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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 18 Dec 2012 14:54:25 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/18/t1355862002cbi038atxgeztpu.htm/, Retrieved Thu, 28 Mar 2024 08:38:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201630, Retrieved Thu, 28 Mar 2024 08:38:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-01 13:37:53] [b98453cac15ba1066b407e146608df68]
- R PD  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [paper 1way anova] [2012-12-11 12:19:36] [e01c78beec4051e03ee053d8bc2c6384]
- RMPD      [Multiple Regression] [paper deel 5 rfc ...] [2012-12-18 19:54:25] [074a00bbc2315ea54a3f557bcf69eecf] [Current]
- R  D        [Multiple Regression] [paper rfc multipl...] [2012-12-20 17:55:31] [e01c78beec4051e03ee053d8bc2c6384]
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Dataseries X:
1	0	0	0	1
0	0	0	0	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
1	0	0	0	0
0	0	0	0	1
0	0	0	0	0
1	0	0	0	0
0	0	0	0	0
0	1	0	1	0
1	0	0	0	0
0	1	0	1	1
1	1	0	1	1
1	1	1	1	0
1	0	0	0	0
0	0	0	0	1
1	1	1	1	1
0	0	0	1	0
0	1	0	1	1
0	0	0	1	1
0	0	0	1	1
1	1	0	0	1
0	1	0	1	0
0	0	0	0	1
0	1	0	0	0
0	0	0	0	1
0	0	0	1	0
0	0	0	0	0
0	0	0	0	0
0	0	0	1	0
1	0	0	0	1
0	0	0	0	0
0	0	0	0	0
1	1	0	1	0
0	1	0	0	1
0	0	0	1	1
1	0	0	1	0
0	1	1	1	1
0	1	0	0	1
0	0	0	1	1
1	0	0	0	0
0	0	0	1	0
0	0	0	1	1
0	0	0	0	0
0	0	0	0	1
0	0	0	1	1
0	0	0	0	0
1	1	0	0	0
1	1	1	1	0
0	0	0	0	1
0	1	1	0	0
0	0	0	0	0
1	1	0	0	1
0	1	0	1	1
0	0	0	0	1
0	0	0	0	1
1	1	1	1	1
1	0	0	0	1
0	1	0	1	0
0	0	0	0	0
1	0	0	0	1
0	0	0	0	0
0	0	0	0	0
1	1	1	1	0
0	0	0	0	0
0	0	0	0	1
0	1	0	0	0
0	0	0	0	0
0	0	0	0	1
0	1	0	0	1
0	1	0	0	0
0	0	0	0	1
1	0	0	1	1
0	0	0	0	1
0	1	0	1	1
1	1	1	0	1
1	0	0	1	0
0	0	0	0	0
0	1	0	0	1
0	0	0	0	0
0	1	1	0	0
0	0	0	1	1
0	0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201630&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201630&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201630&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0272654428933923 + 0.148049856809862T40[t] + 0.281155242564863Used[t] + 0.0632764567718833Useful[t] -0.0457739778191937Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0272654428933923 +  0.148049856809862T40[t] +  0.281155242564863Used[t] +  0.0632764567718833Useful[t] -0.0457739778191937Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201630&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0272654428933923 +  0.148049856809862T40[t] +  0.281155242564863Used[t] +  0.0632764567718833Useful[t] -0.0457739778191937Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201630&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201630&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] = -0.0272654428933923 + 0.148049856809862T40[t] + 0.281155242564863Used[t] + 0.0632764567718833Useful[t] -0.0457739778191937Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.02726544289339230.04629-0.5890.5574890.278744
T400.1480498568098620.0656282.25590.0267740.013387
Used0.2811552425648630.0641634.38193.5e-051.7e-05
Useful0.06327645677188330.0626181.01050.3152560.157628
Outcome-0.04577397781919370.057751-0.79260.4303230.215162

\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) & -0.0272654428933923 & 0.04629 & -0.589 & 0.557489 & 0.278744 \tabularnewline
T40 & 0.148049856809862 & 0.065628 & 2.2559 & 0.026774 & 0.013387 \tabularnewline
Used & 0.281155242564863 & 0.064163 & 4.3819 & 3.5e-05 & 1.7e-05 \tabularnewline
Useful & 0.0632764567718833 & 0.062618 & 1.0105 & 0.315256 & 0.157628 \tabularnewline
Outcome & -0.0457739778191937 & 0.057751 & -0.7926 & 0.430323 & 0.215162 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201630&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]-0.0272654428933923[/C][C]0.04629[/C][C]-0.589[/C][C]0.557489[/C][C]0.278744[/C][/ROW]
[ROW][C]T40[/C][C]0.148049856809862[/C][C]0.065628[/C][C]2.2559[/C][C]0.026774[/C][C]0.013387[/C][/ROW]
[ROW][C]Used[/C][C]0.281155242564863[/C][C]0.064163[/C][C]4.3819[/C][C]3.5e-05[/C][C]1.7e-05[/C][/ROW]
[ROW][C]Useful[/C][C]0.0632764567718833[/C][C]0.062618[/C][C]1.0105[/C][C]0.315256[/C][C]0.157628[/C][/ROW]
[ROW][C]Outcome[/C][C]-0.0457739778191937[/C][C]0.057751[/C][C]-0.7926[/C][C]0.430323[/C][C]0.215162[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201630&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201630&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)-0.02726544289339230.04629-0.5890.5574890.278744
T400.1480498568098620.0656282.25590.0267740.013387
Used0.2811552425648630.0641634.38193.5e-051.7e-05
Useful0.06327645677188330.0626181.01050.3152560.157628
Outcome-0.04577397781919370.057751-0.79260.4303230.215162







Multiple Linear Regression - Regression Statistics
Multiple R0.548918435951736
R-squared0.3013114493277
Adjusted R-squared0.266808311022895
F-TEST (value)8.73287080919648
F-TEST (DF numerator)4
F-TEST (DF denominator)81
p-value6.5187336977246e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.263643273596876
Sum Squared Residuals5.63012983274307

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.548918435951736 \tabularnewline
R-squared & 0.3013114493277 \tabularnewline
Adjusted R-squared & 0.266808311022895 \tabularnewline
F-TEST (value) & 8.73287080919648 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 81 \tabularnewline
