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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 04:43:02 -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/21/t1356083000bwe933xnm4hbrjv.htm/, Retrieved Fri, 29 Mar 2024 15:05:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203384, Retrieved Fri, 29 Mar 2024 15:05:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-12-21 09:17:53] [c6861060a9fe16fe372e7d046f8d5b70]
- R       [Multiple Regression] [] [2012-12-21 09:43:02] [90f4fc95bc23bd40c615363dd079f863] [Current]
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Dataseries X:
1	0
2	0
2	0
2	0
2	0
2	0
2	0
1	0
2	0
2	0
1	0
2	0
2	0
1	0
2	0
1	0
1	1
1	0
2	0
1	1
2	0
2	0
2	0
2	0
1	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
1	0
2	0
2	0
1	0
2	0
2	0
1	0
2	1
2	0
2	0
1	0
2	0
2	0
2	0
2	0
2	0
2	0
1	0
1	1
2	0
2	1
2	0
1	0
2	0
2	0
2	0
1	1
1	0
2	0
2	0
1	0
2	0
2	0
1	1
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
1	0
2	0
2	0
1	1
1	0
2	0
2	0
2	0
2	1
2	0
2	0
4	0
3	0
4	0
4	0
4	0
3	0
4	0
4	0
3	0
4	0
3	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
3	0
4	0
4	0
3	0
4	0
4	0
3	0
3	0
4	0
3	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
3	0
4	0
4	0
3	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
3	0
3	0
4	0
4	1
3	0
4	0
4	0
4	0
3	0
3	0
3	0
4	0
4	0
4	0
4	1
4	1
4	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203384&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203384&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203384&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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = + 0.184485190409027 -0.0406205923836389Treatment[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  +  0.184485190409027 -0.0406205923836389Treatment[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203384&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  +  0.184485190409027 -0.0406205923836389Treatment[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203384&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203384&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] = + 0.184485190409027 -0.0406205923836389Treatment[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1844851904090270.0557273.31050.0011630.000582
Treatment-0.04062059238363890.019607-2.07170.039980.01999

\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) & 0.184485190409027 & 0.055727 & 3.3105 & 0.001163 & 0.000582 \tabularnewline
Treatment & -0.0406205923836389 & 0.019607 & -2.0717 & 0.03998 & 0.01999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203384&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]0.184485190409027[/C][C]0.055727[/C][C]3.3105[/C][C]0.001163[/C][C]0.000582[/C][/ROW]
[ROW][C]Treatment[/C][C]-0.0406205923836389[/C][C]0.019607[/C][C]-2.0717[/C][C]0.03998[/C][C]0.01999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203384&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203384&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)0.1844851904090270.0557273.31050.0011630.000582
Treatment-0.04062059238363890.019607-2.07170.039980.01999







Multiple Linear Regression - Regression Statistics
Multiple R0.165716049118418
R-squared0.0274618089354179
Adjusted R-squared0.0210635313626245
F-TEST (value)4.29206276579664
F-TEST (DF numerator)1
F-TEST (DF denominator)152
p-value0.0399804472617082
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.266076165543606
Sum Squared Residuals10.761071932299

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.165716049118418 \tabularnewline
R-squared & 0.0274618089354179 \tabularnewline
Adjusted R-squared & 0.0210635313626245 \tabularnewline
F-TEST (value) & 4.29206276579664 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0.0399804472617082 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.266076165543606 \tabularnewline
Sum Squared Residuals & 10.761071932299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203384&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.165716049118418[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0274618089354179[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0210635313626245[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.29206276579664[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]0.0399804472617082[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.266076165543606[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.761071932299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203384&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203384&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 R0.165716049118418
R-squared0.0274618089354179
Adjusted R-squared0.0210635313626245
F-TEST (value)4.29206276579664
F-TEST (DF numerator)1
F-TEST (DF denominator)152
p-value0.0399804472617082
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.266076165543606
Sum Squared Residuals10.761071932299







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.143864598025388-0.143864598025388
200.103244005641749-0.103244005641749
300.103244005641749-0.103244005641749
400.103244005641749-0.103244005641749
500.103244005641749-0.103244005641749
600.103244005641749-0.103244005641749
700.103244005641749-0.103244005641749
800.143864598025388-0.143864598025388
900.103244005641749-0.103244005641749
1000.103244005641749-0.103244005641749
1100.143864598025388-0.143864598025388
1200.103244005641749-0.103244005641749
1300.103244005641749-0.103244005641749
1400.143864598025388-0.143864598025388
1500.103244005641749-0.103244005641749
1600.143864598025388-0.143864598025388
1710.1438645980253880.856135401974612
1800.143864598025388-0.143864598025388
1900.103244005641749-0.103244005641749
2010.1438645980253880.856135401974612
2100.103244005641749-0.103244005641749
2200.103244005641749-0.103244005641749
2300.103244005641749-0.103244005641749
2400.103244005641749-0.103244005641749
2500.143864598025388-0.143864598025388
2600.103244005641749-0.103244005641749
2700.103244005641749-0.103244005641749
2800.103244005641749-0.103244005641749
2900.103244005641749-0.103244005641749
3000.103244005641749-0.103244005641749
3100.103244005641749-0.103244005641749
3200.103244005641749-0.103244005641749
3300.103244005641749-0.103244005641749
3400.143864598025388-0.143864598025388
3500.103244005641749-0.103244005641749
3600.103244005641749-0.103244005641749
3700.143864598025388-0.143864598025388
3800.103244005641749-0.103244005641749
3900.103244005641749-0.103244005641749
4000.143864598025388-0.143864598025388
4110.1032440056417490.896755994358251
4200.103244005641749-0.103244005641749
4300.103244005641749-0.103244005641749
4400.143864598025388-0.143864598025388
4500.103244005641749-0.103244005641749
4600.103244005641749-0.103244005641749
4700.103244005641749-0.103244005641749
4800.103244005641749-0.103244005641749
4900.103244005641749-0.103244005641749
5000.103244005641749-0.103244005641749
5100.143864598025388-0.143864598025388
5210.1438645980253880.856135401974612
5300.103244005641749-0.103244005641749
5410.1032440056417490.896755994358251
5500.103244005641749-0.103244005641749
5600.143864598025388-0.143864598025388
5700.103244005641749-0.103244005641749
5800.103244005641749-0.103244005641749
5900.103244005641749-0.103244005641749
6010.1438645980253880.856135401974612
6100.143864598025388-0.143864598025388
6200.103244005641749-0.103244005641749
6300.103244005641749-0.103244005641749
6400.143864598025388-0.143864598025388
6500.103244005641749-0.103244005641749
6600.103244005641749-0.103244005641749
6710.1438645980253880.856135401974612
6800.103244005641749-0.103244005641749
6900.103244005641749-0.103244005641749
7000.103244005641749-0.103244005641749
7100.103244005641749-0.103244005641749
7200.103244005641749-0.103244005641749
7300.103244005641749-0.103244005641749
7400.103244005641749-0.103244005641749
7500.103244005641749-0.103244005641749
7600.143864598025388-0.143864598025388
7700.103244005641749-0.103244005641749
7800.103244005641749-0.103244005641749
7910.1438645980253880.856135401974612
8000.143864598025388-0.143864598025388
8100.103244005641749-0.103244005641749
8200.103244005641749-0.103244005641749
