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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 26 Nov 2011 13:10:13 -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/2011/Nov/26/t13223310324bydu1jlsha3w9i.htm/, Retrieved Mon, 30 Jan 2023 02:24:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147432, Retrieved Mon, 30 Jan 2023 02:24:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97
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] [Multivariate regr...] [2009-11-19 08:22:38] [21324e9cdf3569788a3d630236984d87]
-    D      [Multiple Regression] [] [2010-12-07 12:10:30] [f47feae0308dca73181bb669fbad1c56]
- R             [Multiple Regression] [] [2011-11-26 18:10:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R P             [Multiple Regression] [] [2011-11-27 16:41:22] [3931071255a6f7f4a767409781cc5f7d]
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Dataseries X:
112.3	0
117.3	0
111.1	1
102.2	1
104.3	1
122.9	1
107.6	1
121.3	1
131.5	1
89	1
104.4	1
128.9	1
135.9	1
133.3	1
121.3	1
120.5	0
120.4	0
137.9	0
126.1	0
133.2	0
151.1	0
105	0
119	0
140.4	0
156.6	0
137.1	0
122.7	0
125.8	0
139.3	0
134.9	0
149.2	0
132.3	0
149	0
117.2	0
119.6	0
152	0
149.4	0
127.3	0
114.1	0
102.1	0
107.7	0
104.4	0
102.1	0
96	1
109.3	0
90	1
83.9	1
112	1
114.3	1
103.6	1
91.7	1
80.8	1
87.2	1
109.2	1
102.7	1
95.1	1
117.5	1
85.1	1
92.1	1
113.5	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147432&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147432&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Promet[t] = + 126.945161290323 -20.4486095661847Dummy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Promet[t] =  +  126.945161290323 -20.4486095661847Dummy[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147432&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Promet[t] =  +  126.945161290323 -20.4486095661847Dummy[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147432&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147432&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
Promet[t] = + 126.945161290323 -20.4486095661847Dummy[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)126.9451612903232.8849144.003200
Dummy-20.44860956618474.149626-4.92787e-064e-06

\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) & 126.945161290323 & 2.88491 & 44.0032 & 0 & 0 \tabularnewline
Dummy & -20.4486095661847 & 4.149626 & -4.9278 & 7e-06 & 4e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147432&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]126.945161290323[/C][C]2.88491[/C][C]44.0032[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]-20.4486095661847[/C][C]4.149626[/C][C]-4.9278[/C][C]7e-06[/C][C]4e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147432&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147432&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)126.9451612903232.8849144.003200
Dummy-20.44860956618474.149626-4.92787e-064e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.543248724887231
R-squared0.295119177091602
Adjusted R-squared0.282966059455251
F-TEST (value)24.2834131884694
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value7.29177659353208e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation16.0624999285474
Sum Squared Residuals14964.226429366

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.543248724887231 \tabularnewline
R-squared & 0.295119177091602 \tabularnewline
Adjusted R-squared & 0.282966059455251 \tabularnewline
F-TEST (value) & 24.2834131884694 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 7.29177659353208e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 16.0624999285474 \tabularnewline
Sum Squared Residuals & 14964.226429366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147432&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.543248724887231[/C][/ROW]
[ROW][C]R-squared[/C][C]0.295119177091602[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.282966059455251[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.2834131884694[/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]7.29177659353208e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]16.0624999285474[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14964.226429366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147432&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147432&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 R0.543248724887231
