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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 15:11:40 +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/t1418915534uqp0sgpsip6du2c.htm/, Retrieved Sun, 19 May 2024 18:45:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271051, Retrieved Sun, 19 May 2024 18:45:55 +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 15:11:40] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 7 18 12 21 12 149 18 68 1.8
12.2 18 20 23 20 22 8 139 31 39 2.1
12.8 12 9 22 14 21 11 148 39 32 2.2
7.4 24 19 22 25 21 13 158 46 62 2.3
6.7 16 12 19 15 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 21 21 7 159 35 62 2.1
13.3 15 9 16 15 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 11 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 22 23 10 159 36 63 2.2
10.6 19 17 24 27 22 10 119 38 30 2.2
12 22 12 26 24 21 14 176 69 78 2.7
6.3 25 18 28 23 21 6 54 21 19 1.9
11.9 16 12 20 21 21 14 163 54 66 2.5
9.3 19 16 26 20 21 11 124 36 35 2.2
10 26 21 28 25 24 12 121 23 45 1.9
6.4 24 15 27 16 23 15 153 34 21 2.1
13.8 20 17 23 24 21 13 148 112 25 3.5
10.8 19 17 24 21 24 11 221 35 44 2.1
13.8 19 17 24 22 23 12 188 47 69 2.3
11.7 23 18 22 25 21 7 149 47 54 2.3
10.9 18 15 21 23 22 11 244 37 74 2.2
9.9 21 21 21 22 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 23 22 9 94 23 46 1.9
11.7 19 13 21 19 21 11 156 32 39 2.1
9 19 19 27 21 21 12 132 30 34 2
9.7 7 12 25 19 25 15 161 92 51 3.2
10.8 20 14 23 25 22 12 105 43 42 2.3
10.3 20 13 25 16 22 6 97 55 31 2.5
10.4 19 12 23 24 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 28 21 6 157 43 53 2.3
5.9 14 10 19 15 22 12 111 29 31 2
11.4 17 11 21 17 21 10 145 56 39 2.5
13 17 11 27 18 24 6 162 46 54 2.3
10.8 8 10 25 26 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 19 22 12 109 30 55 2
12.7 22 20 25 26 22 8 105 7 50 1.6
10.9 14 11 24 12 23 12 148 19 30 1.8
13.3 21 17 20 20 23 14 125 48 45 2.4
10.1 20 14 19 24 21 12 116 23 35 1.9
14.3 18 16 21 22 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 19 21 10 162 48 17 2.4
7.6 16 10 25 24 21 7 99 18 40 1.8
15.9 16 10 20 21 21 12 202 43 64 2.3
9.2 16 18 21 16 21 7 186 33 37 2.1
11.1 22 22 27 23 21 12 183 71 65 2.8
13 21 16 24 20 22 10 214 26 100 2
14.5 15 10 27 19 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 21 23 12 126 29 29 2
13 16 10 25 17 21 10 162 43 59 2.3
13.2 26 16 24 24 20 11 159 29 61 2
7.7 18 16 23 22 21 12 110 32 51 2.1
4.35 17 15 22 14 22 9 48 23 12 1
12.7 6 4 24 5 22 11 50 16 45 1
18.1 22 9 19 25 22 12 150 33 37 4
17.85 20 18 25 21 20 12 154 32 37 4
17.1 17 12 24 9 22 12 194 52 68 4
19.1 20 16 28 15 21 12 158 75 72 4
16.1 23 17 23 23 21 10 159 72 143 4
13.35 18 14 19 21 21 15 67 15 9 2
18.4 13 13 19 9 21 10 147 29 55 4
14.7 22 20 27 24 21 15 39 13 17 1
10.6 20 16 24 16 21 10 100 40 37 3
12.6 20 15 26 20 21 15 111 19 27 3
13.6 16 16 25 18 24 15 101 121 58 3
14.1 16 15 19 21 22 13 101 36 21 3
14.5 15 16 20 21 20 12 114 23 19 3
16.15 19 19 26 21 21 12 165 85 78 4
14.75 19 9 27 20 24 8 114 41 35 3
14.8 24 19 23 24 25 9 111 46 48 3
12.45 9 7 18 15 22 15 75 18 27 2
12.65 22 23 23 24 21 12 82 35 43 2
17.35 15 14 21 18 21 12 121 17 30 3
8.6 22 10 23 24 22 15 32 4 25 1
18.4 22 16 22 24 23 11 150 28 69 4
16.1 24 12 21 15 24 12 117 44 72 3
17.75 21 7 24 20 22 14 165 38 13 4
15.25 25 20 26 26 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 13 21 11 145 22 67 4
13.6 28 19 20 16 21 12 120 40 36 3
14.35 21 11 18 22 22 12 109 31 44 3
14.75 16 15 18 21 22 12 132 11 45 4
18.25 16 14 25 11 21 12 172 38 34 4
9.9 25 11 28 23 22 8 169 24 36 4
16 21 14 23 18 23 8 114 37 72 3
18.25 22 15 20 19 21 12 156 37 39 4
16.85 9 7 22 15 21 12 172 22 43 4
18.95 24 22 23 21 21 11 167 43 80 4
15.6 22 11 20 25 21 12 113 31 40 3
17.1 10 12 24 12 22 10 173 31 61 4
15.4 21 13 23 19 21 11 165 21 29 4
15.4 20 15 21 21 21 11 165 21 29 4
13.35 17 11 19 19 25 13 118 32 54 3
19.1 7 7 19 18 21 7 158 26 43 4
7.6 14 13 25 23 25 8 49 32 20 1
19.1 23 7 18 23 22 11 155 33 61 4
14.75 18 11 22 27 21 8 151 30 57 4
19.25 17 22 5 6 23 14 220 67 54 4
13.6 20 15 24 22 20 9 141 22 36 4
12.75 19 15 28 23 22 13 122 33 16 4
9.85 19 11 27 20 25 13 44 24 40 1
15.25 23 10 23 23 20 11 152 28 27 4
11.9 20 18 24 27 21 9 107 41 61 3
16.35 19 14 25 24 21 12 154 31 69 4
12.4 16 16 19 12 23 12 103 33 34 3
18.15 21 16 24 24 22 13 175 21 34 4
17.75 20 17 28 24 21 11 143 52 34 4
12.35 20 14 19 19 21 11 110 29 13 3
15.6 19 10 23 28 21 9 131 11 12 4
19.3 19 16 23 23 21 12 167 26 51 4
17.1 20 16 26 19 21 15 137 7 19 4
18.4 22 17 25 23 21 14 121 13 81 3
19.05 19 12 24 20 21 12 149 20 42 4
18.55 23 17 23 18 22 9 168 52 22 4
19.1 16 11 22 20 21 9 140 28 85 4
12.85 18 12 26 21 22 13 168 39 25 4
9.5 23 8 23 25 22 15 94 9 22 2
4.5 20 17 22 18 22 11 51 19 19 1
13.6 23 17 22 28 22 10 145 60 45 4
11.7 13 7 17 9 23 11 66 19 45 2
13.35 26 18 22 26 22 14 109 14 51 3
17.6 13 14 26 12 21 12 164 -2 73 4
14.05 10 13 24 12 21 13 119 51 24 3
16.1 21 19 27 20 20 11 126 2 61 4
13.35 24 15 22 25 20 11 132 24 23 4
11.85 21 15 23 24 21 13 142 40 14 4
11.95 23 8 22 23 21 12 83 20 54 2
13.2 16 11 20 22 21 9 166 20 36 4
7.7 26 17 27 28 24 13 93 25 26 2
14.6 16 12 20 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 time6 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 & 6 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=271051&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]'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=271051&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271051&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'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] = + 8.94717 -0.0511611AMS.I2[t] -0.0425106AMS.I3[t] -0.0238388AMS.E1[t] -0.045493AMS.E2[t] -0.128713age[t] + 0.135166CONFSOFTTOT[t] -0.00286435LFM[t] -0.0206924PRH[t] + 0.0368072CH[t] + 2.81058PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.94717 -0.0511611AMS.I2[t] -0.0425106AMS.I3[t] -0.0238388AMS.E1[t] -0.045493AMS.E2[t] -0.128713age[t] +  0.135166CONFSOFTTOT[t] -0.00286435LFM[t] -0.0206924PRH[t] +  0.0368072CH[t] +  2.81058PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271051&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.94717 -0.0511611AMS.I2[t] -0.0425106AMS.I3[t] -0.0238388AMS.E1[t] -0.045493AMS.E2[t] -0.128713age[t] +  0.135166CONFSOFTTOT[t] -0.00286435LFM[t] -0.0206924PRH[t] +  0.0368072CH[t] +  2.81058PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271051&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271051&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] = + 8.94717 -0.0511611AMS.I2[t] -0.0425106AMS.I3[t] -0.0238388AMS.E1[t] -0.045493AMS.E2[t] -0.128713age[t] + 0.135166CONFSOFTTOT[t] -0.00286435LFM[t] -0.0206924PRH[t] + 0.0368072CH[t] + 2.81058PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.947174.28212.0890.03864870.0193244
AMS.I2-0.05116110.0604174-0.84680.398690.199345
AMS.I3-0.04251060.0565444-0.75180.4535460.226773
