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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 11 Nov 2014 20:37:50 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/11/t14157382830v0hwsc5wohq6q8.htm/, Retrieved Sun, 19 May 2024 21:23:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253729, Retrieved Sun, 19 May 2024 21:23:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-11-11 20:37:50] [92b9176a7d614ba60c8f41dcecd4e71d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41 38 13 12 14 12 53
39 32 16 11 18 11 83
30 35 19 15 11 14 66
31 33 15 6 12 12 67
34 37 14 13 16 21 76
35 29 13 10 18 12 78
39 31 19 12 14 22 53
34 36 15 14 14 11 80
36 35 14 12 15 10 74
37 38 15 9 15 13 76
38 31 16 10 17 10 79
36 34 16 12 19 8 54
38 35 16 12 10 15 67
39 38 16 11 16 14 54
33 37 17 15 18 10 87
32 33 15 12 14 14 58
36 32 15 10 14 14 75
38 38 20 12 17 11 88
39 38 18 11 14 10 64
32 32 16 12 16 13 57
32 33 16 11 18 9.5 66
31 31 16 12 11 14 68
39 38 19 13 14 12 54
37 39 16 11 12 14 56
39 32 17 12 17 11 86
41 32 17 13 9 9 80
36 35 16 10 16 11 76
33 37 15 14 14 15 69
33 33 16 12 15 14 78
34 33 14 10 11 13 67
31 31 15 12 16 9 80
27 32 12 8 13 15 54
37 31 14 10 17 10 71
34 37 16 12 15 11 84
34 30 14 12 14 13 74
32 33 10 7 16 8 71
29 31 10 9 9 20 63
36 33 14 12 15 12 71
29 31 16 10 17 10 76
35 33 16 10 13 10 69
37 32 16 10 15 9 74
34 33 14 12 16 14 75
38 32 20 15 16 8 54
35 33 14 10 12 14 52
38 28 14 10 15 11 69
37 35 11 12 11 13 68
38 39 14 13 15 9 65
33 34 15 11 15 11 75
36 38 16 11 17 15 74
38 32 14 12 13 11 75
32 38 16 14 16 10 72
32 30 14 10 14 14 67
32 33 12 12 11 18 63
34 38 16 13 12 14 62
32 32 9 5 12 11 63
37 35 14 6 15 14.5 76
39 34 16 12 16 13 74
29 34 16 12 15 9 67
37 36 15 11 12 10 73
35 34 16 10 12 15 70
30 28 12 7 8 20 53
38 34 16 12 13 12 77
34 35 16 14 11 12 80
31 35 14 11 14 14 52
34 31 16 12 15 13 54
35 37 17 13 10 11 80
36 35 18 14 11 17 66
30 27 18 11 12 12 73
39 40 12 12 15 13 63
35 37 16 12 15 14 69
38 36 10 8 14 13 67
31 38 14 11 16 15 54
34 39 18 14 15 13 81
38 41 18 14 15 10 69
34 27 16 12 13 11 84
39 30 17 9 12 19 80
37 37 16 13 17 13 70
34 31 16 11 13 17 69
28 31 13 12 15 13 77
37 27 16 12 13 9 54
33 36 16 12 15 11 79
35 37 16 12 15 9 71
37 33 15 12 16 12 73
32 34 15 11 15 12 72
33 31 16 10 14 13 77
38 39 14 9 15 13 75
33 34 16 12 14 12 69
29 32 16 12 13 15 54
33 33 15 12 7 22 70
31 36 12 9 17 13 73
36 32 17 15 13 15 54
35 41 16 12 15 13 77
32 28 15 12 14 15 82
29 30 13 12 13 12.5 80
39 36 16 10 16 11 80
37 35 16 13 12 16 69
35 31 16 9 14 11 78
37 34 16 12 17 11 81
32 36 14 10 15 10 76
38 36 16 14 17 10 76
37 35 16 11 12 16 73
36 37 20 15 16 12 85
32 28 15 11 11 11 66
33 39 16 11 15 16 79
40 32 13 12 9 19 68
38 35 17 12 16 11 76
41 39 16 12 15 16 71
36 35 16 11 10 15 54
43 42 12 7 10 24 46
30 34 16 12 15 14 85
31 33 16 14 11 15 74
32 41 17 11 13 11 88
32 33 13 11 14 15 38
37 34 12 10 18 12 76
37 32 18 13 16 10 86
33 40 14 13 14 14 54
34 40 14 8 14 13 67
33 35 13 11 14 9 69
38 36 16 12 14 15 90
33 37 13 11 12 15 54
31 27 16 13 14 14 76
38 39 13 12 15 11 89
37 38 16 14 15 8 76
36 31 15 13 15 11 73
31 33 16 15 13 11 79
39 32 15 10 17 8 90
44 39 17 11 17 10 74
33 36 15 9 19 11 81
35 33 12 11 15 13 72
32 33 16 10 13 11 71
28 32 10 11 9 20 66
40 37 16 8 15 10 77
27 30 12 11 15 15 65
37 38 14 12 15 12 74
32 29 15 12 16 14 85
28 22 13 9 11 23 54
34 35 15 11 14 14 63
30 35 11 10 11 16 54
35 34 12 8 15 11 64
31 35 11 9 13 12 69
32 34 16 8 15 10 54
30 37 15 9 16 14 84
30 35 17 15 14 12 86
31 23 16 11 15 12 77
40 31 10 8 16 11 89
32 27 18 13 16 12 76
36 36 13 12 11 13 60
32 31 16 12 12 11 75
35 32 13 9 9 19 73
38 39 10 7 16 12 85
42 37 15 13 13 17 79
34 38 16 9 16 9 71
35 39 16 6 12 12 72
38 34 14 8 9 19 69
33 31 10 8 13 18 78
36 32 17 15 13 15 54
32 37 13 6 14 14 69
33 36 15 9 19 11 81
34 32 16 11 13 9 84
32 38 12 8 12 18 84
34 36 13 8 13 16 69
27 26 13 10 10 24 66
31 26 12 8 14 14 81
38 33 17 14 16 20 82
34 39 15 10 10 18 72
24 30 10 8 11 23 54
30 33 14 11 14 12 78
26 25 11 12 12 14 74
34 38 13 12 9 16 82
27 37 16 12 9 18 73
37 31 12 5 11 20 55
36 37 16 12 16 12 72
41 35 12 10 9 12 78
29 25 9 7 13 17 59
36 28 12 12 16 13 72
32 35 15 11 13 9 78
37 33 12 8 9 16 68
30 30 12 9 12 18 69
31 31 14 10 16 10 67
38 37 12 9 11 14 74
36 36 16 12 14 11 54
35 30 11 6 13 9 67
31 36 19 15 15 11 70
38 32 15 12 14 10 80
22 28 8 12 16 11 89
32 36 16 12 13 19 76
36 34 17 11 14 14 74
39 31 12 7 15 12 87
28 28 11 7 13 14 54
32 36 11 5 11 21 61
32 36 14 12 11 13 38
38 40 16 12 14 10 75
32 33 12 3 15 15 69
35 37 16 11 11 16 62
32 32 13 10 15 14 72
37 38 15 12 12 12 70
34 31 16 9 14 19 79
33 37 16 12 14 15 87
33 33 14 9 8 19 62
26 32 16 12 13 13 77
30 30 16 12 9 17 69
24 30 14 10 15 12 69
34 31 11 9 17 11 75
34 32 12 12 13 14 54
33 34 15 8 15 11 72
34 36 15 11 15 13 74
35 37 16 11 14 12 85
35 36 16 12 16 15 52
36 33 11 10 13 14 70
34 33 15 10 16 12 84
34 33 12 12 9 17 64
41 44 12 12 16 11 84
32 39 15 11 11 18 87
30 32 15 8 10 13 79
35 35 16 12 11 17 67
28 25 14 10 15 13 65
33 35 17 11 17 11 85
39 34 14 10 14 12 83
36 35 13 8 8 22 61
36 39 15 12 15 14 82
35 33 13 12 11 12 76
38 36 14 10 16 12 58
33 32 15 12 10 17 72
31 32 12 9 15 9 72
34 36 13 9 9 21 38
32 36 8 6 16 10 78
31 32 14 10 19 11 54
33 34 14 9 12 12 63
34 33 11 9 8 23 66
34 35 12 9 11 13 70
34 30 13 6 14 12 71
33 38 10 10 9 16 67
32 34 16 6 15 9 58
41 33 18 14 13 17 72
34 32 13 10 16 9 72
36 31 11 10 11 14 70
37 30 4 6 12 17 76
36 27 13 12 13 13 50
29 31 16 12 10 11 72
37 30 10 7 11 12 72
27 32 12 8 12 10 88
35 35 12 11 8 19 53
28 28 10 3 12 16 58
35 33 13 6 12 16 66
37 31 15 10 15 14 82
29 35 12 8 11 20 69
32 35 14 9 13 15 68
36 32 10 9 14 23 44
19 21 12 8 10 20 56
21 20 12 9 12 16 53
31 34 11 7 15 14 70
33 32 10 7 13 17 78
36 34 12 6 13 11 71
33 32 16 9 13 13 72
37 33 12 10 12 17 68
34 33 14 11 12 15 67
35 37 16 12 9 21 75
31 32 14 8 9 18 62
37 34 13 11 15 15 67
35 30 4 3 10 8 83
27 30 15 11 14 12 64
34 38 11 12 15 12 68
40 36 11 7 7 22 62
29 32 14 9 14 12 72

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253729&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253729&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253729&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
(1-B)Depression[t] = + 0.00799723 + 0.022195`(1-B)Connected`[t] + 0.00845042`(1-B)Separate`[t] -0.151222`(1-B)Learning`[t] + 0.0990058`(1-B)Software`[t] -0.64817`(1-B)Happiness`[t] -0.0704205`(1-B)Sport1`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-B)Depression[t] =  +  0.00799723 +  0.022195`(1-B)Connected`[t] +  0.00845042`(1-B)Separate`[t] -0.151222`(1-B)Learning`[t] +  0.0990058`(1-B)Software`[t] -0.64817`(1-B)Happiness`[t] -0.0704205`(1-B)Sport1`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253729&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-B)Depression[t] =  +  0.00799723 +  0.022195`(1-B)Connected`[t] +  0.00845042`(1-B)Separate`[t] -0.151222`(1-B)Learning`[t] +  0.0990058`(1-B)Software`[t] -0.64817`(1-B)Happiness`[t] -0.0704205`(1-B)Sport1`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253729&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253729&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
(1-B)Depression[t] = + 0.00799723 + 0.022195`(1-B)Connected`[t] + 0.00845042`(1-B)Separate`[t] -0.151222`(1-B)Learning`[t] + 0.0990058`(1-B)Software`[t] -0.64817`(1-B)Happiness`[t] -0.0704205`(1-B)Sport1`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.007997230.2375430.033670.9731690.486585
`(1-B)Connected`0.0221950.05062880.43840.6614750.330738
`(1-B)Separate`0.008450420.05276970.16010.8728990.436449
`(1-B)Learning`-0.1512220.0929684-1.6270.1050530.0525266
`(1-B)Software`0.09900580.09390071.0540.2927090.146354
`(1-B)Happiness`-0.648170.072828-8.91.04591e-165.22956e-17
`(1-B)Sport1`-0.07042050.0170316-4.1354.8219e-052.41095e-05

\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) & 0.00799723 & 0.237543 & 0.03367 & 0.973169 & 0.486585 \tabularnewline
`(1-B)Connected` & 0.022195 & 0.0506288 & 0.4384 & 0.661475 & 0.330738 \tabularnewline
`(1-B)Separate` & 0.00845042 & 0.0527697 & 0.1601 & 0.872899 & 0.436449 \tabularnewline
`(1-B)Learning` & -0.151222 & 0.0929684 & -1.627 & 0.105053 & 0.0525266 \tabularnewline
`(1-B)Software` & 0.0990058 & 0.0939007 & 1.054 & 0.292709 & 0.146354 \tabularnewline
`(1-B)Happiness` & -0.64817 & 0.072828 & -8.9 & 1.04591e-16 & 5.22956e-17 \tabularnewline
`(1-B)Sport1` & -0.0704205 & 0.0170316 & -4.135 & 4.8219e-05 & 2.41095e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253729&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]0.00799723[/C][C]0.237543[/C][C]0.03367[/C][C]0.973169[/C][C]0.486585[/C][/ROW]
[ROW][C]`(1-B)Connected`[/C][C]0.022195[/C][C]0.0506288[/C][C]0.4384[/C][C]0.661475[/C][C]0.330738[/C][/ROW]
[ROW][C]`(1-B)Separate`[/C][C]0.00845042[/C][C]0.0527697[/C][C]0.1601[/C][C]0.872899[/C][C]0.436449[/C][/ROW]
[ROW][C]`(1-B)Learning`[/C][C]-0.151222[/C][C]0.0929684[/C][C]-1.627[/C][C]0.105053[/C][C]0.0525266[/C][/ROW]
[ROW][C]`(1-B)Software`[/C][C]0.0990058[/C][C]0.0939007[/C][C]1.054[/C][C]0.292709[/C][C]0.146354[/C][/ROW]
[ROW][C]`(1-B)Happiness`[/C][C]-0.64817[/C][C]0.072828[/C][C]-8.9[/C][C]1.04591e-16[/C][C]5.22956e-17[/C][/ROW]
[ROW][C]`(1-B)Sport1`[/C][C]-0.0704205[/C][C]0.0170316[/C][C]-4.135[/C][C]4.8219e-05[/C][C]2.41095e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253729&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253729&T=2

As an alternative you can also use a QR Code:  
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)0.007997230.2375430.033670.9731690.486585
`(1-B)Connected`0.0221950.05062880.43840.6614750.330738
`(1-B)Separate`0.008450420.05276970.16010.8728990.436449
`(1-B)Learning`-0.1512220.0929684-1.6270.1050530.0525266
`(1-B)Software`0.09900580.09390071.0540.2927090.146354
`(1-B)Happiness`-0.648170.072828-8.91.04591e-165.22956e-17
`(1-B)Sport1`-0.07042050.0170316-4.1354.8219e-052.41095e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.603847
R-squared0.364631
Adjusted R-squared0.34974
F-TEST (value)24.4859
F-TEST (DF numerator)6
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.85202
Sum Squared Residuals3798.55

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.603847 \tabularnewline
R-squared & 0.364631 \tabularnewline
Adjusted R-squared & 0.34974 \tabularnewline
F-TEST (value) & 24.4859 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.85202 \tabularnewline
Sum Squared Residuals & 3798.55 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253729&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.603847[/C][/ROW]
[ROW][C]R-squared[/C][C]0.364631[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.34974[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.4859[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.85202[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3798.55[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253729&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253729&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.603847
R-squared0.364631
Adjusted R-squared0.34974
F-TEST (value)24.4859
F-TEST (DF numerator)6
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.85202
Sum Squared Residuals3798.55






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1-5.345064.34506
235.51029-2.51029
3-2-0.991463-1.00854
49-2.2738211.2738
5-9-1.62039-7.37961
6103.757556.24245
7-11-1.15918-9.84082
8-1-0.2285-0.7715
93-0.5335373.53354
10-3-1.58878-1.41122
11-20.651142-2.65114
1274.97892.0211
13-1-3.017022.01702
14-4-3.50904-0.490963
1544.5923-0.592301
160-1.306831.30683
17-3-3.314980.314985
18-13.86823-4.86823
193-0.6000173.60002
20-3.5-2.01268-1.48732
214.54.464260.0357437
22-2-1.06857-0.931426
2321.383210.616789
24-3-5.412452.41245
25-25.75928-7.75928
262-4.478936.47893
2742.294841.70516
28-1-1.656990.656992
29-13.50193-4.50193
30-4-4.185020.185015
3163.760752.23925
32-5-3.67276-1.32724
