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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 12:11:09 -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/t12587443309gvm2i6z6qj9z6k.htm/, Retrieved Thu, 28 Mar 2024 11:25:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58430, Retrieved Thu, 28 Mar 2024 11:25:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact179
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] [WS7 Multiple Regr...] [2009-11-20 19:11:09] [c6e373ff11c42d4585d53e9e88ed5606] [Current]
-   P         [Multiple Regression] [WS7 Multiple Regr...] [2009-11-20 19:53:18] [8733f8ed033058987ec00f5e71b74854]
-   P           [Multiple Regression] [WS7 Multiple Regr...] [2009-11-20 20:25:38] [8733f8ed033058987ec00f5e71b74854]
-    D            [Multiple Regression] [W7 review ] [2009-11-26 16:27:42] [315ba876df544ad397193b5931d5f354]
-   P             [Multiple Regression] [ws 7 lineaire trend] [2009-11-27 18:35:28] [bd8e774728cf1f2f4e6868fd314defe3]
-   P         [Multiple Regression] [ws7 lineair and s...] [2009-11-27 18:30:38] [bd8e774728cf1f2f4e6868fd314defe3]
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Dataseries X:
2.7	0
2.3	0
1.9	0
2.0	0
2.3	0
2.8	0
2.4	0
2.3	0
2.7	0
2.7	0
2.9	0
3.0	0
2.2	0
2.3	0
2.8	0
2.8	0
2.8	0
2.2	0
2.6	0
2.8	0
2.5	0
2.4	0
2.3	0
1.9	0
1.7	0
2.0	0
2.1	0
1.7	0
1.8	0
1.8	0
1.8	0
1.3	0
1.3	0
1.3	1
1.2	1
1.4	1
2.2	1
2.9	1
3.1	1
3.5	1
3.6	1
4.4	1
4.1	1
5.1	1
5.8	1
5.9	1
5.4	1
5.5	1
4.8	1
3.2	1
2.7	1
2.1	1
1.9	1
0.6	1
0.7	1
-0.2	1
-1.0	1
-1.7	1
-0.7	1
-1.0	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=58430&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=58430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58430&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
Inflatie[t] = + 2.27575757575757 + 0.198316498316499Kredietcrisis[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Inflatie[t] =  +  2.27575757575757 +  0.198316498316499Kredietcrisis[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58430&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Inflatie[t] =  +  2.27575757575757 +  0.198316498316499Kredietcrisis[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58430&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
Inflatie[t] = + 2.27575757575757 + 0.198316498316499Kredietcrisis[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.275757575757570.2700028.428700
Kredietcrisis0.1983164983164990.4024950.49270.6240720.312036

\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) & 2.27575757575757 & 0.270002 & 8.4287 & 0 & 0 \tabularnewline
Kredietcrisis & 0.198316498316499 & 0.402495 & 0.4927 & 0.624072 & 0.312036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58430&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]2.27575757575757[/C][C]0.270002[/C][C]8.4287[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Kredietcrisis[/C][C]0.198316498316499[/C][C]0.402495[/C][C]0.4927[/C][C]0.624072[/C][C]0.312036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58430&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)2.275757575757570.2700028.428700
Kredietcrisis0.1983164983164990.4024950.49270.6240720.312036







Multiple Linear Regression - Regression Statistics
Multiple R0.0645620678760925
R-squared0.00416826060843718
Adjusted R-squared-0.0130012521396932
F-TEST (value)0.242771048286799
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.624072026784833
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.55104224087335
Sum Squared Residuals139.532457912458

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0645620678760925 \tabularnewline
R-squared & 0.00416826060843718 \tabularnewline
Adjusted R-squared & -0.0130012521396932 \tabularnewline
F-TEST (value) & 0.242771048286799 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.624072026784833 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.55104224087335 \tabularnewline
Sum Squared Residuals & 139.532457912458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58430&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0645620678760925[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00416826060843718[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0130012521396932[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.242771048286799[/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]0.624072026784833[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.55104224087335[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]139.532457912458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58430&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58430&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 R0.0645620678760925