p-value & 6.5187336977246e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.263643273596876 \tabularnewline
Sum Squared Residuals & 5.63012983274307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201630&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.548918435951736[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3013114493277[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.266808311022895[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.73287080919648[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]81[/C][/ROW]
[ROW][C]p-value[/C][C]6.5187336977246e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.263643273596876[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5.63012983274307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201630&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201630&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 R0.548918435951736
R-squared0.3013114493277
Adjusted R-squared0.266808311022895
F-TEST (value)8.73287080919648
F-TEST (DF numerator)4
F-TEST (DF denominator)81
p-value6.5187336977246e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.263643273596876
Sum Squared Residuals5.63012983274307







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0750104360972761-0.0750104360972761
20-0.02726544289339250.0272654428933925
30-0.02726544289339210.0272654428933921
40-0.02726544289339220.0272654428933922
50-0.02726544289339230.0272654428933923
60-0.009762963940702730.00976296394070273
70-0.02726544289339230.0272654428933923
800.12078441391647-0.12078441391647
90-0.0730394207125860.073039420712586
100-0.02726544289339230.0272654428933923
1100.12078441391647-0.12078441391647
120-0.02726544289339230.0272654428933923
1300.317166256443354-0.317166256443354
1400.12078441391647-0.12078441391647
1500.27139227862416-0.27139227862416
1600.419442135434022-0.419442135434022
1710.4652161132532160.534783886746784
1800.12078441391647-0.12078441391647
190-0.0730394207125860.073039420712586
2010.4194421354340220.580557864565978
2100.036011013878491-0.036011013878491
2200.27139227862416-0.27139227862416
230-0.009762963940702730.00976296394070273
240-0.009762963940702730.00976296394070273
2500.356165678662139-0.356165678662139
2600.317166256443354-0.317166256443354
270-0.0730394207125860.073039420712586
2800.25388979967147-0.25388979967147
290-0.0730394207125860.073039420712586
3000.036011013878491-0.036011013878491
310-0.02726544289339230.0272654428933923
320-0.02726544289339230.0272654428933923
3300.036011013878491-0.036011013878491
3400.0750104360972762-0.0750104360972762
350-0.02726544289339230.0272654428933923
360-0.02726544289339230.0272654428933923
3700.465216113253216-0.465216113253216
3800.208115821852277-0.208115821852277
390-0.009762963940702730.00976296394070273
4000.184060870688353-0.184060870688353
4110.271392278624160.72860772137584
4200.208115821852277-0.208115821852277
430-0.009762963940702730.00976296394070273
4400.12078441391647-0.12078441391647
4500.036011013878491-0.036011013878491
460-0.009762963940702730.00976296394070273
470-0.02726544289339230.0272654428933923
480-0.0730394207125860.073039420712586
490-0.009762963940702730.00976296394070273
500-0.02726544289339230.0272654428933923
5100.401939656481333-0.401939656481333
5210.4652161132532160.534783886746784
530-0.0730394207125860.073039420712586
5410.253889799671470.74611020032853
550-0.02726544289339230.0272654428933923
5600.356165678662139-0.356165678662139
5700.27139227862416-0.27139227862416
580-0.0730394207125860.073039420712586
590-0.0730394207125860.073039420712586
6010.4194421354340220.580557864565978
6100.0750104360972762-0.0750104360972762
6200.317166256443354-0.317166256443354
630-0.02726544289339230.0272654428933923
6400.0750104360972762-0.0750104360972762
650-0.02726544289339230.0272654428933923
660-0.02726544289339230.0272654428933923
6710.4652161132532160.534783886746784
680-0.02726544289339230.0272654428933923
690-0.0730394207125860.073039420712586
7000.25388979967147-0.25388979967147
710-0.02726544289339230.0272654428933923
720-0.0730394207125860.073039420712586
7300.208115821852277-0.208115821852277
7400.25388979967147-0.25388979967147
750-0.0730394207125860.073039420712586
7600.138286892869159-0.138286892869159
770-0.0730394207125860.073039420712586
7800.27139227862416-0.27139227862416
7910.3561656786621390.643834321337861
8000.184060870688353-0.184060870688353
810-0.02726544289339230.0272654428933923
8200.208115821852277-0.208115821852277
830-0.02726544289339230.0272654428933923
8410.253889799671470.74611020032853
850-0.009762963940702730.00976296394070273
860-0.02726544289339230.0272654428933923

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0750104360972761 & -0.0750104360972761 \tabularnewline
2 & 0 & -0.0272654428933925 & 0.0272654428933925 \tabularnewline
3 & 0 & -0.0272654428933921 & 0.0272654428933921 \tabularnewline
4 & 0 & -0.0272654428933922 & 0.0272654428933922 \tabularnewline
5 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
6 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
7 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
8 & 0 & 0.12078441391647 & -0.12078441391647 \tabularnewline
9 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
10 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
11 & 0 & 0.12078441391647 & -0.12078441391647 \tabularnewline
