190476-0.0476190476190476
700.0476190476190476-0.0476190476190476
800.260869565217391-0.260869565217391
900.0476190476190476-0.0476190476190476
1000.0476190476190476-0.0476190476190476
1100.260869565217391-0.260869565217391
1200.0476190476190476-0.0476190476190476
1300.0476190476190476-0.0476190476190476
1400.260869565217391-0.260869565217391
1500.0476190476190476-0.0476190476190476
1600.260869565217391-0.260869565217391
1710.2608695652173910.739130434782609
1800.260869565217391-0.260869565217391
1900.0476190476190476-0.0476190476190476
2010.2608695652173910.739130434782609
2100.0476190476190476-0.0476190476190476
2200.0476190476190476-0.0476190476190476
2300.0476190476190476-0.0476190476190476
2400.0476190476190476-0.0476190476190476
2500.260869565217391-0.260869565217391
2600.0476190476190476-0.0476190476190476
2700.0476190476190476-0.0476190476190476
2800.0476190476190476-0.0476190476190476
2900.0476190476190476-0.0476190476190476
3000.0476190476190476-0.0476190476190476
3100.0476190476190476-0.0476190476190476
3200.0476190476190476-0.0476190476190476
3300.0476190476190476-0.0476190476190476
3400.260869565217391-0.260869565217391
3500.0476190476190476-0.0476190476190476
3600.0476190476190476-0.0476190476190476
3700.260869565217391-0.260869565217391
3800.0476190476190476-0.0476190476190476
3900.0476190476190476-0.0476190476190476
4000.260869565217391-0.260869565217391
4110.04761904761904780.952380952380952
4200.0476190476190476-0.0476190476190476
4300.0476190476190476-0.0476190476190476
4400.260869565217391-0.260869565217391
4500.0476190476190476-0.0476190476190476
4600.0476190476190476-0.0476190476190476
4700.0476190476190476-0.0476190476190476
4800.0476190476190476-0.0476190476190476
4900.0476190476190476-0.0476190476190476
5000.0476190476190476-0.0476190476190476
5100.260869565217391-0.260869565217391
5210.2608695652173910.739130434782609
5300.0476190476190476-0.0476190476190476
5410.04761904761904780.952380952380952
5500.0476190476190476-0.0476190476190476
5600.260869565217391-0.260869565217391
5700.0476190476190476-0.0476190476190476
5800.0476190476190476-0.0476190476190476
5900.0476190476190476-0.0476190476190476
6010.2608695652173910.739130434782609
6100.260869565217391-0.260869565217391
6200.0476190476190476-0.0476190476190476
6300.0476190476190476-0.0476190476190476
6400.260869565217391-0.260869565217391
6500.0476190476190476-0.0476190476190476
6600.0476190476190476-0.0476190476190476
6710.2608695652173910.739130434782609
6800.0476190476190476-0.0476190476190476
6900.0476190476190476-0.0476190476190476
7000.0476190476190476-0.0476190476190476
7100.0476190476190476-0.0476190476190476
7200.0476190476190476-0.0476190476190476
7300.0476190476190476-0.0476190476190476
7400.0476190476190476-0.0476190476190476
7500.0476190476190476-0.0476190476190476
7600.260869565217391-0.260869565217391
7700.0476190476190476-0.0476190476190476
7800.0476190476190476-0.0476190476190476
7910.2608695652173910.739130434782609
8000.260869565217391-0.260869565217391
8100.0476190476190476-0.0476190476190476
8200.0476190476190476-0.0476190476190476
8300.0476190476190476-0.0476190476190476
8410.04761904761904780.952380952380952
8500.0476190476190476-0.0476190476190476
8600.0476190476190476-0.0476190476190476

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
2 & 0 & 0.0476190476190475 & -0.0476190476190475 \tabularnewline
3 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
4 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
5 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
6 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
7 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
8 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
9 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
10 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
11 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
12 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
13 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
14 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
15 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
16 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
