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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 computationWed, 19 Dec 2012 07:32:37 -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/19/t1355920363ikp74h5s4zqq6ml.htm/, Retrieved Thu, 31 Oct 2024 22:50:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201875, Retrieved Thu, 31 Oct 2024 22:50:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-12-19 12:32:37] [da376ddf2178406f716e1f42a370275c] [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
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	1
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	1
0	1
0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201875&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201875&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201875&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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectA0lysis[t] = + 0.0750409449600476 + 0.00297781527619237T40[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectA0lysis[t] =  +  0.0750409449600476 +  0.00297781527619237T40[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201875&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectA0lysis[t] =  +  0.0750409449600476 +  0.00297781527619237T40[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201875&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201875&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
CorrectA0lysis[t] = + 0.0750409449600476 + 0.00297781527619237T40[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.07504094496004760.0315192.38080.0185120.009256
T400.002977815276192370.0235860.12630.89970.44985

\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) & 0.0750409449600476 & 0.031519 & 2.3808 & 0.018512 & 0.009256 \tabularnewline
T40 & 0.00297781527619237 & 0.023586 & 0.1263 & 0.8997 & 0.44985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201875&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]0.0750409449600476[/C][C]0.031519[/C][C]2.3808[/C][C]0.018512[/C][C]0.009256[/C][/ROW]
[ROW][C]T40[/C][C]0.00297781527619237[/C][C]0.023586[/C][C]0.1263[/C][C]0.8997[/C][C]0.44985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201875&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201875&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)0.07504094496004760.0315192.38080.0185120.009256
T400.002977815276192370.0235860.12630.89970.44985







Multiple Linear Regression - Regression Statistics
Multiple R0.0102397583257689
R-squared0.000104852650570154
Adjusted R-squared-0.00647340489778125
F-TEST (value)0.0159392741618074
F-TEST (DF numerator)1
F-TEST (DF denominator)152
p-value0.899700215159793
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.269792499178403
Sum Squared Residuals11.0637748771651

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0102397583257689 \tabularnewline
R-squared & 0.000104852650570154 \tabularnewline
Adjusted R-squared & -0.00647340489778125 \tabularnewline
F-TEST (value) & 0.0159392741618074 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0.899700215159793 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.269792499178403 \tabularnewline
Sum Squared Residuals & 11.0637748771651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201875&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0102397583257689[/C][/ROW]
[ROW][C]R-squared[/C][C]0.000104852650570154[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00647340489778125[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.0159392741618074[/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]0.899700215159793[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.269792499178403[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11.0637748771651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201875&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201875&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 R0.0102397583257689
R-squared0.000104852650570154
Adjusted R-squared-0.00647340489778125
F-TEST (value)0.0159392741618074
F-TEST (DF numerator)1
F-TEST (DF denominator)152
p-value0.899700215159793
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.269792499178403
Sum Squared Residuals11.0637748771651







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.07801876023624-0.07801876023624
200.0809965755124324-0.0809965755124324
300.0809965755124324-0.0809965755124324
400.0809965755124324-0.0809965755124324
500.0809965755124324-0.0809965755124324
600.0809965755124324-0.0809965755124324
700.0809965755124324-0.0809965755124324
800.07801876023624-0.07801876023624
900.0809965755124324-0.0809965755124324
1000.0809965755124324-0.0809965755124324
1100.07801876023624-0.07801876023624
1200.0809965755124324-0.0809965755124324
1300.0809965755124324-0.0809965755124324
1400.07801876023624-0.07801876023624
1500.0809965755124324-0.0809965755124324
1600.07801876023624-0.07801876023624
1710.078018760236240.92198123976376
1800.07801876023624-0.07801876023624
1900.0809965755124324-0.0809965755124324
2010.078018760236240.92198123976376
2100.0809965755124324-0.0809965755124324
2200.0809965755124324-0.0809965755124324
2300.0809965755124324-0.0809965755124324
2400.0809965755124324-0.0809965755124324
2500.07801876023624-0.07801876023624
2600.0809965755124324-0.0809965755124324
2700.0809965755124324-0.0809965755124324
2800.0809965755124324-0.0809965755124324
2900.0809965755124324-0.0809965755124324
3000.0809965755124324-0.0809965755124324
3100.0809965755124324-0.0809965755124324
3200.0809965755124324-0.0809965755124324
3300.0809965755124324-0.0809965755124324
3400.07801876023624-0.07801876023624
3500.0809965755124324-0.0809965755124324
3600.0809965755124324-0.0809965755124324
3700.07801876023624-0.07801876023624
3800.0809965755124324-0.0809965755124324
3900.0809965755124324-0.0809965755124324
4000.07801876023624-0.07801876023624
4110.08099657551243230.919003424487568
4200.0809965755124324-0.0809965755124324
4300.0809965755124324-0.0809965755124324
4400.07801876023624-0.07801876023624
4500.0809965755124324-0.0809965755124324
4600.0809965755124324-0.0809965755124324
4700.0809965755124324-0.0809965755124324
4800.0809965755124324-0.0809965755124324
4900.0809965755124324-0.0809965755124324
5000.0809965755124324-0.0809965755124324
5100.07801876023624-0.07801876023624
5210.078018760236240.92198123976376
5300.0809965755124324-0.0809965755124324
5410.08099657551243230.919003424487568
5500.0809965755124324-0.0809965755124324
5600.07801876023624-0.07801876023624
5700.0809965755124324-0.0809965755124324
5800.0809965755124324-0.0809965755124324
5900.0809965755124324-0.0809965755124324
6010.078018760236240.92198123976376
6100.07801876023624-0.07801876023624
6200.0809965755124324-0.0809965755124324
6300.0809965755124324-0.0809965755124324
6400.07801876023624-0.07801876023624
6500.0809965755124324-0.0809965755124324
6600.0809965755124324-0.0809965755124324
6710.078018760236240.92198123976376
6800.0809965755124324-0.0809965755124324
6900.0809965755124324-0.0809965755124324
7000.0809965755124324-0.0809965755124324
7100.0809965755124324-0.0809965755124324
7200.0809965755124324-0.0809965755124324
7300.0809965755124324-0.0809965755124324
7400.0809965755124324-0.0809965755124324
7500.0809965755124324-0.0809965755124324
7600.07801876023624-0.07801876023624
7700.0809965755124324-0.0809965755124324
7800.0809965755124324-0.0809965755124324
7910.078018760236240.92198123976376
8000.07801876023624-0.07801876023624
8100.0809965755124324-0.0809965755124324
8200.0809965755124324-0.0809965755124324
8300.0809965755124324-0.0809965755124324
8410.08099657551243230.919003424487568
8500.0809965755124324-0.0809965755124324
8600.0809965755124324-0.0809965755124324
8700.0750409449600476-0.0750409449600476
8800.0750409449600476-0.0750409449600476
8900.0750409449600476-0.0750409449600476
9000.0750409449600476-0.0750409449600476
9100.0750409449600476-0.0750409449600476
9200.0750409449600476-0.0750409449600476
9300.0750409449600476-0.0750409449600476
9400.0750409449600476-0.0750409449600476
9500.0750409449600476-0.0750409449600476
9600.0750409449600476-0.0750409449600476
9700.0750409449600476-0.0750409449600476
9800.0750409449600476-0.0750409449600476
9900.0750409449600476-0.0750409449600476
