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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 15:12:41 +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/18/t1418915606ndta3tzbb5mitgx.htm/, Retrieved Fri, 17 May 2024 08:09:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271055, Retrieved Fri, 17 May 2024 08:09:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-18 14:22:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:12:41] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 7 12 21 12 149 18 68 1.8
12.2 18 20 20 22 8 139 31 39 2.1
12.8 12 9 14 21 11 148 39 32 2.2
7.4 24 19 25 21 13 158 46 62 2.3
6.7 16 12 15 21 11 128 31 33 2.1
12.6 19 16 20 21 10 224 67 52 2.7
14.8 16 17 21 21 7 159 35 62 2.1
13.3 15 9 15 23 10 105 52 77 2.4
11.1 28 28 28 22 15 159 77 76 2.9
8.2 21 20 11 25 12 167 37 41 2.2
11.4 18 16 22 21 12 165 32 48 2.1
6.4 22 22 22 23 10 159 36 63 2.2
10.6 19 17 27 22 10 119 38 30 2.2
12 22 12 24 21 14 176 69 78 2.7
6.3 25 18 23 21 6 54 21 19 1.9
11.9 16 12 21 21 14 163 54 66 2.5
9.3 19 16 20 21 11 124 36 35 2.2
10 26 21 25 24 12 121 23 45 1.9
6.4 24 15 16 23 15 153 34 21 2.1
13.8 20 17 24 21 13 148 112 25 3.5
10.8 19 17 21 24 11 221 35 44 2.1
13.8 19 17 22 23 12 188 47 69 2.3
11.7 23 18 25 21 7 149 47 54 2.3
10.9 18 15 23 22 11 244 37 74 2.2
9.9 21 21 22 21 12 150 20 61 1.9
11.5 20 12 25 22 13 153 22 41 1.9
8.3 15 6 23 22 9 94 23 46 1.9
11.7 19 13 19 21 11 156 32 39 2.1
9 19 19 21 21 12 132 30 34 2
9.7 7 12 19 25 15 161 92 51 3.2
10.8 20 14 25 22 12 105 43 42 2.3
10.3 20 13 16 22 6 97 55 31 2.5
10.4 19 12 24 20 5 151 16 39 1.8
9.3 20 19 18 21 11 166 71 49 2.8
11.8 18 10 28 21 6 157 43 53 2.3
5.9 14 10 15 22 12 111 29 31 2
11.4 17 11 17 21 10 145 56 39 2.5
13 17 11 18 24 6 162 46 54 2.3
10.8 8 10 26 22 12 163 19 49 1.8
11.3 22 22 22 21 6 187 59 46 2.6
11.8 20 12 19 22 12 109 30 55 2
12.7 22 20 26 22 8 105 7 50 1.6
10.9 14 11 12 23 12 148 19 30 1.8
13.3 21 17 20 23 14 125 48 45 2.4
10.1 20 14 24 21 12 116 23 35 1.9
14.3 18 16 22 21 14 138 33 41 2.1
9.3 24 15 23 22 11 164 34 73 2.1
12.5 19 15 19 21 10 162 48 17 2.4
7.6 16 10 24 21 7 99 18 40 1.8
15.9 16 10 21 21 12 202 43 64 2.3
9.2 16 18 16 21 7 186 33 37 2.1
11.1 22 22 23 21 12 183 71 65 2.8
13 21 16 20 22 10 214 26 100 2
14.5 15 10 19 22 10 188 67 28 2.7
12.3 15 16 18 21 12 177 80 56 2.9
11.4 14 16 21 23 12 126 29 29 2
13 16 10 17 21 10 162 43 59 2.3
13.2 26 16 24 20 11 159 29 61 2
7.7 18 16 22 21 12 110 32 51 2.1
4.35 17 15 14 22 9 48 23 12 1
12.7 6 4 5 22 11 50 16 45 1
18.1 22 9 25 22 12 150 33 37 4
17.85 20 18 21 20 12 154 32 37 4
17.1 17 12 9 22 12 194 52 68 4
19.1 20 16 15 21 12 158 75 72 4
16.1 23 17 23 21 10 159 72 143 4
13.35 18 14 21 21 15 67 15 9 2
18.4 13 13 9 21 10 147 29 55 4
14.7 22 20 24 21 15 39 13 17 1
10.6 20 16 16 21 10 100 40 37 3
12.6 20 15 20 21 15 111 19 27 3
13.6 16 16 18 24 15 101 121 58 3
14.1 16 15 21 22 13 101 36 21 3
14.5 15 16 21 20 12 114 23 19 3
16.15 19 19 21 21 12 165 85 78 4
14.75 19 9 20 24 8 114 41 35 3
14.8 24 19 24 25 9 111 46 48 3
12.45 9 7 15 22 15 75 18 27 2
12.65 22 23 24 21 12 82 35 43 2
17.35 15 14 18 21 12 121 17 30 3
8.6 22 10 24 22 15 32 4 25 1
18.4 22 16 24 23 11 150 28 69 4
16.1 24 12 15 24 12 117 44 72 3
17.75 21 7 20 22 14 165 38 13 4
15.25 25 20 26 25 12 154 57 61 4
17.65 26 9 26 22 12 126 23 43 4
16.35 21 12 23 21 12 149 36 51 4
17.65 14 10 13 21 11 145 22 67 4
13.6 28 19 16 21 12 120 40 36 3
14.35 21 11 22 22 12 109 31 44 3
14.75 16 15 21 22 12 132 11 45 4
18.25 16 14 11 21 12 172 38 34 4
9.9 25 11 23 22 8 169 24 36 4
16 21 14 18 23 8 114 37 72 3
18.25 22 15 19 21 12 156 37 39 4
16.85 9 7 15 21 12 172 22 43 4
18.95 24 22 21 21 11 167 43 80 4
15.6 22 11 25 21 12 113 31 40 3
17.1 10 12 12 22 10 173 31 61 4
15.4 21 13 19 21 11 165 21 29 4
15.4 20 15 21 21 11 165 21 29 4
13.35 17 11 19 25 13 118 32 54 3
19.1 7 7 18 21 7 158 26 43 4
7.6 14 13 23 25 8 49 32 20 1
19.1 23 7 23 22 11 155 33 61 4
14.75 18 11 27 21 8 151 30 57 4
19.25 17 22 6 23 14 220 67 54 4
13.6 20 15 22 20 9 141 22 36 4
12.75 19 15 23 22 13 122 33 16 4
9.85 19 11 20 25 13 44 24 40 1
15.25 23 10 23 20 11 152 28 27 4
11.9 20 18 27 21 9 107 41 61 3
16.35 19 14 24 21 12 154 31 69 4
12.4 16 16 12 23 12 103 33 34 3
18.15 21 16 24 22 13 175 21 34 4
17.75 20 17 24 21 11 143 52 34 4
12.35 20 14 19 21 11 110 29 13 3
15.6 19 10 28 21 9 131 11 12 4
19.3 19 16 23 21 12 167 26 51 4
17.1 20 16 19 21 15 137 7 19 4
18.4 22 17 23 21 14 121 13 81 3
19.05 19 12 20 21 12 149 20 42 4
18.55 23 17 18 22 9 168 52 22 4
19.1 16 11 20 21 9 140 28 85 4
12.85 18 12 21 22 13 168 39 25 4
9.5 23 8 25 22 15 94 9 22 2
4.5 20 17 18 22 11 51 19 19 1
13.6 23 17 28 22 10 145 60 45 4
11.7 13 7 9 23 11 66 19 45 2
13.35 26 18 26 22 14 109 14 51 3
17.6 13 14 12 21 12 164 -2 73 4
14.05 10 13 12 21 13 119 51 24 3
16.1 21 19 20 20 11 126 2 61 4
13.35 24 15 25 20 11 132 24 23 4
11.85 21 15 24 21 13 142 40 14 4
11.95 23 8 23 21 12 83 20 54 2
13.2 16 11 22 21 9 166 20 36 4
7.7 26 17 28 24 13 93 25 26 2
14.6 16 12 15 22 12 117 38 30 3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271055&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271055&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271055&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.53642 -0.050978AMS.I2[t] -0.0448663AMS.I3[t] -0.0502973AMS.E2[t] -0.131376age[t] + 0.138739CONFSOFTTOT[t] -0.00285325LFM[t] -0.0210143PRH[t] + 0.0371301CH[t] + 2.81288PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.53642 -0.050978AMS.I2[t] -0.0448663AMS.I3[t] -0.0502973AMS.E2[t] -0.131376age[t] +  0.138739CONFSOFTTOT[t] -0.00285325LFM[t] -0.0210143PRH[t] +  0.0371301CH[t] +  2.81288PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271055&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.53642 -0.050978AMS.I2[t] -0.0448663AMS.I3[t] -0.0502973AMS.E2[t] -0.131376age[t] +  0.138739CONFSOFTTOT[t] -0.00285325LFM[t] -0.0210143PRH[t] +  0.0371301CH[t] +  2.81288PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271055&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271055&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.53642 -0.050978AMS.I2[t] -0.0448663AMS.I3[t] -0.0502973AMS.E2[t] -0.131376age[t] + 0.138739CONFSOFTTOT[t] -0.00285325LFM[t] -0.0210143PRH[t] + 0.0371301CH[t] + 2.81288PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.536424.129172.0670.04070090.0203504
AMS.I2-0.0509780.0602147-0.84660.3987830.199392
AMS.I3-0.04486630.0560156-0.8010.4246270.212314
AMS.E2-0.05029730.0485099-1.0370.3017480.150874
age-0.1313760.170483-0.77060.4423480.221174
CONFSOFTTOT0.1387390.08636131.6060.1106110.0553056
LFM-0.002853250.00610596-0.46730.6410820.320541
PRH-0.02101430.0108119-1.9440.05411780.0270589
CH0.03713010.01029353.6070.0004412690.000220635
PR2.812880.23571511.931.40435e-227.02175e-23

