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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 09 Dec 2014 08:03:29 +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/Dec/09/t1418112439gmrtqxt6sgqd6v3.htm/, Retrieved Thu, 31 Oct 2024 23:19:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264322, Retrieved Thu, 31 Oct 2024 23:19:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-09 08:03:29] [2493f5613ad82cc6ad9068825c65e4dc] [Current]
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Dataseries X:
12.9 2011 0 26 50 0 21 68
12.8 2011 0 37 54 0 22 32
7.4 2011 0 67 71 1 18 62
6.7 2011 0 43 54 1 23 33
12.6 2011 0 52 65 1 12 52
14.8 2011 0 52 73 0 20 62
13.3 2011 0 43 52 1 22 77
11.1 2011 0 84 84 1 21 76
8.2 2011 0 67 42 1 19 41
11.4 2011 0 49 66 1 22 48
6.4 2011 0 70 65 1 15 63
12 2011 0 58 73 0 19 78
6.3 2011 0 68 75 0 18 19
11.3 2011 1 62 72 0 15 31
11.9 2011 0 43 66 1 20 66
9.3 2011 0 56 70 0 21 35
10 2011 0 74 81 0 15 45
13.8 2011 0 63 69 1 23 25
10.8 2011 0 58 71 0 21 44
11.7 2011 0 63 68 1 25 54
10.9 2011 0 53 70 1 9 74
16.1 2011 1 57 68 1 30 80
9.9 2011 0 64 67 1 23 61
11.5 2011 0 53 76 0 16 41
8.3 2011 0 29 70 0 16 46
11.7 2011 0 54 60 0 19 39
9 2011 0 58 72 1 25 34
10.8 2011 0 51 71 1 23 42
10.4 2011 0 54 70 0 10 39
12.7 2011 1 56 64 1 14 20
11.8 2011 0 47 76 0 26 53
13 2011 0 50 68 1 24 54
10.8 2011 0 35 76 1 24 49
12.3 2011 1 30 65 1 18 34
11.3 2011 0 68 67 0 23 46
11.6 2011 1 56 75 1 23 37
10.9 2011 0 43 60 1 19 30
12.1 2011 1 67 73 1 21 28
13.3 2011 0 62 63 1 18 45
10.1 2011 0 57 70 1 27 35
14.3 2011 0 54 66 1 13 41
9.3 2011 0 61 64 1 28 73
12.5 2011 0 56 70 0 23 17
7.6 2011 0 41 75 0 21 40
9.2 2011 0 53 60 0 19 37
14.5 2011 0 46 66 1 17 28
12.3 2011 0 51 59 0 25 56
12.6 2011 0 37 78 0 14 50
13 2011 0 42 67 0 16 59
12.6 2011 1 38 59 1 24 27
13.2 2011 0 66 66 0 20 61
7.7 2011 0 53 71 1 24 51
10.5 2011 1 49 66 0 22 35
10.9 2011 1 49 72 0 22 48
4.3 2011 1 59 71 1 20 25
10.3 2011 1 40 59 0 10 44
11.4 2011 1 63 78 0 22 20
5.6 2011 1 34 65 1 20 26
8.8 2011 1 32 65 0 22 23
9 2011 1 67 71 0 20 21
9.6 2011 1 61 72 1 17 41
6.4 2011 1 60 66 0 18 22
11.6 2011 1 63 69 0 19 27
4.35 2012 0 52 51 1 23 12
12.7 2012 0 16 56 1 22 45
18.1 2012 0 46 67 1 21 37
17.85 2012 0 56 69 1 25 37
16.6 2012 1 52 57 0 30 108
12.6 2012 1 55 56 1 17 10
17.1 2012 0 50 55 1 27 68
19.1 2012 0 59 63 0 23 72
16.1 2012 0 60 67 1 23 143
13.35 2012 0 52 65 0 18 9
18.4 2012 0 44 47 0 18 55
14.7 2012 0 67 76 1 23 17
10.6 2012 0 52 64 1 19 37
12.6 2012 0 55 68 1 15 27
16.2 2012 0 37 64 1 20 37
13.6 2012 0 54 65 1 16 58
18.9 2012 1 72 71 1 24 66
14.1 2012 0 51 63 1 25 21
14.5 2012 0 48 60 1 25 19
16.15 2012 0 60 68 0 19 78
14.75 2012 0 50 72 1 19 35
14.8 2012 0 63 70 1 16 48
12.45 2012 0 33 61 1 19 27
12.65 2012 0 67 61 1 19 43
17.35 2012 0 46 62 1 23 30
8.6 2012 0 54 71 1 21 25
18.4 2012 0 59 71 0 22 69
16.1 2012 0 61 51 1 19 72
11.6 2012 1 33 56 1 20 23
17.75 2012 0 47 70 1 20 13
15.25 2012 0 69 73 1 3 61
17.65 2012 0 52 76 1 23 43
16.35 2012 0 55 68 0 23 51
17.65 2012 0 41 48 0 20 67
13.6 2012 0 73 52 1 15 36
14.35 2012 0 52 60 0 16 44
14.75 2012 0 50 59 0 7 45
18.25 2012 0 51 57 1 24 34
9.9 2012 0 60 79 0 17 36
16 2012 0 56 60 1 24 72
18.25 2012 0 56 60 1 24 39
16.85 2012 0 29 59 0 19 43
14.6 2012 1 66 62 1 25 25
13.85 2012 1 66 59 1 20 56
18.95 2012 0 73 61 1 28 80
15.6 2012 0 55 71 0 23 40
14.85 2012 1 64 57 0 27 73
11.75 2012 1 40 66 0 18 34
18.45 2012 1 46 63 0 28 72
15.9 2012 1 58 69 1 21 42
17.1 2012 0 43 58 0 19 61
16.1 2012 0 61 59 1 23 23
19.9 2012 1 51 48 0 27 74
10.95 2012 1 50 66 1 22 16
18.45 2012 1 52 73 0 28 66
15.1 2012 1 54 67 1 25 9
15 2012 1 66 61 0 21 41
11.35 2012 1 61 68 0 22 57
15.95 2012 1 80 75 1 28 48
18.1 2012 1 51 62 0 20 51
14.6 2012 1 56 69 1 29 53
15.4 2012 0 56 58 1 25 29
15.4 2012 0 56 60 1 25 29
17.6 2012 1 53 74 1 20 55
13.35 2012 0 47 55 1 20 54
19.1 2012 0 25 62 0 16 43
15.35 2012 1 47 63 1 20 51
7.6 2012 0 46 69 0 20 20
13.4 2012 1 50 58 0 23 79
13.9 2012 1 39 58 0 18 39
19.1 2012 0 51 68 1 25 61
15.25 2012 1 58 72 0 18 55
12.9 2012 1 35 62 1 19 30
16.1 2012 1 58 62 0 25 55
17.35 2012 1 60 65 0 25 22
13.15 2012 1 62 69 0 25 37
12.15 2012 1 63 66 0 24 2
12.6 2012 1 53 72 1 19 38
10.35 2012 1 46 62 1 26 27
15.4 2012 1 67 75 1 10 56
9.6 2012 1 59 58 1 17 25
18.2 2012 1 64 66 0 13 39
13.6 2012 1 38 55 0 17 33
14.85 2012 1 50 47 1 30 43
14.75 2012 0 48 72 0 25 57
14.1 2012 1 48 62 0 4 43
14.9 2012 1 47 64 0 16 23
16.25 2012 1 66 64 0 21 44
19.25 2012 0 47 19 1 23 54
13.6 2012 1 63 50 1 22 28
13.6 2012 0 58 68 0 17 36
15.65 2012 1 44 70 0 20 39
12.75 2012 0 51 79 1 20 16
14.6 2012 1 43 69 0 22 23
9.85 2012 0 55 71 1 16 40
12.65 2012 1 38 48 1 23 24
19.2 2012 1 45 73 0 0 78
16.6 2012 1 50 74 1 18 57
11.2 2012 1 54 66 1 25 37
15.25 2012 0 57 71 1 23 27
11.9 2012 0 60 74 0 12 61
13.2 2012 1 55 78 0 18 27
16.35 2012 0 56 75 0 24 69
12.4 2012 0 49 53 1 11 34
15.85 2012 1 37 60 1 18 44
18.15 2012 0 59 70 1 23 34
11.15 2012 1 46 69 1 24 39
15.65 2012 1 51 65 0 29 51
17.75 2012 0 58 78 0 18 34
7.65 2012 1 64 78 0 15 31
12.35 2012 0 53 59 1 29 13
15.6 2012 0 48 72 1 16 12
19.3 2012 0 51 70 0 19 51
15.2 2012 1 47 63 0 22 24
17.1 2012 0 59 63 0 16 19
15.6 2012 1 62 71 1 23 30
18.4 2012 0 62 74 1 23 81
19.05 2012 0 51 67 0 19 42
18.55 2012 0 64 66 0 4 22
19.1 2012 0 52 62 0 20 85
13.1 2012 1 67 80 1 24 27
12.85 2012 0 50 73 1 20 25
9.5 2012 0 54 67 1 4 22
4.5 2012 0 58 61 1 24 19
11.85 2012 1 56 73 0 22 14
13.6 2012 0 63 74 1 16 45
11.7 2012 0 31 32 1 3 45
12.4 2012 1 65 69 1 15 28
13.35 2012 0 71 69 0 24 51
11.4 2012 1 50 84 0 17 41
14.9 2012 1 57 64 1 20 31
19.9 2012 1 47 58 0 27 74
11.2 2012 1 47 59 1 26 19
14.6 2012 1 57 78 1 23 51
17.6 2012 0 43 57 0 17 73
14.05 2012 0 41 60 1 20 24
16.1 2012 0 63 68 0 22 61
13.35 2012 0 63 68 1 19 23
11.85 2012 0 56 73 1 24 14
11.95 2012 0 51 69 0 19 54
14.75 2012 1 50 67 1 23 51
15.15 2012 1 22 60 0 15 62
13.2 2012 0 41 65 1 27 36
16.85 2012 1 59 66 0 26 59
7.85 2012 1 56 74 1 22 24
7.7 2012 0 66 81 0 22 26
12.6 2012 1 53 72 0 18 54
7.85 2012 1 42 55 1 15 39
10.95 2012 1 52 49 1 22 16
12.35 2012 1 54 74 0 27 36
9.95 2012 1 44 53 1 10 31
14.9 2012 1 62 64 1 20 31
16.65 2012 1 53 65 0 17 42
13.4 2012 1 50 57 1 23 39
13.95 2012 1 36 51 0 19 25
15.7 2012 1 76 80 0 13 31
16.85 2012 1 66 67 1 27 38
10.95 2012 1 62 70 1 23 31
15.35 2012 1 59 74 0 16 17
12.2 2012 1 47 75 1 25 22
15.1 2012 1 55 70 0 2 55
17.75 2012 1 58 69 0 26 62
15.2 2012 1 60 65 1 20 51
14.6 2012 0 44 55 0 23 30
16.65 2012 1 57 71 0 22 49




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264322&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264322&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264322&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
TOT[t] = -7920.07 + 3.9427year[t] -0.377017group[t] -0.0024756AMS.I[t] -0.0133645AMS.E[t] -0.808473gender[t] + 0.0538805NUMERACYTOT[t] + 0.0587806CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  -7920.07 +  3.9427year[t] -0.377017group[t] -0.0024756AMS.I[t] -0.0133645AMS.E[t] -0.808473gender[t] +  0.0538805NUMERACYTOT[t] +  0.0587806CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264322&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  -7920.07 +  3.9427year[t] -0.377017group[t] -0.0024756AMS.I[t] -0.0133645AMS.E[t] -0.808473gender[t] +  0.0538805NUMERACYTOT[t] +  0.0587806CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264322&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264322&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = -7920.07 + 3.9427year[t] -0.377017group[t] -0.0024756AMS.I[t] -0.0133645AMS.E[t] -0.808473gender[t] + 0.0538805NUMERACYTOT[t] + 0.0587806CH[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-7920.07790.717-10.021.07008e-195.35042e-20
year3.94270.39297610.039.53406e-204.76703e-20
group-0.3770170.35598-1.0590.2907170.145359
AMS.I-0.00247560.0175701-0.14090.8880790.44404
AMS.E-0.01336450.0219649-0.60840.5435190.27176
gender-0.8084730.357691-2.260.02478410.0123921
NUMERACYTOT0.05388050.03315571.6250.1055780.0527891
CH0.05878060.009062066.4865.72263e-102.86132e-10

\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) & -7920.07 & 790.717 & -10.02 & 1.07008e-19 & 5.35042e-20 \tabularnewline
year & 3.9427 & 0.392976 & 10.03 & 9.53406e-20 & 4.76703e-20 \tabularnewline
group & -0.377017 & 0.35598 & -1.059 & 0.290717 & 0.145359 \tabularnewline
AMS.I & -0.0024756 & 0.0175701 & -0.1409 & 0.888079 & 0.44404 \tabularnewline
AMS.E & -0.0133645 & 0.0219649 & -0.6084 & 0.543519 & 0.27176 \tabularnewline
gender & -0.808473 & 0.357691 & -2.26 & 0.0247841 & 0.0123921 \tabularnewline
NUMERACYTOT & 0.0538805 & 0.0331557 & 1.625 & 0.105578 & 0.0527891 \tabularnewline
CH & 0.0587806 & 0.00906206 & 6.486 & 5.72263e-10 & 2.86132e-10 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264322&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]-7920.07[/C][C]790.717[/C][C]-10.02[/C][C]1.07008e-19[/C][C]5.35042e-20[/C][/ROW]
[ROW][C]year[/C][C]3.9427[/C][C]0.392976[/C][C]10.03[/C][C]9.53406e-20[/C][C]4.76703e-20[/C][/ROW]
[ROW][C]group[/C][C]-0.377017[/C][C]0.35598[/C][C]-1.059[/C][C]0.290717[/C][C]0.145359[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.0024756[/C][C]0.0175701[/C][C]-0.1409[/C][C]0.888079[/C][C]0.44404[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0133645[/C][C]0.0219649[/C][C]-0.6084[/C][C]0.543519[/C][C]0.27176[/C][/ROW]
[ROW][C]gender[/C][C]-0.808473[/C][C]0.357691[/C][C]-2.26[/C][C]0.0247841[/C][C]0.0123921[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0538805[/C][C]0.0331557[/C][C]1.625[/C][C]0.105578[/C][C]0.0527891[/C][/ROW]
[ROW][C]CH[/C][C]0.0587806[/C][C]0.00906206[/C][C]6.486[/C][C]5.72263e-10[/C][C]2.86132e-10[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264322&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264322&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-7920.07790.717-10.021.07008e-195.35042e-20
year3.94270.39297610.039.53406e-204.76703e-20
group-0.3770170.35598-1.0590.2907170.145359
AMS.I-0.00247560.0175701-0.14090.8880790.44404
AMS.E-0.01336450.0219649-0.60840.5435190.27176
gender-0.8084730.357691-2.260.02478410.0123921
NUMERACYTOT0.05388050.03315571.6250.1055780.0527891
CH0.05878060.009062066.4865.72263e-102.86132e-10







Multiple Linear Regression - Regression Statistics
Multiple R0.647792
R-squared0.419634
Adjusted R-squared0.401168
F-TEST (value)22.7244
F-TEST (DF numerator)7
F-TEST (DF denominator)220
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.56394
Sum Squared Residuals1446.23

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.647792 \tabularnewline
