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 computationTue, 18 Dec 2012 12:21:02 -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/18/t1355851455zj0hbbyz9jcvnvr.htm/, Retrieved Thu, 28 Mar 2024 17:16:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201514, Retrieved Thu, 28 Mar 2024 17:16:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper deel 5 RFC CHI] [2012-12-18 16:04:40] [48f852fd41a4fa7d41d1802199989991]
- R P   [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper deel 5 RFC CHI] [2012-12-18 16:20:42] [48f852fd41a4fa7d41d1802199989991]
-    D    [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper deel 5 RFC CHI] [2012-12-18 16:34:32] [48f852fd41a4fa7d41d1802199989991]
-           [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper deel 5 RFC CHI] [2012-12-18 16:38:10] [48f852fd41a4fa7d41d1802199989991]
- RMPD          [Multiple Regression] [Paper deel 5 RFC ...] [2012-12-18 17:21:02] [951f0bbf00246852608bf7fcd40f4937] [Current]
- R P             [Multiple Regression] [Paper deel 5 RFC ...] [2012-12-19 22:58:29] [48f852fd41a4fa7d41d1802199989991]
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Dataseries X:
0	0	0
1	1	0
0	0	0
0	0	0
0	0	1
1	0	0
0	0	1
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	1	0
0	0	0
0	0	0
1	1	0
0	0	0
0	0	0
1	1	1
1	0	0
0	1	0
1	1	0
0	0	0
0	0	0
0	0	0
0	0	0
0	0	0
0	0	0
0	0	0
0	0	0
1	1	0
0	1	1
0	0	0
1	0	0
0	0	1
0	0	0
0	0	0
0	0	0
0	0	0
0	0	0
0	1	0
0	0	0
0	0	0
0	0	0
0	1	1
1	1	1
1	0	0
0	0	0
0	1	0
1	1	0
0	0	0
0	0	1
0	0	1
1	0	0
1	1	0
1	0	0
0	0	0
0	0	1
0	0	0
0	1	0
0	1	1
0	1	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201514&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201514&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201514&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 time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
T20[t] = + 0.178039215686275 + 0.404313725490196Used[t] -0.18Useful[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
T20[t] =  +  0.178039215686275 +  0.404313725490196Used[t] -0.18Useful[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201514&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]T20[t] =  +  0.178039215686275 +  0.404313725490196Used[t] -0.18Useful[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201514&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201514&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
T20[t] = + 0.178039215686275 + 0.404313725490196Used[t] -0.18Useful[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1780392156862750.0590273.01620.003650.001825
Used0.4043137254901960.1161253.48170.0008960.000448
Useful-0.180.136553-1.31820.1920750.096037

\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) & 0.178039215686275 & 0.059027 & 3.0162 & 0.00365 & 0.001825 \tabularnewline
Used & 0.404313725490196 & 0.116125 & 3.4817 & 0.000896 & 0.000448 \tabularnewline
Useful & -0.18 & 0.136553 & -1.3182 & 0.192075 & 0.096037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201514&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]0.178039215686275[/C][C]0.059027[/C][C]3.0162[/C][C]0.00365[/C][C]0.001825[/C][/ROW]
[ROW][C]Used[/C][C]0.404313725490196[/C][C]0.116125[/C][C]3.4817[/C][C]0.000896[/C][C]0.000448[/C][/ROW]
[ROW][C]Useful[/C][C]-0.18[/C][C]0.136553[/C][C]-1.3182[/C][C]0.192075[/C][C]0.096037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201514&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201514&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)0.1780392156862750.0590273.01620.003650.001825
Used0.4043137254901960.1161253.48170.0008960.000448
Useful-0.180.136553-1.31820.1920750.096037







Multiple Linear Regression - Regression Statistics
Multiple R0.401515774708149
R-squared0.161214917339485
Adjusted R-squared0.135406145565315
F-TEST (value)6.24651644604158
F-TEST (DF numerator)2
F-TEST (DF denominator)65
p-value0.00330101646474878
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.40562411178372
Sum Squared Residuals10.6945098039216

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.401515774708149 \tabularnewline
R-squared & 0.161214917339485 \tabularnewline
Adjusted R-squared & 0.135406145565315 \tabularnewline
F-TEST (value) & 6.24651644604158 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 65 \tabularnewline