\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) & 8.53642 & 4.12917 & 2.067 & 0.0407009 & 0.0203504 \tabularnewline
AMS.I2 & -0.050978 & 0.0602147 & -0.8466 & 0.398783 & 0.199392 \tabularnewline
AMS.I3 & -0.0448663 & 0.0560156 & -0.801 & 0.424627 & 0.212314 \tabularnewline
AMS.E2 & -0.0502973 & 0.0485099 & -1.037 & 0.301748 & 0.150874 \tabularnewline
age & -0.131376 & 0.170483 & -0.7706 & 0.442348 & 0.221174 \tabularnewline
CONFSOFTTOT & 0.138739 & 0.0863613 & 1.606 & 0.110611 & 0.0553056 \tabularnewline
LFM & -0.00285325 & 0.00610596 & -0.4673 & 0.641082 & 0.320541 \tabularnewline
PRH & -0.0210143 & 0.0108119 & -1.944 & 0.0541178 & 0.0270589 \tabularnewline
CH & 0.0371301 & 0.0102935 & 3.607 & 0.000441269 & 0.000220635 \tabularnewline
PR & 2.81288 & 0.235715 & 11.93 & 1.40435e-22 & 7.02175e-23 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271055&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]8.53642[/C][C]4.12917[/C][C]2.067[/C][C]0.0407009[/C][C]0.0203504[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.050978[/C][C]0.0602147[/C][C]-0.8466[/C][C]0.398783[/C][C]0.199392[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0448663[/C][C]0.0560156[/C][C]-0.801[/C][C]0.424627[/C][C]0.212314[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0502973[/C][C]0.0485099[/C][C]-1.037[/C][C]0.301748[/C][C]0.150874[/C][/ROW]
[ROW][C]age[/C][C]-0.131376[/C][C]0.170483[/C][C]-0.7706[/C][C]0.442348[/C][C]0.221174[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.138739[/C][C]0.0863613[/C][C]1.606[/C][C]0.110611[/C][C]0.0553056[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00285325[/C][C]0.00610596[/C][C]-0.4673[/C][C]0.641082[/C][C]0.320541[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0210143[/C][C]0.0108119[/C][C]-1.944[/C][C]0.0541178[/C][C]0.0270589[/C][/ROW]
[ROW][C]CH[/C][C]0.0371301[/C][C]0.0102935[/C][C]3.607[/C][C]0.000441269[/C][C]0.000220635[/C][/ROW]
[ROW][C]PR[/C][C]2.81288[/C][C]0.235715[/C][C]11.93[/C][C]1.40435e-22[/C][C]7.02175e-23[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271055&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271055&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.536424.129172.0670.04070090.0203504
AMS.I2-0.0509780.0602147-0.84660.3987830.199392
AMS.I3-0.04486630.0560156-0.8010.4246270.212314
AMS.E2-0.05029730.0485099-1.0370.3017480.150874
age-0.1313760.170483-0.77060.4423480.221174
CONFSOFTTOT0.1387390.08636131.6060.1106110.0553056
LFM-0.002853250.00610596-0.46730.6410820.320541
PRH-0.02101430.0108119-1.9440.05411780.0270589
CH0.03713010.01029353.6070.0004412690.000220635
PR2.812880.23571511.931.40435e-227.02175e-23






Multiple Linear Regression - Regression Statistics
Multiple R0.792777
R-squared0.628496
Adjusted R-squared0.602577
F-TEST (value)24.2486
F-TEST (DF numerator)9
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20893
Sum Squared Residuals629.438

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.792777 \tabularnewline
R-squared & 0.628496 \tabularnewline
Adjusted R-squared & 0.602577 \tabularnewline
F-TEST (value) & 24.2486 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.20893 \tabularnewline
Sum Squared Residuals & 629.438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271055&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.792777[/C][/ROW]
[ROW][C]R-squared[/C][C]0.628496[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.602577[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.2486[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/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]2.20893[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]629.438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271055&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271055&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.792777
R-squared0.628496
Adjusted R-squared0.602577
F-TEST (value)24.2486
F-TEST (DF numerator)9
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20893
Sum Squared Residuals629.438






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9016-0.00157805
212.210.24231.95773
312.811.71861.08138
47.411.602-4.20199
56.711.3108-4.61084
612.611.9510.649014
714.811.1343.66597
813.313.19680.103165
911.112.5427-1.44274
108.210.8524-2.65241
1111.411.24640.15356
126.411.0044-4.6044
1310.610.10840.491633
141213.3916-1.39159
156.38.82565-2.52565
1611.913.1925-1.29252
179.310.9888-1.68884
18109.709970.29003
196.410.4304-4.03038
2013.812.58921.21083
2110.810.29670.503318
2213.811.84931.95069
2311.710.5731.12697
2410.911.8871-0.987086
259.911.0843-1.18426
2611.510.60230.897685
278.311.005-2.70502
2811.711.03370.666277
29910.4462-1.44624
309.713.9844-4.28438
3110.811.2318-0.431782
3210.310.8217-0.521693
3310.49.632680.767323
349.312.2561-2.95607
3511.810.92130.878683
365.911.2449-5.34487
3711.411.8395-0.439462
381310.99612.0039
3910.811.165-0.365011
4011.310.64290.657078
4111.811.52390.276102
4212.79.339923.36008
4310.910.72440.17561
4413.311.67431.62566
4510.110.4173-0.317293
4614.311.322.97997
479.311.5541-2.2541
4812.510.47892.02109
497.610.1649-2.56492
5015.912.48783.41218
519.210.3774-1.17739
5211.112.4523-1.35234
531312.4210.579001
5414.511.55462.94539
5512.313.105-0.804983
5611.410.42550.974542
571312.340.659988
5813.211.01222.18778
597.711.5148-3.81476
604.357.28918-2.93918
6112.710.44032.2597
6218.116.03222.06784
6317.8516.20391.64614
6417.117.5834-0.48342
6519.116.84852.25148
6616.118.6673-2.56729
6713.3510.71022.63981
6818.417.73110.668893
6914.77.692267.00774
7010.613.3093-2.7093
7112.613.8853-1.28529
7213.612.78690.813099
7314.113.07861.02144
7414.513.37051.12948
7516.1516.4558-0.305781
7614.7512.66632.08367
7714.812.15512.64487
7812.4512.23590.214067
7912.6510.33472.31529
8017.3513.99433.35569
818.68.515690.0843088
8218.416.79151.60849
8316.114.38551.71451
8417.7515.66282.08716
8515.2515.3166-0.0666329
8617.6516.27931.37065
8716.3516.6401-0.290137
8817.6518.3506-0.700644
8913.612.95020.64984
9014.3513.75030.599668
9114.7517.0807-2.33073
9218.2516.671.58
939.915.4329-5.53292
941614.02991.97012
9518.2516.16922.0808
9616.8517.8101-0.960118
9718.9516.87872.07129
9815.613.51992.0801
9917.117.7532-0.653206
10015.416.1104-0.710423
10115.415.9711-0.571074
10213.3514.1744-0.824355
10319.117.02342.07662
1047.66.651360.94864
10519.116.90962.19038
10614.7516.425-1.67495
10719.2516.52252.72747
10813.616.082-2.48205
10912.7515.4554-2.70539
1109.858.255771.59423
11115.2515.889-0.638984
11211.913.3777-1.47769
11316.3517.3612-1.01121
11412.413.7563-1.35628
11518.1516.02762.12244
11617.7515.32742.42257
11712.3512.6984-0.348384
11815.615.29280.307232
11919.316.72142.57859
12017.116.58450.515452
12118.415.50652.89346
12219.0516.8952.15496
12318.5514.55053.99947
12419.118.13080.969215
12512.8515.8184-2.96839
1269.510.9237-1.42367
1274.57.45821-2.95821
12813.614.9378-1.3378
12911.712.3205-0.62048