17 & 1 & 0.260869565217391 & 0.739130434782609 \tabularnewline
18 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
19 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
20 & 1 & 0.260869565217391 & 0.739130434782609 \tabularnewline
21 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
22 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
23 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
24 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
25 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
26 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
27 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
28 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
29 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
30 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
31 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
32 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
33 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
34 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
35 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
36 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
37 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
38 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
39 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
40 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
41 & 1 & 0.0476190476190478 & 0.952380952380952 \tabularnewline
42 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
43 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
44 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
45 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
46 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
47 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
48 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
49 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
50 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
51 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
52 & 1 & 0.260869565217391 & 0.739130434782609 \tabularnewline
53 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
54 & 1 & 0.0476190476190478 & 0.952380952380952 \tabularnewline
55 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
56 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
57 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
58 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
59 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
60 & 1 & 0.260869565217391 & 0.739130434782609 \tabularnewline
61 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
62 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
63 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
64 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
65 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
66 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
67 & 1 & 0.260869565217391 & 0.739130434782609 \tabularnewline
68 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
69 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
70 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
71 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
72 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
73 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
74 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
75 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
76 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
77 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
78 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
79 & 1 & 0.260869565217391 & 0.739130434782609 \tabularnewline
80 & 0 & 0.260869565217391 & -0.260869565217391 \tabularnewline
81 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
82 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
83 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
84 & 1 & 0.0476190476190478 & 0.952380952380952 \tabularnewline
85 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