AMS.E1-0.02383880.0628432-0.37930.7050660.352533
AMS.E2-0.0454930.0502925-0.90460.3673940.183697
age-0.1287130.171196-0.75180.4535220.226761
CONFSOFTTOT0.1351660.08715971.5510.1234230.0617115
LFM-0.002864350.0061264-0.46750.6409070.320453
PRH-0.02069240.0108811-1.9020.05946030.0297302
CH0.03680720.01036283.5520.0005361410.000268071
PR2.810580.23657911.882.12755e-221.06377e-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) & 8.94717 & 4.2821 & 2.089 & 0.0386487 & 0.0193244 \tabularnewline
AMS.I2 & -0.0511611 & 0.0604174 & -0.8468 & 0.39869 & 0.199345 \tabularnewline
AMS.I3 & -0.0425106 & 0.0565444 & -0.7518 & 0.453546 & 0.226773 \tabularnewline
AMS.E1 & -0.0238388 & 0.0628432 & -0.3793 & 0.705066 & 0.352533 \tabularnewline
AMS.E2 & -0.045493 & 0.0502925 & -0.9046 & 0.367394 & 0.183697 \tabularnewline
age & -0.128713 & 0.171196 & -0.7518 & 0.453522 & 0.226761 \tabularnewline
CONFSOFTTOT & 0.135166 & 0.0871597 & 1.551 & 0.123423 & 0.0617115 \tabularnewline
LFM & -0.00286435 & 0.0061264 & -0.4675 & 0.640907 & 0.320453 \tabularnewline
PRH & -0.0206924 & 0.0108811 & -1.902 & 0.0594603 & 0.0297302 \tabularnewline
CH & 0.0368072 & 0.0103628 & 3.552 & 0.000536141 & 0.000268071 \tabularnewline
PR & 2.81058 & 0.236579 & 11.88 & 2.12755e-22 & 1.06377e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271051&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]8.94717[/C][C]4.2821[/C][C]2.089[/C][C]0.0386487[/C][C]0.0193244[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0511611[/C][C]0.0604174[/C][C]-0.8468[/C][C]0.39869[/C][C]0.199345[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0425106[/C][C]0.0565444[/C][C]-0.7518[/C][C]0.453546[/C][C]0.226773[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0238388[/C][C]0.0628432[/C][C]-0.3793[/C][C]0.705066[/C][C]0.352533[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.045493[/C][C]0.0502925[/C][C]-0.9046[/C][C]0.367394[/C][C]0.183697[/C][/ROW]
[ROW][C]age[/C][C]-0.128713[/C][C]0.171196[/C][C]-0.7518[/C][C]0.453522[/C][C]0.226761[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.135166[/C][C]0.0871597[/C][C]1.551[/C][C]0.123423[/C][C]0.0617115[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00286435[/C][C]0.0061264[/C][C]-0.4675[/C][C]0.640907[/C][C]0.320453[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0206924[/C][C]0.0108811[/C][C]-1.902[/C][C]0.0594603[/C][C]0.0297302[/C][/ROW]
[ROW][C]CH[/C][C]0.0368072[/C][C]0.0103628[/C][C]3.552[/C][C]0.000536141[/C][C]0.000268071[/C][/ROW]
[ROW][C]PR[/C][C]2.81058[/C][C]0.236579[/C][C]11.88[/C][C]2.12755e-22[/C][C]1.06377e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271051&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271051&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)8.947174.28212.0890.03864870.0193244
AMS.I2-0.05116110.0604174-0.84680.398690.199345
AMS.I3-0.04251060.0565444-0.75180.4535460.226773
AMS.E1-0.02383880.0628432-0.37930.7050660.352533
AMS.E2-0.0454930.0502925-0.90460.3673940.183697
age-0.1287130.171196-0.75180.4535220.226761
CONFSOFTTOT0.1351660.08715971.5510.1234230.0617115
LFM-0.002864350.0061264-0.46750.6409070.320453
PRH-0.02069240.0108811-1.9020.05946030.0297302
CH0.03680720.01036283.5520.0005361410.000268071
PR2.810580.23657911.882.12755e-221.06377e-22






Multiple Linear Regression - Regression Statistics
Multiple R0.79304
R-squared0.628913
Adjusted R-squared0.599922
F-TEST (value)21.6933
F-TEST (DF numerator)10
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2163
Sum Squared Residuals628.732

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.79304 \tabularnewline
R-squared & 0.628913 \tabularnewline
Adjusted R-squared & 0.599922 \tabularnewline
F-TEST (value) & 21.6933 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 128 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.2163 \tabularnewline
Sum Squared Residuals & 628.732 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271051&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.79304[/C][/ROW]
[ROW][C]R-squared[/C][C]0.628913[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.599922[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.6933[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]128[/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]2.2163[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]628.732[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271051&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271051&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 R0.79304
R-squared0.628913
Adjusted R-squared0.599922
F-TEST (value)21.6933
F-TEST (DF numerator)10
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2163
Sum Squared Residuals628.732






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.947-0.0469867
212.210.26561.93437
312.811.70331.09669
47.411.646-4.24596
56.711.3757-4.67573
612.611.91230.687676
714.811.03093.76915
813.313.3274-0.0273814
911.112.4781-1.37815
108.210.8741-2.6741
1111.411.2740.125999
126.411.0064-4.60644
1310.610.13230.467725
141213.3168-1.31683
156.38.74737-2.44737
1611.913.2471-1.34713
179.310.9205-1.62054
18109.626230.373767
196.410.3106-3.91057
2013.812.63111.16894
2110.810.28710.512871
2213.811.8341.96597
2311.710.63931.06073
2410.911.9393-1.03928
259.911.1395-1.23946
2611.510.53790.962131
278.311.0029-2.70292
2811.711.070.629962
29910.3612-1.36116
309.713.9344-4.2344
3110.811.2532-0.453186
3210.310.7783-0.478304
3310.49.657350.742649
349.312.2853-2.9853
3511.810.93890.861114
365.911.3049-5.40493
3711.411.8723-0.472324
381310.90522.09478
3910.811.1268-0.326837
4011.310.68940.610575
4111.811.64710.152938
4212.79.333953.36605
4310.910.6530.246985
4413.311.74581.55418
4510.110.5232-0.423205
4614.311.36722.93283
479.311.6772-2.37719
4812.510.3922.10803
497.610.1342-2.53417
5015.912.5423.35801
519.210.4265-1.22655
5211.112.3842-1.28417
531312.38150.618499
5414.511.46093.03912
5512.313.1009-0.800932
5611.410.41240.987575
571312.2650.735024
5813.210.99632.2037
597.711.5181-3.81812
604.357.30207-2.95207
6112.710.31832.38169
6218.116.13081.96925
6317.8516.15611.69392
6417.117.4896-0.389555
6519.116.70082.39915
6616.118.6623-2.5623
6713.3510.79752.55253
6818.417.76130.638679
6914.77.616037.08397
7010.613.2719-2.67191
7112.613.7956-1.19556
7213.612.74540.854575
7314.113.17860.921429
7414.513.44381.05621
7516.1516.3931-0.243068
7614.7512.57632.17372
7714.812.19882.60117
7812.4512.30110.148889
7912.6510.36752.28247
8017.3514.02173.32827
818.68.508520.0914795
8218.416.82461.57543
8316.114.39531.7047
8417.7515.61572.13427
8515.2515.2864-0.0364446
8617.6516.25791.39207
8716.3516.6587-0.308651
8817.6518.3593-0.709309
8913.613.00670.593315
9014.3513.86310.486898
9114.7517.1897-2.43971
9218.2516.57081.67915
939.915.323-5.42302
941614.02111.9789
9518.2516.22722.02282
9616.8517.7784-0.928437
9718.9516.8832.06695
9815.613.59782.00219
9917.117.6779-0.577914