R-squared0.295119177091602
Adjusted R-squared0.282966059455251
F-TEST (value)24.2834131884694
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value7.29177659353208e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation16.0624999285474
Sum Squared Residuals14964.226429366







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112.3126.945161290322-14.6451612903223
2117.3126.945161290323-9.64516129032261
3111.1106.4965517241384.60344827586206
4102.2106.496551724138-4.29655172413793
5104.3106.496551724138-2.19655172413793
6122.9106.49655172413816.4034482758621
7107.6106.4965517241381.10344827586206
8121.3106.49655172413814.8034482758621
9131.5106.49655172413825.0034482758621
1089106.496551724138-17.4965517241379
11104.4106.496551724138-2.09655172413793
12128.9106.49655172413822.4034482758621
13135.9106.49655172413829.4034482758621
14133.3106.49655172413826.8034482758621
15121.3106.49655172413814.8034482758621
16120.5126.945161290323-6.44516129032259
17120.4126.945161290323-6.54516129032258
18137.9126.94516129032310.9548387096774
19126.1126.945161290323-0.845161290322596
20133.2126.9451612903236.2548387096774
21151.1126.94516129032324.1548387096774
22105126.945161290323-21.9451612903226
23119126.945161290323-7.94516129032259
24140.4126.94516129032313.4548387096774
25156.6126.94516129032329.6548387096774
26137.1126.94516129032310.1548387096774
27122.7126.945161290323-4.24516129032259
28125.8126.945161290323-1.14516129032259
29139.3126.94516129032312.3548387096774
30134.9126.9451612903237.95483870967742
31149.2126.94516129032322.2548387096774
32132.3126.9451612903235.35483870967742
33149126.94516129032322.0548387096774
34117.2126.945161290323-9.74516129032259
35119.6126.945161290323-7.3451612903226
36152126.94516129032325.0548387096774
37149.4126.94516129032322.4548387096774
38127.3126.9451612903230.354838709677407
39114.1126.945161290323-12.8451612903226
40102.1126.945161290323-24.8451612903226
41107.7126.945161290323-19.2451612903226
42104.4126.945161290323-22.5451612903226
43102.1126.945161290323-24.8451612903226
4496106.496551724138-10.4965517241379
45109.3126.945161290323-17.6451612903226
4690106.496551724138-16.4965517241379
4783.9106.496551724138-22.5965517241379
48112106.4965517241385.50344827586207
49114.3106.4965517241387.80344827586207
50103.6106.496551724138-2.89655172413794
5191.7106.496551724138-14.7965517241379
5280.8106.496551724138-25.6965517241379
5387.2106.496551724138-19.2965517241379
54109.2106.4965517241382.70344827586207
55102.7106.496551724138-3.79655172413793
5695.1106.496551724138-11.3965517241379
57117.5106.49655172413811.0034482758621
5885.1106.496551724138-21.3965517241379
5992.1106.496551724138-14.3965517241379
60113.5106.4965517241387.00344827586207

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112.3 & 126.945161290322 & -14.6451612903223 \tabularnewline
2 & 117.3 & 126.945161290323 & -9.64516129032261 \tabularnewline
3 & 111.1 & 106.496551724138 & 4.60344827586206 \tabularnewline
4 & 102.2 & 106.496551724138 & -4.29655172413793 \tabularnewline
5 & 104.3 & 106.496551724138 & -2.19655172413793 \tabularnewline
6 & 122.9 & 106.496551724138 & 16.4034482758621 \tabularnewline
7 & 107.6 & 106.496551724138 & 1.10344827586206 \tabularnewline
8 & 121.3 & 106.496551724138 & 14.8034482758621 \tabularnewline
9 & 131.5 & 106.496551724138 & 25.0034482758621 \tabularnewline
10 & 89 & 106.496551724138 & -17.4965517241379 \tabularnewline
11 & 104.4 & 106.496551724138 & -2.09655172413793 \tabularnewline
12 & 128.9 & 106.496551724138 & 22.4034482758621 \tabularnewline
13 & 135.9 & 106.496551724138 & 29.4034482758621 \tabularnewline
14 & 133.3 & 106.496551724138 & 26.8034482758621 \tabularnewline
15 & 121.3 & 106.496551724138 & 14.8034482758621 \tabularnewline
16 & 120.5 & 126.945161290323 & -6.44516129032259 \tabularnewline
17 & 120.4 & 126.945161290323 & -6.54516129032258 \tabularnewline
18 & 137.9 & 126.945161290323 & 10.9548387096774 \tabularnewline
19 & 126.1 & 126.945161290323 & -0.845161290322596 \tabularnewline
20 & 133.2 & 126.945161290323 & 6.2548387096774 \tabularnewline