R-squared0.00416826060843718
Adjusted R-squared-0.0130012521396932
F-TEST (value)0.242771048286799
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.624072026784833
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.55104224087335
Sum Squared Residuals139.532457912458







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.72.275757575757580.424242424242415
22.32.275757575757570.0242424242424247
31.92.27575757575758-0.375757575757575
422.27575757575758-0.275757575757575
52.32.275757575757580.0242424242424245
62.82.275757575757580.524242424242425
72.42.275757575757580.124242424242425
82.32.275757575757580.0242424242424245
92.72.275757575757580.424242424242425
102.72.275757575757580.424242424242425
112.92.275757575757580.624242424242425
1232.275757575757580.724242424242425
132.22.27575757575758-0.0757575757575752
142.32.275757575757580.0242424242424245
152.82.275757575757580.524242424242425
162.82.275757575757580.524242424242425
172.82.275757575757580.524242424242425
182.22.27575757575758-0.0757575757575752
192.62.275757575757580.324242424242425
202.82.275757575757580.524242424242425
212.52.275757575757580.224242424242425
222.42.275757575757580.124242424242425
232.32.275757575757580.0242424242424245
241.92.27575757575758-0.375757575757575
251.72.27575757575758-0.575757575757575
2622.27575757575758-0.275757575757575
272.12.27575757575758-0.175757575757575
281.72.27575757575758-0.575757575757575
291.82.27575757575758-0.475757575757575
301.82.27575757575758-0.475757575757575
311.82.27575757575758-0.475757575757575
321.32.27575757575758-0.975757575757575
331.32.27575757575758-0.975757575757575
341.32.47407407407407-1.17407407407407
351.22.47407407407407-1.27407407407407
361.42.47407407407407-1.07407407407407
372.22.47407407407407-0.274074074074074
382.92.474074074074070.425925925925926
393.12.474074074074070.625925925925926
403.52.474074074074071.02592592592593
413.62.474074074074071.12592592592593
424.42.474074074074071.92592592592593
434.12.474074074074071.62592592592593
445.12.474074074074072.62592592592593
455.82.474074074074073.32592592592593
465.92.474074074074073.42592592592593
475.42.474074074074072.92592592592593
485.52.474074074074073.02592592592593
494.82.474074074074072.32592592592593
503.22.474074074074070.725925925925926
512.72.474074074074070.225925925925926
522.12.47407407407407-0.374074074074074
531.92.47407407407407-0.574074074074074
540.62.47407407407407-1.87407407407407
550.72.47407407407407-1.77407407407407
56-0.22.47407407407407-2.67407407407407
57-12.47407407407407-3.47407407407407
58-1.72.47407407407407-4.17407407407407
59-0.72.47407407407407-3.17407407407407
60-12.47407407407407-3.47407407407407

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.7 & 2.27575757575758 & 0.424242424242415 \tabularnewline
2 & 2.3 & 2.27575757575757 & 0.0242424242424247 \tabularnewline
3 & 1.9 & 2.27575757575758 & -0.375757575757575 \tabularnewline
4 & 2 & 2.27575757575758 & -0.275757575757575 \tabularnewline
5 & 2.3 & 2.27575757575758 & 0.0242424242424245 \tabularnewline
6 & 2.8 & 2.27575757575758 & 0.524242424242425 \tabularnewline
7 & 2.4 & 2.27575757575758 & 0.124242424242425 \tabularnewline
8 & 2.3 & 2.27575757575758 & 0.0242424242424245 \tabularnewline
9 & 2.7 & 2.27575757575758 & 0.424242424242425 \tabularnewline
10 & 2.7 & 2.27575757575758 & 0.424242424242425 \tabularnewline
11 & 2.9 & 2.27575757575758 & 0.624242424242425 \tabularnewline
12 & 3 & 2.27575757575758 & 0.724242424242425 \tabularnewline
13 & 2.2 & 2.27575757575758 & -0.0757575757575752 \tabularnewline
14 & 2.3 & 2.27575757575758 & 0.0242424242424245 \tabularnewline
15 & 2.8 & 2.27575757575758 & 0.524242424242425 \tabularnewline
16 & 2.8 & 2.27575757575758 & 0.524242424242425 \tabularnewline
17 & 2.8 & 2.27575757575758 & 0.524242424242425 \tabularnewline
18 & 2.2 & 2.27575757575758 & -0.0757575757575752 \tabularnewline
19 & 2.6 & 2.27575757575758 & 0.324242424242425 \tabularnewline
20 & 2.8 & 2.27575757575758 & 0.524242424242425 \tabularnewline
21 & 2.5 & 2.27575757575758 & 0.224242424242425 \tabularnewline
22 & 2.4 & 2.27575757575758 & 0.124242424242425 \tabularnewline
