Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 03:57:49 -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/20/t1355993937aq9nytz5vho3i2m.htm/, Retrieved Fri, 29 Mar 2024 05:24:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202547, Retrieved Fri, 29 Mar 2024 05:24:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [WS7 0299787] [2012-11-02 13:18:17] [a2dcdd13e1df6929c5c71aa45a46cc8e]
- R       [Multiple Regression] [Paper 0299787] [2012-12-05 10:17:15] [a2dcdd13e1df6929c5c71aa45a46cc8e]
- R PD        [Multiple Regression] [Paper 0299787] [2012-12-20 08:57:49] [b443327ccd50424d9f9aaa9d8bba1a6f] [Current]
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Dataseries X:
1	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
0	0
1	0
1	1
1	0
0	0
1	1
0	0
0	0
0	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
1	0
0	1
0	0
0	0
1	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
1	1
0	0
0	1
0	0
1	0
0	0
0	0
0	0
1	1
1	0
0	0
0	0
1	0
0	0
0	0
1	1
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
0	0
0	0
1	1
1	0
0	0
0	0
0	0
0	1
0	0
0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202547&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202547&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202547&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
T40[t] = + 0.220779220779221 + 0.445887445887446Correctanalysis[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
T40[t] =  +  0.220779220779221 +  0.445887445887446Correctanalysis[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202547&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]T40[t] =  +  0.220779220779221 +  0.445887445887446Correctanalysis[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202547&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202547&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
T40[t] = + 0.220779220779221 + 0.445887445887446Correctanalysis[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2207792207792210.0485524.54731.8e-059e-06
Correctanalysis0.4458874458874460.1500832.97090.0038710.001935

\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) & 0.220779220779221 & 0.048552 & 4.5473 & 1.8e-05 & 9e-06 \tabularnewline
Correctanalysis & 0.445887445887446 & 0.150083 & 2.9709 & 0.003871 & 0.001935 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202547&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]0.220779220779221[/C][C]0.048552[/C][C]4.5473[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]Correctanalysis[/C][C]0.445887445887446[/C][C]0.150083[/C][C]2.9709[/C][C]0.003871[/C][C]0.001935[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202547&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202547&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)0.2207792207792210.0485524.54731.8e-059e-06
Correctanalysis0.4458874458874460.1500832.97090.0038710.001935







Multiple Linear Regression - Regression Statistics
Multiple R0.308359738983709
R-squared0.0950857286261013
Adjusted R-squared0.084312939681174
F-TEST (value)8.82647280218692
F-TEST (DF numerator)1
F-TEST (DF denominator)84
p-value0.00387075225747924
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.426038692166865
Sum Squared Residuals15.2467532467532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.308359738983709 \tabularnewline
R-squared & 0.0950857286261013 \tabularnewline
Adjusted R-squared & 0.084312939681174 \tabularnewline
F-TEST (value) & 8.82647280218692 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value & 0.00387075225747924 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.426038692166865 \tabularnewline
Sum Squared Residuals & 15.2467532467532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202547&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.308359738983709[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0950857286261013[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.084312939681174[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.82647280218692[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/C][/ROW]
[ROW][C]p-value[/C][C]0.00387075225747924[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.426038692166865[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]15.2467532467532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202547&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202547&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 R0.308359738983709
R-squared0.0950857286261013
Adjusted R-squared0.084312939681174
F-TEST (value)8.82647280218692
F-TEST (DF numerator)1
F-TEST (DF denominator)84
p-value0.00387075225747924
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.426038692166865
Sum Squared Residuals15.2467532467532







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.2207792207792210.779220779220779
200.220779220779221-0.220779220779221
300.220779220779221-0.220779220779221
400.220779220779221-0.220779220779221
500.220779220779221-0.220779220779221
600.220779220779221-0.220779220779221