21 & 151.1 & 126.945161290323 & 24.1548387096774 \tabularnewline
22 & 105 & 126.945161290323 & -21.9451612903226 \tabularnewline
23 & 119 & 126.945161290323 & -7.94516129032259 \tabularnewline
24 & 140.4 & 126.945161290323 & 13.4548387096774 \tabularnewline
25 & 156.6 & 126.945161290323 & 29.6548387096774 \tabularnewline
26 & 137.1 & 126.945161290323 & 10.1548387096774 \tabularnewline
27 & 122.7 & 126.945161290323 & -4.24516129032259 \tabularnewline
28 & 125.8 & 126.945161290323 & -1.14516129032259 \tabularnewline
29 & 139.3 & 126.945161290323 & 12.3548387096774 \tabularnewline
30 & 134.9 & 126.945161290323 & 7.95483870967742 \tabularnewline
31 & 149.2 & 126.945161290323 & 22.2548387096774 \tabularnewline
32 & 132.3 & 126.945161290323 & 5.35483870967742 \tabularnewline
33 & 149 & 126.945161290323 & 22.0548387096774 \tabularnewline
34 & 117.2 & 126.945161290323 & -9.74516129032259 \tabularnewline
35 & 119.6 & 126.945161290323 & -7.3451612903226 \tabularnewline
36 & 152 & 126.945161290323 & 25.0548387096774 \tabularnewline
37 & 149.4 & 126.945161290323 & 22.4548387096774 \tabularnewline
38 & 127.3 & 126.945161290323 & 0.354838709677407 \tabularnewline
39 & 114.1 & 126.945161290323 & -12.8451612903226 \tabularnewline
40 & 102.1 & 126.945161290323 & -24.8451612903226 \tabularnewline
41 & 107.7 & 126.945161290323 & -19.2451612903226 \tabularnewline
42 & 104.4 & 126.945161290323 & -22.5451612903226 \tabularnewline
43 & 102.1 & 126.945161290323 & -24.8451612903226 \tabularnewline
44 & 96 & 106.496551724138 & -10.4965517241379 \tabularnewline
45 & 109.3 & 126.945161290323 & -17.6451612903226 \tabularnewline
46 & 90 & 106.496551724138 & -16.4965517241379 \tabularnewline
47 & 83.9 & 106.496551724138 & -22.5965517241379 \tabularnewline
48 & 112 & 106.496551724138 & 5.50344827586207 \tabularnewline
49 & 114.3 & 106.496551724138 & 7.80344827586207 \tabularnewline
50 & 103.6 & 106.496551724138 & -2.89655172413794 \tabularnewline
51 & 91.7 & 106.496551724138 & -14.7965517241379 \tabularnewline
52 & 80.8 & 106.496551724138 & -25.6965517241379 \tabularnewline
53 & 87.2 & 106.496551724138 & -19.2965517241379 \tabularnewline
54 & 109.2 & 106.496551724138 & 2.70344827586207 \tabularnewline
55 & 102.7 & 106.496551724138 & -3.79655172413793 \tabularnewline
56 & 95.1 & 106.496551724138 & -11.3965517241379 \tabularnewline
57 & 117.5 & 106.496551724138 & 11.0034482758621 \tabularnewline
58 & 85.1 & 106.496551724138 & -21.3965517241379 \tabularnewline
59 & 92.1 & 106.496551724138 & -14.3965517241379 \tabularnewline
60 & 113.5 & 106.496551724138 & 7.00344827586207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147432&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]112.3[/C][C]126.945161290322[/C][C]-14.6451612903223[/C][/ROW]
[ROW][C]2[/C][C]117.3[/C][C]126.945161290323[/C][C]-9.64516129032261[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]106.496551724138[/C][C]4.60344827586206[/C][/ROW]
[ROW][C]4[/C][C]102.2[/C][C]106.496551724138[/C][C]-4.29655172413793[/C][/ROW]
[ROW][C]5[/C][C]104.3[/C][C]106.496551724138[/C][C]-2.19655172413793[/C][/ROW]
[ROW][C]6[/C][C]122.9[/C][C]106.496551724138[/C][C]16.4034482758621[/C][/ROW]
[ROW][C]7[/C][C]107.6[/C][C]106.496551724138[/C][C]1.10344827586206[/C][/ROW]
[ROW][C]8[/C][C]121.3[/C][C]106.496551724138[/C][C]14.8034482758621[/C][/ROW]
[ROW][C]9[/C][C]131.5[/C][C]106.496551724138[/C][C]25.0034482758621[/C][/ROW]
[ROW][C]10[/C][C]89[/C][C]106.496551724138[/C][C]-17.4965517241379[/C][/ROW]
[ROW][C]11[/C][C]104.4[/C][C]106.496551724138[/C][C]-2.09655172413793[/C][/ROW]
[ROW][C]12[/C][C]128.9[/C][C]106.496551724138[/C][C]22.4034482758621[/C][/ROW]
[ROW][C]13[/C][C]135.9[/C][C]106.496551724138[/C][C]29.4034482758621[/C][/ROW]
[ROW][C]14[/C][C]133.3[/C][C]106.496551724138[/C][C]26.8034482758621[/C][/ROW]
[ROW][C]15[/C][C]121.3[/C][C]106.496551724138[/C][C]14.8034482758621[/C][/ROW]
[ROW][C]16[/C][C]120.5[/C][C]126.945161290323[/C][C]-6.44516129032259[/C][/ROW]
[ROW][C]17[/C][C]120.4[/C][C]126.945161290323[/C][C]-6.54516129032258[/C][/ROW]
[ROW][C]18[/C][C]137.9[/C][C]126.945161290323[/C][C]10.9548387096774[/C][/ROW]
[ROW][C]19[/C][C]126.1[/C][C]126.945161290323[/C][C]-0.845161290322596[/C][/ROW]
