Free Statistics

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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, 20 Nov 2009 11:02:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258740264oavm88irgyoyho7.htm/, Retrieved Fri, 29 Mar 2024 09:52:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58378, Retrieved Fri, 29 Mar 2024 09:52:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS 7 Model 1
Estimated Impact162
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS 7] [2009-11-19 23:41:15] [9717cb857c153ca3061376906953b329]
-    D        [Multiple Regression] [WS 7 Model 1] [2009-11-20 18:02:51] [52b85b290d6f50b0921ad6729b8a5af2] [Current]
-   P           [Multiple Regression] [WS 7 Model 2] [2009-11-20 18:37:24] [9717cb857c153ca3061376906953b329]
-   P             [Multiple Regression] [WS 7 Model 3] [2009-11-20 18:55:39] [9717cb857c153ca3061376906953b329]
-    D              [Multiple Regression] [WS 7 Model 4] [2009-11-22 16:57:43] [9717cb857c153ca3061376906953b329]
-    D                [Multiple Regression] [WS 7 Model 5] [2009-11-22 20:04:45] [9717cb857c153ca3061376906953b329]
-    D                  [Multiple Regression] [WS 7 Model 6] [2009-11-23 16:53:08] [9717cb857c153ca3061376906953b329]
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Dataseries X:
286602	0
283042	0
276687	0
277915	0
277128	0
277103	0
275037	0
270150	0
267140	0
264993	0
287259	0
291186	0
292300	0
288186	0
281477	0
282656	0
280190	0
280408	0
276836	0
275216	0
274352	0
271311	0
289802	0
290726	0
292300	0
278506	0
269826	0
265861	0
269034	0
264176	0
255198	0
253353	0
246057	0
235372	0
258556	0
260993	0
254663	0
250643	0
243422	0
247105	0
248541	0
245039	0
237080	0
237085	0
225554	0
226839	1
247934	1
248333	1
246969	1
245098	1
246263	1
255765	1
264319	1
268347	1
273046	1
273963	1
267430	1
271993	1
292710	1
295881	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58378&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58378&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
nwwmb[t] = + 267912.577777778 -6253.24444444445dummy_variable[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
nwwmb[t] =  +  267912.577777778 -6253.24444444445dummy_variable[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58378&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]nwwmb[t] =  +  267912.577777778 -6253.24444444445dummy_variable[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58378&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
nwwmb[t] = + 267912.577777778 -6253.24444444445dummy_variable[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)267912.5777777782671.648691100.279900
dummy_variable-6253.244444444455343.297382-1.17030.2466680.123334

\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) & 267912.577777778 & 2671.648691 & 100.2799 & 0 & 0 \tabularnewline
dummy_variable & -6253.24444444445 & 5343.297382 & -1.1703 & 0.246668 & 0.123334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58378&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]267912.577777778[/C][C]2671.648691[/C][C]100.2799[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]dummy_variable[/C][C]-6253.24444444445[/C][C]5343.297382[/C][C]-1.1703[/C][C]0.246668[/C][C]0.123334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58378&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58378&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)267912.5777777782671.648691100.279900
dummy_variable-6253.244444444455343.297382-1.17030.2466680.123334







Multiple Linear Regression - Regression Statistics
Multiple R0.151884696481867
R-squared0.0230689610253889
Adjusted R-squared0.0062253224223785
F-TEST (value)1.36959487015268
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.246667802057425
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17921.964256143
Sum Squared Residuals18629414562.3111

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.151884696481867 \tabularnewline
R-squared & 0.0230689610253889 \tabularnewline
Adjusted R-squared & 0.0062253224223785 \tabularnewline
F-TEST (value) & 1.36959487015268 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.246667802057425 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 17921.964256143 \tabularnewline