86 & 0 & 0.0476190476190476 & -0.0476190476190476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203689&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]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.0476190476190475[/C][C]-0.0476190476190475[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.260869565217391[/C][C]0.739130434782609[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.260869565217391[/C][C]0.739130434782609[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.0476190476190478[/C][C]0.952380952380952[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.260869565217391[/C][C]0.739130434782609[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.0476190476190478[/C][C]0.952380952380952[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.260869565217391[/C][C]0.739130434782609[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.260869565217391[/C][C]0.739130434782609[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.260869565217391[/C][C]0.739130434782609[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.260869565217391[/C][C]-0.260869565217391[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.0476190476190478[/C][C]0.952380952380952[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.0476190476190476[/C][C]-0.0476190476190476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203689&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203689&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
100.260869565217391-0.260869565217391
200.0476190476190475-0.0476190476190475
300.0476190476190476-0.0476190476190476
400.0476190476190476-0.0476190476190476
500.0476190476190476-0.0476190476190476
600.0476190476190476-0.0476190476190476
700.0476190476190476-0.0476190476190476
800.260869565217391-0.260869565217391
900.0476190476190476-0.0476190476190476
1000.0476190476190476-0.0476190476190476
1100.260869565217391-0.260869565217391
1200.0476190476190476-0.0476190476190476
1300.0476190476190476-0.0476190476190476
1400.260869565217391-0.260869565217391
1500.0476190476190476-0.0476190476190476
1600.260869565217391-0.260869565217391
1710.2608695652173910.739130434782609
1800.260869565217391-0.260869565217391
1900.0476190476190476-0.0476190476190476
2010.2608695652173910.739130434782609
2100.0476190476190476-0.0476190476190476
2200.0476190476190476-0.0476190476190476
2300.0476190476190476-0.0476190476190476
2400.0476190476190476-0.0476190476190476
2500.260869565217391-0.260869565217391
2600.0476190476190476-0.0476190476190476
2700.0476190476190476-0.0476190476190476
2800.0476190476190476-0.0476190476190476
2900.0476190476190476-0.0476190476190476
3000.0476190476190476-0.0476190476190476
3100.0476190476190476-0.0476190476190476
3200.0476190476190476-0.0476190476190476
3300.0476190476190476-0.0476190476190476
3400.260869565217391-0.260869565217391
3500.0476190476190476-0.0476190476190476
3600.0476190476190476-0.0476190476190476
3700.260869565217391-0.260869565217391
3800.0476190476190476-0.0476190476190476
3900.0476190476190476-0.0476190476190476
4000.260869565217391-0.260869565217391
4110.04761904761904780.952380952380952
4200.0476190476190476-0.0476190476190476
4300.0476190476190476-0.0476190476190476
4400.260869565217391-0.260869565217391
4500.0476190476190476-0.0476190476190476
4600.0476190476190476-0.0476190476190476
4700.0476190476190476-0.0476190476190476
4800.0476190476190476-0.0476190476190476
4900.0476190476190476-0.0476190476190476
5000.0476190476190476-0.0476190476190476
5100.260869565217391-0.260869565217391
5210.2608695652173910.739130434782609
5300.0476190476190476-0.0476190476190476
5410.04761904761904780.952380952380952
5500.0476190476190476-0.0476190476190476
5600.260869565217391-0.260869565217391
5700.0476190476190476-0.0476190476190476
5800.0476190476190476-0.0476190476190476
5900.0476190476190476-0.0476190476190476
6010.2608695652173910.739130434782609
6100.260869565217391-0.260869565217391
6200.0476190476190476-0.0476190476190476
6300.0476190476190476-0.0476190476190476
6400.260869565217391-0.260869565217391
6500.0476190476190476-0.0476190476190476
6600.0476190476190476-0.0476190476190476
6710.2608695652173910.739130434782609
6800.0476190476190476-0.0476190476190476
6900.0476190476190476-0.0476190476190476
7000.0476190476190476-0.0476190476190476
7100.0476190476190476-0.0476190476190476