12 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
13 & 0 & 0.317166256443354 & -0.317166256443354 \tabularnewline
14 & 0 & 0.12078441391647 & -0.12078441391647 \tabularnewline
15 & 0 & 0.27139227862416 & -0.27139227862416 \tabularnewline
16 & 0 & 0.419442135434022 & -0.419442135434022 \tabularnewline
17 & 1 & 0.465216113253216 & 0.534783886746784 \tabularnewline
18 & 0 & 0.12078441391647 & -0.12078441391647 \tabularnewline
19 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
20 & 1 & 0.419442135434022 & 0.580557864565978 \tabularnewline
21 & 0 & 0.036011013878491 & -0.036011013878491 \tabularnewline
22 & 0 & 0.27139227862416 & -0.27139227862416 \tabularnewline
23 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
24 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
25 & 0 & 0.356165678662139 & -0.356165678662139 \tabularnewline
26 & 0 & 0.317166256443354 & -0.317166256443354 \tabularnewline
27 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
28 & 0 & 0.25388979967147 & -0.25388979967147 \tabularnewline
29 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
30 & 0 & 0.036011013878491 & -0.036011013878491 \tabularnewline
31 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
32 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
33 & 0 & 0.036011013878491 & -0.036011013878491 \tabularnewline
34 & 0 & 0.0750104360972762 & -0.0750104360972762 \tabularnewline
35 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
36 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
37 & 0 & 0.465216113253216 & -0.465216113253216 \tabularnewline
38 & 0 & 0.208115821852277 & -0.208115821852277 \tabularnewline
39 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
40 & 0 & 0.184060870688353 & -0.184060870688353 \tabularnewline
41 & 1 & 0.27139227862416 & 0.72860772137584 \tabularnewline
42 & 0 & 0.208115821852277 & -0.208115821852277 \tabularnewline
43 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
44 & 0 & 0.12078441391647 & -0.12078441391647 \tabularnewline
45 & 0 & 0.036011013878491 & -0.036011013878491 \tabularnewline
46 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
47 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
48 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
49 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
50 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
51 & 0 & 0.401939656481333 & -0.401939656481333 \tabularnewline
52 & 1 & 0.465216113253216 & 0.534783886746784 \tabularnewline
53 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
54 & 1 & 0.25388979967147 & 0.74611020032853 \tabularnewline
55 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
56 & 0 & 0.356165678662139 & -0.356165678662139 \tabularnewline
57 & 0 & 0.27139227862416 & -0.27139227862416 \tabularnewline
58 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
59 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
60 & 1 & 0.419442135434022 & 0.580557864565978 \tabularnewline
61 & 0 & 0.0750104360972762 & -0.0750104360972762 \tabularnewline
62 & 0 & 0.317166256443354 & -0.317166256443354 \tabularnewline
63 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
64 & 0 & 0.0750104360972762 & -0.0750104360972762 \tabularnewline
65 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
66 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
67 & 1 & 0.465216113253216 & 0.534783886746784 \tabularnewline
68 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
69 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
70 & 0 & 0.25388979967147 & -0.25388979967147 \tabularnewline
71 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
72 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
73 & 0 & 0.208115821852277 & -0.208115821852277 \tabularnewline
74 & 0 & 0.25388979967147 & -0.25388979967147 \tabularnewline
75 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
76 & 0 & 0.138286892869159 & -0.138286892869159 \tabularnewline
77 & 0 & -0.073039420712586 & 0.073039420712586 \tabularnewline
78 & 0 & 0.27139227862416 & -0.27139227862416 \tabularnewline
79 & 1 & 0.356165678662139 & 0.643834321337861 \tabularnewline
80 & 0 & 0.184060870688353 & -0.184060870688353 \tabularnewline
81 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
82 & 0 & 0.208115821852277 & -0.208115821852277 \tabularnewline
83 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
84 & 1 & 0.25388979967147 & 0.74611020032853 \tabularnewline
85 & 0 & -0.00976296394070273 & 0.00976296394070273 \tabularnewline