Sum Squared Residuals & 18629414562.3111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58378&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.151884696481867[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0230689610253889[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0062253224223785[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.36959487015268[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.246667802057425[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]17921.964256143[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18629414562.3111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58378&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58378&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 R0.151884696481867
R-squared0.0230689610253889
Adjusted R-squared0.0062253224223785
F-TEST (value)1.36959487015268
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.246667802057425
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17921.964256143
Sum Squared Residuals18629414562.3111







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1286602267912.57777777718689.4222222226
2283042267912.57777777815129.4222222222
3276687267912.5777777788774.42222222221
4277915267912.57777777810002.4222222222
5277128267912.5777777789215.42222222221
6277103267912.5777777789190.42222222221
7275037267912.5777777787124.42222222221
8270150267912.5777777782237.42222222221
9267140267912.577777778-772.577777777788
10264993267912.577777778-2919.57777777779
11287259267912.57777777819346.4222222222
12291186267912.57777777823273.4222222222
13292300267912.57777777824387.4222222222
14288186267912.57777777820273.4222222222
15281477267912.57777777813564.4222222222
16282656267912.57777777814743.4222222222
17280190267912.57777777812277.4222222222
18280408267912.57777777812495.4222222222
19276836267912.5777777788923.42222222221
20275216267912.5777777787303.42222222221
21274352267912.5777777786439.42222222221
22271311267912.5777777783398.42222222221
23289802267912.57777777821889.4222222222
24290726267912.57777777822813.4222222222
25292300267912.57777777824387.4222222222
26278506267912.57777777810593.4222222222
27269826267912.5777777781913.42222222221
28265861267912.577777778-2051.57777777779
29269034267912.5777777781121.42222222221
30264176267912.577777778-3736.57777777779
31255198267912.577777778-12714.5777777778
32253353267912.577777778-14559.5777777778
33246057267912.577777778-21855.5777777778
34235372267912.577777778-32540.5777777778
35258556267912.577777778-9356.57777777779
36260993267912.577777778-6919.57777777779
37254663267912.577777778-13249.5777777778
38250643267912.577777778-17269.5777777778
39243422267912.577777778-24490.5777777778
40247105267912.577777778-20807.5777777778
41248541267912.577777778-19371.5777777778
42245039267912.577777778-22873.5777777778
43237080267912.577777778-30832.5777777778
44237085267912.577777778-30827.5777777778
45225554267912.577777778-42358.5777777778
46226839261659.333333333-34820.3333333333
47247934261659.333333333-13725.3333333333
48248333261659.333333333-13326.3333333333
49246969261659.333333333-14690.3333333333
50245098261659.333333333-16561.3333333333
51246263261659.333333333-15396.3333333333
52255765261659.333333333-5894.33333333333
53264319261659.3333333332659.66666666667
54268347261659.3333333336687.66666666667
55273046261659.33333333311386.6666666667
56273963261659.33333333312303.6666666667
57267430261659.3333333335770.66666666667
58271993261659.33333333310333.6666666667
59292710261659.33333333331050.6666666667
60295881261659.33333333334221.6666666667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 286602 & 267912.577777777 & 18689.4222222226 \tabularnewline
2 & 283042 & 267912.577777778 & 15129.4222222222 \tabularnewline
3 & 276687 & 267912.577777778 & 8774.42222222221 \tabularnewline
4 & 277915 & 267912.577777778 & 10002.4222222222 \tabularnewline
5 & 277128 & 267912.577777778 & 9215.42222222221 \tabularnewline
6 & 277103 & 267912.577777778 & 9190.42222222221 \tabularnewline
7 & 275037 & 267912.577777778 & 7124.42222222221 \tabularnewline
8 & 270150 & 267912.577777778 & 2237.42222222221 \tabularnewline
9 & 267140 & 267912.577777778 & -772.577777777788 \tabularnewline
10 & 264993 & 267912.577777778 & -2919.57777777779 \tabularnewline
11 & 287259 & 267912.577777778 & 19346.4222222222 \tabularnewline
12 & 291186 & 267912.577777778 & 23273.4222222222 \tabularnewline
13 & 292300 & 267912.577777778 & 24387.4222222222 \tabularnewline
14 & 288186 & 267912.577777778 & 20273.4222222222 \tabularnewline