7200.0476190476190476-0.0476190476190476
7300.0476190476190476-0.0476190476190476
7400.0476190476190476-0.0476190476190476
7500.0476190476190476-0.0476190476190476
7600.260869565217391-0.260869565217391
7700.0476190476190476-0.0476190476190476
7800.0476190476190476-0.0476190476190476
7910.2608695652173910.739130434782609
8000.260869565217391-0.260869565217391
8100.0476190476190476-0.0476190476190476
8200.0476190476190476-0.0476190476190476
8300.0476190476190476-0.0476190476190476
8410.04761904761904780.952380952380952
8500.0476190476190476-0.0476190476190476
8600.0476190476190476-0.0476190476190476







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5001
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1241092388295030.2482184776590060.875890761170497
180.1009544109824330.2019088219648650.899045589017567
190.06862045476437480.137240909528750.931379545235625
200.4487798660602040.8975597321204090.551220133939796
210.3748719239576310.7497438479152620.625128076042369
220.3062237190206340.6124474380412690.693776280979366
230.2445452668958580.4890905337917170.755454733104142
240.1908770602194020.3817541204388040.809122939780598
250.177827177717150.35565435543430.82217282228285
260.1352649851046460.2705299702092930.864735014895354
270.1005897903521050.201179580704210.899410209647895
280.07313193339283260.1462638667856650.926868066607167
290.05198358863763880.1039671772752780.948016411362361
300.03612989937854820.07225979875709640.963870100621452
310.02455581041079570.04911162082159140.975444189589204
320.01632234046955250.03264468093910510.983677659530448
330.0106123301046510.02122466020930190.989387669895349
340.009407374774330810.01881474954866160.990592625225669
350.005967976306482180.01193595261296440.994032023693518
360.003705115429739580.007410230859479170.99629488457026
370.003208872540298270.006417745080596550.996791127459702
380.001943358178762150.003886716357524310.998056641821238
390.001152260808668540.002304521617337070.998847739191331
400.000993652562997670.001987305125995340.999006347437002
410.09206383401568890.1841276680313780.907936165984311
420.06900692017747140.1380138403549430.930993079822529
430.05071042771971360.1014208554394270.949289572280286
440.04833686402284380.09667372804568760.951663135977156
450.03473381909965750.0694676381993150.965266180900342
460.02445678708992330.04891357417984650.975543212910077
470.01687045337202090.03374090674404190.983129546627979
480.01139854856986150.02279709713972290.988601451430139
490.007542057968429270.01508411593685850.992457942031571
500.004886266256797940.009772532513595880.995113733743202
510.004917486803605830.009834973607211650.995082513196394
520.04114567948613930.08229135897227860.958854320513861
530.02928459906325580.05856919812651160.970715400936744
540.3297851983776740.6595703967553470.670214801622326
550.2742211123804680.5484422247609370.725778887619532
560.2884578796370360.5769157592740720.711542120362964
570.2358950340733150.471790068146630.764104965926685
580.1888514811594420.3777029623188830.811148518840558
590.1478619823130720.2957239646261440.852138017686928
600.3612996182798890.7225992365597780.638700381720111
610.3734188146204570.7468376292409140.626581185379543
620.3107040111079650.6214080222159290.689295988892035
630.2526048021787690.5052096043575380.747395197821231
640.2938367696260910.5876735392521830.706163230373909
650.2359309825157580.4718619650315160.764069017484242
660.1844653348104040.3689306696208070.815534665189596
670.3890125314489720.7780250628979440.610987468551028
680.3190628124356250.6381256248712510.680937187564375
690.2542570581930240.5085141163860480.745742941806976
700.1964085800079960.3928171600159920.803591419992004
710.1467349473254860.2934698946509730.853265052674514
720.1057761491751430.2115522983502850.894223850824857
730.07340873000565220.1468174600113040.926591269994348
740.0489476267073660.09789525341473210.951052373292634
750.03131047092570570.06262094185141140.968689529074294
760.03545039462762560.07090078925525110.964549605372374
770.02139888981900080.04279777963800160.978601110180999
780.01231346819948440.02462693639896870.987686531800516
790.08238416498678830.1647683299735770.917615835013212