10000.0750409449600476-0.0750409449600476
10100.0750409449600476-0.0750409449600476
10200.0750409449600476-0.0750409449600476
10300.0750409449600476-0.0750409449600476
10400.0750409449600476-0.0750409449600476
10500.0750409449600476-0.0750409449600476
10600.0750409449600476-0.0750409449600476
10700.0750409449600476-0.0750409449600476
10800.0750409449600476-0.0750409449600476
10900.0750409449600476-0.0750409449600476
11000.0750409449600476-0.0750409449600476
11100.0750409449600476-0.0750409449600476
11200.0750409449600476-0.0750409449600476
11300.0750409449600476-0.0750409449600476
11400.0750409449600476-0.0750409449600476
11500.0750409449600476-0.0750409449600476
11600.0750409449600476-0.0750409449600476
11700.0750409449600476-0.0750409449600476
11800.0750409449600476-0.0750409449600476
11900.0750409449600476-0.0750409449600476
12000.0750409449600476-0.0750409449600476
12100.0750409449600476-0.0750409449600476
12200.0750409449600476-0.0750409449600476
12300.0750409449600476-0.0750409449600476
12400.0750409449600476-0.0750409449600476
12500.0750409449600476-0.0750409449600476
12600.0750409449600476-0.0750409449600476
12700.0750409449600476-0.0750409449600476
12800.0750409449600476-0.0750409449600476
12900.0750409449600476-0.0750409449600476
13000.0750409449600476-0.0750409449600476
13100.0750409449600476-0.0750409449600476
13200.0750409449600476-0.0750409449600476
13300.0750409449600476-0.0750409449600476
13400.0750409449600476-0.0750409449600476
13500.0750409449600476-0.0750409449600476
13600.0750409449600476-0.0750409449600476
13700.0750409449600476-0.0750409449600476
13800.0750409449600476-0.0750409449600476
13900.0750409449600476-0.0750409449600476
14000.0750409449600476-0.0750409449600476
14110.07504094496004770.924959055039952
14200.0750409449600476-0.0750409449600476
14300.0750409449600476-0.0750409449600476
14400.0750409449600476-0.0750409449600476
14500.0750409449600476-0.0750409449600476
14600.0750409449600476-0.0750409449600476
14700.0750409449600476-0.0750409449600476
14800.0750409449600476-0.0750409449600476
14900.0750409449600476-0.0750409449600476
15000.0750409449600476-0.0750409449600476
15100.0750409449600476-0.0750409449600476
15210.07504094496004770.924959055039952
15310.07504094496004770.924959055039952
15400.0750409449600476-0.0750409449600476

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
2 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
3 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
4 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
5 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
6 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
7 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
8 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
9 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
10 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
11 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
12 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
13 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
14 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
15 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
16 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
17 & 1 & 0.07801876023624 & 0.92198123976376 \tabularnewline
18 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
19 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
20 & 1 & 0.07801876023624 & 0.92198123976376 \tabularnewline
21 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
22 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
23 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
24 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
25 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
26 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
27 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
28 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
29 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
30 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
31 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
32 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
33 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
34 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
35 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
36 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
37 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
38 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
39 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
40 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
41 & 1 & 0.0809965755124323 & 0.919003424487568 \tabularnewline
42 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
43 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
44 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
45 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
46 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
47 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
48 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
49 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
50 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
51 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
52 & 1 & 0.07801876023624 & 0.92198123976376 \tabularnewline
53 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
54 & 1 & 0.0809965755124323 & 0.919003424487568 \tabularnewline
55 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
56 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
57 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
58 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
59 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
60 & 1 & 0.07801876023624 & 0.92198123976376 \tabularnewline
61 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
62 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
63 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
64 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
65 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
66 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
67 & 1 & 0.07801876023624 & 0.92198123976376 \tabularnewline
68 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
69 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
70 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
71 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
72 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
73 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
74 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
75 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
76 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
77 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
78 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
79 & 1 & 0.07801876023624 & 0.92198123976376 \tabularnewline
80 & 0 & 0.07801876023624 & -0.07801876023624 \tabularnewline
81 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
82 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
83 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
84 & 1 & 0.0809965755124323 & 0.919003424487568 \tabularnewline
85 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
86 & 0 & 0.0809965755124324 & -0.0809965755124324 \tabularnewline
87 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
88 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
89 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
90 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
91 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
92 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
93 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
94 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
95 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
96 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
97 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
98 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
99 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
100 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
101 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
102 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
103 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
104 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
