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 computationWed, 05 Dec 2012 09:37:41 -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/05/t1354718280k9dz7y226me1bl1.htm/, Retrieved Fri, 29 Mar 2024 01:43:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196847, Retrieved Fri, 29 Mar 2024 01:43:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared and McNemar Tests] [] [2010-11-16 14:33:59] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [] [2012-12-05 14:37:41] [0147150632114142a3940e53d29550b4] [Current]
Feedback Forum

Post a new message
Dataseries X:
0	0
0	1
0	0
0	0
1	0
0	1
1	0
0	0
0	1
0	0
0	1
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	1
0	0
0	0
0	1
0	0
0	0
1	1
0	1
0	0
0	1
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	1
1	0
0	0
0	1
1	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
1	0
1	1
0	1
0	0
0	0
0	1
0	0
1	0
1	0
0	1
0	1
0	1
0	0
1	0
0	0
0	0
1	0
0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196847&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196847&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196847&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = + 0.176470588235294 -0.0588235294117647T20[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  +  0.176470588235294 -0.0588235294117647T20[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196847&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  +  0.176470588235294 -0.0588235294117647T20[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196847&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196847&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
CorrectAnalysis[t] = + 0.176470588235294 -0.0588235294117647T20[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1764705882352940.0522133.37980.0012220.000611
T20-0.05882352941176470.104427-0.56330.5751390.287569

\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) & 0.176470588235294 & 0.052213 & 3.3798 & 0.001222 & 0.000611 \tabularnewline
T20 & -0.0588235294117647 & 0.104427 & -0.5633 & 0.575139 & 0.287569 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196847&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]0.176470588235294[/C][C]0.052213[/C][C]3.3798[/C][C]0.001222[/C][C]0.000611[/C][/ROW]
[ROW][C]T20[/C][C]-0.0588235294117647[/C][C]0.104427[/C][C]-0.5633[/C][C]0.575139[/C][C]0.287569[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196847&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196847&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)0.1764705882352940.0522133.37980.0012220.000611
T20-0.05882352941176470.104427-0.56330.5751390.287569







Multiple Linear Regression - Regression Statistics
Multiple R0.0691714463866075
R-squared0.00478468899521532
Adjusted R-squared-0.0102943308684935
F-TEST (value)0.317307692307693
F-TEST (DF numerator)1
F-TEST (DF denominator)66
p-value0.575138960716296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.372877236037654
Sum Squared Residuals9.17647058823529

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0691714463866075 \tabularnewline
R-squared & 0.00478468899521532 \tabularnewline
Adjusted R-squared & -0.0102943308684935 \tabularnewline
F-TEST (value) & 0.317307692307693 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 0.575138960716296 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.372877236037654 \tabularnewline
Sum Squared Residuals & 9.17647058823529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196847&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0691714463866075[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00478468899521532[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0102943308684935[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.317307692307693[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/C][/ROW]
[ROW][C]p-value[/C][C]0.575138960716296[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.372877236037654[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.17647058823529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196847&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196847&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 R0.0691714463866075