8300.103244005641749-0.103244005641749
8410.1032440056417490.896755994358251
8500.103244005641749-0.103244005641749
8600.103244005641749-0.103244005641749
8700.0220028208744711-0.0220028208744711
8800.06262341325811-0.06262341325811
8900.0220028208744711-0.0220028208744711
9000.0220028208744711-0.0220028208744711
9100.0220028208744711-0.0220028208744711
9200.06262341325811-0.06262341325811
9300.0220028208744711-0.0220028208744711
9400.0220028208744711-0.0220028208744711
9500.06262341325811-0.06262341325811
9600.0220028208744711-0.0220028208744711
9700.06262341325811-0.06262341325811
9800.0220028208744711-0.0220028208744711
9900.0220028208744711-0.0220028208744711
10000.0220028208744711-0.0220028208744711
10100.0220028208744711-0.0220028208744711
10200.0220028208744711-0.0220028208744711
10300.0220028208744711-0.0220028208744711
10400.0220028208744711-0.0220028208744711
10500.06262341325811-0.06262341325811
10600.0220028208744711-0.0220028208744711
10700.0220028208744711-0.0220028208744711
10800.06262341325811-0.06262341325811
10900.0220028208744711-0.0220028208744711
11000.0220028208744711-0.0220028208744711
11100.06262341325811-0.06262341325811
11200.06262341325811-0.06262341325811
11300.0220028208744711-0.0220028208744711
11400.06262341325811-0.06262341325811
11500.0220028208744711-0.0220028208744711
11600.0220028208744711-0.0220028208744711
11700.0220028208744711-0.0220028208744711
11800.0220028208744711-0.0220028208744711
11900.0220028208744711-0.0220028208744711
12000.0220028208744711-0.0220028208744711
12100.0220028208744711-0.0220028208744711
12200.0220028208744711-0.0220028208744711
12300.06262341325811-0.06262341325811
12400.0220028208744711-0.0220028208744711
12500.0220028208744711-0.0220028208744711
12600.06262341325811-0.06262341325811
12700.0220028208744711-0.0220028208744711
12800.0220028208744711-0.0220028208744711
12900.0220028208744711-0.0220028208744711
13000.0220028208744711-0.0220028208744711
13100.0220028208744711-0.0220028208744711
13200.0220028208744711-0.0220028208744711
13300.0220028208744711-0.0220028208744711
13400.0220028208744711-0.0220028208744711
13500.0220028208744711-0.0220028208744711
13600.0220028208744711-0.0220028208744711
13700.0220028208744711-0.0220028208744711
13800.06262341325811-0.06262341325811
13900.06262341325811-0.06262341325811
14000.0220028208744711-0.0220028208744711
14110.02200282087447110.977997179125529
14200.06262341325811-0.06262341325811
14300.0220028208744711-0.0220028208744711
14400.0220028208744711-0.0220028208744711
14500.0220028208744711-0.0220028208744711
14600.06262341325811-0.06262341325811
14700.06262341325811-0.06262341325811
14800.06262341325811-0.06262341325811
14900.0220028208744711-0.0220028208744711
15000.0220028208744711-0.0220028208744711
15100.0220028208744711-0.0220028208744711
15210.02200282087447110.977997179125529
15310.02200282087447110.977997179125529
15400.0220028208744711-0.0220028208744711

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
2 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
3 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
4 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
5 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
6 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
7 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
8 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
9 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
10 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
11 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
12 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
13 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
14 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
15 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
16 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
17 & 1 & 0.143864598025388 & 0.856135401974612 \tabularnewline
18 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
19 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
20 & 1 & 0.143864598025388 & 0.856135401974612 \tabularnewline
21 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
22 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
23 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
24 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
25 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
26 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
27 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
28 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
29 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
30 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
31 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
32 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
33 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
34 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
35 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
36 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
37 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
38 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
39 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
40 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
41 & 1 & 0.103244005641749 & 0.896755994358251 \tabularnewline
42 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
43 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
44 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
45 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
46 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
47 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
48 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
49 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
50 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
51 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
52 & 1 & 0.143864598025388 & 0.856135401974612 \tabularnewline
53 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
54 & 1 & 0.103244005641749 & 0.896755994358251 \tabularnewline
55 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
56 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
57 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
58 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
59 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
60 & 1 & 0.143864598025388 & 0.856135401974612 \tabularnewline
61 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
62 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
63 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
64 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
65 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
66 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
67 & 1 & 0.143864598025388 & 0.856135401974612 \tabularnewline
68 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
69 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
70 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
71 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
72 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
73 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
74 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
75 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
76 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
77 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
78 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
79 & 1 & 0.143864598025388 & 0.856135401974612 \tabularnewline
80 & 0 & 0.143864598025388 & -0.143864598025388 \tabularnewline
81 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
82 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
83 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
84 & 1 & 0.103244005641749 & 0.896755994358251 \tabularnewline
85 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
86 & 0 & 0.103244005641749 & -0.103244005641749 \tabularnewline
87 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
88 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
89 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
90 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
91 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