10015.416.0939-0.693905
10115.416.0167-0.616737
10213.3514.251-0.901013
10319.117.09732.0027
1047.66.643680.956324
10519.117.01332.08668
10614.7516.4713-1.72128
10719.2516.89532.35468
10813.616.0638-2.46381
10912.7515.348-2.59802
1109.858.153531.69647
11115.2515.8846-0.634631
11211.913.3939-1.49393
11316.3517.3106-0.960613
11412.413.8165-1.41646
11518.1516.01212.13792
11617.7515.2342.51605
11712.3512.7904-0.440415
11815.615.32260.27742
11919.316.72252.57754
12017.116.48850.611496
12118.415.44352.95648
12219.0516.84962.20041
12318.5514.56033.98972
12419.118.13070.969286
12512.8515.7407-2.89073
1269.510.916-1.41599
1274.57.48375-2.98375
12813.615.0113-1.41126
12911.712.3915-0.691505
13013.3513.9121-0.562148
13117.618.9411-1.34109
13214.0513.7380.312008
13316.117.5094-1.4094
13413.3515.5466-2.1966
13511.8515.1724-3.32237
13611.9511.73580.214242
13713.216.3749-3.17492
1387.79.43934-1.73934
13914.613.66410.935893

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.947 & -0.0469867 \tabularnewline
2 & 12.2 & 10.2656 & 1.93437 \tabularnewline
3 & 12.8 & 11.7033 & 1.09669 \tabularnewline
4 & 7.4 & 11.646 & -4.24596 \tabularnewline
5 & 6.7 & 11.3757 & -4.67573 \tabularnewline
6 & 12.6 & 11.9123 & 0.687676 \tabularnewline
7 & 14.8 & 11.0309 & 3.76915 \tabularnewline
8 & 13.3 & 13.3274 & -0.0273814 \tabularnewline
9 & 11.1 & 12.4781 & -1.37815 \tabularnewline
10 & 8.2 & 10.8741 & -2.6741 \tabularnewline
11 & 11.4 & 11.274 & 0.125999 \tabularnewline
12 & 6.4 & 11.0064 & -4.60644 \tabularnewline
13 & 10.6 & 10.1323 & 0.467725 \tabularnewline
14 & 12 & 13.3168 & -1.31683 \tabularnewline
15 & 6.3 & 8.74737 & -2.44737 \tabularnewline
16 & 11.9 & 13.2471 & -1.34713 \tabularnewline
17 & 9.3 & 10.9205 & -1.62054 \tabularnewline
18 & 10 & 9.62623 & 0.373767 \tabularnewline
19 & 6.4 & 10.3106 & -3.91057 \tabularnewline
20 & 13.8 & 12.6311 & 1.16894 \tabularnewline
21 & 10.8 & 10.2871 & 0.512871 \tabularnewline
22 & 13.8 & 11.834 & 1.96597 \tabularnewline
23 & 11.7 & 10.6393 & 1.06073 \tabularnewline
24 & 10.9 & 11.9393 & -1.03928 \tabularnewline
25 & 9.9 & 11.1395 & -1.23946 \tabularnewline
26 & 11.5 & 10.5379 & 0.962131 \tabularnewline
27 & 8.3 & 11.0029 & -2.70292 \tabularnewline
28 & 11.7 & 11.07 & 0.629962 \tabularnewline
29 & 9 & 10.3612 & -1.36116 \tabularnewline
30 & 9.7 & 13.9344 & -4.2344 \tabularnewline
31 & 10.8 & 11.2532 & -0.453186 \tabularnewline
32 & 10.3 & 10.7783 & -0.478304 \tabularnewline
33 & 10.4 & 9.65735 & 0.742649 \tabularnewline
34 & 9.3 & 12.2853 & -2.9853 \tabularnewline
35 & 11.8 & 10.9389 & 0.861114 \tabularnewline
36 & 5.9 & 11.3049 & -5.40493 \tabularnewline
37 & 11.4 & 11.8723 & -0.472324 \tabularnewline
38 & 13 & 10.9052 & 2.09478 \tabularnewline
39 & 10.8 & 11.1268 & -0.326837 \tabularnewline
40 & 11.3 & 10.6894 & 0.610575 \tabularnewline
41 & 11.8 & 11.6471 & 0.152938 \tabularnewline
42 & 12.7 & 9.33395 & 3.36605 \tabularnewline
43 & 10.9 & 10.653 & 0.246985 \tabularnewline
44 & 13.3 & 11.7458 & 1.55418 \tabularnewline
45 & 10.1 & 10.5232 & -0.423205 \tabularnewline
46 & 14.3 & 11.3672 & 2.93283 \tabularnewline
47 & 9.3 & 11.6772 & -2.37719 \tabularnewline
48 & 12.5 & 10.392 & 2.10803 \tabularnewline
49 & 7.6 & 10.1342 & -2.53417 \tabularnewline
50 & 15.9 & 12.542 & 3.35801 \tabularnewline
51 & 9.2 & 10.4265 & -1.22655 \tabularnewline
52 & 11.1 & 12.3842 & -1.28417 \tabularnewline
53 & 13 & 12.3815 & 0.618499 \tabularnewline
54 & 14.5 & 11.4609 & 3.03912 \tabularnewline
55 & 12.3 & 13.1009 & -0.800932 \tabularnewline
56 & 11.4 & 10.4124 & 0.987575 \tabularnewline
57 & 13 & 12.265 & 0.735024 \tabularnewline
58 & 13.2 & 10.9963 & 2.2037 \tabularnewline
59 & 7.7 & 11.5181 & -3.81812 \tabularnewline
60 & 4.35 & 7.30207 & -2.95207 \tabularnewline
61 & 12.7 & 10.3183 & 2.38169 \tabularnewline
62 & 18.1 & 16.1308 & 1.96925 \tabularnewline
63 & 17.85 & 16.1561 & 1.69392 \tabularnewline
64 & 17.1 & 17.4896 & -0.389555 \tabularnewline
65 & 19.1 & 16.7008 & 2.39915 \tabularnewline
66 & 16.1 & 18.6623 & -2.5623 \tabularnewline
67 & 13.35 & 10.7975 & 2.55253 \tabularnewline
68 & 18.4 & 17.7613 & 0.638679 \tabularnewline
69 & 14.7 & 7.61603 & 7.08397 \tabularnewline
70 & 10.6 & 13.2719 & -2.67191 \tabularnewline
71 & 12.6 & 13.7956 & -1.19556 \tabularnewline
72 & 13.6 & 12.7454 & 0.854575 \tabularnewline
73 & 14.1 & 13.1786 & 0.921429 \tabularnewline
74 & 14.5 & 13.4438 & 1.05621 \tabularnewline
75 & 16.15 & 16.3931 & -0.243068 \tabularnewline
76 & 14.75 & 12.5763 & 2.17372 \tabularnewline
77 & 14.8 & 12.1988 & 2.60117 \tabularnewline
78 & 12.45 & 12.3011 & 0.148889 \tabularnewline
79 & 12.65 & 10.3675 & 2.28247 \tabularnewline
80 & 17.35 & 14.0217 & 3.32827 \tabularnewline
81 & 8.6 & 8.50852 & 0.0914795 \tabularnewline
82 & 18.4 & 16.8246 & 1.57543 \tabularnewline
83 & 16.1 & 14.3953 & 1.7047 \tabularnewline
84 & 17.75 & 15.6157 & 2.13427 \tabularnewline
85 & 15.25 & 15.2864 & -0.0364446 \tabularnewline
86 & 17.65 & 16.2579 & 1.39207 \tabularnewline
87 & 16.35 & 16.6587 & -0.308651 \tabularnewline
88 & 17.65 & 18.3593 & -0.709309 \tabularnewline
89 & 13.6 & 13.0067 & 0.593315 \tabularnewline
90 & 14.35 & 13.8631 & 0.486898 \tabularnewline
91 & 14.75 & 17.1897 & -2.43971 \tabularnewline
92 & 18.25 & 16.5708 & 1.67915 \tabularnewline
93 & 9.9 & 15.323 & -5.42302 \tabularnewline
94 & 16 & 14.0211 & 1.9789 \tabularnewline
95 & 18.25 & 16.2272 & 2.02282 \tabularnewline
96 & 16.85 & 17.7784 & -0.928437 \tabularnewline
97 & 18.95 & 16.883 & 2.06695 \tabularnewline
98 & 15.6 & 13.5978 & 2.00219 \tabularnewline
99 & 17.1 & 17.6779 & -0.577914 \tabularnewline
100 & 15.4 & 16.0939 & -0.693905 \tabularnewline
101 & 15.4 & 16.0167 & -0.616737 \tabularnewline
102 & 13.35 & 14.251 & -0.901013 \tabularnewline
103 & 19.1 & 17.0973 & 2.0027 \tabularnewline
104 & 7.6 & 6.64368 & 0.956324 \tabularnewline
105 & 19.1 & 17.0133 & 2.08668 \tabularnewline
106 & 14.75 & 16.4713 & -1.72128 \tabularnewline
107 & 19.25 & 16.8953 & 2.35468 \tabularnewline
108 & 13.6 & 16.0638 & -2.46381 \tabularnewline
109 & 12.75 & 15.348 & -2.59802 \tabularnewline
110 & 9.85 & 8.15353 & 1.69647 \tabularnewline
111 & 15.25 & 15.8846 & -0.634631 \tabularnewline
112 & 11.9 & 13.3939 & -1.49393 \tabularnewline
113 & 16.35 & 17.3106 & -0.960613 \tabularnewline
114 & 12.4 & 13.8165 & -1.41646 \tabularnewline
115 & 18.15 & 16.0121 & 2.13792 \tabularnewline
116 & 17.75 & 15.234 & 2.51605 \tabularnewline
117 & 12.35 & 12.7904 & -0.440415 \tabularnewline
118 & 15.6 & 15.3226 & 0.27742 \tabularnewline
119 & 19.3 & 16.7225 & 2.57754 \tabularnewline
120 & 17.1 & 16.4885 & 0.611496 \tabularnewline
121 & 18.4 & 15.4435 & 2.95648 \tabularnewline
122 & 19.05 & 16.8496 & 2.20041 \tabularnewline
123 & 18.55 & 14.5603 & 3.98972 \tabularnewline
124 & 19.1 & 18.1307 & 0.969286 \tabularnewline
125 & 12.85 & 15.7407 & -2.89073 \tabularnewline
126 & 9.5 & 10.916 & -1.41599 \tabularnewline