R-squared0.00478468899521532
Adjusted R-squared-0.0102943308684935
F-TEST (value)0.317307692307693
F-TEST (DF numerator)1
F-TEST (DF denominator)66
p-value0.575138960716296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.372877236037654
Sum Squared Residuals9.17647058823529







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.176470588235294-0.176470588235294
200.117647058823529-0.117647058823529
300.176470588235294-0.176470588235294
400.176470588235294-0.176470588235294
510.1764705882352940.823529411764706
600.117647058823529-0.117647058823529
710.1764705882352940.823529411764706
800.176470588235294-0.176470588235294
900.117647058823529-0.117647058823529
1000.176470588235294-0.176470588235294
1100.117647058823529-0.117647058823529
1200.176470588235294-0.176470588235294
1300.176470588235294-0.176470588235294
1400.176470588235294-0.176470588235294
1500.176470588235294-0.176470588235294
1600.176470588235294-0.176470588235294
1700.176470588235294-0.176470588235294
1800.176470588235294-0.176470588235294
1900.117647058823529-0.117647058823529
2000.176470588235294-0.176470588235294
2100.176470588235294-0.176470588235294
2200.117647058823529-0.117647058823529
2300.176470588235294-0.176470588235294
2400.176470588235294-0.176470588235294
2510.1176470588235290.882352941176471
2600.117647058823529-0.117647058823529
2700.176470588235294-0.176470588235294
2800.117647058823529-0.117647058823529
2900.176470588235294-0.176470588235294
3000.176470588235294-0.176470588235294
3100.176470588235294-0.176470588235294
3200.176470588235294-0.176470588235294
3300.176470588235294-0.176470588235294
3400.176470588235294-0.176470588235294
3500.176470588235294-0.176470588235294
3600.176470588235294-0.176470588235294
3700.117647058823529-0.117647058823529
3810.1764705882352940.823529411764706
3900.176470588235294-0.176470588235294
4000.117647058823529-0.117647058823529
4110.1764705882352940.823529411764706
4200.176470588235294-0.176470588235294
4300.176470588235294-0.176470588235294
4400.176470588235294-0.176470588235294
4500.176470588235294-0.176470588235294
4600.176470588235294-0.176470588235294
4700.176470588235294-0.176470588235294
4800.176470588235294-0.176470588235294
4900.176470588235294-0.176470588235294
5000.176470588235294-0.176470588235294
5110.1764705882352940.823529411764706
5210.1176470588235290.882352941176471
5300.117647058823529-0.117647058823529
5400.176470588235294-0.176470588235294
5500.176470588235294-0.176470588235294
5600.117647058823529-0.117647058823529
5700.176470588235294-0.176470588235294
5810.1764705882352940.823529411764706
5910.1764705882352940.823529411764706
6000.117647058823529-0.117647058823529
6100.117647058823529-0.117647058823529
6200.117647058823529-0.117647058823529
6300.176470588235294-0.176470588235294
6410.1764705882352940.823529411764706
6500.176470588235294-0.176470588235294
6600.176470588235294-0.176470588235294
6710.1764705882352940.823529411764706
6800.176470588235294-0.176470588235294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
2 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
3 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
4 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
5 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
6 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
7 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
8 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
9 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
10 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
11 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
12 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
13 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
14 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
15 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
16 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
17 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
