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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 14:33:01 +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/t14189132244pd6chf3guzwt9w.htm/, Retrieved Fri, 17 May 2024 19:57:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271000, Retrieved Fri, 17 May 2024 19:57:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75

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 14:33:01] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 7 18 20 21 12 149 18 68 1.8
12.2 18 20 23 19 22 8 139 31 39 2.1
12.8 12 9 22 18 21 11 148 39 32 2.2
7.4 24 19 22 24 21 13 158 46 62 2.3
6.7 16 12 19 20 21 11 128 31 33 2.1
12.6 19 16 25 20 21 10 224 67 52 2.7
14.8 16 17 28 24 21 7 159 35 62 2.1
13.3 15 9 16 21 23 10 105 52 77 2.4
11.1 28 28 28 28 22 15 159 77 76 2.9
8.2 21 20 21 10 25 12 167 37 41 2.2
11.4 18 16 22 22 21 12 165 32 48 2.1
6.4 22 22 24 19 23 10 159 36 63 2.2
10.6 19 17 24 27 22 10 119 38 30 2.2
12 22 12 26 23 21 14 176 69 78 2.7
6.3 25 18 28 24 21 6 54 21 19 1.9
11.9 16 12 20 25 21 14 163 54 66 2.5
9.3 19 16 26 24 21 11 124 36 35 2.2
10 26 21 28 28 24 12 121 23 45 1.9
6.4 24 15 27 28 23 15 153 34 21 2.1
13.8 20 17 23 22 21 13 148 112 25 3.5
10.8 19 17 24 26 24 11 221 35 44 2.1
13.8 19 17 24 26 23 12 188 47 69 2.3
11.7 23 18 22 21 21 7 149 47 54 2.3
10.9 18 15 21 26 22 11 244 37 74 2.2
9.9 21 21 21 24 21 12 150 20 61 1.9
11.5 20 12 26 25 22 13 153 22 41 1.9
8.3 15 6 23 24 22 9 94 23 46 1.9
11.7 19 13 21 20 21 11 156 32 39 2.1
9 19 19 27 24 21 12 132 30 34 2
9.7 7 12 25 25 25 15 161 92 51 3.2
10.8 20 14 23 23 22 12 105 43 42 2.3
10.3 20 13 25 21 22 6 97 55 31 2.5
10.4 19 12 23 23 20 5 151 16 39 1.8
9.3 20 19 22 18 21 11 166 71 49 2.8
11.8 18 10 24 24 21 6 157 43 53 2.3
5.9 14 10 19 18 22 12 111 29 31 2
11.4 17 11 21 21 21 10 145 56 39 2.5
13 17 11 27 23 24 6 162 46 54 2.3
10.8 8 10 25 25 22 12 163 19 49 1.8
11.3 22 22 23 22 21 6 187 59 46 2.6
11.8 20 12 17 23 22 12 109 30 55 2
12.7 22 20 25 25 22 8 105 7 50 1.6
10.9 14 11 24 24 23 12 148 19 30 1.8
13.3 21 17 20 23 23 14 125 48 45 2.4
10.1 20 14 19 27 21 12 116 23 35 1.9
14.3 18 16 21 23 21 14 138 33 41 2.1
9.3 24 15 18 23 22 11 164 34 73 2.1
12.5 19 15 27 24 21 10 162 48 17 2.4
7.6 16 10 25 26 21 7 99 18 40 1.8
15.9 16 10 20 20 21 12 202 43 64 2.3
9.2 16 18 21 23 21 7 186 33 37 2.1
11.1 22 22 27 23 21 12 183 71 65 2.8
13 21 16 24 17 22 10 214 26 100 2
14.5 15 10 27 20 22 10 188 67 28 2.7
12.3 15 16 23 18 21 12 177 80 56 2.9
11.4 14 16 24 19 23 12 126 29 29 2
12.6 14 5 27 26 21 5 139 32 50 2.1
NA 19 18 24 14 21 10 78 47 3 2.3
13 16 10 25 25 21 10 162 43 59 2.3
13.2 26 16 24 18 20 11 159 29 61 2
7.7 18 16 23 26 21 12 110 32 51 2.1
4.35 17 15 22 15 22 9 48 23 12 1
12.7 6 4 24 27 22 11 50 16 45 1
18.1 22 9 19 23 22 12 150 33 37 4
17.85 20 18 25 23 20 12 154 32 37 4
17.1 17 12 24 22 22 12 194 52 68 4
19.1 20 16 28 20 21 12 158 75 72 4
16.1 23 17 23 21 21 10 159 72 143 4
13.35 18 14 19 25 21 15 67 15 9 2
18.4 13 13 19 19 21 10 147 29 55 4
14.7 22 20 27 25 21 15 39 13 17 1
10.6 20 16 24 24 21 10 100 40 37 3
12.6 20 15 26 22 21 15 111 19 27 3
16.2 13 10 21 28 22 9 138 24 37 4
13.6 16 16 25 22 24 15 101 121 58 3
14.1 16 15 19 23 22 13 101 36 21 3
14.5 15 16 20 19 20 12 114 23 19 3
16.15 19 19 26 21 21 12 165 85 78 4
14.75 19 9 27 25 24 8 114 41 35 3
14.8 24 19 23 23 25 9 111 46 48 3
12.45 9 7 18 28 22 15 75 18 27 2
12.65 22 23 23 14 21 12 82 35 43 2
17.35 15 14 21 23 21 12 121 17 30 3
8.6 22 10 23 24 22 15 32 4 25 1
18.4 22 16 22 25 23 11 150 28 69 4
16.1 24 12 21 15 24 12 117 44 72 3
17.75 21 7 24 26 22 14 165 38 13 4
15.25 25 20 26 21 25 12 154 57 61 4
17.65 26 9 24 26 22 12 126 23 43 4
16.35 21 12 22 23 21 12 149 36 51 4
17.65 14 10 20 15 21 11 145 22 67 4
13.6 28 19 20 16 21 12 120 40 36 3
14.35 21 11 18 20 22 12 109 31 44 3
14.75 16 15 18 20 22 12 132 11 45 4
18.25 16 14 25 21 21 12 172 38 34 4
9.9 25 11 28 28 22 8 169 24 36 4
16 21 14 23 19 23 8 114 37 72 3
18.25 22 15 20 21 21 12 156 37 39 4
16.85 9 7 22 22 21 12 172 22 43 4
18.95 24 22 23 17 21 11 167 43 80 4
15.6 22 11 20 26 21 12 113 31 40 3
17.1 10 12 24 22 22 10 173 31 61 4
16.1 22 17 18 17 22 11 2 -4 23 1
15.4 21 13 23 16 21 11 165 21 29 4
15.4 20 15 21 18 21 11 165 21 29 4
13.35 17 11 19 17 25 13 118 32 54 3
19.1 7 7 19 25 21 7 158 26 43 4
7.6 14 13 25 21 25 8 49 32 20 1
19.1 23 7 18 27 22 11 155 33 61 4
14.75 18 11 22 23 21 8 151 30 57 4
19.25 17 22 5 8 23 14 220 67 54 4
13.6 20 15 24 22 20 9 141 22 36 4
12.75 19 15 28 28 22 13 122 33 16 4
9.85 19 11 27 24 25 13 44 24 40 1
15.25 23 10 23 25 20 11 152 28 27 4
11.9 20 18 24 23 21 9 107 41 61 3
16.35 19 14 25 26 21 12 154 31 69 4
12.4 16 16 19 22 23 12 103 33 34 3
18.15 21 16 24 22 22 13 175 21 34 4
17.75 20 17 28 26 21 11 143 52 34 4
12.35 20 14 19 21 21 11 110 29 13 3
15.6 19 10 23 21 21 9 131 11 12 4
19.3 19 16 23 24 21 12 167 26 51 4
17.1 20 16 26 18 21 15 137 7 19 4
18.4 22 17 25 26 21 14 121 13 81 3
19.05 19 12 24 23 21 12 149 20 42 4
18.55 23 17 23 25 22 9 168 52 22 4
19.1 16 11 22 20 21 9 140 28 85 4
12.85 18 12 26 26 22 13 168 39 25 4
9.5 23 8 23 19 22 15 94 9 22 2
4.5 20 17 22 21 22 11 51 19 19 1
13.6 23 17 22 24 22 10 145 60 45 4
11.7 13 7 17 6 23 11 66 19 45 2
13.35 26 18 22 21 22 14 109 14 51 3
17.6 13 14 26 19 21 12 164 -2 73 4
14.05 10 13 24 24 21 13 119 51 24 3
16.1 21 19 27 21 20 11 126 2 61 4
13.35 24 15 22 21 20 11 132 24 23 4
11.85 21 15 23 26 21 13 142 40 14 4
11.95 23 8 22 24 21 12 83 20 54 2
13.2 16 11 20 23 21 9 166 20 36 4
7.7 26 17 27 26 24 13 93 25 26 2
14.6 16 12 20 20 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 time6 seconds
R Server'George Udny Yule' @ yule.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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.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=271000&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.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=271000&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271000&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 time6 seconds
R Server'George Udny Yule' @ yule.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] = + 10.4856 -0.0697501AMS.I2[t] -0.0337005AMS.I3[t] -0.0452417AMS.E1[t] -0.0331666AMS.E3[t] -0.131487age[t] + 0.120834CONFSOFTTOT[t] -0.00686327LFM[t] -0.0226934PRH[t] + 0.0370415CH[t] + 2.76799PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.4856 -0.0697501AMS.I2[t] -0.0337005AMS.I3[t] -0.0452417AMS.E1[t] -0.0331666AMS.E3[t] -0.131487age[t] +  0.120834CONFSOFTTOT[t] -0.00686327LFM[t] -0.0226934PRH[t] +  0.0370415CH[t] +  2.76799PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271000&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.4856 -0.0697501AMS.I2[t] -0.0337005AMS.I3[t] -0.0452417AMS.E1[t] -0.0331666AMS.E3[t] -0.131487age[t] +  0.120834CONFSOFTTOT[t] -0.00686327LFM[t] -0.0226934PRH[t] +  0.0370415CH[t] +  2.76799PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271000&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271000&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] = + 10.4856 -0.0697501AMS.I2[t] -0.0337005AMS.I3[t] -0.0452417AMS.E1[t] -0.0331666AMS.E3[t] -0.131487age[t] + 0.120834CONFSOFTTOT[t] -0.00686327LFM[t] -0.0226934PRH[t] + 0.0370415CH[t] + 2.76799PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.48564.424612.370.01925560.00962779