127 & 4.5 & 7.48375 & -2.98375 \tabularnewline
128 & 13.6 & 15.0113 & -1.41126 \tabularnewline
129 & 11.7 & 12.3915 & -0.691505 \tabularnewline
130 & 13.35 & 13.9121 & -0.562148 \tabularnewline
131 & 17.6 & 18.9411 & -1.34109 \tabularnewline
132 & 14.05 & 13.738 & 0.312008 \tabularnewline
133 & 16.1 & 17.5094 & -1.4094 \tabularnewline
134 & 13.35 & 15.5466 & -2.1966 \tabularnewline
135 & 11.85 & 15.1724 & -3.32237 \tabularnewline
136 & 11.95 & 11.7358 & 0.214242 \tabularnewline
137 & 13.2 & 16.3749 & -3.17492 \tabularnewline
138 & 7.7 & 9.43934 & -1.73934 \tabularnewline
139 & 14.6 & 13.6641 & 0.935893 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271051&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]12.947[/C][C]-0.0469867[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.2656[/C][C]1.93437[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.7033[/C][C]1.09669[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.646[/C][C]-4.24596[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.3757[/C][C]-4.67573[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9123[/C][C]0.687676[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.0309[/C][C]3.76915[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.3274[/C][C]-0.0273814[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4781[/C][C]-1.37815[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.8741[/C][C]-2.6741[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.274[/C][C]0.125999[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.0064[/C][C]-4.60644[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1323[/C][C]0.467725[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3168[/C][C]-1.31683[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.74737[/C][C]-2.44737[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.2471[/C][C]-1.34713[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9205[/C][C]-1.62054[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.62623[/C][C]0.373767[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.3106[/C][C]-3.91057[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.6311[/C][C]1.16894[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.2871[/C][C]0.512871[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.834[/C][C]1.96597[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.6393[/C][C]1.06073[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.9393[/C][C]-1.03928[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.1395[/C][C]-1.23946[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5379[/C][C]0.962131[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.0029[/C][C]-2.70292[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.07[/C][C]0.629962[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3612[/C][C]-1.36116[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.9344[/C][C]-4.2344[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.2532[/C][C]-0.453186[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.7783[/C][C]-0.478304[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.65735[/C][C]0.742649[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2853[/C][C]-2.9853[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.9389[/C][C]0.861114[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.3049[/C][C]-5.40493[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8723[/C][C]-0.472324[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.9052[/C][C]2.09478[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.1268[/C][C]-0.326837[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.6894[/C][C]0.610575[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.6471[/C][C]0.152938[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.33395[/C][C]3.36605[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.653[/C][C]0.246985[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.7458[/C][C]1.55418[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.5232[/C][C]-0.423205[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.3672[/C][C]2.93283[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6772[/C][C]-2.37719[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.392[/C][C]2.10803[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.1342[/C][C]-2.53417[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.542[/C][C]3.35801[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.4265[/C][C]-1.22655[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.3842[/C][C]-1.28417[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.3815[/C][C]0.618499[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.4609[/C][C]3.03912[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.1009[/C][C]-0.800932[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.4124[/C][C]0.987575[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.265[/C][C]0.735024[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]10.9963[/C][C]2.2037[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.5181[/C][C]-3.81812[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.30207[/C][C]-2.95207[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.3183[/C][C]2.38169[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.1308[/C][C]1.96925[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.1561[/C][C]1.69392[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.4896[/C][C]-0.389555[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.7008[/C][C]2.39915[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.6623[/C][C]-2.5623[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.7975[/C][C]2.55253[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.7613[/C][C]0.638679[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.61603[/C][C]7.08397[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.2719[/C][C]-2.67191[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.7956[/C][C]-1.19556[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.7454[/C][C]0.854575[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.1786[/C][C]0.921429[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.4438[/C][C]1.05621[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.3931[/C][C]-0.243068[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.5763[/C][C]2.17372[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.1988[/C][C]2.60117[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.3011[/C][C]0.148889[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.3675[/C][C]2.28247[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]14.0217[/C][C]3.32827[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.50852[/C][C]0.0914795[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.8246[/C][C]1.57543[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.3953[/C][C]1.7047[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.6157[/C][C]2.13427[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