92 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
93 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
94 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
95 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
96 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
97 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
98 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
99 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
100 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
101 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
102 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
103 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
104 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
105 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
106 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
107 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
108 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
109 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
110 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
111 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
112 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
113 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
114 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
115 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
116 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
117 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
118 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
119 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
120 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
121 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
122 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
123 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
124 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
125 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
126 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
127 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
128 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
129 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
130 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
131 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
132 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
133 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
134 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
135 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
136 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
137 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
138 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
139 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
140 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
141 & 1 & 0.0220028208744711 & 0.977997179125529 \tabularnewline
142 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
143 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
144 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
145 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
146 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
147 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
148 & 0 & 0.06262341325811 & -0.06262341325811 \tabularnewline
149 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
150 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
151 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
152 & 1 & 0.0220028208744711 & 0.977997179125529 \tabularnewline
153 & 1 & 0.0220028208744711 & 0.977997179125529 \tabularnewline
154 & 0 & 0.0220028208744711 & -0.0220028208744711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203384&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]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.143864598025388[/C][C]0.856135401974612[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.143864598025388[/C][C]0.856135401974612[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.103244005641749[/C][C]0.896755994358251[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.143864598025388[/C][C]0.856135401974612[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.103244005641749[/C][C]0.896755994358251[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.143864598025388[/C][C]0.856135401974612[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.143864598025388[/C][C]0.856135401974612[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.143864598025388[/C][C]0.856135401974612[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.143864598025388[/C][C]-0.143864598025388[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.103244005641749[/C][C]0.896755994358251[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.103244005641749[/C][C]-0.103244005641749[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.0220028208744711[/C][C]0.977997179125529[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.06262341325811[/C][C]-0.06262341325811[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.0220028208744711[/C][C]0.977997179125529[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.0220028208744711[/C][C]0.977997179125529[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.0220028208744711[/C][C]-0.0220028208744711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203384&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203384&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
100.143864598025388-0.143864598025388
200.103244005641749-0.103244005641749
300.103244005641749-0.103244005641749
400.103244005641749-0.103244005641749
500.103244005641749-0.103244005641749
600.103244005641749-0.103244005641749
700.103244005641749-0.103244005641749
800.143864598025388-0.143864598025388
900.103244005641749-0.103244005641749
1000.103244005641749-0.103244005641749
1100.143864598025388-0.143864598025388
1200.103244005641749-0.103244005641749
1300.103244005641749-0.103244005641749
1400.143864598025388-0.143864598025388
1500.103244005641749-0.103244005641749
1600.143864598025388-0.143864598025388
1710.1438645980253880.856135401974612
1800.143864598025388-0.143864598025388
1900.103244005641749-0.103244005641749
2010.1438645980253880.856135401974612
2100.103244005641749-0.103244005641749
2200.103244005641749-0.103244005641749
2300.103244005641749-0.103244005641749
2400.103244005641749-0.103244005641749
2500.143864598025388-0.143864598025388
2600.103244005641749-0.103244005641749
2700.103244005641749-0.103244005641749
2800.103244005641749-0.103244005641749
2900.103244005641749-0.103244005641749
3000.103244005641749-0.103244005641749
3100.103244005641749-0.103244005641749
3200.103244005641749-0.103244005641749
3300.103244005641749-0.103244005641749
3400.143864598025388-0.143864598025388
3500.103244005641749-0.103244005641749
3600.103244005641749-0.103244005641749
3700.143864598025388-0.143864598025388
3800.103244005641749-0.103244005641749
3900.103244005641749-0.103244005641749
4000.143864598025388-0.143864598025388
4110.1032440056417490.896755994358251
4200.103244005641749-0.103244005641749
4300.103244005641749-0.103244005641749
4400.143864598025388-0.143864598025388
4500.103244005641749-0.103244005641749
4600.103244005641749-0.103244005641749
4700.103244005641749-0.103244005641749
4800.103244005641749-0.103244005641749
4900.103244005641749-0.103244005641749
5000.103244005641749-0.103244005641749
5100.143864598025388-0.143864598025388
5210.1438645980253880.856135401974612
5300.103244005641749-0.103244005641749
5410.1032440056417490.896755994358251
5500.103244005641749-0.103244005641749
5600.143864598025388-0.143864598025388
5700.103244005641749-0.103244005641749
5800.103244005641749-0.103244005641749
5900.103244005641749-0.103244005641749
6010.1438645980253880.856135401974612
6100.143864598025388-0.143864598025388
6200.103244005641749-0.103244005641749
6300.103244005641749-0.103244005641749
6400.143864598025388-0.143864598025388
6500.103244005641749-0.103244005641749
6600.103244005641749-0.103244005641749
6710.1438645980253880.856135401974612
6800.103244005641749-0.103244005641749
6900.103244005641749-0.103244005641749