13013.3513.8748-0.524821
13117.619.0841-1.48411
13214.0513.8030.246975
13316.117.621-1.52101
13413.3515.5057-2.15568
13511.8515.1561-3.30608
13611.9511.72780.222187
13713.216.3047-3.10475
1387.79.49096-1.79096
13914.613.62270.97731

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9016 & -0.00157805 \tabularnewline
2 & 12.2 & 10.2423 & 1.95773 \tabularnewline
3 & 12.8 & 11.7186 & 1.08138 \tabularnewline
4 & 7.4 & 11.602 & -4.20199 \tabularnewline
5 & 6.7 & 11.3108 & -4.61084 \tabularnewline
6 & 12.6 & 11.951 & 0.649014 \tabularnewline
7 & 14.8 & 11.134 & 3.66597 \tabularnewline
8 & 13.3 & 13.1968 & 0.103165 \tabularnewline
9 & 11.1 & 12.5427 & -1.44274 \tabularnewline
10 & 8.2 & 10.8524 & -2.65241 \tabularnewline
11 & 11.4 & 11.2464 & 0.15356 \tabularnewline
12 & 6.4 & 11.0044 & -4.6044 \tabularnewline
13 & 10.6 & 10.1084 & 0.491633 \tabularnewline
14 & 12 & 13.3916 & -1.39159 \tabularnewline
15 & 6.3 & 8.82565 & -2.52565 \tabularnewline
16 & 11.9 & 13.1925 & -1.29252 \tabularnewline
17 & 9.3 & 10.9888 & -1.68884 \tabularnewline
18 & 10 & 9.70997 & 0.29003 \tabularnewline
19 & 6.4 & 10.4304 & -4.03038 \tabularnewline
20 & 13.8 & 12.5892 & 1.21083 \tabularnewline
21 & 10.8 & 10.2967 & 0.503318 \tabularnewline
22 & 13.8 & 11.8493 & 1.95069 \tabularnewline
23 & 11.7 & 10.573 & 1.12697 \tabularnewline
24 & 10.9 & 11.8871 & -0.987086 \tabularnewline
25 & 9.9 & 11.0843 & -1.18426 \tabularnewline
26 & 11.5 & 10.6023 & 0.897685 \tabularnewline
27 & 8.3 & 11.005 & -2.70502 \tabularnewline
28 & 11.7 & 11.0337 & 0.666277 \tabularnewline
29 & 9 & 10.4462 & -1.44624 \tabularnewline
30 & 9.7 & 13.9844 & -4.28438 \tabularnewline
31 & 10.8 & 11.2318 & -0.431782 \tabularnewline
32 & 10.3 & 10.8217 & -0.521693 \tabularnewline
33 & 10.4 & 9.63268 & 0.767323 \tabularnewline
34 & 9.3 & 12.2561 & -2.95607 \tabularnewline
35 & 11.8 & 10.9213 & 0.878683 \tabularnewline
36 & 5.9 & 11.2449 & -5.34487 \tabularnewline
37 & 11.4 & 11.8395 & -0.439462 \tabularnewline
38 & 13 & 10.9961 & 2.0039 \tabularnewline
39 & 10.8 & 11.165 & -0.365011 \tabularnewline
40 & 11.3 & 10.6429 & 0.657078 \tabularnewline
41 & 11.8 & 11.5239 & 0.276102 \tabularnewline
42 & 12.7 & 9.33992 & 3.36008 \tabularnewline
43 & 10.9 & 10.7244 & 0.17561 \tabularnewline
44 & 13.3 & 11.6743 & 1.62566 \tabularnewline
45 & 10.1 & 10.4173 & -0.317293 \tabularnewline
46 & 14.3 & 11.32 & 2.97997 \tabularnewline
47 & 9.3 & 11.5541 & -2.2541 \tabularnewline
48 & 12.5 & 10.4789 & 2.02109 \tabularnewline
49 & 7.6 & 10.1649 & -2.56492 \tabularnewline
50 & 15.9 & 12.4878 & 3.41218 \tabularnewline
51 & 9.2 & 10.3774 & -1.17739 \tabularnewline
52 & 11.1 & 12.4523 & -1.35234 \tabularnewline
53 & 13 & 12.421 & 0.579001 \tabularnewline
54 & 14.5 & 11.5546 & 2.94539 \tabularnewline
55 & 12.3 & 13.105 & -0.804983 \tabularnewline
56 & 11.4 & 10.4255 & 0.974542 \tabularnewline
57 & 13 & 12.34 & 0.659988 \tabularnewline
58 & 13.2 & 11.0122 & 2.18778 \tabularnewline
59 & 7.7 & 11.5148 & -3.81476 \tabularnewline
60 & 4.35 & 7.28918 & -2.93918 \tabularnewline
61 & 12.7 & 10.4403 & 2.2597 \tabularnewline
62 & 18.1 & 16.0322 & 2.06784 \tabularnewline
63 & 17.85 & 16.2039 & 1.64614 \tabularnewline
64 & 17.1 & 17.5834 & -0.48342 \tabularnewline
65 & 19.1 & 16.8485 & 2.25148 \tabularnewline
66 & 16.1 & 18.6673 & -2.56729 \tabularnewline
67 & 13.35 & 10.7102 & 2.63981 \tabularnewline
68 & 18.4 & 17.7311 & 0.668893 \tabularnewline
69 & 14.7 & 7.69226 & 7.00774 \tabularnewline
70 & 10.6 & 13.3093 & -2.7093 \tabularnewline
71 & 12.6 & 13.8853 & -1.28529 \tabularnewline
72 & 13.6 & 12.7869 & 0.813099 \tabularnewline
73 & 14.1 & 13.0786 & 1.02144 \tabularnewline
74 & 14.5 & 13.3705 & 1.12948 \tabularnewline
75 & 16.15 & 16.4558 & -0.305781 \tabularnewline
76 & 14.75 & 12.6663 & 2.08367 \tabularnewline
77 & 14.8 & 12.1551 & 2.64487 \tabularnewline
78 & 12.45 & 12.2359 & 0.214067 \tabularnewline
79 & 12.65 & 10.3347 & 2.31529 \tabularnewline
80 & 17.35 & 13.9943 & 3.35569 \tabularnewline
81 & 8.6 & 8.51569 & 0.0843088 \tabularnewline
82 & 18.4 & 16.7915 & 1.60849 \tabularnewline
83 & 16.1 & 14.3855 & 1.71451 \tabularnewline
84 & 17.75 & 15.6628 & 2.08716 \tabularnewline
85 & 15.25 & 15.3166 & -0.0666329 \tabularnewline
86 & 17.65 & 16.2793 & 1.37065 \tabularnewline
87 & 16.35 & 16.6401 & -0.290137 \tabularnewline
88 & 17.65 & 18.3506 & -0.700644 \tabularnewline
89 & 13.6 & 12.9502 & 0.64984 \tabularnewline
90 & 14.35 & 13.7503 & 0.599668 \tabularnewline
91 & 14.75 & 17.0807 & -2.33073 \tabularnewline
92 & 18.25 & 16.67 & 1.58 \tabularnewline
93 & 9.9 & 15.4329 & -5.53292 \tabularnewline
94 & 16 & 14.0299 & 1.97012 \tabularnewline
95 & 18.25 & 16.1692 & 2.0808 \tabularnewline
96 & 16.85 & 17.8101 & -0.960118 \tabularnewline
97 & 18.95 & 16.8787 & 2.07129 \tabularnewline
98 & 15.6 & 13.5199 & 2.0801 \tabularnewline
99 & 17.1 & 17.7532 & -0.653206 \tabularnewline
100 & 15.4 & 16.1104 & -0.710423 \tabularnewline
101 & 15.4 & 15.9711 & -0.571074 \tabularnewline
102 & 13.35 & 14.1744 & -0.824355 \tabularnewline
103 & 19.1 & 17.0234 & 2.07662 \tabularnewline
104 & 7.6 & 6.65136 & 0.94864 \tabularnewline
105 & 19.1 & 16.9096 & 2.19038 \tabularnewline
106 & 14.75 & 16.425 & -1.67495 \tabularnewline
107 & 19.25 & 16.5225 & 2.72747 \tabularnewline
108 & 13.6 & 16.082 & -2.48205 \tabularnewline
109 & 12.75 & 15.4554 & -2.70539 \tabularnewline
110 & 9.85 & 8.25577 & 1.59423 \tabularnewline
111 & 15.25 & 15.889 & -0.638984 \tabularnewline
112 & 11.9 & 13.3777 & -1.47769 \tabularnewline
113 & 16.35 & 17.3612 & -1.01121 \tabularnewline
114 & 12.4 & 13.7563 & -1.35628 \tabularnewline
115 & 18.15 & 16.0276 & 2.12244 \tabularnewline
116 & 17.75 & 15.3274 & 2.42257 \tabularnewline
117 & 12.35 & 12.6984 & -0.348384 \tabularnewline
118 & 15.6 & 15.2928 & 0.307232 \tabularnewline
119 & 19.3 & 16.7214 & 2.57859 \tabularnewline
120 & 17.1 & 16.5845 & 0.515452 \tabularnewline
121 & 18.4 & 15.5065 & 2.89346 \tabularnewline
122 & 19.05 & 16.895 & 2.15496 \tabularnewline
123 & 18.55 & 14.5505 & 3.99947 \tabularnewline
124 & 19.1 & 18.1308 & 0.969215 \tabularnewline
125 & 12.85 & 15.8184 & -2.96839 \tabularnewline
126 & 9.5 & 10.9237 & -1.42367 \tabularnewline
127 & 4.5 & 7.45821 & -2.95821 \tabularnewline
128 & 13.6 & 14.9378 & -1.3378 \tabularnewline
129 & 11.7 & 12.3205 & -0.62048 \tabularnewline
130 & 13.35 & 13.8748 & -0.524821 \tabularnewline
131 & 17.6 & 19.0841 & -1.48411 \tabularnewline
132 & 14.05 & 13.803 & 0.246975 \tabularnewline
133 & 16.1 & 17.621 & -1.52101 \tabularnewline
134 & 13.35 & 15.5057 & -2.15568 \tabularnewline
135 & 11.85 & 15.1561 & -3.30608 \tabularnewline
136 & 11.95 & 11.7278 & 0.222187 \tabularnewline
137 & 13.2 & 16.3047 & -3.10475 \tabularnewline
138 & 7.7 & 9.49096 & -1.79096 \tabularnewline