800.0454496371012870.0908992742025740.954550362898713
810.02430528031996110.04861056063992210.975694719680039

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0 & 0 & 1 \tabularnewline
6 & 0 & 0 & 1 \tabularnewline
7 & 0 & 0 & 1 \tabularnewline
8 & 0 & 0 & 1 \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 & 0.124109238829503 & 0.248218477659006 & 0.875890761170497 \tabularnewline
18 & 0.100954410982433 & 0.201908821964865 & 0.899045589017567 \tabularnewline
19 & 0.0686204547643748 & 0.13724090952875 & 0.931379545235625 \tabularnewline
20 & 0.448779866060204 & 0.897559732120409 & 0.551220133939796 \tabularnewline
21 & 0.374871923957631 & 0.749743847915262 & 0.625128076042369 \tabularnewline
22 & 0.306223719020634 & 0.612447438041269 & 0.693776280979366 \tabularnewline
23 & 0.244545266895858 & 0.489090533791717 & 0.755454733104142 \tabularnewline
24 & 0.190877060219402 & 0.381754120438804 & 0.809122939780598 \tabularnewline
25 & 0.17782717771715 & 0.3556543554343 & 0.82217282228285 \tabularnewline
26 & 0.135264985104646 & 0.270529970209293 & 0.864735014895354 \tabularnewline
27 & 0.100589790352105 & 0.20117958070421 & 0.899410209647895 \tabularnewline
28 & 0.0731319333928326 & 0.146263866785665 & 0.926868066607167 \tabularnewline
29 & 0.0519835886376388 & 0.103967177275278 & 0.948016411362361 \tabularnewline
30 & 0.0361298993785482 & 0.0722597987570964 & 0.963870100621452 \tabularnewline
31 & 0.0245558104107957 & 0.0491116208215914 & 0.975444189589204 \tabularnewline
32 & 0.0163223404695525 & 0.0326446809391051 & 0.983677659530448 \tabularnewline
33 & 0.010612330104651 & 0.0212246602093019 & 0.989387669895349 \tabularnewline
34 & 0.00940737477433081 & 0.0188147495486616 & 0.990592625225669 \tabularnewline
35 & 0.00596797630648218 & 0.0119359526129644 & 0.994032023693518 \tabularnewline
36 & 0.00370511542973958 & 0.00741023085947917 & 0.99629488457026 \tabularnewline
37 & 0.00320887254029827 & 0.00641774508059655 & 0.996791127459702 \tabularnewline
38 & 0.00194335817876215 & 0.00388671635752431 & 0.998056641821238 \tabularnewline
39 & 0.00115226080866854 & 0.00230452161733707 & 0.998847739191331 \tabularnewline
40 & 0.00099365256299767 & 0.00198730512599534 & 0.999006347437002 \tabularnewline
41 & 0.0920638340156889 & 0.184127668031378 & 0.907936165984311 \tabularnewline
42 & 0.0690069201774714 & 0.138013840354943 & 0.930993079822529 \tabularnewline
43 & 0.0507104277197136 & 0.101420855439427 & 0.949289572280286 \tabularnewline
44 & 0.0483368640228438 & 0.0966737280456876 & 0.951663135977156 \tabularnewline
45 & 0.0347338190996575 & 0.069467638199315 & 0.965266180900342 \tabularnewline
46 & 0.0244567870899233 & 0.0489135741798465 & 0.975543212910077 \tabularnewline
47 & 0.0168704533720209 & 0.0337409067440419 & 0.983129546627979 \tabularnewline
48 & 0.0113985485698615 & 0.0227970971397229 & 0.988601451430139 \tabularnewline
49 & 0.00754205796842927 & 0.0150841159368585 & 0.992457942031571 \tabularnewline
50 & 0.00488626625679794 & 0.00977253251359588 & 0.995113733743202 \tabularnewline
51 & 0.00491748680360583 & 0.00983497360721165 & 0.995082513196394 \tabularnewline
52 & 0.0411456794861393 & 0.0822913589722786 & 0.958854320513861 \tabularnewline
53 & 0.0292845990632558 & 0.0585691981265116 & 0.970715400936744 \tabularnewline
54 & 0.329785198377674 & 0.659570396755347 & 0.670214801622326 \tabularnewline
55 & 0.274221112380468 & 0.548442224760937 & 0.725778887619532 \tabularnewline
56 & 0.288457879637036 & 0.576915759274072 & 0.711542120362964 \tabularnewline
57 & 0.235895034073315 & 0.47179006814663 & 0.764104965926685 \tabularnewline
58 & 0.188851481159442 & 0.377702962318883 & 0.811148518840558 \tabularnewline
59 & 0.147861982313072 & 0.295723964626144 & 0.852138017686928 \tabularnewline
60 & 0.361299618279889 & 0.722599236559778 & 0.638700381720111 \tabularnewline
61 & 0.373418814620457 & 0.746837629240914 & 0.626581185379543 \tabularnewline
62 & 0.310704011107965 & 0.621408022215929 & 0.689295988892035 \tabularnewline
63 & 0.252604802178769 & 0.505209604357538 & 0.747395197821231 \tabularnewline
64 & 0.293836769626091 & 0.587673539252183 & 0.706163230373909 \tabularnewline