700.220779220779221-0.220779220779221
810.2207792207792210.779220779220779
900.220779220779221-0.220779220779221
1000.220779220779221-0.220779220779221
1110.2207792207792210.779220779220779
1200.220779220779221-0.220779220779221
1300.220779220779221-0.220779220779221
1410.2207792207792210.779220779220779
1500.220779220779221-0.220779220779221
1610.2207792207792210.779220779220779
1710.6666666666666670.333333333333333
1810.2207792207792210.779220779220779
1900.220779220779221-0.220779220779221
2010.6666666666666670.333333333333333
2100.220779220779221-0.220779220779221
2200.220779220779221-0.220779220779221
2300.220779220779221-0.220779220779221
2400.220779220779221-0.220779220779221
2510.2207792207792210.779220779220779
2600.220779220779221-0.220779220779221
2700.220779220779221-0.220779220779221
2800.220779220779221-0.220779220779221
2900.220779220779221-0.220779220779221
3000.220779220779221-0.220779220779221
3100.220779220779221-0.220779220779221
3200.220779220779221-0.220779220779221
3300.220779220779221-0.220779220779221
3410.2207792207792210.779220779220779
3500.220779220779221-0.220779220779221
3600.220779220779221-0.220779220779221
3710.2207792207792210.779220779220779
3800.220779220779221-0.220779220779221
3900.220779220779221-0.220779220779221
4010.2207792207792210.779220779220779
4100.666666666666667-0.666666666666667
4200.220779220779221-0.220779220779221
4300.220779220779221-0.220779220779221
4410.2207792207792210.779220779220779
4500.220779220779221-0.220779220779221
4600.220779220779221-0.220779220779221
4700.220779220779221-0.220779220779221
4800.220779220779221-0.220779220779221
4900.220779220779221-0.220779220779221
5000.220779220779221-0.220779220779221
5110.2207792207792210.779220779220779
5210.6666666666666670.333333333333333
5300.220779220779221-0.220779220779221
5400.666666666666667-0.666666666666667
5500.220779220779221-0.220779220779221
5610.2207792207792210.779220779220779
5700.220779220779221-0.220779220779221
5800.220779220779221-0.220779220779221
5900.220779220779221-0.220779220779221
6010.6666666666666670.333333333333333
6110.2207792207792210.779220779220779
6200.220779220779221-0.220779220779221
6300.220779220779221-0.220779220779221
6410.2207792207792210.779220779220779
6500.220779220779221-0.220779220779221
6600.220779220779221-0.220779220779221
6710.6666666666666670.333333333333333
6800.220779220779221-0.220779220779221
6900.220779220779221-0.220779220779221
7000.220779220779221-0.220779220779221
7100.220779220779221-0.220779220779221
7200.220779220779221-0.220779220779221
7300.220779220779221-0.220779220779221
7400.220779220779221-0.220779220779221
7500.220779220779221-0.220779220779221
7610.2207792207792210.779220779220779
7700.220779220779221-0.220779220779221
7800.220779220779221-0.220779220779221
7910.6666666666666670.333333333333333
8010.2207792207792210.779220779220779
8100.220779220779221-0.220779220779221
8200.220779220779221-0.220779220779221
8300.220779220779221-0.220779220779221
8400.666666666666667-0.666666666666667
8500.220779220779221-0.220779220779221
8600.220779220779221-0.220779220779221

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
2 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
3 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
4 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
5 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
6 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
7 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
8 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
9 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
10 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
11 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
12 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
13 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
14 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
15 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
16 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
17 & 1 & 0.666666666666667 & 0.333333333333333 \tabularnewline
18 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
19 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
20 & 1 & 0.666666666666667 & 0.333333333333333 \tabularnewline
21 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
22 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
23 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
24 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
25 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
26 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
27 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
28 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
29 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
30 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
31 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
32 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
33 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