AMS.I2-0.06975010.0539974-1.2920.1987250.0993625
AMS.I3-0.03370050.0588418-0.57270.5678090.283904
AMS.E1-0.04524170.0692925-0.65290.514960.25748
AMS.E3-0.03316660.0588022-0.5640.5736940.286847
age-0.1314870.175729-0.74820.4556570.227829
CONFSOFTTOT0.1208340.08850541.3650.1745060.0872531
LFM-0.006863270.00621388-1.1050.2713980.135699
PRH-0.02269340.0112046-2.0250.04486160.0224308
CH0.03704150.01075043.4460.0007660940.000383047
PR2.767990.24139911.471.65901e-218.29507e-22

\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) & 10.4856 & 4.42461 & 2.37 & 0.0192556 & 0.00962779 \tabularnewline
AMS.I2 & -0.0697501 & 0.0539974 & -1.292 & 0.198725 & 0.0993625 \tabularnewline
AMS.I3 & -0.0337005 & 0.0588418 & -0.5727 & 0.567809 & 0.283904 \tabularnewline
AMS.E1 & -0.0452417 & 0.0692925 & -0.6529 & 0.51496 & 0.25748 \tabularnewline
AMS.E3 & -0.0331666 & 0.0588022 & -0.564 & 0.573694 & 0.286847 \tabularnewline
age & -0.131487 & 0.175729 & -0.7482 & 0.455657 & 0.227829 \tabularnewline
CONFSOFTTOT & 0.120834 & 0.0885054 & 1.365 & 0.174506 & 0.0872531 \tabularnewline
LFM & -0.00686327 & 0.00621388 & -1.105 & 0.271398 & 0.135699 \tabularnewline
PRH & -0.0226934 & 0.0112046 & -2.025 & 0.0448616 & 0.0224308 \tabularnewline
CH & 0.0370415 & 0.0107504 & 3.446 & 0.000766094 & 0.000383047 \tabularnewline
PR & 2.76799 & 0.241399 & 11.47 & 1.65901e-21 & 8.29507e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271000&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]10.4856[/C][C]4.42461[/C][C]2.37[/C][C]0.0192556[/C][C]0.00962779[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0697501[/C][C]0.0539974[/C][C]-1.292[/C][C]0.198725[/C][C]0.0993625[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0337005[/C][C]0.0588418[/C][C]-0.5727[/C][C]0.567809[/C][C]0.283904[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0452417[/C][C]0.0692925[/C][C]-0.6529[/C][C]0.51496[/C][C]0.25748[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0331666[/C][C]0.0588022[/C][C]-0.564[/C][C]0.573694[/C][C]0.286847[/C][/ROW]
[ROW][C]age[/C][C]-0.131487[/C][C]0.175729[/C][C]-0.7482[/C][C]0.455657[/C][C]0.227829[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.120834[/C][C]0.0885054[/C][C]1.365[/C][C]0.174506[/C][C]0.0872531[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00686327[/C][C]0.00621388[/C][C]-1.105[/C][C]0.271398[/C][C]0.135699[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0226934[/C][C]0.0112046[/C][C]-2.025[/C][C]0.0448616[/C][C]0.0224308[/C][/ROW]
[ROW][C]CH[/C][C]0.0370415[/C][C]0.0107504[/C][C]3.446[/C][C]0.000766094[/C][C]0.000383047[/C][/ROW]
[ROW][C]PR[/C][C]2.76799[/C][C]0.241399[/C][C]11.47[/C][C]1.65901e-21[/C][C]8.29507e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271000&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271000&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)10.48564.424612.370.01925560.00962779
AMS.I2-0.06975010.0539974-1.2920.1987250.0993625
AMS.I3-0.03370050.0588418-0.57270.5678090.283904
AMS.E1-0.04524170.0692925-0.65290.514960.25748
AMS.E3-0.03316660.0588022-0.5640.5736940.286847
age-0.1314870.175729-0.74820.4556570.227829
CONFSOFTTOT0.1208340.08850541.3650.1745060.0872531
LFM-0.006863270.00621388-1.1050.2713980.135699
PRH-0.02269340.0112046-2.0250.04486160.0224308
CH0.03704150.01075043.4460.0007660940.000383047
PR2.767990.24139911.471.65901e-218.29507e-22






Multiple Linear Regression - Regression Statistics
Multiple R0.772729
R-squared0.59711
Adjusted R-squared0.566355
F-TEST (value)19.4151
F-TEST (DF numerator)10
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.29476
Sum Squared Residuals689.837

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.772729 \tabularnewline
R-squared & 0.59711 \tabularnewline
Adjusted R-squared & 0.566355 \tabularnewline
F-TEST (value) & 19.4151 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 131 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.29476 \tabularnewline
Sum Squared Residuals & 689.837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271000&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.772729[/C][/ROW]
[ROW][C]R-squared[/C][C]0.59711[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.566355[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.4151[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]131[/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]2.29476[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]689.837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271000&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271000&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 R0.772729
R-squared0.59711
Adjusted R-squared0.566355
F-TEST (value)19.4151
F-TEST (DF numerator)10
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.29476
Sum Squared Residuals689.837






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9729-0.0728736
212.210.55921.64079
312.811.6951.10499
47.411.7242-4.32423
56.711.4634-4.76335
612.611.61580.984236
714.811.04233.75766
813.313.4945-0.194473
911.112.317-1.21701
108.210.8504-2.65037
1111.411.38680.0131845
126.411.1928-4.79281
1310.610.44350.156505
141213.127-1.12704
156.39.15202-2.85202
1611.913.1822-1.28219
179.310.9348-1.63481
18109.63690.363096
196.49.71319-3.31319
2013.812.61361.18636
2110.89.944330.855669
2213.811.63052.16954
2311.710.94490.755082
2410.911.665-0.764984
259.911.2912-1.39118
2611.510.58740.912597
278.311.3914-3.09136
2811.711.13730.562697
29910.3999-1.39992
309.713.712-4.01196
3110.811.5983-0.798339
3210.310.8116-0.51161
3310.49.954510.445485
349.312.2408-2.94078
3511.811.25130.548719
365.911.5371-5.63711
3711.411.8283-0.428254
381310.7252.27504
3910.811.4352-0.635192
4011.310.68160.618437
4111.811.8559-0.0558935
4212.79.792182.90782
4310.910.32910.570927
4413.311.81061.48941
4510.110.79-0.690011
4614.311.54392.75612
479.311.785-2.48501
4812.510.15622.34384
497.610.4999-2.89991
5015.912.5283.37197
519.210.2925-1.09254
5211.112.205-1.10496
531312.3290.67102
5414.511.23313.26692
5512.313.0226-0.722573
5611.410.7670.632982
5712.611.08431.51567
58NANA1.01636
591310.99572.00435
6013.217.1975-3.99751
617.711.2074-3.5074
624.351.766042.58396
6312.710.84681.85322
6418.116.31971.78026
6517.8517.46650.383488
6617.114.26262.83739
6719.121.6622-2.5622
6816.113.93992.16009
6913.3512.49030.859679
7018.411.81276.58732
7114.717.2971-2.59713
7210.611.8415-1.24151
7312.612.9086-0.308608
7416.215.23980.960225
7513.612.99150.60853
7614.113.48880.611207
7714.514.5858-0.0858449
7816.1513.90492.2451
7914.7512.39452.35554
8014.814.76160.0384153
8112.4510.93161.51843
8212.659.442373.20763
8317.3517.8309-0.480943
848.67.055291.54471
8518.416.72341.67663
8616.113.54452.55552
8717.7517.8389-0.0889026
8815.2513.8561.39403