R-squared & 0.419634 \tabularnewline
Adjusted R-squared & 0.401168 \tabularnewline
F-TEST (value) & 22.7244 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 220 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.56394 \tabularnewline
Sum Squared Residuals & 1446.23 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264322&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.647792[/C][/ROW]
[ROW][C]R-squared[/C][C]0.419634[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.401168[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.7244[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]220[/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]2.56394[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1446.23[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264322&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264322&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.647792
R-squared0.419634
Adjusted R-squared0.401168
F-TEST (value)22.7244
F-TEST (DF numerator)7
F-TEST (DF denominator)220
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.56394
Sum Squared Residuals1446.23







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.913.1034-0.203371
212.810.96051.83954
37.411.3984-3.99842
46.710.2498-3.54979
512.610.60471.99535
614.812.32512.47494
713.312.8090.491011
811.112.1672-1.06717
98.210.6055-2.40548
1011.410.90240.497605
116.411.3683-4.96832
121213.1968-1.19681
136.39.62339-3.32339
1411.39.845051.45495
1511.911.86750.0324611
169.310.8221-1.52205
171010.895-0.895006
1813.89.529574.27043
1910.811.3328-0.532763
2011.711.35530.344667
2110.911.6669-0.766884
2216.112.79093.30913
239.911.6699-1.76993
2411.510.83260.667426
258.311.2661-2.96608
2611.711.0880.611989
27910.1386-1.13864
2810.810.53180.268181
2910.410.4694-0.0694413
3012.78.457884.24212
3111.812.0916-0.2916
321311.33361.66636
3310.810.97-0.169951
3412.39.547332.75267
3511.311.5868-0.286787
3611.69.795061.80494
3710.99.777741.12226
3812.19.157772.94223
3913.310.51842.78156
4010.110.3344-0.234387
4114.39.993634.30637
429.312.6922-3.39222
4312.59.871762.62824
447.611.0863-3.48627
459.210.9729-1.77293
4614.59.464815.03519
4712.312.4314-0.131355
4812.611.26671.33328
491312.03810.961863
5012.69.519533.08047
5113.212.32520.87483
527.711.1098-3.40977
5310.510.5697-0.0697041
5410.911.2537-0.353665
554.38.97409-4.67409
5610.310.568-0.267995
5711.49.492961.90704
585.69.17494-3.57494
598.89.91979-1.11979
6099.52763-0.527631
619.69.73462-0.134618
626.49.5628-3.1628
6311.69.863071.73693
644.3512.9759-8.62592
6512.714.8841-2.1841
6618.114.13873.96131
6717.8514.30273.54727
6816.619.3473-2.74729
6912.612.08380.516192
7017.116.43460.665353
7119.117.13351.96648
7216.120.4425-4.34254
7313.3513.15150.198455
7418.416.11582.28418
7514.712.89861.80142
7610.614.0562-3.45617
7712.613.192-0.59196
7816.214.14722.05281
7913.615.1106-1.51061
8018.915.51013.38987
8114.113.45480.645193
8214.513.38481.11523
8316.1517.2014-1.05139
8414.7513.83660.913353
8514.814.43370.3663
8612.4513.5555-1.1055
8712.6514.4118-1.76182
8817.3513.90183.44819
898.613.3601-4.76006
9018.416.79641.60361
9116.116.265-0.164952
9211.613.0641-1.46406
9317.7512.63155.11849
9415.2514.44250.807546
9517.6514.4643.18599
9616.3515.84220.507788
9717.6516.9230.726992
9813.613.8903-0.290257
9914.3515.1679-0.817927
10014.7514.7601-0.0100989
10118.2514.24534.00474
1029.914.4778-4.57783
1031616.4265-0.426452
10418.2514.48673.76331
10516.8515.34111.50891
10614.613.28911.31086
10713.8514.882-1.03203
10818.9517.05681.89323
10915.615.15550.444468
11014.8517.0986-2.24862
11111.7514.2604-2.51039
11218.4517.05811.39191
11315.913.99911.90086
11417.116.37780.722152
11516.113.49332.60669
11619.917.30992.59014
11710.9512.5846-1.63463
11818.4516.55691.89309
11915.112.31152.78846
1201514.83590.164052
12111.3515.7491-4.39915
12215.9514.59431.35566
12318.115.39362.70636
12414.615.0817-0.481726
12515.413.97951.4205
12615.413.95281.44723
12717.614.6552.94503
12813.3515.242-1.89198
12919.115.14933.95074
13015.3514.58170.768292
1317.613.8673-6.26729
13213.417.2571-3.85708
13313.914.6637-0.76368
13419.115.73923.36079
13515.2515.37-0.120031
13612.913.3365-0.436506
13716.115.88080.219161
13817.3513.8963.45397
13913.1514.7193-1.56933
14012.1512.6458-0.495751
14112.613.6285-1.02855
14210.3513.5101-3.1601
14315.414.12691.27308
1449.612.9289-3.32889
14518.214.22553.97453
14613.614.2997-0.699685
14714.8514.8567-0.00667291
14814.7516.2665-1.51653
14914.114.06870.0312629
15014.913.51541.38456
15116.2514.97221.2778
15219.2515.88473.36525
15313.613.47160.128356
15413.614.6298-1.02979
15515.6514.59871.05131
15612.7512.67770.0723306
15714.613.78180.818199
1589.8513.9699-4.1199
15912.6513.379-0.729021
16019.215.7713.42905
16116.614.67221.92781
16211.213.9708-2.77076
16315.2513.5781.67204
16411.915.7448-3.84477
16513.213.6514-0.451414
16616.3516.8581-0.508117
16712.413.6032-1.20322
16815.8514.12731.72267
16918.1513.99784.15216
17011.1514.0142-2.86415
17115.6515.8385-0.188474
17217.7514.43253.31753
1737.6513.7026-6.05261
17412.3513.2486-0.898591
17515.612.3283.272
17619.315.60993.69014
17715.213.91091.28913
17817.113.6413.45901
17915.613.36492.23509
18018.416.69961.70036
18119.0515.12093.92907
18218.5513.11835.43171
18319.117.76671.33328
18413.113.1098-0.0097872
18512.8513.2894-0.439357
1869.512.3212-2.82121
1874.513.2928-8.79276
18811.8513.1671-1.31713
18913.614.2039-0.6039
19011.714.144-2.44398
19112.412.8356-0.435604
19213.3515.8431-2.49312
19311.414.3527-2.95265
19414.913.3681.53202
19519.917.18612.71388
19611.213.0775-1.87747
19714.614.51810.0818738
19817.616.98880.611181
19914.0513.42660.623405
20016.116.3563-0.256333
20113.3513.15260.197445
20211.8512.8434-0.993439
20311.9515.7996-3.84957
20414.7514.68250.0675354
20515.1515.8693-0.719349
20613.214.4423-1.2423
20716.8516.11390.736092
2087.8512.9331-5.0831
2097.714.1178-6.41785
21012.615.3236-2.72363
2117.8513.7262-5.87623
21210.9512.8069-1.85687
21312.3514.7213-2.3713
2149.9513.0084-3.05836
21514.913.35561.5444
21616.6514.65791.99207
21713.414.1107-0.710742
21813.9513.9956-0.0456103
21915.713.53842.16158
22016.8514.09422.75577
22110.9513.4371-2.48705
22215.3512.99942.3506
22312.212.9861-0.786099
22415.114.54210.557902
22517.7516.25261.49737
22615.214.52280.677204
22714.614.8088-0.208789
22816.6515.24871.40129

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 13.1034 & -0.203371 \tabularnewline
2 & 12.8 & 10.9605 & 1.83954 \tabularnewline
3 & 7.4 & 11.3984 & -3.99842 \tabularnewline
4 & 6.7 & 10.2498 & -3.54979 \tabularnewline
5 & 12.6 & 10.6047 & 1.99535 \tabularnewline
6 & 14.8 & 12.3251 & 2.47494 \tabularnewline
7 & 13.3 & 12.809 & 0.491011 \tabularnewline
8 & 11.1 & 12.1672 & -1.06717 \tabularnewline
9 & 8.2 & 10.6055 & -2.40548 \tabularnewline
10 & 11.4 & 10.9024 & 0.497605 \tabularnewline
11 & 6.4 & 11.3683 & -4.96832 \tabularnewline
12 & 12 & 13.1968 & -1.19681 \tabularnewline
13 & 6.3 & 9.62339 & -3.32339 \tabularnewline
14 & 11.3 & 9.84505 & 1.45495 \tabularnewline
15 & 11.9 & 11.8675 & 0.0324611 \tabularnewline
16 & 9.3 & 10.8221 & -1.52205 \tabularnewline
17 & 10 & 10.895 & -0.895006 \tabularnewline
18 & 13.8 & 9.52957 & 4.27043 \tabularnewline
19 & 10.8 & 11.3328 & -0.532763 \tabularnewline
20 & 11.7 & 11.3553 & 0.344667 \tabularnewline
21 & 10.9 & 11.6669 & -0.766884 \tabularnewline
22 & 16.1 & 12.7909 & 3.30913 \tabularnewline
23 & 9.9 & 11.6699 & -1.76993 \tabularnewline
24 & 11.5 & 10.8326 & 0.667426 \tabularnewline
25 & 8.3 & 11.2661 & -2.96608 \tabularnewline
26 & 11.7 & 11.088 & 0.611989 \tabularnewline
27 & 9 & 10.1386 & -1.13864 \tabularnewline
28 & 10.8 & 10.5318 & 0.268181 \tabularnewline
29 & 10.4 & 10.4694 & -0.0694413 \tabularnewline
30 & 12.7 & 8.45788 & 4.24212 \tabularnewline
31 & 11.8 & 12.0916 & -0.2916 \tabularnewline
32 & 13 & 11.3336 & 1.66636 \tabularnewline
33 & 10.8 & 10.97 & -0.169951 \tabularnewline
34 & 12.3 & 9.54733 & 2.75267 \tabularnewline
35 & 11.3 & 11.5868 & -0.286787 \tabularnewline
36 & 11.6 & 9.79506 & 1.80494 \tabularnewline
37 & 10.9 & 9.77774 & 1.12226 \tabularnewline
38 & 12.1 & 9.15777 & 2.94223 \tabularnewline
39 & 13.3 & 10.5184 & 2.78156 \tabularnewline
40 & 10.1 & 10.3344 & -0.234387 \tabularnewline
41 & 14.3 & 9.99363 & 4.30637 \tabularnewline
42 & 9.3 & 12.6922 & -3.39222 \tabularnewline
43 & 12.5 & 9.87176 & 2.62824 \tabularnewline
44 & 7.6 & 11.0863 & -3.48627 \tabularnewline
45 & 9.2 & 10.9729 & -1.77293 \tabularnewline
46 & 14.5 & 9.46481 & 5.03519 \tabularnewline
47 & 12.3 & 12.4314 & -0.131355 \tabularnewline
48 & 12.6 & 11.2667 & 1.33328 \tabularnewline
49 & 13 & 12.0381 & 0.961863 \tabularnewline
50 & 12.6 & 9.51953 & 3.08047 \tabularnewline
51 & 13.2 & 12.3252 & 0.87483 \tabularnewline
52 & 7.7 & 11.1098 & -3.40977 \tabularnewline
53 & 10.5 & 10.5697 & -0.0697041 \tabularnewline
54 & 10.9 & 11.2537 & -0.353665 \tabularnewline
55 & 4.3 & 8.97409 & -4.67409 \tabularnewline
56 & 10.3 & 10.568 & -0.267995 \tabularnewline
57 & 11.4 & 9.49296 & 1.90704 \tabularnewline
58 & 5.6 & 9.17494 & -3.57494 \tabularnewline
59 & 8.8 & 9.91979 & -1.11979 \tabularnewline
60 & 9 & 9.52763 & -0.527631 \tabularnewline
61 & 9.6 & 9.73462 & -0.134618 \tabularnewline
62 & 6.4 & 9.5628 & -3.1628 \tabularnewline
63 & 11.6 & 9.86307 & 1.73693 \tabularnewline
64 & 4.35 & 12.9759 & -8.62592 \tabularnewline
65 & 12.7 & 14.8841 & -2.1841 \tabularnewline
66 & 18.1 & 14.1387 & 3.96131 \tabularnewline
67 & 17.85 & 14.3027 & 3.54727 \tabularnewline
68 & 16.6 & 19.3473 & -2.74729 \tabularnewline
69 & 12.6 & 12.0838 & 0.516192 \tabularnewline
70 & 17.1 & 16.4346 & 0.665353 \tabularnewline
71 & 19.1 & 17.1335 & 1.96648 \tabularnewline
72 & 16.1 & 20.4425 & -4.34254 \tabularnewline
73 & 13.35 & 13.1515 & 0.198455 \tabularnewline
74 & 18.4 & 16.1158 & 2.28418 \tabularnewline
75 & 14.7 & 12.8986 & 1.80142 \tabularnewline
76 & 10.6 & 14.0562 & -3.45617 \tabularnewline
77 & 12.6 & 13.192 & -0.59196 \tabularnewline
78 & 16.2 & 14.1472 & 2.05281 \tabularnewline
79 & 13.6 & 15.1106 & -1.51061 \tabularnewline
80 & 18.9 & 15.5101 & 3.38987 \tabularnewline
81 & 14.1 & 13.4548 & 0.645193 \tabularnewline
82 & 14.5 & 13.3848 & 1.11523 \tabularnewline
83 & 16.15 & 17.2014 & -1.05139 \tabularnewline
84 & 14.75 & 13.8366 & 0.913353 \tabularnewline
85 & 14.8 & 14.4337 & 0.3663 \tabularnewline
86 & 12.45 & 13.5555 & -1.1055 \tabularnewline
87 & 12.65 & 14.4118 & -1.76182 \tabularnewline
88 & 17.35 & 13.9018 & 3.44819 \tabularnewline
89 & 8.6 & 13.3601 & -4.76006 \tabularnewline
90 & 18.4 & 16.7964 & 1.60361 \tabularnewline
91 & 16.1 & 16.265 & -0.164952 \tabularnewline
92 & 11.6 & 13.0641 & -1.46406 \tabularnewline