105 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
106 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
107 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
108 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
109 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
110 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
111 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
112 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
113 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
114 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
115 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
116 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
117 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
118 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
119 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
120 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
121 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
122 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
123 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
124 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
125 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
126 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
127 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
128 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
129 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
130 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
131 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
132 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
133 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
134 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
135 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
136 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
137 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
138 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
139 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
140 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
141 & 1 & 0.0750409449600477 & 0.924959055039952 \tabularnewline
142 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
143 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
144 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
145 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
146 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
147 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
148 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
149 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
150 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
151 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
152 & 1 & 0.0750409449600477 & 0.924959055039952 \tabularnewline
153 & 1 & 0.0750409449600477 & 0.924959055039952 \tabularnewline
154 & 0 & 0.0750409449600476 & -0.0750409449600476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201875&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]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.07801876023624[/C][C]0.92198123976376[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.07801876023624[/C][C]0.92198123976376[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.0809965755124323[/C][C]0.919003424487568[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.07801876023624[/C][C]0.92198123976376[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.0809965755124323[/C][C]0.919003424487568[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.07801876023624[/C][C]0.92198123976376[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.07801876023624[/C][C]0.92198123976376[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.07801876023624[/C][C]0.92198123976376[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.07801876023624[/C][C]-0.07801876023624[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.0809965755124323[/C][C]0.919003424487568[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.0809965755124324[/C][C]-0.0809965755124324[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.0750409449600477[/C][C]0.924959055039952[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.0750409449600477[/C][C]0.924959055039952[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.0750409449600477[/C][C]0.924959055039952[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.0750409449600476[/C][C]-0.0750409449600476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201875&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201875&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
100.07801876023624-0.07801876023624
200.0809965755124324-0.0809965755124324
300.0809965755124324-0.0809965755124324
400.0809965755124324-0.0809965755124324
500.0809965755124324-0.0809965755124324
600.0809965755124324-0.0809965755124324
700.0809965755124324-0.0809965755124324
800.07801876023624-0.07801876023624
900.0809965755124324-0.0809965755124324
1000.0809965755124324-0.0809965755124324
1100.07801876023624-0.07801876023624
1200.0809965755124324-0.0809965755124324
1300.0809965755124324-0.0809965755124324
1400.07801876023624-0.07801876023624
1500.0809965755124324-0.0809965755124324
1600.07801876023624-0.07801876023624
1710.078018760236240.92198123976376
1800.07801876023624-0.07801876023624
1900.0809965755124324-0.0809965755124324
2010.078018760236240.92198123976376
2100.0809965755124324-0.0809965755124324
2200.0809965755124324-0.0809965755124324
2300.0809965755124324-0.0809965755124324
2400.0809965755124324-0.0809965755124324
2500.07801876023624-0.07801876023624
2600.0809965755124324-0.0809965755124324
2700.0809965755124324-0.0809965755124324
2800.0809965755124324-0.0809965755124324
2900.0809965755124324-0.0809965755124324
3000.0809965755124324-0.0809965755124324
3100.0809965755124324-0.0809965755124324
3200.0809965755124324-0.0809965755124324
3300.0809965755124324-0.0809965755124324
3400.07801876023624-0.07801876023624
3500.0809965755124324-0.0809965755124324
3600.0809965755124324-0.0809965755124324
3700.07801876023624-0.07801876023624
3800.0809965755124324-0.0809965755124324
3900.0809965755124324-0.0809965755124324
4000.07801876023624-0.07801876023624
4110.08099657551243230.919003424487568
4200.0809965755124324-0.0809965755124324
4300.0809965755124324-0.0809965755124324
4400.07801876023624-0.07801876023624
4500.0809965755124324-0.0809965755124324
4600.0809965755124324-0.0809965755124324
4700.0809965755124324-0.0809965755124324
4800.0809965755124324-0.0809965755124324
4900.0809965755124324-0.0809965755124324
5000.0809965755124324-0.0809965755124324
5100.07801876023624-0.07801876023624
5210.078018760236240.92198123976376
5300.0809965755124324-0.0809965755124324
5410.08099657551243230.919003424487568
5500.0809965755124324-0.0809965755124324
5600.07801876023624-0.07801876023624
5700.0809965755124324-0.0809965755124324
5800.0809965755124324-0.0809965755124324
5900.0809965755124324-0.0809965755124324
6010.078018760236240.92198123976376
6100.07801876023624-0.07801876023624
6200.0809965755124324-0.0809965755124324
6300.0809965755124324-0.0809965755124324
6400.07801876023624-0.07801876023624
6500.0809965755124324-0.0809965755124324
6600.0809965755124324-0.0809965755124324
6710.078018760236240.92198123976376
6800.0809965755124324-0.0809965755124324
6900.0809965755124324-0.0809965755124324
7000.0809965755124324-0.0809965755124324
7100.0809965755124324-0.0809965755124324
7200.0809965755124324-0.0809965755124324
7300.0809965755124324-0.0809965755124324
7400.0809965755124324-0.0809965755124324
7500.0809965755124324-0.0809965755124324
7600.07801876023624-0.07801876023624
7700.0809965755124324-0.0809965755124324
7800.0809965755124324-0.0809965755124324
7910.078018760236240.92198123976376
8000.07801876023624-0.07801876023624
8100.0809965755124324-0.0809965755124324
8200.0809965755124324-0.0809965755124324
8300.0809965755124324-0.0809965755124324
8410.08099657551243230.919003424487568
8500.0809965755124324-0.0809965755124324
8600.0809965755124324-0.0809965755124324
8700.0750409449600476-0.0750409449600476
8800.0750409449600476-0.0750409449600476
8900.0750409449600476-0.0750409449600476
9000.0750409449600476-0.0750409449600476
9100.0750409449600476-0.0750409449600476
9200.0750409449600476-0.0750409449600476
