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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 14:36:18 +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/t14189133891ysf2zgb1q2vhrx.htm/, Retrieved Fri, 17 May 2024 10:59:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271003, Retrieved Fri, 17 May 2024 10:59:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

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 14:36:18] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
12.9 8 7 18 12 20 21 12 149 18 68 1.8
12.2 18 20 23 20 19 22 8 139 31 39 2.1
12.8 12 9 22 14 18 21 11 148 39 32 2.2
7.4 24 19 22 25 24 21 13 158 46 62 2.3
6.7 16 12 19 15 20 21 11 128 31 33 2.1
12.6 19 16 25 20 20 21 10 224 67 52 2.7
14.8 16 17 28 21 24 21 7 159 35 62 2.1
13.3 15 9 16 15 21 23 10 105 52 77 2.4
11.1 28 28 28 28 28 22 15 159 77 76 2.9
8.2 21 20 21 11 10 25 12 167 37 41 2.2
11.4 18 16 22 22 22 21 12 165 32 48 2.1
6.4 22 22 24 22 19 23 10 159 36 63 2.2
10.6 19 17 24 27 27 22 10 119 38 30 2.2
12 22 12 26 24 23 21 14 176 69 78 2.7
6.3 25 18 28 23 24 21 6 54 21 19 1.9
11.9 16 12 20 21 25 21 14 163 54 66 2.5
9.3 19 16 26 20 24 21 11 124 36 35 2.2
10 26 21 28 25 28 24 12 121 23 45 1.9
6.4 24 15 27 16 28 23 15 153 34 21 2.1
13.8 20 17 23 24 22 21 13 148 112 25 3.5
10.8 19 17 24 21 26 24 11 221 35 44 2.1
13.8 19 17 24 22 26 23 12 188 47 69 2.3
11.7 23 18 22 25 21 21 7 149 47 54 2.3
10.9 18 15 21 23 26 22 11 244 37 74 2.2
9.9 21 21 21 22 24 21 12 150 20 61 1.9
11.5 20 12 26 25 25 22 13 153 22 41 1.9
8.3 15 6 23 23 24 22 9 94 23 46 1.9
11.7 19 13 21 19 20 21 11 156 32 39 2.1
9 19 19 27 21 24 21 12 132 30 34 2
9.7 7 12 25 19 25 25 15 161 92 51 3.2
10.8 20 14 23 25 23 22 12 105 43 42 2.3
10.3 20 13 25 16 21 22 6 97 55 31 2.5
10.4 19 12 23 24 23 20 5 151 16 39 1.8
9.3 20 19 22 18 18 21 11 166 71 49 2.8
11.8 18 10 24 28 24 21 6 157 43 53 2.3
5.9 14 10 19 15 18 22 12 111 29 31 2
11.4 17 11 21 17 21 21 10 145 56 39 2.5
13 17 11 27 18 23 24 6 162 46 54 2.3
10.8 8 10 25 26 25 22 12 163 19 49 1.8
11.3 22 22 23 22 22 21 6 187 59 46 2.6
11.8 20 12 17 19 23 22 12 109 30 55 2
12.7 22 20 25 26 25 22 8 105 7 50 1.6
10.9 14 11 24 12 24 23 12 148 19 30 1.8
13.3 21 17 20 20 23 23 14 125 48 45 2.4
10.1 20 14 19 24 27 21 12 116 23 35 1.9
14.3 18 16 21 22 23 21 14 138 33 41 2.1
9.3 24 15 18 23 23 22 11 164 34 73 2.1
12.5 19 15 27 19 24 21 10 162 48 17 2.4
7.6 16 10 25 24 26 21 7 99 18 40 1.8
15.9 16 10 20 21 20 21 12 202 43 64 2.3
9.2 16 18 21 16 23 21 7 186 33 37 2.1
11.1 22 22 27 23 23 21 12 183 71 65 2.8
13 21 16 24 20 17 22 10 214 26 100 2
14.5 15 10 27 19 20 22 10 188 67 28 2.7
12.3 15 16 23 18 18 21 12 177 80 56 2.9
11.4 14 16 24 21 19 23 12 126 29 29 2
12.6 14 5 27 25 26 21 5 139 32 50 2.1
NA 19 18 24 15 14 21 10 78 47 3 2.3
13 16 10 25 17 25 21 10 162 43 59 2.3
13.2 26 16 24 24 18 20 11 159 29 61 2
7.7 18 16 23 22 26 21 12 110 32 51 2.1
4.35 17 15 22 14 15 22 9 48 23 12 1
12.7 6 4 24 5 27 22 11 50 16 45 1
18.1 22 9 19 25 23 22 12 150 33 37 4
17.85 20 18 25 21 23 20 12 154 32 37 4
17.1 17 12 24 9 22 22 12 194 52 68 4
19.1 20 16 28 15 20 21 12 158 75 72 4
16.1 23 17 23 23 21 21 10 159 72 143 4
13.35 18 14 19 21 25 21 15 67 15 9 2
18.4 13 13 19 9 19 21 10 147 29 55 4
14.7 22 20 27 24 25 21 15 39 13 17 1
10.6 20 16 24 16 24 21 10 100 40 37 3
12.6 20 15 26 20 22 21 15 111 19 27 3
16.2 13 10 21 15 28 22 9 138 24 37 4
13.6 16 16 25 18 22 24 15 101 121 58 3
14.1 16 15 19 21 23 22 13 101 36 21 3
14.5 15 16 20 21 19 20 12 114 23 19 3
16.15 19 19 26 21 21 21 12 165 85 78 4
14.75 19 9 27 20 25 24 8 114 41 35 3
14.8 24 19 23 24 23 25 9 111 46 48 3
12.45 9 7 18 15 28 22 15 75 18 27 2
12.65 22 23 23 24 14 21 12 82 35 43 2
17.35 15 14 21 18 23 21 12 121 17 30 3
8.6 22 10 23 24 24 22 15 32 4 25 1
18.4 22 16 22 24 25 23 11 150 28 69 4
16.1 24 12 21 15 15 24 12 117 44 72 3
17.75 21 7 24 20 26 22 14 165 38 13 4
15.25 25 20 26 26 21 25 12 154 57 61 4
17.65 26 9 24 26 26 22 12 126 23 43 4
16.35 21 12 22 23 23 21 12 149 36 51 4
17.65 14 10 20 13 15 21 11 145 22 67 4
13.6 28 19 20 16 16 21 12 120 40 36 3
14.35 21 11 18 22 20 22 12 109 31 44 3
14.75 16 15 18 21 20 22 12 132 11 45 4
18.25 16 14 25 11 21 21 12 172 38 34 4
9.9 25 11 28 23 28 22 8 169 24 36 4
16 21 14 23 18 19 23 8 114 37 72 3
18.25 22 15 20 19 21 21 12 156 37 39 4
16.85 9 7 22 15 22 21 12 172 22 43 4
18.95 24 22 23 21 17 21 11 167 43 80 4
15.6 22 11 20 25 26 21 12 113 31 40 3
17.1 10 12 24 12 22 22 10 173 31 61 4
16.1 22 17 18 24 17 22 11 2 -4 23 1
15.4 21 13 23 19 16 21 11 165 21 29 4
15.4 20 15 21 21 18 21 11 165 21 29 4
13.35 17 11 19 19 17 25 13 118 32 54 3
19.1 7 7 19 18 25 21 7 158 26 43 4
7.6 14 13 25 23 21 25 8 49 32 20 1
19.1 23 7 18 23 27 22 11 155 33 61 4
14.75 18 11 22 27 23 21 8 151 30 57 4
19.25 17 22 5 6 8 23 14 220 67 54 4
13.6 20 15 24 22 22 20 9 141 22 36 4
12.75 19 15 28 23 28 22 13 122 33 16 4
9.85 19 11 27 20 24 25 13 44 24 40 1
15.25 23 10 23 23 25 20 11 152 28 27 4
11.9 20 18 24 27 23 21 9 107 41 61 3
16.35 19 14 25 24 26 21 12 154 31 69 4
12.4 16 16 19 12 22 23 12 103 33 34 3
18.15 21 16 24 24 22 22 13 175 21 34 4
17.75 20 17 28 24 26 21 11 143 52 34 4
12.35 20 14 19 19 21 21 11 110 29 13 3
15.6 19 10 23 28 21 21 9 131 11 12 4
19.3 19 16 23 23 24 21 12 167 26 51 4
17.1 20 16 26 19 18 21 15 137 7 19 4
18.4 22 17 25 23 26 21 14 121 13 81 3
19.05 19 12 24 20 23 21 12 149 20 42 4
18.55 23 17 23 18 25 22 9 168 52 22 4
19.1 16 11 22 20 20 21 9 140 28 85 4
12.85 18 12 26 21 26 22 13 168 39 25 4
9.5 23 8 23 25 19 22 15 94 9 22 2
4.5 20 17 22 18 21 22 11 51 19 19 1
13.6 23 17 22 28 24 22 10 145 60 45 4
11.7 13 7 17 9 6 23 11 66 19 45 2
13.35 26 18 22 26 21 22 14 109 14 51 3
17.6 13 14 26 12 19 21 12 164 -2 73 4
14.05 10 13 24 12 24 21 13 119 51 24 3
16.1 21 19 27 20 21 20 11 126 2 61 4
13.35 24 15 22 25 21 20 11 132 24 23 4
11.85 21 15 23 24 26 21 13 142 40 14 4
11.95 23 8 22 23 24 21 12 83 20 54 2
13.2 16 11 20 22 23 21 9 166 20 36 4
7.7 26 17 27 28 26 24 13 93 25 26 2
14.6 16 12 20 15 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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.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=271003&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.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=271003&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271003&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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] = + 10.6612 -0.0571626AMS.I2[t] -0.0334088AMS.I3[t] -0.0437643AMS.E1[t] -0.0221151AMS.E2[t] -0.0227041AMS.E3[t] -0.139069age[t] + 0.1169CONFSOFTTOT[t] -0.00694527LFM[t] -0.0228316PRH[t] + 0.0372313CH[t] + 2.76184PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.6612 -0.0571626AMS.I2[t] -0.0334088AMS.I3[t] -0.0437643AMS.E1[t] -0.0221151AMS.E2[t] -0.0227041AMS.E3[t] -0.139069age[t] +  0.1169CONFSOFTTOT[t] -0.00694527LFM[t] -0.0228316PRH[t] +  0.0372313CH[t] +  2.76184PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271003&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.6612 -0.0571626AMS.I2[t] -0.0334088AMS.I3[t] -0.0437643AMS.E1[t] -0.0221151AMS.E2[t] -0.0227041AMS.E3[t] -0.139069age[t] +  0.1169CONFSOFTTOT[t] -0.00694527LFM[t] -0.0228316PRH[t] +  0.0372313CH[t] +  2.76184PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271003&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271003&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] = + 10.6612 -0.0571626AMS.I2[t] -0.0334088AMS.I3[t] -0.0437643AMS.E1[t] -0.0221151AMS.E2[t] -0.0227041AMS.E3[t] -0.139069age[t] + 0.1169CONFSOFTTOT[t] -0.00694527LFM[t] -0.0228316PRH[t] + 0.0372313CH[t] + 2.76184PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.66124.462082.3890.01831740.00915872