93 & 17.75 & 12.6315 & 5.11849 \tabularnewline
94 & 15.25 & 14.4425 & 0.807546 \tabularnewline
95 & 17.65 & 14.464 & 3.18599 \tabularnewline
96 & 16.35 & 15.8422 & 0.507788 \tabularnewline
97 & 17.65 & 16.923 & 0.726992 \tabularnewline
98 & 13.6 & 13.8903 & -0.290257 \tabularnewline
99 & 14.35 & 15.1679 & -0.817927 \tabularnewline
100 & 14.75 & 14.7601 & -0.0100989 \tabularnewline
101 & 18.25 & 14.2453 & 4.00474 \tabularnewline
102 & 9.9 & 14.4778 & -4.57783 \tabularnewline
103 & 16 & 16.4265 & -0.426452 \tabularnewline
104 & 18.25 & 14.4867 & 3.76331 \tabularnewline
105 & 16.85 & 15.3411 & 1.50891 \tabularnewline
106 & 14.6 & 13.2891 & 1.31086 \tabularnewline
107 & 13.85 & 14.882 & -1.03203 \tabularnewline
108 & 18.95 & 17.0568 & 1.89323 \tabularnewline
109 & 15.6 & 15.1555 & 0.444468 \tabularnewline
110 & 14.85 & 17.0986 & -2.24862 \tabularnewline
111 & 11.75 & 14.2604 & -2.51039 \tabularnewline
112 & 18.45 & 17.0581 & 1.39191 \tabularnewline
113 & 15.9 & 13.9991 & 1.90086 \tabularnewline
114 & 17.1 & 16.3778 & 0.722152 \tabularnewline
115 & 16.1 & 13.4933 & 2.60669 \tabularnewline
116 & 19.9 & 17.3099 & 2.59014 \tabularnewline
117 & 10.95 & 12.5846 & -1.63463 \tabularnewline
118 & 18.45 & 16.5569 & 1.89309 \tabularnewline
119 & 15.1 & 12.3115 & 2.78846 \tabularnewline
120 & 15 & 14.8359 & 0.164052 \tabularnewline
121 & 11.35 & 15.7491 & -4.39915 \tabularnewline
122 & 15.95 & 14.5943 & 1.35566 \tabularnewline
123 & 18.1 & 15.3936 & 2.70636 \tabularnewline
124 & 14.6 & 15.0817 & -0.481726 \tabularnewline
125 & 15.4 & 13.9795 & 1.4205 \tabularnewline
126 & 15.4 & 13.9528 & 1.44723 \tabularnewline
127 & 17.6 & 14.655 & 2.94503 \tabularnewline
128 & 13.35 & 15.242 & -1.89198 \tabularnewline
129 & 19.1 & 15.1493 & 3.95074 \tabularnewline
130 & 15.35 & 14.5817 & 0.768292 \tabularnewline
131 & 7.6 & 13.8673 & -6.26729 \tabularnewline
132 & 13.4 & 17.2571 & -3.85708 \tabularnewline
133 & 13.9 & 14.6637 & -0.76368 \tabularnewline
134 & 19.1 & 15.7392 & 3.36079 \tabularnewline
135 & 15.25 & 15.37 & -0.120031 \tabularnewline
136 & 12.9 & 13.3365 & -0.436506 \tabularnewline
137 & 16.1 & 15.8808 & 0.219161 \tabularnewline
138 & 17.35 & 13.896 & 3.45397 \tabularnewline
139 & 13.15 & 14.7193 & -1.56933 \tabularnewline
140 & 12.15 & 12.6458 & -0.495751 \tabularnewline
141 & 12.6 & 13.6285 & -1.02855 \tabularnewline
142 & 10.35 & 13.5101 & -3.1601 \tabularnewline
143 & 15.4 & 14.1269 & 1.27308 \tabularnewline
144 & 9.6 & 12.9289 & -3.32889 \tabularnewline
145 & 18.2 & 14.2255 & 3.97453 \tabularnewline
146 & 13.6 & 14.2997 & -0.699685 \tabularnewline
147 & 14.85 & 14.8567 & -0.00667291 \tabularnewline
148 & 14.75 & 16.2665 & -1.51653 \tabularnewline
149 & 14.1 & 14.0687 & 0.0312629 \tabularnewline
150 & 14.9 & 13.5154 & 1.38456 \tabularnewline
151 & 16.25 & 14.9722 & 1.2778 \tabularnewline
152 & 19.25 & 15.8847 & 3.36525 \tabularnewline
153 & 13.6 & 13.4716 & 0.128356 \tabularnewline
154 & 13.6 & 14.6298 & -1.02979 \tabularnewline
155 & 15.65 & 14.5987 & 1.05131 \tabularnewline
156 & 12.75 & 12.6777 & 0.0723306 \tabularnewline
157 & 14.6 & 13.7818 & 0.818199 \tabularnewline
158 & 9.85 & 13.9699 & -4.1199 \tabularnewline
159 & 12.65 & 13.379 & -0.729021 \tabularnewline
160 & 19.2 & 15.771 & 3.42905 \tabularnewline
161 & 16.6 & 14.6722 & 1.92781 \tabularnewline
162 & 11.2 & 13.9708 & -2.77076 \tabularnewline
163 & 15.25 & 13.578 & 1.67204 \tabularnewline
164 & 11.9 & 15.7448 & -3.84477 \tabularnewline
165 & 13.2 & 13.6514 & -0.451414 \tabularnewline
166 & 16.35 & 16.8581 & -0.508117 \tabularnewline
167 & 12.4 & 13.6032 & -1.20322 \tabularnewline
168 & 15.85 & 14.1273 & 1.72267 \tabularnewline
169 & 18.15 & 13.9978 & 4.15216 \tabularnewline
170 & 11.15 & 14.0142 & -2.86415 \tabularnewline
171 & 15.65 & 15.8385 & -0.188474 \tabularnewline
172 & 17.75 & 14.4325 & 3.31753 \tabularnewline
173 & 7.65 & 13.7026 & -6.05261 \tabularnewline
174 & 12.35 & 13.2486 & -0.898591 \tabularnewline
175 & 15.6 & 12.328 & 3.272 \tabularnewline
176 & 19.3 & 15.6099 & 3.69014 \tabularnewline
177 & 15.2 & 13.9109 & 1.28913 \tabularnewline
178 & 17.1 & 13.641 & 3.45901 \tabularnewline
179 & 15.6 & 13.3649 & 2.23509 \tabularnewline
180 & 18.4 & 16.6996 & 1.70036 \tabularnewline
181 & 19.05 & 15.1209 & 3.92907 \tabularnewline
182 & 18.55 & 13.1183 & 5.43171 \tabularnewline
183 & 19.1 & 17.7667 & 1.33328 \tabularnewline
184 & 13.1 & 13.1098 & -0.0097872 \tabularnewline
185 & 12.85 & 13.2894 & -0.439357 \tabularnewline
186 & 9.5 & 12.3212 & -2.82121 \tabularnewline
187 & 4.5 & 13.2928 & -8.79276 \tabularnewline
188 & 11.85 & 13.1671 & -1.31713 \tabularnewline
189 & 13.6 & 14.2039 & -0.6039 \tabularnewline
190 & 11.7 & 14.144 & -2.44398 \tabularnewline
191 & 12.4 & 12.8356 & -0.435604 \tabularnewline
192 & 13.35 & 15.8431 & -2.49312 \tabularnewline
193 & 11.4 & 14.3527 & -2.95265 \tabularnewline
194 & 14.9 & 13.368 & 1.53202 \tabularnewline
195 & 19.9 & 17.1861 & 2.71388 \tabularnewline
196 & 11.2 & 13.0775 & -1.87747 \tabularnewline
197 & 14.6 & 14.5181 & 0.0818738 \tabularnewline
198 & 17.6 & 16.9888 & 0.611181 \tabularnewline
199 & 14.05 & 13.4266 & 0.623405 \tabularnewline
200 & 16.1 & 16.3563 & -0.256333 \tabularnewline
201 & 13.35 & 13.1526 & 0.197445 \tabularnewline
202 & 11.85 & 12.8434 & -0.993439 \tabularnewline
203 & 11.95 & 15.7996 & -3.84957 \tabularnewline
204 & 14.75 & 14.6825 & 0.0675354 \tabularnewline
205 & 15.15 & 15.8693 & -0.719349 \tabularnewline
206 & 13.2 & 14.4423 & -1.2423 \tabularnewline
207 & 16.85 & 16.1139 & 0.736092 \tabularnewline
208 & 7.85 & 12.9331 & -5.0831 \tabularnewline
209 & 7.7 & 14.1178 & -6.41785 \tabularnewline
210 & 12.6 & 15.3236 & -2.72363 \tabularnewline
211 & 7.85 & 13.7262 & -5.87623 \tabularnewline
212 & 10.95 & 12.8069 & -1.85687 \tabularnewline
213 & 12.35 & 14.7213 & -2.3713 \tabularnewline
214 & 9.95 & 13.0084 & -3.05836 \tabularnewline
215 & 14.9 & 13.3556 & 1.5444 \tabularnewline
216 & 16.65 & 14.6579 & 1.99207 \tabularnewline
217 & 13.4 & 14.1107 & -0.710742 \tabularnewline
218 & 13.95 & 13.9956 & -0.0456103 \tabularnewline
219 & 15.7 & 13.5384 & 2.16158 \tabularnewline
220 & 16.85 & 14.0942 & 2.75577 \tabularnewline
221 & 10.95 & 13.4371 & -2.48705 \tabularnewline
222 & 15.35 & 12.9994 & 2.3506 \tabularnewline
223 & 12.2 & 12.9861 & -0.786099 \tabularnewline
224 & 15.1 & 14.5421 & 0.557902 \tabularnewline
225 & 17.75 & 16.2526 & 1.49737 \tabularnewline
226 & 15.2 & 14.5228 & 0.677204 \tabularnewline
227 & 14.6 & 14.8088 & -0.208789 \tabularnewline
228 & 16.65 & 15.2487 & 1.40129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264322&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]12.9[/C][C]13.1034[/C][C]-0.203371[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]10.9605[/C][C]1.83954[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]11.3984[/C][C]-3.99842[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]10.2498[/C][C]-3.54979[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]10.6047[/C][C]1.99535[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]12.3251[/C][C]2.47494[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]12.809[/C][C]0.491011[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]12.1672[/C][C]-1.06717[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]10.6055[/C][C]-2.40548[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]10.9024[/C][C]0.497605[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]11.3683[/C][C]-4.96832[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.1968[/C][C]-1.19681[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]9.62339[/C][C]-3.32339[/C][/ROW]
[ROW][C]14[/C][C]11.3[/C][C]9.84505[/C][C]1.45495[/C][/ROW]
[ROW][C]15[/C][C]11.9[/C][C]11.8675[/C][C]0.0324611[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]10.8221[/C][C]-1.52205[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.895[/C][C]-0.895006[/C][/ROW]
[ROW][C]18[/C][C]13.8[/C][C]9.52957[/C][C]4.27043[/C][/ROW]
[ROW][C]19[/C][C]10.8[/C][C]11.3328[/C][C]-0.532763[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]11.3553[/C][C]0.344667[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]11.6669[/C][C]-0.766884[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]12.7909[/C][C]3.30913[/C][/ROW]
[ROW][C]23[/C][C]9.9[/C][C]11.6699[/C][C]-1.76993[/C][/ROW]
[ROW][C]24[/C][C]11.5[/C][C]10.8326[/C][C]0.667426[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]11.2661[/C][C]-2.96608[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.088[/C][C]0.611989[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]10.1386[/C][C]-1.13864[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]10.5318[/C][C]0.268181[/C][/ROW]
[ROW][C]29[/C][C]10.4[/C][C]10.4694[/C][C]-0.0694413[/C][/ROW]
[ROW][C]30[/C][C]12.7[/C][C]8.45788[/C][C]4.24212[/C][/ROW]
[ROW][C]31[/C][C]11.8[/C][C]12.0916[/C][C]-0.2916[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]11.3336[/C][C]1.66636[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]10.97[/C][C]-0.169951[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]9.54733[/C][C]2.75267[/C][/ROW]
[ROW][C]35[/C][C]11.3[/C][C]11.5868[/C][C]-0.286787[/C][/ROW]
[ROW][C]36[/C][C]11.6[/C][C]9.79506[/C][C]1.80494[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]9.77774[/C][C]1.12226[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]9.15777[/C][C]2.94223[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]10.5184[/C][C]2.78156[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]10.3344[/C][C]-0.234387[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]9.99363[/C][C]4.30637[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]12.6922[/C][C]-3.39222[/C][/ROW]
[ROW][C]43[/C][C]12.5[/C][C]9.87176[/C][C]2.62824[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]11.0863[/C][C]-3.48627[/C][/ROW]
[ROW][C]45[/C][C]9.2[/C][C]10.9729[/C][C]-1.77293[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]9.46481[/C][C]5.03519[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]12.4314[/C][C]-0.131355[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]11.2667[/C][C]1.33328[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]12.0381[/C][C]0.961863[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]9.51953[/C][C]3.08047[/C][/ROW]
[ROW][C]51[/C][C]13.2[/C][C]12.3252[/C][C]0.87483[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]11.1098[/C][C]-3.40977[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]10.5697[/C][C]-0.0697041[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]11.2537[/C][C]-0.353665[/C][/ROW]
[ROW][C]55[/C][C]4.3[/C][C]8.97409[/C][C]-4.67409[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]10.568[/C][C]-0.267995[/C][/ROW]