65 & 0.235930982515758 & 0.471861965031516 & 0.764069017484242 \tabularnewline
66 & 0.184465334810404 & 0.368930669620807 & 0.815534665189596 \tabularnewline
67 & 0.389012531448972 & 0.778025062897944 & 0.610987468551028 \tabularnewline
68 & 0.319062812435625 & 0.638125624871251 & 0.680937187564375 \tabularnewline
69 & 0.254257058193024 & 0.508514116386048 & 0.745742941806976 \tabularnewline
70 & 0.196408580007996 & 0.392817160015992 & 0.803591419992004 \tabularnewline
71 & 0.146734947325486 & 0.293469894650973 & 0.853265052674514 \tabularnewline
72 & 0.105776149175143 & 0.211552298350285 & 0.894223850824857 \tabularnewline
73 & 0.0734087300056522 & 0.146817460011304 & 0.926591269994348 \tabularnewline
74 & 0.048947626707366 & 0.0978952534147321 & 0.951052373292634 \tabularnewline
75 & 0.0313104709257057 & 0.0626209418514114 & 0.968689529074294 \tabularnewline
76 & 0.0354503946276256 & 0.0709007892552511 & 0.964549605372374 \tabularnewline
77 & 0.0213988898190008 & 0.0427977796380016 & 0.978601110180999 \tabularnewline
78 & 0.0123134681994844 & 0.0246269363989687 & 0.987686531800516 \tabularnewline
79 & 0.0823841649867883 & 0.164768329973577 & 0.917615835013212 \tabularnewline
80 & 0.045449637101287 & 0.090899274202574 & 0.954550362898713 \tabularnewline
81 & 0.0243052803199611 & 0.0486105606399221 & 0.975694719680039 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203689&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]5[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0[/C][C]1[/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]0.124109238829503[/C][C]0.248218477659006[/C][C]0.875890761170497[/C][/ROW]
[ROW][C]18[/C][C]0.100954410982433[/C][C]0.201908821964865[/C][C]0.899045589017567[/C][/ROW]
[ROW][C]19[/C][C]0.0686204547643748[/C][C]0.13724090952875[/C][C]0.931379545235625[/C][/ROW]
[ROW][C]20[/C][C]0.448779866060204[/C][C]0.897559732120409[/C][C]0.551220133939796[/C][/ROW]
[ROW][C]21[/C][C]0.374871923957631[/C][C]0.749743847915262[/C][C]0.625128076042369[/C][/ROW]
[ROW][C]22[/C][C]0.306223719020634[/C][C]0.612447438041269[/C][C]0.693776280979366[/C][/ROW]
[ROW][C]23[/C][C]0.244545266895858[/C][C]0.489090533791717[/C][C]0.755454733104142[/C][/ROW]
[ROW][C]24[/C][C]0.190877060219402[/C][C]0.381754120438804[/C][C]0.809122939780598[/C][/ROW]
[ROW][C]25[/C][C]0.17782717771715[/C][C]0.3556543554343[/C][C]0.82217282228285[/C][/ROW]
[ROW][C]26[/C][C]0.135264985104646[/C][C]0.270529970209293[/C][C]0.864735014895354[/C][/ROW]
[ROW][C]27[/C][C]0.100589790352105[/C][C]0.20117958070421[/C][C]0.899410209647895[/C][/ROW]
[ROW][C]28[/C][C]0.0731319333928326[/C][C]0.146263866785665[/C][C]0.926868066607167[/C][/ROW]
[ROW][C]29[/C][C]0.0519835886376388[/C][C]0.103967177275278[/C][C]0.948016411362361[/C][/ROW]
[ROW][C]30[/C][C]0.0361298993785482[/C][C]0.0722597987570964[/C][C]0.963870100621452[/C][/ROW]
[ROW][C]31[/C][C]0.0245558104107957[/C][C]0.0491116208215914[/C][C]0.975444189589204[/C][/ROW]
[ROW][C]32[/C][C]0.0163223404695525[/C][C]0.0326446809391051[/C][C]0.983677659530448[/C][/ROW]
[ROW][C]33[/C][C]0.010612330104651[/C][C]0.0212246602093019[/C][C]0.989387669895349[/C][/ROW]
[ROW][C]34[/C][C]0.00940737477433081[/C][C]0.0188147495486616[/C][C]0.990592625225669[/C][/ROW]
[ROW][C]35[/C][C]0.00596797630648218[/C][C]0.0119359526129644[/C][C]0.994032023693518[/C][/ROW]
[ROW][C]36[/C][C]0.00370511542973958[/C][C]0.00741023085947917[/C][C]0.99629488457026[/C][/ROW]
[ROW][C]37[/C][C]0.00320887254029827[/C][C]0.00641774508059655[/C][C]0.996791127459702[/C][/ROW]
[ROW][C]38[/C][C]0.00194335817876215[/C][C]0.00388671635752431[/C][C]0.998056641821238[/C][/ROW]
[ROW][C]39[/C][C]0.00115226080866854[/C][C]0.00230452161733707[/C][C]0.998847739191331[/C][/ROW]
[ROW][C]40[/C][C]0.00099365256299767[/C][C]0.00198730512599534[/C][C]0.999006347437002[/C][/ROW]
[ROW][C]41[/C][C]0.0920638340156889[/C][C]0.184127668031378[/C][C]0.907936165984311[/C][/ROW]
[ROW][C]42[/C][C]0.0690069201774714[/C][C]0.138013840354943[/C][C]0.930993079822529[/C][/ROW]
[ROW][C]43[/C][C]0.0507104277197136[/C][C]0.101420855439427[/C][C]0.949289572280286[/C][/ROW]
[ROW][C]44[/C][C]0.0483368640228438[/C][C]0.0966737280456876[/C][C]0.951663135977156[/C][/ROW]