AMS.I2-0.05716260.0631553-0.90510.3670810.18354
AMS.I3-0.03340880.0590383-0.56590.5724490.286224
AMS.E1-0.04376430.0696226-0.62860.5307190.265359
AMS.E2-0.02211510.0570344-0.38780.6988350.349417
AMS.E3-0.02270410.0648717-0.350.7269170.363458
age-0.1390690.177382-0.7840.4344620.217231
CONFSOFTTOT0.11690.08937161.3080.1931730.0965864
LFM-0.006945270.00623771-1.1130.2675780.133789
PRH-0.02283160.0112468-2.030.04439150.0221957
CH0.03723130.01079653.4480.0007603490.000380174
PR2.761840.24270411.383.02067e-211.51033e-21

\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) & 10.6612 & 4.46208 & 2.389 & 0.0183174 & 0.00915872 \tabularnewline
AMS.I2 & -0.0571626 & 0.0631553 & -0.9051 & 0.367081 & 0.18354 \tabularnewline
AMS.I3 & -0.0334088 & 0.0590383 & -0.5659 & 0.572449 & 0.286224 \tabularnewline
AMS.E1 & -0.0437643 & 0.0696226 & -0.6286 & 0.530719 & 0.265359 \tabularnewline
AMS.E2 & -0.0221151 & 0.0570344 & -0.3878 & 0.698835 & 0.349417 \tabularnewline
AMS.E3 & -0.0227041 & 0.0648717 & -0.35 & 0.726917 & 0.363458 \tabularnewline
age & -0.139069 & 0.177382 & -0.784 & 0.434462 & 0.217231 \tabularnewline
CONFSOFTTOT & 0.1169 & 0.0893716 & 1.308 & 0.193173 & 0.0965864 \tabularnewline
LFM & -0.00694527 & 0.00623771 & -1.113 & 0.267578 & 0.133789 \tabularnewline
PRH & -0.0228316 & 0.0112468 & -2.03 & 0.0443915 & 0.0221957 \tabularnewline
CH & 0.0372313 & 0.0107965 & 3.448 & 0.000760349 & 0.000380174 \tabularnewline
PR & 2.76184 & 0.242704 & 11.38 & 3.02067e-21 & 1.51033e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271003&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]10.6612[/C][C]4.46208[/C][C]2.389[/C][C]0.0183174[/C][C]0.00915872[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0571626[/C][C]0.0631553[/C][C]-0.9051[/C][C]0.367081[/C][C]0.18354[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0334088[/C][C]0.0590383[/C][C]-0.5659[/C][C]0.572449[/C][C]0.286224[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0437643[/C][C]0.0696226[/C][C]-0.6286[/C][C]0.530719[/C][C]0.265359[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0221151[/C][C]0.0570344[/C][C]-0.3878[/C][C]0.698835[/C][C]0.349417[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0227041[/C][C]0.0648717[/C][C]-0.35[/C][C]0.726917[/C][C]0.363458[/C][/ROW]
[ROW][C]age[/C][C]-0.139069[/C][C]0.177382[/C][C]-0.784[/C][C]0.434462[/C][C]0.217231[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.1169[/C][C]0.0893716[/C][C]1.308[/C][C]0.193173[/C][C]0.0965864[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00694527[/C][C]0.00623771[/C][C]-1.113[/C][C]0.267578[/C][C]0.133789[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0228316[/C][C]0.0112468[/C][C]-2.03[/C][C]0.0443915[/C][C]0.0221957[/C][/ROW]
[ROW][C]CH[/C][C]0.0372313[/C][C]0.0107965[/C][C]3.448[/C][C]0.000760349[/C][C]0.000380174[/C][/ROW]
[ROW][C]PR[/C][C]2.76184[/C][C]0.242704[/C][C]11.38[/C][C]3.02067e-21[/C][C]1.51033e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271003&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271003&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)10.66124.462082.3890.01831740.00915872
AMS.I2-0.05716260.0631553-0.90510.3670810.18354
AMS.I3-0.03340880.0590383-0.56590.5724490.286224
AMS.E1-0.04376430.0696226-0.62860.5307190.265359
AMS.E2-0.02211510.0570344-0.38780.6988350.349417
AMS.E3-0.02270410.0648717-0.350.7269170.363458
age-0.1390690.177382-0.7840.4344620.217231
CONFSOFTTOT0.11690.08937161.3080.1931730.0965864
LFM-0.006945270.00623771-1.1130.2675780.133789
PRH-0.02283160.0112468-2.030.04439150.0221957
CH0.03723130.01079653.4480.0007603490.000380174
PR2.761840.24270411.383.02067e-211.51033e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.77303
R-squared0.597575
Adjusted R-squared0.563524
F-TEST (value)17.5492
F-TEST (DF numerator)11
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.30224
Sum Squared Residuals689.04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.77303 \tabularnewline
R-squared & 0.597575 \tabularnewline
Adjusted R-squared & 0.563524 \tabularnewline
F-TEST (value) & 17.5492 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 130 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.30224 \tabularnewline
Sum Squared Residuals & 689.04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271003&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.77303[/C][/ROW]
[ROW][C]R-squared[/C][C]0.597575[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.563524[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.5492[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]130[/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]2.30224[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]689.04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271003&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271003&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 R0.77303
R-squared0.597575
Adjusted R-squared0.563524
F-TEST (value)17.5492
F-TEST (DF numerator)11
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.30224
Sum Squared Residuals689.04






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.913.0024-0.102395
212.210.53821.66177
312.811.7081.09196
47.411.7062-4.30616
56.711.5255-4.82554
612.611.60620.993829
714.811.04663.75341
813.313.5435-0.243486
911.112.3154-1.21544
108.210.9272-2.72723
1111.411.34160.0583664
126.411.1662-4.76618
1310.610.35510.244919
141213.0913-1.09131
156.39.23316-2.93316
1611.913.1514-1.25142
179.310.9769-1.67694
18109.681940.31806
196.49.91471-3.51471
2013.812.52461.27539
2110.89.954370.845629
2213.811.62662.17341
2311.710.90530.794733
2410.911.6313-0.731271
259.911.3113-1.41125
2611.510.52790.972068
278.311.3179-3.01792
2811.711.15070.54926
29910.4196-1.41956
309.713.573-3.87303
3110.811.5168-0.716793
3210.310.9301-0.630122
3310.49.927080.472922
349.312.2626-2.9626
3511.811.1180.682003
365.911.543-5.643
3711.411.8697-0.469686
381310.77112.22888
3910.811.2062-0.406158
4011.310.70460.595425
4111.811.9034-0.10337
4212.79.765862.93414
4310.910.46360.436432
4413.311.83081.46918
4510.110.7771-0.677095
4614.311.50062.79943
479.311.7984-2.4984
4812.510.21612.28389
497.610.4572-2.8572
5015.912.45143.44859
519.210.3802-1.18016
5211.112.1997-1.09967
531312.3240.675985
5414.511.18733.31273
5512.312.9767-0.6767
5611.410.65060.749375
5712.610.99851.60146
58NANA0.934617
591310.98352.01645
6013.217.2007-4.00074
617.711.268-3.56801