23 & 2.3 & 2.27575757575758 & 0.0242424242424245 \tabularnewline
24 & 1.9 & 2.27575757575758 & -0.375757575757575 \tabularnewline
25 & 1.7 & 2.27575757575758 & -0.575757575757575 \tabularnewline
26 & 2 & 2.27575757575758 & -0.275757575757575 \tabularnewline
27 & 2.1 & 2.27575757575758 & -0.175757575757575 \tabularnewline
28 & 1.7 & 2.27575757575758 & -0.575757575757575 \tabularnewline
29 & 1.8 & 2.27575757575758 & -0.475757575757575 \tabularnewline
30 & 1.8 & 2.27575757575758 & -0.475757575757575 \tabularnewline
31 & 1.8 & 2.27575757575758 & -0.475757575757575 \tabularnewline
32 & 1.3 & 2.27575757575758 & -0.975757575757575 \tabularnewline
33 & 1.3 & 2.27575757575758 & -0.975757575757575 \tabularnewline
34 & 1.3 & 2.47407407407407 & -1.17407407407407 \tabularnewline
35 & 1.2 & 2.47407407407407 & -1.27407407407407 \tabularnewline
36 & 1.4 & 2.47407407407407 & -1.07407407407407 \tabularnewline
37 & 2.2 & 2.47407407407407 & -0.274074074074074 \tabularnewline
38 & 2.9 & 2.47407407407407 & 0.425925925925926 \tabularnewline
39 & 3.1 & 2.47407407407407 & 0.625925925925926 \tabularnewline
40 & 3.5 & 2.47407407407407 & 1.02592592592593 \tabularnewline
41 & 3.6 & 2.47407407407407 & 1.12592592592593 \tabularnewline
42 & 4.4 & 2.47407407407407 & 1.92592592592593 \tabularnewline
43 & 4.1 & 2.47407407407407 & 1.62592592592593 \tabularnewline
44 & 5.1 & 2.47407407407407 & 2.62592592592593 \tabularnewline
45 & 5.8 & 2.47407407407407 & 3.32592592592593 \tabularnewline
46 & 5.9 & 2.47407407407407 & 3.42592592592593 \tabularnewline
47 & 5.4 & 2.47407407407407 & 2.92592592592593 \tabularnewline
48 & 5.5 & 2.47407407407407 & 3.02592592592593 \tabularnewline
49 & 4.8 & 2.47407407407407 & 2.32592592592593 \tabularnewline
50 & 3.2 & 2.47407407407407 & 0.725925925925926 \tabularnewline
51 & 2.7 & 2.47407407407407 & 0.225925925925926 \tabularnewline
52 & 2.1 & 2.47407407407407 & -0.374074074074074 \tabularnewline
53 & 1.9 & 2.47407407407407 & -0.574074074074074 \tabularnewline
54 & 0.6 & 2.47407407407407 & -1.87407407407407 \tabularnewline
55 & 0.7 & 2.47407407407407 & -1.77407407407407 \tabularnewline
56 & -0.2 & 2.47407407407407 & -2.67407407407407 \tabularnewline
57 & -1 & 2.47407407407407 & -3.47407407407407 \tabularnewline
58 & -1.7 & 2.47407407407407 & -4.17407407407407 \tabularnewline
59 & -0.7 & 2.47407407407407 & -3.17407407407407 \tabularnewline
60 & -1 & 2.47407407407407 & -3.47407407407407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58430&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]2.7[/C][C]2.27575757575758[/C][C]0.424242424242415[/C][/ROW]
[ROW][C]2[/C][C]2.3[/C][C]2.27575757575757[/C][C]0.0242424242424247[/C][/ROW]
[ROW][C]3[/C][C]1.9[/C][C]2.27575757575758[/C][C]-0.375757575757575[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]2.27575757575758[/C][C]-0.275757575757575[/C][/ROW]
[ROW][C]5[/C][C]2.3[/C][C]2.27575757575758[/C][C]0.0242424242424245[/C][/ROW]
[ROW][C]6[/C][C]2.8[/C][C]2.27575757575758[/C][C]0.524242424242425[/C][/ROW]
[ROW][C]7[/C][C]2.4[/C][C]2.27575757575758[/C][C]0.124242424242425[/C][/ROW]
[ROW][C]8[/C][C]2.3[/C][C]2.27575757575758[/C][C]0.0242424242424245[/C][/ROW]
[ROW][C]9[/C][C]2.7[/C][C]2.27575757575758[/C][C]0.424242424242425[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]2.27575757575758[/C][C]0.424242424242425[/C][/ROW]
[ROW][C]11[/C][C]2.9[/C][C]2.27575757575758[/C][C]0.624242424242425[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]2.27575757575758[/C][C]0.724242424242425[/C][/ROW]
[ROW][C]13[/C][C]2.2[/C][C]2.27575757575758[/C][C]-0.0757575757575752[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]2.27575757575758[/C][C]0.0242424242424245[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]2.27575757575758[/C][C]0.524242424242425[/C][/ROW]
[ROW][C]16[/C][C]2.8[/C][C]2.27575757575758[/C][C]0.524242424242425[/C][/ROW]
[ROW][C]17[/C][C]2.8[/C][C]2.27575757575758[/C][C]0.524242424242425[/C][/ROW]
[ROW][C]18[/C][C]2.2[/C][C]2.27575757575758[/C][C]-0.0757575757575752[/C][/ROW]
[ROW][C]19[/C][C]2.6[/C][C]2.27575757575758[/C][C]0.324242424242425[/C][/ROW]
[ROW][C]20[/C][C]2.8[/C][C]2.27575757575758[/C][C]0.524242424242425[/C][/ROW]
[ROW][C]21[/C][C]2.5[/C][C]2.27575757575758[/C][C]0.224242424242425[/C][/ROW]
[ROW][C]22[/C][C]2.4[/C][C]2.27575757575758[/C][C]0.124242424242425[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]2.27575757575758[/C][C]0.0242424242424245[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]2.27575757575758[/C][C]-0.375757575757575[/C][/ROW]