[ROW][C]20[/C][C]133.2[/C][C]126.945161290323[/C][C]6.2548387096774[/C][/ROW]
[ROW][C]21[/C][C]151.1[/C][C]126.945161290323[/C][C]24.1548387096774[/C][/ROW]
[ROW][C]22[/C][C]105[/C][C]126.945161290323[/C][C]-21.9451612903226[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]126.945161290323[/C][C]-7.94516129032259[/C][/ROW]
[ROW][C]24[/C][C]140.4[/C][C]126.945161290323[/C][C]13.4548387096774[/C][/ROW]
[ROW][C]25[/C][C]156.6[/C][C]126.945161290323[/C][C]29.6548387096774[/C][/ROW]
[ROW][C]26[/C][C]137.1[/C][C]126.945161290323[/C][C]10.1548387096774[/C][/ROW]
[ROW][C]27[/C][C]122.7[/C][C]126.945161290323[/C][C]-4.24516129032259[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]126.945161290323[/C][C]-1.14516129032259[/C][/ROW]
[ROW][C]29[/C][C]139.3[/C][C]126.945161290323[/C][C]12.3548387096774[/C][/ROW]
[ROW][C]30[/C][C]134.9[/C][C]126.945161290323[/C][C]7.95483870967742[/C][/ROW]
[ROW][C]31[/C][C]149.2[/C][C]126.945161290323[/C][C]22.2548387096774[/C][/ROW]
[ROW][C]32[/C][C]132.3[/C][C]126.945161290323[/C][C]5.35483870967742[/C][/ROW]
[ROW][C]33[/C][C]149[/C][C]126.945161290323[/C][C]22.0548387096774[/C][/ROW]
[ROW][C]34[/C][C]117.2[/C][C]126.945161290323[/C][C]-9.74516129032259[/C][/ROW]
[ROW][C]35[/C][C]119.6[/C][C]126.945161290323[/C][C]-7.3451612903226[/C][/ROW]
[ROW][C]36[/C][C]152[/C][C]126.945161290323[/C][C]25.0548387096774[/C][/ROW]
[ROW][C]37[/C][C]149.4[/C][C]126.945161290323[/C][C]22.4548387096774[/C][/ROW]
[ROW][C]38[/C][C]127.3[/C][C]126.945161290323[/C][C]0.354838709677407[/C][/ROW]
[ROW][C]39[/C][C]114.1[/C][C]126.945161290323[/C][C]-12.8451612903226[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]126.945161290323[/C][C]-24.8451612903226[/C][/ROW]
[ROW][C]41[/C][C]107.7[/C][C]126.945161290323[/C][C]-19.2451612903226[/C][/ROW]
[ROW][C]42[/C][C]104.4[/C][C]126.945161290323[/C][C]-22.5451612903226[/C][/ROW]
[ROW][C]43[/C][C]102.1[/C][C]126.945161290323[/C][C]-24.8451612903226[/C][/ROW]
[ROW][C]44[/C][C]96[/C][C]106.496551724138[/C][C]-10.4965517241379[/C][/ROW]
[ROW][C]45[/C][C]109.3[/C][C]126.945161290323[/C][C]-17.6451612903226[/C][/ROW]
[ROW][C]46[/C][C]90[/C][C]106.496551724138[/C][C]-16.4965517241379[/C][/ROW]
[ROW][C]47[/C][C]83.9[/C][C]106.496551724138[/C][C]-22.5965517241379[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]106.496551724138[/C][C]5.50344827586207[/C][/ROW]
[ROW][C]49[/C][C]114.3[/C][C]106.496551724138[/C][C]7.80344827586207[/C][/ROW]
[ROW][C]50[/C][C]103.6[/C][C]106.496551724138[/C][C]-2.89655172413794[/C][/ROW]
[ROW][C]51[/C][C]91.7[/C][C]106.496551724138[/C][C]-14.7965517241379[/C][/ROW]
[ROW][C]52[/C][C]80.8[/C][C]106.496551724138[/C][C]-25.6965517241379[/C][/ROW]
[ROW][C]53[/C][C]87.2[/C][C]106.496551724138[/C][C]-19.2965517241379[/C][/ROW]
[ROW][C]54[/C][C]109.2[/C][C]106.496551724138[/C][C]2.70344827586207[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]106.496551724138[/C][C]-3.79655172413793[/C][/ROW]
[ROW][C]56[/C][C]95.1[/C][C]106.496551724138[/C][C]-11.3965517241379[/C][/ROW]
[ROW][C]57[/C][C]117.5[/C][C]106.496551724138[/C][C]11.0034482758621[/C][/ROW]
[ROW][C]58[/C][C]85.1[/C][C]106.496551724138[/C][C]-21.3965517241379[/C][/ROW]
[ROW][C]59[/C][C]92.1[/C][C]106.496551724138[/C][C]-14.3965517241379[/C][/ROW]
[ROW][C]60[/C][C]113.5[/C][C]106.496551724138[/C][C]7.00344827586207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147432&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147432&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
1112.3126.945161290322-14.6451612903223
2117.3126.945161290323-9.64516129032261
3111.1106.4965517241384.60344827586206
4102.2106.496551724138-4.29655172413793
5104.3106.496551724138-2.19655172413793
6122.9106.49655172413816.4034482758621
7107.6106.4965517241381.10344827586206
8121.3106.49655172413814.8034482758621
9131.5106.49655172413825.0034482758621
1089106.496551724138-17.4965517241379
11104.4106.496551724138-2.09655172413793
12128.9106.49655172413822.4034482758621
13135.9106.49655172413829.4034482758621
14133.3106.49655172413826.8034482758621
15121.3106.49655172413814.8034482758621
16120.5126.945161290323-6.44516129032259
17120.4126.945161290323-6.54516129032258
18137.9126.94516129032310.9548387096774
19126.1126.945161290323-0.845161290322596
20133.2126.9451612903236.2548387096774
