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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 11 Dec 2012 07:16:20 -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/11/t13552282113qbc0vot9c8ojy0.htm/, Retrieved Fri, 29 Mar 2024 09:16:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198470, Retrieved Fri, 29 Mar 2024 09:16:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111
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]
- RMPD      [Multiple Regression] [] [2012-12-11 12:16:20] [54fc54845b7758f1604e617c5518fca2] [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'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198470&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198470&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198470&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'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Correct[t] = + 0.0588235294117647 -0.0588235294117647T20[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Correct[t] =  +  0.0588235294117647 -0.0588235294117647T20[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198470&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Correct[t] =  +  0.0588235294117647 -0.0588235294117647T20[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198470&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198470&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
Correct[t] = + 0.0588235294117647 -0.0588235294117647T20[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.05882352941176470.0289632.0310.0462850.023143
T20-0.05882352941176470.057925-1.01550.3135740.156787

\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) & 0.0588235294117647 & 0.028963 & 2.031 & 0.046285 & 0.023143 \tabularnewline
T20 & -0.0588235294117647 & 0.057925 & -1.0155 & 0.313574 & 0.156787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198470&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]0.0588235294117647[/C][C]0.028963[/C][C]2.031[/C][C]0.046285[/C][C]0.023143[/C][/ROW]
[ROW][C]T20[/C][C]-0.0588235294117647[/C][C]0.057925[/C][C]-1.0155[/C][C]0.313574[/C][C]0.156787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198470&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198470&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)0.05882352941176470.0289632.0310.0462850.023143
T20-0.05882352941176470.057925-1.01550.3135740.156787







Multiple Linear Regression - Regression Statistics
Multiple R0.124034734589208
R-squared0.0153846153846154
Adjusted R-squared0.000466200466200495
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 Deviation0.206835075998008
Sum Squared Residuals2.82352941176471

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.124034734589208 \tabularnewline
R-squared & 0.0153846153846154 \tabularnewline
Adjusted R-squared & 0.000466200466200495 \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 & 0.206835075998008 \tabularnewline
Sum Squared Residuals & 2.82352941176471 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198470&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]0.000466200466200495[/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]0.206835075998008[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.82352941176471[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198470&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198470&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-squared0.000466200466200495
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 Deviation0.206835075998008
Sum Squared Residuals2.82352941176471