9300.0750409449600476-0.0750409449600476
9400.0750409449600476-0.0750409449600476
9500.0750409449600476-0.0750409449600476
9600.0750409449600476-0.0750409449600476
9700.0750409449600476-0.0750409449600476
9800.0750409449600476-0.0750409449600476
9900.0750409449600476-0.0750409449600476
10000.0750409449600476-0.0750409449600476
10100.0750409449600476-0.0750409449600476
10200.0750409449600476-0.0750409449600476
10300.0750409449600476-0.0750409449600476
10400.0750409449600476-0.0750409449600476
10500.0750409449600476-0.0750409449600476
10600.0750409449600476-0.0750409449600476
10700.0750409449600476-0.0750409449600476
10800.0750409449600476-0.0750409449600476
10900.0750409449600476-0.0750409449600476
11000.0750409449600476-0.0750409449600476
11100.0750409449600476-0.0750409449600476
11200.0750409449600476-0.0750409449600476
11300.0750409449600476-0.0750409449600476
11400.0750409449600476-0.0750409449600476
11500.0750409449600476-0.0750409449600476
11600.0750409449600476-0.0750409449600476
11700.0750409449600476-0.0750409449600476
11800.0750409449600476-0.0750409449600476
11900.0750409449600476-0.0750409449600476
12000.0750409449600476-0.0750409449600476
12100.0750409449600476-0.0750409449600476
12200.0750409449600476-0.0750409449600476
12300.0750409449600476-0.0750409449600476
12400.0750409449600476-0.0750409449600476
12500.0750409449600476-0.0750409449600476
12600.0750409449600476-0.0750409449600476
12700.0750409449600476-0.0750409449600476
12800.0750409449600476-0.0750409449600476
12900.0750409449600476-0.0750409449600476
13000.0750409449600476-0.0750409449600476
13100.0750409449600476-0.0750409449600476
13200.0750409449600476-0.0750409449600476
13300.0750409449600476-0.0750409449600476
13400.0750409449600476-0.0750409449600476
13500.0750409449600476-0.0750409449600476
13600.0750409449600476-0.0750409449600476
13700.0750409449600476-0.0750409449600476
13800.0750409449600476-0.0750409449600476
13900.0750409449600476-0.0750409449600476
14000.0750409449600476-0.0750409449600476
14110.07504094496004770.924959055039952
14200.0750409449600476-0.0750409449600476
14300.0750409449600476-0.0750409449600476
14400.0750409449600476-0.0750409449600476
14500.0750409449600476-0.0750409449600476
14600.0750409449600476-0.0750409449600476
14700.0750409449600476-0.0750409449600476
14800.0750409449600476-0.0750409449600476
14900.0750409449600476-0.0750409449600476
15000.0750409449600476-0.0750409449600476
15100.0750409449600476-0.0750409449600476
15210.07504094496004770.924959055039952
15310.07504094496004770.924959055039952
15400.0750409449600476-0.0750409449600476







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5001
6001
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.262188426691310.524376853382620.73781157330869
180.2210408536007030.4420817072014060.778959146399297
190.1676153453970910.3352306907941820.832384654602909
200.7221640057332890.5556719885334210.277835994266711
210.6615181384073310.6769637231853380.338481861592669
220.5974548737384570.8050902525230860.402545126261543
230.5317096225239580.9365807549520850.468290377476042
240.4660758892768240.9321517785536480.533924110723176
250.4452509764175720.8905019528351440.554749023582428
260.3831687514773390.7663375029546780.616831248522661
270.324706638159590.6494132763191810.675293361840409
280.2709331769610160.5418663539220330.729066823038984
290.2225813143279830.4451626286559660.777418685672017
300.1800458800872260.3600917601744520.819954119912774
310.1434105505191930.2868211010383860.856589449480807
320.1124961191061120.2249922382122230.887503880893888
330.08692095381410950.1738419076282190.913079046185891
340.07840584013340680.1568116802668140.921594159866593
350.05949206545304080.1189841309060820.940507934546959
360.04449592607852530.08899185215705060.955504073921475
370.03842941785404250.07685883570808510.961570582145957
380.02820435177983530.05640870355967060.971795648220165
390.020420914436950.04084182887389990.97957908556305
400.01690830502059320.03381661004118640.983091694979407
410.3742939460332030.7485878920664060.625706053966797
420.3273787897563450.654757579512690.672621210243655
430.2833751611691590.5667503223383170.716624838830841
440.2531451581073640.5062903162147280.746854841892636
450.2152402862562190.4304805725124380.784759713743781
460.1811130734783480.3622261469566960.818886926521652
470.1508274573601060.3016549147202110.849172542639894
480.1243273772891180.2486547545782360.875672622710882
490.1014566022550780.2029132045101560.898543397744922
500.08198109633369840.1639621926673970.918018903666302
510.06906192334261180.1381238466852240.930938076657388
520.3841601820271860.7683203640543730.615839817972814
530.3412764921816960.6825529843633920.658723507818304
540.8267721761847450.346455647630510.173227823815255
550.7964350563492830.4071298873014330.203564943650717
560.7731163848494540.4537672303010920.226883615150546
570.7382227344289820.5235545311420350.261777265571018
580.7010314010831010.5979371978337970.298968598916899
590.6619183009936140.6761633980127720.338081699006386
600.9289095812595780.1421808374808430.0710904187404216
610.9190620116997670.1618759766004650.0809379883002326
620.9013993987291880.1972012025416240.0986006012708121
630.8812299489408940.2375401021182130.118770051059106
640.8661676112053820.2676647775892370.133832388794618
650.8417062228943950.3165875542112110.158293777105605
660.8147781374432440.3704437251135110.185221862556756
670.9757606814062380.04847863718752450.0242393185937622
680.9689442401975220.06211151960495540.0310557598024777
690.9607021086918980.07859578261620350.0392978913081017
700.9508903737435550.09821925251289040.0491096262564452
710.9394003248555770.1211993502888450.0605996751444226
720.9261801661566290.1476396676867420.0738198338433709
730.9112636391517430.1774727216965130.0887363608482565
740.8948091556534460.2103816886931090.105190844346554
750.8771567040308260.2456865919383480.122843295969174
760.8645410834339860.2709178331320290.135458916566014
770.8456559619147220.3086880761705560.154344038085278
780.8277256951006650.3445486097986710.172274304899335
790.9789332136647170.04213357267056610.021066786335283
800.9758618427779970.04827631444400540.0241381572220027
810.9710754260110350.05784914797792940.0289245739889647
820.966988081103460.06602383779308050.0330119188965402
830.9655964075149080.06880718497018450.0344035924850923
840.9991110212541620.001777957491675210.000888978745837605
850.9986806804473420.002638639105315320.00131931955265766
860.9980656323273430.003868735345314630.00193436767265731
870.9979804743846930.004039051230613490.00201952561530674
880.9976786058851450.004642788229710630.00232139411485532
890.9971759550885070.005648089822986260.00282404491149313
900.9964447240374650.007110551925069980.00355527596253499
910.9954310347704820.009137930459035470.00456896522951774
920.994058647807690.01188270438462040.00594135219231022
930.9922284440933930.01554311181321380.00777155590660691
940.9898164850049460.02036702999010770.0101835149950539
950.9866717753047730.02665644939045490.0133282246952274
960.9826143917069320.03477121658613670.0173856082930683
970.9774345205323450.04513095893530920.0225654794676546
980.9708929252601890.05821414947962190.0291070747398109
990.9627233520892340.07455329582153110.0372766479107655
1000.9526373392216730.09472532155665340.0473626607783267
1010.9403318009506620.1193363980986760.059668199049338
1020.9254995980454190.1490008039091620.0745004019545809
1030.9078430778236020.1843138443527960.0921569221763982
1040.8870902775546370.2258194448907250.112909722445363
1050.8630131519775620.2739736960448750.136986848022438
1060.835446840018950.32910631996210.16455315998105
1070.8043086675859330.3913826648281330.195691332414067
1080.7696153391437050.460769321712590.230384660856295
1090.7314966476088230.5370067047823550.268503352391177
1100.6902040699043390.6195918601913220.309795930095661
1110.6461128396089440.7077743207821130.353887160391057
1120.5997165021399820.8005669957200360.400283497860018
1130.5516135393232350.896772921353530.448386460676765
1140.5024863494775350.995027301044930.497513650522465
1150.4530736133436340.9061472266872680.546926386656366
1160.4041377771762670.8082755543525340.595862222823733
1170.3564299506505740.7128599013011480.643570049349426
1180.3106548680345980.6213097360691960.689345131965402