86 & 0 & -0.0272654428933923 & 0.0272654428933923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201630&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]0.0750104360972761[/C][C]-0.0750104360972761[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.0272654428933925[/C][C]0.0272654428933925[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0272654428933921[/C][C]0.0272654428933921[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0272654428933922[/C][C]0.0272654428933922[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.12078441391647[/C][C]-0.12078441391647[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.12078441391647[/C][C]-0.12078441391647[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.317166256443354[/C][C]-0.317166256443354[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.12078441391647[/C][C]-0.12078441391647[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.27139227862416[/C][C]-0.27139227862416[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.419442135434022[/C][C]-0.419442135434022[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.465216113253216[/C][C]0.534783886746784[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.12078441391647[/C][C]-0.12078441391647[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.419442135434022[/C][C]0.580557864565978[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.036011013878491[/C][C]-0.036011013878491[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.27139227862416[/C][C]-0.27139227862416[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.356165678662139[/C][C]-0.356165678662139[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.317166256443354[/C][C]-0.317166256443354[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.25388979967147[/C][C]-0.25388979967147[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.036011013878491[/C][C]-0.036011013878491[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.036011013878491[/C][C]-0.036011013878491[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.0750104360972762[/C][C]-0.0750104360972762[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.465216113253216[/C][C]-0.465216113253216[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.208115821852277[/C][C]-0.208115821852277[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.184060870688353[/C][C]-0.184060870688353[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.27139227862416[/C][C]0.72860772137584[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.208115821852277[/C][C]-0.208115821852277[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.12078441391647[/C][C]-0.12078441391647[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.036011013878491[/C][C]-0.036011013878491[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.401939656481333[/C][C]-0.401939656481333[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.465216113253216[/C][C]0.534783886746784[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.25388979967147[/C][C]0.74611020032853[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.356165678662139[/C][C]-0.356165678662139[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.27139227862416[/C][C]-0.27139227862416[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.419442135434022[/C][C]0.580557864565978[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.0750104360972762[/C][C]-0.0750104360972762[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.317166256443354[/C][C]-0.317166256443354[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.0750104360972762[/C][C]-0.0750104360972762[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.465216113253216[/C][C]0.534783886746784[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.25388979967147[/C][C]-0.25388979967147[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.208115821852277[/C][C]-0.208115821852277[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.25388979967147[/C][C]-0.25388979967147[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.138286892869159[/C][C]-0.138286892869159[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.073039420712586[/C][C]0.073039420712586[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.27139227862416[/C][C]-0.27139227862416[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.356165678662139[/C][C]0.643834321337861[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.184060870688353[/C][C]-0.184060870688353[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.208115821852277[/C][C]-0.208115821852277[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.25388979967147[/C][C]0.74611020032853[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.00976296394070273[/C][C]0.00976296394070273[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]-0.0272654428933923[/C][C]0.0272654428933923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201630&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201630&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
100.0750104360972761-0.0750104360972761
20-0.02726544289339250.0272654428933925
30-0.02726544289339210.0272654428933921
40-0.02726544289339220.0272654428933922
50-0.02726544289339230.0272654428933923
60-0.009762963940702730.00976296394070273
70-0.02726544289339230.0272654428933923
800.12078441391647-0.12078441391647
90-0.0730394207125860.073039420712586
100-0.02726544289339230.0272654428933923
1100.12078441391647-0.12078441391647
120-0.02726544289339230.0272654428933923
1300.317166256443354-0.317166256443354
1400.12078441391647-0.12078441391647
1500.27139227862416-0.27139227862416
1600.419442135434022-0.419442135434022
1710.4652161132532160.534783886746784
1800.12078441391647-0.12078441391647
190-0.0730394207125860.073039420712586
2010.4194421354340220.580557864565978
2100.036011013878491-0.036011013878491
2200.27139227862416-0.27139227862416
230-0.009762963940702730.00976296394070273
240-0.009762963940702730.00976296394070273
2500.356165678662139-0.356165678662139
2600.317166256443354-0.317166256443354
270-0.0730394207125860.073039420712586
2800.25388979967147-0.25388979967147