8917.6517.9686-0.318551
9016.3517.097-0.74701
9117.6517.10170.548309
9213.613.46190.138055
9314.3517.1269-2.77694
9414.7512.54762.20244
9518.2523.3006-5.05058
969.98.069661.83034
971613.88932.11072
9818.2518.9707-0.720742
9916.8514.8472.00297
10018.9517.15861.79142
10115.615.8245-0.224494
10217.110.13346.96656
10316.116.8166-0.716617
10415.416.1431-0.743116
10515.416.6075-1.20753
10613.3511.39761.95239
10719.118.86930.230743
1087.65.390852.20915
10919.121.1228-2.02285
11014.7512.27622.47382
11119.2521.8159-2.56586
11213.616.0659-2.46595
11312.7511.35881.39121
1149.8510.3675-0.517491
11515.2517.2103-1.96032
11611.912.8013-0.901324
11716.3517.7245-1.37451
11812.410.2482.15197
11918.1515.52642.62361
12017.7518.4031-0.653074
12112.3512.5303-0.180296
12215.612.99822.60176
12319.318.7060.593991
12417.114.19412.90588
12518.416.09732.30271
12619.0514.68734.36272
12718.5517.74070.809272
12819.121.6751-2.57512
12912.8514.7123-1.86231
1309.512.9529-3.45289
1314.56.11479-1.61479
13213.614.8985-1.29849
13311.712.6499-0.949906
13413.3514.4352-1.08517
13517.617.04670.553332
13614.0515.5333-1.48327
13716.118.537-2.43702
13813.3516.6303-3.28028
13911.8511.9219-0.0718953
14011.9515.2198-3.26978
14113.215.2239-2.02391
1427.76.804170.89583
14314.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9729 & -0.0728736 \tabularnewline
2 & 12.2 & 10.5592 & 1.64079 \tabularnewline
3 & 12.8 & 11.695 & 1.10499 \tabularnewline
4 & 7.4 & 11.7242 & -4.32423 \tabularnewline
5 & 6.7 & 11.4634 & -4.76335 \tabularnewline
6 & 12.6 & 11.6158 & 0.984236 \tabularnewline
7 & 14.8 & 11.0423 & 3.75766 \tabularnewline
8 & 13.3 & 13.4945 & -0.194473 \tabularnewline
9 & 11.1 & 12.317 & -1.21701 \tabularnewline
10 & 8.2 & 10.8504 & -2.65037 \tabularnewline
11 & 11.4 & 11.3868 & 0.0131845 \tabularnewline
12 & 6.4 & 11.1928 & -4.79281 \tabularnewline
13 & 10.6 & 10.4435 & 0.156505 \tabularnewline
14 & 12 & 13.127 & -1.12704 \tabularnewline
15 & 6.3 & 9.15202 & -2.85202 \tabularnewline
16 & 11.9 & 13.1822 & -1.28219 \tabularnewline
17 & 9.3 & 10.9348 & -1.63481 \tabularnewline
18 & 10 & 9.6369 & 0.363096 \tabularnewline
19 & 6.4 & 9.71319 & -3.31319 \tabularnewline
20 & 13.8 & 12.6136 & 1.18636 \tabularnewline
21 & 10.8 & 9.94433 & 0.855669 \tabularnewline
22 & 13.8 & 11.6305 & 2.16954 \tabularnewline
23 & 11.7 & 10.9449 & 0.755082 \tabularnewline
24 & 10.9 & 11.665 & -0.764984 \tabularnewline
25 & 9.9 & 11.2912 & -1.39118 \tabularnewline
26 & 11.5 & 10.5874 & 0.912597 \tabularnewline
27 & 8.3 & 11.3914 & -3.09136 \tabularnewline
28 & 11.7 & 11.1373 & 0.562697 \tabularnewline
29 & 9 & 10.3999 & -1.39992 \tabularnewline
30 & 9.7 & 13.712 & -4.01196 \tabularnewline
31 & 10.8 & 11.5983 & -0.798339 \tabularnewline
32 & 10.3 & 10.8116 & -0.51161 \tabularnewline
33 & 10.4 & 9.95451 & 0.445485 \tabularnewline
34 & 9.3 & 12.2408 & -2.94078 \tabularnewline
35 & 11.8 & 11.2513 & 0.548719 \tabularnewline
36 & 5.9 & 11.5371 & -5.63711 \tabularnewline
37 & 11.4 & 11.8283 & -0.428254 \tabularnewline
38 & 13 & 10.725 & 2.27504 \tabularnewline
39 & 10.8 & 11.4352 & -0.635192 \tabularnewline
40 & 11.3 & 10.6816 & 0.618437 \tabularnewline
41 & 11.8 & 11.8559 & -0.0558935 \tabularnewline
42 & 12.7 & 9.79218 & 2.90782 \tabularnewline
43 & 10.9 & 10.3291 & 0.570927 \tabularnewline
44 & 13.3 & 11.8106 & 1.48941 \tabularnewline
45 & 10.1 & 10.79 & -0.690011 \tabularnewline
46 & 14.3 & 11.5439 & 2.75612 \tabularnewline
47 & 9.3 & 11.785 & -2.48501 \tabularnewline
48 & 12.5 & 10.1562 & 2.34384 \tabularnewline
49 & 7.6 & 10.4999 & -2.89991 \tabularnewline
50 & 15.9 & 12.528 & 3.37197 \tabularnewline
51 & 9.2 & 10.2925 & -1.09254 \tabularnewline
52 & 11.1 & 12.205 & -1.10496 \tabularnewline
53 & 13 & 12.329 & 0.67102 \tabularnewline
54 & 14.5 & 11.2331 & 3.26692 \tabularnewline
55 & 12.3 & 13.0226 & -0.722573 \tabularnewline
56 & 11.4 & 10.767 & 0.632982 \tabularnewline
57 & 12.6 & 11.0843 & 1.51567 \tabularnewline
58 & NA & NA & 1.01636 \tabularnewline
59 & 13 & 10.9957 & 2.00435 \tabularnewline
60 & 13.2 & 17.1975 & -3.99751 \tabularnewline
61 & 7.7 & 11.2074 & -3.5074 \tabularnewline
62 & 4.35 & 1.76604 & 2.58396 \tabularnewline
63 & 12.7 & 10.8468 & 1.85322 \tabularnewline
64 & 18.1 & 16.3197 & 1.78026 \tabularnewline
65 & 17.85 & 17.4665 & 0.383488 \tabularnewline
66 & 17.1 & 14.2626 & 2.83739 \tabularnewline
67 & 19.1 & 21.6622 & -2.5622 \tabularnewline
68 & 16.1 & 13.9399 & 2.16009 \tabularnewline
69 & 13.35 & 12.4903 & 0.859679 \tabularnewline
70 & 18.4 & 11.8127 & 6.58732 \tabularnewline
71 & 14.7 & 17.2971 & -2.59713 \tabularnewline
72 & 10.6 & 11.8415 & -1.24151 \tabularnewline
73 & 12.6 & 12.9086 & -0.308608 \tabularnewline
74 & 16.2 & 15.2398 & 0.960225 \tabularnewline
75 & 13.6 & 12.9915 & 0.60853 \tabularnewline
76 & 14.1 & 13.4888 & 0.611207 \tabularnewline
77 & 14.5 & 14.5858 & -0.0858449 \tabularnewline
78 & 16.15 & 13.9049 & 2.2451 \tabularnewline
79 & 14.75 & 12.3945 & 2.35554 \tabularnewline
80 & 14.8 & 14.7616 & 0.0384153 \tabularnewline
81 & 12.45 & 10.9316 & 1.51843 \tabularnewline
82 & 12.65 & 9.44237 & 3.20763 \tabularnewline
83 & 17.35 & 17.8309 & -0.480943 \tabularnewline
84 & 8.6 & 7.05529 & 1.54471 \tabularnewline
85 & 18.4 & 16.7234 & 1.67663 \tabularnewline
86 & 16.1 & 13.5445 & 2.55552 \tabularnewline
87 & 17.75 & 17.8389 & -0.0889026 \tabularnewline
88 & 15.25 & 13.856 & 1.39403 \tabularnewline
89 & 17.65 & 17.9686 & -0.318551 \tabularnewline
90 & 16.35 & 17.097 & -0.74701 \tabularnewline
91 & 17.65 & 17.1017 & 0.548309 \tabularnewline
92 & 13.6 & 13.4619 & 0.138055 \tabularnewline
93 & 14.35 & 17.1269 & -2.77694 \tabularnewline
94 & 14.75 & 12.5476 & 2.20244 \tabularnewline
95 & 18.25 & 23.3006 & -5.05058 \tabularnewline
96 & 9.9 & 8.06966 & 1.83034 \tabularnewline
97 & 16 & 13.8893 & 2.11072 \tabularnewline
98 & 18.25 & 18.9707 & -0.720742 \tabularnewline
99 & 16.85 & 14.847 & 2.00297 \tabularnewline
100 & 18.95 & 17.1586 & 1.79142 \tabularnewline
101 & 15.6 & 15.8245 & -0.224494 \tabularnewline
102 & 17.1 & 10.1334 & 6.96656 \tabularnewline
103 & 16.1 & 16.8166 & -0.716617 \tabularnewline
104 & 15.4 & 16.1431 & -0.743116 \tabularnewline
105 & 15.4 & 16.6075 & -1.20753 \tabularnewline
106 & 13.35 & 11.3976 & 1.95239 \tabularnewline
107 & 19.1 & 18.8693 & 0.230743 \tabularnewline
108 & 7.6 & 5.39085 & 2.20915 \tabularnewline
109 & 19.1 & 21.1228 & -2.02285 \tabularnewline
110 & 14.75 & 12.2762 & 2.47382 \tabularnewline
111 & 19.25 & 21.8159 & -2.56586 \tabularnewline
112 & 13.6 & 16.0659 & -2.46595 \tabularnewline
113 & 12.75 & 11.3588 & 1.39121 \tabularnewline
114 & 9.85 & 10.3675 & -0.517491 \tabularnewline
115 & 15.25 & 17.2103 & -1.96032 \tabularnewline
116 & 11.9 & 12.8013 & -0.901324 \tabularnewline
117 & 16.35 & 17.7245 & -1.37451 \tabularnewline
118 & 12.4 & 10.248 & 2.15197 \tabularnewline
119 & 18.15 & 15.5264 & 2.62361 \tabularnewline
120 & 17.75 & 18.4031 & -0.653074 \tabularnewline
121 & 12.35 & 12.5303 & -0.180296 \tabularnewline
122 & 15.6 & 12.9982 & 2.60176 \tabularnewline
123 & 19.3 & 18.706 & 0.593991 \tabularnewline
