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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 15:10:27 +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/t14189154553n3f1jov3latai5.htm/, Retrieved Fri, 17 May 2024 19:44:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271049, Retrieved Fri, 17 May 2024 19:44:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73

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 15:10:27] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
12.9 8 7 18 12 21 13 12 149 18 68 1.8
12.2 18 20 23 20 22 8 8 139 31 39 2.1
12.8 12 9 22 14 21 14 11 148 39 32 2.2
7.4 24 19 22 25 21 16 13 158 46 62 2.3
6.7 16 12 19 15 21 14 11 128 31 33 2.1
12.6 19 16 25 20 21 13 10 224 67 52 2.7
14.8 16 17 28 21 21 15 7 159 35 62 2.1
13.3 15 9 16 15 23 13 10 105 52 77 2.4
11.1 28 28 28 28 22 20 15 159 77 76 2.9
8.2 21 20 21 11 25 17 12 167 37 41 2.2
11.4 18 16 22 22 21 15 12 165 32 48 2.1
6.4 22 22 24 22 23 16 10 159 36 63 2.2
10.6 19 17 24 27 22 12 10 119 38 30 2.2
12 22 12 26 24 21 17 14 176 69 78 2.7
6.3 25 18 28 23 21 11 6 54 21 19 1.9
11.9 16 12 20 21 21 16 14 163 54 66 2.5
9.3 19 16 26 20 21 15 11 124 36 35 2.2
10 26 21 28 25 24 14 12 121 23 45 1.9
6.4 24 15 27 16 23 19 15 153 34 21 2.1
13.8 20 17 23 24 21 16 13 148 112 25 3.5
10.8 19 17 24 21 24 17 11 221 35 44 2.1
13.8 19 17 24 22 23 10 12 188 47 69 2.3
11.7 23 18 22 25 21 15 7 149 47 54 2.3
10.9 18 15 21 23 22 14 11 244 37 74 2.2
9.9 21 21 21 22 21 15 12 150 20 61 1.9
11.5 20 12 26 25 22 17 13 153 22 41 1.9
8.3 15 6 23 23 22 14 9 94 23 46 1.9
11.7 19 13 21 19 21 16 11 156 32 39 2.1
9 19 19 27 21 21 15 12 132 30 34 2
9.7 7 12 25 19 25 16 15 161 92 51 3.2
10.8 20 14 23 25 22 16 12 105 43 42 2.3
10.3 20 13 25 16 22 10 6 97 55 31 2.5
10.4 19 12 23 24 20 8 5 151 16 39 1.8
9.3 20 19 22 18 21 14 11 166 71 49 2.8
11.8 18 10 24 28 21 10 6 157 43 53 2.3
5.9 14 10 19 15 22 14 12 111 29 31 2
11.4 17 11 21 17 21 12 10 145 56 39 2.5
13 17 11 27 18 24 16 6 162 46 54 2.3
10.8 8 10 25 26 22 16 12 163 19 49 1.8
11.3 22 22 23 22 21 8 6 187 59 46 2.6
11.8 20 12 17 19 22 16 12 109 30 55 2
12.7 22 20 25 26 22 8 8 105 7 50 1.6
10.9 14 11 24 12 23 16 12 148 19 30 1.8
13.3 21 17 20 20 23 19 14 125 48 45 2.4
10.1 20 14 19 24 21 14 12 116 23 35 1.9
14.3 18 16 21 22 21 13 14 138 33 41 2.1
9.3 24 15 18 23 22 15 11 164 34 73 2.1
12.5 19 15 27 19 21 11 10 162 48 17 2.4
7.6 16 10 25 24 21 9 7 99 18 40 1.8
15.9 16 10 20 21 21 16 12 202 43 64 2.3
9.2 16 18 21 16 21 12 7 186 33 37 2.1
11.1 22 22 27 23 21 14 12 183 71 65 2.8
13 21 16 24 20 22 14 10 214 26 100 2
14.5 15 10 27 19 22 13 10 188 67 28 2.7
12.3 15 16 23 18 21 17 12 177 80 56 2.9
11.4 14 16 24 21 23 14 12 126 29 29 2
13 16 10 25 17 21 15 10 162 43 59 2.3
13.2 26 16 24 24 20 15 11 159 29 61 2
7.7 18 16 23 22 21 16 12 110 32 51 2.1
4.35 17 15 22 14 22 16 9 48 23 12 1
12.7 6 4 24 5 22 16 11 50 16 45 1
18.1 22 9 19 25 22 16 12 150 33 37 4
