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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 18:32:56 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356132799fku9ypn25r93na0.htm/, Retrieved Thu, 28 Mar 2024 10:21:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204404, Retrieved Thu, 28 Mar 2024 10:21:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared and McNemar Tests] [] [2010-11-16 14:33:59] [b98453cac15ba1066b407e146608df68]
- R PD  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2012-12-10 15:11:14] [717eeab792c979f6e7c3b92a165ad49e]
- R  D    [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [t 20] [2012-12-15 13:51:15] [bb9c1d1bc63af35ef3129489f33fa7df]
- RMPD      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [1 way anova] [2012-12-15 14:10:59] [bb9c1d1bc63af35ef3129489f33fa7df]
- RM          [Multiple Regression] [t 40] [2012-12-15 14:25:12] [bb9c1d1bc63af35ef3129489f33fa7df]
- R  D          [Multiple Regression] [t 20] [2012-12-15 14:31:28] [bb9c1d1bc63af35ef3129489f33fa7df]
- R P             [Multiple Regression] [T20] [2012-12-19 16:23:16] [5b4f808c567607ef83d7f50fc3056dec]
-   P                 [Multiple Regression] [Multiple Regression] [2012-12-21 23:32:56] [1d531bcf045614ce640502618c1f452f] [Current]
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Dataseries X:
0	0
1	0
0	0
0	0
0	0
1	0
0	0
0	0
1	0
0	0
1	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
0	0
0	0
1	0
0	0
0	0
1	0
1	0
0	0
1	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
0	0
0	0
1	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
1	0
0	0
0	1
1	0
0	0
0	0
0	0
1	0
1	0
1	0
0	0
0	0
0	0
0	1
0	1
0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204404&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204404&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204404&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
T20[t] = + 0.261538461538462 -0.261538461538462Correct[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
T20[t] =  +  0.261538461538462 -0.261538461538462Correct[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204404&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]T20[t] =  +  0.261538461538462 -0.261538461538462Correct[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204404&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204404&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
T20[t] = + 0.261538461538462 -0.261538461538462Correct[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2615384615384620.0540954.83488e-064e-06
Correct-0.2615384615384620.257545-1.01550.3135740.156787

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.261538461538462 & 0.054095 & 4.8348 & 8e-06 & 4e-06 \tabularnewline
Correct & -0.261538461538462 & 0.257545 & -1.0155 & 0.313574 & 0.156787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204404&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.261538461538462[/C][C]0.054095[/C][C]4.8348[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Correct[/C][C]-0.261538461538462[/C][C]0.257545[/C][C]-1.0155[/C][C]0.313574[/C][C]0.156787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204404&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204404&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2615384615384620.0540954.83488e-064e-06
Correct-0.2615384615384620.257545-1.01550.3135740.156787







Multiple Linear Regression - Regression Statistics
Multiple R0.124034734589208
R-squared0.0153846153846154
Adjusted R-squared0.000466200466200384
F-TEST (value)1.03125
F-TEST (DF numerator)1
F-TEST (DF denominator)66
p-value0.313573600345893
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.436130473837578
Sum Squared Residuals12.5538461538462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.124034734589208 \tabularnewline
R-squared & 0.0153846153846154 \tabularnewline
Adjusted R-squared & 0.000466200466200384 \tabularnewline
F-TEST (value) & 1.03125 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 0.313573600345893 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.436130473837578 \tabularnewline