[ROW][C]25[/C][C]1.7[/C][C]2.27575757575758[/C][C]-0.575757575757575[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]2.27575757575758[/C][C]-0.275757575757575[/C][/ROW]
[ROW][C]27[/C][C]2.1[/C][C]2.27575757575758[/C][C]-0.175757575757575[/C][/ROW]
[ROW][C]28[/C][C]1.7[/C][C]2.27575757575758[/C][C]-0.575757575757575[/C][/ROW]
[ROW][C]29[/C][C]1.8[/C][C]2.27575757575758[/C][C]-0.475757575757575[/C][/ROW]
[ROW][C]30[/C][C]1.8[/C][C]2.27575757575758[/C][C]-0.475757575757575[/C][/ROW]
[ROW][C]31[/C][C]1.8[/C][C]2.27575757575758[/C][C]-0.475757575757575[/C][/ROW]
[ROW][C]32[/C][C]1.3[/C][C]2.27575757575758[/C][C]-0.975757575757575[/C][/ROW]
[ROW][C]33[/C][C]1.3[/C][C]2.27575757575758[/C][C]-0.975757575757575[/C][/ROW]
[ROW][C]34[/C][C]1.3[/C][C]2.47407407407407[/C][C]-1.17407407407407[/C][/ROW]
[ROW][C]35[/C][C]1.2[/C][C]2.47407407407407[/C][C]-1.27407407407407[/C][/ROW]
[ROW][C]36[/C][C]1.4[/C][C]2.47407407407407[/C][C]-1.07407407407407[/C][/ROW]
[ROW][C]37[/C][C]2.2[/C][C]2.47407407407407[/C][C]-0.274074074074074[/C][/ROW]
[ROW][C]38[/C][C]2.9[/C][C]2.47407407407407[/C][C]0.425925925925926[/C][/ROW]
[ROW][C]39[/C][C]3.1[/C][C]2.47407407407407[/C][C]0.625925925925926[/C][/ROW]
[ROW][C]40[/C][C]3.5[/C][C]2.47407407407407[/C][C]1.02592592592593[/C][/ROW]
[ROW][C]41[/C][C]3.6[/C][C]2.47407407407407[/C][C]1.12592592592593[/C][/ROW]
[ROW][C]42[/C][C]4.4[/C][C]2.47407407407407[/C][C]1.92592592592593[/C][/ROW]
[ROW][C]43[/C][C]4.1[/C][C]2.47407407407407[/C][C]1.62592592592593[/C][/ROW]
[ROW][C]44[/C][C]5.1[/C][C]2.47407407407407[/C][C]2.62592592592593[/C][/ROW]
[ROW][C]45[/C][C]5.8[/C][C]2.47407407407407[/C][C]3.32592592592593[/C][/ROW]
[ROW][C]46[/C][C]5.9[/C][C]2.47407407407407[/C][C]3.42592592592593[/C][/ROW]
[ROW][C]47[/C][C]5.4[/C][C]2.47407407407407[/C][C]2.92592592592593[/C][/ROW]
[ROW][C]48[/C][C]5.5[/C][C]2.47407407407407[/C][C]3.02592592592593[/C][/ROW]
[ROW][C]49[/C][C]4.8[/C][C]2.47407407407407[/C][C]2.32592592592593[/C][/ROW]
[ROW][C]50[/C][C]3.2[/C][C]2.47407407407407[/C][C]0.725925925925926[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.47407407407407[/C][C]0.225925925925926[/C][/ROW]
[ROW][C]52[/C][C]2.1[/C][C]2.47407407407407[/C][C]-0.374074074074074[/C][/ROW]
[ROW][C]53[/C][C]1.9[/C][C]2.47407407407407[/C][C]-0.574074074074074[/C][/ROW]
[ROW][C]54[/C][C]0.6[/C][C]2.47407407407407[/C][C]-1.87407407407407[/C][/ROW]
[ROW][C]55[/C][C]0.7[/C][C]2.47407407407407[/C][C]-1.77407407407407[/C][/ROW]
[ROW][C]56[/C][C]-0.2[/C][C]2.47407407407407[/C][C]-2.67407407407407[/C][/ROW]
[ROW][C]57[/C][C]-1[/C][C]2.47407407407407[/C][C]-3.47407407407407[/C][/ROW]
[ROW][C]58[/C][C]-1.7[/C][C]2.47407407407407[/C][C]-4.17407407407407[/C][/ROW]
[ROW][C]59[/C][C]-0.7[/C][C]2.47407407407407[/C][C]-3.17407407407407[/C][/ROW]
[ROW][C]60[/C][C]-1[/C][C]2.47407407407407[/C][C]-3.47407407407407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58430&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58430&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
12.72.275757575757580.424242424242415
22.32.275757575757570.0242424242424247
31.92.27575757575758-0.375757575757575
422.27575757575758-0.275757575757575
52.32.275757575757580.0242424242424245
62.82.275757575757580.524242424242425
72.42.275757575757580.124242424242425
82.32.275757575757580.0242424242424245
92.72.275757575757580.424242424242425
102.72.275757575757580.424242424242425
112.92.275757575757580.624242424242425
1232.275757575757580.724242424242425
132.22.27575757575758-0.0757575757575752
142.32.275757575757580.0242424242424245
152.82.275757575757580.524242424242425
162.82.275757575757580.524242424242425
172.82.275757575757580.524242424242425
182.22.27575757575758-0.0757575757575752
192.62.275757575757580.324242424242425
202.82.275757575757580.524242424242425
212.52.275757575757580.224242424242425
222.42.275757575757580.124242424242425
232.32.275757575757580.0242424242424245
241.92.27575757575758-0.375757575757575
251.72.27575757575758-0.575757575757575
2622.27575757575758-0.275757575757575
272.12.27575757575758-0.175757575757575
281.72.27575757575758-0.575757575757575
291.82.27575757575758-0.475757575757575
301.82.27575757575758-0.475757575757575
311.82.27575757575758-0.475757575757575
321.32.27575757575758-0.975757575757575
331.32.27575757575758-0.975757575757575