18 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
19 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
20 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
21 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
22 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
23 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
24 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
25 & 1 & 0.117647058823529 & 0.882352941176471 \tabularnewline
26 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
27 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
28 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
29 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
30 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
31 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
32 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
33 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
34 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
35 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
36 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
37 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
38 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
39 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
40 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
41 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
42 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
43 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
44 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
45 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
46 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
47 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
48 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
49 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
50 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
51 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
52 & 1 & 0.117647058823529 & 0.882352941176471 \tabularnewline
53 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
54 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
55 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
56 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
57 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
58 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
59 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
60 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
61 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
62 & 0 & 0.117647058823529 & -0.117647058823529 \tabularnewline
63 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
64 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
65 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
66 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
67 & 1 & 0.176470588235294 & 0.823529411764706 \tabularnewline
68 & 0 & 0.176470588235294 & -0.176470588235294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196847&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]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.117647058823529[/C][C]0.882352941176471[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.117647058823529[/C][C]0.882352941176471[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.117647058823529[/C][C]-0.117647058823529[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.176470588235294[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.176470588235294[/C][C]-0.176470588235294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196847&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196847&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
100.176470588235294-0.176470588235294
200.117647058823529-0.117647058823529
300.176470588235294-0.176470588235294
400.176470588235294-0.176470588235294
510.1764705882352940.823529411764706
600.117647058823529-0.117647058823529
710.1764705882352940.823529411764706
800.176470588235294-0.176470588235294
900.117647058823529-0.117647058823529
1000.176470588235294-0.176470588235294
1100.117647058823529-0.117647058823529