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0588235294117648-0.0588235294117648
20-4.85705632614561e-174.85705632614561e-17
300.0588235294117647-0.0588235294117647
400.0588235294117647-0.0588235294117647
500.0588235294117647-0.0588235294117647
601.73641754187132e-18-1.73641754187132e-18
700.0588235294117647-0.0588235294117647
800.0588235294117647-0.0588235294117647
901.73641754187132e-18-1.73641754187132e-18
1000.0588235294117647-0.0588235294117647
1101.73641754187132e-18-1.73641754187132e-18
1200.0588235294117647-0.0588235294117647
1300.0588235294117647-0.0588235294117647
1400.0588235294117647-0.0588235294117647
1500.0588235294117647-0.0588235294117647
1600.0588235294117647-0.0588235294117647
1700.0588235294117647-0.0588235294117647
1800.0588235294117647-0.0588235294117647
1901.73641754187132e-18-1.73641754187132e-18
2000.0588235294117647-0.0588235294117647
2100.0588235294117647-0.0588235294117647
2201.73641754187132e-18-1.73641754187132e-18
2300.0588235294117647-0.0588235294117647
2400.0588235294117647-0.0588235294117647
2501.73641754187132e-18-1.73641754187132e-18
2601.73641754187132e-18-1.73641754187132e-18
2700.0588235294117647-0.0588235294117647
2801.73641754187132e-18-1.73641754187132e-18
2900.0588235294117647-0.0588235294117647
3000.0588235294117647-0.0588235294117647
3100.0588235294117647-0.0588235294117647
3200.0588235294117647-0.0588235294117647
3300.0588235294117647-0.0588235294117647
3400.0588235294117647-0.0588235294117647
3500.0588235294117647-0.0588235294117647
3600.0588235294117647-0.0588235294117647
3701.73641754187132e-18-1.73641754187132e-18
3800.0588235294117647-0.0588235294117647
3900.0588235294117647-0.0588235294117647
4001.73641754187132e-18-1.73641754187132e-18
4100.0588235294117647-0.0588235294117647
4200.0588235294117647-0.0588235294117647
4300.0588235294117647-0.0588235294117647
4400.0588235294117647-0.0588235294117647
4500.0588235294117647-0.0588235294117647
4600.0588235294117647-0.0588235294117647
4700.0588235294117647-0.0588235294117647
4800.0588235294117647-0.0588235294117647
4900.0588235294117647-0.0588235294117647
5000.0588235294117647-0.0588235294117647
5100.0588235294117647-0.0588235294117647
5201.73641754187132e-18-1.73641754187132e-18
5301.73641754187132e-18-1.73641754187132e-18
5400.0588235294117647-0.0588235294117647
5510.05882352941176470.941176470588235
5601.73641754187132e-18-1.73641754187132e-18
5700.0588235294117647-0.0588235294117647
5800.0588235294117647-0.0588235294117647
5900.0588235294117647-0.0588235294117647
6001.73641754187132e-18-1.73641754187132e-18
6101.73641754187132e-18-1.73641754187132e-18
6201.73641754187132e-18-1.73641754187132e-18
6300.0588235294117647-0.0588235294117647
6400.0588235294117647-0.0588235294117647
6500.0588235294117647-0.0588235294117647
6610.05882352941176470.941176470588235
6710.05882352941176470.941176470588235
6800.0588235294117647-0.0588235294117647