341.32.47407407407407-1.17407407407407
351.22.47407407407407-1.27407407407407
361.42.47407407407407-1.07407407407407
372.22.47407407407407-0.274074074074074
382.92.474074074074070.425925925925926
393.12.474074074074070.625925925925926
403.52.474074074074071.02592592592593
413.62.474074074074071.12592592592593
424.42.474074074074071.92592592592593
434.12.474074074074071.62592592592593
445.12.474074074074072.62592592592593
455.82.474074074074073.32592592592593
465.92.474074074074073.42592592592593
475.42.474074074074072.92592592592593
485.52.474074074074073.02592592592593
494.82.474074074074072.32592592592593
503.22.474074074074070.725925925925926
512.72.474074074074070.225925925925926
522.12.47407407407407-0.374074074074074
531.92.47407407407407-0.574074074074074
540.62.47407407407407-1.87407407407407
550.72.47407407407407-1.77407407407407
56-0.22.47407407407407-2.67407407407407
57-12.47407407407407-3.47407407407407
58-1.72.47407407407407-4.17407407407407
59-0.72.47407407407407-3.17407407407407
60-12.47407407407407-3.47407407407407







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01484714731051500.02969429462103000.985152852689485
60.007121763151642280.01424352630328460.992878236848358
70.001489148218483670.002978296436967340.998510851781516
80.0002829807427034360.0005659614854068710.999717019257297
98.62068375277507e-050.0001724136750555010.999913793162472
102.37142053068401e-054.74284106136803e-050.999976285794693
111.11213024583831e-052.22426049167663e-050.999988878697542
126.13621006065755e-061.22724201213151e-050.99999386378994
131.63514824760816e-063.27029649521633e-060.999998364851752
143.4989362679033e-076.9978725358066e-070.999999650106373
151.00669082576210e-072.01338165152421e-070.999999899330917
162.73770813866171e-085.47541627732342e-080.999999972622919
177.09143345714384e-091.41828669142877e-080.999999992908567
181.89934692735003e-093.79869385470006e-090.999999998100653
193.48674400143995e-106.9734880028799e-100.999999999651326
208.76708169097404e-111.75341633819481e-100.99999999991233
211.48077311051323e-112.96154622102647e-110.999999999985192
222.57682505134154e-125.15365010268308e-120.999999999997423
235.0822573453605e-131.0164514690721e-120.999999999999492
244.29231533160146e-138.58463066320292e-130.99999999999957
257.81642937451154e-131.56328587490231e-120.999999999999218
262.76958291020261e-135.53916582040522e-130.999999999999723
277.05741766811054e-141.41148353362211e-130.99999999999993
286.8669777446128e-141.37339554892256e-130.999999999999931
293.59891999722198e-147.19783999444396e-140.999999999999964
301.68716283761260e-143.37432567522521e-140.999999999999983
317.24654924290656e-151.44930984858131e-140.999999999999993
322.06059776094847e-144.12119552189693e-140.99999999999998
333.59938477203944e-147.19876954407888e-140.999999999999964
348.06824261346828e-151.61364852269366e-140.999999999999992
351.86609812632569e-153.73219625265138e-150.999999999999998
364.13357516356128e-168.26715032712256e-161
372.94960222944211e-165.89920445888422e-161
381.11808767164257e-152.23617534328514e-150.999999999999999
392.46938831972150e-154.93877663944299e-150.999999999999998
408.98375204738692e-151.79675040947738e-140.99999999999999
411.80965549389571e-143.61931098779141e-140.999999999999982
423.07740191073765e-136.15480382147531e-130.999999999999692
436.4945000692612e-131.29890001385224e-120.99999999999935
442.23531161926481e-114.47062323852962e-110.999999999977647
453.24446566878358e-096.48893133756716e-090.999999996755534
462.54308988515684e-075.08617977031368e-070.999999745691011
474.59265107849147e-069.18530215698294e-060.999995407348921
480.0002053379387831620.0004106758775663240.999794662061217
490.004939042293655060.009878084587310120.995060957706345
500.01824372602129390.03648745204258770.981756273978706
510.06278644252771070.1255728850554210.93721355747229
520.1661869078260780.3323738156521570.833813092173922
530.4818762913152360.9637525826304710.518123708684764
540.5816171570097020.8367656859805970.418382842990298
550.8178909462906410.3642181074187180.182109053709359

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0148471473105150 & 0.0296942946210300 & 0.985152852689485 \tabularnewline
6 & 0.00712176315164228 & 0.0142435263032846 & 0.992878236848358 \tabularnewline
7 & 0.00148914821848367 & 0.00297829643696734 & 0.998510851781516 \tabularnewline