3310.2685560.731444
3421.603660.396337
35-5-0.986261-4.01374
36125.223086.77692
37-8-4.57999-3.42001
38-2-2.313170.313167
3903.24369-3.24369
40-1-1.604510.604506
415-0.2682725.26827
42-60.956842-6.95684
4363.095692.90431
44-3-3.109330.109328
4523.35973-1.35973
46-4-2.67208-1.32792
472-1.198673.19867
484-1.268765.26876
49-42.92539-6.92539
50-1-1.912150.912151
5141.495262.50474
5242.761.24
53-4-0.988993-3.01101
54-30.108992-3.10899
553.5-3.372766.87276
56-1.5-0.171801-1.3282
57-40.927161-4.92716
5811.77666-0.776661
595-0.09226015.09226
6053.944021.05598
61-8-4.80454-3.19546
6201.21076-1.21076
632-0.02589812.0259
64-1-0.951669-0.0483314
65-21.4386-3.4386
6660.2987925.70121
67-5-1.63091-3.36909
6810.08364040.91636
691-1.133542.13354
70-11.36645-2.36645
712-0.8192112.81921
72-2-1.47802-0.521979
73-30.958724-3.95872
7410.1453770.854623
7580.6259367.37406
76-6-1.96664-4.03336
7742.35581.6442
78-4-1.4322-2.5678
79-42.6363-6.6363
802-3.061585.06158
81-20.624201-2.6242
823-0.6192033.6192
8300.525057-0.525057
8410.05068080.949319
850-0.1173150.117315
86-10.920036-1.92004
8731.606791.39321
8873.018743.98126
89-9-6.54735-2.45265
9023.85376-1.85376
91-2-2.999950.99995
9220.2788461.72115
93-2.51.04977-3.54977
94-1.5-2.315540.815538
9553.619481.38052
96-5-2.39634-2.60366
970-1.781021.78102
98-11.6668-2.6668
990-1.061591.06159
10063.132452.86755
101-4-3.64389-0.356113
102-14.78209-5.78209
1035-3.536228.53622
10435.32053-2.32053
105-8-5.71648-2.28352
10651.259883.74012
107-14.20221-5.20221
10890.9947448.00526
109-10-6.44525-3.55475
11013.58706-2.58706
111-4-2.63267-1.36733
11243.418140.581865
113-3-5.089022.08902
114-2-0.027083-1.97292
11544.1415-0.141504
116-1-1.38030.380303
117-40.250949-4.25095
1186-1.706077.70607
11904.09161-4.09161
120-1-3.222142.22214
121-3-0.944209-2.05579
122-30.637163-3.63716
12330.1901272.80987
12400.83453-0.83453
125-3-3.534010.534006
12621.101410.898586
1271-1.946352.94635
12823.90518-1.90518
129-20.604279-2.60428
13093.861895.13811
131-10-5.55141-4.44859
13251.407263.59274
133-3-0.539672-2.46033
1342-1.753053.75305
13595.289383.71062
136-9-2.4317-6.5683
13723.00339-1.00339
138-5-3.5356-1.4644
13911.12213-0.122133
140-2-1.07341-0.926593
1414-2.52166.5216
142-21.43819-3.43819
1430-0.33040.3304
144-1-0.607545-0.392455
1451-0.002645441.00265
14615.19751-4.19751
147-2-2.281180.281178
14882.325035.67497
149-7-4.99285-2.00715
15052.284832.71517
151-8-2.0895-5.9105
15232.263880.736115
15372.688564.31144
154-1-2.749911.74991
155-31.40761-4.40761
156-1-2.029171.02917
157-3-4.069581.06958
158-23.72094-5.72094
15990.970358.02965
160-20.292401-2.2924
16182.121915.87809
162-10-3.599-6.401
1636-1.306327.30632
164-24.46957-6.46957
16550.887494.11251
166-11-3.77595-7.22405
16721.982310.0176923
16821.374110.625885
16920.02429991.9757
17020.06232041.93768
171-8-4.31334-3.68666
17204.62362-4.62362
1735-1.440896.44089
174-4-2.6299-1.3701
175-40.947685-4.94769
17673.55563.4444
1772-2.088644.08864
178-8-2.61663-5.38337
17943.165410.83459
180-3-0.888814-2.11119
181-2-0.170121-1.82988
1822-1.856413.85641
183-10.381396-1.3814
1841-1.252492.25249
18581.947756.05225
186-5-0.677681-4.32232
187-2-1.15432-0.845682
18823.50994-1.50994
18970.7697666.23023
190-81.86704-9.86704
191-3-4.677541.67754
1925-0.6961375.69614
19313.38117-2.38117
194-2-3.043061.04306
195-22.15059-4.15059
1967-2.49619.4961
197-4-0.229841-3.77016
19845.62915-1.62915
199-6-4.4584-1.5416
20043.235920.76408
201-5-3.90976-1.09024
202-1-1.12580.125805
20334.23375-1.23375
204-3-3.410890.410895
20520.203271.79673
206-1-0.239035-0.760965
20731.126091.87391
208-11.23988-2.23988
209-2-3.571681.57168
21056.60527-1.60527
211-6-5.68928-0.310717
21272.242914.75709
213-50.818971-5.81897
21440.5863.414
215-4-2.57928-1.42072
216-2-2.855930.855933
21712.57273-1.57273
218105.341344.65866
219-8-5.88064-2.11936
220-23.25275-5.25275
2210-2.222582.22258
22252.813142.18686
223-8-3.12059-4.87941
224126.240485.75952
225-11-6.93131-4.06869
2261-0.8137271.81373
22713.87369-2.87369
228112.856838.14317
229-10-2.35252-7.64748
230-1-2.497421.49742
23144.42563-0.425627
232-7-4.60659-2.39341
23380.9993587.00064
234-8-1.74024-6.25976
23553.728071.27193
2363-0.386423.38642
237-40.37625-4.37625
238-2-0.171972-1.82803
2391-0.05876021.05876
240-2-2.175390.175388
24195.565323.43468
242-3-3.640910.640906
2430-0.5143980.514398
244-2-2.942170.942172
24563.628042.37196
246-5-1.35478-3.64522
24781.718246.28176
248-30.883912-3.88391
249-4-0.942136-3.05786
250-2-2.840190.840195
25130.9196852.08032
252-60.182976-6.18298
2532-0.453782.45378
25441.738972.26103
255-2-0.191606-1.80839
25661.24174.7583
257-30.698852-3.69885
258-3-3.634810.634815
259-72.61288-9.61288
2604-2.295656.29565
26100.00500767-0.00500767
262105.237124.76288
263-10-5.767-4.233
264-2NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1 & -5.34506 & 4.34506 \tabularnewline
2 & 3 & 5.51029 & -2.51029 \tabularnewline
3 & -2 & -0.991463 & -1.00854 \tabularnewline
4 & 9 & -2.27382 & 11.2738 \tabularnewline
5 & -9 & -1.62039 & -7.37961 \tabularnewline
6 & 10 & 3.75755 & 6.24245 \tabularnewline
7 & -11 & -1.15918 & -9.84082 \tabularnewline
8 & -1 & -0.2285 & -0.7715 \tabularnewline
9 & 3 & -0.533537 & 3.53354 \tabularnewline
10 & -3 & -1.58878 & -1.41122 \tabularnewline
11 & -2 & 0.651142 & -2.65114 \tabularnewline
12 & 7 & 4.9789 & 2.0211 \tabularnewline
13 & -1 & -3.01702 & 2.01702 \tabularnewline
14 & -4 & -3.50904 & -0.490963 \tabularnewline
15 & 4 & 4.5923 & -0.592301 \tabularnewline
16 & 0 & -1.30683 & 1.30683 \tabularnewline
17 & -3 & -3.31498 & 0.314985 \tabularnewline
18 & -1 & 3.86823 & -4.86823 \tabularnewline
19 & 3 & -0.600017 & 3.60002 \tabularnewline
20 & -3.5 & -2.01268 & -1.48732 \tabularnewline
21 & 4.5 & 4.46426 & 0.0357437 \tabularnewline
22 & -2 & -1.06857 & -0.931426 \tabularnewline
23 & 2 & 1.38321 & 0.616789 \tabularnewline
24 & -3 & -5.41245 & 2.41245 \tabularnewline
25 & -2 & 5.75928 & -7.75928 \tabularnewline
26 & 2 & -4.47893 & 6.47893 \tabularnewline
27 & 4 & 2.29484 & 1.70516 \tabularnewline
28 & -1 & -1.65699 & 0.656992 \tabularnewline
29 & -1 & 3.50193 & -4.50193 \tabularnewline
30 & -4 & -4.18502 & 0.185015 \tabularnewline
31 & 6 & 3.76075 & 2.23925 \tabularnewline
32 & -5 & -3.67276 & -1.32724 \tabularnewline
33 & 1 & 0.268556 & 0.731444 \tabularnewline
34 & 2 & 1.60366 & 0.396337 \tabularnewline
35 & -5 & -0.986261 & -4.01374 \tabularnewline
36 & 12 & 5.22308 & 6.77692 \tabularnewline
37 & -8 & -4.57999 & -3.42001 \tabularnewline
38 & -2 & -2.31317 & 0.313167 \tabularnewline
39 & 0 & 3.24369 & -3.24369 \tabularnewline
40 & -1 & -1.60451 & 0.604506 \tabularnewline
41 & 5 & -0.268272 & 5.26827 \tabularnewline
42 & -6 & 0.956842 & -6.95684 \tabularnewline
43 & 6 & 3.09569 & 2.90431 \tabularnewline
44 & -3 & -3.10933 & 0.109328 \tabularnewline
45 & 2 & 3.35973 & -1.35973 \tabularnewline
46 & -4 & -2.67208 & -1.32792 \tabularnewline
47 & 2 & -1.19867 & 3.19867 \tabularnewline
48 & 4 & -1.26876 & 5.26876 \tabularnewline
49 & -4 & 2.92539 & -6.92539 \tabularnewline
50 & -1 & -1.91215 & 0.912151 \tabularnewline
51 & 4 & 1.49526 & 2.50474 \tabularnewline
52 & 4 & 2.76 & 1.24 \tabularnewline
53 & -4 & -0.988993 & -3.01101 \tabularnewline
54 & -3 & 0.108992 & -3.10899 \tabularnewline
55 & 3.5 & -3.37276 & 6.87276 \tabularnewline
56 & -1.5 & -0.171801 & -1.3282 \tabularnewline
57 & -4 & 0.927161 & -4.92716 \tabularnewline
58 & 1 & 1.77666 & -0.776661 \tabularnewline
59 & 5 & -0.0922601 & 5.09226 \tabularnewline
60 & 5 & 3.94402 & 1.05598 \tabularnewline
61 & -8 & -4.80454 & -3.19546 \tabularnewline
62 & 0 & 1.21076 & -1.21076 \tabularnewline
63 & 2 & -0.0258981 & 2.0259 \tabularnewline
64 & -1 & -0.951669 & -0.0483314 \tabularnewline
65 & -2 & 1.4386 & -3.4386 \tabularnewline
66 & 6 & 0.298792 & 5.70121 \tabularnewline
67 & -5 & -1.63091 & -3.36909 \tabularnewline
68 & 1 & 0.0836404 & 0.91636 \tabularnewline
69 & 1 & -1.13354 & 2.13354 \tabularnewline
70 & -1 & 1.36645 & -2.36645 \tabularnewline
71 & 2 & -0.819211 & 2.81921 \tabularnewline
72 & -2 & -1.47802 & -0.521979 \tabularnewline
73 & -3 & 0.958724 & -3.95872 \tabularnewline
74 & 1 & 0.145377 & 0.854623 \tabularnewline
75 & 8 & 0.625936 & 7.37406 \tabularnewline
76 & -6 & -1.96664 & -4.03336 \tabularnewline
77 & 4 & 2.3558 & 1.6442 \tabularnewline
78 & -4 & -1.4322 & -2.5678 \tabularnewline
79 & -4 & 2.6363 & -6.6363 \tabularnewline
80 & 2 & -3.06158 & 5.06158 \tabularnewline
81 & -2 & 0.624201 & -2.6242 \tabularnewline
82 & 3 & -0.619203 & 3.6192 \tabularnewline
83 & 0 & 0.525057 & -0.525057 \tabularnewline
84 & 1 & 0.0506808 & 0.949319 \tabularnewline
85 & 0 & -0.117315 & 0.117315 \tabularnewline
86 & -1 & 0.920036 & -1.92004 \tabularnewline
87 & 3 & 1.60679 & 1.39321 \tabularnewline
88 & 7 & 3.01874 & 3.98126 \tabularnewline
89 & -9 & -6.54735 & -2.45265 \tabularnewline
90 & 2 & 3.85376 & -1.85376 \tabularnewline
91 & -2 & -2.99995 & 0.99995 \tabularnewline
92 & 2 & 0.278846 & 1.72115 \tabularnewline
93 & -2.5 & 1.04977 & -3.54977 \tabularnewline
94 & -1.5 & -2.31554 & 0.815538 \tabularnewline
95 & 5 & 3.61948 & 1.38052 \tabularnewline
96 & -5 & -2.39634 & -2.60366 \tabularnewline
97 & 0 & -1.78102 & 1.78102 \tabularnewline
98 & -1 & 1.6668 & -2.6668 \tabularnewline
99 & 0 & -1.06159 & 1.06159 \tabularnewline
100 & 6 & 3.13245 & 2.86755 \tabularnewline
101 & -4 & -3.64389 & -0.356113 \tabularnewline
102 & -1 & 4.78209 & -5.78209 \tabularnewline
103 & 5 & -3.53622 & 8.53622 \tabularnewline
104 & 3 & 5.32053 & -2.32053 \tabularnewline
105 & -8 & -5.71648 & -2.28352 \tabularnewline
106 & 5 & 1.25988 & 3.74012 \tabularnewline
107 & -1 & 4.20221 & -5.20221 \tabularnewline
108 & 9 & 0.994744 & 8.00526 \tabularnewline
109 & -10 & -6.44525 & -3.55475 \tabularnewline
110 & 1 & 3.58706 & -2.58706 \tabularnewline
111 & -4 & -2.63267 & -1.36733 \tabularnewline
112 & 4 & 3.41814 & 0.581865 \tabularnewline
113 & -3 & -5.08902 & 2.08902 \tabularnewline
114 & -2 & -0.027083 & -1.97292 \tabularnewline
115 & 4 & 4.1415 & -0.141504 \tabularnewline
116 & -1 & -1.3803 & 0.380303 \tabularnewline
117 & -4 & 0.250949 & -4.25095 \tabularnewline
118 & 6 & -1.70607 & 7.70607 \tabularnewline
119 & 0 & 4.09161 & -4.09161 \tabularnewline
120 & -1 & -3.22214 & 2.22214 \tabularnewline
121 & -3 & -0.944209 & -2.05579 \tabularnewline
122 & -3 & 0.637163 & -3.63716 \tabularnewline
123 & 3 & 0.190127 & 2.80987 \tabularnewline
124 & 0 & 0.83453 & -0.83453 \tabularnewline
125 & -3 & -3.53401 & 0.534006 \tabularnewline
126 & 2 & 1.10141 & 0.898586 \tabularnewline
127 & 1 & -1.94635 & 2.94635 \tabularnewline
128 & 2 & 3.90518 & -1.90518 \tabularnewline
129 & -2 & 0.604279 & -2.60428 \tabularnewline
130 & 9 & 3.86189 & 5.13811 \tabularnewline
131 & -10 & -5.55141 & -4.44859 \tabularnewline
132 & 5 & 1.40726 & 3.59274 \tabularnewline
133 & -3 & -0.539672 & -2.46033 \tabularnewline
134 & 2 & -1.75305 & 3.75305 \tabularnewline
135 & 9 & 5.28938 & 3.71062 \tabularnewline
136 & -9 & -2.4317 & -6.5683 \tabularnewline
137 & 2 & 3.00339 & -1.00339 \tabularnewline
138 & -5 & -3.5356 & -1.4644 \tabularnewline
139 & 1 & 1.12213 & -0.122133 \tabularnewline
140 & -2 & -1.07341 & -0.926593 \tabularnewline
141 & 4 & -2.5216 & 6.5216 \tabularnewline
142 & -2 & 1.43819 & -3.43819 \tabularnewline
143 & 0 & -0.3304 & 0.3304 \tabularnewline
144 & -1 & -0.607545 & -0.392455 \tabularnewline
145 & 1 & -0.00264544 & 1.00265 \tabularnewline
146 & 1 & 5.19751 & -4.19751 \tabularnewline
147 & -2 & -2.28118 & 0.281178 \tabularnewline
148 & 8 & 2.32503 & 5.67497 \tabularnewline
149 & -7 & -4.99285 & -2.00715 \tabularnewline
150 & 5 & 2.28483 & 2.71517 \tabularnewline
151 & -8 & -2.0895 & -5.9105 \tabularnewline
152 & 3 & 2.26388 & 0.736115 \tabularnewline
153 & 7 & 2.68856 & 4.31144 \tabularnewline
154 & -1 & -2.74991 & 1.74991 \tabularnewline
155 & -3 & 1.40761 & -4.40761 \tabularnewline