290-0.0730394207125860.073039420712586
3000.036011013878491-0.036011013878491
310-0.02726544289339230.0272654428933923
320-0.02726544289339230.0272654428933923
3300.036011013878491-0.036011013878491
3400.0750104360972762-0.0750104360972762
350-0.02726544289339230.0272654428933923
360-0.02726544289339230.0272654428933923
3700.465216113253216-0.465216113253216
3800.208115821852277-0.208115821852277
390-0.009762963940702730.00976296394070273
4000.184060870688353-0.184060870688353
4110.271392278624160.72860772137584
4200.208115821852277-0.208115821852277
430-0.009762963940702730.00976296394070273
4400.12078441391647-0.12078441391647
4500.036011013878491-0.036011013878491
460-0.009762963940702730.00976296394070273
470-0.02726544289339230.0272654428933923
480-0.0730394207125860.073039420712586
490-0.009762963940702730.00976296394070273
500-0.02726544289339230.0272654428933923
5100.401939656481333-0.401939656481333
5210.4652161132532160.534783886746784
530-0.0730394207125860.073039420712586
5410.253889799671470.74611020032853
550-0.02726544289339230.0272654428933923
5600.356165678662139-0.356165678662139
5700.27139227862416-0.27139227862416
580-0.0730394207125860.073039420712586
590-0.0730394207125860.073039420712586
6010.4194421354340220.580557864565978
6100.0750104360972762-0.0750104360972762
6200.317166256443354-0.317166256443354
630-0.02726544289339230.0272654428933923
6400.0750104360972762-0.0750104360972762
650-0.02726544289339230.0272654428933923
660-0.02726544289339230.0272654428933923
6710.4652161132532160.534783886746784
680-0.02726544289339230.0272654428933923
690-0.0730394207125860.073039420712586
7000.25388979967147-0.25388979967147
710-0.02726544289339230.0272654428933923
720-0.0730394207125860.073039420712586
7300.208115821852277-0.208115821852277
7400.25388979967147-0.25388979967147
750-0.0730394207125860.073039420712586
7600.138286892869159-0.138286892869159
770-0.0730394207125860.073039420712586
7800.27139227862416-0.27139227862416
7910.3561656786621390.643834321337861
8000.184060870688353-0.184060870688353
810-0.02726544289339230.0272654428933923
8200.208115821852277-0.208115821852277
830-0.02726544289339230.0272654428933923
8410.253889799671470.74611020032853
850-0.009762963940702730.00976296394070273
860-0.02726544289339230.0272654428933923







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1803769461986850.3607538923973710.819623053801315
180.1376678480748880.2753356961497750.862332151925112
190.1145080688172050.229016137634410.885491931182795
200.4587569739106870.9175139478213740.541243026089313
210.3796648223555790.7593296447111590.620335177644421
220.3656168729694460.7312337459388920.634383127030554
230.2942359345938170.5884718691876350.705764065406183
240.2305963818211570.4611927636423150.769403618178843
250.2332289860694950.466457972138990.766771013930505
260.2346690757218220.4693381514436440.765330924278178
270.1932353436355160.3864706872710330.806764656364484
280.1612152881301090.3224305762602180.838784711869891
290.1274699087445220.2549398174890430.872530091255478
300.09608254752946780.1921650950589360.903917452470532
310.07030176218177310.1406035243635460.929698237818227
320.05019644170495260.1003928834099050.949803558295047
330.03536134099577090.07072268199154170.964638659004229
340.02501570747992880.05003141495985760.974984292520071
350.01674422144669210.03348844289338410.983255778553308
360.01093506515419050.02187013030838090.98906493484581
370.02324715124967740.04649430249935490.976752848750323
380.01772346662795430.03544693325590870.982276533372046
390.0115719232825420.0231438465650840.988428076717458
400.01019294598145980.02038589196291970.98980705401854
410.1628069577222040.3256139154444090.837193042277796
420.1417592428198640.2835184856397280.858240757180136
430.1093127586173630.2186255172347270.890687241382637
440.08971570694565590.1794314138913120.910284293054344
450.06662933537345980.133258670746920.93337066462654
460.04856068271393750.0971213654278750.951439317286062
470.03455267439650050.06910534879300110.965447325603499
480.02448834360631470.04897668721262950.975511656393685
490.01679219643449170.03358439286898350.983207803565508
500.01115167201961630.02230334403923270.988848327980384
510.02765681493509260.05531362987018520.972343185064907
520.08064715685231630.1612943137046330.919352843147684
530.06136844665179680.1227368933035940.938631553348203
540.3147955407754360.6295910815508730.685204459224564
550.2570498683486830.5140997366973670.742950131651317
560.4106596763957680.8213193527915360.589340323604232
570.3845762614447650.769152522889530.615423738555235
580.3283332226055090.6566664452110190.671666777394491
590.2767319279006140.5534638558012280.723268072099386
600.4688113876076430.9376227752152850.531188612392357
610.45315147621290.90630295242580.5468485237871
620.439527047731550.8790540954631010.56047295226845
630.3661993768301280.7323987536602560.633800623169872
640.3861813224546710.7723626449093410.613818677545329
650.3134037985407810.6268075970815630.686596201459219
660.2462269077596450.492453815519290.753773092240355
670.391797838394720.783595676789440.60820216160528
680.3128675350030060.6257350700060130.687132464996994
690.2413759888214430.4827519776428870.758624011178557
700.2486054607575440.4972109215150870.751394539242456