8 & 0.000282980742703436 & 0.000565961485406871 & 0.999717019257297 \tabularnewline
9 & 8.62068375277507e-05 & 0.000172413675055501 & 0.999913793162472 \tabularnewline
10 & 2.37142053068401e-05 & 4.74284106136803e-05 & 0.999976285794693 \tabularnewline
11 & 1.11213024583831e-05 & 2.22426049167663e-05 & 0.999988878697542 \tabularnewline
12 & 6.13621006065755e-06 & 1.22724201213151e-05 & 0.99999386378994 \tabularnewline
13 & 1.63514824760816e-06 & 3.27029649521633e-06 & 0.999998364851752 \tabularnewline
14 & 3.4989362679033e-07 & 6.9978725358066e-07 & 0.999999650106373 \tabularnewline
15 & 1.00669082576210e-07 & 2.01338165152421e-07 & 0.999999899330917 \tabularnewline
16 & 2.73770813866171e-08 & 5.47541627732342e-08 & 0.999999972622919 \tabularnewline
17 & 7.09143345714384e-09 & 1.41828669142877e-08 & 0.999999992908567 \tabularnewline
18 & 1.89934692735003e-09 & 3.79869385470006e-09 & 0.999999998100653 \tabularnewline
19 & 3.48674400143995e-10 & 6.9734880028799e-10 & 0.999999999651326 \tabularnewline
20 & 8.76708169097404e-11 & 1.75341633819481e-10 & 0.99999999991233 \tabularnewline
21 & 1.48077311051323e-11 & 2.96154622102647e-11 & 0.999999999985192 \tabularnewline
22 & 2.57682505134154e-12 & 5.15365010268308e-12 & 0.999999999997423 \tabularnewline
23 & 5.0822573453605e-13 & 1.0164514690721e-12 & 0.999999999999492 \tabularnewline
24 & 4.29231533160146e-13 & 8.58463066320292e-13 & 0.99999999999957 \tabularnewline
25 & 7.81642937451154e-13 & 1.56328587490231e-12 & 0.999999999999218 \tabularnewline
26 & 2.76958291020261e-13 & 5.53916582040522e-13 & 0.999999999999723 \tabularnewline
27 & 7.05741766811054e-14 & 1.41148353362211e-13 & 0.99999999999993 \tabularnewline
28 & 6.8669777446128e-14 & 1.37339554892256e-13 & 0.999999999999931 \tabularnewline
29 & 3.59891999722198e-14 & 7.19783999444396e-14 & 0.999999999999964 \tabularnewline
30 & 1.68716283761260e-14 & 3.37432567522521e-14 & 0.999999999999983 \tabularnewline
31 & 7.24654924290656e-15 & 1.44930984858131e-14 & 0.999999999999993 \tabularnewline
32 & 2.06059776094847e-14 & 4.12119552189693e-14 & 0.99999999999998 \tabularnewline
33 & 3.59938477203944e-14 & 7.19876954407888e-14 & 0.999999999999964 \tabularnewline
34 & 8.06824261346828e-15 & 1.61364852269366e-14 & 0.999999999999992 \tabularnewline
35 & 1.86609812632569e-15 & 3.73219625265138e-15 & 0.999999999999998 \tabularnewline
36 & 4.13357516356128e-16 & 8.26715032712256e-16 & 1 \tabularnewline
37 & 2.94960222944211e-16 & 5.89920445888422e-16 & 1 \tabularnewline
38 & 1.11808767164257e-15 & 2.23617534328514e-15 & 0.999999999999999 \tabularnewline
39 & 2.46938831972150e-15 & 4.93877663944299e-15 & 0.999999999999998 \tabularnewline
40 & 8.98375204738692e-15 & 1.79675040947738e-14 & 0.99999999999999 \tabularnewline
41 & 1.80965549389571e-14 & 3.61931098779141e-14 & 0.999999999999982 \tabularnewline
42 & 3.07740191073765e-13 & 6.15480382147531e-13 & 0.999999999999692 \tabularnewline
43 & 6.4945000692612e-13 & 1.29890001385224e-12 & 0.99999999999935 \tabularnewline
44 & 2.23531161926481e-11 & 4.47062323852962e-11 & 0.999999999977647 \tabularnewline
45 & 3.24446566878358e-09 & 6.48893133756716e-09 & 0.999999996755534 \tabularnewline
46 & 2.54308988515684e-07 & 5.08617977031368e-07 & 0.999999745691011 \tabularnewline
47 & 4.59265107849147e-06 & 9.18530215698294e-06 & 0.999995407348921 \tabularnewline
48 & 0.000205337938783162 & 0.000410675877566324 & 0.999794662061217 \tabularnewline
49 & 0.00493904229365506 & 0.00987808458731012 & 0.995060957706345 \tabularnewline
50 & 0.0182437260212939 & 0.0364874520425877 & 0.981756273978706 \tabularnewline
51 & 0.0627864425277107 & 0.125572885055421 & 0.93721355747229 \tabularnewline
52 & 0.166186907826078 & 0.332373815652157 & 0.833813092173922 \tabularnewline
53 & 0.481876291315236 & 0.963752582630471 & 0.518123708684764 \tabularnewline
54 & 0.581617157009702 & 0.836765685980597 & 0.418382842990298 \tabularnewline
55 & 0.817890946290641 & 0.364218107418718 & 0.182109053709359 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58430&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]0.0148471473105150[/C][C]0.0296942946210300[/C][C]0.985152852689485[/C][/ROW]
[ROW][C]6[/C][C]0.00712176315164228[/C][C]0.0142435263032846[/C][C]0.992878236848358[/C][/ROW]
[ROW][C]7[/C][C]0.00148914821848367[/C][C]0.00297829643696734[/C][C]0.998510851781516[/C][/ROW]
[ROW][C]8[/C][C]0.000282980742703436[/C][C]0.000565961485406871[/C][C]0.999717019257297[/C][/ROW]