1190.2674386395578560.5348772791157110.732561360442144
1200.2273018039554570.4546036079109150.772698196044543
1210.1906397008300910.3812794016601810.809360299169909
1220.1577114698871160.3154229397742330.842288530112884
1230.1286381417456260.2572762834912510.871361858254374
1240.1034094184556670.2068188369113340.896590581544333
1250.08189795915212020.163795918304240.91810204084788
1260.06387938036471930.1277587607294390.936120619635281
1270.04905581527564450.09811163055128910.950944184724355
1280.03708077808294540.07416155616589080.962919221917055
1290.0275832358221690.05516647164433790.972416764177831
1300.02018915362269110.04037830724538230.979810846377309
1310.01453927983540320.02907855967080650.985460720164597
1320.0103024939399260.02060498787985190.989697506060074
1330.007184575295958960.01436915059191790.992815424704041
1340.004932702245963630.009865404491927260.995067297754036
1350.003336318640530340.006672637281060690.99666368135947
1360.002225193022812770.004450386045625540.997774806977187
1370.001465551422952780.002931102845905560.998534448577047
1380.0009551118186629860.001910223637325970.999044888181337
1390.0006177202337355790.001235440467471160.999382279766264
1400.0003981208327028380.0007962416654056750.999601879167297
1410.0125746145523360.02514922910467190.987425385447664
1420.008208403461946730.01641680692389350.991791596538053
1430.005249364272848510.0104987285456970.994750635727151
1440.003302471343067110.006604942686134220.996697528656933
1450.002058928124763370.004117856249526730.997941071875237
1460.001288590035286030.002577180070572050.998711409964714
1470.0008284914274406510.00165698285488130.999171508572559
1480.0005712645646935260.001142529129387050.999428735435307
1490.0004601066667329780.0009202133334659560.999539893333267

\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 & 0.26218842669131 & 0.52437685338262 & 0.73781157330869 \tabularnewline
18 & 0.221040853600703 & 0.442081707201406 & 0.778959146399297 \tabularnewline
19 & 0.167615345397091 & 0.335230690794182 & 0.832384654602909 \tabularnewline
20 & 0.722164005733289 & 0.555671988533421 & 0.277835994266711 \tabularnewline
21 & 0.661518138407331 & 0.676963723185338 & 0.338481861592669 \tabularnewline
22 & 0.597454873738457 & 0.805090252523086 & 0.402545126261543 \tabularnewline
23 & 0.531709622523958 & 0.936580754952085 & 0.468290377476042 \tabularnewline
24 & 0.466075889276824 & 0.932151778553648 & 0.533924110723176 \tabularnewline
25 & 0.445250976417572 & 0.890501952835144 & 0.554749023582428 \tabularnewline
26 & 0.383168751477339 & 0.766337502954678 & 0.616831248522661 \tabularnewline
27 & 0.32470663815959 & 0.649413276319181 & 0.675293361840409 \tabularnewline
28 & 0.270933176961016 & 0.541866353922033 & 0.729066823038984 \tabularnewline
29 & 0.222581314327983 & 0.445162628655966 & 0.777418685672017 \tabularnewline
30 & 0.180045880087226 & 0.360091760174452 & 0.819954119912774 \tabularnewline
31 & 0.143410550519193 & 0.286821101038386 & 0.856589449480807 \tabularnewline
32 & 0.112496119106112 & 0.224992238212223 & 0.887503880893888 \tabularnewline
33 & 0.0869209538141095 & 0.173841907628219 & 0.913079046185891 \tabularnewline
34 & 0.0784058401334068 & 0.156811680266814 & 0.921594159866593 \tabularnewline
35 & 0.0594920654530408 & 0.118984130906082 & 0.940507934546959 \tabularnewline
36 & 0.0444959260785253 & 0.0889918521570506 & 0.955504073921475 \tabularnewline
37 & 0.0384294178540425 & 0.0768588357080851 & 0.961570582145957 \tabularnewline
38 & 0.0282043517798353 & 0.0564087035596706 & 0.971795648220165 \tabularnewline
39 & 0.02042091443695 & 0.0408418288738999 & 0.97957908556305 \tabularnewline
40 & 0.0169083050205932 & 0.0338166100411864 & 0.983091694979407 \tabularnewline
41 & 0.374293946033203 & 0.748587892066406 & 0.625706053966797 \tabularnewline
42 & 0.327378789756345 & 0.65475757951269 & 0.672621210243655 \tabularnewline
43 & 0.283375161169159 & 0.566750322338317 & 0.716624838830841 \tabularnewline
44 & 0.253145158107364 & 0.506290316214728 & 0.746854841892636 \tabularnewline
45 & 0.215240286256219 & 0.430480572512438 & 0.784759713743781 \tabularnewline
46 & 0.181113073478348 & 0.362226146956696 & 0.818886926521652 \tabularnewline
47 & 0.150827457360106 & 0.301654914720211 & 0.849172542639894 \tabularnewline
48 & 0.124327377289118 & 0.248654754578236 & 0.875672622710882 \tabularnewline
49 & 0.101456602255078 & 0.202913204510156 & 0.898543397744922 \tabularnewline
50 & 0.0819810963336984 & 0.163962192667397 & 0.918018903666302 \tabularnewline
51 & 0.0690619233426118 & 0.138123846685224 & 0.930938076657388 \tabularnewline
52 & 0.384160182027186 & 0.768320364054373 & 0.615839817972814 \tabularnewline
53 & 0.341276492181696 & 0.682552984363392 & 0.658723507818304 \tabularnewline
54 & 0.826772176184745 & 0.34645564763051 & 0.173227823815255 \tabularnewline
55 & 0.796435056349283 & 0.407129887301433 & 0.203564943650717 \tabularnewline
56 & 0.773116384849454 & 0.453767230301092 & 0.226883615150546 \tabularnewline
57 & 0.738222734428982 & 0.523554531142035 & 0.261777265571018 \tabularnewline
58 & 0.701031401083101 & 0.597937197833797 & 0.298968598916899 \tabularnewline
59 & 0.661918300993614 & 0.676163398012772 & 0.338081699006386 \tabularnewline
60 & 0.928909581259578 & 0.142180837480843 & 0.0710904187404216 \tabularnewline
61 & 0.919062011699767 & 0.161875976600465 & 0.0809379883002326 \tabularnewline
62 & 0.901399398729188 & 0.197201202541624 & 0.0986006012708121 \tabularnewline
63 & 0.881229948940894 & 0.237540102118213 & 0.118770051059106 \tabularnewline
64 & 0.866167611205382 & 0.267664777589237 & 0.133832388794618 \tabularnewline
65 & 0.841706222894395 & 0.316587554211211 & 0.158293777105605 \tabularnewline
66 & 0.814778137443244 & 0.370443725113511 & 0.185221862556756 \tabularnewline
67 & 0.975760681406238 & 0.0484786371875245 & 0.0242393185937622 \tabularnewline
68 & 0.968944240197522 & 0.0621115196049554 & 0.0310557598024777 \tabularnewline
69 & 0.960702108691898 & 0.0785957826162035 & 0.0392978913081017 \tabularnewline
70 & 0.950890373743555 & 0.0982192525128904 & 0.0491096262564452 \tabularnewline
71 & 0.939400324855577 & 0.121199350288845 & 0.0605996751444226 \tabularnewline
72 & 0.926180166156629 & 0.147639667686742 & 0.0738198338433709 \tabularnewline
73 & 0.911263639151743 & 0.177472721696513 & 0.0887363608482565 \tabularnewline
74 & 0.894809155653446 & 0.210381688693109 & 0.105190844346554 \tabularnewline
75 & 0.877156704030826 & 0.245686591938348 & 0.122843295969174 \tabularnewline
76 & 0.864541083433986 & 0.270917833132029 & 0.135458916566014 \tabularnewline
77 & 0.845655961914722 & 0.308688076170556 & 0.154344038085278 \tabularnewline
78 & 0.827725695100665 & 0.344548609798671 & 0.172274304899335 \tabularnewline
79 & 0.978933213664717 & 0.0421335726705661 & 0.021066786335283 \tabularnewline
80 & 0.975861842777997 & 0.0482763144440054 & 0.0241381572220027 \tabularnewline
81 & 0.971075426011035 & 0.0578491479779294 & 0.0289245739889647 \tabularnewline
82 & 0.96698808110346 & 0.0660238377930805 & 0.0330119188965402 \tabularnewline
83 & 0.965596407514908 & 0.0688071849701845 & 0.0344035924850923 \tabularnewline
84 & 0.999111021254162 & 0.00177795749167521 & 0.000888978745837605 \tabularnewline
85 & 0.998680680447342 & 0.00263863910531532 & 0.00131931955265766 \tabularnewline
86 & 0.998065632327343 & 0.00386873534531463 & 0.00193436767265731 \tabularnewline
87 & 0.997980474384693 & 0.00403905123061349 & 0.00201952561530674 \tabularnewline
88 & 0.997678605885145 & 0.00464278822971063 & 0.00232139411485532 \tabularnewline
89 & 0.997175955088507 & 0.00564808982298626 & 0.00282404491149313 \tabularnewline
90 & 0.996444724037465 & 0.00711055192506998 & 0.00355527596253499 \tabularnewline
91 & 0.995431034770482 & 0.00913793045903547 & 0.00456896522951774 \tabularnewline
92 & 0.99405864780769 & 0.0118827043846204 & 0.00594135219231022 \tabularnewline
93 & 0.992228444093393 & 0.0155431118132138 & 0.00777155590660691 \tabularnewline
94 & 0.989816485004946 & 0.0203670299901077 & 0.0101835149950539 \tabularnewline
95 & 0.986671775304773 & 0.0266564493904549 & 0.0133282246952274 \tabularnewline
96 & 0.982614391706932 & 0.0347712165861367 & 0.0173856082930683 \tabularnewline