p-value & 0.00330101646474878 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.40562411178372 \tabularnewline
Sum Squared Residuals & 10.6945098039216 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201514&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.401515774708149[/C][/ROW]
[ROW][C]R-squared[/C][C]0.161214917339485[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.135406145565315[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.24651644604158[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]65[/C][/ROW]
[ROW][C]p-value[/C][C]0.00330101646474878[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.40562411178372[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.6945098039216[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201514&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201514&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 R0.401515774708149
R-squared0.161214917339485
Adjusted R-squared0.135406145565315
F-TEST (value)6.24651644604158
F-TEST (DF numerator)2
F-TEST (DF denominator)65
p-value0.00330101646474878
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.40562411178372
Sum Squared Residuals10.6945098039216







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.178039215686275-0.178039215686275
210.5823529411764710.417647058823529
300.178039215686274-0.178039215686274
400.178039215686274-0.178039215686274
50-0.00196078431372550.0019607843137255
610.1780392156862750.821960784313725
70-0.00196078431372550.0019607843137255
800.178039215686274-0.178039215686274
910.1780392156862750.821960784313725
1000.178039215686274-0.178039215686274
1110.1780392156862750.821960784313725
1200.178039215686274-0.178039215686274
1300.178039215686274-0.178039215686274
1400.178039215686274-0.178039215686274
1500.178039215686274-0.178039215686274
1600.178039215686274-0.178039215686274
1700.178039215686274-0.178039215686274
1800.178039215686274-0.178039215686274
1910.5823529411764710.417647058823529
2000.178039215686274-0.178039215686274
2100.178039215686274-0.178039215686274
2210.5823529411764710.417647058823529
2300.178039215686274-0.178039215686274
2400.178039215686274-0.178039215686274
2510.4023529411764710.597647058823529
2610.1780392156862750.821960784313725
2700.582352941176471-0.582352941176471
2810.5823529411764710.417647058823529
2900.178039215686274-0.178039215686274
3000.178039215686274-0.178039215686274
3100.178039215686274-0.178039215686274
3200.178039215686274-0.178039215686274
3300.178039215686274-0.178039215686274
3400.178039215686274-0.178039215686274
3500.178039215686274-0.178039215686274
3600.178039215686274-0.178039215686274
3710.5823529411764710.417647058823529
3800.402352941176471-0.402352941176471
3900.178039215686274-0.178039215686274
4010.1780392156862750.821960784313725
410-0.00196078431372550.0019607843137255
4200.178039215686274-0.178039215686274
4300.178039215686274-0.178039215686274
4400.178039215686274-0.178039215686274
4500.178039215686274-0.178039215686274
4600.178039215686274-0.178039215686274
4700.582352941176471-0.582352941176471
4800.178039215686274-0.178039215686274
4900.178039215686274-0.178039215686274
5000.178039215686274-0.178039215686274
5100.402352941176471-0.402352941176471
5210.4023529411764710.597647058823529
5310.1780392156862750.821960784313725
5400.178039215686274-0.178039215686274
5500.582352941176471-0.582352941176471
5610.5823529411764710.417647058823529
5700.178039215686274-0.178039215686274
580-0.00196078431372550.0019607843137255
590-0.00196078431372550.0019607843137255
6010.1780392156862750.821960784313725
6110.5823529411764710.417647058823529
6210.1780392156862750.821960784313725
6300.178039215686274-0.178039215686274
640-0.00196078431372550.0019607843137255
6500.178039215686274-0.178039215686274
6600.582352941176471-0.582352941176471
6700.402352941176471-0.402352941176471
6800.582352941176471-0.582352941176471

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.178039215686275 & -0.178039215686275 \tabularnewline
2 & 1 & 0.582352941176471 & 0.417647058823529 \tabularnewline
3 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
4 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
5 & 0 & -0.0019607843137255 & 0.0019607843137255 \tabularnewline
6 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
7 & 0 & -0.0019607843137255 & 0.0019607843137255 \tabularnewline