1200.176470588235294-0.176470588235294
1300.176470588235294-0.176470588235294
1400.176470588235294-0.176470588235294
1500.176470588235294-0.176470588235294
1600.176470588235294-0.176470588235294
1700.176470588235294-0.176470588235294
1800.176470588235294-0.176470588235294
1900.117647058823529-0.117647058823529
2000.176470588235294-0.176470588235294
2100.176470588235294-0.176470588235294
2200.117647058823529-0.117647058823529
2300.176470588235294-0.176470588235294
2400.176470588235294-0.176470588235294
2510.1176470588235290.882352941176471
2600.117647058823529-0.117647058823529
2700.176470588235294-0.176470588235294
2800.117647058823529-0.117647058823529
2900.176470588235294-0.176470588235294
3000.176470588235294-0.176470588235294
3100.176470588235294-0.176470588235294
3200.176470588235294-0.176470588235294
3300.176470588235294-0.176470588235294
3400.176470588235294-0.176470588235294
3500.176470588235294-0.176470588235294
3600.176470588235294-0.176470588235294
3700.117647058823529-0.117647058823529
3810.1764705882352940.823529411764706
3900.176470588235294-0.176470588235294
4000.117647058823529-0.117647058823529
4110.1764705882352940.823529411764706
4200.176470588235294-0.176470588235294
4300.176470588235294-0.176470588235294
4400.176470588235294-0.176470588235294
4500.176470588235294-0.176470588235294
4600.176470588235294-0.176470588235294
4700.176470588235294-0.176470588235294
4800.176470588235294-0.176470588235294
4900.176470588235294-0.176470588235294
5000.176470588235294-0.176470588235294
5110.1764705882352940.823529411764706
5210.1176470588235290.882352941176471
5300.117647058823529-0.117647058823529
5400.176470588235294-0.176470588235294
5500.176470588235294-0.176470588235294
5600.117647058823529-0.117647058823529
5700.176470588235294-0.176470588235294
5810.1764705882352940.823529411764706
5910.1764705882352940.823529411764706
6000.117647058823529-0.117647058823529
6100.117647058823529-0.117647058823529
6200.117647058823529-0.117647058823529
6300.176470588235294-0.176470588235294
6410.1764705882352940.823529411764706
6500.176470588235294-0.176470588235294
6600.176470588235294-0.176470588235294
6710.1764705882352940.823529411764706
6800.176470588235294-0.176470588235294







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.8466794215435380.3066411569129250.153320578456462
60.735735319473490.528529361053020.26426468052651
70.8823221500133640.2353556999732720.117677849986636
80.8592762126874350.281447574625130.140723787312565
90.7861988141598530.4276023716802940.213801185840147
100.7451399150915520.5097201698168950.254860084908448
110.6562924549387450.687415090122510.343707545061255
120.6008647880275540.7982704239448910.399135211972446
130.5376545865298740.9246908269402510.462345413470126
140.4702675474573730.9405350949147460.529732452542627
150.4019922355906730.8039844711813450.598007764409327
160.33575625056830.67151250113660.6642437494317
170.2739720449625510.5479440899251030.726027955037449
180.2184053438859860.4368106877719710.781594656114014
190.1629027073037810.3258054146075620.837097292696219
200.1240097091205720.2480194182411450.875990290879428
210.09225385823098220.1845077164619640.907746141769018
220.06412014291609740.1282402858321950.935879857083903
230.04559849285233540.09119698570467080.954401507147665
240.03172653303003690.06345306606007380.968273466969963
250.2144481981796980.4288963963593960.785551801820302
260.1689820337916010.3379640675832020.831017966208399
270.1323287851107020.2646575702214040.867671214889298
280.09981679725928030.1996335945185610.90018320274072
290.07535694467661120.1507138893532220.924643055323389
300.05594633128970180.1118926625794040.944053668710298
310.04087908968323280.08175817936646560.959120910316767
320.02942677679983550.0588535535996710.970573223200164
330.02089376356553060.04178752713106130.979106236434469
340.01465379109704910.02930758219409810.985346208902951
350.01016944409906260.02033888819812510.989830555900937
360.006997920768409120.01399584153681820.993002079231591
370.004415315417118430.008830630834236860.995584684582882