17.85 20 18 25 21 20 16 12 154 32 37 4
17.1 17 12 24 9 22 14 12 194 52 68 4
19.1 20 16 28 15 21 15 12 158 75 72 4
16.1 23 17 23 23 21 16 10 159 72 143 4
13.35 18 14 19 21 21 13 15 67 15 9 2
18.4 13 13 19 9 21 10 10 147 29 55 4
14.7 22 20 27 24 21 17 15 39 13 17 1
10.6 20 16 24 16 21 15 10 100 40 37 3
12.6 20 15 26 20 21 18 15 111 19 27 3
13.6 16 16 25 18 24 20 15 101 121 58 3
14.1 16 15 19 21 22 17 13 101 36 21 3
14.5 15 16 20 21 20 16 12 114 23 19 3
16.15 19 19 26 21 21 15 12 165 85 78 4
14.75 19 9 27 20 24 13 8 114 41 35 3
14.8 24 19 23 24 25 16 9 111 46 48 3
12.45 9 7 18 15 22 16 15 75 18 27 2
12.65 22 23 23 24 21 16 12 82 35 43 2
17.35 15 14 21 18 21 17 12 121 17 30 3
8.6 22 10 23 24 22 20 15 32 4 25 1
18.4 22 16 22 24 23 14 11 150 28 69 4
16.1 24 12 21 15 24 17 12 117 44 72 3
17.75 21 7 24 20 22 16 14 165 38 13 4
15.25 25 20 26 26 25 15 12 154 57 61 4
17.65 26 9 24 26 22 16 12 126 23 43 4
16.35 21 12 22 23 21 16 12 149 36 51 4
17.65 14 10 20 13 21 14 11 145 22 67 4
13.6 28 19 20 16 21 16 12 120 40 36 3
14.35 21 11 18 22 22 16 12 109 31 44 3
14.75 16 15 18 21 22 16 12 132 11 45 4
18.25 16 14 25 11 21 14 12 172 38 34 4
9.9 25 11 28 23 22 14 8 169 24 36 4
16 21 14 23 18 23 16 8 114 37 72 3
18.25 22 15 20 19 21 16 12 156 37 39 4
16.85 9 7 22 15 21 15 12 172 22 43 4
18.95 24 22 23 21 21 18 11 167 43 80 4
15.6 22 11 20 25 21 15 12 113 31 40 3
17.1 10 12 24 12 22 14 10 173 31 61 4
15.4 21 13 23 19 21 15 11 165 21 29 4
15.4 20 15 21 21 21 15 11 165 21 29 4
13.35 17 11 19 19 25 16 13 118 32 54 3
19.1 7 7 19 18 21 11 7 158 26 43 4
7.6 14 13 25 23 25 7 8 49 32 20 1
19.1 23 7 18 23 22 15 11 155 33 61 4
14.75 18 11 22 27 21 14 8 151 30 57 4
19.25 17 22 5 6 23 16 14 220 67 54 4
13.6 20 15 24 22 20 14 9 141 22 36 4
12.75 19 15 28 23 22 11 13 122 33 16 4
9.85 19 11 27 20 25 18 13 44 24 40 1
15.25 23 10 23 23 20 18 11 152 28 27 4
11.9 20 18 24 27 21 15 9 107 41 61 3
16.35 19 14 25 24 21 13 12 154 31 69 4
12.4 16 16 19 12 23 13 12 103 33 34 3
18.15 21 16 24 24 22 18 13 175 21 34 4
17.75 20 17 28 24 21 15 11 143 52 34 4
12.35 20 14 19 19 21 16 11 110 29 13 3
15.6 19 10 23 28 21 12 9 131 11 12 4
19.3 19 16 23 23 21 16 12 167 26 51 4
17.1 20 16 26 19 21 16 15 137 7 19 4
18.4 22 17 25 23 21 19 14 121 13 81 3
19.05 19 12 24 20 21 15 12 149 20 42 4
18.55 23 17 23 18 22 14 9 168 52 22 4
19.1 16 11 22 20 21 14 9 140 28 85 4
12.85 18 12 26 21 22 16 13 168 39 25 4
9.5 23 8 23 25 22 20 15 94 9 22 2
4.5 20 17 22 18 22 16 11 51 19 19 1
13.6 23 17 22 28 22 13 10 145 60 45 4
11.7 13 7 17 9 23 15 11 66 19 45 2
13.35 26 18 22 26 22 16 14 109 14 51 3
17.6 13 14 26 12 21 19 12 164 -2 73 4
14.05 10 13 24 12 21 13 13 119 51 24 3
16.1 21 19 27 20 20 14 11 126 2 61 4
13.35 24 15 22 25 20 15 11 132 24 23 4
11.85 21 15 23 24 21 15 13 142 40 14 4
11.95 23 8 22 23 21 14 12 83 20 54 2
13.2 16 11 20 22 21 12 9 166 20 36 4
7.7 26 17 27 28 24 15 13 93 25 26 2