[ROW][C]45[/C][C]0.0347338190996575[/C][C]0.069467638199315[/C][C]0.965266180900342[/C][/ROW]
[ROW][C]46[/C][C]0.0244567870899233[/C][C]0.0489135741798465[/C][C]0.975543212910077[/C][/ROW]
[ROW][C]47[/C][C]0.0168704533720209[/C][C]0.0337409067440419[/C][C]0.983129546627979[/C][/ROW]
[ROW][C]48[/C][C]0.0113985485698615[/C][C]0.0227970971397229[/C][C]0.988601451430139[/C][/ROW]
[ROW][C]49[/C][C]0.00754205796842927[/C][C]0.0150841159368585[/C][C]0.992457942031571[/C][/ROW]
[ROW][C]50[/C][C]0.00488626625679794[/C][C]0.00977253251359588[/C][C]0.995113733743202[/C][/ROW]
[ROW][C]51[/C][C]0.00491748680360583[/C][C]0.00983497360721165[/C][C]0.995082513196394[/C][/ROW]
[ROW][C]52[/C][C]0.0411456794861393[/C][C]0.0822913589722786[/C][C]0.958854320513861[/C][/ROW]
[ROW][C]53[/C][C]0.0292845990632558[/C][C]0.0585691981265116[/C][C]0.970715400936744[/C][/ROW]
[ROW][C]54[/C][C]0.329785198377674[/C][C]0.659570396755347[/C][C]0.670214801622326[/C][/ROW]
[ROW][C]55[/C][C]0.274221112380468[/C][C]0.548442224760937[/C][C]0.725778887619532[/C][/ROW]
[ROW][C]56[/C][C]0.288457879637036[/C][C]0.576915759274072[/C][C]0.711542120362964[/C][/ROW]
[ROW][C]57[/C][C]0.235895034073315[/C][C]0.47179006814663[/C][C]0.764104965926685[/C][/ROW]
[ROW][C]58[/C][C]0.188851481159442[/C][C]0.377702962318883[/C][C]0.811148518840558[/C][/ROW]
[ROW][C]59[/C][C]0.147861982313072[/C][C]0.295723964626144[/C][C]0.852138017686928[/C][/ROW]
[ROW][C]60[/C][C]0.361299618279889[/C][C]0.722599236559778[/C][C]0.638700381720111[/C][/ROW]
[ROW][C]61[/C][C]0.373418814620457[/C][C]0.746837629240914[/C][C]0.626581185379543[/C][/ROW]
[ROW][C]62[/C][C]0.310704011107965[/C][C]0.621408022215929[/C][C]0.689295988892035[/C][/ROW]
[ROW][C]63[/C][C]0.252604802178769[/C][C]0.505209604357538[/C][C]0.747395197821231[/C][/ROW]
[ROW][C]64[/C][C]0.293836769626091[/C][C]0.587673539252183[/C][C]0.706163230373909[/C][/ROW]
[ROW][C]65[/C][C]0.235930982515758[/C][C]0.471861965031516[/C][C]0.764069017484242[/C][/ROW]
[ROW][C]66[/C][C]0.184465334810404[/C][C]0.368930669620807[/C][C]0.815534665189596[/C][/ROW]
[ROW][C]67[/C][C]0.389012531448972[/C][C]0.778025062897944[/C][C]0.610987468551028[/C][/ROW]
[ROW][C]68[/C][C]0.319062812435625[/C][C]0.638125624871251[/C][C]0.680937187564375[/C][/ROW]
[ROW][C]69[/C][C]0.254257058193024[/C][C]0.508514116386048[/C][C]0.745742941806976[/C][/ROW]
[ROW][C]70[/C][C]0.196408580007996[/C][C]0.392817160015992[/C][C]0.803591419992004[/C][/ROW]
[ROW][C]71[/C][C]0.146734947325486[/C][C]0.293469894650973[/C][C]0.853265052674514[/C][/ROW]
[ROW][C]72[/C][C]0.105776149175143[/C][C]0.211552298350285[/C][C]0.894223850824857[/C][/ROW]
[ROW][C]73[/C][C]0.0734087300056522[/C][C]0.146817460011304[/C][C]0.926591269994348[/C][/ROW]
[ROW][C]74[/C][C]0.048947626707366[/C][C]0.0978952534147321[/C][C]0.951052373292634[/C][/ROW]
[ROW][C]75[/C][C]0.0313104709257057[/C][C]0.0626209418514114[/C][C]0.968689529074294[/C][/ROW]
[ROW][C]76[/C][C]0.0354503946276256[/C][C]0.0709007892552511[/C][C]0.964549605372374[/C][/ROW]
[ROW][C]77[/C][C]0.0213988898190008[/C][C]0.0427977796380016[/C][C]0.978601110180999[/C][/ROW]
[ROW][C]78[/C][C]0.0123134681994844[/C][C]0.0246269363989687[/C][C]0.987686531800516[/C][/ROW]
[ROW][C]79[/C][C]0.0823841649867883[/C][C]0.164768329973577[/C][C]0.917615835013212[/C][/ROW]
[ROW][C]80[/C][C]0.045449637101287[/C][C]0.090899274202574[/C][C]0.954550362898713[/C][/ROW]
[ROW][C]81[/C][C]0.0243052803199611[/C][C]0.0486105606399221[/C][C]0.975694719680039[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203689&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203689&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
5001
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1241092388295030.2482184776590060.875890761170497
180.1009544109824330.2019088219648650.899045589017567
190.06862045476437480.137240909528750.931379545235625
200.4487798660602040.8975597321204090.551220133939796
210.3748719239576310.7497438479152620.625128076042369
220.3062237190206340.6124474380412690.693776280979366
230.2445452668958580.4890905337917170.755454733104142
240.1908770602194020.3817541204388040.809122939780598