380.03494426336666330.06988852673332660.965055736633337
390.02582585693283590.05165171386567170.974174143067164
400.01759052710021790.03518105420043580.982409472899782
410.07711397995321260.1542279599064250.922886020046787
420.05945714263096920.1189142852619380.940542857369031
430.04539515096459880.09079030192919760.954604849035401
440.03440100479809780.06880200959619570.965598995201902
450.02595702246505660.05191404493011310.974042977534943
460.01958339832334780.03916679664669550.980416601676652
470.01485693358733730.02971386717467450.985143066412663
480.01142136463281930.02284272926563850.988578635367181
490.008991930115694640.01798386023138930.991008069884305
500.007358276160869820.01471655232173960.99264172383913
510.02697952712721350.05395905425442710.973020472872786
520.1440660138408760.2881320276817510.855933986159124
530.1030876080965390.2061752161930780.896912391903461
540.09153233757099030.1830646751419810.90846766242901
550.08617696930085420.1723539386017080.913823030699146
560.05640463203254410.1128092640650880.943595367967456
570.05763866622369360.1152773324473870.942361333776306
580.1092263596758810.2184527193517610.890773640324119
590.2138011858401470.4276023716802940.786198814159853
600.1407237873125650.2814475746251310.859276212687435
610.08414261752602290.1682852350520460.915857382473977
620.04454681758105870.08909363516211740.955453182418941
630.03056311264249180.06112622528498360.969436887357508

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.846679421543538 & 0.306641156912925 & 0.153320578456462 \tabularnewline
6 & 0.73573531947349 & 0.52852936105302 & 0.26426468052651 \tabularnewline
7 & 0.882322150013364 & 0.235355699973272 & 0.117677849986636 \tabularnewline
8 & 0.859276212687435 & 0.28144757462513 & 0.140723787312565 \tabularnewline
9 & 0.786198814159853 & 0.427602371680294 & 0.213801185840147 \tabularnewline
10 & 0.745139915091552 & 0.509720169816895 & 0.254860084908448 \tabularnewline
11 & 0.656292454938745 & 0.68741509012251 & 0.343707545061255 \tabularnewline
12 & 0.600864788027554 & 0.798270423944891 & 0.399135211972446 \tabularnewline
13 & 0.537654586529874 & 0.924690826940251 & 0.462345413470126 \tabularnewline
14 & 0.470267547457373 & 0.940535094914746 & 0.529732452542627 \tabularnewline
15 & 0.401992235590673 & 0.803984471181345 & 0.598007764409327 \tabularnewline
16 & 0.3357562505683 & 0.6715125011366 & 0.6642437494317 \tabularnewline
17 & 0.273972044962551 & 0.547944089925103 & 0.726027955037449 \tabularnewline
18 & 0.218405343885986 & 0.436810687771971 & 0.781594656114014 \tabularnewline
19 & 0.162902707303781 & 0.325805414607562 & 0.837097292696219 \tabularnewline
20 & 0.124009709120572 & 0.248019418241145 & 0.875990290879428 \tabularnewline
21 & 0.0922538582309822 & 0.184507716461964 & 0.907746141769018 \tabularnewline
22 & 0.0641201429160974 & 0.128240285832195 & 0.935879857083903 \tabularnewline
23 & 0.0455984928523354 & 0.0911969857046708 & 0.954401507147665 \tabularnewline
24 & 0.0317265330300369 & 0.0634530660600738 & 0.968273466969963 \tabularnewline
25 & 0.214448198179698 & 0.428896396359396 & 0.785551801820302 \tabularnewline
26 & 0.168982033791601 & 0.337964067583202 & 0.831017966208399 \tabularnewline
27 & 0.132328785110702 & 0.264657570221404 & 0.867671214889298 \tabularnewline
28 & 0.0998167972592803 & 0.199633594518561 & 0.90018320274072 \tabularnewline
29 & 0.0753569446766112 & 0.150713889353222 & 0.924643055323389 \tabularnewline
30 & 0.0559463312897018 & 0.111892662579404 & 0.944053668710298 \tabularnewline
31 & 0.0408790896832328 & 0.0817581793664656 & 0.959120910316767 \tabularnewline
32 & 0.0294267767998355 & 0.058853553599671 & 0.970573223200164 \tabularnewline
33 & 0.0208937635655306 & 0.0417875271310613 & 0.979106236434469 \tabularnewline
34 & 0.0146537910970491 & 0.0293075821940981 & 0.985346208902951 \tabularnewline
35 & 0.0101694440990626 & 0.0203388881981251 & 0.989830555900937 \tabularnewline
36 & 0.00699792076840912 & 0.0139958415368182 & 0.993002079231591 \tabularnewline