15 & 281477 & 267912.577777778 & 13564.4222222222 \tabularnewline
16 & 282656 & 267912.577777778 & 14743.4222222222 \tabularnewline
17 & 280190 & 267912.577777778 & 12277.4222222222 \tabularnewline
18 & 280408 & 267912.577777778 & 12495.4222222222 \tabularnewline
19 & 276836 & 267912.577777778 & 8923.42222222221 \tabularnewline
20 & 275216 & 267912.577777778 & 7303.42222222221 \tabularnewline
21 & 274352 & 267912.577777778 & 6439.42222222221 \tabularnewline
22 & 271311 & 267912.577777778 & 3398.42222222221 \tabularnewline
23 & 289802 & 267912.577777778 & 21889.4222222222 \tabularnewline
24 & 290726 & 267912.577777778 & 22813.4222222222 \tabularnewline
25 & 292300 & 267912.577777778 & 24387.4222222222 \tabularnewline
26 & 278506 & 267912.577777778 & 10593.4222222222 \tabularnewline
27 & 269826 & 267912.577777778 & 1913.42222222221 \tabularnewline
28 & 265861 & 267912.577777778 & -2051.57777777779 \tabularnewline
29 & 269034 & 267912.577777778 & 1121.42222222221 \tabularnewline
30 & 264176 & 267912.577777778 & -3736.57777777779 \tabularnewline
31 & 255198 & 267912.577777778 & -12714.5777777778 \tabularnewline
32 & 253353 & 267912.577777778 & -14559.5777777778 \tabularnewline
33 & 246057 & 267912.577777778 & -21855.5777777778 \tabularnewline
34 & 235372 & 267912.577777778 & -32540.5777777778 \tabularnewline
35 & 258556 & 267912.577777778 & -9356.57777777779 \tabularnewline
36 & 260993 & 267912.577777778 & -6919.57777777779 \tabularnewline
37 & 254663 & 267912.577777778 & -13249.5777777778 \tabularnewline
38 & 250643 & 267912.577777778 & -17269.5777777778 \tabularnewline
39 & 243422 & 267912.577777778 & -24490.5777777778 \tabularnewline
40 & 247105 & 267912.577777778 & -20807.5777777778 \tabularnewline
41 & 248541 & 267912.577777778 & -19371.5777777778 \tabularnewline
42 & 245039 & 267912.577777778 & -22873.5777777778 \tabularnewline
43 & 237080 & 267912.577777778 & -30832.5777777778 \tabularnewline
44 & 237085 & 267912.577777778 & -30827.5777777778 \tabularnewline
45 & 225554 & 267912.577777778 & -42358.5777777778 \tabularnewline
46 & 226839 & 261659.333333333 & -34820.3333333333 \tabularnewline
47 & 247934 & 261659.333333333 & -13725.3333333333 \tabularnewline
48 & 248333 & 261659.333333333 & -13326.3333333333 \tabularnewline
49 & 246969 & 261659.333333333 & -14690.3333333333 \tabularnewline
50 & 245098 & 261659.333333333 & -16561.3333333333 \tabularnewline
51 & 246263 & 261659.333333333 & -15396.3333333333 \tabularnewline
52 & 255765 & 261659.333333333 & -5894.33333333333 \tabularnewline
53 & 264319 & 261659.333333333 & 2659.66666666667 \tabularnewline
54 & 268347 & 261659.333333333 & 6687.66666666667 \tabularnewline
55 & 273046 & 261659.333333333 & 11386.6666666667 \tabularnewline
56 & 273963 & 261659.333333333 & 12303.6666666667 \tabularnewline
57 & 267430 & 261659.333333333 & 5770.66666666667 \tabularnewline
58 & 271993 & 261659.333333333 & 10333.6666666667 \tabularnewline
59 & 292710 & 261659.333333333 & 31050.6666666667 \tabularnewline
60 & 295881 & 261659.333333333 & 34221.6666666667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58378&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]286602[/C][C]267912.577777777[/C][C]18689.4222222226[/C][/ROW]
[ROW][C]2[/C][C]283042[/C][C]267912.577777778[/C][C]15129.4222222222[/C][/ROW]
[ROW][C]3[/C][C]276687[/C][C]267912.577777778[/C][C]8774.42222222221[/C][/ROW]
[ROW][C]4[/C][C]277915[/C][C]267912.577777778[/C][C]10002.4222222222[/C][/ROW]
[ROW][C]5[/C][C]277128[/C][C]267912.577777778[/C][C]9215.42222222221[/C][/ROW]
[ROW][C]6[/C][C]277103[/C][C]267912.577777778[/C][C]9190.42222222221[/C][/ROW]
[ROW][C]7[/C][C]275037[/C][C]267912.577777778[/C][C]7124.42222222221[/C][/ROW]
[ROW][C]8[/C][C]270150[/C][C]267912.577777778[/C][C]2237.42222222221[/C][/ROW]
[ROW][C]9[/C][C]267140[/C][C]267912.577777778[/C][C]-772.577777777788[/C][/ROW]
[ROW][C]10[/C][C]264993[/C][C]267912.577777778[/C][C]-2919.57777777779[/C][/ROW]
[ROW][C]11[/C][C]287259[/C][C]267912.577777778[/C][C]19346.4222222222[/C][/ROW]
[ROW][C]12[/C][C]291186[/C][C]267912.577777778[/C][C]23273.4222222222[/C][/ROW]
[ROW][C]13[/C][C]292300[/C][C]267912.577777778[/C][C]24387.4222222222[/C][/ROW]
[ROW][C]14[/C][C]288186[/C][C]267912.577777778[/C][C]20273.4222222222[/C][/ROW]
[ROW][C]15[/C][C]281477[/C][C]267912.577777778[/C][C]13564.4222222222[/C][/ROW]
[ROW][C]16[/C][C]282656[/C][C]267912.577777778[/C][C]14743.4222222222[/C][/ROW]