124 & 17.1 & 14.1941 & 2.90588 \tabularnewline
125 & 18.4 & 16.0973 & 2.30271 \tabularnewline
126 & 19.05 & 14.6873 & 4.36272 \tabularnewline
127 & 18.55 & 17.7407 & 0.809272 \tabularnewline
128 & 19.1 & 21.6751 & -2.57512 \tabularnewline
129 & 12.85 & 14.7123 & -1.86231 \tabularnewline
130 & 9.5 & 12.9529 & -3.45289 \tabularnewline
131 & 4.5 & 6.11479 & -1.61479 \tabularnewline
132 & 13.6 & 14.8985 & -1.29849 \tabularnewline
133 & 11.7 & 12.6499 & -0.949906 \tabularnewline
134 & 13.35 & 14.4352 & -1.08517 \tabularnewline
135 & 17.6 & 17.0467 & 0.553332 \tabularnewline
136 & 14.05 & 15.5333 & -1.48327 \tabularnewline
137 & 16.1 & 18.537 & -2.43702 \tabularnewline
138 & 13.35 & 16.6303 & -3.28028 \tabularnewline
139 & 11.85 & 11.9219 & -0.0718953 \tabularnewline
140 & 11.95 & 15.2198 & -3.26978 \tabularnewline
141 & 13.2 & 15.2239 & -2.02391 \tabularnewline
142 & 7.7 & 6.80417 & 0.89583 \tabularnewline
143 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271000&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]12.9729[/C][C]-0.0728736[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5592[/C][C]1.64079[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.695[/C][C]1.10499[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.7242[/C][C]-4.32423[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.4634[/C][C]-4.76335[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.6158[/C][C]0.984236[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.0423[/C][C]3.75766[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.4945[/C][C]-0.194473[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.317[/C][C]-1.21701[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.8504[/C][C]-2.65037[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.3868[/C][C]0.0131845[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.1928[/C][C]-4.79281[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.4435[/C][C]0.156505[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.127[/C][C]-1.12704[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.15202[/C][C]-2.85202[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.1822[/C][C]-1.28219[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9348[/C][C]-1.63481[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.6369[/C][C]0.363096[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]9.71319[/C][C]-3.31319[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.6136[/C][C]1.18636[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]9.94433[/C][C]0.855669[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.6305[/C][C]2.16954[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.9449[/C][C]0.755082[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.665[/C][C]-0.764984[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.2912[/C][C]-1.39118[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5874[/C][C]0.912597[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.3914[/C][C]-3.09136[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1373[/C][C]0.562697[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3999[/C][C]-1.39992[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.712[/C][C]-4.01196[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.5983[/C][C]-0.798339[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.8116[/C][C]-0.51161[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.95451[/C][C]0.445485[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2408[/C][C]-2.94078[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.2513[/C][C]0.548719[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.5371[/C][C]-5.63711[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8283[/C][C]-0.428254[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.725[/C][C]2.27504[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.4352[/C][C]-0.635192[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.6816[/C][C]0.618437[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.8559[/C][C]-0.0558935[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.79218[/C][C]2.90782[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.3291[/C][C]0.570927[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.8106[/C][C]1.48941[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.79[/C][C]-0.690011[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.5439[/C][C]2.75612[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.785[/C][C]-2.48501[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.1562[/C][C]2.34384[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.4999[/C][C]-2.89991[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.528[/C][C]3.37197[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.2925[/C][C]-1.09254[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.205[/C][C]-1.10496[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.329[/C][C]0.67102[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.2331[/C][C]3.26692[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0226[/C][C]-0.722573[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.767[/C][C]0.632982[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]11.0843[/C][C]1.51567[/C][/ROW]
[ROW][C]58[/C][C]NA[/C][C]NA[/C][C]1.01636[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.9957[/C][C]2.00435[/C][/ROW]
[ROW][C]60[/C][C]13.2[/C][C]17.1975[/C][C]-3.99751[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]11.2074[/C][C]-3.5074[/C][/ROW]
[ROW][C]62[/C][C]4.35[/C][C]1.76604[/C][C]2.58396[/C][/ROW]
[ROW][C]63[/C][C]12.7[/C][C]10.8468[/C][C]1.85322[/C][/ROW]
[ROW][C]64[/C][C]18.1[/C][C]16.3197[/C][C]1.78026[/C][/ROW]
[ROW][C]65[/C][C]17.85[/C][C]17.4665[/C][C]0.383488[/C][/ROW]
[ROW][C]66[/C][C]17.1[/C][C]14.2626[/C][C]2.83739[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]21.6622[/C][C]-2.5622[/C][/ROW]
[ROW][C]68[/C][C]16.1[/C][C]13.9399[/C][C]2.16009[/C][/ROW]
[ROW][C]69[/C][C]13.35[/C][C]12.4903[/C][C]0.859679[/C][/ROW]
[ROW][C]70[/C][C]18.4[/C][C]11.8127[/C][C]6.58732[/C][/ROW]
[ROW][C]71[/C][C]14.7[/C][C]17.2971[/C][C]-2.59713[/C][/ROW]
[ROW][C]72[/C][C]10.6[/C][C]11.8415[/C][C]-1.24151[/C][/ROW]
[ROW][C]73[/C][C]12.6[/C][C]12.9086[/C][C]-0.308608[/C][/ROW]
[ROW][C]74[/C][C]16.2[/C][C]15.2398[/C][C]0.960225[/C][/ROW]
[ROW][C]75[/C][C]13.6[/C][C]12.9915[/C][C]0.60853[/C][/ROW]
[ROW][C]76[/C][C]14.1[/C][C]13.4888[/C][C]0.611207[/C][/ROW]
[ROW][C]77[/C][C]14.5[/C][C]14.5858[/C][C]-0.0858449[/C][/ROW]
[ROW][C]78[/C][C]16.15[/C][C]13.9049[/C][C]2.2451[/C][/ROW]
[ROW][C]79[/C][C]14.75[/C][C]12.3945[/C][C]2.35554[/C][/ROW]
[ROW][C]80[/C][C]14.8[/C][C]14.7616[/C][C]0.0384153[/C][/ROW]
[ROW][C]81[/C][C]12.45[/C][C]10.9316[/C][C]1.51843[/C][/ROW]
[ROW][C]82[/C][C]12.65[/C][C]9.44237[/C][C]3.20763[/C][/ROW]
[ROW][C]83[/C][C]17.35[/C][C]17.8309[/C][C]-0.480943[/C][/ROW]
[ROW][C]84[/C][C]8.6[/C][C]7.05529[/C][C]1.54471[/C][/ROW]
[ROW][C]85[/C][C]18.4[/C][C]16.7234[/C][C]1.67663[/C][/ROW]
[ROW][C]86[/C][C]16.1[/C][C]13.5445[/C][C]2.55552[/C][/ROW]