37 & 0.00441531541711843 & 0.00883063083423686 & 0.995584684582882 \tabularnewline
38 & 0.0349442633666633 & 0.0698885267333266 & 0.965055736633337 \tabularnewline
39 & 0.0258258569328359 & 0.0516517138656717 & 0.974174143067164 \tabularnewline
40 & 0.0175905271002179 & 0.0351810542004358 & 0.982409472899782 \tabularnewline
41 & 0.0771139799532126 & 0.154227959906425 & 0.922886020046787 \tabularnewline
42 & 0.0594571426309692 & 0.118914285261938 & 0.940542857369031 \tabularnewline
43 & 0.0453951509645988 & 0.0907903019291976 & 0.954604849035401 \tabularnewline
44 & 0.0344010047980978 & 0.0688020095961957 & 0.965598995201902 \tabularnewline
45 & 0.0259570224650566 & 0.0519140449301131 & 0.974042977534943 \tabularnewline
46 & 0.0195833983233478 & 0.0391667966466955 & 0.980416601676652 \tabularnewline
47 & 0.0148569335873373 & 0.0297138671746745 & 0.985143066412663 \tabularnewline
48 & 0.0114213646328193 & 0.0228427292656385 & 0.988578635367181 \tabularnewline
49 & 0.00899193011569464 & 0.0179838602313893 & 0.991008069884305 \tabularnewline
50 & 0.00735827616086982 & 0.0147165523217396 & 0.99264172383913 \tabularnewline
51 & 0.0269795271272135 & 0.0539590542544271 & 0.973020472872786 \tabularnewline
52 & 0.144066013840876 & 0.288132027681751 & 0.855933986159124 \tabularnewline
53 & 0.103087608096539 & 0.206175216193078 & 0.896912391903461 \tabularnewline
54 & 0.0915323375709903 & 0.183064675141981 & 0.90846766242901 \tabularnewline
55 & 0.0861769693008542 & 0.172353938601708 & 0.913823030699146 \tabularnewline
56 & 0.0564046320325441 & 0.112809264065088 & 0.943595367967456 \tabularnewline
57 & 0.0576386662236936 & 0.115277332447387 & 0.942361333776306 \tabularnewline
58 & 0.109226359675881 & 0.218452719351761 & 0.890773640324119 \tabularnewline
59 & 0.213801185840147 & 0.427602371680294 & 0.786198814159853 \tabularnewline
60 & 0.140723787312565 & 0.281447574625131 & 0.859276212687435 \tabularnewline
61 & 0.0841426175260229 & 0.168285235052046 & 0.915857382473977 \tabularnewline
62 & 0.0445468175810587 & 0.0890936351621174 & 0.955453182418941 \tabularnewline
63 & 0.0305631126424918 & 0.0611262252849836 & 0.969436887357508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196847&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]0.846679421543538[/C][C]0.306641156912925[/C][C]0.153320578456462[/C][/ROW]
[ROW][C]6[/C][C]0.73573531947349[/C][C]0.52852936105302[/C][C]0.26426468052651[/C][/ROW]
[ROW][C]7[/C][C]0.882322150013364[/C][C]0.235355699973272[/C][C]0.117677849986636[/C][/ROW]
[ROW][C]8[/C][C]0.859276212687435[/C][C]0.28144757462513[/C][C]0.140723787312565[/C][/ROW]
[ROW][C]9[/C][C]0.786198814159853[/C][C]0.427602371680294[/C][C]0.213801185840147[/C][/ROW]
[ROW][C]10[/C][C]0.745139915091552[/C][C]0.509720169816895[/C][C]0.254860084908448[/C][/ROW]
[ROW][C]11[/C][C]0.656292454938745[/C][C]0.68741509012251[/C][C]0.343707545061255[/C][/ROW]
[ROW][C]12[/C][C]0.600864788027554[/C][C]0.798270423944891[/C][C]0.399135211972446[/C][/ROW]
[ROW][C]13[/C][C]0.537654586529874[/C][C]0.924690826940251[/C][C]0.462345413470126[/C][/ROW]
[ROW][C]14[/C][C]0.470267547457373[/C][C]0.940535094914746[/C][C]0.529732452542627[/C][/ROW]
[ROW][C]15[/C][C]0.401992235590673[/C][C]0.803984471181345[/C][C]0.598007764409327[/C][/ROW]
[ROW][C]16[/C][C]0.3357562505683[/C][C]0.6715125011366[/C][C]0.6642437494317[/C][/ROW]
[ROW][C]17[/C][C]0.273972044962551[/C][C]0.547944089925103[/C][C]0.726027955037449[/C][/ROW]
[ROW][C]18[/C][C]0.218405343885986[/C][C]0.436810687771971[/C][C]0.781594656114014[/C][/ROW]
[ROW][C]19[/C][C]0.162902707303781[/C][C]0.325805414607562[/C][C]0.837097292696219[/C][/ROW]
[ROW][C]20[/C][C]0.124009709120572[/C][C]0.248019418241145[/C][C]0.875990290879428[/C][/ROW]
[ROW][C]21[/C][C]0.0922538582309822[/C][C]0.184507716461964[/C][C]0.907746141769018[/C][/ROW]
[ROW][C]22[/C][C]0.0641201429160974[/C][C]0.128240285832195[/C][C]0.935879857083903[/C][/ROW]
[ROW][C]23[/C][C]0.0455984928523354[/C][C]0.0911969857046708[/C][C]0.954401507147665[/C][/ROW]
[ROW][C]24[/C][C]0.0317265330300369[/C][C]0.0634530660600738[/C][C]0.968273466969963[/C][/ROW]