710.1819233724870660.3638467449741310.818076627512934
720.1269318279093020.2538636558186040.873068172090698
730.1303502364192250.2607004728384490.869649763580775
740.2348943278459490.4697886556918990.765105672154051
750.1598838221170940.3197676442341880.840116177882906
760.1000279987413660.2000559974827330.899972001258634
770.05778764890078820.1155752978015760.942212351099212
780.0507427748409230.1014855496818460.949257225159077

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \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 & 0.180376946198685 & 0.360753892397371 & 0.819623053801315 \tabularnewline
18 & 0.137667848074888 & 0.275335696149775 & 0.862332151925112 \tabularnewline
19 & 0.114508068817205 & 0.22901613763441 & 0.885491931182795 \tabularnewline
20 & 0.458756973910687 & 0.917513947821374 & 0.541243026089313 \tabularnewline
21 & 0.379664822355579 & 0.759329644711159 & 0.620335177644421 \tabularnewline
22 & 0.365616872969446 & 0.731233745938892 & 0.634383127030554 \tabularnewline
23 & 0.294235934593817 & 0.588471869187635 & 0.705764065406183 \tabularnewline
24 & 0.230596381821157 & 0.461192763642315 & 0.769403618178843 \tabularnewline
25 & 0.233228986069495 & 0.46645797213899 & 0.766771013930505 \tabularnewline
26 & 0.234669075721822 & 0.469338151443644 & 0.765330924278178 \tabularnewline
27 & 0.193235343635516 & 0.386470687271033 & 0.806764656364484 \tabularnewline
28 & 0.161215288130109 & 0.322430576260218 & 0.838784711869891 \tabularnewline
29 & 0.127469908744522 & 0.254939817489043 & 0.872530091255478 \tabularnewline
30 & 0.0960825475294678 & 0.192165095058936 & 0.903917452470532 \tabularnewline
31 & 0.0703017621817731 & 0.140603524363546 & 0.929698237818227 \tabularnewline
32 & 0.0501964417049526 & 0.100392883409905 & 0.949803558295047 \tabularnewline
33 & 0.0353613409957709 & 0.0707226819915417 & 0.964638659004229 \tabularnewline
34 & 0.0250157074799288 & 0.0500314149598576 & 0.974984292520071 \tabularnewline
35 & 0.0167442214466921 & 0.0334884428933841 & 0.983255778553308 \tabularnewline
36 & 0.0109350651541905 & 0.0218701303083809 & 0.98906493484581 \tabularnewline
37 & 0.0232471512496774 & 0.0464943024993549 & 0.976752848750323 \tabularnewline
38 & 0.0177234666279543 & 0.0354469332559087 & 0.982276533372046 \tabularnewline
39 & 0.011571923282542 & 0.023143846565084 & 0.988428076717458 \tabularnewline
40 & 0.0101929459814598 & 0.0203858919629197 & 0.98980705401854 \tabularnewline
41 & 0.162806957722204 & 0.325613915444409 & 0.837193042277796 \tabularnewline
42 & 0.141759242819864 & 0.283518485639728 & 0.858240757180136 \tabularnewline
43 & 0.109312758617363 & 0.218625517234727 & 0.890687241382637 \tabularnewline
44 & 0.0897157069456559 & 0.179431413891312 & 0.910284293054344 \tabularnewline
45 & 0.0666293353734598 & 0.13325867074692 & 0.93337066462654 \tabularnewline
46 & 0.0485606827139375 & 0.097121365427875 & 0.951439317286062 \tabularnewline
47 & 0.0345526743965005 & 0.0691053487930011 & 0.965447325603499 \tabularnewline
48 & 0.0244883436063147 & 0.0489766872126295 & 0.975511656393685 \tabularnewline
49 & 0.0167921964344917 & 0.0335843928689835 & 0.983207803565508 \tabularnewline
50 & 0.0111516720196163 & 0.0223033440392327 & 0.988848327980384 \tabularnewline
51 & 0.0276568149350926 & 0.0553136298701852 & 0.972343185064907 \tabularnewline
52 & 0.0806471568523163 & 0.161294313704633 & 0.919352843147684 \tabularnewline
53 & 0.0613684466517968 & 0.122736893303594 & 0.938631553348203 \tabularnewline
54 & 0.314795540775436 & 0.629591081550873 & 0.685204459224564 \tabularnewline
55 & 0.257049868348683 & 0.514099736697367 & 0.742950131651317 \tabularnewline
56 & 0.410659676395768 & 0.821319352791536 & 0.589340323604232 \tabularnewline
57 & 0.384576261444765 & 0.76915252288953 & 0.615423738555235 \tabularnewline
58 & 0.328333222605509 & 0.656666445211019 & 0.671666777394491 \tabularnewline
59 & 0.276731927900614 & 0.553463855801228 & 0.723268072099386 \tabularnewline
60 & 0.468811387607643 & 0.937622775215285 & 0.531188612392357 \tabularnewline
61 & 0.4531514762129 & 0.9063029524258 & 0.5468485237871 \tabularnewline
62 & 0.43952704773155 & 0.879054095463101 & 0.56047295226845 \tabularnewline
63 & 0.366199376830128 & 0.732398753660256 & 0.633800623169872 \tabularnewline
64 & 0.386181322454671 & 0.772362644909341 & 0.613818677545329 \tabularnewline
65 & 0.313403798540781 & 0.626807597081563 & 0.686596201459219 \tabularnewline
66 & 0.246226907759645 & 0.49245381551929 & 0.753773092240355 \tabularnewline
67 & 0.39179783839472 & 0.78359567678944 & 0.60820216160528 \tabularnewline
68 & 0.312867535003006 & 0.625735070006013 & 0.687132464996994 \tabularnewline
69 & 0.241375988821443 & 0.482751977642887 & 0.758624011178557 \tabularnewline
70 & 0.248605460757544 & 0.497210921515087 & 0.751394539242456 \tabularnewline
71 & 0.181923372487066 & 0.363846744974131 & 0.818076627512934 \tabularnewline
72 & 0.126931827909302 & 0.253863655818604 & 0.873068172090698 \tabularnewline
73 & 0.130350236419225 & 0.260700472838449 & 0.869649763580775 \tabularnewline
74 & 0.234894327845949 & 0.469788655691899 & 0.765105672154051 \tabularnewline
75 & 0.159883822117094 & 0.319767644234188 & 0.840116177882906 \tabularnewline
76 & 0.100027998741366 & 0.200055997482733 & 0.899972001258634 \tabularnewline
77 & 0.0577876489007882 & 0.115575297801576 & 0.942212351099212 \tabularnewline