21151.1126.94516129032324.1548387096774
22105126.945161290323-21.9451612903226
23119126.945161290323-7.94516129032259
24140.4126.94516129032313.4548387096774
25156.6126.94516129032329.6548387096774
26137.1126.94516129032310.1548387096774
27122.7126.945161290323-4.24516129032259
28125.8126.945161290323-1.14516129032259
29139.3126.94516129032312.3548387096774
30134.9126.9451612903237.95483870967742
31149.2126.94516129032322.2548387096774
32132.3126.9451612903235.35483870967742
33149126.94516129032322.0548387096774
34117.2126.945161290323-9.74516129032259
35119.6126.945161290323-7.3451612903226
36152126.94516129032325.0548387096774
37149.4126.94516129032322.4548387096774
38127.3126.9451612903230.354838709677407
39114.1126.945161290323-12.8451612903226
40102.1126.945161290323-24.8451612903226
41107.7126.945161290323-19.2451612903226
42104.4126.945161290323-22.5451612903226
43102.1126.945161290323-24.8451612903226
4496106.496551724138-10.4965517241379
45109.3126.945161290323-17.6451612903226
4690106.496551724138-16.4965517241379
4783.9106.496551724138-22.5965517241379
48112106.4965517241385.50344827586207
49114.3106.4965517241387.80344827586207
50103.6106.496551724138-2.89655172413794
5191.7106.496551724138-14.7965517241379
5280.8106.496551724138-25.6965517241379
5387.2106.496551724138-19.2965517241379
54109.2106.4965517241382.70344827586207
55102.7106.496551724138-3.79655172413793
5695.1106.496551724138-11.3965517241379
57117.5106.49655172413811.0034482758621
5885.1106.496551724138-21.3965517241379
5992.1106.496551724138-14.3965517241379
60113.5106.4965517241387.00344827586207







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.02308077564848990.04616155129697970.97691922435151
60.09058062673776890.1811612534755380.909419373262231
70.03836084365465430.07672168730930860.961639156345346
80.03596145486210410.07192290972420820.964038545137896
90.09302489269862030.1860497853972410.90697510730138
100.2223410354745550.444682070949110.777658964525445
110.1576108881743440.3152217763486880.842389111825656
120.199439281456320.398878562912640.80056071854368
130.3360520739869720.6721041479739440.663947926013028
140.4277092239283650.855418447856730.572290776071635
150.3850040469687910.7700080939375830.614995953031209
160.3113854401575660.6227708803151310.688614559842434
170.2434107879692880.4868215759385760.756589212030712
180.2668296534122790.5336593068245580.733170346587721
190.2046238317296360.4092476634592720.795376168270364
200.1700958839167480.3401917678334960.829904116083252
210.2890915577840650.5781831155681290.710908442215935
220.3673239296487860.7346478592975730.632676070351214
230.3093970811607680.6187941623215370.690602918839232
240.3050821535020350.6101643070040710.694917846497965
250.5199980415660750.960003916867850.480001958433925
260.4744919575303750.948983915060750.525508042469625
270.4064194079192830.8128388158385650.593580592080717
280.3349074848285060.6698149696570120.665092515171494
290.3113899562571610.6227799125143220.688610043742839
300.2652986903666620.5305973807333240.734701309633338
310.353734057273740.7074681145474810.64626594272626
320.3023736151377330.6047472302754670.697626384862267
330.4276092126032840.8552184252065690.572390787396716
340.3847005993032410.7694011986064810.615299400696759
350.3307454776179960.6614909552359920.669254522382004
360.5908777783536990.8182444432926020.409122221646301
370.8809258362410470.2381483275179050.119074163758953
380.9088002119321950.182399576135610.0911997880678051
390.9012269971013820.1975460057972360.0987730028986179
400.9066484166993910.1867031666012170.0933515833006085
410.8913341130452050.2173317739095910.108665886954795
420.8764498239042390.2471003521915210.123550176095761
430.8652542661472610.2694914677054770.134745733852739
440.8312464825276990.3375070349446020.168753517472301
450.7841004560875560.4317990878248880.215899543912444
460.7655698495062690.4688603009874620.234430150493731
470.8030187699220270.3939624601559460.196981230077973
480.7713280829496570.4573438341006870.228671917050343
490.7709578969510030.4580842060979940.229042103048997
500.6929418244896830.6141163510206340.307058175510317
510.6163276767602380.7673446464795240.383672323239762
520.6898813739282640.6202372521434720.310118626071736