624.352.011722.33828
6312.710.76931.93068
6418.116.3321.76797
6517.8517.67740.172586
6617.114.37282.72715
6719.121.6583-2.55832
6816.113.9352.16499
6913.3512.63240.717597
7018.411.81566.58441
7114.717.4382-2.73816
7210.611.8562-1.25625
7312.613.0086-0.408606
7416.215.21710.98292
7513.612.93150.668515
7614.113.39560.704412
7714.514.5584-0.0583743
7816.1513.94122.2088
7914.7512.37182.37818
8014.814.79870.00127997
8112.4510.81841.63162
8212.659.453023.19698
8317.3517.8103-0.460322
848.67.030471.56953
8518.416.81121.58881
8616.113.58892.51114
8717.7517.74790.00211207
8815.2513.851.39999
8917.6517.938-0.287954
9016.3517.1214-0.771446
9117.6517.2450.405009
9213.613.41710.182915
9314.3517.0373-2.68734
9414.7512.69812.05189
9518.2523.3865-5.13654
969.98.123291.77671
971613.93372.06626
9818.2518.9526-0.702624
9916.8514.8472.00296
10018.9517.13221.81781
10115.615.8921-0.2921
10217.110.05327.04684
10316.116.8035-0.70353
10415.416.0918-0.691765
10515.416.5231-1.1231
10613.3511.33522.01481
10719.118.74110.358924
1087.65.418082.18192
10919.120.9833-1.88328
11014.7512.35952.39051
11119.2521.8073-2.55728
11213.616.0567-2.4567
11312.7511.37931.37071
1149.8510.3917-0.541708
11515.2517.106-1.85599
11611.912.7635-0.863538
11716.3517.8525-1.50246
11812.410.17422.22583
11918.1515.50182.64821
12017.7518.4306-0.680613
12112.3512.3532-0.00324627
12215.612.95552.64452
12319.318.69320.606838
12417.114.22282.87717
12518.416.11132.28868
12619.0514.81124.23876
12718.5517.7020.848042
12819.121.6699-2.56986
12912.8514.6168-1.76677
1309.513.0199-3.51993
1314.56.00687-1.50687
13213.614.8992-1.29918
13311.712.5937-0.893746
13413.3514.52-1.16997
13517.617.13210.467936
13614.0515.5775-1.52752
13716.118.4892-2.38916
13813.3516.6004-3.25037
13911.8511.9463-0.0962623
14011.9515.1549-3.20488
14113.215.1729-1.9729
1427.76.850140.849855
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 & 13.0024 & -0.102395 \tabularnewline
2 & 12.2 & 10.5382 & 1.66177 \tabularnewline
3 & 12.8 & 11.708 & 1.09196 \tabularnewline
4 & 7.4 & 11.7062 & -4.30616 \tabularnewline
5 & 6.7 & 11.5255 & -4.82554 \tabularnewline
6 & 12.6 & 11.6062 & 0.993829 \tabularnewline
7 & 14.8 & 11.0466 & 3.75341 \tabularnewline
8 & 13.3 & 13.5435 & -0.243486 \tabularnewline
9 & 11.1 & 12.3154 & -1.21544 \tabularnewline
10 & 8.2 & 10.9272 & -2.72723 \tabularnewline
11 & 11.4 & 11.3416 & 0.0583664 \tabularnewline
12 & 6.4 & 11.1662 & -4.76618 \tabularnewline
13 & 10.6 & 10.3551 & 0.244919 \tabularnewline
14 & 12 & 13.0913 & -1.09131 \tabularnewline
15 & 6.3 & 9.23316 & -2.93316 \tabularnewline
16 & 11.9 & 13.1514 & -1.25142 \tabularnewline
17 & 9.3 & 10.9769 & -1.67694 \tabularnewline
18 & 10 & 9.68194 & 0.31806 \tabularnewline
19 & 6.4 & 9.91471 & -3.51471 \tabularnewline
20 & 13.8 & 12.5246 & 1.27539 \tabularnewline
21 & 10.8 & 9.95437 & 0.845629 \tabularnewline
22 & 13.8 & 11.6266 & 2.17341 \tabularnewline
23 & 11.7 & 10.9053 & 0.794733 \tabularnewline
24 & 10.9 & 11.6313 & -0.731271 \tabularnewline
25 & 9.9 & 11.3113 & -1.41125 \tabularnewline
26 & 11.5 & 10.5279 & 0.972068 \tabularnewline
27 & 8.3 & 11.3179 & -3.01792 \tabularnewline
28 & 11.7 & 11.1507 & 0.54926 \tabularnewline
29 & 9 & 10.4196 & -1.41956 \tabularnewline
30 & 9.7 & 13.573 & -3.87303 \tabularnewline
31 & 10.8 & 11.5168 & -0.716793 \tabularnewline
32 & 10.3 & 10.9301 & -0.630122 \tabularnewline
33 & 10.4 & 9.92708 & 0.472922 \tabularnewline
34 & 9.3 & 12.2626 & -2.9626 \tabularnewline
35 & 11.8 & 11.118 & 0.682003 \tabularnewline
36 & 5.9 & 11.543 & -5.643 \tabularnewline
37 & 11.4 & 11.8697 & -0.469686 \tabularnewline
38 & 13 & 10.7711 & 2.22888 \tabularnewline
39 & 10.8 & 11.2062 & -0.406158 \tabularnewline
40 & 11.3 & 10.7046 & 0.595425 \tabularnewline
41 & 11.8 & 11.9034 & -0.10337 \tabularnewline
42 & 12.7 & 9.76586 & 2.93414 \tabularnewline
43 & 10.9 & 10.4636 & 0.436432 \tabularnewline
44 & 13.3 & 11.8308 & 1.46918 \tabularnewline
45 & 10.1 & 10.7771 & -0.677095 \tabularnewline
46 & 14.3 & 11.5006 & 2.79943 \tabularnewline
47 & 9.3 & 11.7984 & -2.4984 \tabularnewline
48 & 12.5 & 10.2161 & 2.28389 \tabularnewline
49 & 7.6 & 10.4572 & -2.8572 \tabularnewline
50 & 15.9 & 12.4514 & 3.44859 \tabularnewline
51 & 9.2 & 10.3802 & -1.18016 \tabularnewline
52 & 11.1 & 12.1997 & -1.09967 \tabularnewline
53 & 13 & 12.324 & 0.675985 \tabularnewline
54 & 14.5 & 11.1873 & 3.31273 \tabularnewline
55 & 12.3 & 12.9767 & -0.6767 \tabularnewline
56 & 11.4 & 10.6506 & 0.749375 \tabularnewline
57 & 12.6 & 10.9985 & 1.60146 \tabularnewline
58 & NA & NA & 0.934617 \tabularnewline
59 & 13 & 10.9835 & 2.01645 \tabularnewline
60 & 13.2 & 17.2007 & -4.00074 \tabularnewline
61 & 7.7 & 11.268 & -3.56801 \tabularnewline
62 & 4.35 & 2.01172 & 2.33828 \tabularnewline
63 & 12.7 & 10.7693 & 1.93068 \tabularnewline
64 & 18.1 & 16.332 & 1.76797 \tabularnewline
65 & 17.85 & 17.6774 & 0.172586 \tabularnewline
66 & 17.1 & 14.3728 & 2.72715 \tabularnewline
67 & 19.1 & 21.6583 & -2.55832 \tabularnewline
68 & 16.1 & 13.935 & 2.16499 \tabularnewline
69 & 13.35 & 12.6324 & 0.717597 \tabularnewline
70 & 18.4 & 11.8156 & 6.58441 \tabularnewline
71 & 14.7 & 17.4382 & -2.73816 \tabularnewline
72 & 10.6 & 11.8562 & -1.25625 \tabularnewline
73 & 12.6 & 13.0086 & -0.408606 \tabularnewline
74 & 16.2 & 15.2171 & 0.98292 \tabularnewline
75 & 13.6 & 12.9315 & 0.668515 \tabularnewline
76 & 14.1 & 13.3956 & 0.704412 \tabularnewline
77 & 14.5 & 14.5584 & -0.0583743 \tabularnewline
78 & 16.15 & 13.9412 & 2.2088 \tabularnewline
79 & 14.75 & 12.3718 & 2.37818 \tabularnewline
80 & 14.8 & 14.7987 & 0.00127997 \tabularnewline
81 & 12.45 & 10.8184 & 1.63162 \tabularnewline
82 & 12.65 & 9.45302 & 3.19698 \tabularnewline
83 & 17.35 & 17.8103 & -0.460322 \tabularnewline
84 & 8.6 & 7.03047 & 1.56953 \tabularnewline
85 & 18.4 & 16.8112 & 1.58881 \tabularnewline
86 & 16.1 & 13.5889 & 2.51114 \tabularnewline
87 & 17.75 & 17.7479 & 0.00211207 \tabularnewline
88 & 15.25 & 13.85 & 1.39999 \tabularnewline
89 & 17.65 & 17.938 & -0.287954 \tabularnewline
90 & 16.35 & 17.1214 & -0.771446 \tabularnewline
91 & 17.65 & 17.245 & 0.405009 \tabularnewline
92 & 13.6 & 13.4171 & 0.182915 \tabularnewline
93 & 14.35 & 17.0373 & -2.68734 \tabularnewline
94 & 14.75 & 12.6981 & 2.05189 \tabularnewline
95 & 18.25 & 23.3865 & -5.13654 \tabularnewline
96 & 9.9 & 8.12329 & 1.77671 \tabularnewline
97 & 16 & 13.9337 & 2.06626 \tabularnewline
98 & 18.25 & 18.9526 & -0.702624 \tabularnewline
99 & 16.85 & 14.847 & 2.00296 \tabularnewline
100 & 18.95 & 17.1322 & 1.81781 \tabularnewline
101 & 15.6 & 15.8921 & -0.2921 \tabularnewline
102 & 17.1 & 10.0532 & 7.04684 \tabularnewline
103 & 16.1 & 16.8035 & -0.70353 \tabularnewline
104 & 15.4 & 16.0918 & -0.691765 \tabularnewline
105 & 15.4 & 16.5231 & -1.1231 \tabularnewline
106 & 13.35 & 11.3352 & 2.01481 \tabularnewline
107 & 19.1 & 18.7411 & 0.358924 \tabularnewline
108 & 7.6 & 5.41808 & 2.18192 \tabularnewline
109 & 19.1 & 20.9833 & -1.88328 \tabularnewline
110 & 14.75 & 12.3595 & 2.39051 \tabularnewline
111 & 19.25 & 21.8073 & -2.55728 \tabularnewline