78 & 0.050742774840923 & 0.101485549681846 & 0.949257225159077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201630&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]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]0.180376946198685[/C][C]0.360753892397371[/C][C]0.819623053801315[/C][/ROW]
[ROW][C]18[/C][C]0.137667848074888[/C][C]0.275335696149775[/C][C]0.862332151925112[/C][/ROW]
[ROW][C]19[/C][C]0.114508068817205[/C][C]0.22901613763441[/C][C]0.885491931182795[/C][/ROW]
[ROW][C]20[/C][C]0.458756973910687[/C][C]0.917513947821374[/C][C]0.541243026089313[/C][/ROW]
[ROW][C]21[/C][C]0.379664822355579[/C][C]0.759329644711159[/C][C]0.620335177644421[/C][/ROW]
[ROW][C]22[/C][C]0.365616872969446[/C][C]0.731233745938892[/C][C]0.634383127030554[/C][/ROW]
[ROW][C]23[/C][C]0.294235934593817[/C][C]0.588471869187635[/C][C]0.705764065406183[/C][/ROW]
[ROW][C]24[/C][C]0.230596381821157[/C][C]0.461192763642315[/C][C]0.769403618178843[/C][/ROW]
[ROW][C]25[/C][C]0.233228986069495[/C][C]0.46645797213899[/C][C]0.766771013930505[/C][/ROW]
[ROW][C]26[/C][C]0.234669075721822[/C][C]0.469338151443644[/C][C]0.765330924278178[/C][/ROW]
[ROW][C]27[/C][C]0.193235343635516[/C][C]0.386470687271033[/C][C]0.806764656364484[/C][/ROW]
[ROW][C]28[/C][C]0.161215288130109[/C][C]0.322430576260218[/C][C]0.838784711869891[/C][/ROW]
[ROW][C]29[/C][C]0.127469908744522[/C][C]0.254939817489043[/C][C]0.872530091255478[/C][/ROW]
[ROW][C]30[/C][C]0.0960825475294678[/C][C]0.192165095058936[/C][C]0.903917452470532[/C][/ROW]
[ROW][C]31[/C][C]0.0703017621817731[/C][C]0.140603524363546[/C][C]0.929698237818227[/C][/ROW]
[ROW][C]32[/C][C]0.0501964417049526[/C][C]0.100392883409905[/C][C]0.949803558295047[/C][/ROW]
[ROW][C]33[/C][C]0.0353613409957709[/C][C]0.0707226819915417[/C][C]0.964638659004229[/C][/ROW]
[ROW][C]34[/C][C]0.0250157074799288[/C][C]0.0500314149598576[/C][C]0.974984292520071[/C][/ROW]
[ROW][C]35[/C][C]0.0167442214466921[/C][C]0.0334884428933841[/C][C]0.983255778553308[/C][/ROW]
[ROW][C]36[/C][C]0.0109350651541905[/C][C]0.0218701303083809[/C][C]0.98906493484581[/C][/ROW]
[ROW][C]37[/C][C]0.0232471512496774[/C][C]0.0464943024993549[/C][C]0.976752848750323[/C][/ROW]
[ROW][C]38[/C][C]0.0177234666279543[/C][C]0.0354469332559087[/C][C]0.982276533372046[/C][/ROW]
[ROW][C]39[/C][C]0.011571923282542[/C][C]0.023143846565084[/C][C]0.988428076717458[/C][/ROW]
[ROW][C]40[/C][C]0.0101929459814598[/C][C]0.0203858919629197[/C][C]0.98980705401854[/C][/ROW]
[ROW][C]41[/C][C]0.162806957722204[/C][C]0.325613915444409[/C][C]0.837193042277796[/C][/ROW]
[ROW][C]42[/C][C]0.141759242819864[/C][C]0.283518485639728[/C][C]0.858240757180136[/C][/ROW]
[ROW][C]43[/C][C]0.109312758617363[/C][C]0.218625517234727[/C][C]0.890687241382637[/C][/ROW]
[ROW][C]44[/C][C]0.0897157069456559[/C][C]0.179431413891312[/C][C]0.910284293054344[/C][/ROW]
[ROW][C]45[/C][C]0.0666293353734598[/C][C]0.13325867074692[/C][C]0.93337066462654[/C][/ROW]
[ROW][C]46[/C][C]0.0485606827139375[/C][C]0.097121365427875[/C][C]0.951439317286062[/C][/ROW]
[ROW][C]47[/C][C]0.0345526743965005[/C][C]0.0691053487930011[/C][C]0.965447325603499[/C][/ROW]
[ROW][C]48[/C][C]0.0244883436063147[/C][C]0.0489766872126295[/C][C]0.975511656393685[/C][/ROW]
[ROW][C]49[/C][C]0.0167921964344917[/C][C]0.0335843928689835[/C][C]0.983207803565508[/C][/ROW]
[ROW][C]50[/C][C]0.0111516720196163[/C][C]0.0223033440392327[/C][C]0.988848327980384[/C][/ROW]
[ROW][C]51[/C][C]0.0276568149350926[/C][C]0.0553136298701852[/C][C]0.972343185064907[/C][/ROW]
[ROW][C]52[/C][C]0.0806471568523163[/C][C]0.161294313704633[/C][C]0.919352843147684[/C][/ROW]
[ROW][C]53[/C][C]0.0613684466517968[/C][C]0.122736893303594[/C][C]0.938631553348203[/C][/ROW]
[ROW][C]54[/C][C]0.314795540775436[/C][C]0.629591081550873[/C][C]0.685204459224564[/C][/ROW]
[ROW][C]55[/C][C]0.257049868348683[/C][C]0.514099736697367[/C][C]0.742950131651317[/C][/ROW]
[ROW][C]56[/C][C]0.410659676395768[/C][C]0.821319352791536[/C][C]0.589340323604232[/C][/ROW]
[ROW][C]57[/C][C]0.384576261444765[/C][C]0.76915252288953[/C][C]0.615423738555235[/C][/ROW]
[ROW][C]58[/C][C]0.328333222605509[/C][C]0.656666445211019[/C][C]0.671666777394491[/C][/ROW]
[ROW][C]59[/C][C]0.276731927900614[/C][C]0.553463855801228[/C][C]0.723268072099386[/C][/ROW]
[ROW][C]60[/C][C]0.468811387607643[/C][C]0.937622775215285[/C][C]0.531188612392357[/C][/ROW]
[ROW][C]61[/C][C]0.4531514762129[/C][C]0.9063029524258[/C][C]0.5468485237871[/C][/ROW]
[ROW][C]62[/C][C]0.43952704773155[/C][C]0.879054095463101[/C][C]0.56047295226845[/C][/ROW]
[ROW][C]63[/C][C]0.366199376830128[/C][C]0.732398753660256[/C][C]0.633800623169872[/C][/ROW]
[ROW][C]64[/C][C]0.386181322454671[/C][C]0.772362644909341[/C][C]0.613818677545329[/C][/ROW]
[ROW][C]65[/C][C]0.313403798540781[/C][C]0.626807597081563[/C][C]0.686596201459219[/C][/ROW]
[ROW][C]66[/C][C]0.246226907759645[/C][C]0.49245381551929[/C][C]0.753773092240355[/C][/ROW]
[ROW][C]67[/C][C]0.39179783839472[/C][C]0.78359567678944[/C][C]0.60820216160528[/C][/ROW]
[ROW][C]68[/C][C]0.312867535003006[/C][C]0.625735070006013[/C][C]0.687132464996994[/C][/ROW]
[ROW][C]69[/C][C]0.241375988821443[/C][C]0.482751977642887[/C][C]0.758624011178557[/C][/ROW]
[ROW][C]70[/C][C]0.248605460757544[/C][C]0.497210921515087[/C][C]0.751394539242456[/C][/ROW]