8 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
9 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
10 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
11 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
12 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
13 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
14 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
15 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
16 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
17 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
18 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
19 & 1 & 0.582352941176471 & 0.417647058823529 \tabularnewline
20 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
21 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
22 & 1 & 0.582352941176471 & 0.417647058823529 \tabularnewline
23 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
24 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
25 & 1 & 0.402352941176471 & 0.597647058823529 \tabularnewline
26 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
27 & 0 & 0.582352941176471 & -0.582352941176471 \tabularnewline
28 & 1 & 0.582352941176471 & 0.417647058823529 \tabularnewline
29 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
30 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
31 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
32 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
33 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
34 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
35 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
36 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
37 & 1 & 0.582352941176471 & 0.417647058823529 \tabularnewline
38 & 0 & 0.402352941176471 & -0.402352941176471 \tabularnewline
39 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
40 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
41 & 0 & -0.0019607843137255 & 0.0019607843137255 \tabularnewline
42 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
43 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
44 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
45 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
46 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
47 & 0 & 0.582352941176471 & -0.582352941176471 \tabularnewline
48 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
49 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
50 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
51 & 0 & 0.402352941176471 & -0.402352941176471 \tabularnewline
52 & 1 & 0.402352941176471 & 0.597647058823529 \tabularnewline
53 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
54 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
55 & 0 & 0.582352941176471 & -0.582352941176471 \tabularnewline
56 & 1 & 0.582352941176471 & 0.417647058823529 \tabularnewline
57 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
58 & 0 & -0.0019607843137255 & 0.0019607843137255 \tabularnewline
59 & 0 & -0.0019607843137255 & 0.0019607843137255 \tabularnewline
60 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
61 & 1 & 0.582352941176471 & 0.417647058823529 \tabularnewline
62 & 1 & 0.178039215686275 & 0.821960784313725 \tabularnewline
63 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
64 & 0 & -0.0019607843137255 & 0.0019607843137255 \tabularnewline
65 & 0 & 0.178039215686274 & -0.178039215686274 \tabularnewline
66 & 0 & 0.582352941176471 & -0.582352941176471 \tabularnewline
67 & 0 & 0.402352941176471 & -0.402352941176471 \tabularnewline
68 & 0 & 0.582352941176471 & -0.582352941176471 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201514&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]0.178039215686275[/C][C]-0.178039215686275[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.582352941176471[/C][C]0.417647058823529[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0019607843137255[/C][C]0.0019607843137255[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0019607843137255[/C][C]0.0019607843137255[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.582352941176471[/C][C]0.417647058823529[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.582352941176471[/C][C]0.417647058823529[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.402352941176471[/C][C]0.597647058823529[