[ROW][C]17[/C][C]280190[/C][C]267912.577777778[/C][C]12277.4222222222[/C][/ROW]
[ROW][C]18[/C][C]280408[/C][C]267912.577777778[/C][C]12495.4222222222[/C][/ROW]
[ROW][C]19[/C][C]276836[/C][C]267912.577777778[/C][C]8923.42222222221[/C][/ROW]
[ROW][C]20[/C][C]275216[/C][C]267912.577777778[/C][C]7303.42222222221[/C][/ROW]
[ROW][C]21[/C][C]274352[/C][C]267912.577777778[/C][C]6439.42222222221[/C][/ROW]
[ROW][C]22[/C][C]271311[/C][C]267912.577777778[/C][C]3398.42222222221[/C][/ROW]
[ROW][C]23[/C][C]289802[/C][C]267912.577777778[/C][C]21889.4222222222[/C][/ROW]
[ROW][C]24[/C][C]290726[/C][C]267912.577777778[/C][C]22813.4222222222[/C][/ROW]
[ROW][C]25[/C][C]292300[/C][C]267912.577777778[/C][C]24387.4222222222[/C][/ROW]
[ROW][C]26[/C][C]278506[/C][C]267912.577777778[/C][C]10593.4222222222[/C][/ROW]
[ROW][C]27[/C][C]269826[/C][C]267912.577777778[/C][C]1913.42222222221[/C][/ROW]
[ROW][C]28[/C][C]265861[/C][C]267912.577777778[/C][C]-2051.57777777779[/C][/ROW]
[ROW][C]29[/C][C]269034[/C][C]267912.577777778[/C][C]1121.42222222221[/C][/ROW]
[ROW][C]30[/C][C]264176[/C][C]267912.577777778[/C][C]-3736.57777777779[/C][/ROW]
[ROW][C]31[/C][C]255198[/C][C]267912.577777778[/C][C]-12714.5777777778[/C][/ROW]
[ROW][C]32[/C][C]253353[/C][C]267912.577777778[/C][C]-14559.5777777778[/C][/ROW]
[ROW][C]33[/C][C]246057[/C][C]267912.577777778[/C][C]-21855.5777777778[/C][/ROW]
[ROW][C]34[/C][C]235372[/C][C]267912.577777778[/C][C]-32540.5777777778[/C][/ROW]
[ROW][C]35[/C][C]258556[/C][C]267912.577777778[/C][C]-9356.57777777779[/C][/ROW]
[ROW][C]36[/C][C]260993[/C][C]267912.577777778[/C][C]-6919.57777777779[/C][/ROW]
[ROW][C]37[/C][C]254663[/C][C]267912.577777778[/C][C]-13249.5777777778[/C][/ROW]
[ROW][C]38[/C][C]250643[/C][C]267912.577777778[/C][C]-17269.5777777778[/C][/ROW]
[ROW][C]39[/C][C]243422[/C][C]267912.577777778[/C][C]-24490.5777777778[/C][/ROW]
[ROW][C]40[/C][C]247105[/C][C]267912.577777778[/C][C]-20807.5777777778[/C][/ROW]
[ROW][C]41[/C][C]248541[/C][C]267912.577777778[/C][C]-19371.5777777778[/C][/ROW]
[ROW][C]42[/C][C]245039[/C][C]267912.577777778[/C][C]-22873.5777777778[/C][/ROW]
[ROW][C]43[/C][C]237080[/C][C]267912.577777778[/C][C]-30832.5777777778[/C][/ROW]
[ROW][C]44[/C][C]237085[/C][C]267912.577777778[/C][C]-30827.5777777778[/C][/ROW]
[ROW][C]45[/C][C]225554[/C][C]267912.577777778[/C][C]-42358.5777777778[/C][/ROW]
[ROW][C]46[/C][C]226839[/C][C]261659.333333333[/C][C]-34820.3333333333[/C][/ROW]
[ROW][C]47[/C][C]247934[/C][C]261659.333333333[/C][C]-13725.3333333333[/C][/ROW]
[ROW][C]48[/C][C]248333[/C][C]261659.333333333[/C][C]-13326.3333333333[/C][/ROW]
[ROW][C]49[/C][C]246969[/C][C]261659.333333333[/C][C]-14690.3333333333[/C][/ROW]
[ROW][C]50[/C][C]245098[/C][C]261659.333333333[/C][C]-16561.3333333333[/C][/ROW]
[ROW][C]51[/C][C]246263[/C][C]261659.333333333[/C][C]-15396.3333333333[/C][/ROW]
[ROW][C]52[/C][C]255765[/C][C]261659.333333333[/C][C]-5894.33333333333[/C][/ROW]
[ROW][C]53[/C][C]264319[/C][C]261659.333333333[/C][C]2659.66666666667[/C][/ROW]
[ROW][C]54[/C][C]268347[/C][C]261659.333333333[/C][C]6687.66666666667[/C][/ROW]
[ROW][C]55[/C][C]273046[/C][C]261659.333333333[/C][C]11386.6666666667[/C][/ROW]
[ROW][C]56[/C][C]273963[/C][C]261659.333333333[/C][C]12303.6666666667[/C][/ROW]
[ROW][C]57[/C][C]267430[/C][C]261659.333333333[/C][C]5770.66666666667[/C][/ROW]
[ROW][C]58[/C][C]271993[/C][C]261659.333333333[/C][C]10333.6666666667[/C][/ROW]
[ROW][C]59[/C][C]292710[/C][C]261659.333333333[/C][C]31050.6666666667[/C][/ROW]
[ROW][C]60[/C][C]295881[/C][C]261659.333333333[/C][C]34221.6666666667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58378&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58378&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
1286602267912.57777777718689.4222222226
2283042267912.57777777815129.4222222222
3276687267912.5777777788774.42222222221
4277915267912.57777777810002.4222222222
5277128267912.5777777789215.42222222221
6277103267912.5777777789190.42222222221
7275037267912.5777777787124.42222222221
8270150267912.5777777782237.42222222221
9267140267912.577777778-772.577777777788
10264993267912.577777778-2919.57777777779
11287259267912.57777777819346.4222222222
12291186267912.57777777823273.4222222222
13292300267912.57777777824387.4222222222
14288186267912.57777777820273.4222222222
15281477267912.57777777813564.4222222222
16282656267912.57777777814743.4222222222
17280190267912.57777777812277.4222222222
18280408267912.57777777812495.4222222222