[ROW][C]71[/C][C]0.181923372487066[/C][C]0.363846744974131[/C][C]0.818076627512934[/C][/ROW]
[ROW][C]72[/C][C]0.126931827909302[/C][C]0.253863655818604[/C][C]0.873068172090698[/C][/ROW]
[ROW][C]73[/C][C]0.130350236419225[/C][C]0.260700472838449[/C][C]0.869649763580775[/C][/ROW]
[ROW][C]74[/C][C]0.234894327845949[/C][C]0.469788655691899[/C][C]0.765105672154051[/C][/ROW]
[ROW][C]75[/C][C]0.159883822117094[/C][C]0.319767644234188[/C][C]0.840116177882906[/C][/ROW]
[ROW][C]76[/C][C]0.100027998741366[/C][C]0.200055997482733[/C][C]0.899972001258634[/C][/ROW]
[ROW][C]77[/C][C]0.0577876489007882[/C][C]0.115575297801576[/C][C]0.942212351099212[/C][/ROW]
[ROW][C]78[/C][C]0.050742774840923[/C][C]0.101485549681846[/C][C]0.949257225159077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201630&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201630&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
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1803769461986850.3607538923973710.819623053801315
180.1376678480748880.2753356961497750.862332151925112
190.1145080688172050.229016137634410.885491931182795
200.4587569739106870.9175139478213740.541243026089313
210.3796648223555790.7593296447111590.620335177644421
220.3656168729694460.7312337459388920.634383127030554
230.2942359345938170.5884718691876350.705764065406183
240.2305963818211570.4611927636423150.769403618178843
250.2332289860694950.466457972138990.766771013930505
260.2346690757218220.4693381514436440.765330924278178
270.1932353436355160.3864706872710330.806764656364484
280.1612152881301090.3224305762602180.838784711869891
290.1274699087445220.2549398174890430.872530091255478
300.09608254752946780.1921650950589360.903917452470532
310.07030176218177310.1406035243635460.929698237818227
320.05019644170495260.1003928834099050.949803558295047
330.03536134099577090.07072268199154170.964638659004229
340.02501570747992880.05003141495985760.974984292520071
350.01674422144669210.03348844289338410.983255778553308
360.01093506515419050.02187013030838090.98906493484581
370.02324715124967740.04649430249935490.976752848750323
380.01772346662795430.03544693325590870.982276533372046
390.0115719232825420.0231438465650840.988428076717458
400.01019294598145980.02038589196291970.98980705401854
410.1628069577222040.3256139154444090.837193042277796
420.1417592428198640.2835184856397280.858240757180136
430.1093127586173630.2186255172347270.890687241382637
440.08971570694565590.1794314138913120.910284293054344
450.06662933537345980.133258670746920.93337066462654
460.04856068271393750.0971213654278750.951439317286062
470.03455267439650050.06910534879300110.965447325603499
480.02448834360631470.04897668721262950.975511656393685
490.01679219643449170.03358439286898350.983207803565508
500.01115167201961630.02230334403923270.988848327980384
510.02765681493509260.05531362987018520.972343185064907
520.08064715685231630.1612943137046330.919352843147684
530.06136844665179680.1227368933035940.938631553348203
540.3147955407754360.6295910815508730.685204459224564
550.2570498683486830.5140997366973670.742950131651317
560.4106596763957680.8213193527915360.589340323604232
570.3845762614447650.769152522889530.615423738555235
580.3283332226055090.6566664452110190.671666777394491
590.2767319279006140.5534638558012280.723268072099386
600.4688113876076430.9376227752152850.531188612392357
610.45315147621290.90630295242580.5468485237871
620.439527047731550.8790540954631010.56047295226845
630.3661993768301280.7323987536602560.633800623169872
640.3861813224546710.7723626449093410.613818677545329
650.3134037985407810.6268075970815630.686596201459219
660.2462269077596450.492453815519290.753773092240355
670.391797838394720.783595676789440.60820216160528
680.3128675350030060.6257350700060130.687132464996994
690.2413759888214430.4827519776428870.758624011178557
700.2486054607575440.4972109215150870.751394539242456
710.1819233724870660.3638467449741310.818076627512934
720.1269318279093020.2538636558186040.873068172090698
730.1303502364192250.2607004728384490.869649763580775
740.2348943278459490.4697886556918990.765105672154051
750.1598838221170940.3197676442341880.840116177882906
760.1000279987413660.2000559974827330.899972001258634
770.05778764890078820.1155752978015760.942212351099212
780.0507427748409230.1014855496818460.949257225159077







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.126760563380282NOK
5% type I error level180.253521126760563NOK
10% type I error level230.323943661971831NOK

\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 & 9 & 0.126760563380282 & NOK \tabularnewline
5% type I error level & 18 & 0.253521126760563 & NOK \tabularnewline
10% type I error level & 23 & 0.323943661971831 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201630&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]9[/C][C]0.126760563380282[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.253521126760563[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.323943661971831[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201630&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201630&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 level90.126760563380282NOK
5% type I error level180.253521126760563NOK
10% type I error level230.323943661971831NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}