Sum Squared Residuals & 12.5538461538462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204404&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.124034734589208[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0153846153846154[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.000466200466200384[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.03125[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/C][/ROW]
[ROW][C]p-value[/C][C]0.313573600345893[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.436130473837578[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12.5538461538462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204404&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204404&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 R0.124034734589208
R-squared0.0153846153846154
Adjusted R-squared0.000466200466200384
F-TEST (value)1.03125
F-TEST (DF numerator)1
F-TEST (DF denominator)66
p-value0.313573600345893
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.436130473837578
Sum Squared Residuals12.5538461538462







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.261538461538462-0.261538461538462
210.2615384615384610.738461538461539
300.261538461538462-0.261538461538462
400.261538461538462-0.261538461538462
500.261538461538462-0.261538461538462
610.2615384615384610.738461538461539
700.261538461538462-0.261538461538462
800.261538461538462-0.261538461538462
910.2615384615384610.738461538461539
1000.261538461538462-0.261538461538462
1110.2615384615384610.738461538461539
1200.261538461538462-0.261538461538462
1300.261538461538462-0.261538461538462
1400.261538461538462-0.261538461538462
1500.261538461538462-0.261538461538462
1600.261538461538462-0.261538461538462
1700.261538461538462-0.261538461538462
1800.261538461538462-0.261538461538462
1910.2615384615384610.738461538461539
2000.261538461538462-0.261538461538462
2100.261538461538462-0.261538461538462
2210.2615384615384610.738461538461539
2300.261538461538462-0.261538461538462
2400.261538461538462-0.261538461538462
2510.2615384615384610.738461538461539
2610.2615384615384610.738461538461539
2700.261538461538462-0.261538461538462
2810.2615384615384610.738461538461539
2900.261538461538462-0.261538461538462
3000.261538461538462-0.261538461538462
3100.261538461538462-0.261538461538462
3200.261538461538462-0.261538461538462
3300.261538461538462-0.261538461538462
3400.261538461538462-0.261538461538462
3500.261538461538462-0.261538461538462
3600.261538461538462-0.261538461538462
3710.2615384615384610.738461538461539
3800.261538461538462-0.261538461538462
3900.261538461538462-0.261538461538462
4010.2615384615384610.738461538461539
4100.261538461538462-0.261538461538462
4200.261538461538462-0.261538461538462
4300.261538461538462-0.261538461538462
4400.261538461538462-0.261538461538462
4500.261538461538462-0.261538461538462
4600.261538461538462-0.261538461538462
4700.261538461538462-0.261538461538462
4800.261538461538462-0.261538461538462
4900.261538461538462-0.261538461538462
5000.261538461538462-0.261538461538462
5100.261538461538462-0.261538461538462
5210.2615384615384610.738461538461539
5310.2615384615384610.738461538461539
5400.261538461538462-0.261538461538462
5506.78659737999091e-17-6.78659737999091e-17
5610.2615384615384610.738461538461539
5700.261538461538462-0.261538461538462
5800.261538461538462-0.261538461538462
5900.261538461538462-0.261538461538462
6010.2615384615384610.738461538461539
6110.2615384615384610.738461538461539
6210.2615384615384610.738461538461539
6300.261538461538462-0.261538461538462
6400.261538461538462-0.261538461538462
6500.261538461538462-0.261538461538462
6606.78659737999091e-17-6.78659737999091e-17
6706.78659737999091e-17-6.78659737999091e-17
6800.261538461538462-0.261538461538462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
2 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
3 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
4 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