112 & 13.6 & 16.0567 & -2.4567 \tabularnewline
113 & 12.75 & 11.3793 & 1.37071 \tabularnewline
114 & 9.85 & 10.3917 & -0.541708 \tabularnewline
115 & 15.25 & 17.106 & -1.85599 \tabularnewline
116 & 11.9 & 12.7635 & -0.863538 \tabularnewline
117 & 16.35 & 17.8525 & -1.50246 \tabularnewline
118 & 12.4 & 10.1742 & 2.22583 \tabularnewline
119 & 18.15 & 15.5018 & 2.64821 \tabularnewline
120 & 17.75 & 18.4306 & -0.680613 \tabularnewline
121 & 12.35 & 12.3532 & -0.00324627 \tabularnewline
122 & 15.6 & 12.9555 & 2.64452 \tabularnewline
123 & 19.3 & 18.6932 & 0.606838 \tabularnewline
124 & 17.1 & 14.2228 & 2.87717 \tabularnewline
125 & 18.4 & 16.1113 & 2.28868 \tabularnewline
126 & 19.05 & 14.8112 & 4.23876 \tabularnewline
127 & 18.55 & 17.702 & 0.848042 \tabularnewline
128 & 19.1 & 21.6699 & -2.56986 \tabularnewline
129 & 12.85 & 14.6168 & -1.76677 \tabularnewline
130 & 9.5 & 13.0199 & -3.51993 \tabularnewline
131 & 4.5 & 6.00687 & -1.50687 \tabularnewline
132 & 13.6 & 14.8992 & -1.29918 \tabularnewline
133 & 11.7 & 12.5937 & -0.893746 \tabularnewline
134 & 13.35 & 14.52 & -1.16997 \tabularnewline
135 & 17.6 & 17.1321 & 0.467936 \tabularnewline
136 & 14.05 & 15.5775 & -1.52752 \tabularnewline
137 & 16.1 & 18.4892 & -2.38916 \tabularnewline
138 & 13.35 & 16.6004 & -3.25037 \tabularnewline
139 & 11.85 & 11.9463 & -0.0962623 \tabularnewline
140 & 11.95 & 15.1549 & -3.20488 \tabularnewline
141 & 13.2 & 15.1729 & -1.9729 \tabularnewline
142 & 7.7 & 6.85014 & 0.849855 \tabularnewline
143 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271003&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12.9[/C][C]13.0024[/C][C]-0.102395[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5382[/C][C]1.66177[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.708[/C][C]1.09196[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.7062[/C][C]-4.30616[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.5255[/C][C]-4.82554[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.6062[/C][C]0.993829[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.0466[/C][C]3.75341[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.5435[/C][C]-0.243486[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.3154[/C][C]-1.21544[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.9272[/C][C]-2.72723[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.3416[/C][C]0.0583664[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.1662[/C][C]-4.76618[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.3551[/C][C]0.244919[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.0913[/C][C]-1.09131[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.23316[/C][C]-2.93316[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.1514[/C][C]-1.25142[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9769[/C][C]-1.67694[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.68194[/C][C]0.31806[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]9.91471[/C][C]-3.51471[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.5246[/C][C]1.27539[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]9.95437[/C][C]0.845629[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.6266[/C][C]2.17341[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.9053[/C][C]0.794733[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.6313[/C][C]-0.731271[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.3113[/C][C]-1.41125[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5279[/C][C]0.972068[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.3179[/C][C]-3.01792[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1507[/C][C]0.54926[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.4196[/C][C]-1.41956[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.573[/C][C]-3.87303[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.5168[/C][C]-0.716793[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.9301[/C][C]-0.630122[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.92708[/C][C]0.472922[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2626[/C][C]-2.9626[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.118[/C][C]0.682003[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.543[/C][C]-5.643[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8697[/C][C]-0.469686[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.7711[/C][C]2.22888[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.2062[/C][C]-0.406158[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7046[/C][C]0.595425[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.9034[/C][C]-0.10337[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.76586[/C][C]2.93414[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.4636[/C][C]0.436432[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.8308[/C][C]1.46918[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.7771[/C][C]-0.677095[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.5006[/C][C]2.79943[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.7984[/C][C]-2.4984[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.2161[/C][C]2.28389[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.4572[/C][C]-2.8572[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4514[/C][C]3.44859[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.3802[/C][C]-1.18016[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.1997[/C][C]-1.09967[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.324[/C][C]0.675985[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.1873[/C][C]3.31273[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]12.9767[/C][C]-0.6767[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.6506[/C][C]0.749375[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]10.9985[/C][C]1.60146[/C][/ROW]
[ROW][C]58[/C][C]NA[/C][C]NA[/C][C]0.934617[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.9835[/C][C]2.01645[/C][/ROW]
[ROW][C]60[/C][C]13.2[/C][C]17.2007[/C][C]-4.00074[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]11.268[/C][C]-3.56801[/C][/ROW]
[ROW][C]62[/C][C]4.35[/C][C]2.01172[/C][C]2.33828[/C][/ROW]
[ROW][C]63[/C][C]12.7[/C][C]10.7693[/C][C]1.93068[/C][/ROW]
[ROW][C]64[/C][C]18.1[/C][C]16.332[/C][C]1.76797[/C][/ROW]
[ROW][C]65[/C][C]17.85[/C][C]17.6774[/C][C]0.172586[/C][/ROW]
[ROW][C]66[/C][C]17.1[/C][C]14.3728[/C][C]2.72715[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]21.6583[/C][C]-2.55832[/C][/ROW]
[ROW][C]68[/C][C]16.1[/C][C]13.935[/C][C]2.16499[/C][/ROW]
[ROW][C]69[/C][C]13.35[/C][C]12.6324[/C][C]0.717597[/C][/ROW]
[ROW][C]70[/C][C]18.4[/C][C]11.8156[/C][C]6.58441[/C][/ROW]
[ROW][C]71[/C][C]14.7[/C][C]17.4382[/C][C]-2.73816[/C][/ROW]
[ROW][C]72[/C][C]10.6[/C][C]11.8562[/C][C]-1.25625[/C][/ROW]
[ROW][C]73[/C][C]12.6[/C][C]13.0086[/C][C]-0.408606[/C][/ROW]
[ROW][C]74[/C][C]16.2[/C][C]15.2171[/C][C]0.98292[/C][/ROW]
[ROW][C]75[/C][C]13.6[/C][C]12.9315[/C][C]0.668515[/C][/ROW]
[ROW][C]76[/C][C]14.1[/C][C]13.3956[/C][C]0.704412[/C][/ROW]
[ROW][C]77[/C][C]14.5[/C][C]14.5584[/C][C]-0.0583743[/C][/ROW]