[ROW][C]57[/C][C]11.4[/C][C]9.49296[/C][C]1.90704[/C][/ROW]
[ROW][C]58[/C][C]5.6[/C][C]9.17494[/C][C]-3.57494[/C][/ROW]
[ROW][C]59[/C][C]8.8[/C][C]9.91979[/C][C]-1.11979[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]9.52763[/C][C]-0.527631[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]9.73462[/C][C]-0.134618[/C][/ROW]
[ROW][C]62[/C][C]6.4[/C][C]9.5628[/C][C]-3.1628[/C][/ROW]
[ROW][C]63[/C][C]11.6[/C][C]9.86307[/C][C]1.73693[/C][/ROW]
[ROW][C]64[/C][C]4.35[/C][C]12.9759[/C][C]-8.62592[/C][/ROW]
[ROW][C]65[/C][C]12.7[/C][C]14.8841[/C][C]-2.1841[/C][/ROW]
[ROW][C]66[/C][C]18.1[/C][C]14.1387[/C][C]3.96131[/C][/ROW]
[ROW][C]67[/C][C]17.85[/C][C]14.3027[/C][C]3.54727[/C][/ROW]
[ROW][C]68[/C][C]16.6[/C][C]19.3473[/C][C]-2.74729[/C][/ROW]
[ROW][C]69[/C][C]12.6[/C][C]12.0838[/C][C]0.516192[/C][/ROW]
[ROW][C]70[/C][C]17.1[/C][C]16.4346[/C][C]0.665353[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]17.1335[/C][C]1.96648[/C][/ROW]
[ROW][C]72[/C][C]16.1[/C][C]20.4425[/C][C]-4.34254[/C][/ROW]
[ROW][C]73[/C][C]13.35[/C][C]13.1515[/C][C]0.198455[/C][/ROW]
[ROW][C]74[/C][C]18.4[/C][C]16.1158[/C][C]2.28418[/C][/ROW]
[ROW][C]75[/C][C]14.7[/C][C]12.8986[/C][C]1.80142[/C][/ROW]
[ROW][C]76[/C][C]10.6[/C][C]14.0562[/C][C]-3.45617[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]13.192[/C][C]-0.59196[/C][/ROW]
[ROW][C]78[/C][C]16.2[/C][C]14.1472[/C][C]2.05281[/C][/ROW]
[ROW][C]79[/C][C]13.6[/C][C]15.1106[/C][C]-1.51061[/C][/ROW]
[ROW][C]80[/C][C]18.9[/C][C]15.5101[/C][C]3.38987[/C][/ROW]
[ROW][C]81[/C][C]14.1[/C][C]13.4548[/C][C]0.645193[/C][/ROW]
[ROW][C]82[/C][C]14.5[/C][C]13.3848[/C][C]1.11523[/C][/ROW]
[ROW][C]83[/C][C]16.15[/C][C]17.2014[/C][C]-1.05139[/C][/ROW]
[ROW][C]84[/C][C]14.75[/C][C]13.8366[/C][C]0.913353[/C][/ROW]
[ROW][C]85[/C][C]14.8[/C][C]14.4337[/C][C]0.3663[/C][/ROW]
[ROW][C]86[/C][C]12.45[/C][C]13.5555[/C][C]-1.1055[/C][/ROW]
[ROW][C]87[/C][C]12.65[/C][C]14.4118[/C][C]-1.76182[/C][/ROW]
[ROW][C]88[/C][C]17.35[/C][C]13.9018[/C][C]3.44819[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]13.3601[/C][C]-4.76006[/C][/ROW]
[ROW][C]90[/C][C]18.4[/C][C]16.7964[/C][C]1.60361[/C][/ROW]
[ROW][C]91[/C][C]16.1[/C][C]16.265[/C][C]-0.164952[/C][/ROW]
[ROW][C]92[/C][C]11.6[/C][C]13.0641[/C][C]-1.46406[/C][/ROW]
[ROW][C]93[/C][C]17.75[/C][C]12.6315[/C][C]5.11849[/C][/ROW]
[ROW][C]94[/C][C]15.25[/C][C]14.4425[/C][C]0.807546[/C][/ROW]
[ROW][C]95[/C][C]17.65[/C][C]14.464[/C][C]3.18599[/C][/ROW]
[ROW][C]96[/C][C]16.35[/C][C]15.8422[/C][C]0.507788[/C][/ROW]
[ROW][C]97[/C][C]17.65[/C][C]16.923[/C][C]0.726992[/C][/ROW]
[ROW][C]98[/C][C]13.6[/C][C]13.8903[/C][C]-0.290257[/C][/ROW]
[ROW][C]99[/C][C]14.35[/C][C]15.1679[/C][C]-0.817927[/C][/ROW]
[ROW][C]100[/C][C]14.75[/C][C]14.7601[/C][C]-0.0100989[/C][/ROW]
[ROW][C]101[/C][C]18.25[/C][C]14.2453[/C][C]4.00474[/C][/ROW]
[ROW][C]102[/C][C]9.9[/C][C]14.4778[/C][C]-4.57783[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]16.4265[/C][C]-0.426452[/C][/ROW]
[ROW][C]104[/C][C]18.25[/C][C]14.4867[/C][C]3.76331[/C][/ROW]
[ROW][C]105[/C][C]16.85[/C][C]15.3411[/C][C]1.50891[/C][/ROW]
[ROW][C]106[/C][C]14.6[/C][C]13.2891[/C][C]1.31086[/C][/ROW]
[ROW][C]107[/C][C]13.85[/C][C]14.882[/C][C]-1.03203[/C][/ROW]
[ROW][C]108[/C][C]18.95[/C][C]17.0568[/C][C]1.89323[/C][/ROW]
[ROW][C]109[/C][C]15.6[/C][C]15.1555[/C][C]0.444468[/C][/ROW]
[ROW][C]110[/C][C]14.85[/C][C]17.0986[/C][C]-2.24862[/C][/ROW]
[ROW][C]111[/C][C]11.75[/C][C]14.2604[/C][C]-2.51039[/C][/ROW]
[ROW][C]112[/C][C]18.45[/C][C]17.0581[/C][C]1.39191[/C][/ROW]
[ROW][C]113[/C][C]15.9[/C][C]13.9991[/C][C]1.90086[/C][/ROW]
[ROW][C]114[/C][C]17.1[/C][C]16.3778[/C][C]0.722152[/C][/ROW]
[ROW][C]115[/C][C]16.1[/C][C]13.4933[/C][C]2.60669[/C][/ROW]
[ROW][C]116[/C][C]19.9[/C][C]17.3099[/C][C]2.59014[/C][/ROW]
[ROW][C]117[/C][C]10.95[/C][C]12.5846[/C][C]-1.63463[/C][/ROW]
[ROW][C]118[/C][C]18.45[/C][C]16.5569[/C][C]1.89309[/C][/ROW]
[ROW][C]119[/C][C]15.1[/C][C]12.3115[/C][C]2.78846[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]14.8359[/C][C]0.164052[/C][/ROW]
[ROW][C]121[/C][C]11.35[/C][C]15.7491[/C][C]-4.39915[/C][/ROW]
[ROW][C]122[/C][C]15.95[/C][C]14.5943[/C][C]1.35566[/C][/ROW]
[ROW][C]123[/C][C]18.1[/C][C]15.3936[/C][C]2.70636[/C][/ROW]
[ROW][C]124[/C][C]14.6[/C][C]15.0817[/C][C]-0.481726[/C][/ROW]
[ROW][C]125[/C][C]15.4[/C][C]13.9795[/C][C]1.4205[/C][/ROW]
[ROW][C]126[/C][C]15.4[/C][C]13.9528[/C][C]1.44723[/C][/ROW]
[ROW][C]127[/C][C]17.6[/C][C]14.655[/C][C]2.94503[/C][/ROW]
[ROW][C]128[/C][C]13.35[/C][C]15.242[/C][C]-1.89198[/C][/ROW]
[ROW][C]129[/C][C]19.1[/C][C]15.1493[/C][C]3.95074[/C][/ROW]
[ROW][C]130[/C][C]15.35[/C][C]14.5817[/C][C]0.768292[/C][/ROW]
[ROW][C]131[/C][C]7.6[/C][C]13.8673[/C][C]-6.26729[/C][/ROW]
[ROW][C]132[/C][C]13.4[/C][C]17.2571[/C][C]-3.85708[/C][/ROW]
[ROW][C]133[/C][C]13.9[/C][C]14.6637[/C][C]-0.76368[/C][/ROW]
[ROW][C]134[/C][C]19.1[/C][C]15.7392[/C][C]3.36079[/C][/ROW]
[ROW][C]135[/C][C]15.25[/C][C]15.37[/C][C]-0.120031[/C][/ROW]
[ROW][C]136[/C][C]12.9[/C][C]13.3365[/C][C]-0.436506[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.8808[/C][C]0.219161[/C][/ROW]
[ROW][C]138[/C][C]17.35[/C][C]13.896[/C][C]3.45397[/C][/ROW]
[ROW][C]139[/C][C]13.15[/C][C]14.7193[/C][C]-1.56933[/C][/ROW]
[ROW][C]140[/C][C]12.15[/C][C]12.6458[/C][C]-0.495751[/C][/ROW]
[ROW][C]141[/C][C]12.6[/C][C]13.6285[/C][C]-1.02855[/C][/ROW]
[ROW][C]142[/C][C]10.35[/C][C]13.5101[/C][C]-3.1601[/C][/ROW]
[ROW][C]143[/C][C]15.4[/C][C]14.1269[/C][C]1.27308[/C][/ROW]
[ROW][C]144[/C][C]9.6[/C][C]12.9289[/C][C]-3.32889[/C][/ROW]
[ROW][C]145[/C][C]18.2[/C][C]14.2255[/C][C]3.97453[/C][/ROW]
[ROW][C]146[/C][C]13.6[/C][C]14.2997[/C][C]-0.699685[/C][/ROW]
[ROW][C]147[/C][C]14.85[/C][C]14.8567[/C][C]-0.00667291[/C][/ROW]
[ROW][C]148[/C][C]14.75[/C][C]16.2665[/C][C]-1.51653[/C][/ROW]
[ROW][C]149[/C][C]14.1[/C][C]14.0687[/C][C]0.0312629[/C][/ROW]
[ROW][C]150[/C][C]14.9[/C][C]13.5154[/C][C]1.38456[/C][/ROW]
[ROW][C]151[/C][C]16.25[/C][C]14.9722[/C][C]1.2778[/C][/ROW]
[ROW][C]152[/C][C]19.25[/C][C]15.8847[/C][C]3.36525[/C][/ROW]
[ROW][C]153[/C][C]13.6[/C][C]13.4716[/C][C]0.128356[/C][/ROW]
[ROW][C]154[/C][C]13.6[/C][C]14.6298[/C][C]-1.02979[/C][/ROW]
[ROW][C]155[/C][C]15.65[/C][C]14.5987[/C][C]1.05131[/C][/ROW]
[ROW][C]156[/C][C]12.75[/C][C]12.6777[/C][C]0.0723306[/C][/ROW]
[ROW][C]157[/C][C]14.6[/C][C]13.7818[/C][C]0.818199[/C][/ROW]
[ROW][C]158[/C][C]9.85[/C][C]13.9699[/C][C]-4.1199[/C][/ROW]
[ROW][C]159[/C][C]12.65[/C][C]13.379[/C][C]-0.729021[/C][/ROW]
[ROW][C]160[/C][C]19.2[/C][C]15.771[/C][C]3.42905[/C][/ROW]
[ROW][C]161[/C][C]16.6[/C][C]14.6722[/C][C]1.92781[/C][/ROW]
[ROW][C]162[/C][C]11.2[/C][C]13.9708[/C][C]-2.77076[/C][/ROW]
[ROW][C]163[/C][C]15.25[/C][C]13.578[/C][C]1.67204[/C][/ROW]
[ROW][C]164[/C][C]11.9[/C][C]15.7448[/C][C]-3.84477[/C][/ROW]
[ROW][C]165[/C][C]13.2[/C][C]13.6514[/C][C]-0.451414[/C][/ROW]
[ROW][C]166[/C][C]16.35[/C][C]16.8581[/C][C]-0.508117[/C][/ROW]
[ROW][C]167[/C][C]12.4[/C][C]13.6032[/C][C]-1.20322[/C][/ROW]
[ROW][C]168[/C][C]15.85[/C][C]14.1273[/C][C]1.72267[/C][/ROW]
[ROW][C]169[/C][C]18.15[/C][C]13.9978[/C][C]4.15216[/C][/ROW]
[ROW][C]170[/C][C]11.15[/C][C]14.0142[/C][C]-2.86415[/C][/ROW]
[ROW][C]171[/C][C]15.65[/C][C]15.8385[/C][C]-0.188474[/C][/ROW]
[ROW][C]172[/C][C]17.75[/C][C]14.4325[/C][C]3.31753[/C][/ROW]
[ROW][C]173[/C][C]7.65[/C][C]13.7026[/C][C]-6.05261[/C][/ROW]
[ROW][C]174[/C][C]12.35[/C][C]13.2486[/C][C]-0.898591[/C][/ROW]
[ROW][C]175[/C][C]15.6[/C][C]12.328[/C][C]3.272[/C][/ROW]
[ROW][C]176[/C][C]19.3[/C][C]15.6099[/C][C]3.69014[/C][/ROW]
[ROW][C]177[/C][C]15.2[/C][C]13.9109[/C][C]1.28913[/C][/ROW]
[ROW][C]178[/C][C]17.1[/C][C]13.641[/C][C]3.45901[/C][/ROW]
[ROW][C]179[/C][C]15.6[/C][C]13.3649[/C][C]2.23509[/C][/ROW]
[ROW][C]180[/C][C]18.4[/C][C]16.6996[/C][C]1.70036[/C][/ROW]
[ROW][C]181[/C][C]19.05[/C][C]15.1209[/C][C]3.92907[/C][/ROW]
[ROW][C]182[/C][C]18.55[/C][C]13.1183[/C][C]5.43171[/C][/ROW]
[ROW][C]183[/C][C]19.1[/C][C]17.7667[/C][C]1.33328[/C][/ROW]
[ROW][C]184[/C][C]13.1[/C][C]13.1098[/C][C]-0.0097872[/C][/ROW]
[ROW][C]185[/C][C]12.85[/C][C]13.2894[/C][C]-0.439357[/C][/ROW]
[ROW][C]186[/C][C]9.5[/C][C]12.3212[/C][C]-2.82121[/C][/ROW]
[ROW][C]187[/C][C]4.5[/C][C]13.2928[/C][C]-8.79276[/C][/ROW]
[ROW][C]188[/C][C]11.85[/C][C]13.1671[/C][C]-1.31713[/C][/ROW]
[ROW][C]189[/C][C]13.6[/C][C]14.2039[/C][C]-0.6039[/C][/ROW]
[ROW][C]190[/C][C]11.7[/C][C]14.144[/C][C]-2.44398[/C][/ROW]
[ROW][C]191[/C][C]12.4[/C][C]12.8356[/C][C]-0.435604[/C][/ROW]
[ROW][C]192[/C][C]13.35[/C][C]15.8431[/C][C]-2.49312[/C][/ROW]
[ROW][C]193[/C][C]11.4[/C][C]14.3527[/C][C]-2.95265[/C][/ROW]
[ROW][C]194[/C][C]14.9[/C][C]13.368[/C][C]1.53202[/C][/ROW]
[ROW][C]195[/C][C]19.9[/C][C]17.1861[/C][C]2.71388[/C][/ROW]
[ROW][C]196[/C][C]11.2[/C][C]13.0775[/C][C]-1.87747[/C][/ROW]
[ROW][C]197[/C][C]14.6[/C][C]14.5181[/C][C]0.0818738[/C][/ROW]
[ROW][C]198[/C][C]17.6[/C][C]16.9888[/C][C]0.611181[/C][/ROW]
[ROW][C]199[/C][C]14.05[/C][C]13.4266[/C][C]0.623405[/C][/ROW]
[ROW][C]200[/C][C]16.1[/C][C]16.3563[/C][C]-0.256333[/C][/ROW]
[ROW][C]201[/C][C]13.35[/C][C]13.1526[/C][C]0.197445[/C][/ROW]
[ROW][C]202[/C][C]11.85[/C][C]12.8434[/C][C]-0.993439[/C][/ROW]
[ROW][C]203[/C][C]11.95[/C][C]15.7996[/C][C]-3.84957[/C][/ROW]
[ROW][C]204[/C][C]14.75[/C][C]14.6825[/C][C]0.0675354[/C][/ROW]
[ROW][C]205[/C][C]15.15[/C][C]15.8693[/C][C]-0.719349[/C][/ROW]
[ROW][C]206[/C][C]13.2[/C][C]14.4423[/C][C]-1.2423[/C][/ROW]
[ROW][C]207[/C][C]16.85[/C][C]16.1139[/C][C]0.736092[/C][/ROW]
[ROW][C]208[/C][C]7.85[/C][C]12.9331[/C][C]-5.0831[/C][/ROW]
[ROW][C]209[/C][C]7.7[/C][C]14.1178[/C][C]-6.41785[/C][/ROW]
[ROW][C]210[/C][C]12.6[/C][C]15.3236[/C][C]-2.72363[/C][/ROW]
[ROW][C]211[/C][C]7.85[/C][C]13.7262[/C][C]-5.87623[/C][/ROW]
[ROW][C]212[/C][C]10.95[/C][C]12.8069[/C][C]-1.85687[/C][/ROW]
[ROW][C]213[/C][C]12.35[/C][C]14.7213[/C][C]-2.3713[/C][/ROW]
[ROW][C]214[/C][C]9.95[/C][C]13.0084[/C][C]-3.05836[/C][/ROW]
[ROW][C]215[/C][C]14.9[/C][C]13.3556[/C][C]1.5444[/C][/ROW]
[ROW][C]216[/C][C]16.65[/C][C]14.6579[/C][C]1.99207[/C][/ROW]
[ROW][C]217[/C][C]13.4[/C][C]14.1107[/C][C]-0.710742[/C][/ROW]
[ROW][C]218[/C][C]13.95[/C][C]13.9956[/C][C]-0.0456103[/C][/ROW]
[ROW][C]219[/C][C]15.7[/C][C]13.5384[/C][C]2.16158[/C][/ROW]
[ROW][C]220[/C][C]16.85[/C][C]14.0942[/C][C]2.75577[/C][/ROW]
[ROW][C]221[/C][C]10.95[/C][C]13.4371[/C][C]-2.48705[/C][/ROW]
[ROW][C]222[/C][C]15.35[/C][C]12.9994[/C][C]2.3506[/C][/ROW]
[ROW][C]223[/C][C]12.2[/C][C]12.9861[/C][C]-0.786099[/C][/ROW]
[ROW][C]224[/C][C]15.1[/C][C]14.5421[/C][C]0.557902[/C][/ROW]