156 & -1 & -2.02917 & 1.02917 \tabularnewline
157 & -3 & -4.06958 & 1.06958 \tabularnewline
158 & -2 & 3.72094 & -5.72094 \tabularnewline
159 & 9 & 0.97035 & 8.02965 \tabularnewline
160 & -2 & 0.292401 & -2.2924 \tabularnewline
161 & 8 & 2.12191 & 5.87809 \tabularnewline
162 & -10 & -3.599 & -6.401 \tabularnewline
163 & 6 & -1.30632 & 7.30632 \tabularnewline
164 & -2 & 4.46957 & -6.46957 \tabularnewline
165 & 5 & 0.88749 & 4.11251 \tabularnewline
166 & -11 & -3.77595 & -7.22405 \tabularnewline
167 & 2 & 1.98231 & 0.0176923 \tabularnewline
168 & 2 & 1.37411 & 0.625885 \tabularnewline
169 & 2 & 0.0242999 & 1.9757 \tabularnewline
170 & 2 & 0.0623204 & 1.93768 \tabularnewline
171 & -8 & -4.31334 & -3.68666 \tabularnewline
172 & 0 & 4.62362 & -4.62362 \tabularnewline
173 & 5 & -1.44089 & 6.44089 \tabularnewline
174 & -4 & -2.6299 & -1.3701 \tabularnewline
175 & -4 & 0.947685 & -4.94769 \tabularnewline
176 & 7 & 3.5556 & 3.4444 \tabularnewline
177 & 2 & -2.08864 & 4.08864 \tabularnewline
178 & -8 & -2.61663 & -5.38337 \tabularnewline
179 & 4 & 3.16541 & 0.83459 \tabularnewline
180 & -3 & -0.888814 & -2.11119 \tabularnewline
181 & -2 & -0.170121 & -1.82988 \tabularnewline
182 & 2 & -1.85641 & 3.85641 \tabularnewline
183 & -1 & 0.381396 & -1.3814 \tabularnewline
184 & 1 & -1.25249 & 2.25249 \tabularnewline
185 & 8 & 1.94775 & 6.05225 \tabularnewline
186 & -5 & -0.677681 & -4.32232 \tabularnewline
187 & -2 & -1.15432 & -0.845682 \tabularnewline
188 & 2 & 3.50994 & -1.50994 \tabularnewline
189 & 7 & 0.769766 & 6.23023 \tabularnewline
190 & -8 & 1.86704 & -9.86704 \tabularnewline
191 & -3 & -4.67754 & 1.67754 \tabularnewline
192 & 5 & -0.696137 & 5.69614 \tabularnewline
193 & 1 & 3.38117 & -2.38117 \tabularnewline
194 & -2 & -3.04306 & 1.04306 \tabularnewline
195 & -2 & 2.15059 & -4.15059 \tabularnewline
196 & 7 & -2.4961 & 9.4961 \tabularnewline
197 & -4 & -0.229841 & -3.77016 \tabularnewline
198 & 4 & 5.62915 & -1.62915 \tabularnewline
199 & -6 & -4.4584 & -1.5416 \tabularnewline
200 & 4 & 3.23592 & 0.76408 \tabularnewline
201 & -5 & -3.90976 & -1.09024 \tabularnewline
202 & -1 & -1.1258 & 0.125805 \tabularnewline
203 & 3 & 4.23375 & -1.23375 \tabularnewline
204 & -3 & -3.41089 & 0.410895 \tabularnewline
205 & 2 & 0.20327 & 1.79673 \tabularnewline
206 & -1 & -0.239035 & -0.760965 \tabularnewline
207 & 3 & 1.12609 & 1.87391 \tabularnewline
208 & -1 & 1.23988 & -2.23988 \tabularnewline
209 & -2 & -3.57168 & 1.57168 \tabularnewline
210 & 5 & 6.60527 & -1.60527 \tabularnewline
211 & -6 & -5.68928 & -0.310717 \tabularnewline
212 & 7 & 2.24291 & 4.75709 \tabularnewline
213 & -5 & 0.818971 & -5.81897 \tabularnewline
214 & 4 & 0.586 & 3.414 \tabularnewline
215 & -4 & -2.57928 & -1.42072 \tabularnewline
216 & -2 & -2.85593 & 0.855933 \tabularnewline
217 & 1 & 2.57273 & -1.57273 \tabularnewline
218 & 10 & 5.34134 & 4.65866 \tabularnewline
219 & -8 & -5.88064 & -2.11936 \tabularnewline
220 & -2 & 3.25275 & -5.25275 \tabularnewline
221 & 0 & -2.22258 & 2.22258 \tabularnewline
222 & 5 & 2.81314 & 2.18686 \tabularnewline
223 & -8 & -3.12059 & -4.87941 \tabularnewline
224 & 12 & 6.24048 & 5.75952 \tabularnewline
225 & -11 & -6.93131 & -4.06869 \tabularnewline
226 & 1 & -0.813727 & 1.81373 \tabularnewline
227 & 1 & 3.87369 & -2.87369 \tabularnewline
228 & 11 & 2.85683 & 8.14317 \tabularnewline
229 & -10 & -2.35252 & -7.64748 \tabularnewline
230 & -1 & -2.49742 & 1.49742 \tabularnewline
231 & 4 & 4.42563 & -0.425627 \tabularnewline
232 & -7 & -4.60659 & -2.39341 \tabularnewline
233 & 8 & 0.999358 & 7.00064 \tabularnewline
234 & -8 & -1.74024 & -6.25976 \tabularnewline
235 & 5 & 3.72807 & 1.27193 \tabularnewline
236 & 3 & -0.38642 & 3.38642 \tabularnewline
237 & -4 & 0.37625 & -4.37625 \tabularnewline
238 & -2 & -0.171972 & -1.82803 \tabularnewline
239 & 1 & -0.0587602 & 1.05876 \tabularnewline
240 & -2 & -2.17539 & 0.175388 \tabularnewline
241 & 9 & 5.56532 & 3.43468 \tabularnewline
242 & -3 & -3.64091 & 0.640906 \tabularnewline
243 & 0 & -0.514398 & 0.514398 \tabularnewline
244 & -2 & -2.94217 & 0.942172 \tabularnewline
245 & 6 & 3.62804 & 2.37196 \tabularnewline
246 & -5 & -1.35478 & -3.64522 \tabularnewline
247 & 8 & 1.71824 & 6.28176 \tabularnewline
248 & -3 & 0.883912 & -3.88391 \tabularnewline
249 & -4 & -0.942136 & -3.05786 \tabularnewline
250 & -2 & -2.84019 & 0.840195 \tabularnewline
251 & 3 & 0.919685 & 2.08032 \tabularnewline
252 & -6 & 0.182976 & -6.18298 \tabularnewline
253 & 2 & -0.45378 & 2.45378 \tabularnewline
254 & 4 & 1.73897 & 2.26103 \tabularnewline
255 & -2 & -0.191606 & -1.80839 \tabularnewline
256 & 6 & 1.2417 & 4.7583 \tabularnewline
257 & -3 & 0.698852 & -3.69885 \tabularnewline
258 & -3 & -3.63481 & 0.634815 \tabularnewline
259 & -7 & 2.61288 & -9.61288 \tabularnewline
260 & 4 & -2.29565 & 6.29565 \tabularnewline
261 & 0 & 0.00500767 & -0.00500767 \tabularnewline
262 & 10 & 5.23712 & 4.76288 \tabularnewline
263 & -10 & -5.767 & -4.233 \tabularnewline
264 & -2 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253729&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]-1[/C][C]-5.34506[/C][C]4.34506[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]5.51029[/C][C]-2.51029[/C][/ROW]
[ROW][C]3[/C][C]-2[/C][C]-0.991463[/C][C]-1.00854[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]-2.27382[/C][C]11.2738[/C][/ROW]
[ROW][C]5[/C][C]-9[/C][C]-1.62039[/C][C]-7.37961[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]3.75755[/C][C]6.24245[/C][/ROW]
[ROW][C]7[/C][C]-11[/C][C]-1.15918[/C][C]-9.84082[/C][/ROW]
[ROW][C]8[/C][C]-1[/C][C]-0.2285[/C][C]-0.7715[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]-0.533537[/C][C]3.53354[/C][/ROW]
[ROW][C]10[/C][C]-3[/C][C]-1.58878[/C][C]-1.41122[/C][/ROW]
[ROW][C]11[/C][C]-2[/C][C]0.651142[/C][C]-2.65114[/C][/ROW]
[ROW][C]12[/C][C]7[/C][C]4.9789[/C][C]2.0211[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-3.01702[/C][C]2.01702[/C][/ROW]
[ROW][C]14[/C][C]-4[/C][C]-3.50904[/C][C]-0.490963[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]4.5923[/C][C]-0.592301[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]-1.30683[/C][C]1.30683[/C][/ROW]
[ROW][C]17[/C][C]-3[/C][C]-3.31498[/C][C]0.314985[/C][/ROW]
[ROW][C]18[/C][C]-1[/C][C]3.86823[/C][C]-4.86823[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]-0.600017[/C][C]3.60002[/C][/ROW]
[ROW][C]20[/C][C]-3.5[/C][C]-2.01268[/C][C]-1.48732[/C][/ROW]
[ROW][C]21[/C][C]4.5[/C][C]4.46426[/C][C]0.0357437[/C][/ROW]
[ROW][C]22[/C][C]-2[/C][C]-1.06857[/C][C]-0.931426[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.38321[/C][C]0.616789[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-5.41245[/C][C]2.41245[/C][/ROW]
[ROW][C]25[/C][C]-2[/C][C]5.75928[/C][C]-7.75928[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]-4.47893[/C][C]6.47893[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]2.29484[/C][C]1.70516[/C][/ROW]
[ROW][C]28[/C][C]-1[/C][C]-1.65699[/C][C]0.656992[/C][/ROW]
[ROW][C]29[/C][C]-1[/C][C]3.50193[/C][C]-4.50193[/C][/ROW]
[ROW][C]30[/C][C]-4[/C][C]-4.18502[/C][C]0.185015[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]3.76075[/C][C]2.23925[/C][/ROW]
[ROW][C]32[/C][C]-5[/C][C]-3.67276[/C][C]-1.32724[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.268556[/C][C]0.731444[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.60366[/C][C]0.396337[/C][/ROW]
[ROW][C]35[/C][C]-5[/C][C]-0.986261[/C][C]-4.01374[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]5.22308[/C][C]6.77692[/C][/ROW]
[ROW][C]37[/C][C]-8[/C][C]-4.57999[/C][C]-3.42001[/C][/ROW]
[ROW][C]38[/C][C]-2[/C][C]-2.31317[/C][C]0.313167[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]3.24369[/C][C]-3.24369[/C][/ROW]
[ROW][C]40[/C][C]-1[/C][C]-1.60451[/C][C]0.604506[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]-0.268272[/C][C]5.26827[/C][/ROW]
[ROW][C]42[/C][C]-6[/C][C]0.956842[/C][C]-6.95684[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]3.09569[/C][C]2.90431[/C][/ROW]
[ROW][C]44[/C][C]-3[/C][C]-3.10933[/C][C]0.109328[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]3.35973[/C][C]-1.35973[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-2.67208[/C][C]-1.32792[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]-1.19867[/C][C]3.19867[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]-1.26876[/C][C]5.26876[/C][/ROW]
[ROW][C]49[/C][C]-4[/C][C]2.92539[/C][C]-6.92539[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]-1.91215[/C][C]0.912151[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]1.49526[/C][C]2.50474[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]2.76[/C][C]1.24[/C][/ROW]
[ROW][C]53[/C][C]-4[/C][C]-0.988993[/C][C]-3.01101[/C][/ROW]
[ROW][C]54[/C][C]-3[/C][C]0.108992[/C][C]-3.10899[/C][/ROW]
[ROW][C]55[/C][C]3.5[/C][C]-3.37276[/C][C]6.87276[/C][/ROW]
[ROW][C]56[/C][C]-1.5[/C][C]-0.171801[/C][C]-1.3282[/C][/ROW]
[ROW][C]57[/C][C]-4[/C][C]0.927161[/C][C]-4.92716[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.77666[/C][C]-0.776661[/C][/ROW]
[ROW][C]59[/C][C]5[/C][C]-0.0922601[/C][C]5.09226[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]3.94402[/C][C]1.05598[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-4.80454[/C][C]-3.19546[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]1.21076[/C][C]-1.21076[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]-0.0258981[/C][C]2.0259[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-0.951669[/C][C]-0.0483314[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]1.4386[/C][C]-3.4386[/C][/ROW]
[ROW][C]66[/C][C]6[/C][C]0.298792[/C][C]5.70121[/C][/ROW]
[ROW][C]67[/C][C]-5[/C][C]-1.63091[/C][C]-3.36909[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.0836404[/C][C]0.91636[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]-1.13354[/C][C]2.13354[/C][/ROW]
[ROW][C]70[/C][C]-1[/C][C]1.36645[/C][C]-2.36645[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]-0.819211[/C][C]2.81921[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-1.47802[/C][C]-0.521979[/C][/ROW]
[ROW][C]73[/C][C]-3[/C][C]0.958724[/C][C]-3.95872[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.145377[/C][C]0.854623[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]0.625936[/C][C]7.37406[/C][/ROW]
[ROW][C]76[/C][C]-6[/C][C]-1.96664[/C][C]-4.03336[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]2.3558[/C][C]1.6442[/C][/ROW]
[ROW][C]78[/C][C]-4[/C][C]-1.4322[/C][C]-2.5678[/C][/ROW]
[ROW][C]79[/C][C]-4[/C][C]2.6363[/C][C]-6.6363[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]-3.06158[/C][C]5.06158[/C][/ROW]
[ROW][C]81[/C][C]-2[/C][C]0.624201[/C][C]-2.6242[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]-0.619203[/C][C]3.6192[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.525057[/C][C]-0.525057[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.0506808[/C][C]0.949319[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.117315[/C][C]0.117315[/C][/ROW]
[ROW][C]86[/C][C]-1[/C][C]0.920036[/C][C]-1.92004[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]1.60679[/C][C]1.39321[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]3.01874[/C][C]3.98126[/C][/ROW]
[ROW][C]89[/C][C]-9[/C][C]-6.54735[/C][C]-2.45265[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]3.85376[/C][C]-1.85376[/C][/ROW]
[ROW][C]91[/C][C]-2[/C][C]-2.99995[/C][C]0.99995[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]0.278846[/C][C]1.72115[/C][/ROW]
[ROW][C]93[/C][C]-2.5[/C][C]1.04977[/C][C]-3.54977[/C][/ROW]
[ROW][C]94[/C][C]-1.5[/C][C]-2.31554[/C][C]0.815538[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]3.61948[/C][C]1.38052[/C][/ROW]
[ROW][C]96[/C][C]-5[/C][C]-2.39634[/C][C]-2.60366[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]-1.78102[/C][C]1.78102[/C][/ROW]
[ROW][C]98[/C][C]-1[/C][C]1.6668[/C][C]-2.6668[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]-1.06159[/C][C]1.06159[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]3.13245[/C][C]2.86755[/C][/ROW]