5 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
6 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
7 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
8 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
9 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
10 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
11 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
12 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
13 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
14 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
15 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
16 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
17 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
18 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
19 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
20 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
21 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
22 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
23 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
24 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
25 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
26 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
27 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
28 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
29 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
30 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
31 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
32 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
33 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
34 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
35 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
36 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
37 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
38 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
39 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
40 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
41 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
42 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
43 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
44 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
45 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
46 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
47 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
48 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
49 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
50 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
51 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
52 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
53 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
54 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
55 & 0 & 6.78659737999091e-17 & -6.78659737999091e-17 \tabularnewline
56 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
57 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
58 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
59 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
60 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
61 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
62 & 1 & 0.261538461538461 & 0.738461538461539 \tabularnewline
63 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
64 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
65 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
66 & 0 & 6.78659737999091e-17 & -6.78659737999091e-17 \tabularnewline
67 & 0 & 6.78659737999091e-17 & -6.78659737999091e-17 \tabularnewline
68 & 0 & 0.261538461538462 & -0.261538461538462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204404&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]6.78659737999091e-17[/C][C]-6.78659737999091e-17[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.261538461538461[/C][C]0.738461538461539[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]6.78659737999091e-17[/C][C]-6.78659737999091e-17[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]6.78659737999091e-17[/C][C]-6.78659737999091e-17[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.261538461538462[/C][C]-0.261538461538462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204404&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204404&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.261538461538462-0.261538461538462