139 & 14.6 & 13.6227 & 0.97731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271055&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]12.9016[/C][C]-0.00157805[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.2423[/C][C]1.95773[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.7186[/C][C]1.08138[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.602[/C][C]-4.20199[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.3108[/C][C]-4.61084[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.951[/C][C]0.649014[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.134[/C][C]3.66597[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.1968[/C][C]0.103165[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.5427[/C][C]-1.44274[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.8524[/C][C]-2.65241[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2464[/C][C]0.15356[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.0044[/C][C]-4.6044[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1084[/C][C]0.491633[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3916[/C][C]-1.39159[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.82565[/C][C]-2.52565[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.1925[/C][C]-1.29252[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9888[/C][C]-1.68884[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.70997[/C][C]0.29003[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.4304[/C][C]-4.03038[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.5892[/C][C]1.21083[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.2967[/C][C]0.503318[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.8493[/C][C]1.95069[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.573[/C][C]1.12697[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.8871[/C][C]-0.987086[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.0843[/C][C]-1.18426[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.6023[/C][C]0.897685[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.005[/C][C]-2.70502[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.0337[/C][C]0.666277[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.4462[/C][C]-1.44624[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.9844[/C][C]-4.28438[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.2318[/C][C]-0.431782[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.8217[/C][C]-0.521693[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.63268[/C][C]0.767323[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2561[/C][C]-2.95607[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.9213[/C][C]0.878683[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.2449[/C][C]-5.34487[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8395[/C][C]-0.439462[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.9961[/C][C]2.0039[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.165[/C][C]-0.365011[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.6429[/C][C]0.657078[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.5239[/C][C]0.276102[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.33992[/C][C]3.36008[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.7244[/C][C]0.17561[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.6743[/C][C]1.62566[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.4173[/C][C]-0.317293[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.32[/C][C]2.97997[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.5541[/C][C]-2.2541[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.4789[/C][C]2.02109[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.1649[/C][C]-2.56492[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4878[/C][C]3.41218[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.3774[/C][C]-1.17739[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.4523[/C][C]-1.35234[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.421[/C][C]0.579001[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.5546[/C][C]2.94539[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.105[/C][C]-0.804983[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.4255[/C][C]0.974542[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.34[/C][C]0.659988[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]11.0122[/C][C]2.18778[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.5148[/C][C]-3.81476[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.28918[/C][C]-2.93918[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.4403[/C][C]2.2597[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.0322[/C][C]2.06784[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.2039[/C][C]1.64614[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.5834[/C][C]-0.48342[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.8485[/C][C]2.25148[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.6673[/C][C]-2.56729[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.7102[/C][C]2.63981[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.7311[/C][C]0.668893[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.69226[/C][C]7.00774[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.3093[/C][C]-2.7093[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.8853[/C][C]-1.28529[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.7869[/C][C]0.813099[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.0786[/C][C]1.02144[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.3705[/C][C]1.12948[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.4558[/C][C]-0.305781[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.6663[/C][C]2.08367[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.1551[/C][C]2.64487[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.2359[/C][C]0.214067[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.3347[/C][C]2.31529[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.9943[/C][C]3.35569[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.51569[/C][C]0.0843088[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.7915[/C][C]1.60849[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.3855[/C][C]1.71451[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.6628[/C][C]2.08716[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.3166[/C][C]-0.0666329[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.2793[/C][C]1.37065[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.6401[/C][C]-0.290137[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.3506[/C][C]-0.700644[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.9502[/C][C]0.64984[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.7503[/C][C]0.599668[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.0807[/C][C]-2.33073[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.67[/C][C]1.58[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