250.177827177717150.35565435543430.82217282228285
260.1352649851046460.2705299702092930.864735014895354
270.1005897903521050.201179580704210.899410209647895
280.07313193339283260.1462638667856650.926868066607167
290.05198358863763880.1039671772752780.948016411362361
300.03612989937854820.07225979875709640.963870100621452
310.02455581041079570.04911162082159140.975444189589204
320.01632234046955250.03264468093910510.983677659530448
330.0106123301046510.02122466020930190.989387669895349
340.009407374774330810.01881474954866160.990592625225669
350.005967976306482180.01193595261296440.994032023693518
360.003705115429739580.007410230859479170.99629488457026
370.003208872540298270.006417745080596550.996791127459702
380.001943358178762150.003886716357524310.998056641821238
390.001152260808668540.002304521617337070.998847739191331
400.000993652562997670.001987305125995340.999006347437002
410.09206383401568890.1841276680313780.907936165984311
420.06900692017747140.1380138403549430.930993079822529
430.05071042771971360.1014208554394270.949289572280286
440.04833686402284380.09667372804568760.951663135977156
450.03473381909965750.0694676381993150.965266180900342
460.02445678708992330.04891357417984650.975543212910077
470.01687045337202090.03374090674404190.983129546627979
480.01139854856986150.02279709713972290.988601451430139
490.007542057968429270.01508411593685850.992457942031571
500.004886266256797940.009772532513595880.995113733743202
510.004917486803605830.009834973607211650.995082513196394
520.04114567948613930.08229135897227860.958854320513861
530.02928459906325580.05856919812651160.970715400936744
540.3297851983776740.6595703967553470.670214801622326
550.2742211123804680.5484422247609370.725778887619532
560.2884578796370360.5769157592740720.711542120362964
570.2358950340733150.471790068146630.764104965926685
580.1888514811594420.3777029623188830.811148518840558
590.1478619823130720.2957239646261440.852138017686928
600.3612996182798890.7225992365597780.638700381720111
610.3734188146204570.7468376292409140.626581185379543
620.3107040111079650.6214080222159290.689295988892035
630.2526048021787690.5052096043575380.747395197821231
640.2938367696260910.5876735392521830.706163230373909
650.2359309825157580.4718619650315160.764069017484242
660.1844653348104040.3689306696208070.815534665189596
670.3890125314489720.7780250628979440.610987468551028
680.3190628124356250.6381256248712510.680937187564375
690.2542570581930240.5085141163860480.745742941806976
700.1964085800079960.3928171600159920.803591419992004
710.1467349473254860.2934698946509730.853265052674514
720.1057761491751430.2115522983502850.894223850824857
730.07340873000565220.1468174600113040.926591269994348
740.0489476267073660.09789525341473210.951052373292634
750.03131047092570570.06262094185141140.968689529074294
760.03545039462762560.07090078925525110.964549605372374
770.02139888981900080.04279777963800160.978601110180999
780.01231346819948440.02462693639896870.987686531800516
790.08238416498678830.1647683299735770.917615835013212
800.0454496371012870.0908992742025740.954550362898713
810.02430528031996110.04861056063992210.975694719680039







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level190.246753246753247NOK
5% type I error level310.402597402597403NOK
10% type I error level400.519480519480519NOK

\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 & 19 & 0.246753246753247 & NOK \tabularnewline
5% type I error level & 31 & 0.402597402597403 & NOK \tabularnewline
10% type I error level & 40 & 0.519480519480519 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203689&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]19[/C][C]0.246753246753247[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.402597402597403[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]40[/C][C]0.519480519480519[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203689&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203689&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 level190.246753246753247NOK
5% type I error level310.402597402597403NOK
10% type I error level400.519480519480519NOK



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')
}