210.2615384615384610.738461538461539
300.261538461538462-0.261538461538462
400.261538461538462-0.261538461538462
500.261538461538462-0.261538461538462
610.2615384615384610.738461538461539
700.261538461538462-0.261538461538462
800.261538461538462-0.261538461538462
910.2615384615384610.738461538461539
1000.261538461538462-0.261538461538462
1110.2615384615384610.738461538461539
1200.261538461538462-0.261538461538462
1300.261538461538462-0.261538461538462
1400.261538461538462-0.261538461538462
1500.261538461538462-0.261538461538462
1600.261538461538462-0.261538461538462
1700.261538461538462-0.261538461538462
1800.261538461538462-0.261538461538462
1910.2615384615384610.738461538461539
2000.261538461538462-0.261538461538462
2100.261538461538462-0.261538461538462
2210.2615384615384610.738461538461539
2300.261538461538462-0.261538461538462
2400.261538461538462-0.261538461538462
2510.2615384615384610.738461538461539
2610.2615384615384610.738461538461539
2700.261538461538462-0.261538461538462
2810.2615384615384610.738461538461539
2900.261538461538462-0.261538461538462
3000.261538461538462-0.261538461538462
3100.261538461538462-0.261538461538462
3200.261538461538462-0.261538461538462
3300.261538461538462-0.261538461538462
3400.261538461538462-0.261538461538462
3500.261538461538462-0.261538461538462
3600.261538461538462-0.261538461538462
3710.2615384615384610.738461538461539
3800.261538461538462-0.261538461538462
3900.261538461538462-0.261538461538462
4010.2615384615384610.738461538461539
4100.261538461538462-0.261538461538462
4200.261538461538462-0.261538461538462
4300.261538461538462-0.261538461538462
4400.261538461538462-0.261538461538462
4500.261538461538462-0.261538461538462
4600.261538461538462-0.261538461538462
4700.261538461538462-0.261538461538462
4800.261538461538462-0.261538461538462
4900.261538461538462-0.261538461538462
5000.261538461538462-0.261538461538462
5100.261538461538462-0.261538461538462
5210.2615384615384610.738461538461539
5310.2615384615384610.738461538461539
5400.261538461538462-0.261538461538462
5506.78659737999091e-17-6.78659737999091e-17
5610.2615384615384610.738461538461539
5700.261538461538462-0.261538461538462
5800.261538461538462-0.261538461538462
5900.261538461538462-0.261538461538462
6010.2615384615384610.738461538461539
6110.2615384615384610.738461538461539
6210.2615384615384610.738461538461539
6300.261538461538462-0.261538461538462
6400.261538461538462-0.261538461538462
6500.261538461538462-0.261538461538462
6606.78659737999091e-17-6.78659737999091e-17
6706.78659737999091e-17-6.78659737999091e-17
6800.261538461538462-0.261538461538462







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.7445208059356250.510958388128750.255479194064375
60.8569165559574150.286166888085170.143083444042585
70.8012852774980530.3974294450038950.198714722501947
80.7335318598136220.5329362803727560.266468140186378
90.8310398804402910.3379202391194170.168960119559709
100.786752080774740.426495838450520.21324791922526
110.8544256812168740.2911486375662520.145574318783126
120.8228185868096780.3543628263806440.177181413190322
130.7847460381814430.4305079236371140.215253961818557
140.7405598373367140.5188803253265720.259440162663286
150.6909029787291110.6181940425417780.309097021270889
160.6367301838993930.7265396322012140.363269816100607
170.5792717793260040.8414564413479920.420728220673996
180.5199548437481640.9600903125036720.480045156251836
190.6587913667814780.6824172664370440.341208633218522
200.6079457696779290.7841084606441420.392054230322071
210.5549277831841610.8901444336316780.445072216815839
220.6794491079496920.6411017841006150.320550892050308
230.6330940289069450.7338119421861110.366905971093055