19276836267912.5777777788923.42222222221
20275216267912.5777777787303.42222222221
21274352267912.5777777786439.42222222221
22271311267912.5777777783398.42222222221
23289802267912.57777777821889.4222222222
24290726267912.57777777822813.4222222222
25292300267912.57777777824387.4222222222
26278506267912.57777777810593.4222222222
27269826267912.5777777781913.42222222221
28265861267912.577777778-2051.57777777779
29269034267912.5777777781121.42222222221
30264176267912.577777778-3736.57777777779
31255198267912.577777778-12714.5777777778
32253353267912.577777778-14559.5777777778
33246057267912.577777778-21855.5777777778
34235372267912.577777778-32540.5777777778
35258556267912.577777778-9356.57777777779
36260993267912.577777778-6919.57777777779
37254663267912.577777778-13249.5777777778
38250643267912.577777778-17269.5777777778
39243422267912.577777778-24490.5777777778
40247105267912.577777778-20807.5777777778
41248541267912.577777778-19371.5777777778
42245039267912.577777778-22873.5777777778
43237080267912.577777778-30832.5777777778
44237085267912.577777778-30827.5777777778
45225554267912.577777778-42358.5777777778
46226839261659.333333333-34820.3333333333
47247934261659.333333333-13725.3333333333
48248333261659.333333333-13326.3333333333
49246969261659.333333333-14690.3333333333
50245098261659.333333333-16561.3333333333
51246263261659.333333333-15396.3333333333
52255765261659.333333333-5894.33333333333
53264319261659.3333333332659.66666666667
54268347261659.3333333336687.66666666667
55273046261659.33333333311386.6666666667
56273963261659.33333333312303.6666666667
57267430261659.3333333335770.66666666667
58271993261659.33333333310333.6666666667
59292710261659.33333333331050.6666666667
60295881261659.33333333334221.6666666667







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.02728951289853640.05457902579707280.972710487101464
60.007408116725938060.01481623345187610.992591883274062
70.002537022842699480.005074045685398960.9974629771573
80.002314436682515520.004628873365031040.997685563317484
90.002657268368066330.005314536736132670.997342731631934
100.002959502158938880.005919004317877750.99704049784106
110.003130452954857320.006260905909714640.996869547045143
120.005009178936030.010018357872060.99499082106397
130.007176211964049110.01435242392809820.99282378803595
140.00566421118652250.0113284223730450.994335788813478
150.002930321343102440.005860642686204890.997069678656898
160.001575664513349140.003151329026698280.99842433548665
170.0007830873208205660.001566174641641130.99921691267918
180.0003914014524602860.0007828029049205720.99960859854754
190.0001953145208545450.000390629041709090.999804685479145
200.0001023788618737310.0002047577237474620.999897621138126
215.56853980804253e-050.0001113707961608510.99994431460192
223.73313706641159e-057.46627413282319e-050.999962668629336
236.69654652638743e-050.0001339309305277490.999933034534736
240.0001581682763354940.0003163365526709890.999841831723665
250.0006018687637575950.001203737527515190.999398131236242
260.0005953513838617990.001190702767723600.999404648616138
270.00074256580882380.00148513161764760.999257434191176
280.001215656404002210.002431312808004420.998784343595998
290.001604136780164200.003208273560328400.998395863219836
300.002751997440364010.005503994880728010.997248002559636
310.008522470171039580.01704494034207920.99147752982896
320.02011969015337020.04023938030674030.97988030984663
330.05618468654972630.1123693730994530.943815313450274
340.190113328511860.380226657023720.80988667148814
350.1889390071752510.3778780143505010.81106099282475
360.1918364202404630.3836728404809260.808163579759537
370.1982245965595150.3964491931190300.801775403440485
380.2092775698016110.4185551396032220.79072243019839
390.2376764262052060.4753528524104130.762323573794794
400.2424009789135190.4848019578270380.757599021086481
410.2427431540969220.4854863081938440.757256845903078
420.2476896679084320.4953793358168630.752310332091568
430.2654178981321870.5308357962643730.734582101867813
440.2761077263246250.552215452649250.723892273675375
450.314504723827570.629009447655140.68549527617243
460.4837177512341710.9674355024683420.516282248765829
470.469264979812260.938529959624520.53073502018774
480.4546766691928680.9093533383857350.545323330807132
490.4709182317051780.9418364634103570.529081768294822
500.55477057520020.89045884959960.4452294247998
510.6994232951347790.6011534097304420.300576704865221