[ROW][C]87[/C][C]17.75[/C][C]17.8389[/C][C]-0.0889026[/C][/ROW]
[ROW][C]88[/C][C]15.25[/C][C]13.856[/C][C]1.39403[/C][/ROW]
[ROW][C]89[/C][C]17.65[/C][C]17.9686[/C][C]-0.318551[/C][/ROW]
[ROW][C]90[/C][C]16.35[/C][C]17.097[/C][C]-0.74701[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]17.1017[/C][C]0.548309[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.4619[/C][C]0.138055[/C][/ROW]
[ROW][C]93[/C][C]14.35[/C][C]17.1269[/C][C]-2.77694[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]12.5476[/C][C]2.20244[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]23.3006[/C][C]-5.05058[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]8.06966[/C][C]1.83034[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.8893[/C][C]2.11072[/C][/ROW]
[ROW][C]98[/C][C]18.25[/C][C]18.9707[/C][C]-0.720742[/C][/ROW]
[ROW][C]99[/C][C]16.85[/C][C]14.847[/C][C]2.00297[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]17.1586[/C][C]1.79142[/C][/ROW]
[ROW][C]101[/C][C]15.6[/C][C]15.8245[/C][C]-0.224494[/C][/ROW]
[ROW][C]102[/C][C]17.1[/C][C]10.1334[/C][C]6.96656[/C][/ROW]
[ROW][C]103[/C][C]16.1[/C][C]16.8166[/C][C]-0.716617[/C][/ROW]
[ROW][C]104[/C][C]15.4[/C][C]16.1431[/C][C]-0.743116[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]16.6075[/C][C]-1.20753[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]11.3976[/C][C]1.95239[/C][/ROW]
[ROW][C]107[/C][C]19.1[/C][C]18.8693[/C][C]0.230743[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]5.39085[/C][C]2.20915[/C][/ROW]
[ROW][C]109[/C][C]19.1[/C][C]21.1228[/C][C]-2.02285[/C][/ROW]
[ROW][C]110[/C][C]14.75[/C][C]12.2762[/C][C]2.47382[/C][/ROW]
[ROW][C]111[/C][C]19.25[/C][C]21.8159[/C][C]-2.56586[/C][/ROW]
[ROW][C]112[/C][C]13.6[/C][C]16.0659[/C][C]-2.46595[/C][/ROW]
[ROW][C]113[/C][C]12.75[/C][C]11.3588[/C][C]1.39121[/C][/ROW]
[ROW][C]114[/C][C]9.85[/C][C]10.3675[/C][C]-0.517491[/C][/ROW]
[ROW][C]115[/C][C]15.25[/C][C]17.2103[/C][C]-1.96032[/C][/ROW]
[ROW][C]116[/C][C]11.9[/C][C]12.8013[/C][C]-0.901324[/C][/ROW]
[ROW][C]117[/C][C]16.35[/C][C]17.7245[/C][C]-1.37451[/C][/ROW]
[ROW][C]118[/C][C]12.4[/C][C]10.248[/C][C]2.15197[/C][/ROW]
[ROW][C]119[/C][C]18.15[/C][C]15.5264[/C][C]2.62361[/C][/ROW]
[ROW][C]120[/C][C]17.75[/C][C]18.4031[/C][C]-0.653074[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]12.5303[/C][C]-0.180296[/C][/ROW]
[ROW][C]122[/C][C]15.6[/C][C]12.9982[/C][C]2.60176[/C][/ROW]
[ROW][C]123[/C][C]19.3[/C][C]18.706[/C][C]0.593991[/C][/ROW]
[ROW][C]124[/C][C]17.1[/C][C]14.1941[/C][C]2.90588[/C][/ROW]
[ROW][C]125[/C][C]18.4[/C][C]16.0973[/C][C]2.30271[/C][/ROW]
[ROW][C]126[/C][C]19.05[/C][C]14.6873[/C][C]4.36272[/C][/ROW]
[ROW][C]127[/C][C]18.55[/C][C]17.7407[/C][C]0.809272[/C][/ROW]
[ROW][C]128[/C][C]19.1[/C][C]21.6751[/C][C]-2.57512[/C][/ROW]
[ROW][C]129[/C][C]12.85[/C][C]14.7123[/C][C]-1.86231[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]12.9529[/C][C]-3.45289[/C][/ROW]
[ROW][C]131[/C][C]4.5[/C][C]6.11479[/C][C]-1.61479[/C][/ROW]
[ROW][C]132[/C][C]13.6[/C][C]14.8985[/C][C]-1.29849[/C][/ROW]
[ROW][C]133[/C][C]11.7[/C][C]12.6499[/C][C]-0.949906[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]14.4352[/C][C]-1.08517[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.0467[/C][C]0.553332[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]15.5333[/C][C]-1.48327[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]18.537[/C][C]-2.43702[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]16.6303[/C][C]-3.28028[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]11.9219[/C][C]-0.0718953[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]15.2198[/C][C]-3.26978[/C][/ROW]
[ROW][C]141[/C][C]13.2[/C][C]15.2239[/C][C]-2.02391[/C][/ROW]
[ROW][C]142[/C][C]7.7[/C][C]6.80417[/C][C]0.89583[/C][/ROW]
[ROW][C]143[/C][C]14.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271000&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271000&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
112.912.9729-0.0728736
212.210.55921.64079
312.811.6951.10499
47.411.7242-4.32423
56.711.4634-4.76335
612.611.61580.984236
714.811.04233.75766
813.313.4945-0.194473
911.112.317-1.21701
108.210.8504-2.65037
1111.411.38680.0131845
126.411.1928-4.79281
1310.610.44350.156505
141213.127-1.12704
156.39.15202-2.85202
1611.913.1822-1.28219
179.310.9348-1.63481
18109.63690.363096
196.49.71319-3.31319
2013.812.61361.18636
2110.89.944330.855669
2213.811.63052.16954
2311.710.94490.755082
2410.911.665-0.764984
259.911.2912-1.39118
2611.510.58740.912597
278.311.3914-3.09136
2811.711.13730.562697
29910.3999-1.39992
309.713.712-4.01196
3110.811.5983-0.798339
3210.310.8116-0.51161
3310.49.954510.445485
349.312.2408-2.94078
3511.811.25130.548719
365.911.5371-5.63711
3711.411.8283-0.428254
381310.7252.27504
3910.811.4352-0.635192
4011.310.68160.618437
4111.811.8559-0.0558935
4212.79.792182.90782
4310.910.32910.570927
4413.311.81061.48941
4510.110.79-0.690011
4614.311.54392.75612
479.311.785-2.48501
4812.510.15622.34384
497.610.4999-2.89991
5015.912.5283.37197
519.210.2925-1.09254
5211.112.205-1.10496
531312.3290.67102
5414.511.23313.26692
5512.313.0226-0.722573
5611.410.7670.632982
5712.611.08431.51567
58NANA1.01636
591310.99572.00435
6013.217.1975-3.99751
617.711.2074-3.5074
624.351.766042.58396
6312.710.84681.85322
6418.116.31971.78026
6517.8517.46650.383488
6617.114.26262.83739
6719.121.6622-2.5622
6816.113.93992.16009
6913.3512.49030.859679
7018.411.81276.58732
7114.717.2971-2.59713
7210.611.8415-1.24151
7312.612.9086-0.308608
7416.215.23980.960225
7513.612.99150.60853
7614.113.48880.611207
7714.514.5858-0.0858449
7816.1513.90492.2451
7914.7512.39452.35554
8014.814.76160.0384153
8112.4510.93161.51843
8212.659.442373.20763
8317.3517.8309-0.480943
848.67.055291.54471
8518.416.72341.67663
8616.113.54452.55552
8717.7517.8389-0.0889026
8815.2513.8561.39403
8917.6517.9686-0.318551
9016.3517.097-0.74701
9117.6517.10170.548309
9213.613.46190.138055
9314.3517.1269-2.77694
9414.7512.54762.20244
9518.2523.3006-5.05058
969.98.069661.83034
971613.88932.11072
9818.2518.9707-0.720742
9916.8514.8472.00297
10018.9517.15861.79142
10115.615.8245-0.224494
10217.110.13346.96656
10316.116.8166-0.716617
10415.416.1431-0.743116
10515.416.6075-1.20753
10613.3511.39761.95239
10719.118.86930.230743
1087.65.390852.20915
10919.121.1228-2.02285
11014.7512.27622.47382
11119.2521.8159-2.56586
11213.616.0659-2.46595
11312.7511.35881.39121
1149.8510.3675-0.517491
11515.2517.2103-1.96032
11611.912.8013-0.901324
11716.3517.7245-1.37451
11812.410.2482.15197
11918.1515.52642.62361
12017.7518.4031-0.653074
12112.3512.5303-0.180296
12215.612.99822.60176
12319.318.7060.593991
12417.114.19412.90588
12518.416.09732.30271
12619.0514.68734.36272
12718.5517.74070.809272
12819.121.6751-2.57512
12912.8514.7123-1.86231
1309.512.9529-3.45289
1314.56.11479-1.61479
13213.614.8985-1.29849
13311.712.6499-0.949906
13413.3514.4352-1.08517
13517.617.04670.553332
13614.0515.5333-1.48327
13716.118.537-2.43702
13813.3516.6303-3.28028
13911.8511.9219-0.0718953
14011.9515.2198-3.26978
14113.215.2239-2.02391