240.5844299778875950.831140044224810.415570022112405
250.7001097257585310.5997805484829370.299890274241469
260.7939655578286860.4120688843426290.206034442171314
270.759521887565180.4809562248696410.24047811243482
280.8423707645744560.3152584708510880.157629235425544
290.8134131914461270.3731736171077470.186586808553873
300.7808861193725820.4382277612548360.219113880627418
310.7449257222655250.5101485554689490.255074277734475
320.7058086307282260.5883827385435490.294191369271774
330.6639528902788290.6720942194423420.336047109721171
340.6199081171589950.760183765682010.380091882841005
350.5743348638044590.8513302723910830.425665136195541
360.5279744939052510.9440510121894990.472025506094749
370.6489185496210530.7021629007578950.351081450378947
380.6037665718308610.7924668563382780.396233428169139
390.5574477962449980.8851044075100040.442552203755002
400.6804989565372460.6390020869255070.319501043462754
410.6352950461772810.7294099076454390.364704953822719
420.5884327130560770.8231345738878460.411567286943923
430.5407807172132170.9184385655735660.459219282786783
440.4932781407324820.9865562814649640.506721859267518
450.4468865361709810.8937730723419620.553113463829019
460.40254322935790.8050864587157990.5974567706421
470.3611228761810660.7222457523621320.638877123818934
480.3234150092646050.646830018529210.676584990735395
490.290126333460920.5802526669218410.70987366653908
500.2619194879664990.5238389759329970.738080512033501
510.2395083600657560.4790167201315130.760491639934244
520.3116902353236820.6233804706473640.688309764676318
530.407870734267120.8157414685342390.59212926573288
540.3666963693415690.7333927386831380.633303630658431
550.2834260082504270.5668520165008540.716573991749573
560.3887225680243880.7774451360487750.611277431975612
570.336013291786260.672026583572520.66398670821374
580.2920907052654280.5841814105308560.707909294734572
590.260364411245210.520728822490420.73963558875479
600.344411842442410.688823684884820.65558815755759
610.5510066536103050.897986692779390.448993346389695
62100
63100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.744520805935625 & 0.51095838812875 & 0.255479194064375 \tabularnewline
6 & 0.856916555957415 & 0.28616688808517 & 0.143083444042585 \tabularnewline
7 & 0.801285277498053 & 0.397429445003895 & 0.198714722501947 \tabularnewline
8 & 0.733531859813622 & 0.532936280372756 & 0.266468140186378 \tabularnewline
9 & 0.831039880440291 & 0.337920239119417 & 0.168960119559709 \tabularnewline
10 & 0.78675208077474 & 0.42649583845052 & 0.21324791922526 \tabularnewline
11 & 0.854425681216874 & 0.291148637566252 & 0.145574318783126 \tabularnewline
12 & 0.822818586809678 & 0.354362826380644 & 0.177181413190322 \tabularnewline
13 & 0.784746038181443 & 0.430507923637114 & 0.215253961818557 \tabularnewline
14 & 0.740559837336714 & 0.518880325326572 & 0.259440162663286 \tabularnewline
15 & 0.690902978729111 & 0.618194042541778 & 0.309097021270889 \tabularnewline
16 & 0.636730183899393 & 0.726539632201214 & 0.363269816100607 \tabularnewline
17 & 0.579271779326004 & 0.841456441347992 & 0.420728220673996 \tabularnewline
18 & 0.519954843748164 & 0.960090312503672 & 0.480045156251836 \tabularnewline
19 & 0.658791366781478 & 0.682417266437044 & 0.341208633218522 \tabularnewline
20 & 0.607945769677929 & 0.784108460644142 & 0.392054230322071 \tabularnewline
21 & 0.554927783184161 & 0.890144433631678 & 0.445072216815839 \tabularnewline
22 & 0.679449107949692 & 0.641101784100615 & 0.320550892050308 \tabularnewline
23 & 0.633094028906945 & 0.733811942186111 & 0.366905971093055 \tabularnewline
24 & 0.584429977887595 & 0.83114004422481 & 0.415570022112405 \tabularnewline
25 & 0.700109725758531 & 0.599780548482937 & 0.299890274241469 \tabularnewline
26 & 0.793965557828686 & 0.412068884342629 & 0.206034442171314 \tabularnewline
27 & 0.75952188756518 & 0.480956224869641 & 0.24047811243482 \tabularnewline
28 & 0.842370764574456 & 0.315258470851088 & 0.157629235425544 \tabularnewline
29 & 0.813413191446127 & 0.373173617107747 & 0.186586808553873 \tabularnewline