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.582352941176471[/C][C]-0.582352941176471[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.582352941176471[/C][C]0.417647058823529[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.582352941176471[/C][C]0.417647058823529[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.402352941176471[/C][C]-0.402352941176471[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]-0.0019607843137255[/C][C]0.0019607843137255[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.582352941176471[/C][C]-0.582352941176471[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.402352941176471[/C][C]-0.402352941176471[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.402352941176471[/C][C]0.597647058823529[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.582352941176471[/C][C]-0.582352941176471[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.582352941176471[/C][C]0.417647058823529[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0019607843137255[/C][C]0.0019607843137255[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.0019607843137255[/C][C]0.0019607843137255[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.582352941176471[/C][C]0.417647058823529[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.178039215686275[/C][C]0.821960784313725[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]-0.0019607843137255[/C][C]0.0019607843137255[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.178039215686274[/C][C]-0.178039215686274[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.582352941176471[/C][C]-0.582352941176471[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.402352941176471[/C][C]-0.402352941176471[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.582352941176471[/C][C]-0.582352941176471[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201514&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201514&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
100.178039215686275-0.178039215686275
210.5823529411764710.417647058823529
300.178039215686274-0.178039215686274
400.178039215686274-0.178039215686274
50-0.00196078431372550.0019607843137255
610.1780392156862750.821960784313725
70-0.00196078431372550.0019607843137255
800.178039215686274-0.178039215686274
910.1780392156862750.821960784313725
1000.178039215686274-0.178039215686274
1110.1780392156862750.821960784313725
1200.178039215686274-0.178039215686274
1300.178039215686274-0.178039215686274
1400.178039215686274-0.178039215686274
1500.178039215686274-0.178039215686274
1600.178039215686274-0.178039215686274
1700.178039215686274-0.178039215686274
1800.178039215686274-0.178039215686274
1910.5823529411764710.417647058823529
2000.178039215686274-0.178039215686274
2100.178039215686274-0.178039215686274
2210.5823529411764710.417647058823529
2300.178039215686274-0.178039215686274
2400.178039215686274-0.178039215686274
2510.4023529411764710.597647058823529
2610.1780392156862750.821960784313725
2700.582352941176471-0.582352941176471
2810.5823529411764710.417647058823529
2900.178039215686274-0.178039215686274
3000.178039215686274-0.178039215686274
3100.178039215686274-0.178039215686274
3200.178039215686274-0.178039215686274
3300.178039215686274-0.178039215686274
3400.178039215686274-0.178039215686274
3500.178039215686274-0.178039215686274
3600.178039215686274-0.178039215686274
3710.5823529411764710.417647058823529
3800.402352941176471-0.402352941176471
3900.178039215686274-0.178039215686274
4010.1780392156862750.821960784313725
410-0.00196078431372550.0019607843137255
4200.178039215686274-0.178039215686274
4300.178039215686274-0.178039215686274
4400.178039215686274-0.178039215686274
4500.178039215686274-0.178039215686274
4600.178039215686274-0.178039215686274
4700.582352941176471-0.582352941176471
4800.178039215686274-0.178039215686274
4900.178039215686274-0.178039215686274
5000.178039215686274-0.178039215686274
5100.402352941176471-0.402352941176471
5210.4023529411764710.597647058823529
5310.1780392156862750.821960784313725
5400.178039215686274-0.178039215686274
5500.582352941176471-0.582352941176471
5610.5823529411764710.417647058823529
5700.178039215686274-0.178039215686274
580-0.00196078431372550.0019607843137255
590-0.00196078431372550.0019607843137255
6010.1780392156862750.821960784313725
6110.5823529411764710.417647058823529