14.6 16 12 20 15 22 16 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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.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=271049&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]'Herman Ole Andreas Wold' @ wold.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=271049&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271049&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'Herman Ole Andreas Wold' @ wold.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] = + 9.04072 -0.0461949AMS.I2[t] -0.044734AMS.I3[t] -0.0222514AMS.E1[t] -0.0477338AMS.E2[t] -0.126098age[t] -0.0337527CONFSTATTOT[t] + 0.159552CONFSOFTTOT[t] -0.0029758LFM[t] -0.0207473PRH[t] + 0.0374891CH[t] + 2.81352PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  9.04072 -0.0461949AMS.I2[t] -0.044734AMS.I3[t] -0.0222514AMS.E1[t] -0.0477338AMS.E2[t] -0.126098age[t] -0.0337527CONFSTATTOT[t] +  0.159552CONFSOFTTOT[t] -0.0029758LFM[t] -0.0207473PRH[t] +  0.0374891CH[t] +  2.81352PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271049&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  9.04072 -0.0461949AMS.I2[t] -0.044734AMS.I3[t] -0.0222514AMS.E1[t] -0.0477338AMS.E2[t] -0.126098age[t] -0.0337527CONFSTATTOT[t] +  0.159552CONFSOFTTOT[t] -0.0029758LFM[t] -0.0207473PRH[t] +  0.0374891CH[t] +  2.81352PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271049&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271049&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] = + 9.04072 -0.0461949AMS.I2[t] -0.044734AMS.I3[t] -0.0222514AMS.E1[t] -0.0477338AMS.E2[t] -0.126098age[t] -0.0337527CONFSTATTOT[t] + 0.159552CONFSOFTTOT[t] -0.0029758LFM[t] -0.0207473PRH[t] + 0.0374891CH[t] + 2.81352PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.040724.30752.0990.03781190.018906
AMS.I2-0.04619490.0626468-0.73740.4622480.231124
AMS.I3-0.0447340.0571816-0.78230.4354860.217743
AMS.E1-0.02225140.0632664-0.35170.7256390.362819
AMS.E2-0.04773380.0509691-0.93650.3507810.175391
age-0.1260980.172002-0.73310.4648370.232418
CONFSTATTOT-0.03375270.107136-0.3150.7532440.376622
CONFSOFTTOT0.1595520.11681.3660.1743460.0871728
LFM-0.00297580.00615824-0.48320.629770.314885
PRH-0.02074730.010921-1.90.05973120.0298656
CH0.03748910.01062233.5290.0005809850.000290493
PR2.813520.23759911.842.96192e-221.48096e-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) & 9.04072 & 4.3075 & 2.099 & 0.0378119 & 0.018906 \tabularnewline
AMS.I2 & -0.0461949 & 0.0626468 & -0.7374 & 0.462248 & 0.231124 \tabularnewline
AMS.I3 & -0.044734 & 0.0571816 & -0.7823 & 0.435486 & 0.217743 \tabularnewline
AMS.E1 & -0.0222514 & 0.0632664 & -0.3517 & 0.725639 & 0.362819 \tabularnewline
AMS.E2 & -0.0477338 & 0.0509691 & -0.9365 & 0.350781 & 0.175391 \tabularnewline
age & -0.126098 & 0.172002 & -0.7331 & 0.464837 & 0.232418 \tabularnewline
CONFSTATTOT & -0.0337527 & 0.107136 & -0.315 & 0.753244 & 0.376622 \tabularnewline