97 & 0.977434520532345 & 0.0451309589353092 & 0.0225654794676546 \tabularnewline
98 & 0.970892925260189 & 0.0582141494796219 & 0.0291070747398109 \tabularnewline
99 & 0.962723352089234 & 0.0745532958215311 & 0.0372766479107655 \tabularnewline
100 & 0.952637339221673 & 0.0947253215566534 & 0.0473626607783267 \tabularnewline
101 & 0.940331800950662 & 0.119336398098676 & 0.059668199049338 \tabularnewline
102 & 0.925499598045419 & 0.149000803909162 & 0.0745004019545809 \tabularnewline
103 & 0.907843077823602 & 0.184313844352796 & 0.0921569221763982 \tabularnewline
104 & 0.887090277554637 & 0.225819444890725 & 0.112909722445363 \tabularnewline
105 & 0.863013151977562 & 0.273973696044875 & 0.136986848022438 \tabularnewline
106 & 0.83544684001895 & 0.3291063199621 & 0.16455315998105 \tabularnewline
107 & 0.804308667585933 & 0.391382664828133 & 0.195691332414067 \tabularnewline
108 & 0.769615339143705 & 0.46076932171259 & 0.230384660856295 \tabularnewline
109 & 0.731496647608823 & 0.537006704782355 & 0.268503352391177 \tabularnewline
110 & 0.690204069904339 & 0.619591860191322 & 0.309795930095661 \tabularnewline
111 & 0.646112839608944 & 0.707774320782113 & 0.353887160391057 \tabularnewline
112 & 0.599716502139982 & 0.800566995720036 & 0.400283497860018 \tabularnewline
113 & 0.551613539323235 & 0.89677292135353 & 0.448386460676765 \tabularnewline
114 & 0.502486349477535 & 0.99502730104493 & 0.497513650522465 \tabularnewline
115 & 0.453073613343634 & 0.906147226687268 & 0.546926386656366 \tabularnewline
116 & 0.404137777176267 & 0.808275554352534 & 0.595862222823733 \tabularnewline
117 & 0.356429950650574 & 0.712859901301148 & 0.643570049349426 \tabularnewline
118 & 0.310654868034598 & 0.621309736069196 & 0.689345131965402 \tabularnewline
119 & 0.267438639557856 & 0.534877279115711 & 0.732561360442144 \tabularnewline
120 & 0.227301803955457 & 0.454603607910915 & 0.772698196044543 \tabularnewline
121 & 0.190639700830091 & 0.381279401660181 & 0.809360299169909 \tabularnewline
122 & 0.157711469887116 & 0.315422939774233 & 0.842288530112884 \tabularnewline
123 & 0.128638141745626 & 0.257276283491251 & 0.871361858254374 \tabularnewline
124 & 0.103409418455667 & 0.206818836911334 & 0.896590581544333 \tabularnewline
125 & 0.0818979591521202 & 0.16379591830424 & 0.91810204084788 \tabularnewline
126 & 0.0638793803647193 & 0.127758760729439 & 0.936120619635281 \tabularnewline
127 & 0.0490558152756445 & 0.0981116305512891 & 0.950944184724355 \tabularnewline
128 & 0.0370807780829454 & 0.0741615561658908 & 0.962919221917055 \tabularnewline
129 & 0.027583235822169 & 0.0551664716443379 & 0.972416764177831 \tabularnewline
130 & 0.0201891536226911 & 0.0403783072453823 & 0.979810846377309 \tabularnewline
131 & 0.0145392798354032 & 0.0290785596708065 & 0.985460720164597 \tabularnewline
132 & 0.010302493939926 & 0.0206049878798519 & 0.989697506060074 \tabularnewline
133 & 0.00718457529595896 & 0.0143691505919179 & 0.992815424704041 \tabularnewline
134 & 0.00493270224596363 & 0.00986540449192726 & 0.995067297754036 \tabularnewline
135 & 0.00333631864053034 & 0.00667263728106069 & 0.99666368135947 \tabularnewline
136 & 0.00222519302281277 & 0.00445038604562554 & 0.997774806977187 \tabularnewline
137 & 0.00146555142295278 & 0.00293110284590556 & 0.998534448577047 \tabularnewline
138 & 0.000955111818662986 & 0.00191022363732597 & 0.999044888181337 \tabularnewline
139 & 0.000617720233735579 & 0.00123544046747116 & 0.999382279766264 \tabularnewline
140 & 0.000398120832702838 & 0.000796241665405675 & 0.999601879167297 \tabularnewline
141 & 0.012574614552336 & 0.0251492291046719 & 0.987425385447664 \tabularnewline
142 & 0.00820840346194673 & 0.0164168069238935 & 0.991791596538053 \tabularnewline
143 & 0.00524936427284851 & 0.010498728545697 & 0.994750635727151 \tabularnewline
144 & 0.00330247134306711 & 0.00660494268613422 & 0.996697528656933 \tabularnewline
145 & 0.00205892812476337 & 0.00411785624952673 & 0.997941071875237 \tabularnewline
146 & 0.00128859003528603 & 0.00257718007057205 & 0.998711409964714 \tabularnewline
147 & 0.000828491427440651 & 0.0016569828548813 & 0.999171508572559 \tabularnewline
148 & 0.000571264564693526 & 0.00114252912938705 & 0.999428735435307 \tabularnewline
149 & 0.000460106666732978 & 0.000920213333465956 & 0.999539893333267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201875&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]0.26218842669131[/C][C]0.52437685338262[/C][C]0.73781157330869[/C][/ROW]
[ROW][C]18[/C][C]0.221040853600703[/C][C]0.442081707201406[/C][C]0.778959146399297[/C][/ROW]
[ROW][C]19[/C][C]0.167615345397091[/C][C]0.335230690794182[/C][C]0.832384654602909[/C][/ROW]
[ROW][C]20[/C][C]0.722164005733289[/C][C]0.555671988533421[/C][C]0.277835994266711[/C][/ROW]
[ROW][C]21[/C][C]0.661518138407331[/C][C]0.676963723185338[/C][C]0.338481861592669[/C][/ROW]
[ROW][C]22[/C][C]0.597454873738457[/C][C]0.805090252523086[/C][C]0.402545126261543[/C][/ROW]
[ROW][C]23[/C][C]0.531709622523958[/C][C]0.936580754952085[/C][C]0.468290377476042[/C][/ROW]
[ROW][C]24[/C][C]0.466075889276824[/C][C]0.932151778553648[/C][C]0.533924110723176[/C][/ROW]
[ROW][C]25[/C][C]0.445250976417572[/C][C]0.890501952835144[/C][C]0.554749023582428[/C][/ROW]
[ROW][C]26[/C][C]0.383168751477339[/C][C]0.766337502954678[/C][C]0.616831248522661[/C][/ROW]
[ROW][C]27[/C][C]0.32470663815959[/C][C]0.649413276319181[/C][C]0.675293361840409[/C][/ROW]
[ROW][C]28[/C][C]0.270933176961016[/C][C]0.541866353922033[/C][C]0.729066823038984[/C][/ROW]
[ROW][C]29[/C][C]0.222581314327983[/C][C]0.445162628655966[/C][C]0.777418685672017[/C][/ROW]
[ROW][C]30[/C][C]0.180045880087226[/C][C]0.360091760174452[/C][C]0.819954119912774[/C][/ROW]
[ROW][C]31[/C][C]0.143410550519193[/C][C]0.286821101038386[/C][C]0.856589449480807[/C][/ROW]
[ROW][C]32[/C][C]0.112496119106112[/C][C]0.224992238212223[/C][C]0.887503880893888[/C][/ROW]
[ROW][C]33[/C][C]0.0869209538141095[/C][C]0.173841907628219[/C][C]0.913079046185891[/C][/ROW]
[ROW][C]34[/C][C]0.0784058401334068[/C][C]0.156811680266814[/C][C]0.921594159866593[/C][/ROW]
[ROW][C]35[/C][C]0.0594920654530408[/C][C]0.118984130906082[/C][C]0.940507934546959[/C][/ROW]
[ROW][C]36[/C][C]0.0444959260785253[/C][C]0.0889918521570506[/C][C]0.955504073921475[/C][/ROW]
[ROW][C]37[/C][C]0.0384294178540425[/C][C]0.0768588357080851[/C][C]0.961570582145957[/C][/ROW]
[ROW][C]38[/C][C]0.0282043517798353[/C][C]0.0564087035596706[/C][C]0.971795648220165[/C][/ROW]
[ROW][C]39[/C][C]0.02042091443695[/C][C]0.0408418288738999[/C][C]0.97957908556305[/C][/ROW]
[ROW][C]40[/C][C]0.0169083050205932[/C][C]0.0338166100411864[/C][C]0.983091694979407[/C][/ROW]
[ROW][C]41[/C][C]0.374293946033203[/C][C]0.748587892066406[/C][C]0.625706053966797[/C][/ROW]
[ROW][C]42[/C][C]0.327378789756345[/C][C]0.65475757951269[/C][C]0.672621210243655[/C][/ROW]
[ROW][C]43[/C][C]0.283375161169159[/C][C]0.566750322338317[/C][C]0.716624838830841[/C][/ROW]
[ROW][C]44[/C][C]0.253145158107364[/C][C]0.506290316214728[/C][C]0.746854841892636[/C][/ROW]
[ROW][C]45[/C][C]0.215240286256219[/C][C]0.430480572512438[/C][C]0.784759713743781[/C][/ROW]
[ROW][C]46[/C][C]0.181113073478348[/C][C]0.362226146956696[/C][C]0.818886926521652[/C][/ROW]
[ROW][C]47[/C][C]0.150827457360106[/C][C]0.301654914720211[/C][C]0.849172542639894[/C][/ROW]
[ROW][C]48[/C][C]0.124327377289118[/C][C]0.248654754578236[/C][C]0.875672622710882[/C][/ROW]
[ROW][C]49[/C][C]0.101456602255078[/C][C]0.202913204510156[/C][C]0.898543397744922[/C][/ROW]
[ROW][C]50[/C][C]0.0819810963336984[/C][C]0.163962192667397[/C][C]0.918018903666302[/C][/ROW]
[ROW][C]51[/C][C]0.0690619233426118[/C][C]0.138123846685224[/C][C]0.930938076657388[/C][/ROW]
[ROW][C]52[/C][C]0.384160182027186[/C][C]0.768320364054373[/C][C]0.615839817972814[/C][/ROW]
[ROW][C]53[/C][C]0.341276492181696[/C][C]0.682552984363392[/C][C]0.658723507818304[/C][/ROW]
[ROW][C]54[/C][C]0.826772176184745[/C][C]0.34645564763051[/C][C]0.173227823815255[/C][/ROW]
[ROW][C]55[/C][C]0.796435056349283[/C][C]0.407129887301433[/C][C]0.203564943650717[/C][/ROW]
[ROW][C]56[/C][C]0.773116384849454[/C][C]0.453767230301092[/C][C]0.226883615150546[/C][/ROW]
[ROW][C]57[/C][C]0.738222734428982[/C][C]0.523554531142035[/C][C]0.261777265571018[/C][/ROW]
[ROW][C]58[/C][C]0.701031401083101[/C][C]0.597937197833797[/C][C]0.298968598916899[/C][/ROW]