6210.1780392156862750.821960784313725
6300.178039215686274-0.178039215686274
640-0.00196078431372550.0019607843137255
6500.178039215686274-0.178039215686274
6600.582352941176471-0.582352941176471
6700.402352941176471-0.402352941176471
6800.582352941176471-0.582352941176471







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.7801108174836930.4397783650326130.219889182516307
70.642804241537630.714391516924740.35719575846237
80.5376290881235660.9247418237528670.462370911876434
90.7621000293715320.4757999412569360.237899970628468
100.7077615361347210.5844769277305570.292238463865279
110.8325582961378310.3348834077243370.167441703862169
120.8010017396951630.3979965206096740.198998260304837
130.760067143102020.4798657137959590.23993285689798
140.7107983334192850.578403333161430.289201666580715
150.6544149745147670.6911700509704660.345585025485233
160.5924997571192950.815000485761410.407500242880705
170.5269887586328110.9460224827343780.473011241367189
180.4600487313757640.9200974627515270.539951268624236
190.4017324624211010.8034649248422020.598267537578899
200.3395992146153990.6791984292307980.660400785384601
210.2813263114177490.5626526228354980.718673688582251
220.2415922866627010.4831845733254010.7584077133373
230.1939378318350690.3878756636701380.806062168164931
240.1524960759639850.3049921519279690.847503924036015
250.1487948605712540.2975897211425090.851205139428746
260.359329323173370.718658646346740.64067067682663
270.5639659650838410.8720680698323190.436034034916159
280.551322931338560.897354137322880.44867706866144
290.4916388926290250.983277785258050.508361107370975
300.4320628836552360.8641257673104720.567937116344764
310.3740339163095180.7480678326190350.625966083690482
320.3188697317776560.6377394635553120.681130268222344
330.2676765402053970.5353530804107940.732323459794603
340.2212848714030560.4425697428061120.778715128596944
350.1802173424534120.3604346849068250.819782657546588
360.14468902067670.2893780413533990.8553109793233
370.1478923272309340.2957846544618690.852107672769066
380.1799025833366820.3598051666733640.820097416663318
390.1447463131193360.2894926262386720.855253686880664
400.3077325122565480.6154650245130950.692267487743452
410.2473690483149940.4947380966299880.752630951685006
420.2026395628123480.4052791256246970.797360437187652
430.1634454994468520.3268909988937040.836554500553148
440.1299629079212810.2599258158425610.870037092078719
450.1020833579165390.2041667158330770.897916642083461
460.07946980760658330.1589396152131670.920530192393417
470.1081142935927080.2162285871854170.891885706407291
480.08543291493360180.1708658298672040.914567085066398
490.06783314886933830.1356662977386770.932166851130662
500.05484163293747030.1096832658749410.94515836706253
510.04742875425656790.09485750851313580.952571245743432
520.1115983071282050.2231966142564090.888401692871795
530.1942520626726920.3885041253453840.805747937327308
540.1693992657864370.3387985315728740.830600734213563
550.1902970340995210.3805940681990420.809702965900479
560.2304126752115870.4608253504231740.769587324788413
570.2189458770842580.4378917541685150.781054122915742
580.1477519536625420.2955039073250850.852248046337458
590.09221074129925310.1844214825985060.907789258700747
600.1445477133189480.2890954266378960.855452286681052
610.3443421549358860.6886843098717710.655657845064114
62100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.780110817483693 & 0.439778365032613 & 0.219889182516307 \tabularnewline
7 & 0.64280424153763 & 0.71439151692474 & 0.35719575846237 \tabularnewline
8 & 0.537629088123566 & 0.924741823752867 & 0.462370911876434 \tabularnewline
9 & 0.762100029371532 & 0.475799941256936 & 0.237899970628468 \tabularnewline
10 & 0.707761536134721 & 0.584476927730557 & 0.292238463865279 \tabularnewline
11 & 0.832558296137831 & 0.334883407724337 & 0.167441703862169 \tabularnewline
12 & 0.801001739695163 & 0.397996520609674 & 0.198998260304837 \tabularnewline
13 & 0.76006714310202 & 0.479865713795959 & 0.23993285689798 \tabularnewline
14 & 0.710798333419285 & 0.57840333316143 & 0.289201666580715 \tabularnewline
15 & 0.654414974514767 & 0.691170050970466 & 0.345585025485233 \tabularnewline
16 & 0.592499757119295 & 0.81500048576141 & 0.407500242880705 \tabularnewline
17 & 0.526988758632811 & 0.946022482734378 & 0.473011241367189 \tabularnewline