[ROW][C]225[/C][C]17.75[/C][C]16.2526[/C][C]1.49737[/C][/ROW]
[ROW][C]226[/C][C]15.2[/C][C]14.5228[/C][C]0.677204[/C][/ROW]
[ROW][C]227[/C][C]14.6[/C][C]14.8088[/C][C]-0.208789[/C][/ROW]
[ROW][C]228[/C][C]16.65[/C][C]15.2487[/C][C]1.40129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264322&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264322&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.913.1034-0.203371
212.810.96051.83954
37.411.3984-3.99842
46.710.2498-3.54979
512.610.60471.99535
614.812.32512.47494
713.312.8090.491011
811.112.1672-1.06717
98.210.6055-2.40548
1011.410.90240.497605
116.411.3683-4.96832
121213.1968-1.19681
136.39.62339-3.32339
1411.39.845051.45495
1511.911.86750.0324611
169.310.8221-1.52205
171010.895-0.895006
1813.89.529574.27043
1910.811.3328-0.532763
2011.711.35530.344667
2110.911.6669-0.766884
2216.112.79093.30913
239.911.6699-1.76993
2411.510.83260.667426
258.311.2661-2.96608
2611.711.0880.611989
27910.1386-1.13864
2810.810.53180.268181
2910.410.4694-0.0694413
3012.78.457884.24212
3111.812.0916-0.2916
321311.33361.66636
3310.810.97-0.169951
3412.39.547332.75267
3511.311.5868-0.286787
3611.69.795061.80494
3710.99.777741.12226
3812.19.157772.94223
3913.310.51842.78156
4010.110.3344-0.234387
4114.39.993634.30637
429.312.6922-3.39222
4312.59.871762.62824
447.611.0863-3.48627
459.210.9729-1.77293
4614.59.464815.03519
4712.312.4314-0.131355
4812.611.26671.33328
491312.03810.961863
5012.69.519533.08047
5113.212.32520.87483
527.711.1098-3.40977
5310.510.5697-0.0697041
5410.911.2537-0.353665
554.38.97409-4.67409
5610.310.568-0.267995
5711.49.492961.90704
585.69.17494-3.57494
598.89.91979-1.11979
6099.52763-0.527631
619.69.73462-0.134618
626.49.5628-3.1628
6311.69.863071.73693
644.3512.9759-8.62592
6512.714.8841-2.1841
6618.114.13873.96131
6717.8514.30273.54727
6816.619.3473-2.74729
6912.612.08380.516192
7017.116.43460.665353
7119.117.13351.96648
7216.120.4425-4.34254
7313.3513.15150.198455
7418.416.11582.28418
7514.712.89861.80142
7610.614.0562-3.45617
7712.613.192-0.59196
7816.214.14722.05281
7913.615.1106-1.51061
8018.915.51013.38987
8114.113.45480.645193
8214.513.38481.11523
8316.1517.2014-1.05139
8414.7513.83660.913353
8514.814.43370.3663
8612.4513.5555-1.1055
8712.6514.4118-1.76182
8817.3513.90183.44819
898.613.3601-4.76006
9018.416.79641.60361
9116.116.265-0.164952
9211.613.0641-1.46406
9317.7512.63155.11849
9415.2514.44250.807546
9517.6514.4643.18599
9616.3515.84220.507788
9717.6516.9230.726992
9813.613.8903-0.290257
9914.3515.1679-0.817927
10014.7514.7601-0.0100989
10118.2514.24534.00474
1029.914.4778-4.57783
1031616.4265-0.426452
10418.2514.48673.76331
10516.8515.34111.50891
10614.613.28911.31086
10713.8514.882-1.03203
10818.9517.05681.89323
10915.615.15550.444468
11014.8517.0986-2.24862
11111.7514.2604-2.51039
11218.4517.05811.39191
11315.913.99911.90086
11417.116.37780.722152
11516.113.49332.60669
11619.917.30992.59014
11710.9512.5846-1.63463
11818.4516.55691.89309
11915.112.31152.78846
1201514.83590.164052
12111.3515.7491-4.39915
12215.9514.59431.35566
12318.115.39362.70636
12414.615.0817-0.481726
12515.413.97951.4205
12615.413.95281.44723
12717.614.6552.94503
12813.3515.242-1.89198
12919.115.14933.95074
13015.3514.58170.768292
1317.613.8673-6.26729
13213.417.2571-3.85708
13313.914.6637-0.76368
13419.115.73923.36079
13515.2515.37-0.120031
13612.913.3365-0.436506
13716.115.88080.219161
13817.3513.8963.45397
13913.1514.7193-1.56933
14012.1512.6458-0.495751
14112.613.6285-1.02855
14210.3513.5101-3.1601
14315.414.12691.27308
1449.612.9289-3.32889
14518.214.22553.97453
14613.614.2997-0.699685
14714.8514.8567-0.00667291
14814.7516.2665-1.51653
14914.114.06870.0312629
15014.913.51541.38456
15116.2514.97221.2778
15219.2515.88473.36525
15313.613.47160.128356
15413.614.6298-1.02979
15515.6514.59871.05131
15612.7512.67770.0723306
15714.613.78180.818199
1589.8513.9699-4.1199
15912.6513.379-0.729021
16019.215.7713.42905
16116.614.67221.92781
16211.213.9708-2.77076
16315.2513.5781.67204
16411.915.7448-3.84477
16513.213.6514-0.451414
16616.3516.8581-0.508117
16712.413.6032-1.20322
16815.8514.12731.72267
16918.1513.99784.15216
17011.1514.0142-2.86415
17115.6515.8385-0.188474
17217.7514.43253.31753
1737.6513.7026-6.05261
17412.3513.2486-0.898591
17515.612.3283.272
17619.315.60993.69014
17715.213.91091.28913
17817.113.6413.45901
17915.613.36492.23509
18018.416.69961.70036
18119.0515.12093.92907
18218.5513.11835.43171
18319.117.76671.33328
18413.113.1098-0.0097872
18512.8513.2894-0.439357
1869.512.3212-2.82121
1874.513.2928-8.79276
18811.8513.1671-1.31713
18913.614.2039-0.6039
19011.714.144-2.44398
19112.412.8356-0.435604
19213.3515.8431-2.49312
19311.414.3527-2.95265
19414.913.3681.53202
19519.917.18612.71388
19611.213.0775-1.87747
19714.614.51810.0818738
19817.616.98880.611181
19914.0513.42660.623405
20016.116.3563-0.256333
20113.3513.15260.197445
20211.8512.8434-0.993439
20311.9515.7996-3.84957
20414.7514.68250.0675354
20515.1515.8693-0.719349
20613.214.4423-1.2423
20716.8516.11390.736092
2087.8512.9331-5.0831
2097.714.1178-6.41785
21012.615.3236-2.72363
2117.8513.7262-5.87623
21210.9512.8069-1.85687
21312.3514.7213-2.3713
2149.9513.0084-3.05836
21514.913.35561.5444
21616.6514.65791.99207
21713.414.1107-0.710742
21813.9513.9956-0.0456103
21915.713.53842.16158
22016.8514.09422.75577
22110.9513.4371-2.48705
22215.3512.99942.3506
22312.212.9861-0.786099
22415.114.54210.557902
22517.7516.25261.49737
22615.214.52280.677204
22714.614.8088-0.208789
22816.6515.24871.40129







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8825770.2348460.117423
120.8226830.3546340.177317
130.7777170.4445670.222283
140.6761930.6476130.323807
150.5689490.8621020.431051
160.4651710.9303420.534829
170.3725210.7450420.627479
180.6758670.6482670.324133
190.5913840.8172320.408616
200.5093370.9813260.490663
210.4267610.8535220.573239
220.3540190.7080380.645981
230.2950220.5900430.704978
240.2326740.4653490.767326
250.3525160.7050320.647484
260.3158950.6317890.684105
270.275250.55050.72475
280.2195520.4391040.780448
290.1829950.365990.817005
300.1686720.3373450.831328
310.1323610.2647220.867639
320.1129580.2259150.887042
330.09334130.1866830.906659
340.07806830.1561370.921932
350.05877120.1175420.941229
360.04769550.09539090.952305
370.03721180.07442350.962788
380.02816490.05632980.971835
390.04205610.08411230.957944
400.0308540.0617080.969146
410.06890920.1378180.931091
420.07312230.1462450.926878
430.0748820.1497640.925118
440.1037950.207590.896205
450.08894950.1778990.911051
460.1436930.2873860.856307
470.1202690.2405380.879731
480.1022050.2044110.897795
490.08745870.1749170.912541
500.07780130.1556030.922199
510.07459580.1491920.925404
520.09028940.1805790.909711
530.08586620.1717320.914134
540.0770480.1540960.922952
550.2335340.4670690.766466
560.213790.427580.78621
570.1913870.3827750.808613
580.3049760.6099510.695024
590.2888650.5777290.711135
600.2552780.5105560.744722
610.2233650.446730.776635
620.2534440.5068880.746556
630.2300780.4601550.769922
640.2995550.5991110.700445
650.345480.6909610.65452
660.6085640.7828730.391436
670.6980290.6039410.301971
680.6766710.6466580.323329
690.6421720.7156560.357828
700.6214480.7571050.378552
710.6288670.7422650.371133
720.6766080.6467840.323392
730.6387010.7225980.361299
740.6529410.6941190.347059
750.627170.7456610.37283
760.6602910.6794190.339709
770.6243030.7513940.375697
780.6070840.7858320.392916
790.5796980.8406040.420302
800.6072230.7855530.392777
810.5691420.8617160.430858
820.5350540.9298920.464946
830.501610.9967790.49839
840.463650.9272990.53635
850.4238060.8476130.576194
860.3951560.7903120.604844
870.3731240.7462490.626876
880.4000770.8001530.599923
890.5096090.9807820.490391
900.488920.9778390.51108
910.4546880.9093760.545312
920.433080.8661610.56692
930.5379260.9241490.462074
940.5004010.9991990.499599
950.5101130.9797750.489887
960.4718480.9436970.528152
970.4425110.8850220.557489
980.4062210.8124410.593779
990.3728650.7457310.627135
1000.3365130.6730250.663487
1010.3868680.7737360.613132
1020.4842370.9684740.515763
1030.4483620.8967240.551638
1040.4866730.9733460.513327
1050.4619340.9238690.538066
1060.4302590.8605180.569741
1070.402170.8043410.59783
1080.3830910.7661820.616909
1090.3469870.6939730.653013
1100.3475980.6951950.652402
1110.3466520.6933040.653348
1120.320590.641180.67941
1130.3012180.6024360.698782
1140.2714680.5429350.728532
1150.2691020.5382030.730898
1160.2657430.5314870.734257
1170.253710.507420.74629
1180.2352590.4705190.764741
1190.2418270.4836540.758173
1200.2125430.4250860.787457
1210.2844170.5688350.715583
1220.2566950.513390.743305
1230.2555750.511150.744425
1240.2289070.4578150.771093
1250.2092930.4185860.790707
1260.1918950.383790.808105
1270.1980040.3960070.801996
1280.1862120.3724250.813788
1290.2329880.4659760.767012
1300.2073030.4146060.792697
1310.3647880.7295770.635212
1320.4315360.8630720.568464
1330.3966230.7932460.603377
1340.4276350.855270.572365
1350.3905390.7810790.609461
1360.3598570.7197150.640143
1370.3243620.6487240.675638
1380.3470530.6941070.652947
1390.3293910.6587820.670609
1400.2965390.5930780.703461
1410.2691260.5382530.730874
1420.2811610.5623230.718839
1430.2544470.5088940.745553
1440.2754460.5508920.724554
1450.3095760.6191520.690424
1460.2770370.5540740.722963
1470.244070.4881390.75593
1480.224420.4488410.77558
1490.1955340.3910690.804466
1500.1763670.3527350.823633
1510.1542620.3085230.845738
1520.1677150.3354290.832285
1530.1444360.2888720.855564
1540.1260240.2520480.873976
1550.1088780.2177550.891122
1560.09307670.1861530.906923
1570.07968540.1593710.920315
1580.1014170.2028340.898583
1590.0860780.1721560.913922
1600.09221530.1844310.907785
1610.08785980.175720.91214
1620.08619460.1723890.913805
1630.07927190.1585440.920728
1640.1078150.2156290.892185
1650.08935810.1787160.910642
1660.07552880.1510580.924471
1670.06338130.1267630.936619
1680.06272650.1254530.937273
1690.09082490.181650.909175
1700.08566860.1713370.914331
1710.06949380.1389880.930506
1720.07750530.1550110.922495
1730.1886920.3773840.811308
1740.1622690.3245370.837731
1750.2375420.4750850.762458
1760.2823490.5646980.717651
1770.2585730.5171460.741427
1780.301710.6034190.69829
1790.3058640.6117290.694136
1800.2829350.565870.717065
1810.3834350.766870.616565
1820.634650.7307010.36535
1830.595360.809280.40464
1840.5477820.9044360.452218
1850.5466470.9067050.453353
1860.5116930.9766130.488307
1870.8511750.2976510.148825
1880.8184360.3631280.181564
1890.7861450.427710.213855
1900.7554730.4890550.244527
1910.7081220.5837560.291878
1920.7304030.5391930.269597
1930.6971020.6057960.302898
1940.6760590.6478820.323941