34 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
35 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
36 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
37 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
38 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
39 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
40 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
41 & 0 & 0.666666666666667 & -0.666666666666667 \tabularnewline
42 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
43 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
44 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
45 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
46 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
47 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
48 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
49 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
50 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
51 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
52 & 1 & 0.666666666666667 & 0.333333333333333 \tabularnewline
53 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
54 & 0 & 0.666666666666667 & -0.666666666666667 \tabularnewline
55 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
56 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
57 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
58 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
59 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
60 & 1 & 0.666666666666667 & 0.333333333333333 \tabularnewline
61 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
62 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
63 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
64 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
65 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
66 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
67 & 1 & 0.666666666666667 & 0.333333333333333 \tabularnewline
68 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
69 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
70 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
71 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
72 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
73 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
74 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
75 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
76 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
77 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
78 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
79 & 1 & 0.666666666666667 & 0.333333333333333 \tabularnewline
80 & 1 & 0.220779220779221 & 0.779220779220779 \tabularnewline
81 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
82 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
83 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
84 & 0 & 0.666666666666667 & -0.666666666666667 \tabularnewline
85 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
86 & 0 & 0.220779220779221 & -0.220779220779221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202547&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]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.666666666666667[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.666666666666667[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.666666666666667[/C][C]-0.666666666666667[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.666666666666667[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.666666666666667[/C][C]-0.666666666666667[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.666666666666667[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.666666666666667[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.666666666666667[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.220779220779221[/C][C]0.779220779220779[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.666666666666667[/C][C]-0.666666666666667[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.220779220779221[/C][C]-0.220779220779221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202547&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202547&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
110.2207792207792210.779220779220779
200.220779220779221-0.220779220779221
300.220779220779221-0.220779220779221
400.220779220779221-0.220779220779221
500.220779220779221-0.220779220779221
600.220779220779221-0.220779220779221
700.220779220779221-0.220779220779221
810.2207792207792210.779220779220779
900.220779220779221-0.220779220779221
1000.220779220779221-0.220779220779221
1110.2207792207792210.779220779220779
1200.220779220779221-0.220779220779221
1300.220779220779221-0.220779220779221
1410.2207792207792210.779220779220779
1500.220779220779221-0.220779220779221
1610.2207792207792210.779220779220779
1710.6666666666666670.333333333333333
1810.2207792207792210.779220779220779
1900.220779220779221-0.220779220779221