530.6803089980878670.6393820038242650.319691001912132
540.5660800431438660.8678399137122680.433919956856134
550.3976542832070180.7953085664140360.602345716792982

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0230807756484899 & 0.0461615512969797 & 0.97691922435151 \tabularnewline
6 & 0.0905806267377689 & 0.181161253475538 & 0.909419373262231 \tabularnewline
7 & 0.0383608436546543 & 0.0767216873093086 & 0.961639156345346 \tabularnewline
8 & 0.0359614548621041 & 0.0719229097242082 & 0.964038545137896 \tabularnewline
9 & 0.0930248926986203 & 0.186049785397241 & 0.90697510730138 \tabularnewline
10 & 0.222341035474555 & 0.44468207094911 & 0.777658964525445 \tabularnewline
11 & 0.157610888174344 & 0.315221776348688 & 0.842389111825656 \tabularnewline
12 & 0.19943928145632 & 0.39887856291264 & 0.80056071854368 \tabularnewline
13 & 0.336052073986972 & 0.672104147973944 & 0.663947926013028 \tabularnewline
14 & 0.427709223928365 & 0.85541844785673 & 0.572290776071635 \tabularnewline
15 & 0.385004046968791 & 0.770008093937583 & 0.614995953031209 \tabularnewline
16 & 0.311385440157566 & 0.622770880315131 & 0.688614559842434 \tabularnewline
17 & 0.243410787969288 & 0.486821575938576 & 0.756589212030712 \tabularnewline
18 & 0.266829653412279 & 0.533659306824558 & 0.733170346587721 \tabularnewline
19 & 0.204623831729636 & 0.409247663459272 & 0.795376168270364 \tabularnewline
20 & 0.170095883916748 & 0.340191767833496 & 0.829904116083252 \tabularnewline
21 & 0.289091557784065 & 0.578183115568129 & 0.710908442215935 \tabularnewline
22 & 0.367323929648786 & 0.734647859297573 & 0.632676070351214 \tabularnewline
23 & 0.309397081160768 & 0.618794162321537 & 0.690602918839232 \tabularnewline
24 & 0.305082153502035 & 0.610164307004071 & 0.694917846497965 \tabularnewline
25 & 0.519998041566075 & 0.96000391686785 & 0.480001958433925 \tabularnewline
26 & 0.474491957530375 & 0.94898391506075 & 0.525508042469625 \tabularnewline
27 & 0.406419407919283 & 0.812838815838565 & 0.593580592080717 \tabularnewline
28 & 0.334907484828506 & 0.669814969657012 & 0.665092515171494 \tabularnewline
29 & 0.311389956257161 & 0.622779912514322 & 0.688610043742839 \tabularnewline
30 & 0.265298690366662 & 0.530597380733324 & 0.734701309633338 \tabularnewline
31 & 0.35373405727374 & 0.707468114547481 & 0.64626594272626 \tabularnewline
32 & 0.302373615137733 & 0.604747230275467 & 0.697626384862267 \tabularnewline
33 & 0.427609212603284 & 0.855218425206569 & 0.572390787396716 \tabularnewline
34 & 0.384700599303241 & 0.769401198606481 & 0.615299400696759 \tabularnewline
35 & 0.330745477617996 & 0.661490955235992 & 0.669254522382004 \tabularnewline
36 & 0.590877778353699 & 0.818244443292602 & 0.409122221646301 \tabularnewline
37 & 0.880925836241047 & 0.238148327517905 & 0.119074163758953 \tabularnewline
38 & 0.908800211932195 & 0.18239957613561 & 0.0911997880678051 \tabularnewline
39 & 0.901226997101382 & 0.197546005797236 & 0.0987730028986179 \tabularnewline
40 & 0.906648416699391 & 0.186703166601217 & 0.0933515833006085 \tabularnewline
41 & 0.891334113045205 & 0.217331773909591 & 0.108665886954795 \tabularnewline
42 & 0.876449823904239 & 0.247100352191521 & 0.123550176095761 \tabularnewline
43 & 0.865254266147261 & 0.269491467705477 & 0.134745733852739 \tabularnewline
44 & 0.831246482527699 & 0.337507034944602 & 0.168753517472301 \tabularnewline
45 & 0.784100456087556 & 0.431799087824888 & 0.215899543912444 \tabularnewline
46 & 0.765569849506269 & 0.468860300987462 & 0.234430150493731 \tabularnewline
47 & 0.803018769922027 & 0.393962460155946 & 0.196981230077973 \tabularnewline
48 & 0.771328082949657 & 0.457343834100687 & 0.228671917050343 \tabularnewline
49 & 0.770957896951003 & 0.458084206097994 & 0.229042103048997 \tabularnewline
50 & 0.692941824489683 & 0.614116351020634 & 0.307058175510317 \tabularnewline
51 & 0.616327676760238 & 0.767344646479524 & 0.383672323239762 \tabularnewline
52 & 0.689881373928264 & 0.620237252143472 & 0.310118626071736 \tabularnewline
53 & 0.680308998087867 & 0.639382003824265 & 0.319691001912132 \tabularnewline
54 & 0.566080043143866 & 0.867839913712268 & 0.433919956856134 \tabularnewline