1950.64570.70860.3543
1960.5961420.8077170.403858
1970.5461660.9076690.453834
1980.4952150.9904290.504785
1990.5435940.9128120.456406
2000.477040.954080.52296
2010.4676260.9352520.532374
2020.4864780.9729570.513522
2030.4578730.9157460.542127
2040.4018080.8036160.598192
2050.3492420.6984850.650758
2060.503690.992620.49631
2070.4462150.892430.553785
2080.4535220.9070430.546478
2090.7449650.5100690.255035
2100.7710720.4578570.228928
2110.8661960.2676070.133804
2120.8378680.3242640.162132
2130.9307960.1384070.0692036
2140.9054840.1890330.0945164
2150.8508610.2982780.149139
2160.7550510.4898990.244949
2170.5965020.8069950.403498

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.882577 & 0.234846 & 0.117423 \tabularnewline
12 & 0.822683 & 0.354634 & 0.177317 \tabularnewline
13 & 0.777717 & 0.444567 & 0.222283 \tabularnewline
14 & 0.676193 & 0.647613 & 0.323807 \tabularnewline
15 & 0.568949 & 0.862102 & 0.431051 \tabularnewline
16 & 0.465171 & 0.930342 & 0.534829 \tabularnewline
17 & 0.372521 & 0.745042 & 0.627479 \tabularnewline
18 & 0.675867 & 0.648267 & 0.324133 \tabularnewline
19 & 0.591384 & 0.817232 & 0.408616 \tabularnewline
20 & 0.509337 & 0.981326 & 0.490663 \tabularnewline
21 & 0.426761 & 0.853522 & 0.573239 \tabularnewline
22 & 0.354019 & 0.708038 & 0.645981 \tabularnewline
23 & 0.295022 & 0.590043 & 0.704978 \tabularnewline
24 & 0.232674 & 0.465349 & 0.767326 \tabularnewline
25 & 0.352516 & 0.705032 & 0.647484 \tabularnewline
26 & 0.315895 & 0.631789 & 0.684105 \tabularnewline
27 & 0.27525 & 0.5505 & 0.72475 \tabularnewline
28 & 0.219552 & 0.439104 & 0.780448 \tabularnewline
29 & 0.182995 & 0.36599 & 0.817005 \tabularnewline
30 & 0.168672 & 0.337345 & 0.831328 \tabularnewline
31 & 0.132361 & 0.264722 & 0.867639 \tabularnewline
32 & 0.112958 & 0.225915 & 0.887042 \tabularnewline
33 & 0.0933413 & 0.186683 & 0.906659 \tabularnewline
34 & 0.0780683 & 0.156137 & 0.921932 \tabularnewline
35 & 0.0587712 & 0.117542 & 0.941229 \tabularnewline
36 & 0.0476955 & 0.0953909 & 0.952305 \tabularnewline
37 & 0.0372118 & 0.0744235 & 0.962788 \tabularnewline
38 & 0.0281649 & 0.0563298 & 0.971835 \tabularnewline
39 & 0.0420561 & 0.0841123 & 0.957944 \tabularnewline
40 & 0.030854 & 0.061708 & 0.969146 \tabularnewline
41 & 0.0689092 & 0.137818 & 0.931091 \tabularnewline
42 & 0.0731223 & 0.146245 & 0.926878 \tabularnewline
43 & 0.074882 & 0.149764 & 0.925118 \tabularnewline
44 & 0.103795 & 0.20759 & 0.896205 \tabularnewline
45 & 0.0889495 & 0.177899 & 0.911051 \tabularnewline
46 & 0.143693 & 0.287386 & 0.856307 \tabularnewline
47 & 0.120269 & 0.240538 & 0.879731 \tabularnewline
48 & 0.102205 & 0.204411 & 0.897795 \tabularnewline
49 & 0.0874587 & 0.174917 & 0.912541 \tabularnewline
50 & 0.0778013 & 0.155603 & 0.922199 \tabularnewline
51 & 0.0745958 & 0.149192 & 0.925404 \tabularnewline
52 & 0.0902894 & 0.180579 & 0.909711 \tabularnewline
53 & 0.0858662 & 0.171732 & 0.914134 \tabularnewline
54 & 0.077048 & 0.154096 & 0.922952 \tabularnewline
55 & 0.233534 & 0.467069 & 0.766466 \tabularnewline
56 & 0.21379 & 0.42758 & 0.78621 \tabularnewline
57 & 0.191387 & 0.382775 & 0.808613 \tabularnewline
58 & 0.304976 & 0.609951 & 0.695024 \tabularnewline
59 & 0.288865 & 0.577729 & 0.711135 \tabularnewline
60 & 0.255278 & 0.510556 & 0.744722 \tabularnewline
61 & 0.223365 & 0.44673 & 0.776635 \tabularnewline
62 & 0.253444 & 0.506888 & 0.746556 \tabularnewline
63 & 0.230078 & 0.460155 & 0.769922 \tabularnewline
64 & 0.299555 & 0.599111 & 0.700445 \tabularnewline
65 & 0.34548 & 0.690961 & 0.65452 \tabularnewline
66 & 0.608564 & 0.782873 & 0.391436 \tabularnewline
67 & 0.698029 & 0.603941 & 0.301971 \tabularnewline
68 & 0.676671 & 0.646658 & 0.323329 \tabularnewline
69 & 0.642172 & 0.715656 & 0.357828 \tabularnewline
70 & 0.621448 & 0.757105 & 0.378552 \tabularnewline
71 & 0.628867 & 0.742265 & 0.371133 \tabularnewline
72 & 0.676608 & 0.646784 & 0.323392 \tabularnewline
73 & 0.638701 & 0.722598 & 0.361299 \tabularnewline
74 & 0.652941 & 0.694119 & 0.347059 \tabularnewline
75 & 0.62717 & 0.745661 & 0.37283 \tabularnewline
76 & 0.660291 & 0.679419 & 0.339709 \tabularnewline
77 & 0.624303 & 0.751394 & 0.375697 \tabularnewline
78 & 0.607084 & 0.785832 & 0.392916 \tabularnewline
79 & 0.579698 & 0.840604 & 0.420302 \tabularnewline
80 & 0.607223 & 0.785553 & 0.392777 \tabularnewline
81 & 0.569142 & 0.861716 & 0.430858 \tabularnewline
82 & 0.535054 & 0.929892 & 0.464946 \tabularnewline
83 & 0.50161 & 0.996779 & 0.49839 \tabularnewline
84 & 0.46365 & 0.927299 & 0.53635 \tabularnewline
85 & 0.423806 & 0.847613 & 0.576194 \tabularnewline
86 & 0.395156 & 0.790312 & 0.604844 \tabularnewline
87 & 0.373124 & 0.746249 & 0.626876 \tabularnewline
88 & 0.400077 & 0.800153 & 0.599923 \tabularnewline
89 & 0.509609 & 0.980782 & 0.490391 \tabularnewline
90 & 0.48892 & 0.977839 & 0.51108 \tabularnewline
91 & 0.454688 & 0.909376 & 0.545312 \tabularnewline
92 & 0.43308 & 0.866161 & 0.56692 \tabularnewline
93 & 0.537926 & 0.924149 & 0.462074 \tabularnewline
94 & 0.500401 & 0.999199 & 0.499599 \tabularnewline
95 & 0.510113 & 0.979775 & 0.489887 \tabularnewline
96 & 0.471848 & 0.943697 & 0.528152 \tabularnewline
97 & 0.442511 & 0.885022 & 0.557489 \tabularnewline
98 & 0.406221 & 0.812441 & 0.593779 \tabularnewline
99 & 0.372865 & 0.745731 & 0.627135 \tabularnewline
100 & 0.336513 & 0.673025 & 0.663487 \tabularnewline
101 & 0.386868 & 0.773736 & 0.613132 \tabularnewline
102 & 0.484237 & 0.968474 & 0.515763 \tabularnewline
103 & 0.448362 & 0.896724 & 0.551638 \tabularnewline
104 & 0.486673 & 0.973346 & 0.513327 \tabularnewline
105 & 0.461934 & 0.923869 & 0.538066 \tabularnewline
106 & 0.430259 & 0.860518 & 0.569741 \tabularnewline
107 & 0.40217 & 0.804341 & 0.59783 \tabularnewline
108 & 0.383091 & 0.766182 & 0.616909 \tabularnewline
109 & 0.346987 & 0.693973 & 0.653013 \tabularnewline
110 & 0.347598 & 0.695195 & 0.652402 \tabularnewline
111 & 0.346652 & 0.693304 & 0.653348 \tabularnewline
112 & 0.32059 & 0.64118 & 0.67941 \tabularnewline
113 & 0.301218 & 0.602436 & 0.698782 \tabularnewline
114 & 0.271468 & 0.542935 & 0.728532 \tabularnewline
115 & 0.269102 & 0.538203 & 0.730898 \tabularnewline
116 & 0.265743 & 0.531487 & 0.734257 \tabularnewline
117 & 0.25371 & 0.50742 & 0.74629 \tabularnewline
118 & 0.235259 & 0.470519 & 0.764741 \tabularnewline
119 & 0.241827 & 0.483654 & 0.758173 \tabularnewline
120 & 0.212543 & 0.425086 & 0.787457 \tabularnewline
121 & 0.284417 & 0.568835 & 0.715583 \tabularnewline
122 & 0.256695 & 0.51339 & 0.743305 \tabularnewline
123 & 0.255575 & 0.51115 & 0.744425 \tabularnewline
124 & 0.228907 & 0.457815 & 0.771093 \tabularnewline
125 & 0.209293 & 0.418586 & 0.790707 \tabularnewline
126 & 0.191895 & 0.38379 & 0.808105 \tabularnewline
127 & 0.198004 & 0.396007 & 0.801996 \tabularnewline
128 & 0.186212 & 0.372425 & 0.813788 \tabularnewline
129 & 0.232988 & 0.465976 & 0.767012 \tabularnewline
130 & 0.207303 & 0.414606 & 0.792697 \tabularnewline
131 & 0.364788 & 0.729577 & 0.635212 \tabularnewline
132 & 0.431536 & 0.863072 & 0.568464 \tabularnewline
133 & 0.396623 & 0.793246 & 0.603377 \tabularnewline
134 & 0.427635 & 0.85527 & 0.572365 \tabularnewline
135 & 0.390539 & 0.781079 & 0.609461 \tabularnewline
136 & 0.359857 & 0.719715 & 0.640143 \tabularnewline
137 & 0.324362 & 0.648724 & 0.675638 \tabularnewline
138 & 0.347053 & 0.694107 & 0.652947 \tabularnewline
139 & 0.329391 & 0.658782 & 0.670609 \tabularnewline
140 & 0.296539 & 0.593078 & 0.703461 \tabularnewline
141 & 0.269126 & 0.538253 & 0.730874 \tabularnewline
142 & 0.281161 & 0.562323 & 0.718839 \tabularnewline
143 & 0.254447 & 0.508894 & 0.745553 \tabularnewline
144 & 0.275446 & 0.550892 & 0.724554 \tabularnewline
145 & 0.309576 & 0.619152 & 0.690424 \tabularnewline
146 & 0.277037 & 0.554074 & 0.722963 \tabularnewline
147 & 0.24407 & 0.488139 & 0.75593 \tabularnewline
148 & 0.22442 & 0.448841 & 0.77558 \tabularnewline
149 & 0.195534 & 0.391069 & 0.804466 \tabularnewline
150 & 0.176367 & 0.352735 & 0.823633 \tabularnewline
151 & 0.154262 & 0.308523 & 0.845738 \tabularnewline
152 & 0.167715 & 0.335429 & 0.832285 \tabularnewline
153 & 0.144436 & 0.288872 & 0.855564 \tabularnewline
154 & 0.126024 & 0.252048 & 0.873976 \tabularnewline
155 & 0.108878 & 0.217755 & 0.891122 \tabularnewline
156 & 0.0930767 & 0.186153 & 0.906923 \tabularnewline
157 & 0.0796854 & 0.159371 & 0.920315 \tabularnewline
158 & 0.101417 & 0.202834 & 0.898583 \tabularnewline
159 & 0.086078 & 0.172156 & 0.913922 \tabularnewline
160 & 0.0922153 & 0.184431 & 0.907785 \tabularnewline
161 & 0.0878598 & 0.17572 & 0.91214 \tabularnewline
162 & 0.0861946 & 0.172389 & 0.913805 \tabularnewline
163 & 0.0792719 & 0.158544 & 0.920728 \tabularnewline
164 & 0.107815 & 0.215629 & 0.892185 \tabularnewline
165 & 0.0893581 & 0.178716 & 0.910642 \tabularnewline
166 & 0.0755288 & 0.151058 & 0.924471 \tabularnewline
167 & 0.0633813 & 0.126763 & 0.936619 \tabularnewline
168 & 0.0627265 & 0.125453 & 0.937273 \tabularnewline
169 & 0.0908249 & 0.18165 & 0.909175 \tabularnewline
170 & 0.0856686 & 0.171337 & 0.914331 \tabularnewline
171 & 0.0694938 & 0.138988 & 0.930506 \tabularnewline
172 & 0.0775053 & 0.155011 & 0.922495 \tabularnewline
173 & 0.188692 & 0.377384 & 0.811308 \tabularnewline
174 & 0.162269 & 0.324537 & 0.837731 \tabularnewline
175 & 0.237542 & 0.475085 & 0.762458 \tabularnewline
176 & 0.282349 & 0.564698 & 0.717651 \tabularnewline
177 & 0.258573 & 0.517146 & 0.741427 \tabularnewline
178 & 0.30171 & 0.603419 & 0.69829 \tabularnewline
179 & 0.305864 & 0.611729 & 0.694136 \tabularnewline
180 & 0.282935 & 0.56587 & 0.717065 \tabularnewline
181 & 0.383435 & 0.76687 & 0.616565 \tabularnewline
182 & 0.63465 & 0.730701 & 0.36535 \tabularnewline
183 & 0.59536 & 0.80928 & 0.40464 \tabularnewline
184 & 0.547782 & 0.904436 & 0.452218 \tabularnewline
185 & 0.546647 & 0.906705 & 0.453353 \tabularnewline
186 & 0.511693 & 0.976613 & 0.488307 \tabularnewline
187 & 0.851175 & 0.297651 & 0.148825 \tabularnewline
188 & 0.818436 & 0.363128 & 0.181564 \tabularnewline
189 & 0.786145 & 0.42771 & 0.213855 \tabularnewline
190 & 0.755473 & 0.489055 & 0.244527 \tabularnewline
191 & 0.708122 & 0.583756 & 0.291878 \tabularnewline
192 & 0.730403 & 0.539193 & 0.269597 \tabularnewline
193 & 0.697102 & 0.605796 & 0.302898 \tabularnewline
194 & 0.676059 & 0.647882 & 0.323941 \tabularnewline
195 & 0.6457 & 0.7086 & 0.3543 \tabularnewline
196 & 0.596142 & 0.807717 & 0.403858 \tabularnewline
197 & 0.546166 & 0.907669 & 0.453834 \tabularnewline
198 & 0.495215 & 0.990429 & 0.504785 \tabularnewline
199 & 0.543594 & 0.912812 & 0.456406 \tabularnewline