2010.6666666666666670.333333333333333
2100.220779220779221-0.220779220779221
2200.220779220779221-0.220779220779221
2300.220779220779221-0.220779220779221
2400.220779220779221-0.220779220779221
2510.2207792207792210.779220779220779
2600.220779220779221-0.220779220779221
2700.220779220779221-0.220779220779221
2800.220779220779221-0.220779220779221
2900.220779220779221-0.220779220779221
3000.220779220779221-0.220779220779221
3100.220779220779221-0.220779220779221
3200.220779220779221-0.220779220779221
3300.220779220779221-0.220779220779221
3410.2207792207792210.779220779220779
3500.220779220779221-0.220779220779221
3600.220779220779221-0.220779220779221
3710.2207792207792210.779220779220779
3800.220779220779221-0.220779220779221
3900.220779220779221-0.220779220779221
4010.2207792207792210.779220779220779
4100.666666666666667-0.666666666666667
4200.220779220779221-0.220779220779221
4300.220779220779221-0.220779220779221
4410.2207792207792210.779220779220779
4500.220779220779221-0.220779220779221
4600.220779220779221-0.220779220779221
4700.220779220779221-0.220779220779221
4800.220779220779221-0.220779220779221
4900.220779220779221-0.220779220779221
5000.220779220779221-0.220779220779221
5110.2207792207792210.779220779220779
5210.6666666666666670.333333333333333
5300.220779220779221-0.220779220779221
5400.666666666666667-0.666666666666667
5500.220779220779221-0.220779220779221
5610.2207792207792210.779220779220779
5700.220779220779221-0.220779220779221
5800.220779220779221-0.220779220779221
5900.220779220779221-0.220779220779221
6010.6666666666666670.333333333333333
6110.2207792207792210.779220779220779
6200.220779220779221-0.220779220779221
6300.220779220779221-0.220779220779221
6410.2207792207792210.779220779220779
6500.220779220779221-0.220779220779221
6600.220779220779221-0.220779220779221
6710.6666666666666670.333333333333333
6800.220779220779221-0.220779220779221
6900.220779220779221-0.220779220779221
7000.220779220779221-0.220779220779221
7100.220779220779221-0.220779220779221
7200.220779220779221-0.220779220779221
7300.220779220779221-0.220779220779221
7400.220779220779221-0.220779220779221
7500.220779220779221-0.220779220779221
7610.2207792207792210.779220779220779
7700.220779220779221-0.220779220779221
7800.220779220779221-0.220779220779221
7910.6666666666666670.333333333333333
8010.2207792207792210.779220779220779
8100.220779220779221-0.220779220779221
8200.220779220779221-0.220779220779221
8300.220779220779221-0.220779220779221
8400.666666666666667-0.666666666666667
8500.220779220779221-0.220779220779221
8600.220779220779221-0.220779220779221







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.7677316251829430.4645367496341140.232268374817057
60.6509337303002320.6981325393995370.349066269699768
70.527486066843920.9450278663121590.47251393315608
80.7676046125909230.4647907748181540.232395387409077
90.6960205198678660.6079589602642670.303979480132134
100.6167799431040110.7664401137919770.383220056895989
110.7769279053811940.4461441892376130.223072094618806
120.7234349622650240.5531300754699520.276565037734976
130.6636179159243470.6727641681513070.336382084075653
140.7902923699650990.4194152600698020.209707630034901
150.7467350846403890.5065298307192220.253264915359611
160.8383643849266710.3232712301466580.161635615073329
170.7921624134647940.4156751730704120.207837586535206
180.8636472043771910.2727055912456190.136352795622809
190.8402533027024190.3194933945951610.159746697297581
200.8019179540551760.3961640918896470.198082045944824
210.7717464135071210.4565071729857590.228253586492879
220.7375172590669270.5249654818661450.262482740933073
230.6994727462203920.6010545075592170.300527253779608
240.657991279953990.684017440092020.34200872004601
250.7695794367541550.4608411264916910.230420563245845
260.7353240886965680.5293518226068640.264675911303432
270.6977116381721510.6045767236556980.302288361827849
280.6570720880188450.685855823962310.342927911981155
290.6138586903770890.7722826192458210.386141309622911
300.5686366702809380.8627266594381250.431363329719062
310.5220625722488320.9558748555023360.477937427751168
320.4748557551331880.9497115102663750.525144244866812
330.4277643872635330.8555287745270670.572235612736467
340.5715063683934990.8569872632130010.428493631606501
350.5257961767564520.9484076464870960.474203823243548
360.4794825250769940.9589650501539870.520517474923006
370.6198213456361210.7603573087277580.380178654363879
380.5756005387416330.8487989225167340.424399461258367
390.5301291906681010.9397416186637980.469870809331899
400.6682769880105460.6634460239789080.331723011989454
410.7641013715174860.4717972569650280.235898628482514
420.726816505215140.546366989569720.27318349478486
430.6865856734720320.6268286530559360.313414326527968
440.8039054730686930.3921890538626140.196094526931307
450.7694251872841590.4611496254316810.230574812715841
460.7315474931722460.5369050136555080.268452506827754
470.690554247162140.6188915056757210.30944575283786