.4329[/C][C]-5.53292[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.0299[/C][C]1.97012[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.1692[/C][C]2.0808[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.8101[/C][C]-0.960118[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.8787[/C][C]2.07129[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.5199[/C][C]2.0801[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.7532[/C][C]-0.653206[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.1104[/C][C]-0.710423[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]15.9711[/C][C]-0.571074[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.1744[/C][C]-0.824355[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0234[/C][C]2.07662[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.65136[/C][C]0.94864[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]16.9096[/C][C]2.19038[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.425[/C][C]-1.67495[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]16.5225[/C][C]2.72747[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.082[/C][C]-2.48205[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.4554[/C][C]-2.70539[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.25577[/C][C]1.59423[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.889[/C][C]-0.638984[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.3777[/C][C]-1.47769[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.3612[/C][C]-1.01121[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.7563[/C][C]-1.35628[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]16.0276[/C][C]2.12244[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.3274[/C][C]2.42257[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.6984[/C][C]-0.348384[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.2928[/C][C]0.307232[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.7214[/C][C]2.57859[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.5845[/C][C]0.515452[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.5065[/C][C]2.89346[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.895[/C][C]2.15496[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.5505[/C][C]3.99947[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.1308[/C][C]0.969215[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.8184[/C][C]-2.96839[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.9237[/C][C]-1.42367[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.45821[/C][C]-2.95821[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]14.9378[/C][C]-1.3378[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.3205[/C][C]-0.62048[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.8748[/C][C]-0.524821[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]19.0841[/C][C]-1.48411[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.803[/C][C]0.246975[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.621[/C][C]-1.52101[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.5057[/C][C]-2.15568[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1561[/C][C]-3.30608[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.7278[/C][C]0.222187[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.3047[/C][C]-3.10475[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.49096[/C][C]-1.79096[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.6227[/C][C]0.97731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271055&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271055&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9016-0.00157805
212.210.24231.95773
312.811.71861.08138
47.411.602-4.20199
56.711.3108-4.61084
612.611.9510.649014
714.811.1343.66597
813.313.19680.103165
911.112.5427-1.44274
108.210.8524-2.65241
1111.411.24640.15356
126.411.0044-4.6044
1310.610.10840.491633
141213.3916-1.39159
156.38.82565-2.52565
1611.913.1925-1.29252
179.310.9888-1.68884
18109.709970.29003
196.410.4304-4.03038
2013.812.58921.21083
2110.810.29670.503318
2213.811.84931.95069
2311.710.5731.12697
2410.911.8871-0.987086
259.911.0843-1.18426
2611.510.60230.897685
278.311.005-2.70502
2811.711.03370.666277
29910.4462-1.44624
309.713.9844-4.28438
3110.811.2318-0.431782
3210.310.8217-0.521693
3310.49.632680.767323
349.312.2561-2.95607
3511.810.92130.878683
365.911.2449-5.34487
3711.411.8395-0.439462
381310.99612.0039
3910.811.165-0.365011
4011.310.64290.657078
4111.811.52390.276102
4212.79.339923.36008
4310.910.72440.17561
4413.311.67431.62566
4510.110.4173-0.317293
4614.311.322.97997
479.311.5541-2.2541
4812.510.47892.02109
497.610.1649-2.56492
5015.912.48783.41218
519.210.3774-1.17739
5211.112.4523-1.35234
531312.4210.579001
5414.511.55462.94539
5512.313.105-0.804983
5611.410.42550.974542
571312.340.659988
5813.211.01222.18778
597.711.5148-3.81476
604.357.28918-2.93918
6112.710.44032.2597
6218.116.03222.06784
6317.8516.20391.64614
6417.117.5834-0.48342
6519.116.84852.25148
6616.118.6673-2.56729
6713.3510.71022.63981
6818.417.73110.668893
6914.77.692267.00774
7010.613.3093-2.7093
7112.613.8853-1.28529
7213.612.78690.813099
7314.113.07861.02144
7414.513.37051.12948
7516.1516.4558-0.305781
7614.7512.66632.08367
7714.812.15512.64487
7812.4512.23590.214067
7912.6510.33472.31529
8017.3513.99433.35569
818.68.515690.0843088
8218.416.79151.60849
8316.114.38551.71451
8417.7515.66282.08716
8515.2515.3166-0.0666329
8617.6516.27931.37065
8716.3516.6401-0.290137
8817.6518.3506-0.700644
8913.612.95020.64984
9014.3513.75030.599668
9114.7517.0807-2.33073
9218.2516.671.58
939.915.4329-5.53292
941614.02991.97012
9518.2516.16922.0808
9616.8517.8101-0.960118
9718.9516.87872.07129
9815.613.51992.0801
9917.117.7532-0.653206
10015.416.1104-0.710423
10115.415.9711-0.571074
10213.3514.1744-0.824355
10319.117.02342.07662
1047.66.651360.94864
10519.116.90962.19038
10614.7516.425-1.67495
10719.2516.52252.72747
10813.616.082-2.48205
10912.7515.4554-2.70539
1109.858.255771.59423
11115.2515.889-0.638984
11211.913.3777-1.47769
11316.3517.3612-1.01121
11412.413.7563-1.35628
11518.1516.02762.12244
11617.7515.32742.42257
11712.3512.6984-0.348384
11815.615.29280.307232
11919.316.72142.57859
12017.116.58450.515452
12118.415.50652.89346
12219.0516.8952.15496
12318.5514.55053.99947
12419.118.13080.969215
12512.8515.8184-2.96839
1269.510.9237-1.42367
1274.57.45821-2.95821
12813.614.9378-1.3378
12911.712.3205-0.62048
13013.3513.8748-0.524821
13117.619.0841-1.48411
13214.0513.8030.246975
13316.117.621-1.52101
13413.3515.5057-2.15568
13511.8515.1561-3.30608
13611.9511.72780.222187
13713.216.3047-3.10475
1387.79.49096-1.79096
13914.613.62270.97731






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.5114290.9771420.488571
140.5251260.9497480.474874
150.4553610.9107220.544639
160.3531820.7063650.646818
170.265760.531520.73424