1427.76.804170.89583
14314.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.6493240.7013530.350676
150.4926780.9853570.507322
160.3748360.7496720.625164
170.2554870.5109740.744513
180.1913830.3827660.808617
190.202060.4041190.79794
200.1437430.2874860.856257
210.1032550.206510.896745
220.06627710.1325540.933723
230.05798470.1159690.942015
240.03993420.07986840.960066
250.03933440.07866870.960666
260.07368540.1473710.926315
270.08787650.1757530.912123
280.1388560.2777120.861144
290.1666710.3333410.833329
300.2981280.5962550.701872
310.28880.5775990.7112
320.2310360.4620720.768964
330.1842920.3685840.815708
340.1932580.3865160.806742
350.1521880.3043760.847812
360.3069820.6139640.693018
370.2547770.5095540.745223
380.2162490.4324970.783751
390.1939710.3879420.806029
400.1569120.3138230.843088
410.1440010.2880020.855999
420.1646260.3292520.835374
430.1326050.265210.867395
440.2329160.4658310.767084
450.194250.3885010.80575
460.2893790.5787590.710621
470.3001540.6003080.699846
480.3103850.6207690.689615
490.3691350.7382710.630865
500.465350.9307010.53465
510.4692950.938590.530705
520.4375770.8751540.562423
530.3933860.7867720.606614
540.4115520.8231040.588448
550.3752570.7505140.624743
560.3404040.6808080.659596
570.307320.614640.69268
580.2680750.5361510.731925
590.2554330.5108650.744567
600.3266540.6533070.673346
610.4088680.8177360.591132
620.4374610.8749210.562539
630.4592680.9185360.540732
640.4219430.8438860.578057
650.3782690.7565370.621731
660.3942630.7885250.605737
670.4302860.8605710.569714
680.4629840.9259680.537016
690.4152430.8304860.584757
700.7809730.4380550.219027
710.7963460.4073070.203654
720.7785110.4429790.221489
730.7394460.5211090.260554
740.7261380.5477240.273862
750.6897280.6205450.310272
760.6457260.7085490.354274
770.6050180.7899630.394982
780.6132750.773450.386725
790.6289370.7421260.371063
800.5848180.8303650.415182
810.5600350.8799310.439965
820.5960910.8078190.403909
830.549370.901260.45063
840.5109660.9780670.489034
850.4818640.9637290.518136
860.4997930.9995850.500207
870.4479010.8958030.552099
880.4293760.8587520.570624
890.3823130.7646270.617687
900.3408450.6816910.659155
910.2948710.5897420.705129
920.2503740.5007490.749626
930.285990.5719810.71401
940.2975680.5951360.702432
950.4591220.9182440.540878
960.4318970.8637940.568103
970.4158460.8316930.584154
980.3665030.7330050.633497
990.3465890.6931770.653411
1000.3132040.6264080.686796
1010.263690.5273810.73631
1020.8155840.3688320.184416
1030.7767980.4464040.223202
1040.7306780.5386440.269322
1050.7139280.5721440.286072
1060.711540.5769190.28846
1070.6757660.6484680.324234
1080.6601140.6797730.339886
1090.6355260.7289480.364474
1100.5924620.8150760.407538
1110.5670210.8659590.432979
1120.5328240.9343520.467176
1130.5442270.9115460.455773
1140.4726580.9453160.527342
1150.4590110.9180210.540989
1160.4256130.8512250.574387
1170.3690860.7381720.630914
1180.3224740.6449480.677526
1190.2832520.5665040.716748
1200.2243950.4487910.775605
1210.3257550.651510.674245
1220.263610.5272210.73639
1230.2625450.525090.737455
1240.2266060.4532120.773394
1250.4027620.8055250.597238
1260.9162710.1674580.083729
1270.831250.33750.16875
1280.7149480.5701040.285052
1290.5092670.9814660.490733

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.649324 & 0.701353 & 0.350676 \tabularnewline
15 & 0.492678 & 0.985357 & 0.507322 \tabularnewline
16 & 0.374836 & 0.749672 & 0.625164 \tabularnewline
17 & 0.255487 & 0.510974 & 0.744513 \tabularnewline
18 & 0.191383 & 0.382766 & 0.808617 \tabularnewline
19 & 0.20206 & 0.404119 & 0.79794 \tabularnewline
20 & 0.143743 & 0.287486 & 0.856257 \tabularnewline
21 & 0.103255 & 0.20651 & 0.896745 \tabularnewline
22 & 0.0662771 & 0.132554 & 0.933723 \tabularnewline
23 & 0.0579847 & 0.115969 & 0.942015 \tabularnewline
24 & 0.0399342 & 0.0798684 & 0.960066 \tabularnewline
25 & 0.0393344 & 0.0786687 & 0.960666 \tabularnewline
26 & 0.0736854 & 0.147371 & 0.926315 \tabularnewline
27 & 0.0878765 & 0.175753 & 0.912123 \tabularnewline
28 & 0.138856 & 0.277712 & 0.861144 \tabularnewline
29 & 0.166671 & 0.333341 & 0.833329 \tabularnewline
30 & 0.298128 & 0.596255 & 0.701872 \tabularnewline
31 & 0.2888 & 0.577599 & 0.7112 \tabularnewline
32 & 0.231036 & 0.462072 & 0.768964 \tabularnewline
33 & 0.184292 & 0.368584 & 0.815708 \tabularnewline
34 & 0.193258 & 0.386516 & 0.806742 \tabularnewline
35 & 0.152188 & 0.304376 & 0.847812 \tabularnewline
36 & 0.306982 & 0.613964 & 0.693018 \tabularnewline
37 & 0.254777 & 0.509554 & 0.745223 \tabularnewline
38 & 0.216249 & 0.432497 & 0.783751 \tabularnewline
39 & 0.193971 & 0.387942 & 0.806029 \tabularnewline
40 & 0.156912 & 0.313823 & 0.843088 \tabularnewline
41 & 0.144001 & 0.288002 & 0.855999 \tabularnewline
42 & 0.164626 & 0.329252 & 0.835374 \tabularnewline
43 & 0.132605 & 0.26521 & 0.867395 \tabularnewline
44 & 0.232916 & 0.465831 & 0.767084 \tabularnewline
45 & 0.19425 & 0.388501 & 0.80575 \tabularnewline
46 & 0.289379 & 0.578759 & 0.710621 \tabularnewline
47 & 0.300154 & 0.600308 & 0.699846 \tabularnewline
48 & 0.310385 & 0.620769 & 0.689615 \tabularnewline
49 & 0.369135 & 0.738271 & 0.630865 \tabularnewline
50 & 0.46535 & 0.930701 & 0.53465 \tabularnewline
51 & 0.469295 & 0.93859 & 0.530705 \tabularnewline
52 & 0.437577 & 0.875154 & 0.562423 \tabularnewline
53 & 0.393386 & 0.786772 & 0.606614 \tabularnewline
54 & 0.411552 & 0.823104 & 0.588448 \tabularnewline
55 & 0.375257 & 0.750514 & 0.624743 \tabularnewline
56 & 0.340404 & 0.680808 & 0.659596 \tabularnewline
57 & 0.30732 & 0.61464 & 0.69268 \tabularnewline
58 & 0.268075 & 0.536151 & 0.731925 \tabularnewline
59 & 0.255433 & 0.510865 & 0.744567 \tabularnewline
60 & 0.326654 & 0.653307 & 0.673346 \tabularnewline
61 & 0.408868 & 0.817736 & 0.591132 \tabularnewline
62 & 0.437461 & 0.874921 & 0.562539 \tabularnewline
63 & 0.459268 & 0.918536 & 0.540732 \tabularnewline
64 & 0.421943 & 0.843886 & 0.578057 \tabularnewline
65 & 0.378269 & 0.756537 & 0.621731 \tabularnewline
66 & 0.394263 & 0.788525 & 0.605737 \tabularnewline
67 & 0.430286 & 0.860571 & 0.569714 \tabularnewline
68 & 0.462984 & 0.925968 & 0.537016 \tabularnewline
69 & 0.415243 & 0.830486 & 0.584757 \tabularnewline
70 & 0.780973 & 0.438055 & 0.219027 \tabularnewline
71 & 0.796346 & 0.407307 & 0.203654 \tabularnewline
72 & 0.778511 & 0.442979 & 0.221489 \tabularnewline
73 & 0.739446 & 0.521109 & 0.260554 \tabularnewline
74 & 0.726138 & 0.547724 & 0.273862 \tabularnewline
75 & 0.689728 & 0.620545 & 0.310272 \tabularnewline
76 & 0.645726 & 0.708549 & 0.354274 \tabularnewline
77 & 0.605018 & 0.789963 & 0.394982 \tabularnewline
78 & 0.613275 & 0.77345 & 0.386725 \tabularnewline
79 & 0.628937 & 0.742126 & 0.371063 \tabularnewline
80 & 0.584818 & 0.830365 & 0.415182 \tabularnewline
81 & 0.560035 & 0.879931 & 0.439965 \tabularnewline
82 & 0.596091 & 0.807819 & 0.403909 \tabularnewline
83 & 0.54937 & 0.90126 & 0.45063 \tabularnewline
84 & 0.510966 & 0.978067 & 0.489034 \tabularnewline
85 & 0.481864 & 0.963729 & 0.518136 \tabularnewline