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0588235294117648 & -0.0588235294117648 \tabularnewline
2 & 0 & -4.85705632614561e-17 & 4.85705632614561e-17 \tabularnewline
3 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
4 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
5 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
6 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
7 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
8 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
9 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
10 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
11 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
12 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
13 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
14 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
15 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
16 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
17 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
18 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
19 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
20 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
21 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
22 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
23 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
24 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
25 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
26 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
27 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
28 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
29 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
30 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
31 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
32 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
33 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
34 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
35 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
36 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
37 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
38 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
39 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
40 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
41 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
42 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
43 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
44 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
45 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
46 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
47 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
48 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
49 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
50 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
51 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
52 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
53 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
54 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
55 & 1 & 0.0588235294117647 & 0.941176470588235 \tabularnewline
56 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
57 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
58 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
59 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
60 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
61 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
62 & 0 & 1.73641754187132e-18 & -1.73641754187132e-18 \tabularnewline
63 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
64 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
65 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
66 & 1 & 0.0588235294117647 & 0.941176470588235 \tabularnewline
67 & 1 & 0.0588235294117647 & 0.941176470588235 \tabularnewline
68 & 0 & 0.0588235294117647 & -0.0588235294117647 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198470&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]0.0588235294117648[/C][C]-0.0588235294117648[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-4.85705632614561e-17[/C][C]4.85705632614561e-17[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.0588235294117647[/C][C]0.941176470588235[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]1.73641754187132e-18[/C][C]-1.73641754187132e-18[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.0588235294117647[/C][C]0.941176470588235[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.0588235294117647[/C][C]0.941176470588235[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0588235294117647[/C][C]-0.0588235294117647[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198470&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198470&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
100.0588235294117648-0.0588235294117648
20-4.85705632614561e-174.85705632614561e-17
300.0588235294117647-0.0588235294117647
400.0588235294117647-0.0588235294117647
500.0588235294117647-0.0588235294117647
601.73641754187132e-18-1.73641754187132e-18
700.0588235294117647-0.0588235294117647
800.0588235294117647-0.0588235294117647
901.73641754187132e-18-1.73641754187132e-18
1000.0588235294117647-0.0588235294117647
1101.73641754187132e-18-1.73641754187132e-18
1200.0588235294117647-0.0588235294117647
1300.0588235294117647-0.0588235294117647
1400.0588235294117647-0.0588235294117647
1500.0588235294117647-0.0588235294117647
1600.0588235294117647-0.0588235294117647
1700.0588235294117647-0.0588235294117647
1800.0588235294117647-0.0588235294117647
1901.73641754187132e-18-1.73641754187132e-18
2000.0588235294117647-0.0588235294117647
2100.0588235294117647-0.0588235294117647
2201.73641754187132e-18-1.73641754187132e-18
2300.0588235294117647-0.0588235294117647
2400.0588235294117647-0.0588235294117647
2501.73641754187132e-18-1.73641754187132e-18
2601.73641754187132e-18-1.73641754187132e-18
2700.0588235294117647-0.0588235294117647
2801.73641754187132e-18-1.73641754187132e-18
2900.0588235294117647-0.0588235294117647
3000.0588235294117647-0.0588235294117647
3100.0588235294117647-0.0588235294117647
3200.0588235294117647-0.0588235294117647
3300.0588235294117647-0.0588235294117647
3400.0588235294117647-0.0588235294117647
3500.0588235294117647-0.0588235294117647
3600.0588235294117647-0.0588235294117647
3701.73641754187132e-18-1.73641754187132e-18
3800.0588235294117647-0.0588235294117647
3900.0588235294117647-0.0588235294117647
4001.73641754187132e-18-1.73641754187132e-18
4100.0588235294117647-0.0588235294117647
4200.0588235294117647-0.0588235294117647
4300.0588235294117647-0.0588235294117647
4400.0588235294117647-0.0588235294117647
4500.0588235294117647-0.0588235294117647
4600.0588235294117647-0.0588235294117647
4700.0588235294117647-0.0588235294117647
4800.0588235294117647-0.0588235294117647
4900.0588235294117647-0.0588235294117647
5000.0588235294117647-0.0588235294117647
5100.0588235294117647-0.0588235294117647
5201.73641754187132e-18-1.73641754187132e-18
5301.73641754187132e-18-1.73641754187132e-18
5400.0588235294117647-0.0588235294117647
5510.05882352941176470.941176470588235
5601.73641754187132e-18-1.73641754187132e-18
5700.0588235294117647-0.0588235294117647
5800.0588235294117647-0.0588235294117647
5900.0588235294117647-0.0588235294117647
6001.73641754187132e-18-1.73641754187132e-18
6101.73641754187132e-18-1.73641754187132e-18
6201.73641754187132e-18-1.73641754187132e-18
6300.0588235294117647-0.0588235294117647
6400.0588235294117647-0.0588235294117647
6500.0588235294117647-0.0588235294117647
6610.05882352941176470.941176470588235
6710.05882352941176470.941176470588235
6800.0588235294117647-0.0588235294117647







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5001
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
17001
18001
19001
20001
21001
22001
23001
24001
25001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
551.00630636057771e-072.01261272115542e-070.999999899369364
563.28456512468558e-086.56913024937115e-080.999999967154349
571.78225494760107e-083.56450989520213e-080.999999982177451
581.12102785992212e-082.24205571984424e-080.999999988789721
599.17981852592534e-091.83596370518507e-080.999999990820181
602.38263562108779e-094.76527124217558e-090.999999997617364
615.55525720911704e-101.11105144182341e-090.999999999444474
621.13556950255464e-102.27113900510929e-100.999999999886443
631.18155890328562e-102.36311780657124e-100.999999999881844