[ROW][C]101[/C][C]-4[/C][C]-3.64389[/C][C]-0.356113[/C][/ROW]
[ROW][C]102[/C][C]-1[/C][C]4.78209[/C][C]-5.78209[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]-3.53622[/C][C]8.53622[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]5.32053[/C][C]-2.32053[/C][/ROW]
[ROW][C]105[/C][C]-8[/C][C]-5.71648[/C][C]-2.28352[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]1.25988[/C][C]3.74012[/C][/ROW]
[ROW][C]107[/C][C]-1[/C][C]4.20221[/C][C]-5.20221[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]0.994744[/C][C]8.00526[/C][/ROW]
[ROW][C]109[/C][C]-10[/C][C]-6.44525[/C][C]-3.55475[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]3.58706[/C][C]-2.58706[/C][/ROW]
[ROW][C]111[/C][C]-4[/C][C]-2.63267[/C][C]-1.36733[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.41814[/C][C]0.581865[/C][/ROW]
[ROW][C]113[/C][C]-3[/C][C]-5.08902[/C][C]2.08902[/C][/ROW]
[ROW][C]114[/C][C]-2[/C][C]-0.027083[/C][C]-1.97292[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]4.1415[/C][C]-0.141504[/C][/ROW]
[ROW][C]116[/C][C]-1[/C][C]-1.3803[/C][C]0.380303[/C][/ROW]
[ROW][C]117[/C][C]-4[/C][C]0.250949[/C][C]-4.25095[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]-1.70607[/C][C]7.70607[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]4.09161[/C][C]-4.09161[/C][/ROW]
[ROW][C]120[/C][C]-1[/C][C]-3.22214[/C][C]2.22214[/C][/ROW]
[ROW][C]121[/C][C]-3[/C][C]-0.944209[/C][C]-2.05579[/C][/ROW]
[ROW][C]122[/C][C]-3[/C][C]0.637163[/C][C]-3.63716[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]0.190127[/C][C]2.80987[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.83453[/C][C]-0.83453[/C][/ROW]
[ROW][C]125[/C][C]-3[/C][C]-3.53401[/C][C]0.534006[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.10141[/C][C]0.898586[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]-1.94635[/C][C]2.94635[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]3.90518[/C][C]-1.90518[/C][/ROW]
[ROW][C]129[/C][C]-2[/C][C]0.604279[/C][C]-2.60428[/C][/ROW]
[ROW][C]130[/C][C]9[/C][C]3.86189[/C][C]5.13811[/C][/ROW]
[ROW][C]131[/C][C]-10[/C][C]-5.55141[/C][C]-4.44859[/C][/ROW]
[ROW][C]132[/C][C]5[/C][C]1.40726[/C][C]3.59274[/C][/ROW]
[ROW][C]133[/C][C]-3[/C][C]-0.539672[/C][C]-2.46033[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]-1.75305[/C][C]3.75305[/C][/ROW]
[ROW][C]135[/C][C]9[/C][C]5.28938[/C][C]3.71062[/C][/ROW]
[ROW][C]136[/C][C]-9[/C][C]-2.4317[/C][C]-6.5683[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]3.00339[/C][C]-1.00339[/C][/ROW]
[ROW][C]138[/C][C]-5[/C][C]-3.5356[/C][C]-1.4644[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.12213[/C][C]-0.122133[/C][/ROW]
[ROW][C]140[/C][C]-2[/C][C]-1.07341[/C][C]-0.926593[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]-2.5216[/C][C]6.5216[/C][/ROW]
[ROW][C]142[/C][C]-2[/C][C]1.43819[/C][C]-3.43819[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]-0.3304[/C][C]0.3304[/C][/ROW]
[ROW][C]144[/C][C]-1[/C][C]-0.607545[/C][C]-0.392455[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]-0.00264544[/C][C]1.00265[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]5.19751[/C][C]-4.19751[/C][/ROW]
[ROW][C]147[/C][C]-2[/C][C]-2.28118[/C][C]0.281178[/C][/ROW]
[ROW][C]148[/C][C]8[/C][C]2.32503[/C][C]5.67497[/C][/ROW]
[ROW][C]149[/C][C]-7[/C][C]-4.99285[/C][C]-2.00715[/C][/ROW]
[ROW][C]150[/C][C]5[/C][C]2.28483[/C][C]2.71517[/C][/ROW]
[ROW][C]151[/C][C]-8[/C][C]-2.0895[/C][C]-5.9105[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]2.26388[/C][C]0.736115[/C][/ROW]
[ROW][C]153[/C][C]7[/C][C]2.68856[/C][C]4.31144[/C][/ROW]
[ROW][C]154[/C][C]-1[/C][C]-2.74991[/C][C]1.74991[/C][/ROW]
[ROW][C]155[/C][C]-3[/C][C]1.40761[/C][C]-4.40761[/C][/ROW]
[ROW][C]156[/C][C]-1[/C][C]-2.02917[/C][C]1.02917[/C][/ROW]
[ROW][C]157[/C][C]-3[/C][C]-4.06958[/C][C]1.06958[/C][/ROW]
[ROW][C]158[/C][C]-2[/C][C]3.72094[/C][C]-5.72094[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]0.97035[/C][C]8.02965[/C][/ROW]
[ROW][C]160[/C][C]-2[/C][C]0.292401[/C][C]-2.2924[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]2.12191[/C][C]5.87809[/C][/ROW]
[ROW][C]162[/C][C]-10[/C][C]-3.599[/C][C]-6.401[/C][/ROW]
[ROW][C]163[/C][C]6[/C][C]-1.30632[/C][C]7.30632[/C][/ROW]
[ROW][C]164[/C][C]-2[/C][C]4.46957[/C][C]-6.46957[/C][/ROW]
[ROW][C]165[/C][C]5[/C][C]0.88749[/C][C]4.11251[/C][/ROW]
[ROW][C]166[/C][C]-11[/C][C]-3.77595[/C][C]-7.22405[/C][/ROW]
[ROW][C]167[/C][C]2[/C][C]1.98231[/C][C]0.0176923[/C][/ROW]
[ROW][C]168[/C][C]2[/C][C]1.37411[/C][C]0.625885[/C][/ROW]
[ROW][C]169[/C][C]2[/C][C]0.0242999[/C][C]1.9757[/C][/ROW]
[ROW][C]170[/C][C]2[/C][C]0.0623204[/C][C]1.93768[/C][/ROW]
[ROW][C]171[/C][C]-8[/C][C]-4.31334[/C][C]-3.68666[/C][/ROW]
[ROW][C]172[/C][C]0[/C][C]4.62362[/C][C]-4.62362[/C][/ROW]
[ROW][C]173[/C][C]5[/C][C]-1.44089[/C][C]6.44089[/C][/ROW]
[ROW][C]174[/C][C]-4[/C][C]-2.6299[/C][C]-1.3701[/C][/ROW]
[ROW][C]175[/C][C]-4[/C][C]0.947685[/C][C]-4.94769[/C][/ROW]
[ROW][C]176[/C][C]7[/C][C]3.5556[/C][C]3.4444[/C][/ROW]
[ROW][C]177[/C][C]2[/C][C]-2.08864[/C][C]4.08864[/C][/ROW]
[ROW][C]178[/C][C]-8[/C][C]-2.61663[/C][C]-5.38337[/C][/ROW]
[ROW][C]179[/C][C]4[/C][C]3.16541[/C][C]0.83459[/C][/ROW]
[ROW][C]180[/C][C]-3[/C][C]-0.888814[/C][C]-2.11119[/C][/ROW]
[ROW][C]181[/C][C]-2[/C][C]-0.170121[/C][C]-1.82988[/C][/ROW]
[ROW][C]182[/C][C]2[/C][C]-1.85641[/C][C]3.85641[/C][/ROW]
[ROW][C]183[/C][C]-1[/C][C]0.381396[/C][C]-1.3814[/C][/ROW]
[ROW][C]184[/C][C]1[/C][C]-1.25249[/C][C]2.25249[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]1.94775[/C][C]6.05225[/C][/ROW]
[ROW][C]186[/C][C]-5[/C][C]-0.677681[/C][C]-4.32232[/C][/ROW]
[ROW][C]187[/C][C]-2[/C][C]-1.15432[/C][C]-0.845682[/C][/ROW]
[ROW][C]188[/C][C]2[/C][C]3.50994[/C][C]-1.50994[/C][/ROW]
[ROW][C]189[/C][C]7[/C][C]0.769766[/C][C]6.23023[/C][/ROW]
[ROW][C]190[/C][C]-8[/C][C]1.86704[/C][C]-9.86704[/C][/ROW]
[ROW][C]191[/C][C]-3[/C][C]-4.67754[/C][C]1.67754[/C][/ROW]
[ROW][C]192[/C][C]5[/C][C]-0.696137[/C][C]5.69614[/C][/ROW]
[ROW][C]193[/C][C]1[/C][C]3.38117[/C][C]-2.38117[/C][/ROW]
[ROW][C]194[/C][C]-2[/C][C]-3.04306[/C][C]1.04306[/C][/ROW]
[ROW][C]195[/C][C]-2[/C][C]2.15059[/C][C]-4.15059[/C][/ROW]
[ROW][C]196[/C][C]7[/C][C]-2.4961[/C][C]9.4961[/C][/ROW]
[ROW][C]197[/C][C]-4[/C][C]-0.229841[/C][C]-3.77016[/C][/ROW]
[ROW][C]198[/C][C]4[/C][C]5.62915[/C][C]-1.62915[/C][/ROW]
[ROW][C]199[/C][C]-6[/C][C]-4.4584[/C][C]-1.5416[/C][/ROW]
[ROW][C]200[/C][C]4[/C][C]3.23592[/C][C]0.76408[/C][/ROW]
[ROW][C]201[/C][C]-5[/C][C]-3.90976[/C][C]-1.09024[/C][/ROW]
[ROW][C]202[/C][C]-1[/C][C]-1.1258[/C][C]0.125805[/C][/ROW]
[ROW][C]203[/C][C]3[/C][C]4.23375[/C][C]-1.23375[/C][/ROW]
[ROW][C]204[/C][C]-3[/C][C]-3.41089[/C][C]0.410895[/C][/ROW]
[ROW][C]205[/C][C]2[/C][C]0.20327[/C][C]1.79673[/C][/ROW]
[ROW][C]206[/C][C]-1[/C][C]-0.239035[/C][C]-0.760965[/C][/ROW]
[ROW][C]207[/C][C]3[/C][C]1.12609[/C][C]1.87391[/C][/ROW]
[ROW][C]208[/C][C]-1[/C][C]1.23988[/C][C]-2.23988[/C][/ROW]
[ROW][C]209[/C][C]-2[/C][C]-3.57168[/C][C]1.57168[/C][/ROW]
[ROW][C]210[/C][C]5[/C][C]6.60527[/C][C]-1.60527[/C][/ROW]
[ROW][C]211[/C][C]-6[/C][C]-5.68928[/C][C]-0.310717[/C][/ROW]
[ROW][C]212[/C][C]7[/C][C]2.24291[/C][C]4.75709[/C][/ROW]
[ROW][C]213[/C][C]-5[/C][C]0.818971[/C][C]-5.81897[/C][/ROW]
[ROW][C]214[/C][C]4[/C][C]0.586[/C][C]3.414[/C][/ROW]
[ROW][C]215[/C][C]-4[/C][C]-2.57928[/C][C]-1.42072[/C][/ROW]
[ROW][C]216[/C][C]-2[/C][C]-2.85593[/C][C]0.855933[/C][/ROW]
[ROW][C]217[/C][C]1[/C][C]2.57273[/C][C]-1.57273[/C][/ROW]
[ROW][C]218[/C][C]10[/C][C]5.34134[/C][C]4.65866[/C][/ROW]
[ROW][C]219[/C][C]-8[/C][C]-5.88064[/C][C]-2.11936[/C][/ROW]
[ROW][C]220[/C][C]-2[/C][C]3.25275[/C][C]-5.25275[/C][/ROW]
[ROW][C]221[/C][C]0[/C][C]-2.22258[/C][C]2.22258[/C][/ROW]
[ROW][C]222[/C][C]5[/C][C]2.81314[/C][C]2.18686[/C][/ROW]
[ROW][C]223[/C][C]-8[/C][C]-3.12059[/C][C]-4.87941[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]6.24048[/C][C]5.75952[/C][/ROW]
[ROW][C]225[/C][C]-11[/C][C]-6.93131[/C][C]-4.06869[/C][/ROW]
[ROW][C]226[/C][C]1[/C][C]-0.813727[/C][C]1.81373[/C][/ROW]
[ROW][C]227[/C][C]1[/C][C]3.87369[/C][C]-2.87369[/C][/ROW]
[ROW][C]228[/C][C]11[/C][C]2.85683[/C][C]8.14317[/C][/ROW]
[ROW][C]229[/C][C]-10[/C][C]-2.35252[/C][C]-7.64748[/C][/ROW]
[ROW][C]230[/C][C]-1[/C][C]-2.49742[/C][C]1.49742[/C][/ROW]
[ROW][C]231[/C][C]4[/C][C]4.42563[/C][C]-0.425627[/C][/ROW]
[ROW][C]232[/C][C]-7[/C][C]-4.60659[/C][C]-2.39341[/C][/ROW]
[ROW][C]233[/C][C]8[/C][C]0.999358[/C][C]7.00064[/C][/ROW]
[ROW][C]234[/C][C]-8[/C][C]-1.74024[/C][C]-6.25976[/C][/ROW]
[ROW][C]235[/C][C]5[/C][C]3.72807[/C][C]1.27193[/C][/ROW]
[ROW][C]236[/C][C]3[/C][C]-0.38642[/C][C]3.38642[/C][/ROW]
[ROW][C]237[/C][C]-4[/C][C]0.37625[/C][C]-4.37625[/C][/ROW]
[ROW][C]238[/C][C]-2[/C][C]-0.171972[/C][C]-1.82803[/C][/ROW]
[ROW][C]239[/C][C]1[/C][C]-0.0587602[/C][C]1.05876[/C][/ROW]
[ROW][C]240[/C][C]-2[/C][C]-2.17539[/C][C]0.175388[/C][/ROW]
[ROW][C]241[/C][C]9[/C][C]5.56532[/C][C]3.43468[/C][/ROW]
[ROW][C]242[/C][C]-3[/C][C]-3.64091[/C][C]0.640906[/C][/ROW]
[ROW][C]243[/C][C]0[/C][C]-0.514398[/C][C]0.514398[/C][/ROW]
[ROW][C]244[/C][C]-2[/C][C]-2.94217[/C][C]0.942172[/C][/ROW]
[ROW][C]245[/C][C]6[/C][C]3.62804[/C][C]2.37196[/C][/ROW]
[ROW][C]246[/C][C]-5[/C][C]-1.35478[/C][C]-3.64522[/C][/ROW]
[ROW][C]247[/C][C]8[/C][C]1.71824[/C][C]6.28176[/C][/ROW]
[ROW][C]248[/C][C]-3[/C][C]0.883912[/C][C]-3.88391[/C][/ROW]
[ROW][C]249[/C][C]-4[/C][C]-0.942136[/C][C]-3.05786[/C][/ROW]
[ROW][C]250[/C][C]-2[/C][C]-2.84019[/C][C]0.840195[/C][/ROW]
[ROW][C]251[/C][C]3[/C][C]0.919685[/C][C]2.08032[/C][/ROW]
[ROW][C]252[/C][C]-6[/C][C]0.182976[/C][C]-6.18298[/C][/ROW]
[ROW][C]253[/C][C]2[/C][C]-0.45378[/C][C]2.45378[/C][/ROW]
[ROW][C]254[/C][C]4[/C][C]1.73897[/C][C]2.26103[/C][/ROW]
[ROW][C]255[/C][C]-2[/C][C]-0.191606[/C][C]-1.80839[/C][/ROW]
[ROW][C]256[/C][C]6[/C][C]1.2417[/C][C]4.7583[/C][/ROW]
[ROW][C]257[/C][C]-3[/C][C]0.698852[/C][C]-3.69885[/C][/ROW]
[ROW][C]258[/C][C]-3[/C][C]-3.63481[/C][C]0.634815[/C][/ROW]
[ROW][C]259[/C][C]-7[/C][C]2.61288[/C][C]-9.61288[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]-2.29565[/C][C]6.29565[/C][/ROW]
[ROW][C]261[/C][C]0[/C][C]0.00500767[/C][C]-0.00500767[/C][/ROW]
[ROW][C]262[/C][C]10[/C][C]5.23712[/C][C]4.76288[/C][/ROW]
[ROW][C]263[/C][C]-10[/C][C]-5.767[/C][C]-4.233[/C][/ROW]
[ROW][C]264[/C][C]-2[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253729&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253729&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1-5.345064.34506
235.51029-2.51029
3-2-0.991463-1.00854
49-2.2738211.2738
5-9-1.62039-7.37961
6103.757556.24245
7-11-1.15918-9.84082
8-1-0.2285-0.7715
93-0.5335373.53354
10-3-1.58878-1.41122
11-20.651142-2.65114
1274.97892.0211
13-1-3.017022.01702
14-4-3.50904-0.490963
1544.5923-0.592301
160-1.306831.30683
17-3-3.314980.314985
18-13.86823-4.86823
193-0.6000173.60002
20-3.5-2.01268-1.48732
214.54.464260.0357437
22-2-1.06857-0.931426
2321.383210.616789
24-3-5.412452.41245
25-25.75928-7.75928
262-4.478936.47893
2742.294841.70516
28-1-1.656990.656992
29-13.50193-4.50193
30-4-4.185020.185015
3163.760752.23925
32-5-3.67276-1.32724
3310.2685560.731444
3421.603660.396337
35-5-0.986261-4.01374
36125.223086.77692
37-8-4.57999-3.42001
38-2-2.313170.313167
3903.24369-3.24369
40-1-1.604510.604506
415-0.2682725.26827
42-60.956842-6.95684
4363.095692.90431
44-3-3.109330.109328
4523.35973-1.35973
46-4-2.67208-1.32792
472-1.198673.19867
484-1.268765.26876
49-42.92539-6.92539
50-1-1.912150.912151
5141.495262.50474
5242.761.24
53-4-0.988993-3.01101
54-30.108992-3.10899
553.5-3.372766.87276
56-1.5-0.171801-1.3282
57-40.927161-4.92716
5811.77666-0.776661
595-0.09226015.09226
6053.944021.05598
61-8-4.80454-3.19546
6201.21076-1.21076
632-0.02589812.0259
64-1-0.951669-0.0483314
65-21.4386-3.4386
6660.2987925.70121
67-5-1.63091-3.36909
6810.08364040.91636