18 & 0.460048731375764 & 0.920097462751527 & 0.539951268624236 \tabularnewline
19 & 0.401732462421101 & 0.803464924842202 & 0.598267537578899 \tabularnewline
20 & 0.339599214615399 & 0.679198429230798 & 0.660400785384601 \tabularnewline
21 & 0.281326311417749 & 0.562652622835498 & 0.718673688582251 \tabularnewline
22 & 0.241592286662701 & 0.483184573325401 & 0.7584077133373 \tabularnewline
23 & 0.193937831835069 & 0.387875663670138 & 0.806062168164931 \tabularnewline
24 & 0.152496075963985 & 0.304992151927969 & 0.847503924036015 \tabularnewline
25 & 0.148794860571254 & 0.297589721142509 & 0.851205139428746 \tabularnewline
26 & 0.35932932317337 & 0.71865864634674 & 0.64067067682663 \tabularnewline
27 & 0.563965965083841 & 0.872068069832319 & 0.436034034916159 \tabularnewline
28 & 0.55132293133856 & 0.89735413732288 & 0.44867706866144 \tabularnewline
29 & 0.491638892629025 & 0.98327778525805 & 0.508361107370975 \tabularnewline
30 & 0.432062883655236 & 0.864125767310472 & 0.567937116344764 \tabularnewline
31 & 0.374033916309518 & 0.748067832619035 & 0.625966083690482 \tabularnewline
32 & 0.318869731777656 & 0.637739463555312 & 0.681130268222344 \tabularnewline
33 & 0.267676540205397 & 0.535353080410794 & 0.732323459794603 \tabularnewline
34 & 0.221284871403056 & 0.442569742806112 & 0.778715128596944 \tabularnewline
35 & 0.180217342453412 & 0.360434684906825 & 0.819782657546588 \tabularnewline
36 & 0.1446890206767 & 0.289378041353399 & 0.8553109793233 \tabularnewline
37 & 0.147892327230934 & 0.295784654461869 & 0.852107672769066 \tabularnewline
38 & 0.179902583336682 & 0.359805166673364 & 0.820097416663318 \tabularnewline
39 & 0.144746313119336 & 0.289492626238672 & 0.855253686880664 \tabularnewline
40 & 0.307732512256548 & 0.615465024513095 & 0.692267487743452 \tabularnewline
41 & 0.247369048314994 & 0.494738096629988 & 0.752630951685006 \tabularnewline
42 & 0.202639562812348 & 0.405279125624697 & 0.797360437187652 \tabularnewline
43 & 0.163445499446852 & 0.326890998893704 & 0.836554500553148 \tabularnewline
44 & 0.129962907921281 & 0.259925815842561 & 0.870037092078719 \tabularnewline
45 & 0.102083357916539 & 0.204166715833077 & 0.897916642083461 \tabularnewline
46 & 0.0794698076065833 & 0.158939615213167 & 0.920530192393417 \tabularnewline
47 & 0.108114293592708 & 0.216228587185417 & 0.891885706407291 \tabularnewline
48 & 0.0854329149336018 & 0.170865829867204 & 0.914567085066398 \tabularnewline
49 & 0.0678331488693383 & 0.135666297738677 & 0.932166851130662 \tabularnewline
50 & 0.0548416329374703 & 0.109683265874941 & 0.94515836706253 \tabularnewline
51 & 0.0474287542565679 & 0.0948575085131358 & 0.952571245743432 \tabularnewline
52 & 0.111598307128205 & 0.223196614256409 & 0.888401692871795 \tabularnewline
53 & 0.194252062672692 & 0.388504125345384 & 0.805747937327308 \tabularnewline
54 & 0.169399265786437 & 0.338798531572874 & 0.830600734213563 \tabularnewline
55 & 0.190297034099521 & 0.380594068199042 & 0.809702965900479 \tabularnewline
56 & 0.230412675211587 & 0.460825350423174 & 0.769587324788413 \tabularnewline
57 & 0.218945877084258 & 0.437891754168515 & 0.781054122915742 \tabularnewline
58 & 0.147751953662542 & 0.295503907325085 & 0.852248046337458 \tabularnewline
59 & 0.0922107412992531 & 0.184421482598506 & 0.907789258700747 \tabularnewline
60 & 0.144547713318948 & 0.289095426637896 & 0.855452286681052 \tabularnewline
61 & 0.344342154935886 & 0.688684309871771 & 0.655657845064114 \tabularnewline
62 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201514&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]6[/C][C]0.780110817483693[/C][C]0.439778365032613[/C][C]0.219889182516307[/C][/ROW]
[ROW][C]7[/C][C]0.64280424153763[/C][C]0.71439151692474[/C][C]0.35719575846237[/C][/ROW]
[ROW][C]8[/C][C]0.537629088123566[/C][C]0.924741823752867[/C][C]0.462370911876434[/C][/ROW]
[ROW][C]9[/C][C]0.762100029371532[/C][C]0.475799941256936[/C][C]0.237899970628468[/C][/ROW]
[ROW][C]10[/C][C]0.707761536134721[/C][C]0.584476927730557[/C][C]0.292238463865279[/C][/ROW]
[ROW][C]11[/C][C]0.832558296137831[/C][C]0.334883407724337[/C][C]0.167441703862169[/C][/ROW]
[ROW][C]12[/C][C]0.801001739695163[/C][C]0.397996520609674[/C][C]0.198998260304837[/C][/ROW]
[ROW][C]13[/C][C]0.76006714310202[/C][C]0.479865713795959[/C][C]0.23993285689798[/C][/ROW]