CONFSOFTTOT & 0.159552 & 0.1168 & 1.366 & 0.174346 & 0.0871728 \tabularnewline
LFM & -0.0029758 & 0.00615824 & -0.4832 & 0.62977 & 0.314885 \tabularnewline
PRH & -0.0207473 & 0.010921 & -1.9 & 0.0597312 & 0.0298656 \tabularnewline
CH & 0.0374891 & 0.0106223 & 3.529 & 0.000580985 & 0.000290493 \tabularnewline
PR & 2.81352 & 0.237599 & 11.84 & 2.96192e-22 & 1.48096e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271049&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]9.04072[/C][C]4.3075[/C][C]2.099[/C][C]0.0378119[/C][C]0.018906[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0461949[/C][C]0.0626468[/C][C]-0.7374[/C][C]0.462248[/C][C]0.231124[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.044734[/C][C]0.0571816[/C][C]-0.7823[/C][C]0.435486[/C][C]0.217743[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0222514[/C][C]0.0632664[/C][C]-0.3517[/C][C]0.725639[/C][C]0.362819[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0477338[/C][C]0.0509691[/C][C]-0.9365[/C][C]0.350781[/C][C]0.175391[/C][/ROW]
[ROW][C]age[/C][C]-0.126098[/C][C]0.172002[/C][C]-0.7331[/C][C]0.464837[/C][C]0.232418[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.0337527[/C][C]0.107136[/C][C]-0.315[/C][C]0.753244[/C][C]0.376622[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.159552[/C][C]0.1168[/C][C]1.366[/C][C]0.174346[/C][C]0.0871728[/C][/ROW]
[ROW][C]LFM[/C][C]-0.0029758[/C][C]0.00615824[/C][C]-0.4832[/C][C]0.62977[/C][C]0.314885[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0207473[/C][C]0.010921[/C][C]-1.9[/C][C]0.0597312[/C][C]0.0298656[/C][/ROW]
[ROW][C]CH[/C][C]0.0374891[/C][C]0.0106223[/C][C]3.529[/C][C]0.000580985[/C][C]0.000290493[/C][/ROW]
[ROW][C]PR[/C][C]2.81352[/C][C]0.237599[/C][C]11.84[/C][C]2.96192e-22[/C][C]1.48096e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271049&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271049&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)9.040724.30752.0990.03781190.018906
AMS.I2-0.04619490.0626468-0.73740.4622480.231124
AMS.I3-0.0447340.0571816-0.78230.4354860.217743
AMS.E1-0.02225140.0632664-0.35170.7256390.362819
AMS.E2-0.04773380.0509691-0.93650.3507810.175391
age-0.1260980.172002-0.73310.4648370.232418
CONFSTATTOT-0.03375270.107136-0.3150.7532440.376622
CONFSOFTTOT0.1595520.11681.3660.1743460.0871728
LFM-0.00297580.00615824-0.48320.629770.314885
PRH-0.02074730.010921-1.90.05973120.0298656
CH0.03748910.01062233.5290.0005809850.000290493
PR2.813520.23759911.842.96192e-221.48096e-22






Multiple Linear Regression - Regression Statistics
Multiple R0.793223
R-squared0.629203
Adjusted R-squared0.597087
F-TEST (value)19.5914
F-TEST (DF numerator)11
F-TEST (DF denominator)127
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22413
Sum Squared Residuals628.241

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.793223 \tabularnewline