480.6468587981442920.7062824037114160.353141201855708
490.6009972160528130.7980055678943750.399002783947187
500.5536102338088320.8927795323823350.446389766191168
510.6979111695511230.6041776608977540.302088830448877
520.6765923430358290.6468153139283430.323407656964171
530.6299823409401320.7400353181197370.370017659059868
540.7162035898207120.5675928203585760.283796410179288
550.6711793173002640.6576413653994730.328820682699736
560.8126207871071230.3747584257857540.187379212892877
570.7738282040867260.4523435918265470.226171795913274
580.7306264916733070.5387470166533860.269373508326693
590.6834314709337670.6331370581324660.316568529066233
600.6537864783354550.6924270433290890.346213521664544
610.8164535029776620.3670929940446750.183546497022338
620.773574513692120.4528509726157590.226425486307879
630.7251313933407160.5497372133185690.274868606659285
640.8893293373965310.2213413252069380.110670662603469
650.853937165388120.2921256692237590.146062834611879
660.8110391789997370.3779216420005270.188960821000263
670.807862870484020.384274259031960.19213712951598
680.7553557903806530.4892884192386930.244644209619347
690.695088706383710.6098225872325810.30491129361629
700.6280670094643980.7438659810712030.371932990535602
710.5559710548264760.8880578903470480.444028945173524
720.4810868352803570.9621736705607150.518913164719643
730.4061324905994320.8122649811988630.593867509400568
740.3339948519455290.6679897038910580.666005148054471
750.2674157552570470.5348315105140930.732584244742953
760.4994244258373010.9988488516746030.500575574162699
770.4003165159144340.8006330318288690.599683484085566
780.3045530625135160.6091061250270330.695446937486484
790.47040286722230.9408057344445990.5295971327777
80100
81100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.767731625182943 & 0.464536749634114 & 0.232268374817057 \tabularnewline
6 & 0.650933730300232 & 0.698132539399537 & 0.349066269699768 \tabularnewline
7 & 0.52748606684392 & 0.945027866312159 & 0.47251393315608 \tabularnewline
8 & 0.767604612590923 & 0.464790774818154 & 0.232395387409077 \tabularnewline
9 & 0.696020519867866 & 0.607958960264267 & 0.303979480132134 \tabularnewline
10 & 0.616779943104011 & 0.766440113791977 & 0.383220056895989 \tabularnewline
11 & 0.776927905381194 & 0.446144189237613 & 0.223072094618806 \tabularnewline
12 & 0.723434962265024 & 0.553130075469952 & 0.276565037734976 \tabularnewline
13 & 0.663617915924347 & 0.672764168151307 & 0.336382084075653 \tabularnewline
14 & 0.790292369965099 & 0.419415260069802 & 0.209707630034901 \tabularnewline
15 & 0.746735084640389 & 0.506529830719222 & 0.253264915359611 \tabularnewline
16 & 0.838364384926671 & 0.323271230146658 & 0.161635615073329 \tabularnewline
17 & 0.792162413464794 & 0.415675173070412 & 0.207837586535206 \tabularnewline
18 & 0.863647204377191 & 0.272705591245619 & 0.136352795622809 \tabularnewline
19 & 0.840253302702419 & 0.319493394595161 & 0.159746697297581 \tabularnewline
20 & 0.801917954055176 & 0.396164091889647 & 0.198082045944824 \tabularnewline
21 & 0.771746413507121 & 0.456507172985759 & 0.228253586492879 \tabularnewline
22 & 0.737517259066927 & 0.524965481866145 & 0.262482740933073 \tabularnewline
23 & 0.699472746220392 & 0.601054507559217 & 0.300527253779608 \tabularnewline
24 & 0.65799127995399 & 0.68401744009202 & 0.34200872004601 \tabularnewline
25 & 0.769579436754155 & 0.460841126491691 & 0.230420563245845 \tabularnewline
26 & 0.735324088696568 & 0.529351822606864 & 0.264675911303432 \tabularnewline
27 & 0.697711638172151 & 0.604576723655698 & 0.302288361827849 \tabularnewline
28 & 0.657072088018845 & 0.68585582396231 & 0.342927911981155 \tabularnewline
29 & 0.613858690377089 & 0.772282619245821 & 0.386141309622911 \tabularnewline
30 & 0.568636670280938 & 0.862726659438125 & 0.431363329719062 \tabularnewline
31 & 0.522062572248832 & 0.955874855502336 & 0.477937427751168 \tabularnewline
32 & 0.474855755133188 & 0.949711510266375 & 0.525144244866812 \tabularnewline
33 & 0.427764387263533 & 0.855528774527067 & 0.572235612736467 \tabularnewline
34 & 0.571506368393499 & 0.856987263213001 & 0.428493631606501 \tabularnewline
35 & 0.525796176756452 & 0.948407646487096 & 0.474203823243548 \tabularnewline
36 & 0.479482525076994 & 0.958965050153987 & 0.520517474923006 \tabularnewline
37 & 0.619821345636121 & 0.760357308727758 & 0.380178654363879 \tabularnewline
38 & 0.575600538741633 & 0.848798922516734 & 0.424399461258367 \tabularnewline
39 & 0.530129190668101 & 0.939741618663798 & 0.469870809331899 \tabularnewline
40 & 0.668276988010546 & 0.663446023978908 & 0.331723011989454 \tabularnewline
41 & 0.764101371517486 & 0.471797256965028 & 0.235898628482514 \tabularnewline
42 & 0.72681650521514 & 0.54636698956972 & 0.27318349478486 \tabularnewline
43 & 0.686585673472032 & 0.626828653055936 & 0.313414326527968 \tabularnewline
44 & 0.803905473068693 & 0.392189053862614 & 0.196094526931307 \tabularnewline
45 & 0.769425187284159 & 0.461149625431681 & 0.230574812715841 \tabularnewline