[ROW][C]25[/C][C]0.214448198179698[/C][C]0.428896396359396[/C][C]0.785551801820302[/C][/ROW]
[ROW][C]26[/C][C]0.168982033791601[/C][C]0.337964067583202[/C][C]0.831017966208399[/C][/ROW]
[ROW][C]27[/C][C]0.132328785110702[/C][C]0.264657570221404[/C][C]0.867671214889298[/C][/ROW]
[ROW][C]28[/C][C]0.0998167972592803[/C][C]0.199633594518561[/C][C]0.90018320274072[/C][/ROW]
[ROW][C]29[/C][C]0.0753569446766112[/C][C]0.150713889353222[/C][C]0.924643055323389[/C][/ROW]
[ROW][C]30[/C][C]0.0559463312897018[/C][C]0.111892662579404[/C][C]0.944053668710298[/C][/ROW]
[ROW][C]31[/C][C]0.0408790896832328[/C][C]0.0817581793664656[/C][C]0.959120910316767[/C][/ROW]
[ROW][C]32[/C][C]0.0294267767998355[/C][C]0.058853553599671[/C][C]0.970573223200164[/C][/ROW]
[ROW][C]33[/C][C]0.0208937635655306[/C][C]0.0417875271310613[/C][C]0.979106236434469[/C][/ROW]
[ROW][C]34[/C][C]0.0146537910970491[/C][C]0.0293075821940981[/C][C]0.985346208902951[/C][/ROW]
[ROW][C]35[/C][C]0.0101694440990626[/C][C]0.0203388881981251[/C][C]0.989830555900937[/C][/ROW]
[ROW][C]36[/C][C]0.00699792076840912[/C][C]0.0139958415368182[/C][C]0.993002079231591[/C][/ROW]
[ROW][C]37[/C][C]0.00441531541711843[/C][C]0.00883063083423686[/C][C]0.995584684582882[/C][/ROW]
[ROW][C]38[/C][C]0.0349442633666633[/C][C]0.0698885267333266[/C][C]0.965055736633337[/C][/ROW]
[ROW][C]39[/C][C]0.0258258569328359[/C][C]0.0516517138656717[/C][C]0.974174143067164[/C][/ROW]
[ROW][C]40[/C][C]0.0175905271002179[/C][C]0.0351810542004358[/C][C]0.982409472899782[/C][/ROW]
[ROW][C]41[/C][C]0.0771139799532126[/C][C]0.154227959906425[/C][C]0.922886020046787[/C][/ROW]
[ROW][C]42[/C][C]0.0594571426309692[/C][C]0.118914285261938[/C][C]0.940542857369031[/C][/ROW]
[ROW][C]43[/C][C]0.0453951509645988[/C][C]0.0907903019291976[/C][C]0.954604849035401[/C][/ROW]
[ROW][C]44[/C][C]0.0344010047980978[/C][C]0.0688020095961957[/C][C]0.965598995201902[/C][/ROW]
[ROW][C]45[/C][C]0.0259570224650566[/C][C]0.0519140449301131[/C][C]0.974042977534943[/C][/ROW]
[ROW][C]46[/C][C]0.0195833983233478[/C][C]0.0391667966466955[/C][C]0.980416601676652[/C][/ROW]
[ROW][C]47[/C][C]0.0148569335873373[/C][C]0.0297138671746745[/C][C]0.985143066412663[/C][/ROW]
[ROW][C]48[/C][C]0.0114213646328193[/C][C]0.0228427292656385[/C][C]0.988578635367181[/C][/ROW]
[ROW][C]49[/C][C]0.00899193011569464[/C][C]0.0179838602313893[/C][C]0.991008069884305[/C][/ROW]
[ROW][C]50[/C][C]0.00735827616086982[/C][C]0.0147165523217396[/C][C]0.99264172383913[/C][/ROW]
[ROW][C]51[/C][C]0.0269795271272135[/C][C]0.0539590542544271[/C][C]0.973020472872786[/C][/ROW]
[ROW][C]52[/C][C]0.144066013840876[/C][C]0.288132027681751[/C][C]0.855933986159124[/C][/ROW]
[ROW][C]53[/C][C]0.103087608096539[/C][C]0.206175216193078[/C][C]0.896912391903461[/C][/ROW]
[ROW][C]54[/C][C]0.0915323375709903[/C][C]0.183064675141981[/C][C]0.90846766242901[/C][/ROW]
[ROW][C]55[/C][C]0.0861769693008542[/C][C]0.172353938601708[/C][C]0.913823030699146[/C][/ROW]
[ROW][C]56[/C][C]0.0564046320325441[/C][C]0.112809264065088[/C][C]0.943595367967456[/C][/ROW]
[ROW][C]57[/C][C]0.0576386662236936[/C][C]0.115277332447387[/C][C]0.942361333776306[/C][/ROW]
[ROW][C]58[/C][C]0.109226359675881[/C][C]0.218452719351761[/C][C]0.890773640324119[/C][/ROW]
[ROW][C]59[/C][C]0.213801185840147[/C][C]0.427602371680294[/C][C]0.786198814159853[/C][/ROW]
[ROW][C]60[/C][C]0.140723787312565[/C][C]0.281447574625131[/C][C]0.859276212687435[/C][/ROW]
[ROW][C]61[/C][C]0.0841426175260229[/C][C]0.168285235052046[/C][C]0.915857382473977[/C][/ROW]
[ROW][C]62[/C][C]0.0445468175810587[/C][C]0.0890936351621174[/C][C]0.955453182418941[/C][/ROW]
[ROW][C]63[/C][C]0.0305631126424918[/C][C]0.0611262252849836[/C][C]0.969436887357508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196847&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196847&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
50.8466794215435380.3066411569129250.153320578456462
60.735735319473490.528529361053020.26426468052651
70.8823221500133640.2353556999732720.117677849986636
80.8592762126874350.281447574625130.140723787312565