180.3208660.6417320.679134
190.2590450.5180890.740955
200.2325850.465170.767415
210.2086770.4173540.791323
220.1510350.3020690.848965
230.1036780.2073560.896322
240.07222690.1444540.927773
250.05871320.1174260.941287
260.06917230.1383450.930828
270.1109490.2218990.889051
280.1540150.308030.845985
290.1881960.3763920.811804
300.3481920.6963830.651808
310.3298780.6597560.670122
320.2709620.5419240.729038
330.2251470.4502940.774853
340.2346990.4693970.765301
350.1894490.3788990.810551
360.3980770.7961540.601923
370.3421580.6843170.657842
380.3038820.6077630.696118
390.2882650.5765310.711735
400.2419990.4839980.758001
410.2232050.446410.776795
420.2665220.5330430.733478
430.2314640.4629280.768536
440.3835280.7670560.616472
450.3331470.6662950.666853
460.4572910.9145810.542709
470.4594810.9189610.540519
480.4754940.9509880.524506
490.5280440.9439130.471956
500.6202050.759590.379795
510.6073560.7852880.392644
520.5839830.8320330.416017
530.5399510.9200980.460049
540.5470540.9058920.452946
550.5153210.9693570.484679
560.4731330.9462670.526867
570.4339840.8679680.566016
580.4174870.8349750.582513
590.4978380.9956750.502162
600.5831080.8337840.416892
610.6375390.7249220.362461
620.670130.6597390.32987
630.6388680.7222630.361132
640.6029490.7941030.397051
650.6021220.7957550.397878
660.6479560.7040870.352044
670.6884920.6230150.311508
680.6449730.7100540.355027
690.9489650.102070.0510349
700.955390.08922030.0446101
710.9478350.104330.0521651
720.9473110.1053780.0526889
730.9354960.1290090.0645043
740.9253440.1493120.0746559
750.9266120.1467770.0733883
760.925690.1486210.0743105
770.9359320.1281360.0640678
780.9175090.1649810.0824907
790.9152530.1694940.0847469
800.947120.105760.0528802
810.9322830.1354340.0677169
820.9210380.1579240.0789619
830.9089960.1820080.0910039
840.8969250.2061510.103075
850.8758870.2482260.124113
860.8642170.2715650.135783
870.8382220.3235560.161778
880.8116410.3767170.188359
890.7737090.4525810.226291
900.7308870.5382270.269113
910.7239360.5521290.276064
920.6894850.621030.310515
930.9185450.162910.0814549
940.9016560.1966880.0983438
950.8918560.2162880.108144
960.8688970.2622060.131103
970.845630.3087390.15437
980.8455930.3088140.154407
990.8277010.3445980.172299
1000.7931710.4136580.206829
1010.7497880.5004240.250212
1020.73230.5353990.2677
1030.7386960.5226080.261304
1040.7468810.5062390.253119
1050.6998870.6002270.300113
1060.664290.6714190.33571
1070.6449730.7100550.355027
1080.6238940.7522130.376106
1090.5986020.8027960.401398
1100.6186360.7627280.381364
1110.5756670.8486660.424333
1120.5083850.983230.491615
1130.4708870.9417730.529113
1140.4086240.8172490.591376
1150.3996360.7992720.600364
1160.4082850.8165690.591715
1170.3310530.6621070.668947
1180.6832050.633590.316795
1190.7658980.4682030.234102
1200.7992450.4015090.200755
1210.7708240.4583510.229176
1220.8884710.2230570.111529
1230.9475820.1048360.0524179
1240.8962610.2074780.103739
1250.8736390.2527230.126361
1260.747090.505820.25291

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.511429 & 0.977142 & 0.488571 \tabularnewline
14 & 0.525126 & 0.949748 & 0.474874 \tabularnewline
15 & 0.455361 & 0.910722 & 0.544639 \tabularnewline
16 & 0.353182 & 0.706365 & 0.646818 \tabularnewline
17 & 0.26576 & 0.53152 & 0.73424 \tabularnewline
18 & 0.320866 & 0.641732 & 0.679134 \tabularnewline
19 & 0.259045 & 0.518089 & 0.740955 \tabularnewline
20 & 0.232585 & 0.46517 & 0.767415 \tabularnewline
21 & 0.208677 & 0.417354 & 0.791323 \tabularnewline
22 & 0.151035 & 0.302069 & 0.848965 \tabularnewline
23 & 0.103678 & 0.207356 & 0.896322 \tabularnewline
24 & 0.0722269 & 0.144454 & 0.927773 \tabularnewline
25 & 0.0587132 & 0.117426 & 0.941287 \tabularnewline
26 & 0.0691723 & 0.138345 & 0.930828 \tabularnewline
27 & 0.110949 & 0.221899 & 0.889051 \tabularnewline
28 & 0.154015 & 0.30803 & 0.845985 \tabularnewline
29 & 0.188196 & 0.376392 & 0.811804 \tabularnewline
30 & 0.348192 & 0.696383 & 0.651808 \tabularnewline
31 & 0.329878 & 0.659756 & 0.670122 \tabularnewline
32 & 0.270962 & 0.541924 & 0.729038 \tabularnewline
33 & 0.225147 & 0.450294 & 0.774853 \tabularnewline
34 & 0.234699 & 0.469397 & 0.765301 \tabularnewline
35 & 0.189449 & 0.378899 & 0.810551 \tabularnewline
36 & 0.398077 & 0.796154 & 0.601923 \tabularnewline
37 & 0.342158 & 0.684317 & 0.657842 \tabularnewline
38 & 0.303882 & 0.607763 & 0.696118 \tabularnewline
39 & 0.288265 & 0.576531 & 0.711735 \tabularnewline
40 & 0.241999 & 0.483998 & 0.758001 \tabularnewline
41 & 0.223205 & 0.44641 & 0.776795 \tabularnewline
42 & 0.266522 & 0.533043 & 0.733478 \tabularnewline
43 & 0.231464 & 0.462928 & 0.768536 \tabularnewline
44 & 0.383528 & 0.767056 & 0.616472 \tabularnewline
45 & 0.333147 & 0.666295 & 0.666853 \tabularnewline
46 & 0.457291 & 0.914581 & 0.542709 \tabularnewline
47 & 0.459481 & 0.918961 & 0.540519 \tabularnewline
48 & 0.475494 & 0.950988 & 0.524506 \tabularnewline
49 & 0.528044 & 0.943913 & 0.471956 \tabularnewline
50 & 0.620205 & 0.75959 & 0.379795 \tabularnewline
51 & 0.607356 & 0.785288 & 0.392644 \tabularnewline
52 & 0.583983 & 0.832033 & 0.416017 \tabularnewline
53 & 0.539951 & 0.920098 & 0.460049 \tabularnewline
54 & 0.547054 & 0.905892 & 0.452946 \tabularnewline
55 & 0.515321 & 0.969357 & 0.484679 \tabularnewline
56 & 0.473133 & 0.946267 & 0.526867 \tabularnewline
57 & 0.433984 & 0.867968 & 0.566016 \tabularnewline
58 & 0.417487 & 0.834975 & 0.582513 \tabularnewline
59 & 0.497838 & 0.995675 & 0.502162 \tabularnewline
60 & 0.583108 & 0.833784 & 0.416892 \tabularnewline
61 & 0.637539 & 0.724922 & 0.362461 \tabularnewline
62 & 0.67013 & 0.659739 & 0.32987 \tabularnewline
63 & 0.638868 & 0.722263 & 0.361132 \tabularnewline
64 & 0.602949 & 0.794103 & 0.397051 \tabularnewline
65 & 0.602122 & 0.795755 & 0.397878 \tabularnewline
66 & 0.647956 & 0.704087 & 0.352044 \tabularnewline
67 & 0.688492 & 0.623015 & 0.311508 \tabularnewline
68 & 0.644973 & 0.710054 & 0.355027 \tabularnewline
69 & 0.948965 & 0.10207 & 0.0510349 \tabularnewline
70 & 0.95539 & 0.0892203 & 0.0446101 \tabularnewline
71 & 0.947835 & 0.10433 & 0.0521651 \tabularnewline
72 & 0.947311 & 0.105378 & 0.0526889 \tabularnewline
73 & 0.935496 & 0.129009 & 0.0645043 \tabularnewline
74 & 0.925344 & 0.149312 & 0.0746559 \tabularnewline
75 & 0.926612 & 0.146777 & 0.0733883 \tabularnewline
76 & 0.92569 & 0.148621 & 0.0743105 \tabularnewline
77 & 0.935932 & 0.128136 & 0.0640678 \tabularnewline
78 & 0.917509 & 0.164981 & 0.0824907 \tabularnewline
79 & 0.915253 & 0.169494 & 0.0847469 \tabularnewline
80 & 0.94712 & 0.10576 & 0.0528802 \tabularnewline
81 & 0.932283 & 0.135434 & 0.0677169 \tabularnewline
82 & 0.921038 & 0.157924 & 0.0789619 \tabularnewline
83 & 0.908996 & 0.182008 & 0.0910039 \tabularnewline
84 & 0.896925 & 0.206151 & 0.103075 \tabularnewline
85 & 0.875887 & 0.248226 & 0.124113 \tabularnewline