200 & 0.47704 & 0.95408 & 0.52296 \tabularnewline
201 & 0.467626 & 0.935252 & 0.532374 \tabularnewline
202 & 0.486478 & 0.972957 & 0.513522 \tabularnewline
203 & 0.457873 & 0.915746 & 0.542127 \tabularnewline
204 & 0.401808 & 0.803616 & 0.598192 \tabularnewline
205 & 0.349242 & 0.698485 & 0.650758 \tabularnewline
206 & 0.50369 & 0.99262 & 0.49631 \tabularnewline
207 & 0.446215 & 0.89243 & 0.553785 \tabularnewline
208 & 0.453522 & 0.907043 & 0.546478 \tabularnewline
209 & 0.744965 & 0.510069 & 0.255035 \tabularnewline
210 & 0.771072 & 0.457857 & 0.228928 \tabularnewline
211 & 0.866196 & 0.267607 & 0.133804 \tabularnewline
212 & 0.837868 & 0.324264 & 0.162132 \tabularnewline
213 & 0.930796 & 0.138407 & 0.0692036 \tabularnewline
214 & 0.905484 & 0.189033 & 0.0945164 \tabularnewline
215 & 0.850861 & 0.298278 & 0.149139 \tabularnewline
216 & 0.755051 & 0.489899 & 0.244949 \tabularnewline
217 & 0.596502 & 0.806995 & 0.403498 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264322&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]11[/C][C]0.882577[/C][C]0.234846[/C][C]0.117423[/C][/ROW]
[ROW][C]12[/C][C]0.822683[/C][C]0.354634[/C][C]0.177317[/C][/ROW]
[ROW][C]13[/C][C]0.777717[/C][C]0.444567[/C][C]0.222283[/C][/ROW]
[ROW][C]14[/C][C]0.676193[/C][C]0.647613[/C][C]0.323807[/C][/ROW]
[ROW][C]15[/C][C]0.568949[/C][C]0.862102[/C][C]0.431051[/C][/ROW]
[ROW][C]16[/C][C]0.465171[/C][C]0.930342[/C][C]0.534829[/C][/ROW]
[ROW][C]17[/C][C]0.372521[/C][C]0.745042[/C][C]0.627479[/C][/ROW]
[ROW][C]18[/C][C]0.675867[/C][C]0.648267[/C][C]0.324133[/C][/ROW]
[ROW][C]19[/C][C]0.591384[/C][C]0.817232[/C][C]0.408616[/C][/ROW]
[ROW][C]20[/C][C]0.509337[/C][C]0.981326[/C][C]0.490663[/C][/ROW]
[ROW][C]21[/C][C]0.426761[/C][C]0.853522[/C][C]0.573239[/C][/ROW]
[ROW][C]22[/C][C]0.354019[/C][C]0.708038[/C][C]0.645981[/C][/ROW]
[ROW][C]23[/C][C]0.295022[/C][C]0.590043[/C][C]0.704978[/C][/ROW]
[ROW][C]24[/C][C]0.232674[/C][C]0.465349[/C][C]0.767326[/C][/ROW]
[ROW][C]25[/C][C]0.352516[/C][C]0.705032[/C][C]0.647484[/C][/ROW]
[ROW][C]26[/C][C]0.315895[/C][C]0.631789[/C][C]0.684105[/C][/ROW]
[ROW][C]27[/C][C]0.27525[/C][C]0.5505[/C][C]0.72475[/C][/ROW]
[ROW][C]28[/C][C]0.219552[/C][C]0.439104[/C][C]0.780448[/C][/ROW]
[ROW][C]29[/C][C]0.182995[/C][C]0.36599[/C][C]0.817005[/C][/ROW]
[ROW][C]30[/C][C]0.168672[/C][C]0.337345[/C][C]0.831328[/C][/ROW]
[ROW][C]31[/C][C]0.132361[/C][C]0.264722[/C][C]0.867639[/C][/ROW]
[ROW][C]32[/C][C]0.112958[/C][C]0.225915[/C][C]0.887042[/C][/ROW]
[ROW][C]33[/C][C]0.0933413[/C][C]0.186683[/C][C]0.906659[/C][/ROW]
[ROW][C]34[/C][C]0.0780683[/C][C]0.156137[/C][C]0.921932[/C][/ROW]
[ROW][C]35[/C][C]0.0587712[/C][C]0.117542[/C][C]0.941229[/C][/ROW]
[ROW][C]36[/C][C]0.0476955[/C][C]0.0953909[/C][C]0.952305[/C][/ROW]
[ROW][C]37[/C][C]0.0372118[/C][C]0.0744235[/C][C]0.962788[/C][/ROW]
[ROW][C]38[/C][C]0.0281649[/C][C]0.0563298[/C][C]0.971835[/C][/ROW]
[ROW][C]39[/C][C]0.0420561[/C][C]0.0841123[/C][C]0.957944[/C][/ROW]
[ROW][C]40[/C][C]0.030854[/C][C]0.061708[/C][C]0.969146[/C][/ROW]
[ROW][C]41[/C][C]0.0689092[/C][C]0.137818[/C][C]0.931091[/C][/ROW]
[ROW][C]42[/C][C]0.0731223[/C][C]0.146245[/C][C]0.926878[/C][/ROW]
[ROW][C]43[/C][C]0.074882[/C][C]0.149764[/C][C]0.925118[/C][/ROW]
[ROW][C]44[/C][C]0.103795[/C][C]0.20759[/C][C]0.896205[/C][/ROW]
[ROW][C]45[/C][C]0.0889495[/C][C]0.177899[/C][C]0.911051[/C][/ROW]
[ROW][C]46[/C][C]0.143693[/C][C]0.287386[/C][C]0.856307[/C][/ROW]
[ROW][C]47[/C][C]0.120269[/C][C]0.240538[/C][C]0.879731[/C][/ROW]
[ROW][C]48[/C][C]0.102205[/C][C]0.204411[/C][C]0.897795[/C][/ROW]
[ROW][C]49[/C][C]0.0874587[/C][C]0.174917[/C][C]0.912541[/C][/ROW]
[ROW][C]50[/C][C]0.0778013[/C][C]0.155603[/C][C]0.922199[/C][/ROW]
[ROW][C]51[/C][C]0.0745958[/C][C]0.149192[/C][C]0.925404[/C][/ROW]
[ROW][C]52[/C][C]0.0902894[/C][C]0.180579[/C][C]0.909711[/C][/ROW]
[ROW][C]53[/C][C]0.0858662[/C][C]0.171732[/C][C]0.914134[/C][/ROW]
[ROW][C]54[/C][C]0.077048[/C][C]0.154096[/C][C]0.922952[/C][/ROW]
[ROW][C]55[/C][C]0.233534[/C][C]0.467069[/C][C]0.766466[/C][/ROW]
[ROW][C]56[/C][C]0.21379[/C][C]0.42758[/C][C]0.78621[/C][/ROW]
[ROW][C]57[/C][C]0.191387[/C][C]0.382775[/C][C]0.808613[/C][/ROW]
[ROW][C]58[/C][C]0.304976[/C][C]0.609951[/C][C]0.695024[/C][/ROW]
[ROW][C]59[/C][C]0.288865[/C][C]0.577729[/C][C]0.711135[/C][/ROW]
[ROW][C]60[/C][C]0.255278[/C][C]0.510556[/C][C]0.744722[/C][/ROW]
[ROW][C]61[/C][C]0.223365[/C][C]0.44673[/C][C]0.776635[/C][/ROW]
[ROW][C]62[/C][C]0.253444[/C][C]0.506888[/C][C]0.746556[/C][/ROW]
[ROW][C]63[/C][C]0.230078[/C][C]0.460155[/C][C]0.769922[/C][/ROW]
[ROW][C]64[/C][C]0.299555[/C][C]0.599111[/C][C]0.700445[/C][/ROW]
[ROW][C]65[/C][C]0.34548[/C][C]0.690961[/C][C]0.65452[/C][/ROW]
[ROW][C]66[/C][C]0.608564[/C][C]0.782873[/C][C]0.391436[/C][/ROW]
[ROW][C]67[/C][C]0.698029[/C][C]0.603941[/C][C]0.301971[/C][/ROW]
[ROW][C]68[/C][C]0.676671[/C][C]0.646658[/C][C]0.323329[/C][/ROW]
[ROW][C]69[/C][C]0.642172[/C][C]0.715656[/C][C]0.357828[/C][/ROW]
[ROW][C]70[/C][C]0.621448[/C][C]0.757105[/C][C]0.378552[/C][/ROW]
[ROW][C]71[/C][C]0.628867[/C][C]0.742265[/C][C]0.371133[/C][/ROW]
[ROW][C]72[/C][C]0.676608[/C][C]0.646784[/C][C]0.323392[/C][/ROW]
[ROW][C]73[/C][C]0.638701[/C][C]0.722598[/C][C]0.361299[/C][/ROW]
[ROW][C]74[/C][C]0.652941[/C][C]0.694119[/C][C]0.347059[/C][/ROW]
[ROW][C]75[/C][C]0.62717[/C][C]0.745661[/C][C]0.37283[/C][/ROW]
[ROW][C]76[/C][C]0.660291[/C][C]0.679419[/C][C]0.339709[/C][/ROW]
[ROW][C]77[/C][C]0.624303[/C][C]0.751394[/C][C]0.375697[/C][/ROW]
[ROW][C]78[/C][C]0.607084[/C][C]0.785832[/C][C]0.392916[/C][/ROW]
[ROW][C]79[/C][C]0.579698[/C][C]0.840604[/C][C]0.420302[/C][/ROW]
[ROW][C]80[/C][C]0.607223[/C][C]0.785553[/C][C]0.392777[/C][/ROW]
[ROW][C]81[/C][C]0.569142[/C][C]0.861716[/C][C]0.430858[/C][/ROW]
[ROW][C]82[/C][C]0.535054[/C][C]0.929892[/C][C]0.464946[/C][/ROW]
[ROW][C]83[/C][C]0.50161[/C][C]0.996779[/C][C]0.49839[/C][/ROW]
[ROW][C]84[/C][C]0.46365[/C][C]0.927299[/C][C]0.53635[/C][/ROW]
[ROW][C]85[/C][C]0.423806[/C][C]0.847613[/C][C]0.576194[/C][/ROW]
[ROW][C]86[/C][C]0.395156[/C][C]0.790312[/C][C]0.604844[/C][/ROW]
[ROW][C]87[/C][C]0.373124[/C][C]0.746249[/C][C]0.626876[/C][/ROW]
[ROW][C]88[/C][C]0.400077[/C][C]0.800153[/C][C]0.599923[/C][/ROW]
[ROW][C]89[/C][C]0.509609[/C][C]0.980782[/C][C]0.490391[/C][/ROW]
[ROW][C]90[/C][C]0.48892[/C][C]0.977839[/C][C]0.51108[/C][/ROW]
[ROW][C]91[/C][C]0.454688[/C][C]0.909376[/C][C]0.545312[/C][/ROW]
[ROW][C]92[/C][C]0.43308[/C][C]0.866161[/C][C]0.56692[/C][/ROW]
[ROW][C]93[/C][C]0.537926[/C][C]0.924149[/C][C]0.462074[/C][/ROW]
[ROW][C]94[/C][C]0.500401[/C][C]0.999199[/C][C]0.499599[/C][/ROW]
[ROW][C]95[/C][C]0.510113[/C][C]0.979775[/C][C]0.489887[/C][/ROW]
[ROW][C]96[/C][C]0.471848[/C][C]0.943697[/C][C]0.528152[/C][/ROW]
[ROW][C]97[/C][C]0.442511[/C][C]0.885022[/C][C]0.557489[/C][/ROW]
[ROW][C]98[/C][C]0.406221[/C][C]0.812441[/C][C]0.593779[/C][/ROW]
[ROW][C]99[/C][C]0.372865[/C][C]0.745731[/C][C]0.627135[/C][/ROW]
[ROW][C]100[/C][C]0.336513[/C][C]0.673025[/C][C]0.663487[/C][/ROW]
[ROW][C]101[/C][C]0.386868[/C][C]0.773736[/C][C]0.613132[/C][/ROW]
[ROW][C]102[/C][C]0.484237[/C][C]0.968474[/C][C]0.515763[/C][/ROW]
[ROW][C]103[/C][C]0.448362[/C][C]0.896724[/C][C]0.551638[/C][/ROW]
[ROW][C]104[/C][C]0.486673[/C][C]0.973346[/C][C]0.513327[/C][/ROW]
[ROW][C]105[/C][C]0.461934[/C][C]0.923869[/C][C]0.538066[/C][/ROW]
[ROW][C]106[/C][C]0.430259[/C][C]0.860518[/C][C]0.569741[/C][/ROW]
[ROW][C]107[/C][C]0.40217[/C][C]0.804341[/C][C]0.59783[/C][/ROW]
[ROW][C]108[/C][C]0.383091[/C][C]0.766182[/C][C]0.616909[/C][/ROW]
[ROW][C]109[/C][C]0.346987[/C][C]0.693973[/C][C]0.653013[/C][/ROW]
[ROW][C]110[/C][C]0.347598[/C][C]0.695195[/C][C]0.652402[/C][/ROW]
[ROW][C]111[/C][C]0.346652[/C][C]0.693304[/C][C]0.653348[/C][/ROW]
[ROW][C]112[/C][C]0.32059[/C][C]0.64118[/C][C]0.67941[/C][/ROW]
[ROW][C]113[/C][C]0.301218[/C][C]0.602436[/C][C]0.698782[/C][/ROW]
[ROW][C]114[/C][C]0.271468[/C][C]0.542935[/C][C]0.728532[/C][/ROW]
[ROW][C]115[/C][C]0.269102[/C][C]0.538203[/C][C]0.730898[/C][/ROW]
[ROW][C]116[/C][C]0.265743[/C][C]0.531487[/C][C]0.734257[/C][/ROW]
[ROW][C]117[/C][C]0.25371[/C][C]0.50742[/C][C]0.74629[/C][/ROW]
[ROW][C]118[/C][C]0.235259[/C][C]0.470519[/C][C]0.764741[/C][/ROW]
[ROW][C]119[/C][C]0.241827[/C][C]0.483654[/C][C]0.758173[/C][/ROW]
[ROW][C]120[/C][C]0.212543[/C][C]0.425086[/C][C]0.787457[/C][/ROW]
[ROW][C]121[/C][C]0.284417[/C][C]0.568835[/C][C]0.715583[/C][/ROW]
[ROW][C]122[/C][C]0.256695[/C][C]0.51339[/C][C]0.743305[/C][/ROW]
[ROW][C]123[/C][C]0.255575[/C][C]0.51115[/C][C]0.744425[/C][/ROW]
[ROW][C]124[/C][C]0.228907[/C][C]0.457815[/C][C]0.771093[/C][/ROW]
[ROW][C]125[/C][C]0.209293[/C][C]0.418586[/C][C]0.790707[/C][/ROW]
[ROW][C]126[/C][C]0.191895[/C][C]0.38379[/C][C]0.808105[/C][/ROW]
[ROW][C]127[/C][C]0.198004[/C][C]0.396007[/C][C]0.801996[/C][/ROW]
[ROW][C]128[/C][C]0.186212[/C][C]0.372425[/C][C]0.813788[/C][/ROW]
[ROW][C]129[/C][C]0.232988[/C][C]0.465976[/C][C]0.767012[/C][/ROW]
[ROW][C]130[/C][C]0.207303[/C][C]0.414606[/C][C]0.792697[/C][/ROW]
[ROW][C]131[/C][C]0.364788[/C][C]0.729577[/C][C]0.635212[/C][/ROW]
[ROW][C]132[/C][C]0.431536[/C][C]0.863072[/C][C]0.568464[/C][/ROW]
[ROW][C]133[/C][C]0.396623[/C][C]0.793246[/C][C]0.603377[/C][/ROW]
[ROW][C]134[/C][C]0.427635[/C][C]0.85527[/C][C]0.572365[/C][/ROW]
[ROW][C]135[/C][C]0.390539[/C][C]0.781079[/C][C]0.609461[/C][/ROW]
[ROW][C]136[/C][C]0.359857[/C][C]0.719715[/C][C]0.640143[/C][/ROW]
[ROW][C]137[/C][C]0.324362[/C][C]0.648724[/C][C]0.675638[/C][/ROW]
[ROW][C]138[/C][C]0.347053[/C][C]0.694107[/C][C]0.652947[/C][/ROW]
[ROW][C]139[/C][C]0.329391[/C][C]0.658782[/C][C]0.670609[/C][/ROW]
[ROW][C]140[/C][C]0.296539[/C][C]0.593078[/C][C]0.703461[/C][/ROW]
[ROW][C]141[/C][C]0.269126[/C][C]0.538253[/C][C]0.730874[/C][/ROW]
[ROW][C]142[/C][C]0.281161[/C][C]0.562323[/C][C]0.718839[/C][/ROW]
[ROW][C]143[/C][C]0.254447[/C][C]0.508894[/C][C]0.745553[/C][/ROW]
[ROW][C]144[/C][C]0.275446[/C][C]0.550892[/C][C]0.724554[/C][/ROW]
[ROW][C]145[/C][C]0.309576[/C][C]0.619152[/C][C]0.690424[/C][/ROW]
[ROW][C]146[/C][C]0.277037[/C][C]0.554074[/C][C]0.722963[/C][/ROW]
[ROW][C]147[/C][C]0.24407[/C][C]0.488139[/C][C]0.75593[/C][/ROW]
[ROW][C]148[/C][C]0.22442[/C][C]0.448841[/C][C]0.77558[/C][/ROW]
[ROW][C]149[/C][C]0.195534[/C][C]0.391069[/C][C]0.804466[/C][/ROW]
[ROW][C]150[/C][C]0.176367[/C][C]0.352735[/C][C]0.823633[/C][/ROW]
[ROW][C]151[/C][C]0.154262[/C][C]0.308523[/C][C]0.845738[/C][/ROW]
[ROW][C]152[/C][C]0.167715[/C][C]0.335429[/C][C]0.832285[/C][/ROW]
[ROW][C]153[/C][C]0.144436[/C][C]0.288872[/C][C]0.855564[/C][/ROW]
[ROW][C]154[/C][C]0.126024[/C][C]0.252048[/C][C]0.873976[/C][/ROW]