.2864[/C][C]-0.0364446[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.2579[/C][C]1.39207[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.6587[/C][C]-0.308651[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.3593[/C][C]-0.709309[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]13.0067[/C][C]0.593315[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.8631[/C][C]0.486898[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.1897[/C][C]-2.43971[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.5708[/C][C]1.67915[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.323[/C][C]-5.42302[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.0211[/C][C]1.9789[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.2272[/C][C]2.02282[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.7784[/C][C]-0.928437[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.883[/C][C]2.06695[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.5978[/C][C]2.00219[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.6779[/C][C]-0.577914[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.0939[/C][C]-0.693905[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0167[/C][C]-0.616737[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.251[/C][C]-0.901013[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0973[/C][C]2.0027[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.64368[/C][C]0.956324[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]17.0133[/C][C]2.08668[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.4713[/C][C]-1.72128[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]16.8953[/C][C]2.35468[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.0638[/C][C]-2.46381[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.348[/C][C]-2.59802[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.15353[/C][C]1.69647[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.8846[/C][C]-0.634631[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.3939[/C][C]-1.49393[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.3106[/C][C]-0.960613[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.8165[/C][C]-1.41646[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]16.0121[/C][C]2.13792[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.234[/C][C]2.51605[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.7904[/C][C]-0.440415[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.3226[/C][C]0.27742[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.7225[/C][C]2.57754[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.4885[/C][C]0.611496[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.4435[/C][C]2.95648[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8496[/C][C]2.20041[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.5603[/C][C]3.98972[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.1307[/C][C]0.969286[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.7407[/C][C]-2.89073[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.916[/C][C]-1.41599[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.48375[/C][C]-2.98375[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]15.0113[/C][C]-1.41126[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.3915[/C][C]-0.691505[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.9121[/C][C]-0.562148[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]18.9411[/C][C]-1.34109[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.738[/C][C]0.312008[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.5094[/C][C]-1.4094[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.5466[/C][C]-2.1966[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1724[/C][C]-3.32237[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.7358[/C][C]0.214242[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.3749[/C][C]-3.17492[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.43934[/C][C]-1.73934[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.6641[/C][C]0.935893[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271051&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271051&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
112.912.947-0.0469867
212.210.26561.93437
312.811.70331.09669
47.411.646-4.24596
56.711.3757-4.67573
612.611.91230.687676
714.811.03093.76915
813.313.3274-0.0273814
911.112.4781-1.37815
108.210.8741-2.6741
1111.411.2740.125999
126.411.0064-4.60644
1310.610.13230.467725
141213.3168-1.31683
156.38.74737-2.44737
1611.913.2471-1.34713
179.310.9205-1.62054
18109.626230.373767
196.410.3106-3.91057
2013.812.63111.16894
2110.810.28710.512871
2213.811.8341.96597
2311.710.63931.06073
2410.911.9393-1.03928
259.911.1395-1.23946
2611.510.53790.962131
278.311.0029-2.70292
2811.711.070.629962
29910.3612-1.36116
309.713.9344-4.2344
3110.811.2532-0.453186
3210.310.7783-0.478304
3310.49.657350.742649
349.312.2853-2.9853
3511.810.93890.861114
365.911.3049-5.40493
3711.411.8723-0.472324
381310.90522.09478
3910.811.1268-0.326837
4011.310.68940.610575
4111.811.64710.152938
4212.79.333953.36605
4310.910.6530.246985
4413.311.74581.55418
4510.110.5232-0.423205
4614.311.36722.93283
479.311.6772-2.37719
4812.510.3922.10803
497.610.1342-2.53417
5015.912.5423.35801
519.210.4265-1.22655
5211.112.3842-1.28417
531312.38150.618499
5414.511.46093.03912
5512.313.1009-0.800932
5611.410.41240.987575
571312.2650.735024
5813.210.99632.2037
597.711.5181-3.81812
604.357.30207-2.95207
6112.710.31832.38169
6218.116.13081.96925
6317.8516.15611.69392
6417.117.4896-0.389555
6519.116.70082.39915
6616.118.6623-2.5623
6713.3510.79752.55253
6818.417.76130.638679
6914.77.616037.08397
7010.613.2719-2.67191
7112.613.7956-1.19556
7213.612.74540.854575
7314.113.17860.921429
7414.513.44381.05621
7516.1516.3931-0.243068
7614.7512.57632.17372
7714.812.19882.60117
7812.4512.30110.148889
7912.6510.36752.28247
8017.3514.02173.32827
818.68.508520.0914795
8218.416.82461.57543
8316.114.39531.7047
8417.7515.61572.13427
8515.2515.2864-0.0364446
8617.6516.25791.39207
8716.3516.6587-0.308651
8817.6518.3593-0.709309
8913.613.00670.593315
9014.3513.86310.486898
9114.7517.1897-2.43971
9218.2516.57081.67915
939.915.323-5.42302
941614.02111.9789
9518.2516.22722.02282
9616.8517.7784-0.928437
9718.9516.8832.06695
9815.613.59782.00219
9917.117.6779-0.577914
10015.416.0939-0.693905
10115.416.0167-0.616737
10213.3514.251-0.901013
10319.117.09732.0027
1047.66.643680.956324
10519.117.01332.08668
10614.7516.4713-1.72128
10719.2516.89532.35468
10813.616.0638-2.46381
10912.7515.348-2.59802
1109.858.153531.69647
11115.2515.8846-0.634631
11211.913.3939-1.49393
11316.3517.3106-0.960613
11412.413.8165-1.41646
11518.1516.01212.13792
11617.7515.2342.51605
11712.3512.7904-0.440415
11815.615.32260.27742
11919.316.72252.57754
12017.116.48850.611496
12118.415.44352.95648
12219.0516.84962.20041
12318.5514.56033.98972