86 & 0.864217 & 0.271565 & 0.135783 \tabularnewline
87 & 0.838222 & 0.323556 & 0.161778 \tabularnewline
88 & 0.811641 & 0.376717 & 0.188359 \tabularnewline
89 & 0.773709 & 0.452581 & 0.226291 \tabularnewline
90 & 0.730887 & 0.538227 & 0.269113 \tabularnewline
91 & 0.723936 & 0.552129 & 0.276064 \tabularnewline
92 & 0.689485 & 0.62103 & 0.310515 \tabularnewline
93 & 0.918545 & 0.16291 & 0.0814549 \tabularnewline
94 & 0.901656 & 0.196688 & 0.0983438 \tabularnewline
95 & 0.891856 & 0.216288 & 0.108144 \tabularnewline
96 & 0.868897 & 0.262206 & 0.131103 \tabularnewline
97 & 0.84563 & 0.308739 & 0.15437 \tabularnewline
98 & 0.845593 & 0.308814 & 0.154407 \tabularnewline
99 & 0.827701 & 0.344598 & 0.172299 \tabularnewline
100 & 0.793171 & 0.413658 & 0.206829 \tabularnewline
101 & 0.749788 & 0.500424 & 0.250212 \tabularnewline
102 & 0.7323 & 0.535399 & 0.2677 \tabularnewline
103 & 0.738696 & 0.522608 & 0.261304 \tabularnewline
104 & 0.746881 & 0.506239 & 0.253119 \tabularnewline
105 & 0.699887 & 0.600227 & 0.300113 \tabularnewline
106 & 0.66429 & 0.671419 & 0.33571 \tabularnewline
107 & 0.644973 & 0.710055 & 0.355027 \tabularnewline
108 & 0.623894 & 0.752213 & 0.376106 \tabularnewline
109 & 0.598602 & 0.802796 & 0.401398 \tabularnewline
110 & 0.618636 & 0.762728 & 0.381364 \tabularnewline
111 & 0.575667 & 0.848666 & 0.424333 \tabularnewline
112 & 0.508385 & 0.98323 & 0.491615 \tabularnewline
113 & 0.470887 & 0.941773 & 0.529113 \tabularnewline
114 & 0.408624 & 0.817249 & 0.591376 \tabularnewline
115 & 0.399636 & 0.799272 & 0.600364 \tabularnewline
116 & 0.408285 & 0.816569 & 0.591715 \tabularnewline
117 & 0.331053 & 0.662107 & 0.668947 \tabularnewline
118 & 0.683205 & 0.63359 & 0.316795 \tabularnewline
119 & 0.765898 & 0.468203 & 0.234102 \tabularnewline
120 & 0.799245 & 0.401509 & 0.200755 \tabularnewline
121 & 0.770824 & 0.458351 & 0.229176 \tabularnewline
122 & 0.888471 & 0.223057 & 0.111529 \tabularnewline
123 & 0.947582 & 0.104836 & 0.0524179 \tabularnewline
124 & 0.896261 & 0.207478 & 0.103739 \tabularnewline
125 & 0.873639 & 0.252723 & 0.126361 \tabularnewline
126 & 0.74709 & 0.50582 & 0.25291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271055&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]13[/C][C]0.511429[/C][C]0.977142[/C][C]0.488571[/C][/ROW]
[ROW][C]14[/C][C]0.525126[/C][C]0.949748[/C][C]0.474874[/C][/ROW]
[ROW][C]15[/C][C]0.455361[/C][C]0.910722[/C][C]0.544639[/C][/ROW]
[ROW][C]16[/C][C]0.353182[/C][C]0.706365[/C][C]0.646818[/C][/ROW]
[ROW][C]17[/C][C]0.26576[/C][C]0.53152[/C][C]0.73424[/C][/ROW]
[ROW][C]18[/C][C]0.320866[/C][C]0.641732[/C][C]0.679134[/C][/ROW]
[ROW][C]19[/C][C]0.259045[/C][C]0.518089[/C][C]0.740955[/C][/ROW]
[ROW][C]20[/C][C]0.232585[/C][C]0.46517[/C][C]0.767415[/C][/ROW]
[ROW][C]21[/C][C]0.208677[/C][C]0.417354[/C][C]0.791323[/C][/ROW]
[ROW][C]22[/C][C]0.151035[/C][C]0.302069[/C][C]0.848965[/C][/ROW]
[ROW][C]23[/C][C]0.103678[/C][C]0.207356[/C][C]0.896322[/C][/ROW]
[ROW][C]24[/C][C]0.0722269[/C][C]0.144454[/C][C]0.927773[/C][/ROW]
[ROW][C]25[/C][C]0.0587132[/C][C]0.117426[/C][C]0.941287[/C][/ROW]
[ROW][C]26[/C][C]0.0691723[/C][C]0.138345[/C][C]0.930828[/C][/ROW]
[ROW][C]27[/C][C]0.110949[/C][C]0.221899[/C][C]0.889051[/C][/ROW]
[ROW][C]28[/C][C]0.154015[/C][C]0.30803[/C][C]0.845985[/C][/ROW]
[ROW][C]29[/C][C]0.188196[/C][C]0.376392[/C][C]0.811804[/C][/ROW]
[ROW][C]30[/C][C]0.348192[/C][C]0.696383[/C][C]0.651808[/C][/ROW]
[ROW][C]31[/C][C]0.329878[/C][C]0.659756[/C][C]0.670122[/C][/ROW]
[ROW][C]32[/C][C]0.270962[/C][C]0.541924[/C][C]0.729038[/C][/ROW]
[ROW][C]33[/C][C]0.225147[/C][C]0.450294[/C][C]0.774853[/C][/ROW]
[ROW][C]34[/C][C]0.234699[/C][C]0.469397[/C][C]0.765301[/C][/ROW]
[ROW][C]35[/C][C]0.189449[/C][C]0.378899[/C][C]0.810551[/C][/ROW]
[ROW][C]36[/C][C]0.398077[/C][C]0.796154[/C][C]0.601923[/C][/ROW]
[ROW][C]37[/C][C]0.342158[/C][C]0.684317[/C][C]0.657842[/C][/ROW]
[ROW][C]38[/C][C]0.303882[/C][C]0.607763[/C][C]0.696118[/C][/ROW]
[ROW][C]39[/C][C]0.288265[/C][C]0.576531[/C][C]0.711735[/C][/ROW]
[ROW][C]40[/C][C]0.241999[/C][C]0.483998[/C][C]0.758001[/C][/ROW]
[ROW][C]41[/C][C]0.223205[/C][C]0.44641[/C][C]0.776795[/C][/ROW]
[ROW][C]42[/C][C]0.266522[/C][C]0.533043[/C][C]0.733478[/C][/ROW]
[ROW][C]43[/C][C]0.231464[/C][C]0.462928[/C][C]0.768536[/C][/ROW]
[ROW][C]44[/C][C]0.383528[/C][C]0.767056[/C][C]0.616472[/C][/ROW]
[ROW][C]45[/C][C]0.333147[/C][C]0.666295[/C][C]0.666853[/C][/ROW]
[ROW][C]46[/C][C]0.457291[/C][C]0.914581[/C][C]0.542709[/C][/ROW]
[ROW][C]47[/C][C]0.459481[/C][C]0.918961[/C][C]0.540519[/C][/ROW]
[ROW][C]48[/C][C]0.475494[/C][C]0.950988[/C][C]0.524506[/C][/ROW]
[ROW][C]49[/C][C]0.528044[/C][C]0.943913[/C][C]0.471956[/C][/ROW]
[ROW][C]50[/C][C]0.620205[/C][C]0.75959[/C][C]0.379795[/C][/ROW]
[ROW][C]51[/C][C]0.607356[/C][C]0.785288[/C][C]0.392644[/C][/ROW]
[ROW][C]52[/C][C]0.583983[/C][C]0.832033[/C][C]0.416017[/C][/ROW]
[ROW][C]53[/C][C]0.539951[/C][C]0.920098[/C][C]0.460049[/C][/ROW]
[ROW][C]54[/C][C]0.547054[/C][C]0.905892[/C][C]0.452946[/C][/ROW]
[ROW][C]55[/C][C]0.515321[/C][C]0.969357[/C][C]0.484679[/C][/ROW]
[ROW][C]56[/C][C]0.473133[/C][C]0.946267[/C][C]0.526867[/C][/ROW]
[ROW][C]57[/C][C]0.433984[/C][C]0.867968[/C][C]0.566016[/C][/ROW]
[ROW][C]58[/C][C]0.417487[/C][C]0.834975[/C][C]0.582513[/C][/ROW]
[ROW][C]59[/C][C]0.497838[/C][C]0.995675[/C][C]0.502162[/C][/ROW]
[ROW][C]60[/C][C]0.583108[/C][C]0.833784[/C][C]0.416892[/C][/ROW]
[ROW][C]61[/C][C]0.637539[/C][C]0.724922[/C][C]0.362461[/C][/ROW]
[ROW][C]62[/C][C]0.67013[/C][C]0.659739[/C][C]0.32987[/C][/ROW]
[ROW][C]63[/C][C]0.638868[/C][C]0.722263[/C][C]0.361132[/C][/ROW]
[ROW][C]64[/C][C]0.602949[/C][C]0.794103[/C][C]0.397051[/C][/ROW]
[ROW][C]65[/C][C]0.602122[/C][C]0.795755[/C][C]0.397878[/C][/ROW]
[ROW][C]66[/C][C]0.647956[/C][C]0.704087[/C][C]0.352044[/C][/ROW]
[ROW][C]67[/C][C]0.688492[/C][C]0.623015[/C][C]0.311508[/C][/ROW]
[ROW][C]68[/C][C]0.644973[/C][C]0.710054[/C][C]0.355027[/C][/ROW]
[ROW][C]69[/C][C]0.948965[/C][C]0.10207[/C][C]0.0510349[/C][/ROW]
[ROW][C]70[/C][C]0.95539[/C][C]0.0892203[/C][C]0.0446101[/C][/ROW]
[ROW][C]71[/C][C]0.947835[/C][C]0.10433[/C][C]0.0521651[/C][/ROW]
[ROW][C]72[/C][C]0.947311[/C][C]0.105378[/C][C]0.0526889[/C][/ROW]
[ROW][C]73[/C][C]0.935496[/C][C]0.129009[/C][C]0.0645043[/C][/ROW]
[ROW][C]74[/C][C]0.925344[/C][C]0.149312[/C][C]0.0746559[/C][/ROW]
[ROW][C]75[/C][C]0.926612[/C][C]0.146777[/C][C]0.0733883[/C][/ROW]
[ROW][C]76[/C][C]0.92569[/C][C]0.148621[/C][C]0.0743105[/C][/ROW]
[ROW][C]77[/C][C]0.935932[/C][C]0.128136[/C][C]0.0640678[/C][/ROW]
[ROW][C]78[/C][C]0.917509[/C][C]0.164981[/C][C]0.0824907[/C][/ROW]
[ROW][C]79[/C][C]0.915253[/C][C]0.169494[/C][C]0.0847469[/C][/ROW]
[ROW][C]80[/C][C]0.94712[/C][C]0.10576[/C][C]0.0528802[/C][/ROW]
[ROW][C]81[/C][C]0.932283[/C][C]0.135434[/C][C]0.0677169[/C][/ROW]