7000.103244005641749-0.103244005641749
7100.103244005641749-0.103244005641749
7200.103244005641749-0.103244005641749
7300.103244005641749-0.103244005641749
7400.103244005641749-0.103244005641749
7500.103244005641749-0.103244005641749
7600.143864598025388-0.143864598025388
7700.103244005641749-0.103244005641749
7800.103244005641749-0.103244005641749
7910.1438645980253880.856135401974612
8000.143864598025388-0.143864598025388
8100.103244005641749-0.103244005641749
8200.103244005641749-0.103244005641749
8300.103244005641749-0.103244005641749
8410.1032440056417490.896755994358251
8500.103244005641749-0.103244005641749
8600.103244005641749-0.103244005641749
8700.0220028208744711-0.0220028208744711
8800.06262341325811-0.06262341325811
8900.0220028208744711-0.0220028208744711
9000.0220028208744711-0.0220028208744711
9100.0220028208744711-0.0220028208744711
9200.06262341325811-0.06262341325811
9300.0220028208744711-0.0220028208744711
9400.0220028208744711-0.0220028208744711
9500.06262341325811-0.06262341325811
9600.0220028208744711-0.0220028208744711
9700.06262341325811-0.06262341325811
9800.0220028208744711-0.0220028208744711
9900.0220028208744711-0.0220028208744711
10000.0220028208744711-0.0220028208744711
10100.0220028208744711-0.0220028208744711
10200.0220028208744711-0.0220028208744711
10300.0220028208744711-0.0220028208744711
10400.0220028208744711-0.0220028208744711
10500.06262341325811-0.06262341325811
10600.0220028208744711-0.0220028208744711
10700.0220028208744711-0.0220028208744711
10800.06262341325811-0.06262341325811
10900.0220028208744711-0.0220028208744711
11000.0220028208744711-0.0220028208744711
11100.06262341325811-0.06262341325811
11200.06262341325811-0.06262341325811
11300.0220028208744711-0.0220028208744711
11400.06262341325811-0.06262341325811
11500.0220028208744711-0.0220028208744711
11600.0220028208744711-0.0220028208744711
11700.0220028208744711-0.0220028208744711
11800.0220028208744711-0.0220028208744711
11900.0220028208744711-0.0220028208744711
12000.0220028208744711-0.0220028208744711
12100.0220028208744711-0.0220028208744711
12200.0220028208744711-0.0220028208744711
12300.06262341325811-0.06262341325811
12400.0220028208744711-0.0220028208744711
12500.0220028208744711-0.0220028208744711
12600.06262341325811-0.06262341325811
12700.0220028208744711-0.0220028208744711
12800.0220028208744711-0.0220028208744711
12900.0220028208744711-0.0220028208744711
13000.0220028208744711-0.0220028208744711
13100.0220028208744711-0.0220028208744711
13200.0220028208744711-0.0220028208744711
13300.0220028208744711-0.0220028208744711
13400.0220028208744711-0.0220028208744711
13500.0220028208744711-0.0220028208744711
13600.0220028208744711-0.0220028208744711
13700.0220028208744711-0.0220028208744711
13800.06262341325811-0.06262341325811
13900.06262341325811-0.06262341325811
14000.0220028208744711-0.0220028208744711
14110.02200282087447110.977997179125529
14200.06262341325811-0.06262341325811
14300.0220028208744711-0.0220028208744711
14400.0220028208744711-0.0220028208744711
14500.0220028208744711-0.0220028208744711
14600.06262341325811-0.06262341325811
14700.06262341325811-0.06262341325811
14800.06262341325811-0.06262341325811
14900.0220028208744711-0.0220028208744711
15000.0220028208744711-0.0220028208744711
15100.0220028208744711-0.0220028208744711
15210.02200282087447110.977997179125529
15310.02200282087447110.977997179125529
15400.0220028208744711-0.0220028208744711







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5001
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.2866545973474380.5733091946948760.713345402652562
180.2453761942929360.4907523885858710.754623805707064
190.1889033442234460.3778066884468930.811096655776554
200.7432643297530940.5134713404938120.256735670246906
210.6852197178452290.6295605643095420.314780282154771
220.6232651605903250.753469678819350.376734839409675
230.5589973606854040.8820052786291920.441002639314596
240.4941218076841270.9882436153682530.505878192315873
250.4758240210903350.951648042180670.524175978909665
260.4135532514187620.8271065028375240.586446748581238
270.3541602150335940.7083204300671890.645839784966406
280.2988065439047970.5976130878095950.701193456095203
290.2483544668721740.4967089337443480.751645533127826
300.2033485442826270.4066970885652530.796651455717374
310.1640268915720150.3280537831440310.835973108427985
320.1303556883030.2607113766060.869644311697
330.1020791850732420.2041583701464830.897920814926758
340.09398162396986540.1879632479397310.906018376030135
350.07241374914406750.1448274982881350.927586250855932
360.05501747332583470.1100349466516690.944982526674165
370.04886516504853440.09773033009706890.951134834951466
380.03651682762670440.07303365525340880.963483172373296
390.02692990157682540.05385980315365090.973070098423175
400.02314811109363920.04629622218727840.976851888906361
410.4289748735338320.8579497470676630.571025126466168
420.3812185813692910.7624371627385820.618781418630709
430.3355609601447410.6711219202894810.664439039855259
440.3078270107078390.6156540214156780.692172989292161
450.2670185103803190.5340370207606380.732981489619681
460.2294326704184180.4588653408368360.770567329581582
470.1952807226112050.390561445222410.804719277388795
480.1646563550348560.3293127100697130.835343644965144
490.1375468448695720.2750936897391450.862453155130428
500.113848296806010.227696593612020.88615170319399
510.1008544076047910.2017088152095820.899145592395209
520.4348459977654680.8696919955309360.565154002234532
530.3913618946024480.7827237892048960.608638105397552
540.8581493106383630.2837013787232740.141850689361637
550.832453624799030.335092750401940.16754637520097
560.8161040548983570.3677918902032870.183895945101643
570.7865868268568570.4268263462862860.213413173143143
580.7547292618137750.4905414763724510.245270738186225
590.7207595843535190.5584808312929610.279240415646481
600.9383832476796540.1232335046406910.0616167523203455
610.9312412625337460.1375174749325070.0687587374662536
620.9161201726404610.1677596547190790.0838798273595393
630.8987607860681670.2024784278636660.101239213931833
640.888619893446420.222760213107160.11138010655358
650.8679901790666540.2640196418666930.132009820933346
660.8451114447751980.3097771104496030.154888555224802
670.9768703581896170.04625928362076520.0231296418103826
680.9703258370122090.05934832597558220.0296741629877911
690.9623667183191510.07526656336169720.0376332816808486
700.9528156629243770.09436867415124630.0471843370756232
710.9415070178663660.1169859642672690.0584929821336344
720.9282970211682470.1434059576635060.0717029788317529
730.9130750886430860.1738498227138280.0869249113569142
740.8957757682985820.2084484634028370.104224231701418
750.8763908977920170.2472182044159660.123609102207983
760.8680810816247120.2638378367505750.131918918375288
770.8467870051581560.3064259896836890.153212994841844
780.8240728472651060.3518543054697880.175927152734894
790.9744093972713050.05118120545739080.0255906027286954
800.970508534839770.05898293032045960.0294914651602298
810.9624254626070980.07514907478580350.0375745373929017
820.9527914252123470.09441714957530660.0472085747876533
830.9415724636472870.1168550727054260.058427536352713
840.9992672371090020.00146552578199550.000732762890997749
850.9989150140573310.002169971885338650.00108498594266932
860.998418807190440.003162385619119590.00158119280955979
870.9981138085980410.003772382803917620.00188619140195881
880.9973055051389210.005388989722157770.00269449486107889
890.9965387238685520.006922552262895050.00346127613144752
900.9954435050440060.00911298991198840.0045564949559942
910.9939423985769960.01211520284600870.00605760142300434
920.991534135940610.01693172811877930.00846586405938966
930.988825620265820.02234875946836010.0111743797341801
940.9852964314430960.02940713711380840.0147035685569042
950.9800948207913760.03981035841724830.0199051792086242
960.9742274179063590.05154516418728230.0257725820936412
970.965905707441160.0681885851176810.0340942925588405
980.9566533183123460.08669336337530820.0433466816876541
990.945354233367820.109291533264360.05464576663218
1000.9317311197907690.1365377604184610.0682688802092307