46 & 0.731547493172246 & 0.536905013655508 & 0.268452506827754 \tabularnewline
47 & 0.69055424716214 & 0.618891505675721 & 0.30944575283786 \tabularnewline
48 & 0.646858798144292 & 0.706282403711416 & 0.353141201855708 \tabularnewline
49 & 0.600997216052813 & 0.798005567894375 & 0.399002783947187 \tabularnewline
50 & 0.553610233808832 & 0.892779532382335 & 0.446389766191168 \tabularnewline
51 & 0.697911169551123 & 0.604177660897754 & 0.302088830448877 \tabularnewline
52 & 0.676592343035829 & 0.646815313928343 & 0.323407656964171 \tabularnewline
53 & 0.629982340940132 & 0.740035318119737 & 0.370017659059868 \tabularnewline
54 & 0.716203589820712 & 0.567592820358576 & 0.283796410179288 \tabularnewline
55 & 0.671179317300264 & 0.657641365399473 & 0.328820682699736 \tabularnewline
56 & 0.812620787107123 & 0.374758425785754 & 0.187379212892877 \tabularnewline
57 & 0.773828204086726 & 0.452343591826547 & 0.226171795913274 \tabularnewline
58 & 0.730626491673307 & 0.538747016653386 & 0.269373508326693 \tabularnewline
59 & 0.683431470933767 & 0.633137058132466 & 0.316568529066233 \tabularnewline
60 & 0.653786478335455 & 0.692427043329089 & 0.346213521664544 \tabularnewline
61 & 0.816453502977662 & 0.367092994044675 & 0.183546497022338 \tabularnewline
62 & 0.77357451369212 & 0.452850972615759 & 0.226425486307879 \tabularnewline
63 & 0.725131393340716 & 0.549737213318569 & 0.274868606659285 \tabularnewline
64 & 0.889329337396531 & 0.221341325206938 & 0.110670662603469 \tabularnewline
65 & 0.85393716538812 & 0.292125669223759 & 0.146062834611879 \tabularnewline
66 & 0.811039178999737 & 0.377921642000527 & 0.188960821000263 \tabularnewline
67 & 0.80786287048402 & 0.38427425903196 & 0.19213712951598 \tabularnewline
68 & 0.755355790380653 & 0.489288419238693 & 0.244644209619347 \tabularnewline
69 & 0.69508870638371 & 0.609822587232581 & 0.30491129361629 \tabularnewline
70 & 0.628067009464398 & 0.743865981071203 & 0.371932990535602 \tabularnewline
71 & 0.555971054826476 & 0.888057890347048 & 0.444028945173524 \tabularnewline
72 & 0.481086835280357 & 0.962173670560715 & 0.518913164719643 \tabularnewline
73 & 0.406132490599432 & 0.812264981198863 & 0.593867509400568 \tabularnewline
74 & 0.333994851945529 & 0.667989703891058 & 0.666005148054471 \tabularnewline
75 & 0.267415755257047 & 0.534831510514093 & 0.732584244742953 \tabularnewline
76 & 0.499424425837301 & 0.998848851674603 & 0.500575574162699 \tabularnewline
77 & 0.400316515914434 & 0.800633031828869 & 0.599683484085566 \tabularnewline
78 & 0.304553062513516 & 0.609106125027033 & 0.695446937486484 \tabularnewline
79 & 0.4704028672223 & 0.940805734444599 & 0.5295971327777 \tabularnewline
80 & 1 & 0 & 0 \tabularnewline
81 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202547&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]0.767731625182943[/C][C]0.464536749634114[/C][C]0.232268374817057[/C][/ROW]
[ROW][C]6[/C][C]0.650933730300232[/C][C]0.698132539399537[/C][C]0.349066269699768[/C][/ROW]
[ROW][C]7[/C][C]0.52748606684392[/C][C]0.945027866312159[/C][C]0.47251393315608[/C][/ROW]
[ROW][C]8[/C][C]0.767604612590923[/C][C]0.464790774818154[/C][C]0.232395387409077[/C][/ROW]
[ROW][C]9[/C][C]0.696020519867866[/C][C]0.607958960264267[/C][C]0.303979480132134[/C][/ROW]
[ROW][C]10[/C][C]0.616779943104011[/C][C]0.766440113791977[/C][C]0.383220056895989[/C][/ROW]
[ROW][C]11[/C][C]0.776927905381194[/C][C]0.446144189237613[/C][C]0.223072094618806[/C][/ROW]
[ROW][C]12[/C][C]0.723434962265024[/C][C]0.553130075469952[/C][C]0.276565037734976[/C][/ROW]
[ROW][C]13[/C][C]0.663617915924347[/C][C]0.672764168151307[/C][C]0.336382084075653[/C][/ROW]
[ROW][C]14[/C][C]0.790292369965099[/C][C]0.419415260069802[/C][C]0.209707630034901[/C][/ROW]
[ROW][C]15[/C][C]0.746735084640389[/C][C]0.506529830719222[/C][C]0.253264915359611[/C][/ROW]
[ROW][C]16[/C][C]0.838364384926671[/C][C]0.323271230146658[/C][C]0.161635615073329[/C][/ROW]
[ROW][C]17[/C][C]0.792162413464794[/C][C]0.415675173070412[/C][C]0.207837586535206[/C][/ROW]
[ROW][C]18[/C][C]0.863647204377191[/C][C]0.272705591245619[/C][C]0.136352795622809[/C][/ROW]
[ROW][C]19[/C][C]0.840253302702419[/C][C]0.319493394595161[/C][C]0.159746697297581[/C][/ROW]
[ROW][C]20[/C][C]0.801917954055176[/C][C]0.396164091889647[/C][C]0.198082045944824[/C][/ROW]
[ROW][C]21[/C][C]0.771746413507121[/C][C]0.456507172985759[/C][C]0.228253586492879[/C][/ROW]
[ROW][C]22[/C][C]0.737517259066927[/C][C]0.524965481866145[/C][C]0.262482740933073[/C][/ROW]
[ROW][C]23[/C][C]0.699472746220392[/C][C]0.601054507559217[/C][C]0.300527253779608[/C][/ROW]
[ROW][C]24[/C][C]0.65799127995399[/C][C]0.68401744009202[/C][C]0.34200872004601[/C][/ROW]
[ROW][C]25[/C][C]0.769579436754155[/C][C]0.460841126491691[/C][C]0.230420563245845[/C][/ROW]
[ROW][C]26[/C][C]0.735324088696568[/C][C]0.529351822606864[/C][C]0.264675911303432[/C][/ROW]
[ROW][C]27[/C][C]0.697711638172151[/C][C]0.604576723655698[/C][C]0.302288361827849[/C][/ROW]