R-squared & 0.629203 \tabularnewline
Adjusted R-squared & 0.597087 \tabularnewline
F-TEST (value) & 19.5914 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 127 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.22413 \tabularnewline
Sum Squared Residuals & 628.241 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271049&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.793223[/C][/ROW]
[ROW][C]R-squared[/C][C]0.629203[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.597087[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.5914[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]127[/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]2.22413[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]628.241[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271049&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271049&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 R0.793223
R-squared0.629203
Adjusted R-squared0.597087
F-TEST (value)19.5914
F-TEST (DF numerator)11
F-TEST (DF denominator)127
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22413
Sum Squared Residuals628.241






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.913.0092-0.10923
212.210.3941.80602
312.811.70031.09972
47.411.6562-4.25617
56.711.382-4.68196
612.611.93430.665719
714.810.91163.88839
813.313.3774-0.0774385
911.112.4169-1.31689
108.210.8342-2.63417
1111.411.2670.132977
126.410.9428-4.54284
1310.610.16790.432071
141213.3407-1.3407
156.38.75929-2.45929
1611.913.2657-1.36566
179.310.9007-1.60075
18109.695030.304965
196.410.2832-3.88319
2013.812.60551.19454
2110.810.19510.604868
2213.812.01851.78152
2311.710.52971.17034
2410.911.9516-1.05162
259.911.1453-1.24528
2611.510.50540.994633
278.310.9723-2.67229
2811.711.01660.683442
29910.3566-1.3566
309.713.9455-4.24546
3110.811.2269-0.426894
3210.310.8271-0.527095
3310.49.719710.680291
349.312.3004-3.0004
3511.810.96170.838292
365.911.333-5.43302
3711.411.9295-0.529547
381310.75332.24669
3910.811.0489-0.248937
4011.310.77670.52332
4111.811.63740.162611
4212.79.483033.21697
4310.910.62380.276196
4413.311.67421.62576
4510.110.5516-0.451619
4614.311.47312.82694
479.311.6889-2.38892
4812.510.47232.02772
497.610.2132-2.61325
5015.912.51053.38949
519.210.3865-1.18648
5211.112.4327-1.33267
531312.41470.585292
5414.511.4723.02801
5512.313.0259-0.725932
5611.410.42120.978756
571312.23640.763582
5813.211.01012.18986
597.711.4872-3.78716
604.357.19173-2.84173
6112.710.27262.42742
6218.116.11631.98372
6317.8516.12451.72547
6417.117.5701-0.470067
6519.116.74942.35062
6616.118.6636-2.56359
6713.3510.9182.43195
6818.417.89310.506933
6914.77.619857.08015
7010.613.2447-2.64466
7112.613.7785-1.17853
7213.612.66620.933812
7314.113.1120.987995
7414.513.37371.12634
7516.1516.4161-0.266104
7614.7512.5792.17097
7714.812.12352.67647
7812.4512.3170.133008
7912.6510.33362.31641
8017.3513.94033.40974