90.7861988141598530.4276023716802940.213801185840147
100.7451399150915520.5097201698168950.254860084908448
110.6562924549387450.687415090122510.343707545061255
120.6008647880275540.7982704239448910.399135211972446
130.5376545865298740.9246908269402510.462345413470126
140.4702675474573730.9405350949147460.529732452542627
150.4019922355906730.8039844711813450.598007764409327
160.33575625056830.67151250113660.6642437494317
170.2739720449625510.5479440899251030.726027955037449
180.2184053438859860.4368106877719710.781594656114014
190.1629027073037810.3258054146075620.837097292696219
200.1240097091205720.2480194182411450.875990290879428
210.09225385823098220.1845077164619640.907746141769018
220.06412014291609740.1282402858321950.935879857083903
230.04559849285233540.09119698570467080.954401507147665
240.03172653303003690.06345306606007380.968273466969963
250.2144481981796980.4288963963593960.785551801820302
260.1689820337916010.3379640675832020.831017966208399
270.1323287851107020.2646575702214040.867671214889298
280.09981679725928030.1996335945185610.90018320274072
290.07535694467661120.1507138893532220.924643055323389
300.05594633128970180.1118926625794040.944053668710298
310.04087908968323280.08175817936646560.959120910316767
320.02942677679983550.0588535535996710.970573223200164
330.02089376356553060.04178752713106130.979106236434469
340.01465379109704910.02930758219409810.985346208902951
350.01016944409906260.02033888819812510.989830555900937
360.006997920768409120.01399584153681820.993002079231591
370.004415315417118430.008830630834236860.995584684582882
380.03494426336666330.06988852673332660.965055736633337
390.02582585693283590.05165171386567170.974174143067164
400.01759052710021790.03518105420043580.982409472899782
410.07711397995321260.1542279599064250.922886020046787
420.05945714263096920.1189142852619380.940542857369031
430.04539515096459880.09079030192919760.954604849035401
440.03440100479809780.06880200959619570.965598995201902
450.02595702246505660.05191404493011310.974042977534943
460.01958339832334780.03916679664669550.980416601676652
470.01485693358733730.02971386717467450.985143066412663
480.01142136463281930.02284272926563850.988578635367181
490.008991930115694640.01798386023138930.991008069884305
500.007358276160869820.01471655232173960.99264172383913
510.02697952712721350.05395905425442710.973020472872786
520.1440660138408760.2881320276817510.855933986159124
530.1030876080965390.2061752161930780.896912391903461
540.09153233757099030.1830646751419810.90846766242901
550.08617696930085420.1723539386017080.913823030699146
560.05640463203254410.1128092640650880.943595367967456
570.05763866622369360.1152773324473870.942361333776306
580.1092263596758810.2184527193517610.890773640324119
590.2138011858401470.4276023716802940.786198814159853
600.1407237873125650.2814475746251310.859276212687435
610.08414261752602290.1682852350520460.915857382473977
620.04454681758105870.08909363516211740.955453182418941
630.03056311264249180.06112622528498360.969436887357508







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0169491525423729NOK
5% type I error level110.186440677966102NOK
10% type I error level230.389830508474576NOK

\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 & 0.0169491525423729 & NOK \tabularnewline
5% type I error level & 11 & 0.186440677966102 & NOK \tabularnewline
10% type I error level & 23 & 0.389830508474576 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196847&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]0.0169491525423729[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]11[/C][C]0.186440677966102[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.389830508474576[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196847&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196847&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 level10.0169491525423729NOK
5% type I error level110.186440677966102NOK
10% type I error level230.389830508474576NOK



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')
}