[ROW][C]14[/C][C]0.710798333419285[/C][C]0.57840333316143[/C][C]0.289201666580715[/C][/ROW]
[ROW][C]15[/C][C]0.654414974514767[/C][C]0.691170050970466[/C][C]0.345585025485233[/C][/ROW]
[ROW][C]16[/C][C]0.592499757119295[/C][C]0.81500048576141[/C][C]0.407500242880705[/C][/ROW]
[ROW][C]17[/C][C]0.526988758632811[/C][C]0.946022482734378[/C][C]0.473011241367189[/C][/ROW]
[ROW][C]18[/C][C]0.460048731375764[/C][C]0.920097462751527[/C][C]0.539951268624236[/C][/ROW]
[ROW][C]19[/C][C]0.401732462421101[/C][C]0.803464924842202[/C][C]0.598267537578899[/C][/ROW]
[ROW][C]20[/C][C]0.339599214615399[/C][C]0.679198429230798[/C][C]0.660400785384601[/C][/ROW]
[ROW][C]21[/C][C]0.281326311417749[/C][C]0.562652622835498[/C][C]0.718673688582251[/C][/ROW]
[ROW][C]22[/C][C]0.241592286662701[/C][C]0.483184573325401[/C][C]0.7584077133373[/C][/ROW]
[ROW][C]23[/C][C]0.193937831835069[/C][C]0.387875663670138[/C][C]0.806062168164931[/C][/ROW]
[ROW][C]24[/C][C]0.152496075963985[/C][C]0.304992151927969[/C][C]0.847503924036015[/C][/ROW]
[ROW][C]25[/C][C]0.148794860571254[/C][C]0.297589721142509[/C][C]0.851205139428746[/C][/ROW]
[ROW][C]26[/C][C]0.35932932317337[/C][C]0.71865864634674[/C][C]0.64067067682663[/C][/ROW]
[ROW][C]27[/C][C]0.563965965083841[/C][C]0.872068069832319[/C][C]0.436034034916159[/C][/ROW]
[ROW][C]28[/C][C]0.55132293133856[/C][C]0.89735413732288[/C][C]0.44867706866144[/C][/ROW]
[ROW][C]29[/C][C]0.491638892629025[/C][C]0.98327778525805[/C][C]0.508361107370975[/C][/ROW]
[ROW][C]30[/C][C]0.432062883655236[/C][C]0.864125767310472[/C][C]0.567937116344764[/C][/ROW]
[ROW][C]31[/C][C]0.374033916309518[/C][C]0.748067832619035[/C][C]0.625966083690482[/C][/ROW]
[ROW][C]32[/C][C]0.318869731777656[/C][C]0.637739463555312[/C][C]0.681130268222344[/C][/ROW]
[ROW][C]33[/C][C]0.267676540205397[/C][C]0.535353080410794[/C][C]0.732323459794603[/C][/ROW]
[ROW][C]34[/C][C]0.221284871403056[/C][C]0.442569742806112[/C][C]0.778715128596944[/C][/ROW]
[ROW][C]35[/C][C]0.180217342453412[/C][C]0.360434684906825[/C][C]0.819782657546588[/C][/ROW]
[ROW][C]36[/C][C]0.1446890206767[/C][C]0.289378041353399[/C][C]0.8553109793233[/C][/ROW]
[ROW][C]37[/C][C]0.147892327230934[/C][C]0.295784654461869[/C][C]0.852107672769066[/C][/ROW]
[ROW][C]38[/C][C]0.179902583336682[/C][C]0.359805166673364[/C][C]0.820097416663318[/C][/ROW]
[ROW][C]39[/C][C]0.144746313119336[/C][C]0.289492626238672[/C][C]0.855253686880664[/C][/ROW]
[ROW][C]40[/C][C]0.307732512256548[/C][C]0.615465024513095[/C][C]0.692267487743452[/C][/ROW]
[ROW][C]41[/C][C]0.247369048314994[/C][C]0.494738096629988[/C][C]0.752630951685006[/C][/ROW]
[ROW][C]42[/C][C]0.202639562812348[/C][C]0.405279125624697[/C][C]0.797360437187652[/C][/ROW]
[ROW][C]43[/C][C]0.163445499446852[/C][C]0.326890998893704[/C][C]0.836554500553148[/C][/ROW]
[ROW][C]44[/C][C]0.129962907921281[/C][C]0.259925815842561[/C][C]0.870037092078719[/C][/ROW]
[ROW][C]45[/C][C]0.102083357916539[/C][C]0.204166715833077[/C][C]0.897916642083461[/C][/ROW]
[ROW][C]46[/C][C]0.0794698076065833[/C][C]0.158939615213167[/C][C]0.920530192393417[/C][/ROW]
[ROW][C]47[/C][C]0.108114293592708[/C][C]0.216228587185417[/C][C]0.891885706407291[/C][/ROW]
[ROW][C]48[/C][C]0.0854329149336018[/C][C]0.170865829867204[/C][C]0.914567085066398[/C][/ROW]
[ROW][C]49[/C][C]0.0678331488693383[/C][C]0.135666297738677[/C][C]0.932166851130662[/C][/ROW]
[ROW][C]50[/C][C]0.0548416329374703[/C][C]0.109683265874941[/C][C]0.94515836706253[/C][/ROW]
[ROW][C]51[/C][C]0.0474287542565679[/C][C]0.0948575085131358[/C][C]0.952571245743432[/C][/ROW]
[ROW][C]52[/C][C]0.111598307128205[/C][C]0.223196614256409[/C][C]0.888401692871795[/C][/ROW]
[ROW][C]53[/C][C]0.194252062672692[/C][C]0.388504125345384[/C][C]0.805747937327308[/C][/ROW]
[ROW][C]54[/C][C]0.169399265786437[/C][C]0.338798531572874[/C][C]0.830600734213563[/C][/ROW]
[ROW][C]55[/C][C]0.190297034099521[/C][C]0.380594068199042[/C][C]0.809702965900479[/C][/ROW]
[ROW][C]56[/C][C]0.230412675211587[/C][C]0.460825350423174[/C][C]0.769587324788413[/C][/ROW]
[ROW][C]57[/C][C]0.218945877084258[/C][C]0.437891754168515[/C][C]0.781054122915742[/C][/ROW]
[ROW][C]58[/C][C]0.147751953662542[/C][C]0.295503907325085[/C][C]0.852248046337458[/C][/ROW]
[ROW][C]59[/C][C]0.0922107412992531[/C][C]0.184421482598506[/C][C]0.907789258700747[/C][/ROW]
[ROW][C]60[/C][C]0.144547713318948[/C][C]0.289095426637896[/C][C]0.855452286681052[/C][/ROW]