[ROW][C]155[/C][C]0.108878[/C][C]0.217755[/C][C]0.891122[/C][/ROW]
[ROW][C]156[/C][C]0.0930767[/C][C]0.186153[/C][C]0.906923[/C][/ROW]
[ROW][C]157[/C][C]0.0796854[/C][C]0.159371[/C][C]0.920315[/C][/ROW]
[ROW][C]158[/C][C]0.101417[/C][C]0.202834[/C][C]0.898583[/C][/ROW]
[ROW][C]159[/C][C]0.086078[/C][C]0.172156[/C][C]0.913922[/C][/ROW]
[ROW][C]160[/C][C]0.0922153[/C][C]0.184431[/C][C]0.907785[/C][/ROW]
[ROW][C]161[/C][C]0.0878598[/C][C]0.17572[/C][C]0.91214[/C][/ROW]
[ROW][C]162[/C][C]0.0861946[/C][C]0.172389[/C][C]0.913805[/C][/ROW]
[ROW][C]163[/C][C]0.0792719[/C][C]0.158544[/C][C]0.920728[/C][/ROW]
[ROW][C]164[/C][C]0.107815[/C][C]0.215629[/C][C]0.892185[/C][/ROW]
[ROW][C]165[/C][C]0.0893581[/C][C]0.178716[/C][C]0.910642[/C][/ROW]
[ROW][C]166[/C][C]0.0755288[/C][C]0.151058[/C][C]0.924471[/C][/ROW]
[ROW][C]167[/C][C]0.0633813[/C][C]0.126763[/C][C]0.936619[/C][/ROW]
[ROW][C]168[/C][C]0.0627265[/C][C]0.125453[/C][C]0.937273[/C][/ROW]
[ROW][C]169[/C][C]0.0908249[/C][C]0.18165[/C][C]0.909175[/C][/ROW]
[ROW][C]170[/C][C]0.0856686[/C][C]0.171337[/C][C]0.914331[/C][/ROW]
[ROW][C]171[/C][C]0.0694938[/C][C]0.138988[/C][C]0.930506[/C][/ROW]
[ROW][C]172[/C][C]0.0775053[/C][C]0.155011[/C][C]0.922495[/C][/ROW]
[ROW][C]173[/C][C]0.188692[/C][C]0.377384[/C][C]0.811308[/C][/ROW]
[ROW][C]174[/C][C]0.162269[/C][C]0.324537[/C][C]0.837731[/C][/ROW]
[ROW][C]175[/C][C]0.237542[/C][C]0.475085[/C][C]0.762458[/C][/ROW]
[ROW][C]176[/C][C]0.282349[/C][C]0.564698[/C][C]0.717651[/C][/ROW]
[ROW][C]177[/C][C]0.258573[/C][C]0.517146[/C][C]0.741427[/C][/ROW]
[ROW][C]178[/C][C]0.30171[/C][C]0.603419[/C][C]0.69829[/C][/ROW]
[ROW][C]179[/C][C]0.305864[/C][C]0.611729[/C][C]0.694136[/C][/ROW]
[ROW][C]180[/C][C]0.282935[/C][C]0.56587[/C][C]0.717065[/C][/ROW]
[ROW][C]181[/C][C]0.383435[/C][C]0.76687[/C][C]0.616565[/C][/ROW]
[ROW][C]182[/C][C]0.63465[/C][C]0.730701[/C][C]0.36535[/C][/ROW]
[ROW][C]183[/C][C]0.59536[/C][C]0.80928[/C][C]0.40464[/C][/ROW]
[ROW][C]184[/C][C]0.547782[/C][C]0.904436[/C][C]0.452218[/C][/ROW]
[ROW][C]185[/C][C]0.546647[/C][C]0.906705[/C][C]0.453353[/C][/ROW]
[ROW][C]186[/C][C]0.511693[/C][C]0.976613[/C][C]0.488307[/C][/ROW]
[ROW][C]187[/C][C]0.851175[/C][C]0.297651[/C][C]0.148825[/C][/ROW]
[ROW][C]188[/C][C]0.818436[/C][C]0.363128[/C][C]0.181564[/C][/ROW]
[ROW][C]189[/C][C]0.786145[/C][C]0.42771[/C][C]0.213855[/C][/ROW]
[ROW][C]190[/C][C]0.755473[/C][C]0.489055[/C][C]0.244527[/C][/ROW]
[ROW][C]191[/C][C]0.708122[/C][C]0.583756[/C][C]0.291878[/C][/ROW]
[ROW][C]192[/C][C]0.730403[/C][C]0.539193[/C][C]0.269597[/C][/ROW]
[ROW][C]193[/C][C]0.697102[/C][C]0.605796[/C][C]0.302898[/C][/ROW]
[ROW][C]194[/C][C]0.676059[/C][C]0.647882[/C][C]0.323941[/C][/ROW]
[ROW][C]195[/C][C]0.6457[/C][C]0.7086[/C][C]0.3543[/C][/ROW]
[ROW][C]196[/C][C]0.596142[/C][C]0.807717[/C][C]0.403858[/C][/ROW]
[ROW][C]197[/C][C]0.546166[/C][C]0.907669[/C][C]0.453834[/C][/ROW]
[ROW][C]198[/C][C]0.495215[/C][C]0.990429[/C][C]0.504785[/C][/ROW]
[ROW][C]199[/C][C]0.543594[/C][C]0.912812[/C][C]0.456406[/C][/ROW]
[ROW][C]200[/C][C]0.47704[/C][C]0.95408[/C][C]0.52296[/C][/ROW]
[ROW][C]201[/C][C]0.467626[/C][C]0.935252[/C][C]0.532374[/C][/ROW]
[ROW][C]202[/C][C]0.486478[/C][C]0.972957[/C][C]0.513522[/C][/ROW]
[ROW][C]203[/C][C]0.457873[/C][C]0.915746[/C][C]0.542127[/C][/ROW]
[ROW][C]204[/C][C]0.401808[/C][C]0.803616[/C][C]0.598192[/C][/ROW]
[ROW][C]205[/C][C]0.349242[/C][C]0.698485[/C][C]0.650758[/C][/ROW]
[ROW][C]206[/C][C]0.50369[/C][C]0.99262[/C][C]0.49631[/C][/ROW]
[ROW][C]207[/C][C]0.446215[/C][C]0.89243[/C][C]0.553785[/C][/ROW]
[ROW][C]208[/C][C]0.453522[/C][C]0.907043[/C][C]0.546478[/C][/ROW]
[ROW][C]209[/C][C]0.744965[/C][C]0.510069[/C][C]0.255035[/C][/ROW]
[ROW][C]210[/C][C]0.771072[/C][C]0.457857[/C][C]0.228928[/C][/ROW]
[ROW][C]211[/C][C]0.866196[/C][C]0.267607[/C][C]0.133804[/C][/ROW]
[ROW][C]212[/C][C]0.837868[/C][C]0.324264[/C][C]0.162132[/C][/ROW]
[ROW][C]213[/C][C]0.930796[/C][C]0.138407[/C][C]0.0692036[/C][/ROW]
[ROW][C]214[/C][C]0.905484[/C][C]0.189033[/C][C]0.0945164[/C][/ROW]
[ROW][C]215[/C][C]0.850861[/C][C]0.298278[/C][C]0.149139[/C][/ROW]
[ROW][C]216[/C][C]0.755051[/C][C]0.489899[/C][C]0.244949[/C][/ROW]
[ROW][C]217[/C][C]0.596502[/C][C]0.806995[/C][C]0.403498[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264322&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264322&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8825770.2348460.117423
120.8226830.3546340.177317
130.7777170.4445670.222283
140.6761930.6476130.323807
150.5689490.8621020.431051
160.4651710.9303420.534829
170.3725210.7450420.627479
180.6758670.6482670.324133
190.5913840.8172320.408616
200.5093370.9813260.490663
210.4267610.8535220.573239
220.3540190.7080380.645981
230.2950220.5900430.704978
240.2326740.4653490.767326
250.3525160.7050320.647484
260.3158950.6317890.684105
270.275250.55050.72475
280.2195520.4391040.780448
290.1829950.365990.817005
300.1686720.3373450.831328
310.1323610.2647220.867639
320.1129580.2259150.887042
330.09334130.1866830.906659
340.07806830.1561370.921932
350.05877120.1175420.941229
360.04769550.09539090.952305
370.03721180.07442350.962788
380.02816490.05632980.971835
390.04205610.08411230.957944
400.0308540.0617080.969146
410.06890920.1378180.931091
420.07312230.1462450.926878
430.0748820.1497640.925118
440.1037950.207590.896205
450.08894950.1778990.911051
460.1436930.2873860.856307
470.1202690.2405380.879731
480.1022050.2044110.897795
490.08745870.1749170.912541
500.07780130.1556030.922199
510.07459580.1491920.925404
520.09028940.1805790.909711
530.08586620.1717320.914134
540.0770480.1540960.922952
550.2335340.4670690.766466
560.213790.427580.78621
570.1913870.3827750.808613
580.3049760.6099510.695024
590.2888650.5777290.711135
600.2552780.5105560.744722
610.2233650.446730.776635
620.2534440.5068880.746556
630.2300780.4601550.769922
640.2995550.5991110.700445
650.345480.6909610.65452
660.6085640.7828730.391436
670.6980290.6039410.301971
680.6766710.6466580.323329
690.6421720.7156560.357828
700.6214480.7571050.378552
710.6288670.7422650.371133
720.6766080.6467840.323392
730.6387010.7225980.361299
740.6529410.6941190.347059
750.627170.7456610.37283
760.6602910.6794190.339709
770.6243030.7513940.375697
780.6070840.7858320.392916
790.5796980.8406040.420302
800.6072230.7855530.392777
810.5691420.8617160.430858
820.5350540.9298920.464946
830.501610.9967790.49839
840.463650.9272990.53635
850.4238060.8476130.576194
860.3951560.7903120.604844
870.3731240.7462490.626876
880.4000770.8001530.599923
890.5096090.9807820.490391
900.488920.9778390.51108
910.4546880.9093760.545312
920.433080.8661610.56692
930.5379260.9241490.462074
940.5004010.9991990.499599
950.5101130.9797750.489887
960.4718480.9436970.528152
970.4425110.8850220.557489
980.4062210.8124410.593779
990.3728650.7457310.627135
1000.3365130.6730250.663487
1010.3868680.7737360.613132
1020.4842370.9684740.515763
1030.4483620.8967240.551638
1040.4866730.9733460.513327
1050.4619340.9238690.538066
1060.4302590.8605180.569741
1070.402170.8043410.59783
1080.3830910.7661820.616909
1090.3469870.6939730.653013
1100.3475980.6951950.652402
1110.3466520.6933040.653348
1120.320590.641180.67941
1130.3012180.6024360.698782
1140.2714680.5429350.728532
1150.2691020.5382030.730898
1160.2657430.5314870.734257
1170.253710.507420.74629
1180.2352590.4705190.764741
1190.2418270.4836540.758173
1200.2125430.4250860.787457
1210.2844170.5688350.715583
1220.2566950.513390.743305
1230.2555750.511150.744425
1240.2289070.4578150.771093
1250.2092930.4185860.790707
1260.1918950.383790.808105
1270.1980040.3960070.801996
1280.1862120.3724250.813788
1290.2329880.4659760.767012
1300.2073030.4146060.792697
1310.3647880.7295770.635212
1320.4315360.8630720.568464
1330.3966230.7932460.603377
1340.4276350.855270.572365
1350.3905390.7810790.609461
1360.3598570.7197150.640143
1370.3243620.6487240.675638
1380.3470530.6941070.652947
1390.3293910.6587820.670609
1400.2965390.5930780.703461
1410.2691260.5382530.730874
1420.2811610.5623230.718839
1430.2544470.5088940.745553
1440.2754460.5508920.724554
1450.3095760.6191520.690424
1460.2770370.5540740.722963
1470.244070.4881390.75593
1480.224420.4488410.77558
1490.1955340.3910690.804466
1500.1763670.3527350.823633
1510.1542620.3085230.845738
1520.1677150.3354290.832285
1530.1444360.2888720.855564
1540.1260240.2520480.873976
1550.1088780.2177550.891122
1560.09307670.1861530.906923
1570.07968540.1593710.920315
1580.1014170.2028340.898583
1590.0860780.1721560.913922
1600.09221530.1844310.907785
1610.08785980.175720.91214
1620.08619460.1723890.913805
1630.07927190.1585440.920728
1640.1078150.2156290.892185
1650.08935810.1787160.910642
1660.07552880.1510580.924471
1670.06338130.1267630.936619
1680.06272650.1254530.937273
1690.09082490.181650.909175
1700.08566860.1713370.914331
1710.06949380.1389880.930506
1720.07750530.1550110.922495
1730.1886920.3773840.811308
1740.1622690.3245370.837731
1750.2375420.4750850.762458
1760.2823490.5646980.717651
1770.2585730.5171460.741427
1780.301710.6034190.69829
1790.3058640.6117290.694136
1800.2829350.565870.717065
1810.3834350.766870.616565
1820.634650.7307010.36535
1830.595360.809280.40464
1840.5477820.9044360.452218
1850.5466470.9067050.453353
1860.5116930.9766130.488307
1870.8511750.2976510.148825
1880.8184360.3631280.181564
1890.7861450.427710.213855
1900.7554730.4890550.244527
1910.7081220.5837560.291878
1920.7304030.5391930.269597
1930.6971020.6057960.302898
1940.6760590.6478820.323941
1950.64570.70860.3543
1960.5961420.8077170.403858
1970.5461660.9076690.453834
1980.4952150.9904290.504785
1990.5435940.9128120.456406
2000.477040.954080.52296
2010.4676260.9352520.532374
2020.4864780.9729570.513522
2030.4578730.9157460.542127
2040.4018080.8036160.598192
2050.3492420.6984850.650758
2060.503690.992620.49631
2070.4462150.892430.553785
2080.4535220.9070430.546478
2090.7449650.5100690.255035
2100.7710720.4578570.228928
2110.8661960.2676070.133804
2120.8378680.3242640.162132
2130.9307960.1384070.0692036
2140.9054840.1890330.0945164
2150.8508610.2982780.149139
2160.7550510.4898990.244949
2170.5965020.8069950.403498







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 level50.0241546OK

\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 & 5 & 0.0241546 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264322&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]5[/C][C]0.0241546[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264322&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264322&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level50.0241546OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}