691-1.133542.13354
70-11.36645-2.36645
712-0.8192112.81921
72-2-1.47802-0.521979
73-30.958724-3.95872
7410.1453770.854623
7580.6259367.37406
76-6-1.96664-4.03336
7742.35581.6442
78-4-1.4322-2.5678
79-42.6363-6.6363
802-3.061585.06158
81-20.624201-2.6242
823-0.6192033.6192
8300.525057-0.525057
8410.05068080.949319
850-0.1173150.117315
86-10.920036-1.92004
8731.606791.39321
8873.018743.98126
89-9-6.54735-2.45265
9023.85376-1.85376
91-2-2.999950.99995
9220.2788461.72115
93-2.51.04977-3.54977
94-1.5-2.315540.815538
9553.619481.38052
96-5-2.39634-2.60366
970-1.781021.78102
98-11.6668-2.6668
990-1.061591.06159
10063.132452.86755
101-4-3.64389-0.356113
102-14.78209-5.78209
1035-3.536228.53622
10435.32053-2.32053
105-8-5.71648-2.28352
10651.259883.74012
107-14.20221-5.20221
10890.9947448.00526
109-10-6.44525-3.55475
11013.58706-2.58706
111-4-2.63267-1.36733
11243.418140.581865
113-3-5.089022.08902
114-2-0.027083-1.97292
11544.1415-0.141504
116-1-1.38030.380303
117-40.250949-4.25095
1186-1.706077.70607
11904.09161-4.09161
120-1-3.222142.22214
121-3-0.944209-2.05579
122-30.637163-3.63716
12330.1901272.80987
12400.83453-0.83453
125-3-3.534010.534006
12621.101410.898586
1271-1.946352.94635
12823.90518-1.90518
129-20.604279-2.60428
13093.861895.13811
131-10-5.55141-4.44859
13251.407263.59274
133-3-0.539672-2.46033
1342-1.753053.75305
13595.289383.71062
136-9-2.4317-6.5683
13723.00339-1.00339
138-5-3.5356-1.4644
13911.12213-0.122133
140-2-1.07341-0.926593
1414-2.52166.5216
142-21.43819-3.43819
1430-0.33040.3304
144-1-0.607545-0.392455
1451-0.002645441.00265
14615.19751-4.19751
147-2-2.281180.281178
14882.325035.67497
149-7-4.99285-2.00715
15052.284832.71517
151-8-2.0895-5.9105
15232.263880.736115
15372.688564.31144
154-1-2.749911.74991
155-31.40761-4.40761
156-1-2.029171.02917
157-3-4.069581.06958
158-23.72094-5.72094
15990.970358.02965
160-20.292401-2.2924
16182.121915.87809
162-10-3.599-6.401
1636-1.306327.30632
164-24.46957-6.46957
16550.887494.11251
166-11-3.77595-7.22405
16721.982310.0176923
16821.374110.625885
16920.02429991.9757
17020.06232041.93768
171-8-4.31334-3.68666
17204.62362-4.62362
1735-1.440896.44089
174-4-2.6299-1.3701
175-40.947685-4.94769
17673.55563.4444
1772-2.088644.08864
178-8-2.61663-5.38337
17943.165410.83459
180-3-0.888814-2.11119
181-2-0.170121-1.82988
1822-1.856413.85641
183-10.381396-1.3814
1841-1.252492.25249
18581.947756.05225
186-5-0.677681-4.32232
187-2-1.15432-0.845682
18823.50994-1.50994
18970.7697666.23023
190-81.86704-9.86704
191-3-4.677541.67754
1925-0.6961375.69614
19313.38117-2.38117
194-2-3.043061.04306
195-22.15059-4.15059
1967-2.49619.4961
197-4-0.229841-3.77016
19845.62915-1.62915
199-6-4.4584-1.5416
20043.235920.76408
201-5-3.90976-1.09024
202-1-1.12580.125805
20334.23375-1.23375
204-3-3.410890.410895
20520.203271.79673
206-1-0.239035-0.760965
20731.126091.87391
208-11.23988-2.23988
209-2-3.571681.57168
21056.60527-1.60527
211-6-5.68928-0.310717
21272.242914.75709
213-50.818971-5.81897
21440.5863.414
215-4-2.57928-1.42072
216-2-2.855930.855933
21712.57273-1.57273
218105.341344.65866
219-8-5.88064-2.11936
220-23.25275-5.25275
2210-2.222582.22258
22252.813142.18686
223-8-3.12059-4.87941
224126.240485.75952
225-11-6.93131-4.06869
2261-0.8137271.81373
22713.87369-2.87369
228112.856838.14317
229-10-2.35252-7.64748
230-1-2.497421.49742
23144.42563-0.425627
232-7-4.60659-2.39341
23380.9993587.00064
234-8-1.74024-6.25976
23553.728071.27193
2363-0.386423.38642
237-40.37625-4.37625
238-2-0.171972-1.82803
2391-0.05876021.05876
240-2-2.175390.175388
24195.565323.43468
242-3-3.640910.640906
2430-0.5143980.514398
244-2-2.942170.942172
24563.628042.37196
246-5-1.35478-3.64522
24781.718246.28176
248-30.883912-3.88391
249-4-0.942136-3.05786
250-2-2.840190.840195
25130.9196852.08032
252-60.182976-6.18298
2532-0.453782.45378
25441.738972.26103
255-2-0.191606-1.80839
25661.24174.7583
257-30.698852-3.69885
258-3-3.634810.634815
259-72.61288-9.61288
2604-2.295656.29565
26100.00500767-0.00500767
262105.237124.76288
263-10-5.767-4.233
264-2NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5199390.9601210.480061
110.8948530.2102950.105147
120.8435540.3128930.156446
130.7683610.4632780.231639
140.6740530.6518930.325947
150.7962690.4074620.203731
160.7453810.5092380.254619
170.8382580.3234840.161742
180.8331350.3337290.166865
190.9234110.1531780.0765888
200.8952280.2095440.104772
210.857330.285340.14267
220.876860.2462790.12314
230.862950.2741010.13705
240.8230360.3539280.176964
250.9024280.1951450.0975723
260.9328920.1342160.0671078
270.9165940.1668120.0834062
280.8900090.2199820.109991
290.8711940.2576130.128806
300.8451150.309770.154885
310.8509670.2980650.149033
320.8318520.3362950.168148
330.7931490.4137020.206851
340.7555470.4889070.244453
350.7341460.5317080.265854
360.836330.3273390.16367
370.8612070.2775860.138793
380.8328730.3342550.167127
390.807930.3841390.19207
400.7711610.4576780.228839
410.7937840.4124310.206216
420.8854740.2290530.114526
430.8840470.2319060.115953
440.8581270.2837450.141873
450.8315690.3368630.168431
460.8061840.3876320.193816
470.7870490.4259020.212951
480.8134290.3731430.186571
490.8473110.3053770.152689
500.8219560.3560880.178044
510.8124610.3750780.187539
520.7856320.4287360.214368
530.7734720.4530570.226528
540.7535460.4929080.246454
550.8096440.3807110.190356
560.7826510.4346970.217349
570.8146050.3707890.185395
580.7843890.4312230.215611
590.8017010.3965980.198299
600.777570.4448610.22243
610.7728380.4543240.227162
620.743130.513740.25687
630.7139220.5721570.286078
640.6766330.6467330.323367
650.6582390.6835220.341761
660.6929130.6141740.307087
670.6926870.6146270.307313
680.6586390.6827220.341361
690.6278050.744390.372195
700.5956390.8087230.404361
710.5644270.8711470.435573
720.5241190.9517610.475881
730.5230.9540.477
740.4867110.9734220.513289
750.6013440.7973120.398656
760.6154460.7691080.384554
770.5842760.8314470.415724
780.561390.8772190.43861
790.6247790.7504430.375221
800.6333290.7333420.366671
810.610760.778480.38924
820.6118940.7762110.388106
830.5764420.8471170.423558
840.5403950.919210.459605
850.5019990.9960020.498001
860.4732220.9464450.526778
870.4396760.8793520.560324
880.4563310.9126620.543669
890.4443460.8886920.555654
900.4117530.8235050.588247
910.3769970.7539940.623003
920.3508090.7016180.649191
930.3440710.6881420.655929
940.3107970.6215940.689203
950.2852470.5704940.714753
960.2733310.5466610.726669
970.2496190.4992380.750381
980.2346560.4693120.765344
990.2094520.4189030.790548
1000.1981920.3963840.801808
1010.1750450.3500910.824955
1020.1954320.3908640.804568
1030.290330.580660.70967
1040.2692270.5384540.730773
1050.262560.5251190.73744
1060.2628260.5256520.737174
1070.2844060.5688120.715594
1080.3959930.7919870.604007
1090.3909660.7819310.609034
1100.3697890.7395790.630211
1110.3499820.6999640.650018
1120.3241790.6483580.675821
1130.2996520.5993030.700348
1140.2772530.5545060.722747
1150.2476960.4953920.752304
1160.2203020.4406040.779698
1170.2232640.4465280.776736
1180.3048190.6096390.695181
1190.3049550.6099090.695045
1200.2858730.5717470.714127
1210.2705350.5410690.729465
1220.2670280.5340550.732972
1230.2557020.5114030.744298
1240.228570.4571410.77143
1250.2040010.4080010.795999
1260.1805070.3610150.819493
1270.1720420.3440830.827958
1280.1561020.3122040.843898
1290.1455240.2910470.854476
1300.166920.3338390.83308
1310.179730.359460.82027
1320.1774320.3548640.822568
1330.1640670.3281330.835933
1340.1623790.3247580.837621
1350.1625880.3251760.837412
1360.2057350.4114710.794265
1370.1829030.3658070.817097
1380.1632980.3265950.836702
1390.1421340.2842690.857866
1400.1238820.2477650.876118
1410.1621280.3242560.837872
1420.1564370.3128740.843563
1430.1361370.2722740.863863
1440.1178910.2357810.882109
1450.1023560.2047120.897644
1460.1056210.2112420.894379
1470.09050350.1810070.909496
1480.1078290.2156590.892171
1490.09676180.1935240.903238
1500.09022670.1804530.909773
1510.1121440.2242880.887856
1520.09634980.19270.90365
1530.09982680.1996540.900173
1540.088150.17630.91185
1550.09348680.1869740.906513
1560.08077160.1615430.919228
1570.06938470.1387690.930615
1580.08471520.169430.915285
1590.143930.2878590.85607
1600.1323750.2647510.867625
1610.1563750.3127490.843625
1620.1942750.3885490.805725
1630.2651180.5302360.734882
1640.3247210.6494410.675279
1650.3274490.6548990.672551
1660.4055680.8111360.594432
1670.370070.740140.62993
1680.3366320.6732630.663368
1690.3111760.6223520.688824
1700.2845080.5690150.715492
1710.2763320.5526650.723668
1720.2927760.5855530.707224
1730.3677760.7355510.632224
1740.3377830.6755660.662217
1750.3664520.7329040.633548
1760.3550360.7100720.644964
1770.3720880.7441750.627912
1780.4044070.8088140.595593
1790.369430.7388590.63057
1800.3438190.6876390.656181
1810.3154040.6308080.684596
1820.3146530.6293070.685347
1830.2860020.5720040.713998
1840.3097820.6195640.690218
1850.3242580.6485160.675742
1860.3443660.6887320.655634
1870.3097570.6195150.690243
1880.2774170.5548340.722583
1890.3195960.6391930.680404
1900.5506810.8986380.449319
1910.5188050.962390.481195
1920.6017490.7965020.398251
1930.6101830.7796330.389817
1940.5841310.8317380.415869
1950.6251020.7497960.374898
1960.8549010.2901980.145099
1970.8612950.2774090.138705
1980.8450260.3099480.154974
1990.8197480.3605040.180252
2000.791150.4176990.20885
2010.7648370.4703260.235163
2020.7296270.5407450.270373
2030.7202620.5594750.279738
2040.6920010.6159990.307999
2050.6560490.6879020.343951
2060.6156240.7687520.384376
2070.576960.8460790.42304
2080.5396950.920610.460305
2090.5130180.9739640.486982
2100.5185480.9629030.481452
2110.4740740.9481490.525926
2120.5222660.9554670.477734
2130.5632510.8734970.436749
2140.5285110.9429780.471489
2150.4877020.9754040.512298
2160.4423470.8846930.557653
2170.4193040.8386070.580696
2180.4177670.8355330.582233
2190.3752060.7504120.624794
2200.4594380.9188760.540562
2210.4396110.8792220.560389
2220.395050.7900990.60495
2230.3774120.7548240.622588
2240.3754760.7509510.624524
2250.3362810.6725630.663719
2260.2904040.5808080.709596
2270.2885640.5771270.711436
2280.4102620.8205230.589738
2290.5440030.9119940.455997
2300.5143170.9713650.485683
2310.4858890.9717780.514111
2320.4325730.8651460.567427
2330.4350180.8700360.564982
2340.4679080.9358160.532092
2350.405790.811580.59421
2360.4192090.8384190.580791
2370.6059230.7881540.394077
2380.5401040.9197910.459896
2390.5032750.9934510.496725
2400.4439950.8879910.556005
2410.4164150.8328290.583585
2420.773990.4520190.22601
2430.7562740.4874510.243726
2440.6873230.6253550.312677
2450.6077410.7845170.392259
2460.6287820.7424370.371218
2470.7848350.430330.215165
2480.7059440.5881120.294056
2490.669230.6615410.33077
2500.7286070.5427860.271393
2510.6123990.7752010.387601
2520.9194910.1610180.0805091
2530.8308510.3382970.169149
2540.7968820.4062360.203118

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.519939 & 0.960121 & 0.480061 \tabularnewline
11 & 0.894853 & 0.210295 & 0.105147 \tabularnewline
12 & 0.843554 & 0.312893 & 0.156446 \tabularnewline
13 & 0.768361 & 0.463278 & 0.231639 \tabularnewline
14 & 0.674053 & 0.651893 & 0.325947 \tabularnewline
15 & 0.796269 & 0.407462 & 0.203731 \tabularnewline
16 & 0.745381 & 0.509238 & 0.254619 \tabularnewline
17 & 0.838258 & 0.323484 & 0.161742 \tabularnewline
18 & 0.833135 & 0.333729 & 0.166865 \tabularnewline
19 & 0.923411 & 0.153178 & 0.0765888 \tabularnewline
20 & 0.895228 & 0.209544 & 0.104772 \tabularnewline
21 & 0.85733 & 0.28534 & 0.14267 \tabularnewline
22 & 0.87686 & 0.246279 & 0.12314 \tabularnewline
23 & 0.86295 & 0.274101 & 0.13705 \tabularnewline
24 & 0.823036 & 0.353928 & 0.176964 \tabularnewline
25 & 0.902428 & 0.195145 & 0.0975723 \tabularnewline
26 & 0.932892 & 0.134216 & 0.0671078 \tabularnewline
27 & 0.916594 & 0.166812 & 0.0834062 \tabularnewline
28 & 0.890009 & 0.219982 & 0.109991 \tabularnewline