[ROW][C]82[/C][C]0.921038[/C][C]0.157924[/C][C]0.0789619[/C][/ROW]
[ROW][C]83[/C][C]0.908996[/C][C]0.182008[/C][C]0.0910039[/C][/ROW]
[ROW][C]84[/C][C]0.896925[/C][C]0.206151[/C][C]0.103075[/C][/ROW]
[ROW][C]85[/C][C]0.875887[/C][C]0.248226[/C][C]0.124113[/C][/ROW]
[ROW][C]86[/C][C]0.864217[/C][C]0.271565[/C][C]0.135783[/C][/ROW]
[ROW][C]87[/C][C]0.838222[/C][C]0.323556[/C][C]0.161778[/C][/ROW]
[ROW][C]88[/C][C]0.811641[/C][C]0.376717[/C][C]0.188359[/C][/ROW]
[ROW][C]89[/C][C]0.773709[/C][C]0.452581[/C][C]0.226291[/C][/ROW]
[ROW][C]90[/C][C]0.730887[/C][C]0.538227[/C][C]0.269113[/C][/ROW]
[ROW][C]91[/C][C]0.723936[/C][C]0.552129[/C][C]0.276064[/C][/ROW]
[ROW][C]92[/C][C]0.689485[/C][C]0.62103[/C][C]0.310515[/C][/ROW]
[ROW][C]93[/C][C]0.918545[/C][C]0.16291[/C][C]0.0814549[/C][/ROW]
[ROW][C]94[/C][C]0.901656[/C][C]0.196688[/C][C]0.0983438[/C][/ROW]
[ROW][C]95[/C][C]0.891856[/C][C]0.216288[/C][C]0.108144[/C][/ROW]
[ROW][C]96[/C][C]0.868897[/C][C]0.262206[/C][C]0.131103[/C][/ROW]
[ROW][C]97[/C][C]0.84563[/C][C]0.308739[/C][C]0.15437[/C][/ROW]
[ROW][C]98[/C][C]0.845593[/C][C]0.308814[/C][C]0.154407[/C][/ROW]
[ROW][C]99[/C][C]0.827701[/C][C]0.344598[/C][C]0.172299[/C][/ROW]
[ROW][C]100[/C][C]0.793171[/C][C]0.413658[/C][C]0.206829[/C][/ROW]
[ROW][C]101[/C][C]0.749788[/C][C]0.500424[/C][C]0.250212[/C][/ROW]
[ROW][C]102[/C][C]0.7323[/C][C]0.535399[/C][C]0.2677[/C][/ROW]
[ROW][C]103[/C][C]0.738696[/C][C]0.522608[/C][C]0.261304[/C][/ROW]
[ROW][C]104[/C][C]0.746881[/C][C]0.506239[/C][C]0.253119[/C][/ROW]
[ROW][C]105[/C][C]0.699887[/C][C]0.600227[/C][C]0.300113[/C][/ROW]
[ROW][C]106[/C][C]0.66429[/C][C]0.671419[/C][C]0.33571[/C][/ROW]
[ROW][C]107[/C][C]0.644973[/C][C]0.710055[/C][C]0.355027[/C][/ROW]
[ROW][C]108[/C][C]0.623894[/C][C]0.752213[/C][C]0.376106[/C][/ROW]
[ROW][C]109[/C][C]0.598602[/C][C]0.802796[/C][C]0.401398[/C][/ROW]
[ROW][C]110[/C][C]0.618636[/C][C]0.762728[/C][C]0.381364[/C][/ROW]
[ROW][C]111[/C][C]0.575667[/C][C]0.848666[/C][C]0.424333[/C][/ROW]
[ROW][C]112[/C][C]0.508385[/C][C]0.98323[/C][C]0.491615[/C][/ROW]
[ROW][C]113[/C][C]0.470887[/C][C]0.941773[/C][C]0.529113[/C][/ROW]
[ROW][C]114[/C][C]0.408624[/C][C]0.817249[/C][C]0.591376[/C][/ROW]
[ROW][C]115[/C][C]0.399636[/C][C]0.799272[/C][C]0.600364[/C][/ROW]
[ROW][C]116[/C][C]0.408285[/C][C]0.816569[/C][C]0.591715[/C][/ROW]
[ROW][C]117[/C][C]0.331053[/C][C]0.662107[/C][C]0.668947[/C][/ROW]
[ROW][C]118[/C][C]0.683205[/C][C]0.63359[/C][C]0.316795[/C][/ROW]
[ROW][C]119[/C][C]0.765898[/C][C]0.468203[/C][C]0.234102[/C][/ROW]
[ROW][C]120[/C][C]0.799245[/C][C]0.401509[/C][C]0.200755[/C][/ROW]
[ROW][C]121[/C][C]0.770824[/C][C]0.458351[/C][C]0.229176[/C][/ROW]
[ROW][C]122[/C][C]0.888471[/C][C]0.223057[/C][C]0.111529[/C][/ROW]
[ROW][C]123[/C][C]0.947582[/C][C]0.104836[/C][C]0.0524179[/C][/ROW]
[ROW][C]124[/C][C]0.896261[/C][C]0.207478[/C][C]0.103739[/C][/ROW]
[ROW][C]125[/C][C]0.873639[/C][C]0.252723[/C][C]0.126361[/C][/ROW]
[ROW][C]126[/C][C]0.74709[/C][C]0.50582[/C][C]0.25291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271055&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271055&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.5114290.9771420.488571
140.5251260.9497480.474874
150.4553610.9107220.544639
160.3531820.7063650.646818
170.265760.531520.73424
180.3208660.6417320.679134
190.2590450.5180890.740955
200.2325850.465170.767415
210.2086770.4173540.791323
220.1510350.3020690.848965
230.1036780.2073560.896322
240.07222690.1444540.927773
250.05871320.1174260.941287
260.06917230.1383450.930828
270.1109490.2218990.889051
280.1540150.308030.845985
290.1881960.3763920.811804
300.3481920.6963830.651808
310.3298780.6597560.670122
320.2709620.5419240.729038
330.2251470.4502940.774853
340.2346990.4693970.765301
350.1894490.3788990.810551
360.3980770.7961540.601923
370.3421580.6843170.657842
380.3038820.6077630.696118
390.2882650.5765310.711735
400.2419990.4839980.758001
410.2232050.446410.776795
420.2665220.5330430.733478
430.2314640.4629280.768536
440.3835280.7670560.616472
450.3331470.6662950.666853
460.4572910.9145810.542709
470.4594810.9189610.540519
480.4754940.9509880.524506
490.5280440.9439130.471956
500.6202050.759590.379795
510.6073560.7852880.392644
520.5839830.8320330.416017
530.5399510.9200980.460049
540.5470540.9058920.452946
550.5153210.9693570.484679
560.4731330.9462670.526867
570.4339840.8679680.566016
580.4174870.8349750.582513
590.4978380.9956750.502162
600.5831080.8337840.416892
610.6375390.7249220.362461
620.670130.6597390.32987
630.6388680.7222630.361132
640.6029490.7941030.397051
650.6021220.7957550.397878
660.6479560.7040870.352044
670.6884920.6230150.311508
680.6449730.7100540.355027
690.9489650.102070.0510349
700.955390.08922030.0446101
710.9478350.104330.0521651
720.9473110.1053780.0526889
730.9354960.1290090.0645043
740.9253440.1493120.0746559
750.9266120.1467770.0733883
760.925690.1486210.0743105
770.9359320.1281360.0640678
780.9175090.1649810.0824907
790.9152530.1694940.0847469
800.947120.105760.0528802
810.9322830.1354340.0677169
820.9210380.1579240.0789619
830.9089960.1820080.0910039
840.8969250.2061510.103075
850.8758870.2482260.124113
860.8642170.2715650.135783
870.8382220.3235560.161778
880.8116410.3767170.188359
890.7737090.4525810.226291
900.7308870.5382270.269113
910.7239360.5521290.276064
920.6894850.621030.310515
930.9185450.162910.0814549
940.9016560.1966880.0983438
950.8918560.2162880.108144
960.8688970.2622060.131103
970.845630.3087390.15437
980.8455930.3088140.154407
990.8277010.3445980.172299
1000.7931710.4136580.206829
1010.7497880.5004240.250212
1020.73230.5353990.2677
1030.7386960.5226080.261304
1040.7468810.5062390.253119
1050.6998870.6002270.300113
1060.664290.6714190.33571
1070.6449730.7100550.355027
1080.6238940.7522130.376106
1090.5986020.8027960.401398
1100.6186360.7627280.381364
1110.5756670.8486660.424333
1120.5083850.983230.491615
1130.4708870.9417730.529113
1140.4086240.8172490.591376
1150.3996360.7992720.600364
1160.4082850.8165690.591715
1170.3310530.6621070.668947
1180.6832050.633590.316795
1190.7658980.4682030.234102
1200.7992450.4015090.200755
1210.7708240.4583510.229176
1220.8884710.2230570.111529
1230.9475820.1048360.0524179
1240.8962610.2074780.103739
1250.8736390.2527230.126361
1260.747090.505820.25291






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 level10.00877193OK

\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 & 1 & 0.00877193 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271055&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]1[/C][C]0.00877193[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271055&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271055&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00877193OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
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')
}