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0 & 0 & 1 \tabularnewline
6 & 0 & 0 & 1 \tabularnewline
7 & 0 & 0 & 1 \tabularnewline
8 & 0 & 0 & 1 \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0 & 0 & 1 \tabularnewline
18 & 0 & 0 & 1 \tabularnewline
19 & 0 & 0 & 1 \tabularnewline
20 & 0 & 0 & 1 \tabularnewline
21 & 0 & 0 & 1 \tabularnewline
22 & 0 & 0 & 1 \tabularnewline
23 & 0 & 0 & 1 \tabularnewline
24 & 0 & 0 & 1 \tabularnewline
25 & 0 & 0 & 1 \tabularnewline
26 & 0 & 0 & 1 \tabularnewline
27 & 0 & 0 & 1 \tabularnewline
28 & 0 & 0 & 1 \tabularnewline
29 & 0 & 0 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 1.00630636057771e-07 & 2.01261272115542e-07 & 0.999999899369364 \tabularnewline
56 & 3.28456512468558e-08 & 6.56913024937115e-08 & 0.999999967154349 \tabularnewline
57 & 1.78225494760107e-08 & 3.56450989520213e-08 & 0.999999982177451 \tabularnewline
58 & 1.12102785992212e-08 & 2.24205571984424e-08 & 0.999999988789721 \tabularnewline
59 & 9.17981852592534e-09 & 1.83596370518507e-08 & 0.999999990820181 \tabularnewline
60 & 2.38263562108779e-09 & 4.76527124217558e-09 & 0.999999997617364 \tabularnewline
61 & 5.55525720911704e-10 & 1.11105144182341e-09 & 0.999999999444474 \tabularnewline
62 & 1.13556950255464e-10 & 2.27113900510929e-10 & 0.999999999886443 \tabularnewline
63 & 1.18155890328562e-10 & 2.36311780657124e-10 & 0.999999999881844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198470&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[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]1.00630636057771e-07[/C][C]2.01261272115542e-07[/C][C]0.999999899369364[/C][/ROW]
[ROW][C]56[/C][C]3.28456512468558e-08[/C][C]6.56913024937115e-08[/C][C]0.999999967154349[/C][/ROW]
[ROW][C]57[/C][C]1.78225494760107e-08[/C][C]3.56450989520213e-08[/C][C]0.999999982177451[/C][/ROW]
[ROW][C]58[/C][C]1.12102785992212e-08[/C][C]2.24205571984424e-08[/C][C]0.999999988789721[/C][/ROW]
[ROW][C]59[/C][C]9.17981852592534e-09[/C][C]1.83596370518507e-08[/C][C]0.999999990820181[/C][/ROW]
[ROW][C]60[/C][C]2.38263562108779e-09[/C][C]4.76527124217558e-09[/C][C]0.999999997617364[/C][/ROW]
[ROW][C]61[/C][C]5.55525720911704e-10[/C][C]1.11105144182341e-09[/C][C]0.999999999444474[/C][/ROW]
[ROW][C]62[/C][C]1.13556950255464e-10[/C][C]2.27113900510929e-10[/C][C]0.999999999886443[/C][/ROW]
[ROW][C]63[/C][C]1.18155890328562e-10[/C][C]2.36311780657124e-10[/C][C]0.999999999881844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198470&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198470&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
5001
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
17001
18001
19001
20001
21001
22001
23001
24001
25001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
551.00630636057771e-072.01261272115542e-070.999999899369364
563.28456512468558e-086.56913024937115e-080.999999967154349
571.78225494760107e-083.56450989520213e-080.999999982177451
581.12102785992212e-082.24205571984424e-080.999999988789721
599.17981852592534e-091.83596370518507e-080.999999990820181
602.38263562108779e-094.76527124217558e-090.999999997617364
615.55525720911704e-101.11105144182341e-090.999999999444474
621.13556950255464e-102.27113900510929e-100.999999999886443
631.18155890328562e-102.36311780657124e-100.999999999881844







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level591NOK
5% type I error level591NOK
10% type I error level591NOK

\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 & 59 & 1 & NOK \tabularnewline
5% type I error level & 59 & 1 & NOK \tabularnewline
10% type I error level & 59 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198470&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]59[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]59[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198470&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198470&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 level591NOK
5% type I error level591NOK
10% type I error level591NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}