520.7504144571474220.4991710857051550.249585542852578
530.7180674755895970.5638650488208070.281932524410403
540.6450791264203550.709841747159290.354920873579645
550.51363936294010.97272127411980.4863606370599

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0272895128985364 & 0.0545790257970728 & 0.972710487101464 \tabularnewline
6 & 0.00740811672593806 & 0.0148162334518761 & 0.992591883274062 \tabularnewline
7 & 0.00253702284269948 & 0.00507404568539896 & 0.9974629771573 \tabularnewline
8 & 0.00231443668251552 & 0.00462887336503104 & 0.997685563317484 \tabularnewline
9 & 0.00265726836806633 & 0.00531453673613267 & 0.997342731631934 \tabularnewline
10 & 0.00295950215893888 & 0.00591900431787775 & 0.99704049784106 \tabularnewline
11 & 0.00313045295485732 & 0.00626090590971464 & 0.996869547045143 \tabularnewline
12 & 0.00500917893603 & 0.01001835787206 & 0.99499082106397 \tabularnewline
13 & 0.00717621196404911 & 0.0143524239280982 & 0.99282378803595 \tabularnewline
14 & 0.0056642111865225 & 0.011328422373045 & 0.994335788813478 \tabularnewline
15 & 0.00293032134310244 & 0.00586064268620489 & 0.997069678656898 \tabularnewline
16 & 0.00157566451334914 & 0.00315132902669828 & 0.99842433548665 \tabularnewline
17 & 0.000783087320820566 & 0.00156617464164113 & 0.99921691267918 \tabularnewline
18 & 0.000391401452460286 & 0.000782802904920572 & 0.99960859854754 \tabularnewline
19 & 0.000195314520854545 & 0.00039062904170909 & 0.999804685479145 \tabularnewline
20 & 0.000102378861873731 & 0.000204757723747462 & 0.999897621138126 \tabularnewline
21 & 5.56853980804253e-05 & 0.000111370796160851 & 0.99994431460192 \tabularnewline
22 & 3.73313706641159e-05 & 7.46627413282319e-05 & 0.999962668629336 \tabularnewline
23 & 6.69654652638743e-05 & 0.000133930930527749 & 0.999933034534736 \tabularnewline
24 & 0.000158168276335494 & 0.000316336552670989 & 0.999841831723665 \tabularnewline
25 & 0.000601868763757595 & 0.00120373752751519 & 0.999398131236242 \tabularnewline
26 & 0.000595351383861799 & 0.00119070276772360 & 0.999404648616138 \tabularnewline
27 & 0.0007425658088238 & 0.0014851316176476 & 0.999257434191176 \tabularnewline
28 & 0.00121565640400221 & 0.00243131280800442 & 0.998784343595998 \tabularnewline
29 & 0.00160413678016420 & 0.00320827356032840 & 0.998395863219836 \tabularnewline
30 & 0.00275199744036401 & 0.00550399488072801 & 0.997248002559636 \tabularnewline
31 & 0.00852247017103958 & 0.0170449403420792 & 0.99147752982896 \tabularnewline
32 & 0.0201196901533702 & 0.0402393803067403 & 0.97988030984663 \tabularnewline
33 & 0.0561846865497263 & 0.112369373099453 & 0.943815313450274 \tabularnewline
34 & 0.19011332851186 & 0.38022665702372 & 0.80988667148814 \tabularnewline
35 & 0.188939007175251 & 0.377878014350501 & 0.81106099282475 \tabularnewline
36 & 0.191836420240463 & 0.383672840480926 & 0.808163579759537 \tabularnewline
37 & 0.198224596559515 & 0.396449193119030 & 0.801775403440485 \tabularnewline
38 & 0.209277569801611 & 0.418555139603222 & 0.79072243019839 \tabularnewline
39 & 0.237676426205206 & 0.475352852410413 & 0.762323573794794 \tabularnewline
40 & 0.242400978913519 & 0.484801957827038 & 0.757599021086481 \tabularnewline
41 & 0.242743154096922 & 0.485486308193844 & 0.757256845903078 \tabularnewline
42 & 0.247689667908432 & 0.495379335816863 & 0.752310332091568 \tabularnewline
43 & 0.265417898132187 & 0.530835796264373 & 0.734582101867813 \tabularnewline
44 & 0.276107726324625 & 0.55221545264925 & 0.723892273675375 \tabularnewline
45 & 0.31450472382757 & 0.62900944765514 & 0.68549527617243 \tabularnewline
46 & 0.483717751234171 & 0.967435502468342 & 0.516282248765829 \tabularnewline
47 & 0.46926497981226 & 0.93852995962452 & 0.53073502018774 \tabularnewline
48 & 0.454676669192868 & 0.909353338385735 & 0.545323330807132 \tabularnewline
49 & 0.470918231705178 & 0.941836463410357 & 0.529081768294822 \tabularnewline
50 & 0.5547705752002 & 0.8904588495996 & 0.4452294247998 \tabularnewline
51 & 0.699423295134779 & 0.601153409730442 & 0.300576704865221 \tabularnewline
52 & 0.750414457147422 & 0.499171085705155 & 0.249585542852578 \tabularnewline
53 & 0.718067475589597 & 0.563865048820807 & 0.281932524410403 \tabularnewline
54 & 0.645079126420355 & 0.70984174715929 & 0.354920873579645 \tabularnewline
55 & 0.5136393629401 & 0.9727212741198 & 0.4863606370599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58378&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]0.0272895128985364[/C][C]0.0545790257970728[/C][C]0.972710487101464[/C][/ROW]