[ROW][C]59[/C][C]0.661918300993614[/C][C]0.676163398012772[/C][C]0.338081699006386[/C][/ROW]
[ROW][C]60[/C][C]0.928909581259578[/C][C]0.142180837480843[/C][C]0.0710904187404216[/C][/ROW]
[ROW][C]61[/C][C]0.919062011699767[/C][C]0.161875976600465[/C][C]0.0809379883002326[/C][/ROW]
[ROW][C]62[/C][C]0.901399398729188[/C][C]0.197201202541624[/C][C]0.0986006012708121[/C][/ROW]
[ROW][C]63[/C][C]0.881229948940894[/C][C]0.237540102118213[/C][C]0.118770051059106[/C][/ROW]
[ROW][C]64[/C][C]0.866167611205382[/C][C]0.267664777589237[/C][C]0.133832388794618[/C][/ROW]
[ROW][C]65[/C][C]0.841706222894395[/C][C]0.316587554211211[/C][C]0.158293777105605[/C][/ROW]
[ROW][C]66[/C][C]0.814778137443244[/C][C]0.370443725113511[/C][C]0.185221862556756[/C][/ROW]
[ROW][C]67[/C][C]0.975760681406238[/C][C]0.0484786371875245[/C][C]0.0242393185937622[/C][/ROW]
[ROW][C]68[/C][C]0.968944240197522[/C][C]0.0621115196049554[/C][C]0.0310557598024777[/C][/ROW]
[ROW][C]69[/C][C]0.960702108691898[/C][C]0.0785957826162035[/C][C]0.0392978913081017[/C][/ROW]
[ROW][C]70[/C][C]0.950890373743555[/C][C]0.0982192525128904[/C][C]0.0491096262564452[/C][/ROW]
[ROW][C]71[/C][C]0.939400324855577[/C][C]0.121199350288845[/C][C]0.0605996751444226[/C][/ROW]
[ROW][C]72[/C][C]0.926180166156629[/C][C]0.147639667686742[/C][C]0.0738198338433709[/C][/ROW]
[ROW][C]73[/C][C]0.911263639151743[/C][C]0.177472721696513[/C][C]0.0887363608482565[/C][/ROW]
[ROW][C]74[/C][C]0.894809155653446[/C][C]0.210381688693109[/C][C]0.105190844346554[/C][/ROW]
[ROW][C]75[/C][C]0.877156704030826[/C][C]0.245686591938348[/C][C]0.122843295969174[/C][/ROW]
[ROW][C]76[/C][C]0.864541083433986[/C][C]0.270917833132029[/C][C]0.135458916566014[/C][/ROW]
[ROW][C]77[/C][C]0.845655961914722[/C][C]0.308688076170556[/C][C]0.154344038085278[/C][/ROW]
[ROW][C]78[/C][C]0.827725695100665[/C][C]0.344548609798671[/C][C]0.172274304899335[/C][/ROW]
[ROW][C]79[/C][C]0.978933213664717[/C][C]0.0421335726705661[/C][C]0.021066786335283[/C][/ROW]
[ROW][C]80[/C][C]0.975861842777997[/C][C]0.0482763144440054[/C][C]0.0241381572220027[/C][/ROW]
[ROW][C]81[/C][C]0.971075426011035[/C][C]0.0578491479779294[/C][C]0.0289245739889647[/C][/ROW]
[ROW][C]82[/C][C]0.96698808110346[/C][C]0.0660238377930805[/C][C]0.0330119188965402[/C][/ROW]
[ROW][C]83[/C][C]0.965596407514908[/C][C]0.0688071849701845[/C][C]0.0344035924850923[/C][/ROW]
[ROW][C]84[/C][C]0.999111021254162[/C][C]0.00177795749167521[/C][C]0.000888978745837605[/C][/ROW]
[ROW][C]85[/C][C]0.998680680447342[/C][C]0.00263863910531532[/C][C]0.00131931955265766[/C][/ROW]
[ROW][C]86[/C][C]0.998065632327343[/C][C]0.00386873534531463[/C][C]0.00193436767265731[/C][/ROW]
[ROW][C]87[/C][C]0.997980474384693[/C][C]0.00403905123061349[/C][C]0.00201952561530674[/C][/ROW]
[ROW][C]88[/C][C]0.997678605885145[/C][C]0.00464278822971063[/C][C]0.00232139411485532[/C][/ROW]
[ROW][C]89[/C][C]0.997175955088507[/C][C]0.00564808982298626[/C][C]0.00282404491149313[/C][/ROW]
[ROW][C]90[/C][C]0.996444724037465[/C][C]0.00711055192506998[/C][C]0.00355527596253499[/C][/ROW]
[ROW][C]91[/C][C]0.995431034770482[/C][C]0.00913793045903547[/C][C]0.00456896522951774[/C][/ROW]
[ROW][C]92[/C][C]0.99405864780769[/C][C]0.0118827043846204[/C][C]0.00594135219231022[/C][/ROW]
[ROW][C]93[/C][C]0.992228444093393[/C][C]0.0155431118132138[/C][C]0.00777155590660691[/C][/ROW]
[ROW][C]94[/C][C]0.989816485004946[/C][C]0.0203670299901077[/C][C]0.0101835149950539[/C][/ROW]
[ROW][C]95[/C][C]0.986671775304773[/C][C]0.0266564493904549[/C][C]0.0133282246952274[/C][/ROW]
[ROW][C]96[/C][C]0.982614391706932[/C][C]0.0347712165861367[/C][C]0.0173856082930683[/C][/ROW]
[ROW][C]97[/C][C]0.977434520532345[/C][C]0.0451309589353092[/C][C]0.0225654794676546[/C][/ROW]
[ROW][C]98[/C][C]0.970892925260189[/C][C]0.0582141494796219[/C][C]0.0291070747398109[/C][/ROW]
[ROW][C]99[/C][C]0.962723352089234[/C][C]0.0745532958215311[/C][C]0.0372766479107655[/C][/ROW]
[ROW][C]100[/C][C]0.952637339221673[/C][C]0.0947253215566534[/C][C]0.0473626607783267[/C][/ROW]
[ROW][C]101[/C][C]0.940331800950662[/C][C]0.119336398098676[/C][C]0.059668199049338[/C][/ROW]
[ROW][C]102[/C][C]0.925499598045419[/C][C]0.149000803909162[/C][C]0.0745004019545809[/C][/ROW]
[ROW][C]103[/C][C]0.907843077823602[/C][C]0.184313844352796[/C][C]0.0921569221763982[/C][/ROW]
[ROW][C]104[/C][C]0.887090277554637[/C][C]0.225819444890725[/C][C]0.112909722445363[/C][/ROW]
[ROW][C]105[/C][C]0.863013151977562[/C][C]0.273973696044875[/C][C]0.136986848022438[/C][/ROW]
[ROW][C]106[/C][C]0.83544684001895[/C][C]0.3291063199621[/C][C]0.16455315998105[/C][/ROW]
[ROW][C]107[/C][C]0.804308667585933[/C][C]0.391382664828133[/C][C]0.195691332414067[/C][/ROW]
[ROW][C]108[/C][C]0.769615339143705[/C][C]0.46076932171259[/C][C]0.230384660856295[/C][/ROW]
[ROW][C]109[/C][C]0.731496647608823[/C][C]0.537006704782355[/C][C]0.268503352391177[/C][/ROW]
[ROW][C]110[/C][C]0.690204069904339[/C][C]0.619591860191322[/C][C]0.309795930095661[/C][/ROW]
[ROW][C]111[/C][C]0.646112839608944[/C][C]0.707774320782113[/C][C]0.353887160391057[/C][/ROW]
[ROW][C]112[/C][C]0.599716502139982[/C][C]0.800566995720036[/C][C]0.400283497860018[/C][/ROW]
[ROW][C]113[/C][C]0.551613539323235[/C][C]0.89677292135353[/C][C]0.448386460676765[/C][/ROW]
[ROW][C]114[/C][C]0.502486349477535[/C][C]0.99502730104493[/C][C]0.497513650522465[/C][/ROW]
[ROW][C]115[/C][C]0.453073613343634[/C][C]0.906147226687268[/C][C]0.546926386656366[/C][/ROW]
[ROW][C]116[/C][C]0.404137777176267[/C][C]0.808275554352534[/C][C]0.595862222823733[/C][/ROW]
[ROW][C]117[/C][C]0.356429950650574[/C][C]0.712859901301148[/C][C]0.643570049349426[/C][/ROW]
[ROW][C]118[/C][C]0.310654868034598[/C][C]0.621309736069196[/C][C]0.689345131965402[/C][/ROW]
[ROW][C]119[/C][C]0.267438639557856[/C][C]0.534877279115711[/C][C]0.732561360442144[/C][/ROW]
[ROW][C]120[/C][C]0.227301803955457[/C][C]0.454603607910915[/C][C]0.772698196044543[/C][/ROW]
[ROW][C]121[/C][C]0.190639700830091[/C][C]0.381279401660181[/C][C]0.809360299169909[/C][/ROW]
[ROW][C]122[/C][C]0.157711469887116[/C][C]0.315422939774233[/C][C]0.842288530112884[/C][/ROW]
[ROW][C]123[/C][C]0.128638141745626[/C][C]0.257276283491251[/C][C]0.871361858254374[/C][/ROW]
[ROW][C]124[/C][C]0.103409418455667[/C][C]0.206818836911334[/C][C]0.896590581544333[/C][/ROW]
[ROW][C]125[/C][C]0.0818979591521202[/C][C]0.16379591830424[/C][C]0.91810204084788[/C][/ROW]
[ROW][C]126[/C][C]0.0638793803647193[/C][C]0.127758760729439[/C][C]0.936120619635281[/C][/ROW]
[ROW][C]127[/C][C]0.0490558152756445[/C][C]0.0981116305512891[/C][C]0.950944184724355[/C][/ROW]
[ROW][C]128[/C][C]0.0370807780829454[/C][C]0.0741615561658908[/C][C]0.962919221917055[/C][/ROW]
[ROW][C]129[/C][C]0.027583235822169[/C][C]0.0551664716443379[/C][C]0.972416764177831[/C][/ROW]
[ROW][C]130[/C][C]0.0201891536226911[/C][C]0.0403783072453823[/C][C]0.979810846377309[/C][/ROW]
[ROW][C]131[/C][C]0.0145392798354032[/C][C]0.0290785596708065[/C][C]0.985460720164597[/C][/ROW]
[ROW][C]132[/C][C]0.010302493939926[/C][C]0.0206049878798519[/C][C]0.989697506060074[/C][/ROW]
[ROW][C]133[/C][C]0.00718457529595896[/C][C]0.0143691505919179[/C][C]0.992815424704041[/C][/ROW]
[ROW][C]134[/C][C]0.00493270224596363[/C][C]0.00986540449192726[/C][C]0.995067297754036[/C][/ROW]
[ROW][C]135[/C][C]0.00333631864053034[/C][C]0.00667263728106069[/C][C]0.99666368135947[/C][/ROW]
[ROW][C]136[/C][C]0.00222519302281277[/C][C]0.00445038604562554[/C][C]0.997774806977187[/C][/ROW]
[ROW][C]137[/C][C]0.00146555142295278[/C][C]0.00293110284590556[/C][C]0.998534448577047[/C][/ROW]
[ROW][C]138[/C][C]0.000955111818662986[/C][C]0.00191022363732597[/C][C]0.999044888181337[/C][/ROW]
[ROW][C]139[/C][C]0.000617720233735579[/C][C]0.00123544046747116[/C][C]0.999382279766264[/C][/ROW]
[ROW][C]140[/C][C]0.000398120832702838[/C][C]0.000796241665405675[/C][C]0.999601879167297[/C][/ROW]
[ROW][C]141[/C][C]0.012574614552336[/C][C]0.0251492291046719[/C][C]0.987425385447664[/C][/ROW]
[ROW][C]142[/C][C]0.00820840346194673[/C][C]0.0164168069238935[/C][C]0.991791596538053[/C][/ROW]
[ROW][C]143[/C][C]0.00524936427284851[/C][C]0.010498728545697[/C][C]0.994750635727151[/C][/ROW]
[ROW][C]144[/C][C]0.00330247134306711[/C][C]0.00660494268613422[/C][C]0.996697528656933[/C][/ROW]
[ROW][C]145[/C][C]0.00205892812476337[/C][C]0.00411785624952673[/C][C]0.997941071875237[/C][/ROW]