12419.118.13070.969286
12512.8515.7407-2.89073
1269.510.916-1.41599
1274.57.48375-2.98375
12813.615.0113-1.41126
12911.712.3915-0.691505
13013.3513.9121-0.562148
13117.618.9411-1.34109
13214.0513.7380.312008
13316.117.5094-1.4094
13413.3515.5466-2.1966
13511.8515.1724-3.32237
13611.9511.73580.214242
13713.216.3749-3.17492
1387.79.43934-1.73934
13914.613.66410.935893






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.6461150.7077690.353885
150.5073990.9852030.492601
160.3858190.7716380.614181
170.2649950.529990.735005
180.2281770.4563540.771823
190.1832370.3664740.816763
200.1417230.2834460.858277
210.1083330.2166660.891667
220.06963020.139260.93037
230.06332860.1266570.936671
240.04417470.08834940.955825
250.05126110.1025220.948739
260.05336820.1067360.946632
270.1045020.2090040.895498
280.1703090.3406180.829691
290.2116390.4232780.788361
300.3943980.7887960.605602
310.3710490.7420980.628951
320.3089690.6179370.691031
330.2589480.5178960.741052
340.268460.5369210.73154
350.2186710.4373420.781329
360.4307480.8614950.569252
370.3731750.746350.626825
380.3299760.6599520.670024
390.3142980.6285950.685702
400.2641350.528270.735865
410.2504570.5009130.749543
420.2956890.5913770.704311
430.2571540.5143080.742846
440.4065270.8130530.593473
450.3539210.7078410.646079
460.469240.938480.53076
470.4843410.9686820.515659
480.506850.9862990.49315
490.5547190.8905620.445281
500.6403360.7193290.359664
510.6310050.7379910.368995
520.6039410.7921190.396059
530.5612670.8774660.438733
540.5722250.855550.427775
550.5399060.9201870.460094
560.4971890.9943790.502811
570.4566130.9132250.543387
580.4397850.879570.560215
590.5197530.9604940.480247
600.6014940.7970130.398506
610.6437440.7125120.356256
620.6675840.6648330.332416
630.6321370.7357260.367863
640.5960710.8078580.403929
650.5875650.8248690.412435
660.6356240.7287520.364376
670.6826590.6346820.317341
680.6378630.7242740.362137
690.9436890.1126210.0563107
700.9500960.09980880.0499044
710.9432750.1134510.0567254
720.9412240.1175530.0587764
730.9286630.1426730.0713366
740.9162910.1674170.0837085
750.913810.1723790.0861897
760.9122820.1754360.0877181
770.9261020.1477970.0738983
780.9057890.1884220.0942112
790.9039250.1921490.0960747
800.9382330.1235340.0617671
810.9212250.157550.0787749
820.9081120.1837760.0918878
830.8956460.2087080.104354
840.8831780.2336440.116822
850.8591920.2816150.140808
860.8466550.3066890.153345
870.8179580.3640830.182042
880.78920.4216010.2108
890.7501380.4997230.249862
900.7042030.5915930.295797
910.6972990.6054020.302701
920.6668890.6662220.333111
930.9062970.1874060.0937031
940.88680.2264010.1132
950.876480.247040.12352
960.8515260.2969480.148474
970.8249150.350170.175085
980.82870.34260.1713
990.819780.3604390.18022
1000.7886470.4227060.211353
1010.7431610.5136780.256839
1020.7148540.5702920.285146
1030.7364560.5270880.263544
1040.7452670.5094650.254733
1050.6997940.6004120.300206
1060.6573540.6852930.342646
1070.6151930.7696150.384807
1080.5991920.8016160.400808
1090.5849050.8301890.415095
1100.5687860.8624280.431214
1110.5256330.9487340.474367
1120.4563730.9127450.543627
1130.417520.8350410.58248
1140.3515530.7031050.648447
1150.3503080.7006170.649692
1160.3424990.6849990.657501
1170.2687210.5374420.731279
1180.633740.7325190.36626
1190.6909850.6180310.309015
1200.7248610.5502790.275139
1210.6827870.6344250.317213
1220.8208950.3582090.179105
1230.9025990.1948020.0974009
1240.8184740.3630530.181526
1250.7499460.5001090.250054

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.646115 & 0.707769 & 0.353885 \tabularnewline
15 & 0.507399 & 0.985203 & 0.492601 \tabularnewline
16 & 0.385819 & 0.771638 & 0.614181 \tabularnewline
17 & 0.264995 & 0.52999 & 0.735005 \tabularnewline
18 & 0.228177 & 0.456354 & 0.771823 \tabularnewline
19 & 0.183237 & 0.366474 & 0.816763 \tabularnewline
20 & 0.141723 & 0.283446 & 0.858277 \tabularnewline
21 & 0.108333 & 0.216666 & 0.891667 \tabularnewline
22 & 0.0696302 & 0.13926 & 0.93037 \tabularnewline
23 & 0.0633286 & 0.126657 & 0.936671 \tabularnewline
24 & 0.0441747 & 0.0883494 & 0.955825 \tabularnewline
25 & 0.0512611 & 0.102522 & 0.948739 \tabularnewline
26 & 0.0533682 & 0.106736 & 0.946632 \tabularnewline
27 & 0.104502 & 0.209004 & 0.895498 \tabularnewline
28 & 0.170309 & 0.340618 & 0.829691 \tabularnewline
29 & 0.211639 & 0.423278 & 0.788361 \tabularnewline
30 & 0.394398 & 0.788796 & 0.605602 \tabularnewline
31 & 0.371049 & 0.742098 & 0.628951 \tabularnewline
32 & 0.308969 & 0.617937 & 0.691031 \tabularnewline
33 & 0.258948 & 0.517896 & 0.741052 \tabularnewline
34 & 0.26846 & 0.536921 & 0.73154 \tabularnewline
35 & 0.218671 & 0.437342 & 0.781329 \tabularnewline
36 & 0.430748 & 0.861495 & 0.569252 \tabularnewline
37 & 0.373175 & 0.74635 & 0.626825 \tabularnewline
38 & 0.329976 & 0.659952 & 0.670024 \tabularnewline
39 & 0.314298 & 0.628595 & 0.685702 \tabularnewline
40 & 0.264135 & 0.52827 & 0.735865 \tabularnewline
41 & 0.250457 & 0.500913 & 0.749543 \tabularnewline
42 & 0.295689 & 0.591377 & 0.704311 \tabularnewline
43 & 0.257154 & 0.514308 & 0.742846 \tabularnewline
44 & 0.406527 & 0.813053 & 0.593473 \tabularnewline
45 & 0.353921 & 0.707841 & 0.646079 \tabularnewline
46 & 0.46924 & 0.93848 & 0.53076 \tabularnewline
47 & 0.484341 & 0.968682 & 0.515659 \tabularnewline
48 & 0.50685 & 0.986299 & 0.49315 \tabularnewline
49 & 0.554719 & 0.890562 & 0.445281 \tabularnewline
50 & 0.640336 & 0.719329 & 0.359664 \tabularnewline
51 & 0.631005 & 0.737991 & 0.368995 \tabularnewline
52 & 0.603941 & 0.792119 & 0.396059 \tabularnewline
53 & 0.561267 & 0.877466 & 0.438733 \tabularnewline
54 & 0.572225 & 0.85555 & 0.427775 \tabularnewline
55 & 0.539906 & 0.920187 & 0.460094 \tabularnewline
56 & 0.497189 & 0.994379 & 0.502811 \tabularnewline
57 & 0.456613 & 0.913225 & 0.543387 \tabularnewline
58 & 0.439785 & 0.87957 & 0.560215 \tabularnewline
59 & 0.519753 & 0.960494 & 0.480247 \tabularnewline
60 & 0.601494 & 0.797013 & 0.398506 \tabularnewline
61 & 0.643744 & 0.712512 & 0.356256 \tabularnewline
62 & 0.667584 & 0.664833 & 0.332416 \tabularnewline
63 & 0.632137 & 0.735726 & 0.367863 \tabularnewline
64 & 0.596071 & 0.807858 & 0.403929 \tabularnewline
65 & 0.587565 & 0.824869 & 0.412435 \tabularnewline
66 & 0.635624 & 0.728752 & 0.364376 \tabularnewline
67 & 0.682659 & 0.634682 & 0.317341 \tabularnewline
68 & 0.637863 & 0.724274 & 0.362137 \tabularnewline
69 & 0.943689 & 0.112621 & 0.0563107 \tabularnewline
70 & 0.950096 & 0.0998088 & 0.0499044 \tabularnewline
71 & 0.943275 & 0.113451 & 0.0567254 \tabularnewline
72 & 0.941224 & 0.117553 & 0.0587764 \tabularnewline
73 & 0.928663 & 0.142673 & 0.0713366 \tabularnewline
74 & 0.916291 & 0.167417 & 0.0837085 \tabularnewline
75 & 0.91381 & 0.172379 & 0.0861897 \tabularnewline
76 & 0.912282 & 0.175436 & 0.0877181 \tabularnewline
77 & 0.926102 & 0.147797 & 0.0738983 \tabularnewline
78 & 0.905789 & 0.188422 & 0.0942112 \tabularnewline