818.68.436310.163689
8218.416.86941.53064
8316.114.40521.69484
8417.7515.65032.09966
8515.2515.3275-0.0774851
8617.6516.27631.37367
8716.3516.6487-0.298663
8817.6518.3935-0.743482
8913.613.01830.581746
9014.3513.85090.499134
9114.7517.1481-2.39815
9218.2516.61651.63353
939.915.3054-5.40542
941613.94242.05763
9518.2516.21232.03774
9616.8517.7644-0.914401
9718.9516.79732.15271
9815.613.6151.98504
9917.117.6669-0.566888
10015.416.0856-0.68565
10115.415.9914-0.591412
10213.3514.2652-0.915216
10319.117.07632.02373
1047.66.799570.800434
10519.117.03632.06367
10614.7516.4138-1.66381
10719.2516.90082.34922
10813.616.0308-2.43077
10912.7515.506-2.75603
1109.858.113881.73612
11115.2515.7798-0.529842
11211.913.3287-1.42873
11316.3517.402-1.05202
11412.413.8899-1.48989
11518.1515.942.21004
11617.7515.21272.53727
11712.3512.7267-0.37671
11815.615.33610.263873
11919.316.69382.60623
12017.116.53420.565762
12118.415.40162.99844
12219.0516.86812.18194
12318.5514.53614.01391
12419.118.12380.976186
12512.8515.7337-2.88366
1269.510.8447-1.34466
1274.57.42833-2.92833
12813.615.039-1.43902
12911.712.3912-0.691153
13013.3513.961-0.611046
13117.618.8321-1.23209
13214.0513.80580.24421
13316.117.5503-1.45027
13413.3515.5306-2.18057
13511.8515.1885-3.33852
13611.9511.81650.133493
13713.216.392-3.19199
1387.79.48971-1.78971
13914.613.63290.967146

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 13.0092 & -0.10923 \tabularnewline
2 & 12.2 & 10.394 & 1.80602 \tabularnewline
3 & 12.8 & 11.7003 & 1.09972 \tabularnewline
4 & 7.4 & 11.6562 & -4.25617 \tabularnewline
5 & 6.7 & 11.382 & -4.68196 \tabularnewline
6 & 12.6 & 11.9343 & 0.665719 \tabularnewline
7 & 14.8 & 10.9116 & 3.88839 \tabularnewline
8 & 13.3 & 13.3774 & -0.0774385 \tabularnewline
9 & 11.1 & 12.4169 & -1.31689 \tabularnewline
10 & 8.2 & 10.8342 & -2.63417 \tabularnewline
11 & 11.4 & 11.267 & 0.132977 \tabularnewline
12 & 6.4 & 10.9428 & -4.54284 \tabularnewline
13 & 10.6 & 10.1679 & 0.432071 \tabularnewline
14 & 12 & 13.3407 & -1.3407 \tabularnewline
15 & 6.3 & 8.75929 & -2.45929 \tabularnewline
16 & 11.9 & 13.2657 & -1.36566 \tabularnewline
17 & 9.3 & 10.9007 & -1.60075 \tabularnewline
18 & 10 & 9.69503 & 0.304965 \tabularnewline
19 & 6.4 & 10.2832 & -3.88319 \tabularnewline
20 & 13.8 & 12.6055 & 1.19454 \tabularnewline
21 & 10.8 & 10.1951 & 0.604868 \tabularnewline
22 & 13.8 & 12.0185 & 1.78152 \tabularnewline
23 & 11.7 & 10.5297 & 1.17034 \tabularnewline
24 & 10.9 & 11.9516 & -1.05162 \tabularnewline
25 & 9.9 & 11.1453 & -1.24528 \tabularnewline
26 & 11.5 & 10.5054 & 0.994633 \tabularnewline
27 & 8.3 & 10.9723 & -2.67229 \tabularnewline
28 & 11.7 & 11.0166 & 0.683442 \tabularnewline
29 & 9 & 10.3566 & -1.3566 \tabularnewline
30 & 9.7 & 13.9455 & -4.24546 \tabularnewline
31 & 10.8 & 11.2269 & -0.426894 \tabularnewline
32 & 10.3 & 10.8271 & -0.527095 \tabularnewline
33 & 10.4 & 9.71971 & 0.680291 \tabularnewline