55 & 0.397654283207018 & 0.795308566414036 & 0.602345716792982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147432&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]0.0230807756484899[/C][C]0.0461615512969797[/C][C]0.97691922435151[/C][/ROW]
[ROW][C]6[/C][C]0.0905806267377689[/C][C]0.181161253475538[/C][C]0.909419373262231[/C][/ROW]
[ROW][C]7[/C][C]0.0383608436546543[/C][C]0.0767216873093086[/C][C]0.961639156345346[/C][/ROW]
[ROW][C]8[/C][C]0.0359614548621041[/C][C]0.0719229097242082[/C][C]0.964038545137896[/C][/ROW]
[ROW][C]9[/C][C]0.0930248926986203[/C][C]0.186049785397241[/C][C]0.90697510730138[/C][/ROW]
[ROW][C]10[/C][C]0.222341035474555[/C][C]0.44468207094911[/C][C]0.777658964525445[/C][/ROW]
[ROW][C]11[/C][C]0.157610888174344[/C][C]0.315221776348688[/C][C]0.842389111825656[/C][/ROW]
[ROW][C]12[/C][C]0.19943928145632[/C][C]0.39887856291264[/C][C]0.80056071854368[/C][/ROW]
[ROW][C]13[/C][C]0.336052073986972[/C][C]0.672104147973944[/C][C]0.663947926013028[/C][/ROW]
[ROW][C]14[/C][C]0.427709223928365[/C][C]0.85541844785673[/C][C]0.572290776071635[/C][/ROW]
[ROW][C]15[/C][C]0.385004046968791[/C][C]0.770008093937583[/C][C]0.614995953031209[/C][/ROW]
[ROW][C]16[/C][C]0.311385440157566[/C][C]0.622770880315131[/C][C]0.688614559842434[/C][/ROW]
[ROW][C]17[/C][C]0.243410787969288[/C][C]0.486821575938576[/C][C]0.756589212030712[/C][/ROW]
[ROW][C]18[/C][C]0.266829653412279[/C][C]0.533659306824558[/C][C]0.733170346587721[/C][/ROW]
[ROW][C]19[/C][C]0.204623831729636[/C][C]0.409247663459272[/C][C]0.795376168270364[/C][/ROW]
[ROW][C]20[/C][C]0.170095883916748[/C][C]0.340191767833496[/C][C]0.829904116083252[/C][/ROW]
[ROW][C]21[/C][C]0.289091557784065[/C][C]0.578183115568129[/C][C]0.710908442215935[/C][/ROW]
[ROW][C]22[/C][C]0.367323929648786[/C][C]0.734647859297573[/C][C]0.632676070351214[/C][/ROW]
[ROW][C]23[/C][C]0.309397081160768[/C][C]0.618794162321537[/C][C]0.690602918839232[/C][/ROW]
[ROW][C]24[/C][C]0.305082153502035[/C][C]0.610164307004071[/C][C]0.694917846497965[/C][/ROW]
[ROW][C]25[/C][C]0.519998041566075[/C][C]0.96000391686785[/C][C]0.480001958433925[/C][/ROW]
[ROW][C]26[/C][C]0.474491957530375[/C][C]0.94898391506075[/C][C]0.525508042469625[/C][/ROW]
[ROW][C]27[/C][C]0.406419407919283[/C][C]0.812838815838565[/C][C]0.593580592080717[/C][/ROW]
[ROW][C]28[/C][C]0.334907484828506[/C][C]0.669814969657012[/C][C]0.665092515171494[/C][/ROW]
[ROW][C]29[/C][C]0.311389956257161[/C][C]0.622779912514322[/C][C]0.688610043742839[/C][/ROW]
[ROW][C]30[/C][C]0.265298690366662[/C][C]0.530597380733324[/C][C]0.734701309633338[/C][/ROW]
[ROW][C]31[/C][C]0.35373405727374[/C][C]0.707468114547481[/C][C]0.64626594272626[/C][/ROW]
[ROW][C]32[/C][C]0.302373615137733[/C][C]0.604747230275467[/C][C]0.697626384862267[/C][/ROW]
[ROW][C]33[/C][C]0.427609212603284[/C][C]0.855218425206569[/C][C]0.572390787396716[/C][/ROW]
[ROW][C]34[/C][C]0.384700599303241[/C][C]0.769401198606481[/C][C]0.615299400696759[/C][/ROW]
[ROW][C]35[/C][C]0.330745477617996[/C][C]0.661490955235992[/C][C]0.669254522382004[/C][/ROW]
[ROW][C]36[/C][C]0.590877778353699[/C][C]0.818244443292602[/C][C]0.409122221646301[/C][/ROW]
[ROW][C]37[/C][C]0.880925836241047[/C][C]0.238148327517905[/C][C]0.119074163758953[/C][/ROW]
[ROW][C]38[/C][C]0.908800211932195[/C][C]0.18239957613561[/C][C]0.0911997880678051[/C][/ROW]
[ROW][C]39[/C][C]0.901226997101382[/C][C]0.197546005797236[/C][C]0.0987730028986179[/C][/ROW]
[ROW][C]40[/C][C]0.906648416699391[/C][C]0.186703166601217[/C][C]0.0933515833006085[/C][/ROW]
[ROW][C]41[/C][C]0.891334113045205[/C][C]0.217331773909591[/C][C]0.108665886954795[/C][/ROW]
[ROW][C]42[/C][C]0.876449823904239[/C][C]0.247100352191521[/C][C]0.123550176095761[/C][/ROW]
[ROW][C]43[/C][C]0.865254266147261[/C][C]0.269491467705477[/C][C]0.134745733852739[/C][/ROW]
[ROW][C]44[/C][C]0.831246482527699[/C][C]0.337507034944602[/C][C]0.168753517472301[/C][/ROW]
[ROW][C]45[/C][C]0.784100456087556[/C][C]0.431799087824888[/C][C]0.215899543912444[/C][/ROW]
[ROW][C]46[/C][C]0.765569849506269[/C][C]0.468860300987462[/C][C]0.234430150493731[/C][/ROW]
[ROW][C]47[/C][C]0.803018769922027[/C][C]0.393962460155946[/C][C]0.196981230077973[/C][/ROW]
[ROW][C]48[/C][C]0.771328082949657[/C][C]0.457343834100687[/C][C]0.228671917050343[/C][/ROW]
[ROW][C]49[/C][C]0.770957896951003[/C][C]0.458084206097994[/C][C]0.229042103048997[/C][/ROW]