29 & 0.871194 & 0.257613 & 0.128806 \tabularnewline
30 & 0.845115 & 0.30977 & 0.154885 \tabularnewline
31 & 0.850967 & 0.298065 & 0.149033 \tabularnewline
32 & 0.831852 & 0.336295 & 0.168148 \tabularnewline
33 & 0.793149 & 0.413702 & 0.206851 \tabularnewline
34 & 0.755547 & 0.488907 & 0.244453 \tabularnewline
35 & 0.734146 & 0.531708 & 0.265854 \tabularnewline
36 & 0.83633 & 0.327339 & 0.16367 \tabularnewline
37 & 0.861207 & 0.277586 & 0.138793 \tabularnewline
38 & 0.832873 & 0.334255 & 0.167127 \tabularnewline
39 & 0.80793 & 0.384139 & 0.19207 \tabularnewline
40 & 0.771161 & 0.457678 & 0.228839 \tabularnewline
41 & 0.793784 & 0.412431 & 0.206216 \tabularnewline
42 & 0.885474 & 0.229053 & 0.114526 \tabularnewline
43 & 0.884047 & 0.231906 & 0.115953 \tabularnewline
44 & 0.858127 & 0.283745 & 0.141873 \tabularnewline
45 & 0.831569 & 0.336863 & 0.168431 \tabularnewline
46 & 0.806184 & 0.387632 & 0.193816 \tabularnewline
47 & 0.787049 & 0.425902 & 0.212951 \tabularnewline
48 & 0.813429 & 0.373143 & 0.186571 \tabularnewline
49 & 0.847311 & 0.305377 & 0.152689 \tabularnewline
50 & 0.821956 & 0.356088 & 0.178044 \tabularnewline
51 & 0.812461 & 0.375078 & 0.187539 \tabularnewline
52 & 0.785632 & 0.428736 & 0.214368 \tabularnewline
53 & 0.773472 & 0.453057 & 0.226528 \tabularnewline
54 & 0.753546 & 0.492908 & 0.246454 \tabularnewline
55 & 0.809644 & 0.380711 & 0.190356 \tabularnewline
56 & 0.782651 & 0.434697 & 0.217349 \tabularnewline
57 & 0.814605 & 0.370789 & 0.185395 \tabularnewline
58 & 0.784389 & 0.431223 & 0.215611 \tabularnewline
59 & 0.801701 & 0.396598 & 0.198299 \tabularnewline
60 & 0.77757 & 0.444861 & 0.22243 \tabularnewline
61 & 0.772838 & 0.454324 & 0.227162 \tabularnewline
62 & 0.74313 & 0.51374 & 0.25687 \tabularnewline
63 & 0.713922 & 0.572157 & 0.286078 \tabularnewline
64 & 0.676633 & 0.646733 & 0.323367 \tabularnewline
65 & 0.658239 & 0.683522 & 0.341761 \tabularnewline
66 & 0.692913 & 0.614174 & 0.307087 \tabularnewline
67 & 0.692687 & 0.614627 & 0.307313 \tabularnewline
68 & 0.658639 & 0.682722 & 0.341361 \tabularnewline
69 & 0.627805 & 0.74439 & 0.372195 \tabularnewline
70 & 0.595639 & 0.808723 & 0.404361 \tabularnewline
71 & 0.564427 & 0.871147 & 0.435573 \tabularnewline
72 & 0.524119 & 0.951761 & 0.475881 \tabularnewline
73 & 0.523 & 0.954 & 0.477 \tabularnewline
74 & 0.486711 & 0.973422 & 0.513289 \tabularnewline
75 & 0.601344 & 0.797312 & 0.398656 \tabularnewline
76 & 0.615446 & 0.769108 & 0.384554 \tabularnewline
77 & 0.584276 & 0.831447 & 0.415724 \tabularnewline
78 & 0.56139 & 0.877219 & 0.43861 \tabularnewline
79 & 0.624779 & 0.750443 & 0.375221 \tabularnewline
80 & 0.633329 & 0.733342 & 0.366671 \tabularnewline
81 & 0.61076 & 0.77848 & 0.38924 \tabularnewline
82 & 0.611894 & 0.776211 & 0.388106 \tabularnewline
83 & 0.576442 & 0.847117 & 0.423558 \tabularnewline
84 & 0.540395 & 0.91921 & 0.459605 \tabularnewline
85 & 0.501999 & 0.996002 & 0.498001 \tabularnewline
86 & 0.473222 & 0.946445 & 0.526778 \tabularnewline
87 & 0.439676 & 0.879352 & 0.560324 \tabularnewline
88 & 0.456331 & 0.912662 & 0.543669 \tabularnewline
89 & 0.444346 & 0.888692 & 0.555654 \tabularnewline
90 & 0.411753 & 0.823505 & 0.588247 \tabularnewline
91 & 0.376997 & 0.753994 & 0.623003 \tabularnewline
92 & 0.350809 & 0.701618 & 0.649191 \tabularnewline
93 & 0.344071 & 0.688142 & 0.655929 \tabularnewline
94 & 0.310797 & 0.621594 & 0.689203 \tabularnewline
95 & 0.285247 & 0.570494 & 0.714753 \tabularnewline
96 & 0.273331 & 0.546661 & 0.726669 \tabularnewline
97 & 0.249619 & 0.499238 & 0.750381 \tabularnewline
98 & 0.234656 & 0.469312 & 0.765344 \tabularnewline
99 & 0.209452 & 0.418903 & 0.790548 \tabularnewline
100 & 0.198192 & 0.396384 & 0.801808 \tabularnewline
101 & 0.175045 & 0.350091 & 0.824955 \tabularnewline
102 & 0.195432 & 0.390864 & 0.804568 \tabularnewline
103 & 0.29033 & 0.58066 & 0.70967 \tabularnewline
104 & 0.269227 & 0.538454 & 0.730773 \tabularnewline
105 & 0.26256 & 0.525119 & 0.73744 \tabularnewline
106 & 0.262826 & 0.525652 & 0.737174 \tabularnewline
107 & 0.284406 & 0.568812 & 0.715594 \tabularnewline
108 & 0.395993 & 0.791987 & 0.604007 \tabularnewline
109 & 0.390966 & 0.781931 & 0.609034 \tabularnewline
110 & 0.369789 & 0.739579 & 0.630211 \tabularnewline
111 & 0.349982 & 0.699964 & 0.650018 \tabularnewline
112 & 0.324179 & 0.648358 & 0.675821 \tabularnewline
113 & 0.299652 & 0.599303 & 0.700348 \tabularnewline
114 & 0.277253 & 0.554506 & 0.722747 \tabularnewline
115 & 0.247696 & 0.495392 & 0.752304 \tabularnewline
116 & 0.220302 & 0.440604 & 0.779698 \tabularnewline
117 & 0.223264 & 0.446528 & 0.776736 \tabularnewline
118 & 0.304819 & 0.609639 & 0.695181 \tabularnewline
119 & 0.304955 & 0.609909 & 0.695045 \tabularnewline
120 & 0.285873 & 0.571747 & 0.714127 \tabularnewline
121 & 0.270535 & 0.541069 & 0.729465 \tabularnewline
122 & 0.267028 & 0.534055 & 0.732972 \tabularnewline
123 & 0.255702 & 0.511403 & 0.744298 \tabularnewline
124 & 0.22857 & 0.457141 & 0.77143 \tabularnewline
125 & 0.204001 & 0.408001 & 0.795999 \tabularnewline
126 & 0.180507 & 0.361015 & 0.819493 \tabularnewline
127 & 0.172042 & 0.344083 & 0.827958 \tabularnewline
128 & 0.156102 & 0.312204 & 0.843898 \tabularnewline
129 & 0.145524 & 0.291047 & 0.854476 \tabularnewline
130 & 0.16692 & 0.333839 & 0.83308 \tabularnewline
131 & 0.17973 & 0.35946 & 0.82027 \tabularnewline
132 & 0.177432 & 0.354864 & 0.822568 \tabularnewline
133 & 0.164067 & 0.328133 & 0.835933 \tabularnewline
134 & 0.162379 & 0.324758 & 0.837621 \tabularnewline
135 & 0.162588 & 0.325176 & 0.837412 \tabularnewline
136 & 0.205735 & 0.411471 & 0.794265 \tabularnewline
137 & 0.182903 & 0.365807 & 0.817097 \tabularnewline
138 & 0.163298 & 0.326595 & 0.836702 \tabularnewline
139 & 0.142134 & 0.284269 & 0.857866 \tabularnewline
140 & 0.123882 & 0.247765 & 0.876118 \tabularnewline
141 & 0.162128 & 0.324256 & 0.837872 \tabularnewline
142 & 0.156437 & 0.312874 & 0.843563 \tabularnewline
143 & 0.136137 & 0.272274 & 0.863863 \tabularnewline
144 & 0.117891 & 0.235781 & 0.882109 \tabularnewline
145 & 0.102356 & 0.204712 & 0.897644 \tabularnewline
146 & 0.105621 & 0.211242 & 0.894379 \tabularnewline
147 & 0.0905035 & 0.181007 & 0.909496 \tabularnewline
148 & 0.107829 & 0.215659 & 0.892171 \tabularnewline
149 & 0.0967618 & 0.193524 & 0.903238 \tabularnewline
150 & 0.0902267 & 0.180453 & 0.909773 \tabularnewline
151 & 0.112144 & 0.224288 & 0.887856 \tabularnewline
152 & 0.0963498 & 0.1927 & 0.90365 \tabularnewline
153 & 0.0998268 & 0.199654 & 0.900173 \tabularnewline
154 & 0.08815 & 0.1763 & 0.91185 \tabularnewline
155 & 0.0934868 & 0.186974 & 0.906513 \tabularnewline
156 & 0.0807716 & 0.161543 & 0.919228 \tabularnewline
157 & 0.0693847 & 0.138769 & 0.930615 \tabularnewline
158 & 0.0847152 & 0.16943 & 0.915285 \tabularnewline
159 & 0.14393 & 0.287859 & 0.85607 \tabularnewline
160 & 0.132375 & 0.264751 & 0.867625 \tabularnewline
161 & 0.156375 & 0.312749 & 0.843625 \tabularnewline
162 & 0.194275 & 0.388549 & 0.805725 \tabularnewline
163 & 0.265118 & 0.530236 & 0.734882 \tabularnewline
164 & 0.324721 & 0.649441 & 0.675279 \tabularnewline
165 & 0.327449 & 0.654899 & 0.672551 \tabularnewline
166 & 0.405568 & 0.811136 & 0.594432 \tabularnewline
167 & 0.37007 & 0.74014 & 0.62993 \tabularnewline
168 & 0.336632 & 0.673263 & 0.663368 \tabularnewline
169 & 0.311176 & 0.622352 & 0.688824 \tabularnewline
170 & 0.284508 & 0.569015 & 0.715492 \tabularnewline
171 & 0.276332 & 0.552665 & 0.723668 \tabularnewline
172 & 0.292776 & 0.585553 & 0.707224 \tabularnewline
173 & 0.367776 & 0.735551 & 0.632224 \tabularnewline
174 & 0.337783 & 0.675566 & 0.662217 \tabularnewline
175 & 0.366452 & 0.732904 & 0.633548 \tabularnewline
176 & 0.355036 & 0.710072 & 0.644964 \tabularnewline
177 & 0.372088 & 0.744175 & 0.627912 \tabularnewline
178 & 0.404407 & 0.808814 & 0.595593 \tabularnewline
179 & 0.36943 & 0.738859 & 0.63057 \tabularnewline
180 & 0.343819 & 0.687639 & 0.656181 \tabularnewline
181 & 0.315404 & 0.630808 & 0.684596 \tabularnewline
182 & 0.314653 & 0.629307 & 0.685347 \tabularnewline
183 & 0.286002 & 0.572004 & 0.713998 \tabularnewline
184 & 0.309782 & 0.619564 & 0.690218 \tabularnewline
185 & 0.324258 & 0.648516 & 0.675742 \tabularnewline
186 & 0.344366 & 0.688732 & 0.655634 \tabularnewline
187 & 0.309757 & 0.619515 & 0.690243 \tabularnewline
188 & 0.277417 & 0.554834 & 0.722583 \tabularnewline
189 & 0.319596 & 0.639193 & 0.680404 \tabularnewline
190 & 0.550681 & 0.898638 & 0.449319 \tabularnewline
191 & 0.518805 & 0.96239 & 0.481195 \tabularnewline
192 & 0.601749 & 0.796502 & 0.398251 \tabularnewline
193 & 0.610183 & 0.779633 & 0.389817 \tabularnewline
194 & 0.584131 & 0.831738 & 0.415869 \tabularnewline
195 & 0.625102 & 0.749796 & 0.374898 \tabularnewline
196 & 0.854901 & 0.290198 & 0.145099 \tabularnewline
197 & 0.861295 & 0.277409 & 0.138705 \tabularnewline
198 & 0.845026 & 0.309948 & 0.154974 \tabularnewline
199 & 0.819748 & 0.360504 & 0.180252 \tabularnewline
200 & 0.79115 & 0.417699 & 0.20885 \tabularnewline
201 & 0.764837 & 0.470326 & 0.235163 \tabularnewline
202 & 0.729627 & 0.540745 & 0.270373 \tabularnewline
203 & 0.720262 & 0.559475 & 0.279738 \tabularnewline
204 & 0.692001 & 0.615999 & 0.307999 \tabularnewline
205 & 0.656049 & 0.687902 & 0.343951 \tabularnewline
206 & 0.615624 & 0.768752 & 0.384376 \tabularnewline
207 & 0.57696 & 0.846079 & 0.42304 \tabularnewline
208 & 0.539695 & 0.92061 & 0.460305 \tabularnewline
209 & 0.513018 & 0.973964 & 0.486982 \tabularnewline
210 & 0.518548 & 0.962903 & 0.481452 \tabularnewline
211 & 0.474074 & 0.948149 & 0.525926 \tabularnewline
212 & 0.522266 & 0.955467 & 0.477734 \tabularnewline
213 & 0.563251 & 0.873497 & 0.436749 \tabularnewline
214 & 0.528511 & 0.942978 & 0.471489 \tabularnewline
215 & 0.487702 & 0.975404 & 0.512298 \tabularnewline
216 & 0.442347 & 0.884693 & 0.557653 \tabularnewline
217 & 0.419304 & 0.838607 & 0.580696 \tabularnewline
218 & 0.417767 & 0.835533 & 0.582233 \tabularnewline
219 & 0.375206 & 0.750412 & 0.624794 \tabularnewline
220 & 0.459438 & 0.918876 & 0.540562 \tabularnewline
221 & 0.439611 & 0.879222 & 0.560389 \tabularnewline
222 & 0.39505 & 0.790099 & 0.60495 \tabularnewline
223 & 0.377412 & 0.754824 & 0.622588 \tabularnewline
224 & 0.375476 & 0.750951 & 0.624524 \tabularnewline
225 & 0.336281 & 0.672563 & 0.663719 \tabularnewline
226 & 0.290404 & 0.580808 & 0.709596 \tabularnewline
227 & 0.288564 & 0.577127 & 0.711436 \tabularnewline
228 & 0.410262 & 0.820523 & 0.589738 \tabularnewline
229 & 0.544003 & 0.911994 & 0.455997 \tabularnewline
230 & 0.514317 & 0.971365 & 0.485683 \tabularnewline
231 & 0.485889 & 0.971778 & 0.514111 \tabularnewline
232 & 0.432573 & 0.865146 & 0.567427 \tabularnewline
233 & 0.435018 & 0.870036 & 0.564982 \tabularnewline
234 & 0.467908 & 0.935816 & 0.532092 \tabularnewline
235 & 0.40579 & 0.81158 & 0.59421 \tabularnewline
236 & 0.419209 & 0.838419 & 0.580791 \tabularnewline
237 & 0.605923 & 0.788154 & 0.394077 \tabularnewline
238 & 0.540104 & 0.919791 & 0.459896 \tabularnewline
239 & 0.503275 & 0.993451 & 0.496725 \tabularnewline
240 & 0.443995 & 0.887991 & 0.556005 \tabularnewline
241 & 0.416415 & 0.832829 & 0.583585 \tabularnewline
242 & 0.77399 & 0.452019 & 0.22601 \tabularnewline
243 & 0.756274 & 0.487451 & 0.243726 \tabularnewline
244 & 0.687323 & 0.625355 & 0.312677 \tabularnewline
245 & 0.607741 & 0.784517 & 0.392259 \tabularnewline
246 & 0.628782 & 0.742437 & 0.371218 \tabularnewline
247 & 0.784835 & 0.43033 & 0.215165 \tabularnewline
248 & 0.705944 & 0.588112 & 0.294056 \tabularnewline
249 & 0.66923 & 0.661541 & 0.33077 \tabularnewline
250 & 0.728607 & 0.542786 & 0.271393 \tabularnewline
251 & 0.612399 & 0.775201 & 0.387601 \tabularnewline
252 & 0.919491 & 0.161018 & 0.0805091 \tabularnewline
253 & 0.830851 & 0.338297 & 0.169149 \tabularnewline