[ROW][C]28[/C][C]0.657072088018845[/C][C]0.68585582396231[/C][C]0.342927911981155[/C][/ROW]
[ROW][C]29[/C][C]0.613858690377089[/C][C]0.772282619245821[/C][C]0.386141309622911[/C][/ROW]
[ROW][C]30[/C][C]0.568636670280938[/C][C]0.862726659438125[/C][C]0.431363329719062[/C][/ROW]
[ROW][C]31[/C][C]0.522062572248832[/C][C]0.955874855502336[/C][C]0.477937427751168[/C][/ROW]
[ROW][C]32[/C][C]0.474855755133188[/C][C]0.949711510266375[/C][C]0.525144244866812[/C][/ROW]
[ROW][C]33[/C][C]0.427764387263533[/C][C]0.855528774527067[/C][C]0.572235612736467[/C][/ROW]
[ROW][C]34[/C][C]0.571506368393499[/C][C]0.856987263213001[/C][C]0.428493631606501[/C][/ROW]
[ROW][C]35[/C][C]0.525796176756452[/C][C]0.948407646487096[/C][C]0.474203823243548[/C][/ROW]
[ROW][C]36[/C][C]0.479482525076994[/C][C]0.958965050153987[/C][C]0.520517474923006[/C][/ROW]
[ROW][C]37[/C][C]0.619821345636121[/C][C]0.760357308727758[/C][C]0.380178654363879[/C][/ROW]
[ROW][C]38[/C][C]0.575600538741633[/C][C]0.848798922516734[/C][C]0.424399461258367[/C][/ROW]
[ROW][C]39[/C][C]0.530129190668101[/C][C]0.939741618663798[/C][C]0.469870809331899[/C][/ROW]
[ROW][C]40[/C][C]0.668276988010546[/C][C]0.663446023978908[/C][C]0.331723011989454[/C][/ROW]
[ROW][C]41[/C][C]0.764101371517486[/C][C]0.471797256965028[/C][C]0.235898628482514[/C][/ROW]
[ROW][C]42[/C][C]0.72681650521514[/C][C]0.54636698956972[/C][C]0.27318349478486[/C][/ROW]
[ROW][C]43[/C][C]0.686585673472032[/C][C]0.626828653055936[/C][C]0.313414326527968[/C][/ROW]
[ROW][C]44[/C][C]0.803905473068693[/C][C]0.392189053862614[/C][C]0.196094526931307[/C][/ROW]
[ROW][C]45[/C][C]0.769425187284159[/C][C]0.461149625431681[/C][C]0.230574812715841[/C][/ROW]
[ROW][C]46[/C][C]0.731547493172246[/C][C]0.536905013655508[/C][C]0.268452506827754[/C][/ROW]
[ROW][C]47[/C][C]0.69055424716214[/C][C]0.618891505675721[/C][C]0.30944575283786[/C][/ROW]
[ROW][C]48[/C][C]0.646858798144292[/C][C]0.706282403711416[/C][C]0.353141201855708[/C][/ROW]
[ROW][C]49[/C][C]0.600997216052813[/C][C]0.798005567894375[/C][C]0.399002783947187[/C][/ROW]
[ROW][C]50[/C][C]0.553610233808832[/C][C]0.892779532382335[/C][C]0.446389766191168[/C][/ROW]
[ROW][C]51[/C][C]0.697911169551123[/C][C]0.604177660897754[/C][C]0.302088830448877[/C][/ROW]
[ROW][C]52[/C][C]0.676592343035829[/C][C]0.646815313928343[/C][C]0.323407656964171[/C][/ROW]
[ROW][C]53[/C][C]0.629982340940132[/C][C]0.740035318119737[/C][C]0.370017659059868[/C][/ROW]
[ROW][C]54[/C][C]0.716203589820712[/C][C]0.567592820358576[/C][C]0.283796410179288[/C][/ROW]
[ROW][C]55[/C][C]0.671179317300264[/C][C]0.657641365399473[/C][C]0.328820682699736[/C][/ROW]
[ROW][C]56[/C][C]0.812620787107123[/C][C]0.374758425785754[/C][C]0.187379212892877[/C][/ROW]
[ROW][C]57[/C][C]0.773828204086726[/C][C]0.452343591826547[/C][C]0.226171795913274[/C][/ROW]
[ROW][C]58[/C][C]0.730626491673307[/C][C]0.538747016653386[/C][C]0.269373508326693[/C][/ROW]
[ROW][C]59[/C][C]0.683431470933767[/C][C]0.633137058132466[/C][C]0.316568529066233[/C][/ROW]
[ROW][C]60[/C][C]0.653786478335455[/C][C]0.692427043329089[/C][C]0.346213521664544[/C][/ROW]
[ROW][C]61[/C][C]0.816453502977662[/C][C]0.367092994044675[/C][C]0.183546497022338[/C][/ROW]
[ROW][C]62[/C][C]0.77357451369212[/C][C]0.452850972615759[/C][C]0.226425486307879[/C][/ROW]
[ROW][C]63[/C][C]0.725131393340716[/C][C]0.549737213318569[/C][C]0.274868606659285[/C][/ROW]
[ROW][C]64[/C][C]0.889329337396531[/C][C]0.221341325206938[/C][C]0.110670662603469[/C][/ROW]
[ROW][C]65[/C][C]0.85393716538812[/C][C]0.292125669223759[/C][C]0.146062834611879[/C][/ROW]
[ROW][C]66[/C][C]0.811039178999737[/C][C]0.377921642000527[/C][C]0.188960821000263[/C][/ROW]
[ROW][C]67[/C][C]0.80786287048402[/C][C]0.38427425903196[/C][C]0.19213712951598[/C][/ROW]
[ROW][C]68[/C][C]0.755355790380653[/C][C]0.489288419238693[/C][C]0.244644209619347[/C][/ROW]
[ROW][C]69[/C][C]0.69508870638371[/C][C]0.609822587232581[/C][C]0.30491129361629[/C][/ROW]
[ROW][C]70[/C][C]0.628067009464398[/C][C]0.743865981071203[/C][C]0.371932990535602[/C][/ROW]
[ROW][C]71[/C][C]0.555971054826476[/C][C]0.888057890347048[/C][C]0.444028945173524[/C][/ROW]
[ROW][C]72[/C][C]0.481086835280357[/C][C]0.962173670560715[/C][C]0.518913164719643[/C][/ROW]
[ROW][C]73[/C][C]0.406132490599432[/C][C]0.812264981198863[/C][C]0.593867509400568[/C][/ROW]
[ROW][C]74[/C][C]0.333994851945529[/C][C]0.667989703891058[/C][C]0.666005148054471[/C][/ROW]
[ROW][C]75[/C][C]0.267415755257047[/C][C]0.534831510514093[/C][C]0.732584244742953[/C][/ROW]
[ROW][C]76[/C][C]0.499424425837301[/C][C]0.998848851674603[/C][C]0.500575574162699[/C][/ROW]
[ROW][C]77[/C][C]0.400316515914434[/C][C]0.800633031828869[/C][C]0.599683484085566[/C][/ROW]
[ROW][C]78[/C][C]0.304553062513516[/C][C]0.609106125027033[/C][C]0.695446937486484[/C][/ROW]
[ROW][C]79[/C][C]0.4704028672223[/C][C]0.940805734444599[/C][C]0.5295971327777[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202547&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202547&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
50.7677316251829430.4645367496341140.232268374817057