86 & 0.499793 & 0.999585 & 0.500207 \tabularnewline
87 & 0.447901 & 0.895803 & 0.552099 \tabularnewline
88 & 0.429376 & 0.858752 & 0.570624 \tabularnewline
89 & 0.382313 & 0.764627 & 0.617687 \tabularnewline
90 & 0.340845 & 0.681691 & 0.659155 \tabularnewline
91 & 0.294871 & 0.589742 & 0.705129 \tabularnewline
92 & 0.250374 & 0.500749 & 0.749626 \tabularnewline
93 & 0.28599 & 0.571981 & 0.71401 \tabularnewline
94 & 0.297568 & 0.595136 & 0.702432 \tabularnewline
95 & 0.459122 & 0.918244 & 0.540878 \tabularnewline
96 & 0.431897 & 0.863794 & 0.568103 \tabularnewline
97 & 0.415846 & 0.831693 & 0.584154 \tabularnewline
98 & 0.366503 & 0.733005 & 0.633497 \tabularnewline
99 & 0.346589 & 0.693177 & 0.653411 \tabularnewline
100 & 0.313204 & 0.626408 & 0.686796 \tabularnewline
101 & 0.26369 & 0.527381 & 0.73631 \tabularnewline
102 & 0.815584 & 0.368832 & 0.184416 \tabularnewline
103 & 0.776798 & 0.446404 & 0.223202 \tabularnewline
104 & 0.730678 & 0.538644 & 0.269322 \tabularnewline
105 & 0.713928 & 0.572144 & 0.286072 \tabularnewline
106 & 0.71154 & 0.576919 & 0.28846 \tabularnewline
107 & 0.675766 & 0.648468 & 0.324234 \tabularnewline
108 & 0.660114 & 0.679773 & 0.339886 \tabularnewline
109 & 0.635526 & 0.728948 & 0.364474 \tabularnewline
110 & 0.592462 & 0.815076 & 0.407538 \tabularnewline
111 & 0.567021 & 0.865959 & 0.432979 \tabularnewline
112 & 0.532824 & 0.934352 & 0.467176 \tabularnewline
113 & 0.544227 & 0.911546 & 0.455773 \tabularnewline
114 & 0.472658 & 0.945316 & 0.527342 \tabularnewline
115 & 0.459011 & 0.918021 & 0.540989 \tabularnewline
116 & 0.425613 & 0.851225 & 0.574387 \tabularnewline
117 & 0.369086 & 0.738172 & 0.630914 \tabularnewline
118 & 0.322474 & 0.644948 & 0.677526 \tabularnewline
119 & 0.283252 & 0.566504 & 0.716748 \tabularnewline
120 & 0.224395 & 0.448791 & 0.775605 \tabularnewline
121 & 0.325755 & 0.65151 & 0.674245 \tabularnewline
122 & 0.26361 & 0.527221 & 0.73639 \tabularnewline
123 & 0.262545 & 0.52509 & 0.737455 \tabularnewline
124 & 0.226606 & 0.453212 & 0.773394 \tabularnewline
125 & 0.402762 & 0.805525 & 0.597238 \tabularnewline
126 & 0.916271 & 0.167458 & 0.083729 \tabularnewline
127 & 0.83125 & 0.3375 & 0.16875 \tabularnewline
128 & 0.714948 & 0.570104 & 0.285052 \tabularnewline
129 & 0.509267 & 0.981466 & 0.490733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271000&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]14[/C][C]0.649324[/C][C]0.701353[/C][C]0.350676[/C][/ROW]
[ROW][C]15[/C][C]0.492678[/C][C]0.985357[/C][C]0.507322[/C][/ROW]
[ROW][C]16[/C][C]0.374836[/C][C]0.749672[/C][C]0.625164[/C][/ROW]
[ROW][C]17[/C][C]0.255487[/C][C]0.510974[/C][C]0.744513[/C][/ROW]
[ROW][C]18[/C][C]0.191383[/C][C]0.382766[/C][C]0.808617[/C][/ROW]
[ROW][C]19[/C][C]0.20206[/C][C]0.404119[/C][C]0.79794[/C][/ROW]
[ROW][C]20[/C][C]0.143743[/C][C]0.287486[/C][C]0.856257[/C][/ROW]
[ROW][C]21[/C][C]0.103255[/C][C]0.20651[/C][C]0.896745[/C][/ROW]
[ROW][C]22[/C][C]0.0662771[/C][C]0.132554[/C][C]0.933723[/C][/ROW]
[ROW][C]23[/C][C]0.0579847[/C][C]0.115969[/C][C]0.942015[/C][/ROW]
[ROW][C]24[/C][C]0.0399342[/C][C]0.0798684[/C][C]0.960066[/C][/ROW]
[ROW][C]25[/C][C]0.0393344[/C][C]0.0786687[/C][C]0.960666[/C][/ROW]
[ROW][C]26[/C][C]0.0736854[/C][C]0.147371[/C][C]0.926315[/C][/ROW]
[ROW][C]27[/C][C]0.0878765[/C][C]0.175753[/C][C]0.912123[/C][/ROW]
[ROW][C]28[/C][C]0.138856[/C][C]0.277712[/C][C]0.861144[/C][/ROW]
[ROW][C]29[/C][C]0.166671[/C][C]0.333341[/C][C]0.833329[/C][/ROW]
[ROW][C]30[/C][C]0.298128[/C][C]0.596255[/C][C]0.701872[/C][/ROW]
[ROW][C]31[/C][C]0.2888[/C][C]0.577599[/C][C]0.7112[/C][/ROW]
[ROW][C]32[/C][C]0.231036[/C][C]0.462072[/C][C]0.768964[/C][/ROW]
[ROW][C]33[/C][C]0.184292[/C][C]0.368584[/C][C]0.815708[/C][/ROW]
[ROW][C]34[/C][C]0.193258[/C][C]0.386516[/C][C]0.806742[/C][/ROW]
[ROW][C]35[/C][C]0.152188[/C][C]0.304376[/C][C]0.847812[/C][/ROW]
[ROW][C]36[/C][C]0.306982[/C][C]0.613964[/C][C]0.693018[/C][/ROW]
[ROW][C]37[/C][C]0.254777[/C][C]0.509554[/C][C]0.745223[/C][/ROW]
[ROW][C]38[/C][C]0.216249[/C][C]0.432497[/C][C]0.783751[/C][/ROW]
[ROW][C]39[/C][C]0.193971[/C][C]0.387942[/C][C]0.806029[/C][/ROW]
[ROW][C]40[/C][C]0.156912[/C][C]0.313823[/C][C]0.843088[/C][/ROW]
[ROW][C]41[/C][C]0.144001[/C][C]0.288002[/C][C]0.855999[/C][/ROW]
[ROW][C]42[/C][C]0.164626[/C][C]0.329252[/C][C]0.835374[/C][/ROW]
[ROW][C]43[/C][C]0.132605[/C][C]0.26521[/C][C]0.867395[/C][/ROW]
[ROW][C]44[/C][C]0.232916[/C][C]0.465831[/C][C]0.767084[/C][/ROW]
[ROW][C]45[/C][C]0.19425[/C][C]0.388501[/C][C]0.80575[/C][/ROW]
[ROW][C]46[/C][C]0.289379[/C][C]0.578759[/C][C]0.710621[/C][/ROW]
[ROW][C]47[/C][C]0.300154[/C][C]0.600308[/C][C]0.699846[/C][/ROW]
[ROW][C]48[/C][C]0.310385[/C][C]0.620769[/C][C]0.689615[/C][/ROW]
[ROW][C]49[/C][C]0.369135[/C][C]0.738271[/C][C]0.630865[/C][/ROW]
[ROW][C]50[/C][C]0.46535[/C][C]0.930701[/C][C]0.53465[/C][/ROW]
[ROW][C]51[/C][C]0.469295[/C][C]0.93859[/C][C]0.530705[/C][/ROW]
[ROW][C]52[/C][C]0.437577[/C][C]0.875154[/C][C]0.562423[/C][/ROW]
[ROW][C]53[/C][C]0.393386[/C][C]0.786772[/C][C]0.606614[/C][/ROW]
[ROW][C]54[/C][C]0.411552[/C][C]0.823104[/C][C]0.588448[/C][/ROW]
[ROW][C]55[/C][C]0.375257[/C][C]0.750514[/C][C]0.624743[/C][/ROW]
[ROW][C]56[/C][C]0.340404[/C][C]0.680808[/C][C]0.659596[/C][/ROW]
[ROW][C]57[/C][C]0.30732[/C][C]0.61464[/C][C]0.69268[/C][/ROW]
[ROW][C]58[/C][C]0.268075[/C][C]0.536151[/C][C]0.731925[/C][/ROW]
[ROW][C]59[/C][C]0.255433[/C][C]0.510865[/C][C]0.744567[/C][/ROW]
[ROW][C]60[/C][C]0.326654[/C][C]0.653307[/C][C]0.673346[/C][/ROW]
[ROW][C]61[/C][C]0.408868[/C][C]0.817736[/C][C]0.591132[/C][/ROW]
[ROW][C]62[/C][C]0.437461[/C][C]0.874921[/C][C]0.562539[/C][/ROW]
[ROW][C]63[/C][C]0.459268[/C][C]0.918536[/C][C]0.540732[/C][/ROW]
[ROW][C]64[/C][C]0.421943[/C][C]0.843886[/C][C]0.578057[/C][/ROW]
[ROW][C]65[/C][C]0.378269[/C][C]0.756537[/C][C]0.621731[/C][/ROW]
[ROW][C]66[/C][C]0.394263[/C][C]0.788525[/C][C]0.605737[/C][/ROW]
[ROW][C]67[/C][C]0.430286[/C][C]0.860571[/C][C]0.569714[/C][/ROW]
[ROW][C]68[/C][C]0.462984[/C][C]0.925968[/C][C]0.537016[/C][/ROW]
[ROW][C]69[/C][C]0.415243[/C][C]0.830486[/C][C]0.584757[/C][/ROW]
[ROW][C]70[/C][C]0.780973[/C][C]0.438055[/C][C]0.219027[/C][/ROW]
[ROW][C]71[/C][C]0.796346[/C][C]0.407307[/C][C]0.203654[/C][/ROW]
[ROW][C]72[/C][C]0.778511[/C][C]0.442979[/C][C]0.221489[/C][/ROW]
[ROW][C]73[/C][C]0.739446[/C][C]0.521109[/C][C]0.260554[/C][/ROW]
[ROW][C]74[/C][C]0.726138[/C][C]0.547724[/C][C]0.273862[/C][/ROW]
[ROW][C]75[/C][C]0.689728[/C][C]0.620545[/C][C]0.310272[/C][/ROW]
[ROW][C]76[/C][C]0.645726[/C][C]0.708549[/C][C]0.354274[/C][/ROW]
[ROW][C]77[/C][C]0.605018[/C][C]0.789963[/C][C]0.394982[/C][/ROW]
[ROW][C]78[/C][C]0.613275[/C][C]0.77345[/C][C]0.386725[/C][/ROW]
[ROW][C]79[/C][C]0.628937[/C][C]0.742126[/C][C]0.371063[/C][/ROW]
[ROW][C]80[/C][C]0.584818[/C][C]0.830365[/C][C]0.415182[/C][/ROW]
[ROW][C]81[/C][C]0.560035[/C][C]0.879931[/C][C]0.439965[/C][/ROW]
[ROW][C]82[/C][C]0.596091[/C][C]0.807819[/C][C]0.403909[/C][/ROW]
[ROW][C]83[/C][C]0.54937[/C][C]0.90126[/C][C]0.45063[/C][/ROW]