[ROW][C]61[/C][C]0.344342154935886[/C][C]0.688684309871771[/C][C]0.655657845064114[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201514&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201514&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
60.7801108174836930.4397783650326130.219889182516307
70.642804241537630.714391516924740.35719575846237
80.5376290881235660.9247418237528670.462370911876434
90.7621000293715320.4757999412569360.237899970628468
100.7077615361347210.5844769277305570.292238463865279
110.8325582961378310.3348834077243370.167441703862169
120.8010017396951630.3979965206096740.198998260304837
130.760067143102020.4798657137959590.23993285689798
140.7107983334192850.578403333161430.289201666580715
150.6544149745147670.6911700509704660.345585025485233
160.5924997571192950.815000485761410.407500242880705
170.5269887586328110.9460224827343780.473011241367189
180.4600487313757640.9200974627515270.539951268624236
190.4017324624211010.8034649248422020.598267537578899
200.3395992146153990.6791984292307980.660400785384601
210.2813263114177490.5626526228354980.718673688582251
220.2415922866627010.4831845733254010.7584077133373
230.1939378318350690.3878756636701380.806062168164931
240.1524960759639850.3049921519279690.847503924036015
250.1487948605712540.2975897211425090.851205139428746
260.359329323173370.718658646346740.64067067682663
270.5639659650838410.8720680698323190.436034034916159
280.551322931338560.897354137322880.44867706866144
290.4916388926290250.983277785258050.508361107370975
300.4320628836552360.8641257673104720.567937116344764
310.3740339163095180.7480678326190350.625966083690482
320.3188697317776560.6377394635553120.681130268222344
330.2676765402053970.5353530804107940.732323459794603
340.2212848714030560.4425697428061120.778715128596944
350.1802173424534120.3604346849068250.819782657546588
360.14468902067670.2893780413533990.8553109793233
370.1478923272309340.2957846544618690.852107672769066
380.1799025833366820.3598051666733640.820097416663318
390.1447463131193360.2894926262386720.855253686880664
400.3077325122565480.6154650245130950.692267487743452
410.2473690483149940.4947380966299880.752630951685006
420.2026395628123480.4052791256246970.797360437187652
430.1634454994468520.3268909988937040.836554500553148
440.1299629079212810.2599258158425610.870037092078719
450.1020833579165390.2041667158330770.897916642083461
460.07946980760658330.1589396152131670.920530192393417
470.1081142935927080.2162285871854170.891885706407291
480.08543291493360180.1708658298672040.914567085066398
490.06783314886933830.1356662977386770.932166851130662
500.05484163293747030.1096832658749410.94515836706253
510.04742875425656790.09485750851313580.952571245743432
520.1115983071282050.2231966142564090.888401692871795
530.1942520626726920.3885041253453840.805747937327308
540.1693992657864370.3387985315728740.830600734213563
550.1902970340995210.3805940681990420.809702965900479
560.2304126752115870.4608253504231740.769587324788413
570.2189458770842580.4378917541685150.781054122915742
580.1477519536625420.2955039073250850.852248046337458
590.09221074129925310.1844214825985060.907789258700747
600.1445477133189480.2890954266378960.855452286681052
610.3443421549358860.6886843098717710.655657845064114
62100







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0175438596491228NOK
5% type I error level10.0175438596491228OK
10% type I error level20.0350877192982456OK

\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 & 1 & 0.0175438596491228 & NOK \tabularnewline
5% type I error level & 1 & 0.0175438596491228 & OK \tabularnewline
10% type I error level & 2 & 0.0350877192982456 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201514&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]1[/C][C]0.0175438596491228[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0175438596491228[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0350877192982456[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201514&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201514&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 level10.0175438596491228NOK
5% type I error level10.0175438596491228OK
10% type I error level20.0350877192982456OK



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = FALSE ;
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')
}