[ROW][C]9[/C][C]8.62068375277507e-05[/C][C]0.000172413675055501[/C][C]0.999913793162472[/C][/ROW]
[ROW][C]10[/C][C]2.37142053068401e-05[/C][C]4.74284106136803e-05[/C][C]0.999976285794693[/C][/ROW]
[ROW][C]11[/C][C]1.11213024583831e-05[/C][C]2.22426049167663e-05[/C][C]0.999988878697542[/C][/ROW]
[ROW][C]12[/C][C]6.13621006065755e-06[/C][C]1.22724201213151e-05[/C][C]0.99999386378994[/C][/ROW]
[ROW][C]13[/C][C]1.63514824760816e-06[/C][C]3.27029649521633e-06[/C][C]0.999998364851752[/C][/ROW]
[ROW][C]14[/C][C]3.4989362679033e-07[/C][C]6.9978725358066e-07[/C][C]0.999999650106373[/C][/ROW]
[ROW][C]15[/C][C]1.00669082576210e-07[/C][C]2.01338165152421e-07[/C][C]0.999999899330917[/C][/ROW]
[ROW][C]16[/C][C]2.73770813866171e-08[/C][C]5.47541627732342e-08[/C][C]0.999999972622919[/C][/ROW]
[ROW][C]17[/C][C]7.09143345714384e-09[/C][C]1.41828669142877e-08[/C][C]0.999999992908567[/C][/ROW]
[ROW][C]18[/C][C]1.89934692735003e-09[/C][C]3.79869385470006e-09[/C][C]0.999999998100653[/C][/ROW]
[ROW][C]19[/C][C]3.48674400143995e-10[/C][C]6.9734880028799e-10[/C][C]0.999999999651326[/C][/ROW]
[ROW][C]20[/C][C]8.76708169097404e-11[/C][C]1.75341633819481e-10[/C][C]0.99999999991233[/C][/ROW]
[ROW][C]21[/C][C]1.48077311051323e-11[/C][C]2.96154622102647e-11[/C][C]0.999999999985192[/C][/ROW]
[ROW][C]22[/C][C]2.57682505134154e-12[/C][C]5.15365010268308e-12[/C][C]0.999999999997423[/C][/ROW]
[ROW][C]23[/C][C]5.0822573453605e-13[/C][C]1.0164514690721e-12[/C][C]0.999999999999492[/C][/ROW]
[ROW][C]24[/C][C]4.29231533160146e-13[/C][C]8.58463066320292e-13[/C][C]0.99999999999957[/C][/ROW]
[ROW][C]25[/C][C]7.81642937451154e-13[/C][C]1.56328587490231e-12[/C][C]0.999999999999218[/C][/ROW]
[ROW][C]26[/C][C]2.76958291020261e-13[/C][C]5.53916582040522e-13[/C][C]0.999999999999723[/C][/ROW]
[ROW][C]27[/C][C]7.05741766811054e-14[/C][C]1.41148353362211e-13[/C][C]0.99999999999993[/C][/ROW]
[ROW][C]28[/C][C]6.8669777446128e-14[/C][C]1.37339554892256e-13[/C][C]0.999999999999931[/C][/ROW]
[ROW][C]29[/C][C]3.59891999722198e-14[/C][C]7.19783999444396e-14[/C][C]0.999999999999964[/C][/ROW]
[ROW][C]30[/C][C]1.68716283761260e-14[/C][C]3.37432567522521e-14[/C][C]0.999999999999983[/C][/ROW]
[ROW][C]31[/C][C]7.24654924290656e-15[/C][C]1.44930984858131e-14[/C][C]0.999999999999993[/C][/ROW]
[ROW][C]32[/C][C]2.06059776094847e-14[/C][C]4.12119552189693e-14[/C][C]0.99999999999998[/C][/ROW]
[ROW][C]33[/C][C]3.59938477203944e-14[/C][C]7.19876954407888e-14[/C][C]0.999999999999964[/C][/ROW]
[ROW][C]34[/C][C]8.06824261346828e-15[/C][C]1.61364852269366e-14[/C][C]0.999999999999992[/C][/ROW]
[ROW][C]35[/C][C]1.86609812632569e-15[/C][C]3.73219625265138e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]36[/C][C]4.13357516356128e-16[/C][C]8.26715032712256e-16[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]2.94960222944211e-16[/C][C]5.89920445888422e-16[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.11808767164257e-15[/C][C]2.23617534328514e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]39[/C][C]2.46938831972150e-15[/C][C]4.93877663944299e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]40[/C][C]8.98375204738692e-15[/C][C]1.79675040947738e-14[/C][C]0.99999999999999[/C][/ROW]
[ROW][C]41[/C][C]1.80965549389571e-14[/C][C]3.61931098779141e-14[/C][C]0.999999999999982[/C][/ROW]
[ROW][C]42[/C][C]3.07740191073765e-13[/C][C]6.15480382147531e-13[/C][C]0.999999999999692[/C][/ROW]
[ROW][C]43[/C][C]6.4945000692612e-13[/C][C]1.29890001385224e-12[/C][C]0.99999999999935[/C][/ROW]
[ROW][C]44[/C][C]2.23531161926481e-11[/C][C]4.47062323852962e-11[/C][C]0.999999999977647[/C][/ROW]
[ROW][C]45[/C][C]3.24446566878358e-09[/C][C]6.48893133756716e-09[/C][C]0.999999996755534[/C][/ROW]
[ROW][C]46[/C][C]2.54308988515684e-07[/C][C]5.08617977031368e-07[/C][C]0.999999745691011[/C][/ROW]
[ROW][C]47[/C][C]4.59265107849147e-06[/C][C]9.18530215698294e-06[/C][C]0.999995407348921[/C][/ROW]
[ROW][C]48[/C][C]0.000205337938783162[/C][C]0.000410675877566324[/C][C]0.999794662061217[/C][/ROW]
[ROW][C]49[/C][C]0.00493904229365506[/C][C]0.00987808458731012[/C][C]0.995060957706345[/C][/ROW]
[ROW][C]50[/C][C]0.0182437260212939[/C][C]0.0364874520425877[/C][C]0.981756273978706[/C][/ROW]
[ROW][C]51[/C][C]0.0627864425277107[/C][C]0.125572885055421[/C][C]0.93721355747229[/C][/ROW]
[ROW][C]52[/C][C]0.166186907826078[/C][C]0.332373815652157[/C][C]0.833813092173922[/C][/ROW]