[ROW][C]50[/C][C]0.692941824489683[/C][C]0.614116351020634[/C][C]0.307058175510317[/C][/ROW]
[ROW][C]51[/C][C]0.616327676760238[/C][C]0.767344646479524[/C][C]0.383672323239762[/C][/ROW]
[ROW][C]52[/C][C]0.689881373928264[/C][C]0.620237252143472[/C][C]0.310118626071736[/C][/ROW]
[ROW][C]53[/C][C]0.680308998087867[/C][C]0.639382003824265[/C][C]0.319691001912132[/C][/ROW]
[ROW][C]54[/C][C]0.566080043143866[/C][C]0.867839913712268[/C][C]0.433919956856134[/C][/ROW]
[ROW][C]55[/C][C]0.397654283207018[/C][C]0.795308566414036[/C][C]0.602345716792982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147432&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147432&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
50.02308077564848990.04616155129697970.97691922435151
60.09058062673776890.1811612534755380.909419373262231
70.03836084365465430.07672168730930860.961639156345346
80.03596145486210410.07192290972420820.964038545137896
90.09302489269862030.1860497853972410.90697510730138
100.2223410354745550.444682070949110.777658964525445
110.1576108881743440.3152217763486880.842389111825656
120.199439281456320.398878562912640.80056071854368
130.3360520739869720.6721041479739440.663947926013028
140.4277092239283650.855418447856730.572290776071635
150.3850040469687910.7700080939375830.614995953031209
160.3113854401575660.6227708803151310.688614559842434
170.2434107879692880.4868215759385760.756589212030712
180.2668296534122790.5336593068245580.733170346587721
190.2046238317296360.4092476634592720.795376168270364
200.1700958839167480.3401917678334960.829904116083252
210.2890915577840650.5781831155681290.710908442215935
220.3673239296487860.7346478592975730.632676070351214
230.3093970811607680.6187941623215370.690602918839232
240.3050821535020350.6101643070040710.694917846497965
250.5199980415660750.960003916867850.480001958433925
260.4744919575303750.948983915060750.525508042469625
270.4064194079192830.8128388158385650.593580592080717
280.3349074848285060.6698149696570120.665092515171494
290.3113899562571610.6227799125143220.688610043742839
300.2652986903666620.5305973807333240.734701309633338
310.353734057273740.7074681145474810.64626594272626
320.3023736151377330.6047472302754670.697626384862267
330.4276092126032840.8552184252065690.572390787396716
340.3847005993032410.7694011986064810.615299400696759
350.3307454776179960.6614909552359920.669254522382004
360.5908777783536990.8182444432926020.409122221646301
370.8809258362410470.2381483275179050.119074163758953
380.9088002119321950.182399576135610.0911997880678051
390.9012269971013820.1975460057972360.0987730028986179
400.9066484166993910.1867031666012170.0933515833006085
410.8913341130452050.2173317739095910.108665886954795
420.8764498239042390.2471003521915210.123550176095761
430.8652542661472610.2694914677054770.134745733852739
440.8312464825276990.3375070349446020.168753517472301
450.7841004560875560.4317990878248880.215899543912444
460.7655698495062690.4688603009874620.234430150493731
470.8030187699220270.3939624601559460.196981230077973
480.7713280829496570.4573438341006870.228671917050343
490.7709578969510030.4580842060979940.229042103048997
500.6929418244896830.6141163510206340.307058175510317
510.6163276767602380.7673446464795240.383672323239762
520.6898813739282640.6202372521434720.310118626071736
530.6803089980878670.6393820038242650.319691001912132
540.5660800431438660.8678399137122680.433919956856134
550.3976542832070180.7953085664140360.602345716792982







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0196078431372549OK
10% type I error level30.0588235294117647OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0196078431372549 & OK \tabularnewline
10% type I error level & 3 & 0.0588235294117647 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147432&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0196078431372549[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0588235294117647[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147432&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147432&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 level00OK
5% type I error level10.0196078431372549OK
10% type I error level30.0588235294117647OK



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')
}