1010.9155161815958750.168967636808250.084483818404125
1020.8964655187949390.2070689624101220.103534481205061
1030.8743749623249520.2512500753500960.125625037675048
1040.8490965077256120.3018069845487750.150903492274388
1050.8174261653114020.3651476693771960.182573834688598
1060.7852274981128610.4295450037742780.214772501887139
1070.7498589923976420.5002820152047160.250141007602358
1080.7072539789984550.585492042003090.292746021001545
1090.6658931726611360.6682136546777290.334106827338864
1100.6223318163775440.7553363672449110.377668183622456
1110.5718212212837380.8563575574325250.428178778716262
1120.5198257864978320.9603484270043360.480174213502168
1130.4725462521967240.9450925043934480.527453747803276
1140.4200900218586760.8401800437173510.579909978141324
1150.3740201945010560.7480403890021130.625979805498944
1160.3296293034621820.6592586069243640.670370696537818
1170.2874871079611710.5749742159223410.71251289203883
1180.2480726471664070.4961452943328130.751927352833593
1190.2117577129478170.4235154258956350.788242287052183
1200.1787974468650840.3575948937301670.821202553134917
1210.1493283659268710.2986567318537420.850671634073129
1220.1233734505331360.2467469010662710.876626549466864
1230.09680581775151270.1936116355030250.903194182248487
1240.0778522469725090.1557044939450180.922147753027491
1250.06193901464878180.1238780292975640.938060985351218
1260.04605427649975420.09210855299950850.953945723500246
1270.03561299965799330.07122599931598660.964387000342007
1280.02726490799845960.05452981599691910.97273509200154
1290.02068816997025450.04137633994050890.979311830029746
1300.01558111613408440.03116223226816880.984418883865916
1310.01167063626254640.02334127252509270.988329363737454
1320.008717119403773320.01743423880754660.991282880596227
1330.006516408057287350.01303281611457470.993483591942713
1340.004899418444880880.009798836889761770.995100581555119
1350.003730187882887940.007460375765775880.996269812117112
1360.002903202692911160.005806405385822310.997096797307089
1370.00234107075028530.004682141500570590.997658929249715
1380.001338503989994260.002677007979988510.998661496010006
1390.0007349385260375340.001469877052075070.999265061473962
1400.0005960320512853590.001192064102570720.999403967948715
1410.0154701583357210.0309403166714420.984529841664279
1420.009083061521000450.01816612304200090.990916938479
1430.006680045773562090.01336009154712420.993319954226438
1440.005178348224578020.0103566964491560.994821651775422
1450.004453127461884580.008906254923769160.995546872538115
1460.002126407889019140.004252815778038270.997873592110981
1470.0009187058946585450.001837411789317090.999081294105341
1480.0003504075746410380.0007008151492820760.999649592425359
1490.0002877654975556250.000575530995111250.999712234502444

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0 & 0 & 1 \tabularnewline
6 & 0 & 0 & 1 \tabularnewline
7 & 0 & 0 & 1 \tabularnewline
8 & 0 & 0 & 1 \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.286654597347438 & 0.573309194694876 & 0.713345402652562 \tabularnewline
18 & 0.245376194292936 & 0.490752388585871 & 0.754623805707064 \tabularnewline
19 & 0.188903344223446 & 0.377806688446893 & 0.811096655776554 \tabularnewline
20 & 0.743264329753094 & 0.513471340493812 & 0.256735670246906 \tabularnewline
21 & 0.685219717845229 & 0.629560564309542 & 0.314780282154771 \tabularnewline
22 & 0.623265160590325 & 0.75346967881935 & 0.376734839409675 \tabularnewline
23 & 0.558997360685404 & 0.882005278629192 & 0.441002639314596 \tabularnewline
24 & 0.494121807684127 & 0.988243615368253 & 0.505878192315873 \tabularnewline
25 & 0.475824021090335 & 0.95164804218067 & 0.524175978909665 \tabularnewline
26 & 0.413553251418762 & 0.827106502837524 & 0.586446748581238 \tabularnewline
27 & 0.354160215033594 & 0.708320430067189 & 0.645839784966406 \tabularnewline
28 & 0.298806543904797 & 0.597613087809595 & 0.701193456095203 \tabularnewline
29 & 0.248354466872174 & 0.496708933744348 & 0.751645533127826 \tabularnewline
30 & 0.203348544282627 & 0.406697088565253 & 0.796651455717374 \tabularnewline
31 & 0.164026891572015 & 0.328053783144031 & 0.835973108427985 \tabularnewline
32 & 0.130355688303 & 0.260711376606 & 0.869644311697 \tabularnewline
33 & 0.102079185073242 & 0.204158370146483 & 0.897920814926758 \tabularnewline
34 & 0.0939816239698654 & 0.187963247939731 & 0.906018376030135 \tabularnewline
35 & 0.0724137491440675 & 0.144827498288135 & 0.927586250855932 \tabularnewline
36 & 0.0550174733258347 & 0.110034946651669 & 0.944982526674165 \tabularnewline
37 & 0.0488651650485344 & 0.0977303300970689 & 0.951134834951466 \tabularnewline
38 & 0.0365168276267044 & 0.0730336552534088 & 0.963483172373296 \tabularnewline
39 & 0.0269299015768254 & 0.0538598031536509 & 0.973070098423175 \tabularnewline
40 & 0.0231481110936392 & 0.0462962221872784 & 0.976851888906361 \tabularnewline
41 & 0.428974873533832 & 0.857949747067663 & 0.571025126466168 \tabularnewline
42 & 0.381218581369291 & 0.762437162738582 & 0.618781418630709 \tabularnewline
43 & 0.335560960144741 & 0.671121920289481 & 0.664439039855259 \tabularnewline
44 & 0.307827010707839 & 0.615654021415678 & 0.692172989292161 \tabularnewline
45 & 0.267018510380319 & 0.534037020760638 & 0.732981489619681 \tabularnewline
46 & 0.229432670418418 & 0.458865340836836 & 0.770567329581582 \tabularnewline
47 & 0.195280722611205 & 0.39056144522241 & 0.804719277388795 \tabularnewline
48 & 0.164656355034856 & 0.329312710069713 & 0.835343644965144 \tabularnewline
49 & 0.137546844869572 & 0.275093689739145 & 0.862453155130428 \tabularnewline
50 & 0.11384829680601 & 0.22769659361202 & 0.88615170319399 \tabularnewline
51 & 0.100854407604791 & 0.201708815209582 & 0.899145592395209 \tabularnewline
52 & 0.434845997765468 & 0.869691995530936 & 0.565154002234532 \tabularnewline
53 & 0.391361894602448 & 0.782723789204896 & 0.608638105397552 \tabularnewline
54 & 0.858149310638363 & 0.283701378723274 & 0.141850689361637 \tabularnewline
55 & 0.83245362479903 & 0.33509275040194 & 0.16754637520097 \tabularnewline
56 & 0.816104054898357 & 0.367791890203287 & 0.183895945101643 \tabularnewline
57 & 0.786586826856857 & 0.426826346286286 & 0.213413173143143 \tabularnewline
58 & 0.754729261813775 & 0.490541476372451 & 0.245270738186225 \tabularnewline
59 & 0.720759584353519 & 0.558480831292961 & 0.279240415646481 \tabularnewline
60 & 0.938383247679654 & 0.123233504640691 & 0.0616167523203455 \tabularnewline
61 & 0.931241262533746 & 0.137517474932507 & 0.0687587374662536 \tabularnewline
62 & 0.916120172640461 & 0.167759654719079 & 0.0838798273595393 \tabularnewline
63 & 0.898760786068167 & 0.202478427863666 & 0.101239213931833 \tabularnewline
64 & 0.88861989344642 & 0.22276021310716 & 0.11138010655358 \tabularnewline
65 & 0.867990179066654 & 0.264019641866693 & 0.132009820933346 \tabularnewline
66 & 0.845111444775198 & 0.309777110449603 & 0.154888555224802 \tabularnewline
67 & 0.976870358189617 & 0.0462592836207652 & 0.0231296418103826 \tabularnewline
68 & 0.970325837012209 & 0.0593483259755822 & 0.0296741629877911 \tabularnewline
69 & 0.962366718319151 & 0.0752665633616972 & 0.0376332816808486 \tabularnewline
70 & 0.952815662924377 & 0.0943686741512463 & 0.0471843370756232 \tabularnewline
71 & 0.941507017866366 & 0.116985964267269 & 0.0584929821336344 \tabularnewline
72 & 0.928297021168247 & 0.143405957663506 & 0.0717029788317529 \tabularnewline
73 & 0.913075088643086 & 0.173849822713828 & 0.0869249113569142 \tabularnewline
74 & 0.895775768298582 & 0.208448463402837 & 0.104224231701418 \tabularnewline
75 & 0.876390897792017 & 0.247218204415966 & 0.123609102207983 \tabularnewline
76 & 0.868081081624712 & 0.263837836750575 & 0.131918918375288 \tabularnewline
77 & 0.846787005158156 & 0.306425989683689 & 0.153212994841844 \tabularnewline
78 & 0.824072847265106 & 0.351854305469788 & 0.175927152734894 \tabularnewline
79 & 0.974409397271305 & 0.0511812054573908 & 0.0255906027286954 \tabularnewline
80 & 0.97050853483977 & 0.0589829303204596 & 0.0294914651602298 \tabularnewline
81 & 0.962425462607098 & 0.0751490747858035 & 0.0375745373929017 \tabularnewline