79 & 0.903925 & 0.192149 & 0.0960747 \tabularnewline
80 & 0.938233 & 0.123534 & 0.0617671 \tabularnewline
81 & 0.921225 & 0.15755 & 0.0787749 \tabularnewline
82 & 0.908112 & 0.183776 & 0.0918878 \tabularnewline
83 & 0.895646 & 0.208708 & 0.104354 \tabularnewline
84 & 0.883178 & 0.233644 & 0.116822 \tabularnewline
85 & 0.859192 & 0.281615 & 0.140808 \tabularnewline
86 & 0.846655 & 0.306689 & 0.153345 \tabularnewline
87 & 0.817958 & 0.364083 & 0.182042 \tabularnewline
88 & 0.7892 & 0.421601 & 0.2108 \tabularnewline
89 & 0.750138 & 0.499723 & 0.249862 \tabularnewline
90 & 0.704203 & 0.591593 & 0.295797 \tabularnewline
91 & 0.697299 & 0.605402 & 0.302701 \tabularnewline
92 & 0.666889 & 0.666222 & 0.333111 \tabularnewline
93 & 0.906297 & 0.187406 & 0.0937031 \tabularnewline
94 & 0.8868 & 0.226401 & 0.1132 \tabularnewline
95 & 0.87648 & 0.24704 & 0.12352 \tabularnewline
96 & 0.851526 & 0.296948 & 0.148474 \tabularnewline
97 & 0.824915 & 0.35017 & 0.175085 \tabularnewline
98 & 0.8287 & 0.3426 & 0.1713 \tabularnewline
99 & 0.81978 & 0.360439 & 0.18022 \tabularnewline
100 & 0.788647 & 0.422706 & 0.211353 \tabularnewline
101 & 0.743161 & 0.513678 & 0.256839 \tabularnewline
102 & 0.714854 & 0.570292 & 0.285146 \tabularnewline
103 & 0.736456 & 0.527088 & 0.263544 \tabularnewline
104 & 0.745267 & 0.509465 & 0.254733 \tabularnewline
105 & 0.699794 & 0.600412 & 0.300206 \tabularnewline
106 & 0.657354 & 0.685293 & 0.342646 \tabularnewline
107 & 0.615193 & 0.769615 & 0.384807 \tabularnewline
108 & 0.599192 & 0.801616 & 0.400808 \tabularnewline
109 & 0.584905 & 0.830189 & 0.415095 \tabularnewline
110 & 0.568786 & 0.862428 & 0.431214 \tabularnewline
111 & 0.525633 & 0.948734 & 0.474367 \tabularnewline
112 & 0.456373 & 0.912745 & 0.543627 \tabularnewline
113 & 0.41752 & 0.835041 & 0.58248 \tabularnewline
114 & 0.351553 & 0.703105 & 0.648447 \tabularnewline
115 & 0.350308 & 0.700617 & 0.649692 \tabularnewline
116 & 0.342499 & 0.684999 & 0.657501 \tabularnewline
117 & 0.268721 & 0.537442 & 0.731279 \tabularnewline
118 & 0.63374 & 0.732519 & 0.36626 \tabularnewline
119 & 0.690985 & 0.618031 & 0.309015 \tabularnewline
120 & 0.724861 & 0.550279 & 0.275139 \tabularnewline
121 & 0.682787 & 0.634425 & 0.317213 \tabularnewline
122 & 0.820895 & 0.358209 & 0.179105 \tabularnewline
123 & 0.902599 & 0.194802 & 0.0974009 \tabularnewline
124 & 0.818474 & 0.363053 & 0.181526 \tabularnewline
125 & 0.749946 & 0.500109 & 0.250054 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271051&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]0.646115[/C][C]0.707769[/C][C]0.353885[/C][/ROW]
[ROW][C]15[/C][C]0.507399[/C][C]0.985203[/C][C]0.492601[/C][/ROW]
[ROW][C]16[/C][C]0.385819[/C][C]0.771638[/C][C]0.614181[/C][/ROW]
[ROW][C]17[/C][C]0.264995[/C][C]0.52999[/C][C]0.735005[/C][/ROW]
[ROW][C]18[/C][C]0.228177[/C][C]0.456354[/C][C]0.771823[/C][/ROW]
[ROW][C]19[/C][C]0.183237[/C][C]0.366474[/C][C]0.816763[/C][/ROW]
[ROW][C]20[/C][C]0.141723[/C][C]0.283446[/C][C]0.858277[/C][/ROW]
[ROW][C]21[/C][C]0.108333[/C][C]0.216666[/C][C]0.891667[/C][/ROW]
[ROW][C]22[/C][C]0.0696302[/C][C]0.13926[/C][C]0.93037[/C][/ROW]
[ROW][C]23[/C][C]0.0633286[/C][C]0.126657[/C][C]0.936671[/C][/ROW]
[ROW][C]24[/C][C]0.0441747[/C][C]0.0883494[/C][C]0.955825[/C][/ROW]
[ROW][C]25[/C][C]0.0512611[/C][C]0.102522[/C][C]0.948739[/C][/ROW]
[ROW][C]26[/C][C]0.0533682[/C][C]0.106736[/C][C]0.946632[/C][/ROW]
[ROW][C]27[/C][C]0.104502[/C][C]0.209004[/C][C]0.895498[/C][/ROW]
[ROW][C]28[/C][C]0.170309[/C][C]0.340618[/C][C]0.829691[/C][/ROW]
[ROW][C]29[/C][C]0.211639[/C][C]0.423278[/C][C]0.788361[/C][/ROW]
[ROW][C]30[/C][C]0.394398[/C][C]0.788796[/C][C]0.605602[/C][/ROW]
[ROW][C]31[/C][C]0.371049[/C][C]0.742098[/C][C]0.628951[/C][/ROW]
[ROW][C]32[/C][C]0.308969[/C][C]0.617937[/C][C]0.691031[/C][/ROW]
[ROW][C]33[/C][C]0.258948[/C][C]0.517896[/C][C]0.741052[/C][/ROW]
[ROW][C]34[/C][C]0.26846[/C][C]0.536921[/C][C]0.73154[/C][/ROW]
[ROW][C]35[/C][C]0.218671[/C][C]0.437342[/C][C]0.781329[/C][/ROW]
[ROW][C]36[/C][C]0.430748[/C][C]0.861495[/C][C]0.569252[/C][/ROW]
[ROW][C]37[/C][C]0.373175[/C][C]0.74635[/C][C]0.626825[/C][/ROW]
[ROW][C]38[/C][C]0.329976[/C][C]0.659952[/C][C]0.670024[/C][/ROW]
[ROW][C]39[/C][C]0.314298[/C][C]0.628595[/C][C]0.685702[/C][/ROW]
[ROW][C]40[/C][C]0.264135[/C][C]0.52827[/C][C]0.735865[/C][/ROW]
[ROW][C]41[/C][C]0.250457[/C][C]0.500913[/C][C]0.749543[/C][/ROW]
[ROW][C]42[/C][C]0.295689[/C][C]0.591377[/C][C]0.704311[/C][/ROW]
[ROW][C]43[/C][C]0.257154[/C][C]0.514308[/C][C]0.742846[/C][/ROW]
[ROW][C]44[/C][C]0.406527[/C][C]0.813053[/C][C]0.593473[/C][/ROW]
[ROW][C]45[/C][C]0.353921[/C][C]0.707841[/C][C]0.646079[/C][/ROW]
[ROW][C]46[/C][C]0.46924[/C][C]0.93848[/C][C]0.53076[/C][/ROW]
[ROW][C]47[/C][C]0.484341[/C][C]0.968682[/C][C]0.515659[/C][/ROW]
[ROW][C]48[/C][C]0.50685[/C][C]0.986299[/C][C]0.49315[/C][/ROW]
[ROW][C]49[/C][C]0.554719[/C][C]0.890562[/C][C]0.445281[/C][/ROW]
[ROW][C]50[/C][C]0.640336[/C][C]0.719329[/C][C]0.359664[/C][/ROW]
[ROW][C]51[/C][C]0.631005[/C][C]0.737991[/C][C]0.368995[/C][/ROW]
[ROW][C]52[/C][C]0.603941[/C][C]0.792119[/C][C]0.396059[/C][/ROW]
[ROW][C]53[/C][C]0.561267[/C][C]0.877466[/C][C]0.438733[/C][/ROW]
[ROW][C]54[/C][C]0.572225[/C][C]0.85555[/C][C]0.427775[/C][/ROW]
[ROW][C]55[/C][C]0.539906[/C][C]0.920187[/C][C]0.460094[/C][/ROW]
[ROW][C]56[/C][C]0.497189[/C][C]0.994379[/C][C]0.502811[/C][/ROW]
[ROW][C]57[/C][C]0.456613[/C][C]0.913225[/C][C]0.543387[/C][/ROW]
[ROW][C]58[/C][C]0.439785[/C][C]0.87957[/C][C]0.560215[/C][/ROW]
[ROW][C]59[/C][C]0.519753[/C][C]0.960494[/C][C]0.480247[/C][/ROW]
[ROW][C]60[/C][C]0.601494[/C][C]0.797013[/C][C]0.398506[/C][/ROW]
[ROW][C]61[/C][C]0.643744[/C][C]0.712512[/C][C]0.356256[/C][/ROW]
[ROW][C]62[/C][C]0.667584[/C][C]0.664833[/C][C]0.332416[/C][/ROW]
[ROW][C]63[/C][C]0.632137[/C][C]0.735726[/C][C]0.367863[/C][/ROW]
[ROW][C]64[/C][C]0.596071[/C][C]0.807858[/C][C]0.403929[/C][/ROW]
[ROW][C]65[/C][C]0.587565[/C][C]0.824869[/C][C]0.412435[/C][/ROW]
[ROW][C]66[/C][C]0.635624[/C][C]0.728752[/C][C]0.364376[/C][/ROW]
[ROW][C]67[/C][C]0.682659[/C][C]0.634682[/C][C]0.317341[/C][/ROW]
[ROW][C]68[/C][C]0.637863[/C][C]0.724274[/C][C]0.362137[/C][/ROW]
[ROW][C]69[/C][C]0.943689[/C][C]0.112621[/C][C]0.0563107[/C][/ROW]
[ROW][C]70[/C][C]0.950096[/C][C]0.0998088[/C][C]0.0499044[/C][/ROW]
[ROW][C]71[/C][C]0.943275[/C][C]0.113451[/C][C]0.0567254[/C][/ROW]
[ROW][C]72[/C][C]0.941224[/C][C]0.117553[/C][C]0.0587764[/C][/ROW]
[ROW][C]73[/C][C]0.928663[/C][C]0.142673[/C][C]0.0713366[/C][/ROW]
[ROW][C]74[/C][C]0.916291[/C][C]0.167417[/C][C]0.0837085[/C][/ROW]
[ROW][C]75[/C][C]0.91381[/C][C]0.172379[/C][C]0.0861897[/C][/ROW]
[ROW][C]76[/C][C]0.912282[/C][C]0.175436[/C][C]0.0877181[/C][/ROW]
[ROW][C]77[/C][C]0.926102[/C][C]0.147797[/C][C]0.0738983[/C][/ROW]
[ROW][C]78[/C][C]0.905789[/C][C]0.188422[/C][C]0.0942112[/C][/ROW]
[ROW][C]79[/C][C]0.903925[/C][C]0.192149[/C][C]0.0960747[/C][/ROW]