30 & 0.780886119372582 & 0.438227761254836 & 0.219113880627418 \tabularnewline
31 & 0.744925722265525 & 0.510148555468949 & 0.255074277734475 \tabularnewline
32 & 0.705808630728226 & 0.588382738543549 & 0.294191369271774 \tabularnewline
33 & 0.663952890278829 & 0.672094219442342 & 0.336047109721171 \tabularnewline
34 & 0.619908117158995 & 0.76018376568201 & 0.380091882841005 \tabularnewline
35 & 0.574334863804459 & 0.851330272391083 & 0.425665136195541 \tabularnewline
36 & 0.527974493905251 & 0.944051012189499 & 0.472025506094749 \tabularnewline
37 & 0.648918549621053 & 0.702162900757895 & 0.351081450378947 \tabularnewline
38 & 0.603766571830861 & 0.792466856338278 & 0.396233428169139 \tabularnewline
39 & 0.557447796244998 & 0.885104407510004 & 0.442552203755002 \tabularnewline
40 & 0.680498956537246 & 0.639002086925507 & 0.319501043462754 \tabularnewline
41 & 0.635295046177281 & 0.729409907645439 & 0.364704953822719 \tabularnewline
42 & 0.588432713056077 & 0.823134573887846 & 0.411567286943923 \tabularnewline
43 & 0.540780717213217 & 0.918438565573566 & 0.459219282786783 \tabularnewline
44 & 0.493278140732482 & 0.986556281464964 & 0.506721859267518 \tabularnewline
45 & 0.446886536170981 & 0.893773072341962 & 0.553113463829019 \tabularnewline
46 & 0.4025432293579 & 0.805086458715799 & 0.5974567706421 \tabularnewline
47 & 0.361122876181066 & 0.722245752362132 & 0.638877123818934 \tabularnewline
48 & 0.323415009264605 & 0.64683001852921 & 0.676584990735395 \tabularnewline
49 & 0.29012633346092 & 0.580252666921841 & 0.70987366653908 \tabularnewline
50 & 0.261919487966499 & 0.523838975932997 & 0.738080512033501 \tabularnewline
51 & 0.239508360065756 & 0.479016720131513 & 0.760491639934244 \tabularnewline
52 & 0.311690235323682 & 0.623380470647364 & 0.688309764676318 \tabularnewline
53 & 0.40787073426712 & 0.815741468534239 & 0.59212926573288 \tabularnewline
54 & 0.366696369341569 & 0.733392738683138 & 0.633303630658431 \tabularnewline
55 & 0.283426008250427 & 0.566852016500854 & 0.716573991749573 \tabularnewline
56 & 0.388722568024388 & 0.777445136048775 & 0.611277431975612 \tabularnewline
57 & 0.33601329178626 & 0.67202658357252 & 0.66398670821374 \tabularnewline
58 & 0.292090705265428 & 0.584181410530856 & 0.707909294734572 \tabularnewline
59 & 0.26036441124521 & 0.52072882249042 & 0.73963558875479 \tabularnewline
60 & 0.34441184244241 & 0.68882368488482 & 0.65558815755759 \tabularnewline
61 & 0.551006653610305 & 0.89798669277939 & 0.448993346389695 \tabularnewline
62 & 1 & 0 & 0 \tabularnewline
63 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204404&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]0.744520805935625[/C][C]0.51095838812875[/C][C]0.255479194064375[/C][/ROW]
[ROW][C]6[/C][C]0.856916555957415[/C][C]0.28616688808517[/C][C]0.143083444042585[/C][/ROW]
[ROW][C]7[/C][C]0.801285277498053[/C][C]0.397429445003895[/C][C]0.198714722501947[/C][/ROW]
[ROW][C]8[/C][C]0.733531859813622[/C][C]0.532936280372756[/C][C]0.266468140186378[/C][/ROW]
[ROW][C]9[/C][C]0.831039880440291[/C][C]0.337920239119417[/C][C]0.168960119559709[/C][/ROW]
[ROW][C]10[/C][C]0.78675208077474[/C][C]0.42649583845052[/C][C]0.21324791922526[/C][/ROW]
[ROW][C]11[/C][C]0.854425681216874[/C][C]0.291148637566252[/C][C]0.145574318783126[/C][/ROW]
[ROW][C]12[/C][C]0.822818586809678[/C][C]0.354362826380644[/C][C]0.177181413190322[/C][/ROW]
[ROW][C]13[/C][C]0.784746038181443[/C][C]0.430507923637114[/C][C]0.215253961818557[/C][/ROW]
[ROW][C]14[/C][C]0.740559837336714[/C][C]0.518880325326572[/C][C]0.259440162663286[/C][/ROW]
[ROW][C]15[/C][C]0.690902978729111[/C][C]0.618194042541778[/C][C]0.309097021270889[/C][/ROW]
[ROW][C]16[/C][C]0.636730183899393[/C][C]0.726539632201214[/C][C]0.363269816100607[/C][/ROW]
[ROW][C]17[/C][C]0.579271779326004[/C][C]0.841456441347992[/C][C]0.420728220673996[/C][/ROW]
[ROW][C]18[/C][C]0.519954843748164[/C][C]0.960090312503672[/C][C]0.480045156251836[/C][/ROW]
[ROW][C]19[/C][C]0.658791366781478[/C][C]0.682417266437044[/C][C]0.341208633218522[/C][/ROW]