[ROW][C]84[/C][C]0.510966[/C][C]0.978067[/C][C]0.489034[/C][/ROW]
[ROW][C]85[/C][C]0.481864[/C][C]0.963729[/C][C]0.518136[/C][/ROW]
[ROW][C]86[/C][C]0.499793[/C][C]0.999585[/C][C]0.500207[/C][/ROW]
[ROW][C]87[/C][C]0.447901[/C][C]0.895803[/C][C]0.552099[/C][/ROW]
[ROW][C]88[/C][C]0.429376[/C][C]0.858752[/C][C]0.570624[/C][/ROW]
[ROW][C]89[/C][C]0.382313[/C][C]0.764627[/C][C]0.617687[/C][/ROW]
[ROW][C]90[/C][C]0.340845[/C][C]0.681691[/C][C]0.659155[/C][/ROW]
[ROW][C]91[/C][C]0.294871[/C][C]0.589742[/C][C]0.705129[/C][/ROW]
[ROW][C]92[/C][C]0.250374[/C][C]0.500749[/C][C]0.749626[/C][/ROW]
[ROW][C]93[/C][C]0.28599[/C][C]0.571981[/C][C]0.71401[/C][/ROW]
[ROW][C]94[/C][C]0.297568[/C][C]0.595136[/C][C]0.702432[/C][/ROW]
[ROW][C]95[/C][C]0.459122[/C][C]0.918244[/C][C]0.540878[/C][/ROW]
[ROW][C]96[/C][C]0.431897[/C][C]0.863794[/C][C]0.568103[/C][/ROW]
[ROW][C]97[/C][C]0.415846[/C][C]0.831693[/C][C]0.584154[/C][/ROW]
[ROW][C]98[/C][C]0.366503[/C][C]0.733005[/C][C]0.633497[/C][/ROW]
[ROW][C]99[/C][C]0.346589[/C][C]0.693177[/C][C]0.653411[/C][/ROW]
[ROW][C]100[/C][C]0.313204[/C][C]0.626408[/C][C]0.686796[/C][/ROW]
[ROW][C]101[/C][C]0.26369[/C][C]0.527381[/C][C]0.73631[/C][/ROW]
[ROW][C]102[/C][C]0.815584[/C][C]0.368832[/C][C]0.184416[/C][/ROW]
[ROW][C]103[/C][C]0.776798[/C][C]0.446404[/C][C]0.223202[/C][/ROW]
[ROW][C]104[/C][C]0.730678[/C][C]0.538644[/C][C]0.269322[/C][/ROW]
[ROW][C]105[/C][C]0.713928[/C][C]0.572144[/C][C]0.286072[/C][/ROW]
[ROW][C]106[/C][C]0.71154[/C][C]0.576919[/C][C]0.28846[/C][/ROW]
[ROW][C]107[/C][C]0.675766[/C][C]0.648468[/C][C]0.324234[/C][/ROW]
[ROW][C]108[/C][C]0.660114[/C][C]0.679773[/C][C]0.339886[/C][/ROW]
[ROW][C]109[/C][C]0.635526[/C][C]0.728948[/C][C]0.364474[/C][/ROW]
[ROW][C]110[/C][C]0.592462[/C][C]0.815076[/C][C]0.407538[/C][/ROW]
[ROW][C]111[/C][C]0.567021[/C][C]0.865959[/C][C]0.432979[/C][/ROW]
[ROW][C]112[/C][C]0.532824[/C][C]0.934352[/C][C]0.467176[/C][/ROW]
[ROW][C]113[/C][C]0.544227[/C][C]0.911546[/C][C]0.455773[/C][/ROW]
[ROW][C]114[/C][C]0.472658[/C][C]0.945316[/C][C]0.527342[/C][/ROW]
[ROW][C]115[/C][C]0.459011[/C][C]0.918021[/C][C]0.540989[/C][/ROW]
[ROW][C]116[/C][C]0.425613[/C][C]0.851225[/C][C]0.574387[/C][/ROW]
[ROW][C]117[/C][C]0.369086[/C][C]0.738172[/C][C]0.630914[/C][/ROW]
[ROW][C]118[/C][C]0.322474[/C][C]0.644948[/C][C]0.677526[/C][/ROW]
[ROW][C]119[/C][C]0.283252[/C][C]0.566504[/C][C]0.716748[/C][/ROW]
[ROW][C]120[/C][C]0.224395[/C][C]0.448791[/C][C]0.775605[/C][/ROW]
[ROW][C]121[/C][C]0.325755[/C][C]0.65151[/C][C]0.674245[/C][/ROW]
[ROW][C]122[/C][C]0.26361[/C][C]0.527221[/C][C]0.73639[/C][/ROW]
[ROW][C]123[/C][C]0.262545[/C][C]0.52509[/C][C]0.737455[/C][/ROW]
[ROW][C]124[/C][C]0.226606[/C][C]0.453212[/C][C]0.773394[/C][/ROW]
[ROW][C]125[/C][C]0.402762[/C][C]0.805525[/C][C]0.597238[/C][/ROW]
[ROW][C]126[/C][C]0.916271[/C][C]0.167458[/C][C]0.083729[/C][/ROW]
[ROW][C]127[/C][C]0.83125[/C][C]0.3375[/C][C]0.16875[/C][/ROW]
[ROW][C]128[/C][C]0.714948[/C][C]0.570104[/C][C]0.285052[/C][/ROW]
[ROW][C]129[/C][C]0.509267[/C][C]0.981466[/C][C]0.490733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271000&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271000&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
140.6493240.7013530.350676
150.4926780.9853570.507322
160.3748360.7496720.625164
170.2554870.5109740.744513
180.1913830.3827660.808617
190.202060.4041190.79794
200.1437430.2874860.856257
210.1032550.206510.896745
220.06627710.1325540.933723
230.05798470.1159690.942015
240.03993420.07986840.960066
250.03933440.07866870.960666
260.07368540.1473710.926315
270.08787650.1757530.912123
280.1388560.2777120.861144
290.1666710.3333410.833329
300.2981280.5962550.701872
310.28880.5775990.7112
320.2310360.4620720.768964
330.1842920.3685840.815708
340.1932580.3865160.806742
350.1521880.3043760.847812
360.3069820.6139640.693018
370.2547770.5095540.745223
380.2162490.4324970.783751
390.1939710.3879420.806029
400.1569120.3138230.843088
410.1440010.2880020.855999
420.1646260.3292520.835374
430.1326050.265210.867395
440.2329160.4658310.767084
450.194250.3885010.80575
460.2893790.5787590.710621
470.3001540.6003080.699846
480.3103850.6207690.689615
490.3691350.7382710.630865
500.465350.9307010.53465
510.4692950.938590.530705
520.4375770.8751540.562423
530.3933860.7867720.606614
540.4115520.8231040.588448
550.3752570.7505140.624743
560.3404040.6808080.659596
570.307320.614640.69268
580.2680750.5361510.731925
590.2554330.5108650.744567
600.3266540.6533070.673346
610.4088680.8177360.591132
620.4374610.8749210.562539
630.4592680.9185360.540732
640.4219430.8438860.578057
650.3782690.7565370.621731
660.3942630.7885250.605737
670.4302860.8605710.569714
680.4629840.9259680.537016
690.4152430.8304860.584757
700.7809730.4380550.219027
710.7963460.4073070.203654
720.7785110.4429790.221489
730.7394460.5211090.260554
740.7261380.5477240.273862
750.6897280.6205450.310272
760.6457260.7085490.354274
770.6050180.7899630.394982
780.6132750.773450.386725
790.6289370.7421260.371063
800.5848180.8303650.415182
810.5600350.8799310.439965
820.5960910.8078190.403909
830.549370.901260.45063
840.5109660.9780670.489034
850.4818640.9637290.518136
860.4997930.9995850.500207
870.4479010.8958030.552099
880.4293760.8587520.570624
890.3823130.7646270.617687
900.3408450.6816910.659155
910.2948710.5897420.705129
920.2503740.5007490.749626
930.285990.5719810.71401
940.2975680.5951360.702432
950.4591220.9182440.540878
960.4318970.8637940.568103
970.4158460.8316930.584154
980.3665030.7330050.633497
990.3465890.6931770.653411
1000.3132040.6264080.686796
1010.263690.5273810.73631
1020.8155840.3688320.184416
1030.7767980.4464040.223202
1040.7306780.5386440.269322
1050.7139280.5721440.286072
1060.711540.5769190.28846
1070.6757660.6484680.324234
1080.6601140.6797730.339886
1090.6355260.7289480.364474
1100.5924620.8150760.407538
1110.5670210.8659590.432979
1120.5328240.9343520.467176
1130.5442270.9115460.455773
1140.4726580.9453160.527342
1150.4590110.9180210.540989
1160.4256130.8512250.574387
1170.3690860.7381720.630914
1180.3224740.6449480.677526
1190.2832520.5665040.716748
1200.2243950.4487910.775605
1210.3257550.651510.674245
1220.263610.5272210.73639
1230.2625450.525090.737455
1240.2266060.4532120.773394
1250.4027620.8055250.597238
1260.9162710.1674580.083729
1270.831250.33750.16875
1280.7149480.5701040.285052
1290.5092670.9814660.490733






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 level20.0172414OK

\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 & 2 & 0.0172414 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271000&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]2[/C][C]0.0172414[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271000&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271000&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 level20.0172414OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
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Figure 3
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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')
}