82 & 0.952791425212347 & 0.0944171495753066 & 0.0472085747876533 \tabularnewline
83 & 0.941572463647287 & 0.116855072705426 & 0.058427536352713 \tabularnewline
84 & 0.999267237109002 & 0.0014655257819955 & 0.000732762890997749 \tabularnewline
85 & 0.998915014057331 & 0.00216997188533865 & 0.00108498594266932 \tabularnewline
86 & 0.99841880719044 & 0.00316238561911959 & 0.00158119280955979 \tabularnewline
87 & 0.998113808598041 & 0.00377238280391762 & 0.00188619140195881 \tabularnewline
88 & 0.997305505138921 & 0.00538898972215777 & 0.00269449486107889 \tabularnewline
89 & 0.996538723868552 & 0.00692255226289505 & 0.00346127613144752 \tabularnewline
90 & 0.995443505044006 & 0.0091129899119884 & 0.0045564949559942 \tabularnewline
91 & 0.993942398576996 & 0.0121152028460087 & 0.00605760142300434 \tabularnewline
92 & 0.99153413594061 & 0.0169317281187793 & 0.00846586405938966 \tabularnewline
93 & 0.98882562026582 & 0.0223487594683601 & 0.0111743797341801 \tabularnewline
94 & 0.985296431443096 & 0.0294071371138084 & 0.0147035685569042 \tabularnewline
95 & 0.980094820791376 & 0.0398103584172483 & 0.0199051792086242 \tabularnewline
96 & 0.974227417906359 & 0.0515451641872823 & 0.0257725820936412 \tabularnewline
97 & 0.96590570744116 & 0.068188585117681 & 0.0340942925588405 \tabularnewline
98 & 0.956653318312346 & 0.0866933633753082 & 0.0433466816876541 \tabularnewline
99 & 0.94535423336782 & 0.10929153326436 & 0.05464576663218 \tabularnewline
100 & 0.931731119790769 & 0.136537760418461 & 0.0682688802092307 \tabularnewline
101 & 0.915516181595875 & 0.16896763680825 & 0.084483818404125 \tabularnewline
102 & 0.896465518794939 & 0.207068962410122 & 0.103534481205061 \tabularnewline
103 & 0.874374962324952 & 0.251250075350096 & 0.125625037675048 \tabularnewline
104 & 0.849096507725612 & 0.301806984548775 & 0.150903492274388 \tabularnewline
105 & 0.817426165311402 & 0.365147669377196 & 0.182573834688598 \tabularnewline
106 & 0.785227498112861 & 0.429545003774278 & 0.214772501887139 \tabularnewline
107 & 0.749858992397642 & 0.500282015204716 & 0.250141007602358 \tabularnewline
108 & 0.707253978998455 & 0.58549204200309 & 0.292746021001545 \tabularnewline
109 & 0.665893172661136 & 0.668213654677729 & 0.334106827338864 \tabularnewline
110 & 0.622331816377544 & 0.755336367244911 & 0.377668183622456 \tabularnewline
111 & 0.571821221283738 & 0.856357557432525 & 0.428178778716262 \tabularnewline
112 & 0.519825786497832 & 0.960348427004336 & 0.480174213502168 \tabularnewline
113 & 0.472546252196724 & 0.945092504393448 & 0.527453747803276 \tabularnewline
114 & 0.420090021858676 & 0.840180043717351 & 0.579909978141324 \tabularnewline
115 & 0.374020194501056 & 0.748040389002113 & 0.625979805498944 \tabularnewline
116 & 0.329629303462182 & 0.659258606924364 & 0.670370696537818 \tabularnewline
117 & 0.287487107961171 & 0.574974215922341 & 0.71251289203883 \tabularnewline
118 & 0.248072647166407 & 0.496145294332813 & 0.751927352833593 \tabularnewline
119 & 0.211757712947817 & 0.423515425895635 & 0.788242287052183 \tabularnewline
120 & 0.178797446865084 & 0.357594893730167 & 0.821202553134917 \tabularnewline
121 & 0.149328365926871 & 0.298656731853742 & 0.850671634073129 \tabularnewline
122 & 0.123373450533136 & 0.246746901066271 & 0.876626549466864 \tabularnewline
123 & 0.0968058177515127 & 0.193611635503025 & 0.903194182248487 \tabularnewline
124 & 0.077852246972509 & 0.155704493945018 & 0.922147753027491 \tabularnewline
125 & 0.0619390146487818 & 0.123878029297564 & 0.938060985351218 \tabularnewline
126 & 0.0460542764997542 & 0.0921085529995085 & 0.953945723500246 \tabularnewline
127 & 0.0356129996579933 & 0.0712259993159866 & 0.964387000342007 \tabularnewline
128 & 0.0272649079984596 & 0.0545298159969191 & 0.97273509200154 \tabularnewline
129 & 0.0206881699702545 & 0.0413763399405089 & 0.979311830029746 \tabularnewline
130 & 0.0155811161340844 & 0.0311622322681688 & 0.984418883865916 \tabularnewline
131 & 0.0116706362625464 & 0.0233412725250927 & 0.988329363737454 \tabularnewline
132 & 0.00871711940377332 & 0.0174342388075466 & 0.991282880596227 \tabularnewline
133 & 0.00651640805728735 & 0.0130328161145747 & 0.993483591942713 \tabularnewline
134 & 0.00489941844488088 & 0.00979883688976177 & 0.995100581555119 \tabularnewline
135 & 0.00373018788288794 & 0.00746037576577588 & 0.996269812117112 \tabularnewline
136 & 0.00290320269291116 & 0.00580640538582231 & 0.997096797307089 \tabularnewline
137 & 0.0023410707502853 & 0.00468214150057059 & 0.997658929249715 \tabularnewline
138 & 0.00133850398999426 & 0.00267700797998851 & 0.998661496010006 \tabularnewline
139 & 0.000734938526037534 & 0.00146987705207507 & 0.999265061473962 \tabularnewline
140 & 0.000596032051285359 & 0.00119206410257072 & 0.999403967948715 \tabularnewline
141 & 0.015470158335721 & 0.030940316671442 & 0.984529841664279 \tabularnewline
142 & 0.00908306152100045 & 0.0181661230420009 & 0.990916938479 \tabularnewline
143 & 0.00668004577356209 & 0.0133600915471242 & 0.993319954226438 \tabularnewline
144 & 0.00517834822457802 & 0.010356696449156 & 0.994821651775422 \tabularnewline
145 & 0.00445312746188458 & 0.00890625492376916 & 0.995546872538115 \tabularnewline
146 & 0.00212640788901914 & 0.00425281577803827 & 0.997873592110981 \tabularnewline
147 & 0.000918705894658545 & 0.00183741178931709 & 0.999081294105341 \tabularnewline
148 & 0.000350407574641038 & 0.000700815149282076 & 0.999649592425359 \tabularnewline
149 & 0.000287765497555625 & 0.00057553099511125 & 0.999712234502444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203384&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.286654597347438[/C][C]0.573309194694876[/C][C]0.713345402652562[/C][/ROW]
[ROW][C]18[/C][C]0.245376194292936[/C][C]0.490752388585871[/C][C]0.754623805707064[/C][/ROW]
[ROW][C]19[/C][C]0.188903344223446[/C][C]0.377806688446893[/C][C]0.811096655776554[/C][/ROW]
[ROW][C]20[/C][C]0.743264329753094[/C][C]0.513471340493812[/C][C]0.256735670246906[/C][/ROW]
[ROW][C]21[/C][C]0.685219717845229[/C][C]0.629560564309542[/C][C]0.314780282154771[/C][/ROW]
[ROW][C]22[/C][C]0.623265160590325[/C][C]0.75346967881935[/C][C]0.376734839409675[/C][/ROW]
[ROW][C]23[/C][C]0.558997360685404[/C][C]0.882005278629192[/C][C]0.441002639314596[/C][/ROW]
[ROW][C]24[/C][C]0.494121807684127[/C][C]0.988243615368253[/C][C]0.505878192315873[/C][/ROW]
[ROW][C]25[/C][C]0.475824021090335[/C][C]0.95164804218067[/C][C]0.524175978909665[/C][/ROW]
[ROW][C]26[/C][C]0.413553251418762[/C][C]0.827106502837524[/C][C]0.586446748581238[/C][/ROW]
[ROW][C]27[/C][C]0.354160215033594[/C][C]0.708320430067189[/C][C]0.645839784966406[/C][/ROW]
[ROW][C]28[/C][C]0.298806543904797[/C][C]0.597613087809595[/C][C]0.701193456095203[/C][/ROW]
[ROW][C]29[/C][C]0.248354466872174[/C][C]0.496708933744348[/C][C]0.751645533127826[/C][/ROW]
[ROW][C]30[/C][C]0.203348544282627[/C][C]0.406697088565253[/C][C]0.796651455717374[/C][/ROW]
[ROW][C]31[/C][C]0.164026891572015[/C][C]0.328053783144031[/C][C]0.835973108427985[/C][/ROW]
[ROW][C]32[/C][C]0.130355688303[/C][C]0.260711376606[/C][C]0.869644311697[/C][/ROW]
[ROW][C]33[/C][C]0.102079185073242[/C][C]0.204158370146483[/C][C]0.897920814926758[/C][/ROW]
[ROW][C]34[/C][C]0.0939816239698654[/C][C]0.187963247939731[/C][C]0.906018376030135[/C][/ROW]
[ROW][C]35[/C][C]0.0724137491440675[/C][C]0.144827498288135[/C][C]0.927586250855932[/C][/ROW]
[ROW][C]36[/C][C]0.0550174733258347[/C][C]0.110034946651669[/C][C]0.944982526674165[/C][/ROW]
[ROW][C]37[/C][C]0.0488651650485344[/C][C]0.0977303300970689[/C][C]0.951134834951466[/C][/ROW]
[ROW][C]38[/C][C]0.0365168276267044[/C][C]0.0730336552534088[/C][C]0.963483172373296[/C][/ROW]
[ROW][C]39[/C][C]0.0269299015768254[/C][C]0.0538598031536509[/C][C]0.973070098423175[/C][/ROW]
[ROW][C]40[/C][C]0.0231481110936392[/C][C]0.0462962221872784[/C][C]0.976851888906361[/C][/ROW]
[ROW][C]41[/C][C]0.428974873533832[/C][C]0.857949747067663[/C][C]0.571025126466168[/C][/ROW]
[ROW][C]42[/C][C]0.381218581369291[/C][C]0.762437162738582[/C][C]0.618781418630709[/C][/ROW]
[ROW][C]43[/C][C]0.335560960144741[/C][C]0.671121920289481[/C][C]0.664439039855259[/C][/ROW]
[ROW][C]44[/C][C]0.307827010707839[/C][C]0.615654021415678[/C][C]0.692172989292161[/C][/ROW]