[ROW][C]20[/C][C]0.607945769677929[/C][C]0.784108460644142[/C][C]0.392054230322071[/C][/ROW]
[ROW][C]21[/C][C]0.554927783184161[/C][C]0.890144433631678[/C][C]0.445072216815839[/C][/ROW]
[ROW][C]22[/C][C]0.679449107949692[/C][C]0.641101784100615[/C][C]0.320550892050308[/C][/ROW]
[ROW][C]23[/C][C]0.633094028906945[/C][C]0.733811942186111[/C][C]0.366905971093055[/C][/ROW]
[ROW][C]24[/C][C]0.584429977887595[/C][C]0.83114004422481[/C][C]0.415570022112405[/C][/ROW]
[ROW][C]25[/C][C]0.700109725758531[/C][C]0.599780548482937[/C][C]0.299890274241469[/C][/ROW]
[ROW][C]26[/C][C]0.793965557828686[/C][C]0.412068884342629[/C][C]0.206034442171314[/C][/ROW]
[ROW][C]27[/C][C]0.75952188756518[/C][C]0.480956224869641[/C][C]0.24047811243482[/C][/ROW]
[ROW][C]28[/C][C]0.842370764574456[/C][C]0.315258470851088[/C][C]0.157629235425544[/C][/ROW]
[ROW][C]29[/C][C]0.813413191446127[/C][C]0.373173617107747[/C][C]0.186586808553873[/C][/ROW]
[ROW][C]30[/C][C]0.780886119372582[/C][C]0.438227761254836[/C][C]0.219113880627418[/C][/ROW]
[ROW][C]31[/C][C]0.744925722265525[/C][C]0.510148555468949[/C][C]0.255074277734475[/C][/ROW]
[ROW][C]32[/C][C]0.705808630728226[/C][C]0.588382738543549[/C][C]0.294191369271774[/C][/ROW]
[ROW][C]33[/C][C]0.663952890278829[/C][C]0.672094219442342[/C][C]0.336047109721171[/C][/ROW]
[ROW][C]34[/C][C]0.619908117158995[/C][C]0.76018376568201[/C][C]0.380091882841005[/C][/ROW]
[ROW][C]35[/C][C]0.574334863804459[/C][C]0.851330272391083[/C][C]0.425665136195541[/C][/ROW]
[ROW][C]36[/C][C]0.527974493905251[/C][C]0.944051012189499[/C][C]0.472025506094749[/C][/ROW]
[ROW][C]37[/C][C]0.648918549621053[/C][C]0.702162900757895[/C][C]0.351081450378947[/C][/ROW]
[ROW][C]38[/C][C]0.603766571830861[/C][C]0.792466856338278[/C][C]0.396233428169139[/C][/ROW]
[ROW][C]39[/C][C]0.557447796244998[/C][C]0.885104407510004[/C][C]0.442552203755002[/C][/ROW]
[ROW][C]40[/C][C]0.680498956537246[/C][C]0.639002086925507[/C][C]0.319501043462754[/C][/ROW]
[ROW][C]41[/C][C]0.635295046177281[/C][C]0.729409907645439[/C][C]0.364704953822719[/C][/ROW]
[ROW][C]42[/C][C]0.588432713056077[/C][C]0.823134573887846[/C][C]0.411567286943923[/C][/ROW]
[ROW][C]43[/C][C]0.540780717213217[/C][C]0.918438565573566[/C][C]0.459219282786783[/C][/ROW]
[ROW][C]44[/C][C]0.493278140732482[/C][C]0.986556281464964[/C][C]0.506721859267518[/C][/ROW]
[ROW][C]45[/C][C]0.446886536170981[/C][C]0.893773072341962[/C][C]0.553113463829019[/C][/ROW]
[ROW][C]46[/C][C]0.4025432293579[/C][C]0.805086458715799[/C][C]0.5974567706421[/C][/ROW]
[ROW][C]47[/C][C]0.361122876181066[/C][C]0.722245752362132[/C][C]0.638877123818934[/C][/ROW]
[ROW][C]48[/C][C]0.323415009264605[/C][C]0.64683001852921[/C][C]0.676584990735395[/C][/ROW]
[ROW][C]49[/C][C]0.29012633346092[/C][C]0.580252666921841[/C][C]0.70987366653908[/C][/ROW]
[ROW][C]50[/C][C]0.261919487966499[/C][C]0.523838975932997[/C][C]0.738080512033501[/C][/ROW]
[ROW][C]51[/C][C]0.239508360065756[/C][C]0.479016720131513[/C][C]0.760491639934244[/C][/ROW]
[ROW][C]52[/C][C]0.311690235323682[/C][C]0.623380470647364[/C][C]0.688309764676318[/C][/ROW]
[ROW][C]53[/C][C]0.40787073426712[/C][C]0.815741468534239[/C][C]0.59212926573288[/C][/ROW]
[ROW][C]54[/C][C]0.366696369341569[/C][C]0.733392738683138[/C][C]0.633303630658431[/C][/ROW]
[ROW][C]55[/C][C]0.283426008250427[/C][C]0.566852016500854[/C][C]0.716573991749573[/C][/ROW]
[ROW][C]56[/C][C]0.388722568024388[/C][C]0.777445136048775[/C][C]0.611277431975612[/C][/ROW]
[ROW][C]57[/C][C]0.33601329178626[/C][C]0.67202658357252[/C][C]0.66398670821374[/C][/ROW]
[ROW][C]58[/C][C]0.292090705265428[/C][C]0.584181410530856[/C][C]0.707909294734572[/C][/ROW]
[ROW][C]59[/C][C]0.26036441124521[/C][C]0.52072882249042[/C][C]0.73963558875479[/C][/ROW]
[ROW][C]60[/C][C]0.34441184244241[/C][C]0.68882368488482[/C][C]0.65558815755759[/C][/ROW]
[ROW][C]61[/C][C]0.551006653610305[/C][C]0.89798669277939[/C][C]0.448993346389695[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204404&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204404&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
50.7445208059356250.510958388128750.255479194064375