[ROW][C]53[/C][C]0.481876291315236[/C][C]0.963752582630471[/C][C]0.518123708684764[/C][/ROW]
[ROW][C]54[/C][C]0.581617157009702[/C][C]0.836765685980597[/C][C]0.418382842990298[/C][/ROW]
[ROW][C]55[/C][C]0.817890946290641[/C][C]0.364218107418718[/C][C]0.182109053709359[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58430&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58430&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
50.01484714731051500.02969429462103000.985152852689485
60.007121763151642280.01424352630328460.992878236848358
70.001489148218483670.002978296436967340.998510851781516
80.0002829807427034360.0005659614854068710.999717019257297
98.62068375277507e-050.0001724136750555010.999913793162472
102.37142053068401e-054.74284106136803e-050.999976285794693
111.11213024583831e-052.22426049167663e-050.999988878697542
126.13621006065755e-061.22724201213151e-050.99999386378994
131.63514824760816e-063.27029649521633e-060.999998364851752
143.4989362679033e-076.9978725358066e-070.999999650106373
151.00669082576210e-072.01338165152421e-070.999999899330917
162.73770813866171e-085.47541627732342e-080.999999972622919
177.09143345714384e-091.41828669142877e-080.999999992908567
181.89934692735003e-093.79869385470006e-090.999999998100653
193.48674400143995e-106.9734880028799e-100.999999999651326
208.76708169097404e-111.75341633819481e-100.99999999991233
211.48077311051323e-112.96154622102647e-110.999999999985192
222.57682505134154e-125.15365010268308e-120.999999999997423
235.0822573453605e-131.0164514690721e-120.999999999999492
244.29231533160146e-138.58463066320292e-130.99999999999957
257.81642937451154e-131.56328587490231e-120.999999999999218
262.76958291020261e-135.53916582040522e-130.999999999999723
277.05741766811054e-141.41148353362211e-130.99999999999993
286.8669777446128e-141.37339554892256e-130.999999999999931
293.59891999722198e-147.19783999444396e-140.999999999999964
301.68716283761260e-143.37432567522521e-140.999999999999983
317.24654924290656e-151.44930984858131e-140.999999999999993
322.06059776094847e-144.12119552189693e-140.99999999999998
333.59938477203944e-147.19876954407888e-140.999999999999964
348.06824261346828e-151.61364852269366e-140.999999999999992
351.86609812632569e-153.73219625265138e-150.999999999999998
364.13357516356128e-168.26715032712256e-161
372.94960222944211e-165.89920445888422e-161
381.11808767164257e-152.23617534328514e-150.999999999999999
392.46938831972150e-154.93877663944299e-150.999999999999998
408.98375204738692e-151.79675040947738e-140.99999999999999
411.80965549389571e-143.61931098779141e-140.999999999999982
423.07740191073765e-136.15480382147531e-130.999999999999692
436.4945000692612e-131.29890001385224e-120.99999999999935
442.23531161926481e-114.47062323852962e-110.999999999977647
453.24446566878358e-096.48893133756716e-090.999999996755534
462.54308988515684e-075.08617977031368e-070.999999745691011
474.59265107849147e-069.18530215698294e-060.999995407348921
480.0002053379387831620.0004106758775663240.999794662061217
490.004939042293655060.009878084587310120.995060957706345
500.01824372602129390.03648745204258770.981756273978706
510.06278644252771070.1255728850554210.93721355747229
520.1661869078260780.3323738156521570.833813092173922
530.4818762913152360.9637525826304710.518123708684764
540.5816171570097020.8367656859805970.418382842990298
550.8178909462906410.3642181074187180.182109053709359







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level430.843137254901961NOK
5% type I error level460.901960784313726NOK
10% type I error level460.901960784313726NOK

\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 & 43 & 0.843137254901961 & NOK \tabularnewline
5% type I error level & 46 & 0.901960784313726 & NOK \tabularnewline
10% type I error level & 46 & 0.901960784313726 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58430&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]43[/C][C]0.843137254901961[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]46[/C][C]0.901960784313726[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.901960784313726[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58430&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58430&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 level430.843137254901961NOK
5% type I error level460.901960784313726NOK
10% type I error level460.901960784313726NOK



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')
}