[ROW][C]78[/C][C]16.15[/C][C]13.9412[/C][C]2.2088[/C][/ROW]
[ROW][C]79[/C][C]14.75[/C][C]12.3718[/C][C]2.37818[/C][/ROW]
[ROW][C]80[/C][C]14.8[/C][C]14.7987[/C][C]0.00127997[/C][/ROW]
[ROW][C]81[/C][C]12.45[/C][C]10.8184[/C][C]1.63162[/C][/ROW]
[ROW][C]82[/C][C]12.65[/C][C]9.45302[/C][C]3.19698[/C][/ROW]
[ROW][C]83[/C][C]17.35[/C][C]17.8103[/C][C]-0.460322[/C][/ROW]
[ROW][C]84[/C][C]8.6[/C][C]7.03047[/C][C]1.56953[/C][/ROW]
[ROW][C]85[/C][C]18.4[/C][C]16.8112[/C][C]1.58881[/C][/ROW]
[ROW][C]86[/C][C]16.1[/C][C]13.5889[/C][C]2.51114[/C][/ROW]
[ROW][C]87[/C][C]17.75[/C][C]17.7479[/C][C]0.00211207[/C][/ROW]
[ROW][C]88[/C][C]15.25[/C][C]13.85[/C][C]1.39999[/C][/ROW]
[ROW][C]89[/C][C]17.65[/C][C]17.938[/C][C]-0.287954[/C][/ROW]
[ROW][C]90[/C][C]16.35[/C][C]17.1214[/C][C]-0.771446[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]17.245[/C][C]0.405009[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.4171[/C][C]0.182915[/C][/ROW]
[ROW][C]93[/C][C]14.35[/C][C]17.0373[/C][C]-2.68734[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]12.6981[/C][C]2.05189[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]23.3865[/C][C]-5.13654[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]8.12329[/C][C]1.77671[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.9337[/C][C]2.06626[/C][/ROW]
[ROW][C]98[/C][C]18.25[/C][C]18.9526[/C][C]-0.702624[/C][/ROW]
[ROW][C]99[/C][C]16.85[/C][C]14.847[/C][C]2.00296[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]17.1322[/C][C]1.81781[/C][/ROW]
[ROW][C]101[/C][C]15.6[/C][C]15.8921[/C][C]-0.2921[/C][/ROW]
[ROW][C]102[/C][C]17.1[/C][C]10.0532[/C][C]7.04684[/C][/ROW]
[ROW][C]103[/C][C]16.1[/C][C]16.8035[/C][C]-0.70353[/C][/ROW]
[ROW][C]104[/C][C]15.4[/C][C]16.0918[/C][C]-0.691765[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]16.5231[/C][C]-1.1231[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]11.3352[/C][C]2.01481[/C][/ROW]
[ROW][C]107[/C][C]19.1[/C][C]18.7411[/C][C]0.358924[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]5.41808[/C][C]2.18192[/C][/ROW]
[ROW][C]109[/C][C]19.1[/C][C]20.9833[/C][C]-1.88328[/C][/ROW]
[ROW][C]110[/C][C]14.75[/C][C]12.3595[/C][C]2.39051[/C][/ROW]
[ROW][C]111[/C][C]19.25[/C][C]21.8073[/C][C]-2.55728[/C][/ROW]
[ROW][C]112[/C][C]13.6[/C][C]16.0567[/C][C]-2.4567[/C][/ROW]
[ROW][C]113[/C][C]12.75[/C][C]11.3793[/C][C]1.37071[/C][/ROW]
[ROW][C]114[/C][C]9.85[/C][C]10.3917[/C][C]-0.541708[/C][/ROW]
[ROW][C]115[/C][C]15.25[/C][C]17.106[/C][C]-1.85599[/C][/ROW]
[ROW][C]116[/C][C]11.9[/C][C]12.7635[/C][C]-0.863538[/C][/ROW]
[ROW][C]117[/C][C]16.35[/C][C]17.8525[/C][C]-1.50246[/C][/ROW]
[ROW][C]118[/C][C]12.4[/C][C]10.1742[/C][C]2.22583[/C][/ROW]
[ROW][C]119[/C][C]18.15[/C][C]15.5018[/C][C]2.64821[/C][/ROW]
[ROW][C]120[/C][C]17.75[/C][C]18.4306[/C][C]-0.680613[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]12.3532[/C][C]-0.00324627[/C][/ROW]
[ROW][C]122[/C][C]15.6[/C][C]12.9555[/C][C]2.64452[/C][/ROW]
[ROW][C]123[/C][C]19.3[/C][C]18.6932[/C][C]0.606838[/C][/ROW]
[ROW][C]124[/C][C]17.1[/C][C]14.2228[/C][C]2.87717[/C][/ROW]
[ROW][C]125[/C][C]18.4[/C][C]16.1113[/C][C]2.28868[/C][/ROW]
[ROW][C]126[/C][C]19.05[/C][C]14.8112[/C][C]4.23876[/C][/ROW]
[ROW][C]127[/C][C]18.55[/C][C]17.702[/C][C]0.848042[/C][/ROW]
[ROW][C]128[/C][C]19.1[/C][C]21.6699[/C][C]-2.56986[/C][/ROW]
[ROW][C]129[/C][C]12.85[/C][C]14.6168[/C][C]-1.76677[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]13.0199[/C][C]-3.51993[/C][/ROW]
[ROW][C]131[/C][C]4.5[/C][C]6.00687[/C][C]-1.50687[/C][/ROW]
[ROW][C]132[/C][C]13.6[/C][C]14.8992[/C][C]-1.29918[/C][/ROW]
[ROW][C]133[/C][C]11.7[/C][C]12.5937[/C][C]-0.893746[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]14.52[/C][C]-1.16997[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.1321[/C][C]0.467936[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]15.5775[/C][C]-1.52752[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]18.4892[/C][C]-2.38916[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]16.6004[/C][C]-3.25037[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]11.9463[/C][C]-0.0962623[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]15.1549[/C][C]-3.20488[/C][/ROW]
[ROW][C]141[/C][C]13.2[/C][C]15.1729[/C][C]-1.9729[/C][/ROW]
[ROW][C]142[/C][C]7.7[/C][C]6.85014[/C][C]0.849855[/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=271003&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271003&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
112.913.0024-0.102395
212.210.53821.66177
312.811.7081.09196
47.411.7062-4.30616
56.711.5255-4.82554
612.611.60620.993829
714.811.04663.75341
813.313.5435-0.243486
911.112.3154-1.21544
108.210.9272-2.72723
1111.411.34160.0583664
126.411.1662-4.76618
1310.610.35510.244919
141213.0913-1.09131
156.39.23316-2.93316
1611.913.1514-1.25142
179.310.9769-1.67694
18109.681940.31806
196.49.91471-3.51471
2013.812.52461.27539
2110.89.954370.845629
2213.811.62662.17341
2311.710.90530.794733
2410.911.6313-0.731271
259.911.3113-1.41125
2611.510.52790.972068
278.311.3179-3.01792
2811.711.15070.54926
29910.4196-1.41956
309.713.573-3.87303
3110.811.5168-0.716793
3210.310.9301-0.630122
3310.49.927080.472922
349.312.2626-2.9626
3511.811.1180.682003
365.911.543-5.643
3711.411.8697-0.469686
381310.77112.22888
3910.811.2062-0.406158
4011.310.70460.595425
4111.811.9034-0.10337
4212.79.765862.93414
4310.910.46360.436432
4413.311.83081.46918
4510.110.7771-0.677095
4614.311.50062.79943
479.311.7984-2.4984
4812.510.21612.28389
497.610.4572-2.8572
5015.912.45143.44859
519.210.3802-1.18016
5211.112.1997-1.09967
531312.3240.675985
5414.511.18733.31273
5512.312.9767-0.6767
5611.410.65060.749375
5712.610.99851.60146
58NANA0.934617
591310.98352.01645
6013.217.2007-4.00074
617.711.268-3.56801
624.352.011722.33828
6312.710.76931.93068
6418.116.3321.76797
6517.8517.67740.172586
6617.114.37282.72715
6719.121.6583-2.55832
6816.113.9352.16499
6913.3512.63240.717597
7018.411.81566.58441
7114.717.4382-2.73816
7210.611.8562-1.25625
7312.613.0086-0.408606
7416.215.21710.98292
7513.612.93150.668515
7614.113.39560.704412
7714.514.5584-0.0583743
7816.1513.94122.2088
7914.7512.37182.37818
8014.814.79870.00127997
8112.4510.81841.63162
8212.659.453023.19698
8317.3517.8103-0.460322
848.67.030471.56953
8518.416.81121.58881
8616.113.58892.51114
8717.7517.74790.00211207
8815.2513.851.39999
8917.6517.938-0.287954
9016.3517.1214-0.771446
9117.6517.2450.405009
9213.613.41710.182915
9314.3517.0373-2.68734
9414.7512.69812.05189
9518.2523.3865-5.13654
969.98.123291.77671
971613.93372.06626
9818.2518.9526-0.702624
9916.8514.8472.00296
10018.9517.13221.81781
10115.615.8921-0.2921
10217.110.05327.04684
10316.116.8035-0.70353
10415.416.0918-0.691765
10515.416.5231-1.1231
10613.3511.33522.01481
10719.118.74110.358924
1087.65.418082.18192
10919.120.9833-1.88328
11014.7512.35952.39051
11119.2521.8073-2.55728
11213.616.0567-2.4567
11312.7511.37931.37071
1149.8510.3917-0.541708
11515.2517.106-1.85599
11611.912.7635-0.863538
11716.3517.8525-1.50246
11812.410.17422.22583