[ROW][C]6[/C][C]0.00740811672593806[/C][C]0.0148162334518761[/C][C]0.992591883274062[/C][/ROW]
[ROW][C]7[/C][C]0.00253702284269948[/C][C]0.00507404568539896[/C][C]0.9974629771573[/C][/ROW]
[ROW][C]8[/C][C]0.00231443668251552[/C][C]0.00462887336503104[/C][C]0.997685563317484[/C][/ROW]
[ROW][C]9[/C][C]0.00265726836806633[/C][C]0.00531453673613267[/C][C]0.997342731631934[/C][/ROW]
[ROW][C]10[/C][C]0.00295950215893888[/C][C]0.00591900431787775[/C][C]0.99704049784106[/C][/ROW]
[ROW][C]11[/C][C]0.00313045295485732[/C][C]0.00626090590971464[/C][C]0.996869547045143[/C][/ROW]
[ROW][C]12[/C][C]0.00500917893603[/C][C]0.01001835787206[/C][C]0.99499082106397[/C][/ROW]
[ROW][C]13[/C][C]0.00717621196404911[/C][C]0.0143524239280982[/C][C]0.99282378803595[/C][/ROW]
[ROW][C]14[/C][C]0.0056642111865225[/C][C]0.011328422373045[/C][C]0.994335788813478[/C][/ROW]
[ROW][C]15[/C][C]0.00293032134310244[/C][C]0.00586064268620489[/C][C]0.997069678656898[/C][/ROW]
[ROW][C]16[/C][C]0.00157566451334914[/C][C]0.00315132902669828[/C][C]0.99842433548665[/C][/ROW]
[ROW][C]17[/C][C]0.000783087320820566[/C][C]0.00156617464164113[/C][C]0.99921691267918[/C][/ROW]
[ROW][C]18[/C][C]0.000391401452460286[/C][C]0.000782802904920572[/C][C]0.99960859854754[/C][/ROW]
[ROW][C]19[/C][C]0.000195314520854545[/C][C]0.00039062904170909[/C][C]0.999804685479145[/C][/ROW]
[ROW][C]20[/C][C]0.000102378861873731[/C][C]0.000204757723747462[/C][C]0.999897621138126[/C][/ROW]
[ROW][C]21[/C][C]5.56853980804253e-05[/C][C]0.000111370796160851[/C][C]0.99994431460192[/C][/ROW]
[ROW][C]22[/C][C]3.73313706641159e-05[/C][C]7.46627413282319e-05[/C][C]0.999962668629336[/C][/ROW]
[ROW][C]23[/C][C]6.69654652638743e-05[/C][C]0.000133930930527749[/C][C]0.999933034534736[/C][/ROW]
[ROW][C]24[/C][C]0.000158168276335494[/C][C]0.000316336552670989[/C][C]0.999841831723665[/C][/ROW]
[ROW][C]25[/C][C]0.000601868763757595[/C][C]0.00120373752751519[/C][C]0.999398131236242[/C][/ROW]
[ROW][C]26[/C][C]0.000595351383861799[/C][C]0.00119070276772360[/C][C]0.999404648616138[/C][/ROW]
[ROW][C]27[/C][C]0.0007425658088238[/C][C]0.0014851316176476[/C][C]0.999257434191176[/C][/ROW]
[ROW][C]28[/C][C]0.00121565640400221[/C][C]0.00243131280800442[/C][C]0.998784343595998[/C][/ROW]
[ROW][C]29[/C][C]0.00160413678016420[/C][C]0.00320827356032840[/C][C]0.998395863219836[/C][/ROW]
[ROW][C]30[/C][C]0.00275199744036401[/C][C]0.00550399488072801[/C][C]0.997248002559636[/C][/ROW]
[ROW][C]31[/C][C]0.00852247017103958[/C][C]0.0170449403420792[/C][C]0.99147752982896[/C][/ROW]
[ROW][C]32[/C][C]0.0201196901533702[/C][C]0.0402393803067403[/C][C]0.97988030984663[/C][/ROW]
[ROW][C]33[/C][C]0.0561846865497263[/C][C]0.112369373099453[/C][C]0.943815313450274[/C][/ROW]
[ROW][C]34[/C][C]0.19011332851186[/C][C]0.38022665702372[/C][C]0.80988667148814[/C][/ROW]
[ROW][C]35[/C][C]0.188939007175251[/C][C]0.377878014350501[/C][C]0.81106099282475[/C][/ROW]
[ROW][C]36[/C][C]0.191836420240463[/C][C]0.383672840480926[/C][C]0.808163579759537[/C][/ROW]
[ROW][C]37[/C][C]0.198224596559515[/C][C]0.396449193119030[/C][C]0.801775403440485[/C][/ROW]
[ROW][C]38[/C][C]0.209277569801611[/C][C]0.418555139603222[/C][C]0.79072243019839[/C][/ROW]
[ROW][C]39[/C][C]0.237676426205206[/C][C]0.475352852410413[/C][C]0.762323573794794[/C][/ROW]
[ROW][C]40[/C][C]0.242400978913519[/C][C]0.484801957827038[/C][C]0.757599021086481[/C][/ROW]
[ROW][C]41[/C][C]0.242743154096922[/C][C]0.485486308193844[/C][C]0.757256845903078[/C][/ROW]
[ROW][C]42[/C][C]0.247689667908432[/C][C]0.495379335816863[/C][C]0.752310332091568[/C][/ROW]
[ROW][C]43[/C][C]0.265417898132187[/C][C]0.530835796264373[/C][C]0.734582101867813[/C][/ROW]
[ROW][C]44[/C][C]0.276107726324625[/C][C]0.55221545264925[/C][C]0.723892273675375[/C][/ROW]
[ROW][C]45[/C][C]0.31450472382757[/C][C]0.62900944765514[/C][C]0.68549527617243[/C][/ROW]
[ROW][C]46[/C][C]0.483717751234171[/C][C]0.967435502468342[/C][C]0.516282248765829[/C][/ROW]
[ROW][C]47[/C][C]0.46926497981226[/C][C]0.93852995962452[/C][C]0.53073502018774[/C][/ROW]
[ROW][C]48[/C][C]0.454676669192868[/C][C]0.909353338385735[/C][C]0.545323330807132[/C][/ROW]
[ROW][C]49[/C][C]0.470918231705178[/C][C]0.941836463410357[/C][C]0.529081768294822[/C][/ROW]
[ROW][C]50[/C][C]0.5547705752002[/C][C]0.8904588495996[/C][C]0.4452294247998[/C][/ROW]