[ROW][C]80[/C][C]0.938233[/C][C]0.123534[/C][C]0.0617671[/C][/ROW]
[ROW][C]81[/C][C]0.921225[/C][C]0.15755[/C][C]0.0787749[/C][/ROW]
[ROW][C]82[/C][C]0.908112[/C][C]0.183776[/C][C]0.0918878[/C][/ROW]
[ROW][C]83[/C][C]0.895646[/C][C]0.208708[/C][C]0.104354[/C][/ROW]
[ROW][C]84[/C][C]0.883178[/C][C]0.233644[/C][C]0.116822[/C][/ROW]
[ROW][C]85[/C][C]0.859192[/C][C]0.281615[/C][C]0.140808[/C][/ROW]
[ROW][C]86[/C][C]0.846655[/C][C]0.306689[/C][C]0.153345[/C][/ROW]
[ROW][C]87[/C][C]0.817958[/C][C]0.364083[/C][C]0.182042[/C][/ROW]
[ROW][C]88[/C][C]0.7892[/C][C]0.421601[/C][C]0.2108[/C][/ROW]
[ROW][C]89[/C][C]0.750138[/C][C]0.499723[/C][C]0.249862[/C][/ROW]
[ROW][C]90[/C][C]0.704203[/C][C]0.591593[/C][C]0.295797[/C][/ROW]
[ROW][C]91[/C][C]0.697299[/C][C]0.605402[/C][C]0.302701[/C][/ROW]
[ROW][C]92[/C][C]0.666889[/C][C]0.666222[/C][C]0.333111[/C][/ROW]
[ROW][C]93[/C][C]0.906297[/C][C]0.187406[/C][C]0.0937031[/C][/ROW]
[ROW][C]94[/C][C]0.8868[/C][C]0.226401[/C][C]0.1132[/C][/ROW]
[ROW][C]95[/C][C]0.87648[/C][C]0.24704[/C][C]0.12352[/C][/ROW]
[ROW][C]96[/C][C]0.851526[/C][C]0.296948[/C][C]0.148474[/C][/ROW]
[ROW][C]97[/C][C]0.824915[/C][C]0.35017[/C][C]0.175085[/C][/ROW]
[ROW][C]98[/C][C]0.8287[/C][C]0.3426[/C][C]0.1713[/C][/ROW]
[ROW][C]99[/C][C]0.81978[/C][C]0.360439[/C][C]0.18022[/C][/ROW]
[ROW][C]100[/C][C]0.788647[/C][C]0.422706[/C][C]0.211353[/C][/ROW]
[ROW][C]101[/C][C]0.743161[/C][C]0.513678[/C][C]0.256839[/C][/ROW]
[ROW][C]102[/C][C]0.714854[/C][C]0.570292[/C][C]0.285146[/C][/ROW]
[ROW][C]103[/C][C]0.736456[/C][C]0.527088[/C][C]0.263544[/C][/ROW]
[ROW][C]104[/C][C]0.745267[/C][C]0.509465[/C][C]0.254733[/C][/ROW]
[ROW][C]105[/C][C]0.699794[/C][C]0.600412[/C][C]0.300206[/C][/ROW]
[ROW][C]106[/C][C]0.657354[/C][C]0.685293[/C][C]0.342646[/C][/ROW]
[ROW][C]107[/C][C]0.615193[/C][C]0.769615[/C][C]0.384807[/C][/ROW]
[ROW][C]108[/C][C]0.599192[/C][C]0.801616[/C][C]0.400808[/C][/ROW]
[ROW][C]109[/C][C]0.584905[/C][C]0.830189[/C][C]0.415095[/C][/ROW]
[ROW][C]110[/C][C]0.568786[/C][C]0.862428[/C][C]0.431214[/C][/ROW]
[ROW][C]111[/C][C]0.525633[/C][C]0.948734[/C][C]0.474367[/C][/ROW]
[ROW][C]112[/C][C]0.456373[/C][C]0.912745[/C][C]0.543627[/C][/ROW]
[ROW][C]113[/C][C]0.41752[/C][C]0.835041[/C][C]0.58248[/C][/ROW]
[ROW][C]114[/C][C]0.351553[/C][C]0.703105[/C][C]0.648447[/C][/ROW]
[ROW][C]115[/C][C]0.350308[/C][C]0.700617[/C][C]0.649692[/C][/ROW]
[ROW][C]116[/C][C]0.342499[/C][C]0.684999[/C][C]0.657501[/C][/ROW]
[ROW][C]117[/C][C]0.268721[/C][C]0.537442[/C][C]0.731279[/C][/ROW]
[ROW][C]118[/C][C]0.63374[/C][C]0.732519[/C][C]0.36626[/C][/ROW]
[ROW][C]119[/C][C]0.690985[/C][C]0.618031[/C][C]0.309015[/C][/ROW]
[ROW][C]120[/C][C]0.724861[/C][C]0.550279[/C][C]0.275139[/C][/ROW]
[ROW][C]121[/C][C]0.682787[/C][C]0.634425[/C][C]0.317213[/C][/ROW]
[ROW][C]122[/C][C]0.820895[/C][C]0.358209[/C][C]0.179105[/C][/ROW]
[ROW][C]123[/C][C]0.902599[/C][C]0.194802[/C][C]0.0974009[/C][/ROW]
[ROW][C]124[/C][C]0.818474[/C][C]0.363053[/C][C]0.181526[/C][/ROW]
[ROW][C]125[/C][C]0.749946[/C][C]0.500109[/C][C]0.250054[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271051&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271051&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
140.6461150.7077690.353885
150.5073990.9852030.492601
160.3858190.7716380.614181
170.2649950.529990.735005
180.2281770.4563540.771823
190.1832370.3664740.816763
200.1417230.2834460.858277
210.1083330.2166660.891667
220.06963020.139260.93037
230.06332860.1266570.936671
240.04417470.08834940.955825
250.05126110.1025220.948739
260.05336820.1067360.946632
270.1045020.2090040.895498
280.1703090.3406180.829691
290.2116390.4232780.788361
300.3943980.7887960.605602
310.3710490.7420980.628951
320.3089690.6179370.691031
330.2589480.5178960.741052
340.268460.5369210.73154
350.2186710.4373420.781329
360.4307480.8614950.569252
370.3731750.746350.626825
380.3299760.6599520.670024
390.3142980.6285950.685702
400.2641350.528270.735865
410.2504570.5009130.749543
420.2956890.5913770.704311
430.2571540.5143080.742846
440.4065270.8130530.593473
450.3539210.7078410.646079
460.469240.938480.53076
470.4843410.9686820.515659
480.506850.9862990.49315
490.5547190.8905620.445281
500.6403360.7193290.359664
510.6310050.7379910.368995
520.6039410.7921190.396059
530.5612670.8774660.438733
540.5722250.855550.427775
550.5399060.9201870.460094
560.4971890.9943790.502811
570.4566130.9132250.543387
580.4397850.879570.560215
590.5197530.9604940.480247
600.6014940.7970130.398506
610.6437440.7125120.356256
620.6675840.6648330.332416
630.6321370.7357260.367863
640.5960710.8078580.403929
650.5875650.8248690.412435
660.6356240.7287520.364376
670.6826590.6346820.317341
680.6378630.7242740.362137
690.9436890.1126210.0563107
700.9500960.09980880.0499044
710.9432750.1134510.0567254
720.9412240.1175530.0587764
730.9286630.1426730.0713366
740.9162910.1674170.0837085
750.913810.1723790.0861897
760.9122820.1754360.0877181
770.9261020.1477970.0738983
780.9057890.1884220.0942112
790.9039250.1921490.0960747
800.9382330.1235340.0617671
810.9212250.157550.0787749
820.9081120.1837760.0918878
830.8956460.2087080.104354
840.8831780.2336440.116822
850.8591920.2816150.140808
860.8466550.3066890.153345
870.8179580.3640830.182042
880.78920.4216010.2108
890.7501380.4997230.249862
900.7042030.5915930.295797
910.6972990.6054020.302701
920.6668890.6662220.333111
930.9062970.1874060.0937031
940.88680.2264010.1132
950.876480.247040.12352
960.8515260.2969480.148474
970.8249150.350170.175085
980.82870.34260.1713
990.819780.3604390.18022
1000.7886470.4227060.211353
1010.7431610.5136780.256839
1020.7148540.5702920.285146
1030.7364560.5270880.263544
1040.7452670.5094650.254733
1050.6997940.6004120.300206
1060.6573540.6852930.342646
1070.6151930.7696150.384807
1080.5991920.8016160.400808
1090.5849050.8301890.415095
1100.5687860.8624280.431214
1110.5256330.9487340.474367
1120.4563730.9127450.543627
1130.417520.8350410.58248
1140.3515530.7031050.648447
1150.3503080.7006170.649692
1160.3424990.6849990.657501
1170.2687210.5374420.731279
1180.633740.7325190.36626
1190.6909850.6180310.309015
1200.7248610.5502790.275139
1210.6827870.6344250.317213
1220.8208950.3582090.179105
1230.9025990.1948020.0974009
1240.8184740.3630530.181526
1250.7499460.5001090.250054






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 level20.0178571OK

\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 & 0.0178571 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271051&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]0.0178571[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271051&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271051&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 level20.0178571OK


Figures (Output of Computation)

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

Parameters (Session):
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')
}