254 & 0.796882 & 0.406236 & 0.203118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253729&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]10[/C][C]0.519939[/C][C]0.960121[/C][C]0.480061[/C][/ROW]
[ROW][C]11[/C][C]0.894853[/C][C]0.210295[/C][C]0.105147[/C][/ROW]
[ROW][C]12[/C][C]0.843554[/C][C]0.312893[/C][C]0.156446[/C][/ROW]
[ROW][C]13[/C][C]0.768361[/C][C]0.463278[/C][C]0.231639[/C][/ROW]
[ROW][C]14[/C][C]0.674053[/C][C]0.651893[/C][C]0.325947[/C][/ROW]
[ROW][C]15[/C][C]0.796269[/C][C]0.407462[/C][C]0.203731[/C][/ROW]
[ROW][C]16[/C][C]0.745381[/C][C]0.509238[/C][C]0.254619[/C][/ROW]
[ROW][C]17[/C][C]0.838258[/C][C]0.323484[/C][C]0.161742[/C][/ROW]
[ROW][C]18[/C][C]0.833135[/C][C]0.333729[/C][C]0.166865[/C][/ROW]
[ROW][C]19[/C][C]0.923411[/C][C]0.153178[/C][C]0.0765888[/C][/ROW]
[ROW][C]20[/C][C]0.895228[/C][C]0.209544[/C][C]0.104772[/C][/ROW]
[ROW][C]21[/C][C]0.85733[/C][C]0.28534[/C][C]0.14267[/C][/ROW]
[ROW][C]22[/C][C]0.87686[/C][C]0.246279[/C][C]0.12314[/C][/ROW]
[ROW][C]23[/C][C]0.86295[/C][C]0.274101[/C][C]0.13705[/C][/ROW]
[ROW][C]24[/C][C]0.823036[/C][C]0.353928[/C][C]0.176964[/C][/ROW]
[ROW][C]25[/C][C]0.902428[/C][C]0.195145[/C][C]0.0975723[/C][/ROW]
[ROW][C]26[/C][C]0.932892[/C][C]0.134216[/C][C]0.0671078[/C][/ROW]
[ROW][C]27[/C][C]0.916594[/C][C]0.166812[/C][C]0.0834062[/C][/ROW]
[ROW][C]28[/C][C]0.890009[/C][C]0.219982[/C][C]0.109991[/C][/ROW]
[ROW][C]29[/C][C]0.871194[/C][C]0.257613[/C][C]0.128806[/C][/ROW]
[ROW][C]30[/C][C]0.845115[/C][C]0.30977[/C][C]0.154885[/C][/ROW]
[ROW][C]31[/C][C]0.850967[/C][C]0.298065[/C][C]0.149033[/C][/ROW]
[ROW][C]32[/C][C]0.831852[/C][C]0.336295[/C][C]0.168148[/C][/ROW]
[ROW][C]33[/C][C]0.793149[/C][C]0.413702[/C][C]0.206851[/C][/ROW]
[ROW][C]34[/C][C]0.755547[/C][C]0.488907[/C][C]0.244453[/C][/ROW]
[ROW][C]35[/C][C]0.734146[/C][C]0.531708[/C][C]0.265854[/C][/ROW]
[ROW][C]36[/C][C]0.83633[/C][C]0.327339[/C][C]0.16367[/C][/ROW]
[ROW][C]37[/C][C]0.861207[/C][C]0.277586[/C][C]0.138793[/C][/ROW]
[ROW][C]38[/C][C]0.832873[/C][C]0.334255[/C][C]0.167127[/C][/ROW]
[ROW][C]39[/C][C]0.80793[/C][C]0.384139[/C][C]0.19207[/C][/ROW]
[ROW][C]40[/C][C]0.771161[/C][C]0.457678[/C][C]0.228839[/C][/ROW]
[ROW][C]41[/C][C]0.793784[/C][C]0.412431[/C][C]0.206216[/C][/ROW]
[ROW][C]42[/C][C]0.885474[/C][C]0.229053[/C][C]0.114526[/C][/ROW]
[ROW][C]43[/C][C]0.884047[/C][C]0.231906[/C][C]0.115953[/C][/ROW]
[ROW][C]44[/C][C]0.858127[/C][C]0.283745[/C][C]0.141873[/C][/ROW]
[ROW][C]45[/C][C]0.831569[/C][C]0.336863[/C][C]0.168431[/C][/ROW]
[ROW][C]46[/C][C]0.806184[/C][C]0.387632[/C][C]0.193816[/C][/ROW]
[ROW][C]47[/C][C]0.787049[/C][C]0.425902[/C][C]0.212951[/C][/ROW]
[ROW][C]48[/C][C]0.813429[/C][C]0.373143[/C][C]0.186571[/C][/ROW]
[ROW][C]49[/C][C]0.847311[/C][C]0.305377[/C][C]0.152689[/C][/ROW]
[ROW][C]50[/C][C]0.821956[/C][C]0.356088[/C][C]0.178044[/C][/ROW]
[ROW][C]51[/C][C]0.812461[/C][C]0.375078[/C][C]0.187539[/C][/ROW]
[ROW][C]52[/C][C]0.785632[/C][C]0.428736[/C][C]0.214368[/C][/ROW]
[ROW][C]53[/C][C]0.773472[/C][C]0.453057[/C][C]0.226528[/C][/ROW]
[ROW][C]54[/C][C]0.753546[/C][C]0.492908[/C][C]0.246454[/C][/ROW]
[ROW][C]55[/C][C]0.809644[/C][C]0.380711[/C][C]0.190356[/C][/ROW]
[ROW][C]56[/C][C]0.782651[/C][C]0.434697[/C][C]0.217349[/C][/ROW]
[ROW][C]57[/C][C]0.814605[/C][C]0.370789[/C][C]0.185395[/C][/ROW]
[ROW][C]58[/C][C]0.784389[/C][C]0.431223[/C][C]0.215611[/C][/ROW]
[ROW][C]59[/C][C]0.801701[/C][C]0.396598[/C][C]0.198299[/C][/ROW]
[ROW][C]60[/C][C]0.77757[/C][C]0.444861[/C][C]0.22243[/C][/ROW]
[ROW][C]61[/C][C]0.772838[/C][C]0.454324[/C][C]0.227162[/C][/ROW]
[ROW][C]62[/C][C]0.74313[/C][C]0.51374[/C][C]0.25687[/C][/ROW]
[ROW][C]63[/C][C]0.713922[/C][C]0.572157[/C][C]0.286078[/C][/ROW]
[ROW][C]64[/C][C]0.676633[/C][C]0.646733[/C][C]0.323367[/C][/ROW]
[ROW][C]65[/C][C]0.658239[/C][C]0.683522[/C][C]0.341761[/C][/ROW]
[ROW][C]66[/C][C]0.692913[/C][C]0.614174[/C][C]0.307087[/C][/ROW]
[ROW][C]67[/C][C]0.692687[/C][C]0.614627[/C][C]0.307313[/C][/ROW]
[ROW][C]68[/C][C]0.658639[/C][C]0.682722[/C][C]0.341361[/C][/ROW]
[ROW][C]69[/C][C]0.627805[/C][C]0.74439[/C][C]0.372195[/C][/ROW]
[ROW][C]70[/C][C]0.595639[/C][C]0.808723[/C][C]0.404361[/C][/ROW]
[ROW][C]71[/C][C]0.564427[/C][C]0.871147[/C][C]0.435573[/C][/ROW]
[ROW][C]72[/C][C]0.524119[/C][C]0.951761[/C][C]0.475881[/C][/ROW]
[ROW][C]73[/C][C]0.523[/C][C]0.954[/C][C]0.477[/C][/ROW]
[ROW][C]74[/C][C]0.486711[/C][C]0.973422[/C][C]0.513289[/C][/ROW]
[ROW][C]75[/C][C]0.601344[/C][C]0.797312[/C][C]0.398656[/C][/ROW]
[ROW][C]76[/C][C]0.615446[/C][C]0.769108[/C][C]0.384554[/C][/ROW]
[ROW][C]77[/C][C]0.584276[/C][C]0.831447[/C][C]0.415724[/C][/ROW]
[ROW][C]78[/C][C]0.56139[/C][C]0.877219[/C][C]0.43861[/C][/ROW]
[ROW][C]79[/C][C]0.624779[/C][C]0.750443[/C][C]0.375221[/C][/ROW]
[ROW][C]80[/C][C]0.633329[/C][C]0.733342[/C][C]0.366671[/C][/ROW]
[ROW][C]81[/C][C]0.61076[/C][C]0.77848[/C][C]0.38924[/C][/ROW]
[ROW][C]82[/C][C]0.611894[/C][C]0.776211[/C][C]0.388106[/C][/ROW]
[ROW][C]83[/C][C]0.576442[/C][C]0.847117[/C][C]0.423558[/C][/ROW]
[ROW][C]84[/C][C]0.540395[/C][C]0.91921[/C][C]0.459605[/C][/ROW]
[ROW][C]85[/C][C]0.501999[/C][C]0.996002[/C][C]0.498001[/C][/ROW]
[ROW][C]86[/C][C]0.473222[/C][C]0.946445[/C][C]0.526778[/C][/ROW]
[ROW][C]87[/C][C]0.439676[/C][C]0.879352[/C][C]0.560324[/C][/ROW]
[ROW][C]88[/C][C]0.456331[/C][C]0.912662[/C][C]0.543669[/C][/ROW]
[ROW][C]89[/C][C]0.444346[/C][C]0.888692[/C][C]0.555654[/C][/ROW]
[ROW][C]90[/C][C]0.411753[/C][C]0.823505[/C][C]0.588247[/C][/ROW]
[ROW][C]91[/C][C]0.376997[/C][C]0.753994[/C][C]0.623003[/C][/ROW]
[ROW][C]92[/C][C]0.350809[/C][C]0.701618[/C][C]0.649191[/C][/ROW]
[ROW][C]93[/C][C]0.344071[/C][C]0.688142[/C][C]0.655929[/C][/ROW]
[ROW][C]94[/C][C]0.310797[/C][C]0.621594[/C][C]0.689203[/C][/ROW]
[ROW][C]95[/C][C]0.285247[/C][C]0.570494[/C][C]0.714753[/C][/ROW]
[ROW][C]96[/C][C]0.273331[/C][C]0.546661[/C][C]0.726669[/C][/ROW]
[ROW][C]97[/C][C]0.249619[/C][C]0.499238[/C][C]0.750381[/C][/ROW]
[ROW][C]98[/C][C]0.234656[/C][C]0.469312[/C][C]0.765344[/C][/ROW]
[ROW][C]99[/C][C]0.209452[/C][C]0.418903[/C][C]0.790548[/C][/ROW]
[ROW][C]100[/C][C]0.198192[/C][C]0.396384[/C][C]0.801808[/C][/ROW]
[ROW][C]101[/C][C]0.175045[/C][C]0.350091[/C][C]0.824955[/C][/ROW]
[ROW][C]102[/C][C]0.195432[/C][C]0.390864[/C][C]0.804568[/C][/ROW]
[ROW][C]103[/C][C]0.29033[/C][C]0.58066[/C][C]0.70967[/C][/ROW]
[ROW][C]104[/C][C]0.269227[/C][C]0.538454[/C][C]0.730773[/C][/ROW]
[ROW][C]105[/C][C]0.26256[/C][C]0.525119[/C][C]0.73744[/C][/ROW]
[ROW][C]106[/C][C]0.262826[/C][C]0.525652[/C][C]0.737174[/C][/ROW]
[ROW][C]107[/C][C]0.284406[/C][C]0.568812[/C][C]0.715594[/C][/ROW]
[ROW][C]108[/C][C]0.395993[/C][C]0.791987[/C][C]0.604007[/C][/ROW]
[ROW][C]109[/C][C]0.390966[/C][C]0.781931[/C][C]0.609034[/C][/ROW]
[ROW][C]110[/C][C]0.369789[/C][C]0.739579[/C][C]0.630211[/C][/ROW]
[ROW][C]111[/C][C]0.349982[/C][C]0.699964[/C][C]0.650018[/C][/ROW]
[ROW][C]112[/C][C]0.324179[/C][C]0.648358[/C][C]0.675821[/C][/ROW]
[ROW][C]113[/C][C]0.299652[/C][C]0.599303[/C][C]0.700348[/C][/ROW]
[ROW][C]114[/C][C]0.277253[/C][C]0.554506[/C][C]0.722747[/C][/ROW]
[ROW][C]115[/C][C]0.247696[/C][C]0.495392[/C][C]0.752304[/C][/ROW]
[ROW][C]116[/C][C]0.220302[/C][C]0.440604[/C][C]0.779698[/C][/ROW]
[ROW][C]117[/C][C]0.223264[/C][C]0.446528[/C][C]0.776736[/C][/ROW]
[ROW][C]118[/C][C]0.304819[/C][C]0.609639[/C][C]0.695181[/C][/ROW]
[ROW][C]119[/C][C]0.304955[/C][C]0.609909[/C][C]0.695045[/C][/ROW]
[ROW][C]120[/C][C]0.285873[/C][C]0.571747[/C][C]0.714127[/C][/ROW]
[ROW][C]121[/C][C]0.270535[/C][C]0.541069[/C][C]0.729465[/C][/ROW]
[ROW][C]122[/C][C]0.267028[/C][C]0.534055[/C][C]0.732972[/C][/ROW]
[ROW][C]123[/C][C]0.255702[/C][C]0.511403[/C][C]0.744298[/C][/ROW]
[ROW][C]124[/C][C]0.22857[/C][C]0.457141[/C][C]0.77143[/C][/ROW]
[ROW][C]125[/C][C]0.204001[/C][C]0.408001[/C][C]0.795999[/C][/ROW]
[ROW][C]126[/C][C]0.180507[/C][C]0.361015[/C][C]0.819493[/C][/ROW]
[ROW][C]127[/C][C]0.172042[/C][C]0.344083[/C][C]0.827958[/C][/ROW]
[ROW][C]128[/C][C]0.156102[/C][C]0.312204[/C][C]0.843898[/C][/ROW]
[ROW][C]129[/C][C]0.145524[/C][C]0.291047[/C][C]0.854476[/C][/ROW]
[ROW][C]130[/C][C]0.16692[/C][C]0.333839[/C][C]0.83308[/C][/ROW]
[ROW][C]131[/C][C]0.17973[/C][C]0.35946[/C][C]0.82027[/C][/ROW]
[ROW][C]132[/C][C]0.177432[/C][C]0.354864[/C][C]0.822568[/C][/ROW]
[ROW][C]133[/C][C]0.164067[/C][C]0.328133[/C][C]0.835933[/C][/ROW]
[ROW][C]134[/C][C]0.162379[/C][C]0.324758[/C][C]0.837621[/C][/ROW]
[ROW][C]135[/C][C]0.162588[/C][C]0.325176[/C][C]0.837412[/C][/ROW]
[ROW][C]136[/C][C]0.205735[/C][C]0.411471[/C][C]0.794265[/C][/ROW]
[ROW][C]137[/C][C]0.182903[/C][C]0.365807[/C][C]0.817097[/C][/ROW]
[ROW][C]138[/C][C]0.163298[/C][C]0.326595[/C][C]0.836702[/C][/ROW]
[ROW][C]139[/C][C]0.142134[/C][C]0.284269[/C][C]0.857866[/C][/ROW]
[ROW][C]140[/C][C]0.123882[/C][C]0.247765[/C][C]0.876118[/C][/ROW]
[ROW][C]141[/C][C]0.162128[/C][C]0.324256[/C][C]0.837872[/C][/ROW]
[ROW][C]142[/C][C]0.156437[/C][C]0.312874[/C][C]0.843563[/C][/ROW]
[ROW][C]143[/C][C]0.136137[/C][C]0.272274[/C][C]0.863863[/C][/ROW]
[ROW][C]144[/C][C]0.117891[/C][C]0.235781[/C][C]0.882109[/C][/ROW]
[ROW][C]145[/C][C]0.102356[/C][C]0.204712[/C][C]0.897644[/C][/ROW]
[ROW][C]146[/C][C]0.105621[/C][C]0.211242[/C][C]0.894379[/C][/ROW]
[ROW][C]147[/C][C]0.0905035[/C][C]0.181007[/C][C]0.909496[/C][/ROW]
[ROW][C]148[/C][C]0.107829[/C][C]0.215659[/C][C]0.892171[/C][/ROW]
[ROW][C]149[/C][C]0.0967618[/C][C]0.193524[/C][C]0.903238[/C][/ROW]
[ROW][C]150[/C][C]0.0902267[/C][C]0.180453[/C][C]0.909773[/C][/ROW]
[ROW][C]151[/C][C]0.112144[/C][C]0.224288[/C][C]0.887856[/C][/ROW]
[ROW][C]152[/C][C]0.0963498[/C][C]0.1927[/C][C]0.90365[/C][/ROW]
[ROW][C]153[/C][C]0.0998268[/C][C]0.199654[/C][C]0.900173[/C][/ROW]
[ROW][C]154[/C][C]0.08815[/C][C]0.1763[/C][C]0.91185[/C][/ROW]
[ROW][C]155[/C][C]0.0934868[/C][C]0.186974[/C][C]0.906513[/C][/ROW]
[ROW][C]156[/C][C]0.0807716[/C][C]0.161543[/C][C]0.919228[/C][/ROW]
[ROW][C]157[/C][C]0.0693847[/C][C]0.138769[/C][C]0.930615[/C][/ROW]
[ROW][C]158[/C][C]0.0847152[/C][C]0.16943[/C][C]0.915285[/C][/ROW]
[ROW][C]159[/C][C]0.14393[/C][C]0.287859[/C][C]0.85607[/C][/ROW]
[ROW][C]160[/C][C]0.132375[/C][C]0.264751[/C][C]0.867625[/C][/ROW]
[ROW][C]161[/C][C]0.156375[/C][C]0.312749[/C][C]0.843625[/C][/ROW]
[ROW][C]162[/C][C]0.194275[/C][C]0.388549[/C][C]0.805725[/C][/ROW]
[ROW][C]163[/C][C]0.265118[/C][C]0.530236[/C][C]0.734882[/C][/ROW]
[ROW][C]164[/C][C]0.324721[/C][C]0.649441[/C][C]0.675279[/C][/ROW]
[ROW][C]165[/C][C]0.327449[/C][C]0.654899[/C][C]0.672551[/C][/ROW]
[ROW][C]166[/C][C]0.405568[/C][C]0.811136[/C][C]0.594432[/C][/ROW]
[ROW][C]167[/C][C]0.37007[/C][C]0.74014[/C][C]0.62993[/C][/ROW]
[ROW][C]168[/C][C]0.336632[/C][C]0.673263[/C][C]0.663368[/C][/ROW]
[ROW][C]169[/C][C]0.311176[/C][C]0.622352[/C][C]0.688824[/C][/ROW]
[ROW][C]170[/C][C]0.284508[/C][C]0.569015[/C][C]0.715492[/C][/ROW]
[ROW][C]171[/C][C]0.276332[/C][C]0.552665[/C][C]0.723668[/C][/ROW]
[ROW][C]172[/C][C]0.292776[/C][C]0.585553[/C][C]0.707224[/C][/ROW]
[ROW][C]173[/C][C]0.367776[/C][C]0.735551[/C][C]0.632224[/C][/ROW]
[ROW][C]174[/C][C]0.337783[/C][C]0.675566[/C][C]0.662217[/C][/ROW]
[ROW][C]175[/C][C]0.366452[/C][C]0.732904[/C][C]0.633548[/C][/ROW]
[ROW][C]176[/C][C]0.355036[/C][C]0.710072[/C][C]0.644964[/C][/ROW]
[ROW][C]177[/C][C]0.372088[/C][C]0.744175[/C][C]0.627912[/C][/ROW]
[ROW][C]178[/C][C]0.404407[/C][C]0.808814[/C][C]0.595593[/C][/ROW]
[ROW][C]179[/C][C]0.36943[/C][C]0.738859[/C][C]0.63057[/C][/ROW]
[ROW][C]180[/C][C]0.343819[/C][C]0.687639[/C][C]0.656181[/C][/ROW]
[ROW][C]181[/C][C]0.315404[/C][C]0.630808[/C][C]0.684596[/C][/ROW]
[ROW][C]182[/C][C]0.314653[/C][C]0.629307[/C][C]0.685347[/C][/ROW]
[ROW][C]183[/C][C]0.286002[/C][C]0.572004[/C][C]0.713998[/C][/ROW]
[ROW][C]184[/C][C]0.309782[/C][C]0.619564[/C][C]0.690218[/C][/ROW]
[ROW][C]185[/C][C]0.324258[/C][C]0.648516[/C][C]0.675742[/C][/ROW]
[ROW][C]186[/C][C]0.344366[/C][C]0.688732[/C][C]0.655634[/C][/ROW]