[ROW][C]51[/C][C]0.699423295134779[/C][C]0.601153409730442[/C][C]0.300576704865221[/C][/ROW]
[ROW][C]52[/C][C]0.750414457147422[/C][C]0.499171085705155[/C][C]0.249585542852578[/C][/ROW]
[ROW][C]53[/C][C]0.718067475589597[/C][C]0.563865048820807[/C][C]0.281932524410403[/C][/ROW]
[ROW][C]54[/C][C]0.645079126420355[/C][C]0.70984174715929[/C][C]0.354920873579645[/C][/ROW]
[ROW][C]55[/C][C]0.5136393629401[/C][C]0.9727212741198[/C][C]0.4863606370599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58378&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58378&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
50.02728951289853640.05457902579707280.972710487101464
60.007408116725938060.01481623345187610.992591883274062
70.002537022842699480.005074045685398960.9974629771573
80.002314436682515520.004628873365031040.997685563317484
90.002657268368066330.005314536736132670.997342731631934
100.002959502158938880.005919004317877750.99704049784106
110.003130452954857320.006260905909714640.996869547045143
120.005009178936030.010018357872060.99499082106397
130.007176211964049110.01435242392809820.99282378803595
140.00566421118652250.0113284223730450.994335788813478
150.002930321343102440.005860642686204890.997069678656898
160.001575664513349140.003151329026698280.99842433548665
170.0007830873208205660.001566174641641130.99921691267918
180.0003914014524602860.0007828029049205720.99960859854754
190.0001953145208545450.000390629041709090.999804685479145
200.0001023788618737310.0002047577237474620.999897621138126
215.56853980804253e-050.0001113707961608510.99994431460192
223.73313706641159e-057.46627413282319e-050.999962668629336
236.69654652638743e-050.0001339309305277490.999933034534736
240.0001581682763354940.0003163365526709890.999841831723665
250.0006018687637575950.001203737527515190.999398131236242
260.0005953513838617990.001190702767723600.999404648616138
270.00074256580882380.00148513161764760.999257434191176
280.001215656404002210.002431312808004420.998784343595998
290.001604136780164200.003208273560328400.998395863219836
300.002751997440364010.005503994880728010.997248002559636
310.008522470171039580.01704494034207920.99147752982896
320.02011969015337020.04023938030674030.97988030984663
330.05618468654972630.1123693730994530.943815313450274
340.190113328511860.380226657023720.80988667148814
350.1889390071752510.3778780143505010.81106099282475
360.1918364202404630.3836728404809260.808163579759537
370.1982245965595150.3964491931190300.801775403440485
380.2092775698016110.4185551396032220.79072243019839
390.2376764262052060.4753528524104130.762323573794794
400.2424009789135190.4848019578270380.757599021086481
410.2427431540969220.4854863081938440.757256845903078
420.2476896679084320.4953793358168630.752310332091568
430.2654178981321870.5308357962643730.734582101867813
440.2761077263246250.552215452649250.723892273675375
450.314504723827570.629009447655140.68549527617243
460.4837177512341710.9674355024683420.516282248765829
470.469264979812260.938529959624520.53073502018774
480.4546766691928680.9093533383857350.545323330807132
490.4709182317051780.9418364634103570.529081768294822
500.55477057520020.89045884959960.4452294247998
510.6994232951347790.6011534097304420.300576704865221
520.7504144571474220.4991710857051550.249585542852578
530.7180674755895970.5638650488208070.281932524410403
540.6450791264203550.709841747159290.354920873579645
550.51363936294010.97272127411980.4863606370599







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.411764705882353NOK
5% type I error level270.529411764705882NOK
10% type I error level280.549019607843137NOK

\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 & 21 & 0.411764705882353 & NOK \tabularnewline
5% type I error level & 27 & 0.529411764705882 & NOK \tabularnewline
10% type I error level & 28 & 0.549019607843137 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58378&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]21[/C][C]0.411764705882353[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.529411764705882[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.549019607843137[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58378&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58378&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 level210.411764705882353NOK
5% type I error level270.529411764705882NOK
10% type I error level280.549019607843137NOK



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')
}