[ROW][C]45[/C][C]0.267018510380319[/C][C]0.534037020760638[/C][C]0.732981489619681[/C][/ROW]
[ROW][C]46[/C][C]0.229432670418418[/C][C]0.458865340836836[/C][C]0.770567329581582[/C][/ROW]
[ROW][C]47[/C][C]0.195280722611205[/C][C]0.39056144522241[/C][C]0.804719277388795[/C][/ROW]
[ROW][C]48[/C][C]0.164656355034856[/C][C]0.329312710069713[/C][C]0.835343644965144[/C][/ROW]
[ROW][C]49[/C][C]0.137546844869572[/C][C]0.275093689739145[/C][C]0.862453155130428[/C][/ROW]
[ROW][C]50[/C][C]0.11384829680601[/C][C]0.22769659361202[/C][C]0.88615170319399[/C][/ROW]
[ROW][C]51[/C][C]0.100854407604791[/C][C]0.201708815209582[/C][C]0.899145592395209[/C][/ROW]
[ROW][C]52[/C][C]0.434845997765468[/C][C]0.869691995530936[/C][C]0.565154002234532[/C][/ROW]
[ROW][C]53[/C][C]0.391361894602448[/C][C]0.782723789204896[/C][C]0.608638105397552[/C][/ROW]
[ROW][C]54[/C][C]0.858149310638363[/C][C]0.283701378723274[/C][C]0.141850689361637[/C][/ROW]
[ROW][C]55[/C][C]0.83245362479903[/C][C]0.33509275040194[/C][C]0.16754637520097[/C][/ROW]
[ROW][C]56[/C][C]0.816104054898357[/C][C]0.367791890203287[/C][C]0.183895945101643[/C][/ROW]
[ROW][C]57[/C][C]0.786586826856857[/C][C]0.426826346286286[/C][C]0.213413173143143[/C][/ROW]
[ROW][C]58[/C][C]0.754729261813775[/C][C]0.490541476372451[/C][C]0.245270738186225[/C][/ROW]
[ROW][C]59[/C][C]0.720759584353519[/C][C]0.558480831292961[/C][C]0.279240415646481[/C][/ROW]
[ROW][C]60[/C][C]0.938383247679654[/C][C]0.123233504640691[/C][C]0.0616167523203455[/C][/ROW]
[ROW][C]61[/C][C]0.931241262533746[/C][C]0.137517474932507[/C][C]0.0687587374662536[/C][/ROW]
[ROW][C]62[/C][C]0.916120172640461[/C][C]0.167759654719079[/C][C]0.0838798273595393[/C][/ROW]
[ROW][C]63[/C][C]0.898760786068167[/C][C]0.202478427863666[/C][C]0.101239213931833[/C][/ROW]
[ROW][C]64[/C][C]0.88861989344642[/C][C]0.22276021310716[/C][C]0.11138010655358[/C][/ROW]
[ROW][C]65[/C][C]0.867990179066654[/C][C]0.264019641866693[/C][C]0.132009820933346[/C][/ROW]
[ROW][C]66[/C][C]0.845111444775198[/C][C]0.309777110449603[/C][C]0.154888555224802[/C][/ROW]
[ROW][C]67[/C][C]0.976870358189617[/C][C]0.0462592836207652[/C][C]0.0231296418103826[/C][/ROW]
[ROW][C]68[/C][C]0.970325837012209[/C][C]0.0593483259755822[/C][C]0.0296741629877911[/C][/ROW]
[ROW][C]69[/C][C]0.962366718319151[/C][C]0.0752665633616972[/C][C]0.0376332816808486[/C][/ROW]
[ROW][C]70[/C][C]0.952815662924377[/C][C]0.0943686741512463[/C][C]0.0471843370756232[/C][/ROW]
[ROW][C]71[/C][C]0.941507017866366[/C][C]0.116985964267269[/C][C]0.0584929821336344[/C][/ROW]
[ROW][C]72[/C][C]0.928297021168247[/C][C]0.143405957663506[/C][C]0.0717029788317529[/C][/ROW]
[ROW][C]73[/C][C]0.913075088643086[/C][C]0.173849822713828[/C][C]0.0869249113569142[/C][/ROW]
[ROW][C]74[/C][C]0.895775768298582[/C][C]0.208448463402837[/C][C]0.104224231701418[/C][/ROW]
[ROW][C]75[/C][C]0.876390897792017[/C][C]0.247218204415966[/C][C]0.123609102207983[/C][/ROW]
[ROW][C]76[/C][C]0.868081081624712[/C][C]0.263837836750575[/C][C]0.131918918375288[/C][/ROW]
[ROW][C]77[/C][C]0.846787005158156[/C][C]0.306425989683689[/C][C]0.153212994841844[/C][/ROW]
[ROW][C]78[/C][C]0.824072847265106[/C][C]0.351854305469788[/C][C]0.175927152734894[/C][/ROW]
[ROW][C]79[/C][C]0.974409397271305[/C][C]0.0511812054573908[/C][C]0.0255906027286954[/C][/ROW]
[ROW][C]80[/C][C]0.97050853483977[/C][C]0.0589829303204596[/C][C]0.0294914651602298[/C][/ROW]
[ROW][C]81[/C][C]0.962425462607098[/C][C]0.0751490747858035[/C][C]0.0375745373929017[/C][/ROW]
[ROW][C]82[/C][C]0.952791425212347[/C][C]0.0944171495753066[/C][C]0.0472085747876533[/C][/ROW]
[ROW][C]83[/C][C]0.941572463647287[/C][C]0.116855072705426[/C][C]0.058427536352713[/C][/ROW]
[ROW][C]84[/C][C]0.999267237109002[/C][C]0.0014655257819955[/C][C]0.000732762890997749[/C][/ROW]
[ROW][C]85[/C][C]0.998915014057331[/C][C]0.00216997188533865[/C][C]0.00108498594266932[/C][/ROW]
[ROW][C]86[/C][C]0.99841880719044[/C][C]0.00316238561911959[/C][C]0.00158119280955979[/C][/ROW]
[ROW][C]87[/C][C]0.998113808598041[/C][C]0.00377238280391762[/C][C]0.00188619140195881[/C][/ROW]
[ROW][C]88[/C][C]0.997305505138921[/C][C]0.00538898972215777[/C][C]0.00269449486107889[/C][/ROW]
[ROW][C]89[/C][C]0.996538723868552[/C][C]0.00692255226289505[/C][C]0.00346127613144752[/C][/ROW]
[ROW][C]90[/C][C]0.995443505044006[/C][C]0.0091129899119884[/C][C]0.0045564949559942[/C][/ROW]
[ROW][C]91[/C][C]0.993942398576996[/C][C]0.0121152028460087[/C][C]0.00605760142300434[/C][/ROW]
[ROW][C]92[/C][C]0.99153413594061[/C][C]0.0169317281187793[/C][C]0.00846586405938966[/C][/ROW]
[ROW][C]93[/C][C]0.98882562026582[/C][C]0.0223487594683601[/C][C]0.0111743797341801[/C][/ROW]
[ROW][C]94[/C][C]0.985296431443096[/C][C]0.0294071371138084[/C][C]0.0147035685569042[/C][/ROW]
[ROW][C]95[/C][C]0.980094820791376[/C][C]0.0398103584172483[/C][C]0.0199051792086242[/C][/ROW]
[ROW][C]96[/C][C]0.974227417906359[/C][C]0.0515451641872823[/C][C]0.0257725820936412[/C][/ROW]
[ROW][C]97[/C][C]0.96590570744116[/C][C]0.068188585117681[/C][C]0.0340942925588405[/C][/ROW]
[ROW][C]98[/C][C]0.956653318312346[/C][C]0.0866933633753082[/C][C]0.0433466816876541[/C][/ROW]
[ROW][C]99[/C][C]0.94535423336782[/C][C]0.10929153326436[/C][C]0.05464576663218[/C][/ROW]
[ROW][C]100[/C][C]0.931731119790769[/C][C]0.136537760418461[/C][C]0.0682688802092307[/C][/ROW]
[ROW][C]101[/C][C]0.915516181595875[/C][C]0.16896763680825[/C][C]0.084483818404125[/C][/ROW]
[ROW][C]102[/C][C]0.896465518794939[/C][C]0.207068962410122[/C][C]0.103534481205061[/C][/ROW]
[ROW][C]103[/C][C]0.874374962324952[/C][C]0.251250075350096[/C][C]0.125625037675048[/C][/ROW]
[ROW][C]104[/C][C]0.849096507725612[/C][C]0.301806984548775[/C][C]0.150903492274388[/C][/ROW]
[ROW][C]105[/C][C]0.817426165311402[/C][C]0.365147669377196[/C][C]0.182573834688598[/C][/ROW]
[ROW][C]106[/C][C]0.785227498112861[/C][C]0.429545003774278[/C][C]0.214772501887139[/C][/ROW]
[ROW][C]107[/C][C]0.749858992397642[/C][C]0.500282015204716[/C][C]0.250141007602358[/C][/ROW]
[ROW][C]108[/C][C]0.707253978998455[/C][C]0.58549204200309[/C][C]0.292746021001545[/C][/ROW]
[ROW][C]109[/C][C]0.665893172661136[/C][C]0.668213654677729[/C][C]0.334106827338864[/C][/ROW]
[ROW][C]110[/C][C]0.622331816377544[/C][C]0.755336367244911[/C][C]0.377668183622456[/C][/ROW]
[ROW][C]111[/C][C]0.571821221283738[/C][C]0.856357557432525[/C][C]0.428178778716262[/C][/ROW]
[ROW][C]112[/C][C]0.519825786497832[/C][C]0.960348427004336[/C][C]0.480174213502168[/C][/ROW]
[ROW][C]113[/C][C]0.472546252196724[/C][C]0.945092504393448[/C][C]0.527453747803276[/C][/ROW]
[ROW][C]114[/C][C]0.420090021858676[/C][C]0.840180043717351[/C][C]0.579909978141324[/C][/ROW]
[ROW][C]115[/C][C]0.374020194501056[/C][C]0.748040389002113[/C][C]0.625979805498944[/C][/ROW]
[ROW][C]116[/C][C]0.329629303462182[/C][C]0.659258606924364[/C][C]0.670370696537818[/C][/ROW]
[ROW][C]117[/C][C]0.287487107961171[/C][C]0.574974215922341[/C][C]0.71251289203883[/C][/ROW]
[ROW][C]118[/C][C]0.248072647166407[/C][C]0.496145294332813[/C][C]0.751927352833593[/C][/ROW]
[ROW][C]119[/C][C]0.211757712947817[/C][C]0.423515425895635[/C][C]0.788242287052183[/C][/ROW]
[ROW][C]120[/C][C]0.178797446865084[/C][C]0.357594893730167[/C][C]0.821202553134917[/C][/ROW]
[ROW][C]121[/C][C]0.149328365926871[/C][C]0.298656731853742[/C][C]0.850671634073129[/C][/ROW]
[ROW][C]122[/C][C]0.123373450533136[/C][C]0.246746901066271[/C][C]0.876626549466864[/C][/ROW]
[ROW][C]123[/C][C]0.0968058177515127[/C][C]0.193611635503025[/C][C]0.903194182248487[/C][/ROW]
[ROW][C]124[/C][C]0.077852246972509[/C][C]0.155704493945018[/C][C]0.922147753027491[/C][/ROW]
[ROW][C]125[/C][C]0.0619390146487818[/C][C]0.123878029297564[/C][C]0.938060985351218[/C][/ROW]
[ROW][C]126[/C][C]0.0460542764997542[/C][C]0.0921085529995085[/C][C]0.953945723500246[/C][/ROW]
[ROW][C]127[/C][C]0.0356129996579933[/C][C]0.0712259993159866[/C][C]0.964387000342007[/C][/ROW]
[ROW][C]128[/C][C]0.0272649079984596[/C][C]0.0545298159969191[/C][C]0.97273509200154[/C][/ROW]
[ROW][C]129[/C][C]0.0206881699702545[/C][C]0.0413763399405089[/C][C]0.979311830029746[/C][/ROW]
[ROW][C]130[/C][C]0.0155811161340844[/C][C]0.0311622322681688[/C][C]0.984418883865916[/C][/ROW]