11918.1515.50182.64821
12017.7518.4306-0.680613
12112.3512.3532-0.00324627
12215.612.95552.64452
12319.318.69320.606838
12417.114.22282.87717
12518.416.11132.28868
12619.0514.81124.23876
12718.5517.7020.848042
12819.121.6699-2.56986
12912.8514.6168-1.76677
1309.513.0199-3.51993
1314.56.00687-1.50687
13213.614.8992-1.29918
13311.712.5937-0.893746
13413.3514.52-1.16997
13517.617.13210.467936
13614.0515.5775-1.52752
13716.118.4892-2.38916
13813.3516.6004-3.25037
13911.8511.9463-0.0962623
14011.9515.1549-3.20488
14113.215.1729-1.9729
1427.76.850140.849855
14314.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.5589350.882130.441065
160.4461380.8922760.553862
170.31070.6213990.6893
180.2770860.5541720.722914
190.2201740.4403490.779826
200.1684720.3369440.831528
210.1239370.2478730.876063
220.07941050.1588210.920589
230.06863460.1372690.931365
240.04787890.09575780.952121
250.05305320.1061060.946947
260.05034270.1006850.949657
270.1004170.2008340.899583
280.1568410.3136830.843159
290.1933110.3866210.806689
300.3437450.687490.656255
310.3151320.6302630.684868
320.2580490.5160970.741951
330.2091330.4182660.790867
340.2109280.4218560.789072
350.1654210.3308420.834579
360.3408270.6816540.659173
370.2863880.5727760.713612
380.2453870.4907740.754613
390.2243580.4487170.775642
400.1828950.365790.817105
410.1680720.3361450.831928
420.1885290.3770580.811471
430.1544250.308850.845575
440.2607130.5214250.739287
450.218710.437420.78129
460.3153780.6307560.684622
470.3273370.6546750.672663
480.3380660.6761320.661934
490.3959150.791830.604085
500.4927190.9854380.507281
510.4906730.9813470.509327
520.455810.9116190.54419
530.4112620.8225250.588738
540.4285610.8571230.571439
550.3884180.7768370.611582
560.3448820.6897630.655118
570.3175560.6351120.682444
580.2847550.569510.715245
590.2730550.546110.726945
600.3339060.6678120.666094
610.4233340.8466690.576666
620.4643250.9286490.535675
630.4902960.9805920.509704
640.4513750.902750.548625
650.4081850.8163690.591815
660.4155230.8310470.584477
670.4480890.8961780.551911
680.4803540.9607080.519646
690.429310.858620.57069
700.7886430.4227150.211357
710.8150250.369950.184975
720.7989250.402150.201075
730.764410.471180.23559
740.7504610.4990780.249539
750.714290.5714210.28571
760.6741710.6516580.325829
770.6292670.7414660.370733
780.6282150.743570.371785
790.6370790.7258420.362921
800.5944540.8110920.405546
810.5721040.8557920.427896
820.6049350.7901310.395065
830.5575520.8848960.442448
840.5155920.9688160.484408
850.4845450.9690890.515455
860.496010.9920190.50399
870.4457210.8914420.554279
880.4206940.8413890.579306
890.3740170.7480350.625983
900.3336050.667210.666395
910.2911710.5823420.708829
920.2460870.4921730.753913
930.2790930.5581850.720907
940.2813430.5626860.718657
950.4615330.9230670.538467
960.4250190.8500370.574981
970.4034820.8069630.596518
980.3541620.7083240.645838
990.333340.6666810.66666
1000.3008210.6016420.699179
1010.2519840.5039680.748016
1020.8437410.3125180.156259
1030.8125380.3749250.187462
1040.7695220.4609560.230478
1050.7427930.5144140.257207
1060.7644570.4710860.235543
1070.769590.460820.23041
1080.7315130.5369740.268487
1090.6896970.6206060.310303
1100.6474470.7051060.352553
1110.6303420.7393150.369658
1120.5990570.8018860.400943
1130.5949650.8100710.405035
1140.5445130.9109750.455487
1150.4806030.9612070.519397
1160.4331530.8663070.566847
1170.3574740.7149480.642526
1180.3390660.6781310.660934
1190.3357150.671430.664285
1200.2586420.5172830.741358
1210.5962790.8074430.403721
1220.6560380.6879240.343962
1230.7053890.5892220.294611
1240.6454540.7090930.354546
1250.7469560.5060890.253044
1260.8421390.3157230.157861
1270.7085610.5828770.291439
1280.5750580.8498850.424942

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.558935 & 0.88213 & 0.441065 \tabularnewline
16 & 0.446138 & 0.892276 & 0.553862 \tabularnewline
17 & 0.3107 & 0.621399 & 0.6893 \tabularnewline
18 & 0.277086 & 0.554172 & 0.722914 \tabularnewline
19 & 0.220174 & 0.440349 & 0.779826 \tabularnewline
20 & 0.168472 & 0.336944 & 0.831528 \tabularnewline
21 & 0.123937 & 0.247873 & 0.876063 \tabularnewline
22 & 0.0794105 & 0.158821 & 0.920589 \tabularnewline
23 & 0.0686346 & 0.137269 & 0.931365 \tabularnewline
24 & 0.0478789 & 0.0957578 & 0.952121 \tabularnewline
25 & 0.0530532 & 0.106106 & 0.946947 \tabularnewline
26 & 0.0503427 & 0.100685 & 0.949657 \tabularnewline
27 & 0.100417 & 0.200834 & 0.899583 \tabularnewline
28 & 0.156841 & 0.313683 & 0.843159 \tabularnewline
29 & 0.193311 & 0.386621 & 0.806689 \tabularnewline
30 & 0.343745 & 0.68749 & 0.656255 \tabularnewline
31 & 0.315132 & 0.630263 & 0.684868 \tabularnewline
32 & 0.258049 & 0.516097 & 0.741951 \tabularnewline
33 & 0.209133 & 0.418266 & 0.790867 \tabularnewline
34 & 0.210928 & 0.421856 & 0.789072 \tabularnewline
35 & 0.165421 & 0.330842 & 0.834579 \tabularnewline
36 & 0.340827 & 0.681654 & 0.659173 \tabularnewline
37 & 0.286388 & 0.572776 & 0.713612 \tabularnewline
38 & 0.245387 & 0.490774 & 0.754613 \tabularnewline
39 & 0.224358 & 0.448717 & 0.775642 \tabularnewline
40 & 0.182895 & 0.36579 & 0.817105 \tabularnewline
41 & 0.168072 & 0.336145 & 0.831928 \tabularnewline
42 & 0.188529 & 0.377058 & 0.811471 \tabularnewline
43 & 0.154425 & 0.30885 & 0.845575 \tabularnewline
44 & 0.260713 & 0.521425 & 0.739287 \tabularnewline
45 & 0.21871 & 0.43742 & 0.78129 \tabularnewline
46 & 0.315378 & 0.630756 & 0.684622 \tabularnewline
47 & 0.327337 & 0.654675 & 0.672663 \tabularnewline
48 & 0.338066 & 0.676132 & 0.661934 \tabularnewline
49 & 0.395915 & 0.79183 & 0.604085 \tabularnewline
50 & 0.492719 & 0.985438 & 0.507281 \tabularnewline
51 & 0.490673 & 0.981347 & 0.509327 \tabularnewline
52 & 0.45581 & 0.911619 & 0.54419 \tabularnewline
53 & 0.411262 & 0.822525 & 0.588738 \tabularnewline
54 & 0.428561 & 0.857123 & 0.571439 \tabularnewline
55 & 0.388418 & 0.776837 & 0.611582 \tabularnewline
56 & 0.344882 & 0.689763 & 0.655118 \tabularnewline
57 & 0.317556 & 0.635112 & 0.682444 \tabularnewline
58 & 0.284755 & 0.56951 & 0.715245 \tabularnewline
59 & 0.273055 & 0.54611 & 0.726945 \tabularnewline
60 & 0.333906 & 0.667812 & 0.666094 \tabularnewline
61 & 0.423334 & 0.846669 & 0.576666 \tabularnewline
62 & 0.464325 & 0.928649 & 0.535675 \tabularnewline
63 & 0.490296 & 0.980592 & 0.509704 \tabularnewline
64 & 0.451375 & 0.90275 & 0.548625 \tabularnewline
65 & 0.408185 & 0.816369 & 0.591815 \tabularnewline
66 & 0.415523 & 0.831047 & 0.584477 \tabularnewline
67 & 0.448089 & 0.896178 & 0.551911 \tabularnewline
68 & 0.480354 & 0.960708 & 0.519646 \tabularnewline
69 & 0.42931 & 0.85862 & 0.57069 \tabularnewline
70 & 0.788643 & 0.422715 & 0.211357 \tabularnewline
71 & 0.815025 & 0.36995 & 0.184975 \tabularnewline
72 & 0.798925 & 0.40215 & 0.201075 \tabularnewline
73 & 0.76441 & 0.47118 & 0.23559 \tabularnewline
74 & 0.750461 & 0.499078 & 0.249539 \tabularnewline
75 & 0.71429 & 0.571421 & 0.28571 \tabularnewline
76 & 0.674171 & 0.651658 & 0.325829 \tabularnewline
77 & 0.629267 & 0.741466 & 0.370733 \tabularnewline
78 & 0.628215 & 0.74357 & 0.371785 \tabularnewline