60.6509337303002320.6981325393995370.349066269699768
70.527486066843920.9450278663121590.47251393315608
80.7676046125909230.4647907748181540.232395387409077
90.6960205198678660.6079589602642670.303979480132134
100.6167799431040110.7664401137919770.383220056895989
110.7769279053811940.4461441892376130.223072094618806
120.7234349622650240.5531300754699520.276565037734976
130.6636179159243470.6727641681513070.336382084075653
140.7902923699650990.4194152600698020.209707630034901
150.7467350846403890.5065298307192220.253264915359611
160.8383643849266710.3232712301466580.161635615073329
170.7921624134647940.4156751730704120.207837586535206
180.8636472043771910.2727055912456190.136352795622809
190.8402533027024190.3194933945951610.159746697297581
200.8019179540551760.3961640918896470.198082045944824
210.7717464135071210.4565071729857590.228253586492879
220.7375172590669270.5249654818661450.262482740933073
230.6994727462203920.6010545075592170.300527253779608
240.657991279953990.684017440092020.34200872004601
250.7695794367541550.4608411264916910.230420563245845
260.7353240886965680.5293518226068640.264675911303432
270.6977116381721510.6045767236556980.302288361827849
280.6570720880188450.685855823962310.342927911981155
290.6138586903770890.7722826192458210.386141309622911
300.5686366702809380.8627266594381250.431363329719062
310.5220625722488320.9558748555023360.477937427751168
320.4748557551331880.9497115102663750.525144244866812
330.4277643872635330.8555287745270670.572235612736467
340.5715063683934990.8569872632130010.428493631606501
350.5257961767564520.9484076464870960.474203823243548
360.4794825250769940.9589650501539870.520517474923006
370.6198213456361210.7603573087277580.380178654363879
380.5756005387416330.8487989225167340.424399461258367
390.5301291906681010.9397416186637980.469870809331899
400.6682769880105460.6634460239789080.331723011989454
410.7641013715174860.4717972569650280.235898628482514
420.726816505215140.546366989569720.27318349478486
430.6865856734720320.6268286530559360.313414326527968
440.8039054730686930.3921890538626140.196094526931307
450.7694251872841590.4611496254316810.230574812715841
460.7315474931722460.5369050136555080.268452506827754
470.690554247162140.6188915056757210.30944575283786
480.6468587981442920.7062824037114160.353141201855708
490.6009972160528130.7980055678943750.399002783947187
500.5536102338088320.8927795323823350.446389766191168
510.6979111695511230.6041776608977540.302088830448877
520.6765923430358290.6468153139283430.323407656964171
530.6299823409401320.7400353181197370.370017659059868
540.7162035898207120.5675928203585760.283796410179288
550.6711793173002640.6576413653994730.328820682699736
560.8126207871071230.3747584257857540.187379212892877
570.7738282040867260.4523435918265470.226171795913274
580.7306264916733070.5387470166533860.269373508326693
590.6834314709337670.6331370581324660.316568529066233
600.6537864783354550.6924270433290890.346213521664544
610.8164535029776620.3670929940446750.183546497022338
620.773574513692120.4528509726157590.226425486307879
630.7251313933407160.5497372133185690.274868606659285
640.8893293373965310.2213413252069380.110670662603469
650.853937165388120.2921256692237590.146062834611879
660.8110391789997370.3779216420005270.188960821000263
670.807862870484020.384274259031960.19213712951598
680.7553557903806530.4892884192386930.244644209619347
690.695088706383710.6098225872325810.30491129361629
700.6280670094643980.7438659810712030.371932990535602
710.5559710548264760.8880578903470480.444028945173524
720.4810868352803570.9621736705607150.518913164719643
730.4061324905994320.8122649811988630.593867509400568
740.3339948519455290.6679897038910580.666005148054471
750.2674157552570470.5348315105140930.732584244742953
760.4994244258373010.9988488516746030.500575574162699
770.4003165159144340.8006330318288690.599683484085566
780.3045530625135160.6091061250270330.695446937486484
790.47040286722230.9408057344445990.5295971327777
80100
81100







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.025974025974026NOK
5% type I error level20.025974025974026OK
10% type I error level20.025974025974026OK

\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 & 0.025974025974026 & NOK \tabularnewline
5% type I error level & 2 & 0.025974025974026 & OK \tabularnewline
10% type I error level & 2 & 0.025974025974026 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202547&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]0.025974025974026[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.025974025974026[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.025974025974026[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202547&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202547&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 level20.025974025974026NOK
5% type I error level20.025974025974026OK
10% type I error level20.025974025974026OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}