[ROW][C]131[/C][C]0.0116706362625464[/C][C]0.0233412725250927[/C][C]0.988329363737454[/C][/ROW]
[ROW][C]132[/C][C]0.00871711940377332[/C][C]0.0174342388075466[/C][C]0.991282880596227[/C][/ROW]
[ROW][C]133[/C][C]0.00651640805728735[/C][C]0.0130328161145747[/C][C]0.993483591942713[/C][/ROW]
[ROW][C]134[/C][C]0.00489941844488088[/C][C]0.00979883688976177[/C][C]0.995100581555119[/C][/ROW]
[ROW][C]135[/C][C]0.00373018788288794[/C][C]0.00746037576577588[/C][C]0.996269812117112[/C][/ROW]
[ROW][C]136[/C][C]0.00290320269291116[/C][C]0.00580640538582231[/C][C]0.997096797307089[/C][/ROW]
[ROW][C]137[/C][C]0.0023410707502853[/C][C]0.00468214150057059[/C][C]0.997658929249715[/C][/ROW]
[ROW][C]138[/C][C]0.00133850398999426[/C][C]0.00267700797998851[/C][C]0.998661496010006[/C][/ROW]
[ROW][C]139[/C][C]0.000734938526037534[/C][C]0.00146987705207507[/C][C]0.999265061473962[/C][/ROW]
[ROW][C]140[/C][C]0.000596032051285359[/C][C]0.00119206410257072[/C][C]0.999403967948715[/C][/ROW]
[ROW][C]141[/C][C]0.015470158335721[/C][C]0.030940316671442[/C][C]0.984529841664279[/C][/ROW]
[ROW][C]142[/C][C]0.00908306152100045[/C][C]0.0181661230420009[/C][C]0.990916938479[/C][/ROW]
[ROW][C]143[/C][C]0.00668004577356209[/C][C]0.0133600915471242[/C][C]0.993319954226438[/C][/ROW]
[ROW][C]144[/C][C]0.00517834822457802[/C][C]0.010356696449156[/C][C]0.994821651775422[/C][/ROW]
[ROW][C]145[/C][C]0.00445312746188458[/C][C]0.00890625492376916[/C][C]0.995546872538115[/C][/ROW]
[ROW][C]146[/C][C]0.00212640788901914[/C][C]0.00425281577803827[/C][C]0.997873592110981[/C][/ROW]
[ROW][C]147[/C][C]0.000918705894658545[/C][C]0.00183741178931709[/C][C]0.999081294105341[/C][/ROW]
[ROW][C]148[/C][C]0.000350407574641038[/C][C]0.000700815149282076[/C][C]0.999649592425359[/C][/ROW]
[ROW][C]149[/C][C]0.000287765497555625[/C][C]0.00057553099511125[/C][C]0.999712234502444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203384&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203384&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5001
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.2866545973474380.5733091946948760.713345402652562
180.2453761942929360.4907523885858710.754623805707064
190.1889033442234460.3778066884468930.811096655776554
200.7432643297530940.5134713404938120.256735670246906
210.6852197178452290.6295605643095420.314780282154771
220.6232651605903250.753469678819350.376734839409675
230.5589973606854040.8820052786291920.441002639314596
240.4941218076841270.9882436153682530.505878192315873
250.4758240210903350.951648042180670.524175978909665
260.4135532514187620.8271065028375240.586446748581238
270.3541602150335940.7083204300671890.645839784966406
280.2988065439047970.5976130878095950.701193456095203
290.2483544668721740.4967089337443480.751645533127826
300.2033485442826270.4066970885652530.796651455717374
310.1640268915720150.3280537831440310.835973108427985
320.1303556883030.2607113766060.869644311697
330.1020791850732420.2041583701464830.897920814926758
340.09398162396986540.1879632479397310.906018376030135
350.07241374914406750.1448274982881350.927586250855932
360.05501747332583470.1100349466516690.944982526674165
370.04886516504853440.09773033009706890.951134834951466
380.03651682762670440.07303365525340880.963483172373296
390.02692990157682540.05385980315365090.973070098423175
400.02314811109363920.04629622218727840.976851888906361
410.4289748735338320.8579497470676630.571025126466168
420.3812185813692910.7624371627385820.618781418630709
430.3355609601447410.6711219202894810.664439039855259
440.3078270107078390.6156540214156780.692172989292161
450.2670185103803190.5340370207606380.732981489619681
460.2294326704184180.4588653408368360.770567329581582
470.1952807226112050.390561445222410.804719277388795
480.1646563550348560.3293127100697130.835343644965144
490.1375468448695720.2750936897391450.862453155130428
500.113848296806010.227696593612020.88615170319399
510.1008544076047910.2017088152095820.899145592395209
520.4348459977654680.8696919955309360.565154002234532
530.3913618946024480.7827237892048960.608638105397552
540.8581493106383630.2837013787232740.141850689361637
550.832453624799030.335092750401940.16754637520097
560.8161040548983570.3677918902032870.183895945101643
570.7865868268568570.4268263462862860.213413173143143
580.7547292618137750.4905414763724510.245270738186225
590.7207595843535190.5584808312929610.279240415646481
600.9383832476796540.1232335046406910.0616167523203455
610.9312412625337460.1375174749325070.0687587374662536
620.9161201726404610.1677596547190790.0838798273595393
630.8987607860681670.2024784278636660.101239213931833
640.888619893446420.222760213107160.11138010655358
650.8679901790666540.2640196418666930.132009820933346
660.8451114447751980.3097771104496030.154888555224802
670.9768703581896170.04625928362076520.0231296418103826
680.9703258370122090.05934832597558220.0296741629877911
690.9623667183191510.07526656336169720.0376332816808486
700.9528156629243770.09436867415124630.0471843370756232
710.9415070178663660.1169859642672690.0584929821336344
720.9282970211682470.1434059576635060.0717029788317529
730.9130750886430860.1738498227138280.0869249113569142
740.8957757682985820.2084484634028370.104224231701418
750.8763908977920170.2472182044159660.123609102207983
760.8680810816247120.2638378367505750.131918918375288
770.8467870051581560.3064259896836890.153212994841844
780.8240728472651060.3518543054697880.175927152734894
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800.970508534839770.05898293032045960.0294914651602298
810.9624254626070980.07514907478580350.0375745373929017
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830.9415724636472870.1168550727054260.058427536352713
840.9992672371090020.00146552578199550.000732762890997749
850.9989150140573310.002169971885338650.00108498594266932
860.998418807190440.003162385619119590.00158119280955979
870.9981138085980410.003772382803917620.00188619140195881
880.9973055051389210.005388989722157770.00269449486107889
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900.9954435050440060.00911298991198840.0045564949559942
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920.991534135940610.01693172811877930.00846586405938966
930.988825620265820.02234875946836010.0111743797341801
940.9852964314430960.02940713711380840.0147035685569042
950.9800948207913760.03981035841724830.0199051792086242
960.9742274179063590.05154516418728230.0257725820936412
970.965905707441160.0681885851176810.0340942925588405
980.9566533183123460.08669336337530820.0433466816876541
990.945354233367820.109291533264360.05464576663218
1000.9317311197907690.1365377604184610.0682688802092307
1010.9155161815958750.168967636808250.084483818404125
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1100.6223318163775440.7553363672449110.377668183622456
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1120.5198257864978320.9603484270043360.480174213502168
1130.4725462521967240.9450925043934480.527453747803276
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1480.0003504075746410380.0007008151492820760.999649592425359
1490.0002877654975556250.000575530995111250.999712234502444







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level310.213793103448276NOK
5% type I error level470.324137931034483NOK
10% type I error level630.43448275862069NOK

\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 & 31 & 0.213793103448276 & NOK \tabularnewline
5% type I error level & 47 & 0.324137931034483 & NOK \tabularnewline
10% type I error level & 63 & 0.43448275862069 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203384&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]31[/C][C]0.213793103448276[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]47[/C][C]0.324137931034483[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.43448275862069[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203384&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203384&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 level310.213793103448276NOK
5% type I error level470.324137931034483NOK
10% type I error level630.43448275862069NOK



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