60.8569165559574150.286166888085170.143083444042585
70.8012852774980530.3974294450038950.198714722501947
80.7335318598136220.5329362803727560.266468140186378
90.8310398804402910.3379202391194170.168960119559709
100.786752080774740.426495838450520.21324791922526
110.8544256812168740.2911486375662520.145574318783126
120.8228185868096780.3543628263806440.177181413190322
130.7847460381814430.4305079236371140.215253961818557
140.7405598373367140.5188803253265720.259440162663286
150.6909029787291110.6181940425417780.309097021270889
160.6367301838993930.7265396322012140.363269816100607
170.5792717793260040.8414564413479920.420728220673996
180.5199548437481640.9600903125036720.480045156251836
190.6587913667814780.6824172664370440.341208633218522
200.6079457696779290.7841084606441420.392054230322071
210.5549277831841610.8901444336316780.445072216815839
220.6794491079496920.6411017841006150.320550892050308
230.6330940289069450.7338119421861110.366905971093055
240.5844299778875950.831140044224810.415570022112405
250.7001097257585310.5997805484829370.299890274241469
260.7939655578286860.4120688843426290.206034442171314
270.759521887565180.4809562248696410.24047811243482
280.8423707645744560.3152584708510880.157629235425544
290.8134131914461270.3731736171077470.186586808553873
300.7808861193725820.4382277612548360.219113880627418
310.7449257222655250.5101485554689490.255074277734475
320.7058086307282260.5883827385435490.294191369271774
330.6639528902788290.6720942194423420.336047109721171
340.6199081171589950.760183765682010.380091882841005
350.5743348638044590.8513302723910830.425665136195541
360.5279744939052510.9440510121894990.472025506094749
370.6489185496210530.7021629007578950.351081450378947
380.6037665718308610.7924668563382780.396233428169139
390.5574477962449980.8851044075100040.442552203755002
400.6804989565372460.6390020869255070.319501043462754
410.6352950461772810.7294099076454390.364704953822719
420.5884327130560770.8231345738878460.411567286943923
430.5407807172132170.9184385655735660.459219282786783
440.4932781407324820.9865562814649640.506721859267518
450.4468865361709810.8937730723419620.553113463829019
460.40254322935790.8050864587157990.5974567706421
470.3611228761810660.7222457523621320.638877123818934
480.3234150092646050.646830018529210.676584990735395
490.290126333460920.5802526669218410.70987366653908
500.2619194879664990.5238389759329970.738080512033501
510.2395083600657560.4790167201315130.760491639934244
520.3116902353236820.6233804706473640.688309764676318
530.407870734267120.8157414685342390.59212926573288
540.3666963693415690.7333927386831380.633303630658431
550.2834260082504270.5668520165008540.716573991749573
560.3887225680243880.7774451360487750.611277431975612
570.336013291786260.672026583572520.66398670821374
580.2920907052654280.5841814105308560.707909294734572
590.260364411245210.520728822490420.73963558875479
600.344411842442410.688823684884820.65558815755759
610.5510066536103050.897986692779390.448993346389695
62100
63100







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0338983050847458NOK
5% type I error level20.0338983050847458OK
10% type I error level20.0338983050847458OK

\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 & 2 & 0.0338983050847458 & NOK \tabularnewline
5% type I error level & 2 & 0.0338983050847458 & OK \tabularnewline
10% type I error level & 2 & 0.0338983050847458 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204404&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]2[/C][C]0.0338983050847458[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0338983050847458[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0338983050847458[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204404&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204404&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 level20.0338983050847458NOK
5% type I error level20.0338983050847458OK
10% type I error level20.0338983050847458OK



Parameters (Session):
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')
}