79 & 0.637079 & 0.725842 & 0.362921 \tabularnewline
80 & 0.594454 & 0.811092 & 0.405546 \tabularnewline
81 & 0.572104 & 0.855792 & 0.427896 \tabularnewline
82 & 0.604935 & 0.790131 & 0.395065 \tabularnewline
83 & 0.557552 & 0.884896 & 0.442448 \tabularnewline
84 & 0.515592 & 0.968816 & 0.484408 \tabularnewline
85 & 0.484545 & 0.969089 & 0.515455 \tabularnewline
86 & 0.49601 & 0.992019 & 0.50399 \tabularnewline
87 & 0.445721 & 0.891442 & 0.554279 \tabularnewline
88 & 0.420694 & 0.841389 & 0.579306 \tabularnewline
89 & 0.374017 & 0.748035 & 0.625983 \tabularnewline
90 & 0.333605 & 0.66721 & 0.666395 \tabularnewline
91 & 0.291171 & 0.582342 & 0.708829 \tabularnewline
92 & 0.246087 & 0.492173 & 0.753913 \tabularnewline
93 & 0.279093 & 0.558185 & 0.720907 \tabularnewline
94 & 0.281343 & 0.562686 & 0.718657 \tabularnewline
95 & 0.461533 & 0.923067 & 0.538467 \tabularnewline
96 & 0.425019 & 0.850037 & 0.574981 \tabularnewline
97 & 0.403482 & 0.806963 & 0.596518 \tabularnewline
98 & 0.354162 & 0.708324 & 0.645838 \tabularnewline
99 & 0.33334 & 0.666681 & 0.66666 \tabularnewline
100 & 0.300821 & 0.601642 & 0.699179 \tabularnewline
101 & 0.251984 & 0.503968 & 0.748016 \tabularnewline
102 & 0.843741 & 0.312518 & 0.156259 \tabularnewline
103 & 0.812538 & 0.374925 & 0.187462 \tabularnewline
104 & 0.769522 & 0.460956 & 0.230478 \tabularnewline
105 & 0.742793 & 0.514414 & 0.257207 \tabularnewline
106 & 0.764457 & 0.471086 & 0.235543 \tabularnewline
107 & 0.76959 & 0.46082 & 0.23041 \tabularnewline
108 & 0.731513 & 0.536974 & 0.268487 \tabularnewline
109 & 0.689697 & 0.620606 & 0.310303 \tabularnewline
110 & 0.647447 & 0.705106 & 0.352553 \tabularnewline
111 & 0.630342 & 0.739315 & 0.369658 \tabularnewline
112 & 0.599057 & 0.801886 & 0.400943 \tabularnewline
113 & 0.594965 & 0.810071 & 0.405035 \tabularnewline
114 & 0.544513 & 0.910975 & 0.455487 \tabularnewline
115 & 0.480603 & 0.961207 & 0.519397 \tabularnewline
116 & 0.433153 & 0.866307 & 0.566847 \tabularnewline
117 & 0.357474 & 0.714948 & 0.642526 \tabularnewline
118 & 0.339066 & 0.678131 & 0.660934 \tabularnewline
119 & 0.335715 & 0.67143 & 0.664285 \tabularnewline
120 & 0.258642 & 0.517283 & 0.741358 \tabularnewline
121 & 0.596279 & 0.807443 & 0.403721 \tabularnewline
122 & 0.656038 & 0.687924 & 0.343962 \tabularnewline
123 & 0.705389 & 0.589222 & 0.294611 \tabularnewline
124 & 0.645454 & 0.709093 & 0.354546 \tabularnewline
125 & 0.746956 & 0.506089 & 0.253044 \tabularnewline
126 & 0.842139 & 0.315723 & 0.157861 \tabularnewline
127 & 0.708561 & 0.582877 & 0.291439 \tabularnewline
128 & 0.575058 & 0.849885 & 0.424942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271003&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]15[/C][C]0.558935[/C][C]0.88213[/C][C]0.441065[/C][/ROW]
[ROW][C]16[/C][C]0.446138[/C][C]0.892276[/C][C]0.553862[/C][/ROW]
[ROW][C]17[/C][C]0.3107[/C][C]0.621399[/C][C]0.6893[/C][/ROW]
[ROW][C]18[/C][C]0.277086[/C][C]0.554172[/C][C]0.722914[/C][/ROW]
[ROW][C]19[/C][C]0.220174[/C][C]0.440349[/C][C]0.779826[/C][/ROW]
[ROW][C]20[/C][C]0.168472[/C][C]0.336944[/C][C]0.831528[/C][/ROW]
[ROW][C]21[/C][C]0.123937[/C][C]0.247873[/C][C]0.876063[/C][/ROW]
[ROW][C]22[/C][C]0.0794105[/C][C]0.158821[/C][C]0.920589[/C][/ROW]
[ROW][C]23[/C][C]0.0686346[/C][C]0.137269[/C][C]0.931365[/C][/ROW]
[ROW][C]24[/C][C]0.0478789[/C][C]0.0957578[/C][C]0.952121[/C][/ROW]
[ROW][C]25[/C][C]0.0530532[/C][C]0.106106[/C][C]0.946947[/C][/ROW]
[ROW][C]26[/C][C]0.0503427[/C][C]0.100685[/C][C]0.949657[/C][/ROW]
[ROW][C]27[/C][C]0.100417[/C][C]0.200834[/C][C]0.899583[/C][/ROW]
[ROW][C]28[/C][C]0.156841[/C][C]0.313683[/C][C]0.843159[/C][/ROW]
[ROW][C]29[/C][C]0.193311[/C][C]0.386621[/C][C]0.806689[/C][/ROW]
[ROW][C]30[/C][C]0.343745[/C][C]0.68749[/C][C]0.656255[/C][/ROW]
[ROW][C]31[/C][C]0.315132[/C][C]0.630263[/C][C]0.684868[/C][/ROW]
[ROW][C]32[/C][C]0.258049[/C][C]0.516097[/C][C]0.741951[/C][/ROW]
[ROW][C]33[/C][C]0.209133[/C][C]0.418266[/C][C]0.790867[/C][/ROW]
[ROW][C]34[/C][C]0.210928[/C][C]0.421856[/C][C]0.789072[/C][/ROW]
[ROW][C]35[/C][C]0.165421[/C][C]0.330842[/C][C]0.834579[/C][/ROW]
[ROW][C]36[/C][C]0.340827[/C][C]0.681654[/C][C]0.659173[/C][/ROW]
[ROW][C]37[/C][C]0.286388[/C][C]0.572776[/C][C]0.713612[/C][/ROW]
[ROW][C]38[/C][C]0.245387[/C][C]0.490774[/C][C]0.754613[/C][/ROW]
[ROW][C]39[/C][C]0.224358[/C][C]0.448717[/C][C]0.775642[/C][/ROW]
[ROW][C]40[/C][C]0.182895[/C][C]0.36579[/C][C]0.817105[/C][/ROW]
[ROW][C]41[/C][C]0.168072[/C][C]0.336145[/C][C]0.831928[/C][/ROW]
[ROW][C]42[/C][C]0.188529[/C][C]0.377058[/C][C]0.811471[/C][/ROW]
[ROW][C]43[/C][C]0.154425[/C][C]0.30885[/C][C]0.845575[/C][/ROW]
[ROW][C]44[/C][C]0.260713[/C][C]0.521425[/C][C]0.739287[/C][/ROW]
[ROW][C]45[/C][C]0.21871[/C][C]0.43742[/C][C]0.78129[/C][/ROW]
[ROW][C]46[/C][C]0.315378[/C][C]0.630756[/C][C]0.684622[/C][/ROW]
[ROW][C]47[/C][C]0.327337[/C][C]0.654675[/C][C]0.672663[/C][/ROW]
[ROW][C]48[/C][C]0.338066[/C][C]0.676132[/C][C]0.661934[/C][/ROW]
[ROW][C]49[/C][C]0.395915[/C][C]0.79183[/C][C]0.604085[/C][/ROW]
[ROW][C]50[/C][C]0.492719[/C][C]0.985438[/C][C]0.507281[/C][/ROW]
[ROW][C]51[/C][C]0.490673[/C][C]0.981347[/C][C]0.509327[/C][/ROW]
[ROW][C]52[/C][C]0.45581[/C][C]0.911619[/C][C]0.54419[/C][/ROW]
[ROW][C]53[/C][C]0.411262[/C][C]0.822525[/C][C]0.588738[/C][/ROW]
[ROW][C]54[/C][C]0.428561[/C][C]0.857123[/C][C]0.571439[/C][/ROW]
[ROW][C]55[/C][C]0.388418[/C][C]0.776837[/C][C]0.611582[/C][/ROW]
[ROW][C]56[/C][C]0.344882[/C][C]0.689763[/C][C]0.655118[/C][/ROW]
[ROW][C]57[/C][C]0.317556[/C][C]0.635112[/C][C]0.682444[/C][/ROW]
[ROW][C]58[/C][C]0.284755[/C][C]0.56951[/C][C]0.715245[/C][/ROW]
[ROW][C]59[/C][C]0.273055[/C][C]0.54611[/C][C]0.726945[/C][/ROW]
[ROW][C]60[/C][C]0.333906[/C][C]0.667812[/C][C]0.666094[/C][/ROW]
[ROW][C]61[/C][C]0.423334[/C][C]0.846669[/C][C]0.576666[/C][/ROW]
[ROW][C]62[/C][C]0.464325[/C][C]0.928649[/C][C]0.535675[/C][/ROW]
[ROW][C]63[/C][C]0.490296[/C][C]0.980592[/C][C]0.509704[/C][/ROW]
[ROW][C]64[/C][C]0.451375[/C][C]0.90275[/C][C]0.548625[/C][/ROW]
[ROW][C]65[/C][C]0.408185[/C][C]0.816369[/C][C]0.591815[/C][/ROW]
[ROW][C]66[/C][C]0.415523[/C][C]0.831047[/C][C]0.584477[/C][/ROW]
[ROW][C]67[/C][C]0.448089[/C][C]0.896178[/C][C]0.551911[/C][/ROW]
[ROW][C]68[/C][C]0.480354[/C][C]0.960708[/C][C]0.519646[/C][/ROW]
[ROW][C]69[/C][C]0.42931[/C][C]0.85862[/C][C]0.57069[/C][/ROW]
[ROW][C]70[/C][C]0.788643[/C][C]0.422715[/C][C]0.211357[/C][/ROW]
[ROW][C]71[/C][C]0.815025[/C][C]0.36995[/C][C]0.184975[/C][/ROW]
[ROW][C]72[/C][C]0.798925[/C][C]0.40215[/C][C]0.201075[/C][/ROW]
[ROW][C]73[/C][C]0.76441[/C][C]0.47118[/C][C]0.23559[/C][/ROW]
[ROW][C]74[/C][C]0.750461[/C][C]0.499078[/C][C]0.249539[/C][/ROW]
[ROW][C]75[/C][C]0.71429[/C][C]0.571421[/C][C]0.28571[/C][/ROW]
[ROW][C]76[/C][C]0.674171[/C][C]0.651658[/C][C]0.325829[/C][/ROW]
[ROW][C]77[/C][C]0.629267[/C][C]0.741466[/C][C]0.370733[/C][/ROW]
[ROW][C]78[/C][C]0.628215[/C][C]0.74357[/C][C]0.371785[/C][/ROW]
[ROW][C]79[/C][C]0.637079[/C][C]0.725842[/C][C]0.362921[/C][/ROW]
[ROW][C]80[/C][C]0.594454[/C][C]0.811092[/C][C]0.405546[/C][/ROW]