[ROW][C]187[/C][C]0.309757[/C][C]0.619515[/C][C]0.690243[/C][/ROW]
[ROW][C]188[/C][C]0.277417[/C][C]0.554834[/C][C]0.722583[/C][/ROW]
[ROW][C]189[/C][C]0.319596[/C][C]0.639193[/C][C]0.680404[/C][/ROW]
[ROW][C]190[/C][C]0.550681[/C][C]0.898638[/C][C]0.449319[/C][/ROW]
[ROW][C]191[/C][C]0.518805[/C][C]0.96239[/C][C]0.481195[/C][/ROW]
[ROW][C]192[/C][C]0.601749[/C][C]0.796502[/C][C]0.398251[/C][/ROW]
[ROW][C]193[/C][C]0.610183[/C][C]0.779633[/C][C]0.389817[/C][/ROW]
[ROW][C]194[/C][C]0.584131[/C][C]0.831738[/C][C]0.415869[/C][/ROW]
[ROW][C]195[/C][C]0.625102[/C][C]0.749796[/C][C]0.374898[/C][/ROW]
[ROW][C]196[/C][C]0.854901[/C][C]0.290198[/C][C]0.145099[/C][/ROW]
[ROW][C]197[/C][C]0.861295[/C][C]0.277409[/C][C]0.138705[/C][/ROW]
[ROW][C]198[/C][C]0.845026[/C][C]0.309948[/C][C]0.154974[/C][/ROW]
[ROW][C]199[/C][C]0.819748[/C][C]0.360504[/C][C]0.180252[/C][/ROW]
[ROW][C]200[/C][C]0.79115[/C][C]0.417699[/C][C]0.20885[/C][/ROW]
[ROW][C]201[/C][C]0.764837[/C][C]0.470326[/C][C]0.235163[/C][/ROW]
[ROW][C]202[/C][C]0.729627[/C][C]0.540745[/C][C]0.270373[/C][/ROW]
[ROW][C]203[/C][C]0.720262[/C][C]0.559475[/C][C]0.279738[/C][/ROW]
[ROW][C]204[/C][C]0.692001[/C][C]0.615999[/C][C]0.307999[/C][/ROW]
[ROW][C]205[/C][C]0.656049[/C][C]0.687902[/C][C]0.343951[/C][/ROW]
[ROW][C]206[/C][C]0.615624[/C][C]0.768752[/C][C]0.384376[/C][/ROW]
[ROW][C]207[/C][C]0.57696[/C][C]0.846079[/C][C]0.42304[/C][/ROW]
[ROW][C]208[/C][C]0.539695[/C][C]0.92061[/C][C]0.460305[/C][/ROW]
[ROW][C]209[/C][C]0.513018[/C][C]0.973964[/C][C]0.486982[/C][/ROW]
[ROW][C]210[/C][C]0.518548[/C][C]0.962903[/C][C]0.481452[/C][/ROW]
[ROW][C]211[/C][C]0.474074[/C][C]0.948149[/C][C]0.525926[/C][/ROW]
[ROW][C]212[/C][C]0.522266[/C][C]0.955467[/C][C]0.477734[/C][/ROW]
[ROW][C]213[/C][C]0.563251[/C][C]0.873497[/C][C]0.436749[/C][/ROW]
[ROW][C]214[/C][C]0.528511[/C][C]0.942978[/C][C]0.471489[/C][/ROW]
[ROW][C]215[/C][C]0.487702[/C][C]0.975404[/C][C]0.512298[/C][/ROW]
[ROW][C]216[/C][C]0.442347[/C][C]0.884693[/C][C]0.557653[/C][/ROW]
[ROW][C]217[/C][C]0.419304[/C][C]0.838607[/C][C]0.580696[/C][/ROW]
[ROW][C]218[/C][C]0.417767[/C][C]0.835533[/C][C]0.582233[/C][/ROW]
[ROW][C]219[/C][C]0.375206[/C][C]0.750412[/C][C]0.624794[/C][/ROW]
[ROW][C]220[/C][C]0.459438[/C][C]0.918876[/C][C]0.540562[/C][/ROW]
[ROW][C]221[/C][C]0.439611[/C][C]0.879222[/C][C]0.560389[/C][/ROW]
[ROW][C]222[/C][C]0.39505[/C][C]0.790099[/C][C]0.60495[/C][/ROW]
[ROW][C]223[/C][C]0.377412[/C][C]0.754824[/C][C]0.622588[/C][/ROW]
[ROW][C]224[/C][C]0.375476[/C][C]0.750951[/C][C]0.624524[/C][/ROW]
[ROW][C]225[/C][C]0.336281[/C][C]0.672563[/C][C]0.663719[/C][/ROW]
[ROW][C]226[/C][C]0.290404[/C][C]0.580808[/C][C]0.709596[/C][/ROW]
[ROW][C]227[/C][C]0.288564[/C][C]0.577127[/C][C]0.711436[/C][/ROW]
[ROW][C]228[/C][C]0.410262[/C][C]0.820523[/C][C]0.589738[/C][/ROW]
[ROW][C]229[/C][C]0.544003[/C][C]0.911994[/C][C]0.455997[/C][/ROW]
[ROW][C]230[/C][C]0.514317[/C][C]0.971365[/C][C]0.485683[/C][/ROW]
[ROW][C]231[/C][C]0.485889[/C][C]0.971778[/C][C]0.514111[/C][/ROW]
[ROW][C]232[/C][C]0.432573[/C][C]0.865146[/C][C]0.567427[/C][/ROW]
[ROW][C]233[/C][C]0.435018[/C][C]0.870036[/C][C]0.564982[/C][/ROW]
[ROW][C]234[/C][C]0.467908[/C][C]0.935816[/C][C]0.532092[/C][/ROW]
[ROW][C]235[/C][C]0.40579[/C][C]0.81158[/C][C]0.59421[/C][/ROW]
[ROW][C]236[/C][C]0.419209[/C][C]0.838419[/C][C]0.580791[/C][/ROW]
[ROW][C]237[/C][C]0.605923[/C][C]0.788154[/C][C]0.394077[/C][/ROW]
[ROW][C]238[/C][C]0.540104[/C][C]0.919791[/C][C]0.459896[/C][/ROW]
[ROW][C]239[/C][C]0.503275[/C][C]0.993451[/C][C]0.496725[/C][/ROW]
[ROW][C]240[/C][C]0.443995[/C][C]0.887991[/C][C]0.556005[/C][/ROW]
[ROW][C]241[/C][C]0.416415[/C][C]0.832829[/C][C]0.583585[/C][/ROW]
[ROW][C]242[/C][C]0.77399[/C][C]0.452019[/C][C]0.22601[/C][/ROW]
[ROW][C]243[/C][C]0.756274[/C][C]0.487451[/C][C]0.243726[/C][/ROW]
[ROW][C]244[/C][C]0.687323[/C][C]0.625355[/C][C]0.312677[/C][/ROW]
[ROW][C]245[/C][C]0.607741[/C][C]0.784517[/C][C]0.392259[/C][/ROW]
[ROW][C]246[/C][C]0.628782[/C][C]0.742437[/C][C]0.371218[/C][/ROW]
[ROW][C]247[/C][C]0.784835[/C][C]0.43033[/C][C]0.215165[/C][/ROW]
[ROW][C]248[/C][C]0.705944[/C][C]0.588112[/C][C]0.294056[/C][/ROW]
[ROW][C]249[/C][C]0.66923[/C][C]0.661541[/C][C]0.33077[/C][/ROW]
[ROW][C]250[/C][C]0.728607[/C][C]0.542786[/C][C]0.271393[/C][/ROW]
[ROW][C]251[/C][C]0.612399[/C][C]0.775201[/C][C]0.387601[/C][/ROW]
[ROW][C]252[/C][C]0.919491[/C][C]0.161018[/C][C]0.0805091[/C][/ROW]
[ROW][C]253[/C][C]0.830851[/C][C]0.338297[/C][C]0.169149[/C][/ROW]
[ROW][C]254[/C][C]0.796882[/C][C]0.406236[/C][C]0.203118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253729&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253729&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5199390.9601210.480061
110.8948530.2102950.105147
120.8435540.3128930.156446
130.7683610.4632780.231639
140.6740530.6518930.325947
150.7962690.4074620.203731
160.7453810.5092380.254619
170.8382580.3234840.161742
180.8331350.3337290.166865
190.9234110.1531780.0765888
200.8952280.2095440.104772
210.857330.285340.14267
220.876860.2462790.12314
230.862950.2741010.13705
240.8230360.3539280.176964
250.9024280.1951450.0975723
260.9328920.1342160.0671078
270.9165940.1668120.0834062
280.8900090.2199820.109991
290.8711940.2576130.128806
300.8451150.309770.154885
310.8509670.2980650.149033
320.8318520.3362950.168148
330.7931490.4137020.206851
340.7555470.4889070.244453
350.7341460.5317080.265854
360.836330.3273390.16367
370.8612070.2775860.138793
380.8328730.3342550.167127
390.807930.3841390.19207
400.7711610.4576780.228839
410.7937840.4124310.206216
420.8854740.2290530.114526
430.8840470.2319060.115953
440.8581270.2837450.141873
450.8315690.3368630.168431
460.8061840.3876320.193816
470.7870490.4259020.212951
480.8134290.3731430.186571
490.8473110.3053770.152689
500.8219560.3560880.178044
510.8124610.3750780.187539
520.7856320.4287360.214368
530.7734720.4530570.226528
540.7535460.4929080.246454
550.8096440.3807110.190356
560.7826510.4346970.217349
570.8146050.3707890.185395
580.7843890.4312230.215611
590.8017010.3965980.198299
600.777570.4448610.22243
610.7728380.4543240.227162
620.743130.513740.25687
630.7139220.5721570.286078
640.6766330.6467330.323367
650.6582390.6835220.341761
660.6929130.6141740.307087
670.6926870.6146270.307313
680.6586390.6827220.341361
690.6278050.744390.372195
700.5956390.8087230.404361
710.5644270.8711470.435573
720.5241190.9517610.475881
730.5230.9540.477
740.4867110.9734220.513289
750.6013440.7973120.398656
760.6154460.7691080.384554
770.5842760.8314470.415724
780.561390.8772190.43861
790.6247790.7504430.375221
800.6333290.7333420.366671
810.610760.778480.38924
820.6118940.7762110.388106
830.5764420.8471170.423558
840.5403950.919210.459605
850.5019990.9960020.498001
860.4732220.9464450.526778
870.4396760.8793520.560324
880.4563310.9126620.543669
890.4443460.8886920.555654
900.4117530.8235050.588247
910.3769970.7539940.623003
920.3508090.7016180.649191
930.3440710.6881420.655929
940.3107970.6215940.689203
950.2852470.5704940.714753
960.2733310.5466610.726669
970.2496190.4992380.750381
980.2346560.4693120.765344
990.2094520.4189030.790548
1000.1981920.3963840.801808
1010.1750450.3500910.824955
1020.1954320.3908640.804568
1030.290330.580660.70967
1040.2692270.5384540.730773
1050.262560.5251190.73744
1060.2628260.5256520.737174
1070.2844060.5688120.715594
1080.3959930.7919870.604007
1090.3909660.7819310.609034
1100.3697890.7395790.630211
1110.3499820.6999640.650018
1120.3241790.6483580.675821
1130.2996520.5993030.700348
1140.2772530.5545060.722747
1150.2476960.4953920.752304
1160.2203020.4406040.779698
1170.2232640.4465280.776736
1180.3048190.6096390.695181
1190.3049550.6099090.695045
1200.2858730.5717470.714127
1210.2705350.5410690.729465
1220.2670280.5340550.732972
1230.2557020.5114030.744298
1240.228570.4571410.77143
1250.2040010.4080010.795999
1260.1805070.3610150.819493
1270.1720420.3440830.827958
1280.1561020.3122040.843898
1290.1455240.2910470.854476
1300.166920.3338390.83308
1310.179730.359460.82027
1320.1774320.3548640.822568
1330.1640670.3281330.835933
1340.1623790.3247580.837621
1350.1625880.3251760.837412
1360.2057350.4114710.794265
1370.1829030.3658070.817097
1380.1632980.3265950.836702
1390.1421340.2842690.857866
1400.1238820.2477650.876118
1410.1621280.3242560.837872
1420.1564370.3128740.843563
1430.1361370.2722740.863863
1440.1178910.2357810.882109
1450.1023560.2047120.897644
1460.1056210.2112420.894379
1470.09050350.1810070.909496
1480.1078290.2156590.892171
1490.09676180.1935240.903238
1500.09022670.1804530.909773
1510.1121440.2242880.887856
1520.09634980.19270.90365
1530.09982680.1996540.900173
1540.088150.17630.91185
1550.09348680.1869740.906513
1560.08077160.1615430.919228
1570.06938470.1387690.930615
1580.08471520.169430.915285
1590.143930.2878590.85607
1600.1323750.2647510.867625
1610.1563750.3127490.843625
1620.1942750.3885490.805725
1630.2651180.5302360.734882
1640.3247210.6494410.675279
1650.3274490.6548990.672551
1660.4055680.8111360.594432
1670.370070.740140.62993
1680.3366320.6732630.663368
1690.3111760.6223520.688824
1700.2845080.5690150.715492
1710.2763320.5526650.723668
1720.2927760.5855530.707224
1730.3677760.7355510.632224
1740.3377830.6755660.662217
1750.3664520.7329040.633548
1760.3550360.7100720.644964
1770.3720880.7441750.627912
1780.4044070.8088140.595593
1790.369430.7388590.63057
1800.3438190.6876390.656181
1810.3154040.6308080.684596
1820.3146530.6293070.685347
1830.2860020.5720040.713998
1840.3097820.6195640.690218
1850.3242580.6485160.675742
1860.3443660.6887320.655634
1870.3097570.6195150.690243
1880.2774170.5548340.722583
1890.3195960.6391930.680404
1900.5506810.8986380.449319
1910.5188050.962390.481195
1920.6017490.7965020.398251
1930.6101830.7796330.389817
1940.5841310.8317380.415869
1950.6251020.7497960.374898
1960.8549010.2901980.145099
1970.8612950.2774090.138705
1980.8450260.3099480.154974
1990.8197480.3605040.180252
2000.791150.4176990.20885
2010.7648370.4703260.235163
2020.7296270.5407450.270373
2030.7202620.5594750.279738
2040.6920010.6159990.307999
2050.6560490.6879020.343951
2060.6156240.7687520.384376
2070.576960.8460790.42304
2080.5396950.920610.460305
2090.5130180.9739640.486982
2100.5185480.9629030.481452
2110.4740740.9481490.525926
2120.5222660.9554670.477734
2130.5632510.8734970.436749
2140.5285110.9429780.471489
2150.4877020.9754040.512298
2160.4423470.8846930.557653
2170.4193040.8386070.580696
2180.4177670.8355330.582233
2190.3752060.7504120.624794
2200.4594380.9188760.540562
2210.4396110.8792220.560389
2220.395050.7900990.60495
2230.3774120.7548240.622588
2240.3754760.7509510.624524
2250.3362810.6725630.663719
2260.2904040.5808080.709596
2270.2885640.5771270.711436
2280.4102620.8205230.589738
2290.5440030.9119940.455997
2300.5143170.9713650.485683
2310.4858890.9717780.514111
2320.4325730.8651460.567427
2330.4350180.8700360.564982
2340.4679080.9358160.532092
2350.405790.811580.59421
2360.4192090.8384190.580791
2370.6059230.7881540.394077
2380.5401040.9197910.459896
2390.5032750.9934510.496725
2400.4439950.8879910.556005
2410.4164150.8328290.583585
2420.773990.4520190.22601
2430.7562740.4874510.243726
2440.6873230.6253550.312677
2450.6077410.7845170.392259
2460.6287820.7424370.371218
2470.7848350.430330.215165
2480.7059440.5881120.294056
2490.669230.6615410.33077
2500.7286070.5427860.271393
2510.6123990.7752010.387601
2520.9194910.1610180.0805091
2530.8308510.3382970.169149
2540.7968820.4062360.203118






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253729&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253729&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253729&T=6

As an alternative you can also use a QR Code:  
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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = First Differences ;
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}