[ROW][C]146[/C][C]0.00128859003528603[/C][C]0.00257718007057205[/C][C]0.998711409964714[/C][/ROW]
[ROW][C]147[/C][C]0.000828491427440651[/C][C]0.0016569828548813[/C][C]0.999171508572559[/C][/ROW]
[ROW][C]148[/C][C]0.000571264564693526[/C][C]0.00114252912938705[/C][C]0.999428735435307[/C][/ROW]
[ROW][C]149[/C][C]0.000460106666732978[/C][C]0.000920213333465956[/C][C]0.999539893333267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201875&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201875&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
170.262188426691310.524376853382620.73781157330869
180.2210408536007030.4420817072014060.778959146399297
190.1676153453970910.3352306907941820.832384654602909
200.7221640057332890.5556719885334210.277835994266711
210.6615181384073310.6769637231853380.338481861592669
220.5974548737384570.8050902525230860.402545126261543
230.5317096225239580.9365807549520850.468290377476042
240.4660758892768240.9321517785536480.533924110723176
250.4452509764175720.8905019528351440.554749023582428
260.3831687514773390.7663375029546780.616831248522661
270.324706638159590.6494132763191810.675293361840409
280.2709331769610160.5418663539220330.729066823038984
290.2225813143279830.4451626286559660.777418685672017
300.1800458800872260.3600917601744520.819954119912774
310.1434105505191930.2868211010383860.856589449480807
320.1124961191061120.2249922382122230.887503880893888
330.08692095381410950.1738419076282190.913079046185891
340.07840584013340680.1568116802668140.921594159866593
350.05949206545304080.1189841309060820.940507934546959
360.04449592607852530.08899185215705060.955504073921475
370.03842941785404250.07685883570808510.961570582145957
380.02820435177983530.05640870355967060.971795648220165
390.020420914436950.04084182887389990.97957908556305
400.01690830502059320.03381661004118640.983091694979407
410.3742939460332030.7485878920664060.625706053966797
420.3273787897563450.654757579512690.672621210243655
430.2833751611691590.5667503223383170.716624838830841
440.2531451581073640.5062903162147280.746854841892636
450.2152402862562190.4304805725124380.784759713743781
460.1811130734783480.3622261469566960.818886926521652
470.1508274573601060.3016549147202110.849172542639894
480.1243273772891180.2486547545782360.875672622710882
490.1014566022550780.2029132045101560.898543397744922
500.08198109633369840.1639621926673970.918018903666302
510.06906192334261180.1381238466852240.930938076657388
520.3841601820271860.7683203640543730.615839817972814
530.3412764921816960.6825529843633920.658723507818304
540.8267721761847450.346455647630510.173227823815255
550.7964350563492830.4071298873014330.203564943650717
560.7731163848494540.4537672303010920.226883615150546
570.7382227344289820.5235545311420350.261777265571018
580.7010314010831010.5979371978337970.298968598916899
590.6619183009936140.6761633980127720.338081699006386
600.9289095812595780.1421808374808430.0710904187404216
610.9190620116997670.1618759766004650.0809379883002326
620.9013993987291880.1972012025416240.0986006012708121
630.8812299489408940.2375401021182130.118770051059106
640.8661676112053820.2676647775892370.133832388794618
650.8417062228943950.3165875542112110.158293777105605
660.8147781374432440.3704437251135110.185221862556756
670.9757606814062380.04847863718752450.0242393185937622
680.9689442401975220.06211151960495540.0310557598024777
690.9607021086918980.07859578261620350.0392978913081017
700.9508903737435550.09821925251289040.0491096262564452
710.9394003248555770.1211993502888450.0605996751444226
720.9261801661566290.1476396676867420.0738198338433709
730.9112636391517430.1774727216965130.0887363608482565
740.8948091556534460.2103816886931090.105190844346554
750.8771567040308260.2456865919383480.122843295969174
760.8645410834339860.2709178331320290.135458916566014
770.8456559619147220.3086880761705560.154344038085278
780.8277256951006650.3445486097986710.172274304899335
790.9789332136647170.04213357267056610.021066786335283
800.9758618427779970.04827631444400540.0241381572220027
810.9710754260110350.05784914797792940.0289245739889647
820.966988081103460.06602383779308050.0330119188965402
830.9655964075149080.06880718497018450.0344035924850923
840.9991110212541620.001777957491675210.000888978745837605
850.9986806804473420.002638639105315320.00131931955265766
860.9980656323273430.003868735345314630.00193436767265731
870.9979804743846930.004039051230613490.00201952561530674
880.9976786058851450.004642788229710630.00232139411485532
890.9971759550885070.005648089822986260.00282404491149313
900.9964447240374650.007110551925069980.00355527596253499
910.9954310347704820.009137930459035470.00456896522951774
920.994058647807690.01188270438462040.00594135219231022
930.9922284440933930.01554311181321380.00777155590660691
940.9898164850049460.02036702999010770.0101835149950539
950.9866717753047730.02665644939045490.0133282246952274
960.9826143917069320.03477121658613670.0173856082930683
970.9774345205323450.04513095893530920.0225654794676546
980.9708929252601890.05821414947962190.0291070747398109
990.9627233520892340.07455329582153110.0372766479107655
1000.9526373392216730.09472532155665340.0473626607783267
1010.9403318009506620.1193363980986760.059668199049338
1020.9254995980454190.1490008039091620.0745004019545809
1030.9078430778236020.1843138443527960.0921569221763982
1040.8870902775546370.2258194448907250.112909722445363
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1060.835446840018950.32910631996210.16455315998105
1070.8043086675859330.3913826648281330.195691332414067
1080.7696153391437050.460769321712590.230384660856295
1090.7314966476088230.5370067047823550.268503352391177
1100.6902040699043390.6195918601913220.309795930095661
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1120.5997165021399820.8005669957200360.400283497860018
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1140.5024863494775350.995027301044930.497513650522465
1150.4530736133436340.9061472266872680.546926386656366
1160.4041377771762670.8082755543525340.595862222823733
1170.3564299506505740.7128599013011480.643570049349426
1180.3106548680345980.6213097360691960.689345131965402
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1200.2273018039554570.4546036079109150.772698196044543
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1320.0103024939399260.02060498787985190.989697506060074
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1350.003336318640530340.006672637281060690.99666368135947
1360.002225193022812770.004450386045625540.997774806977187
1370.001465551422952780.002931102845905560.998534448577047
1380.0009551118186629860.001910223637325970.999044888181337
1390.0006177202337355790.001235440467471160.999382279766264
1400.0003981208327028380.0007962416654056750.999601879167297
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1420.008208403461946730.01641680692389350.991791596538053
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1450.002058928124763370.004117856249526730.997941071875237
1460.001288590035286030.002577180070572050.998711409964714
1470.0008284914274406510.00165698285488130.999171508572559
1480.0005712645646935260.001142529129387050.999428735435307
1490.0004601066667329780.0009202133334659560.999539893333267







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level330.227586206896552NOK
5% type I error level510.351724137931034NOK
10% type I error level660.455172413793103NOK

\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 & 33 & 0.227586206896552 & NOK \tabularnewline
5% type I error level & 51 & 0.351724137931034 & NOK \tabularnewline
10% type I error level & 66 & 0.455172413793103 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201875&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]33[/C][C]0.227586206896552[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]51[/C][C]0.351724137931034[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]66[/C][C]0.455172413793103[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201875&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201875&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 level330.227586206896552NOK
5% type I error level510.351724137931034NOK
10% type I error level660.455172413793103NOK



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')
}