[ROW][C]81[/C][C]0.572104[/C][C]0.855792[/C][C]0.427896[/C][/ROW]
[ROW][C]82[/C][C]0.604935[/C][C]0.790131[/C][C]0.395065[/C][/ROW]
[ROW][C]83[/C][C]0.557552[/C][C]0.884896[/C][C]0.442448[/C][/ROW]
[ROW][C]84[/C][C]0.515592[/C][C]0.968816[/C][C]0.484408[/C][/ROW]
[ROW][C]85[/C][C]0.484545[/C][C]0.969089[/C][C]0.515455[/C][/ROW]
[ROW][C]86[/C][C]0.49601[/C][C]0.992019[/C][C]0.50399[/C][/ROW]
[ROW][C]87[/C][C]0.445721[/C][C]0.891442[/C][C]0.554279[/C][/ROW]
[ROW][C]88[/C][C]0.420694[/C][C]0.841389[/C][C]0.579306[/C][/ROW]
[ROW][C]89[/C][C]0.374017[/C][C]0.748035[/C][C]0.625983[/C][/ROW]
[ROW][C]90[/C][C]0.333605[/C][C]0.66721[/C][C]0.666395[/C][/ROW]
[ROW][C]91[/C][C]0.291171[/C][C]0.582342[/C][C]0.708829[/C][/ROW]
[ROW][C]92[/C][C]0.246087[/C][C]0.492173[/C][C]0.753913[/C][/ROW]
[ROW][C]93[/C][C]0.279093[/C][C]0.558185[/C][C]0.720907[/C][/ROW]
[ROW][C]94[/C][C]0.281343[/C][C]0.562686[/C][C]0.718657[/C][/ROW]
[ROW][C]95[/C][C]0.461533[/C][C]0.923067[/C][C]0.538467[/C][/ROW]
[ROW][C]96[/C][C]0.425019[/C][C]0.850037[/C][C]0.574981[/C][/ROW]
[ROW][C]97[/C][C]0.403482[/C][C]0.806963[/C][C]0.596518[/C][/ROW]
[ROW][C]98[/C][C]0.354162[/C][C]0.708324[/C][C]0.645838[/C][/ROW]
[ROW][C]99[/C][C]0.33334[/C][C]0.666681[/C][C]0.66666[/C][/ROW]
[ROW][C]100[/C][C]0.300821[/C][C]0.601642[/C][C]0.699179[/C][/ROW]
[ROW][C]101[/C][C]0.251984[/C][C]0.503968[/C][C]0.748016[/C][/ROW]
[ROW][C]102[/C][C]0.843741[/C][C]0.312518[/C][C]0.156259[/C][/ROW]
[ROW][C]103[/C][C]0.812538[/C][C]0.374925[/C][C]0.187462[/C][/ROW]
[ROW][C]104[/C][C]0.769522[/C][C]0.460956[/C][C]0.230478[/C][/ROW]
[ROW][C]105[/C][C]0.742793[/C][C]0.514414[/C][C]0.257207[/C][/ROW]
[ROW][C]106[/C][C]0.764457[/C][C]0.471086[/C][C]0.235543[/C][/ROW]
[ROW][C]107[/C][C]0.76959[/C][C]0.46082[/C][C]0.23041[/C][/ROW]
[ROW][C]108[/C][C]0.731513[/C][C]0.536974[/C][C]0.268487[/C][/ROW]
[ROW][C]109[/C][C]0.689697[/C][C]0.620606[/C][C]0.310303[/C][/ROW]
[ROW][C]110[/C][C]0.647447[/C][C]0.705106[/C][C]0.352553[/C][/ROW]
[ROW][C]111[/C][C]0.630342[/C][C]0.739315[/C][C]0.369658[/C][/ROW]
[ROW][C]112[/C][C]0.599057[/C][C]0.801886[/C][C]0.400943[/C][/ROW]
[ROW][C]113[/C][C]0.594965[/C][C]0.810071[/C][C]0.405035[/C][/ROW]
[ROW][C]114[/C][C]0.544513[/C][C]0.910975[/C][C]0.455487[/C][/ROW]
[ROW][C]115[/C][C]0.480603[/C][C]0.961207[/C][C]0.519397[/C][/ROW]
[ROW][C]116[/C][C]0.433153[/C][C]0.866307[/C][C]0.566847[/C][/ROW]
[ROW][C]117[/C][C]0.357474[/C][C]0.714948[/C][C]0.642526[/C][/ROW]
[ROW][C]118[/C][C]0.339066[/C][C]0.678131[/C][C]0.660934[/C][/ROW]
[ROW][C]119[/C][C]0.335715[/C][C]0.67143[/C][C]0.664285[/C][/ROW]
[ROW][C]120[/C][C]0.258642[/C][C]0.517283[/C][C]0.741358[/C][/ROW]
[ROW][C]121[/C][C]0.596279[/C][C]0.807443[/C][C]0.403721[/C][/ROW]
[ROW][C]122[/C][C]0.656038[/C][C]0.687924[/C][C]0.343962[/C][/ROW]
[ROW][C]123[/C][C]0.705389[/C][C]0.589222[/C][C]0.294611[/C][/ROW]
[ROW][C]124[/C][C]0.645454[/C][C]0.709093[/C][C]0.354546[/C][/ROW]
[ROW][C]125[/C][C]0.746956[/C][C]0.506089[/C][C]0.253044[/C][/ROW]
[ROW][C]126[/C][C]0.842139[/C][C]0.315723[/C][C]0.157861[/C][/ROW]
[ROW][C]127[/C][C]0.708561[/C][C]0.582877[/C][C]0.291439[/C][/ROW]
[ROW][C]128[/C][C]0.575058[/C][C]0.849885[/C][C]0.424942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271003&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271003&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
150.5589350.882130.441065
160.4461380.8922760.553862
170.31070.6213990.6893
180.2770860.5541720.722914
190.2201740.4403490.779826
200.1684720.3369440.831528
210.1239370.2478730.876063
220.07941050.1588210.920589
230.06863460.1372690.931365
240.04787890.09575780.952121
250.05305320.1061060.946947
260.05034270.1006850.949657
270.1004170.2008340.899583
280.1568410.3136830.843159
290.1933110.3866210.806689
300.3437450.687490.656255
310.3151320.6302630.684868
320.2580490.5160970.741951
330.2091330.4182660.790867
340.2109280.4218560.789072
350.1654210.3308420.834579
360.3408270.6816540.659173
370.2863880.5727760.713612
380.2453870.4907740.754613
390.2243580.4487170.775642
400.1828950.365790.817105
410.1680720.3361450.831928
420.1885290.3770580.811471
430.1544250.308850.845575
440.2607130.5214250.739287
450.218710.437420.78129
460.3153780.6307560.684622
470.3273370.6546750.672663
480.3380660.6761320.661934
490.3959150.791830.604085
500.4927190.9854380.507281
510.4906730.9813470.509327
520.455810.9116190.54419
530.4112620.8225250.588738
540.4285610.8571230.571439
550.3884180.7768370.611582
560.3448820.6897630.655118
570.3175560.6351120.682444
580.2847550.569510.715245
590.2730550.546110.726945
600.3339060.6678120.666094
610.4233340.8466690.576666
620.4643250.9286490.535675
630.4902960.9805920.509704
640.4513750.902750.548625
650.4081850.8163690.591815
660.4155230.8310470.584477
670.4480890.8961780.551911
680.4803540.9607080.519646
690.429310.858620.57069
700.7886430.4227150.211357
710.8150250.369950.184975
720.7989250.402150.201075
730.764410.471180.23559
740.7504610.4990780.249539
750.714290.5714210.28571
760.6741710.6516580.325829
770.6292670.7414660.370733
780.6282150.743570.371785
790.6370790.7258420.362921
800.5944540.8110920.405546
810.5721040.8557920.427896
820.6049350.7901310.395065
830.5575520.8848960.442448
840.5155920.9688160.484408
850.4845450.9690890.515455
860.496010.9920190.50399
870.4457210.8914420.554279
880.4206940.8413890.579306
890.3740170.7480350.625983
900.3336050.667210.666395
910.2911710.5823420.708829
920.2460870.4921730.753913
930.2790930.5581850.720907
940.2813430.5626860.718657
950.4615330.9230670.538467
960.4250190.8500370.574981
970.4034820.8069630.596518
980.3541620.7083240.645838
990.333340.6666810.66666
1000.3008210.6016420.699179
1010.2519840.5039680.748016
1020.8437410.3125180.156259
1030.8125380.3749250.187462
1040.7695220.4609560.230478
1050.7427930.5144140.257207
1060.7644570.4710860.235543
1070.769590.460820.23041
1080.7315130.5369740.268487
1090.6896970.6206060.310303
1100.6474470.7051060.352553
1110.6303420.7393150.369658
1120.5990570.8018860.400943
1130.5949650.8100710.405035
1140.5445130.9109750.455487
1150.4806030.9612070.519397
1160.4331530.8663070.566847
1170.3574740.7149480.642526
1180.3390660.6781310.660934
1190.3357150.671430.664285
1200.2586420.5172830.741358
1210.5962790.8074430.403721
1220.6560380.6879240.343962
1230.7053890.5892220.294611
1240.6454540.7090930.354546
1250.7469560.5060890.253044
1260.8421390.3157230.157861
1270.7085610.5828770.291439
1280.5750580.8498850.424942






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00877193 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271003&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00877193[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271003&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271003&T=6

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

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


Figures (Output of Computation)

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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')
}