34 & 9.3 & 12.3004 & -3.0004 \tabularnewline
35 & 11.8 & 10.9617 & 0.838292 \tabularnewline
36 & 5.9 & 11.333 & -5.43302 \tabularnewline
37 & 11.4 & 11.9295 & -0.529547 \tabularnewline
38 & 13 & 10.7533 & 2.24669 \tabularnewline
39 & 10.8 & 11.0489 & -0.248937 \tabularnewline
40 & 11.3 & 10.7767 & 0.52332 \tabularnewline
41 & 11.8 & 11.6374 & 0.162611 \tabularnewline
42 & 12.7 & 9.48303 & 3.21697 \tabularnewline
43 & 10.9 & 10.6238 & 0.276196 \tabularnewline
44 & 13.3 & 11.6742 & 1.62576 \tabularnewline
45 & 10.1 & 10.5516 & -0.451619 \tabularnewline
46 & 14.3 & 11.4731 & 2.82694 \tabularnewline
47 & 9.3 & 11.6889 & -2.38892 \tabularnewline
48 & 12.5 & 10.4723 & 2.02772 \tabularnewline
49 & 7.6 & 10.2132 & -2.61325 \tabularnewline
50 & 15.9 & 12.5105 & 3.38949 \tabularnewline
51 & 9.2 & 10.3865 & -1.18648 \tabularnewline
52 & 11.1 & 12.4327 & -1.33267 \tabularnewline
53 & 13 & 12.4147 & 0.585292 \tabularnewline
54 & 14.5 & 11.472 & 3.02801 \tabularnewline
55 & 12.3 & 13.0259 & -0.725932 \tabularnewline
56 & 11.4 & 10.4212 & 0.978756 \tabularnewline
57 & 13 & 12.2364 & 0.763582 \tabularnewline
58 & 13.2 & 11.0101 & 2.18986 \tabularnewline
59 & 7.7 & 11.4872 & -3.78716 \tabularnewline
60 & 4.35 & 7.19173 & -2.84173 \tabularnewline
61 & 12.7 & 10.2726 & 2.42742 \tabularnewline
62 & 18.1 & 16.1163 & 1.98372 \tabularnewline
63 & 17.85 & 16.1245 & 1.72547 \tabularnewline
64 & 17.1 & 17.5701 & -0.470067 \tabularnewline
65 & 19.1 & 16.7494 & 2.35062 \tabularnewline
66 & 16.1 & 18.6636 & -2.56359 \tabularnewline
67 & 13.35 & 10.918 & 2.43195 \tabularnewline
68 & 18.4 & 17.8931 & 0.506933 \tabularnewline
69 & 14.7 & 7.61985 & 7.08015 \tabularnewline
70 & 10.6 & 13.2447 & -2.64466 \tabularnewline
71 & 12.6 & 13.7785 & -1.17853 \tabularnewline
72 & 13.6 & 12.6662 & 0.933812 \tabularnewline
73 & 14.1 & 13.112 & 0.987995 \tabularnewline
74 & 14.5 & 13.3737 & 1.12634 \tabularnewline
75 & 16.15 & 16.4161 & -0.266104 \tabularnewline
76 & 14.75 & 12.579 & 2.17097 \tabularnewline
77 & 14.8 & 12.1235 & 2.67647 \tabularnewline
78 & 12.45 & 12.317 & 0.133008 \tabularnewline
79 & 12.65 & 10.3336 & 2.31641 \tabularnewline
80 & 17.35 & 13.9403 & 3.40974 \tabularnewline
81 & 8.6 & 8.43631 & 0.163689 \tabularnewline
82 & 18.4 & 16.8694 & 1.53064 \tabularnewline
83 & 16.1 & 14.4052 & 1.69484 \tabularnewline
84 & 17.75 & 15.6503 & 2.09966 \tabularnewline
85 & 15.25 & 15.3275 & -0.0774851 \tabularnewline
86 & 17.65 & 16.2763 & 1.37367 \tabularnewline
87 & 16.35 & 16.6487 & -0.298663 \tabularnewline
88 & 17.65 & 18.3935 & -0.743482 \tabularnewline
89 & 13.6 & 13.0183 & 0.581746 \tabularnewline
90 & 14.35 & 13.8509 & 0.499134 \tabularnewline
91 & 14.75 & 17.1481 & -2.39815 \tabularnewline
92 & 18.25 & 16.6165 & 1.63353 \tabularnewline
93 & 9.9 & 15.3054 & -5.40542 \tabularnewline
94 & 16 & 13.9424 & 2.05763 \tabularnewline
95 & 18.25 & 16.2123 & 2.03774 \tabularnewline
96 & 16.85 & 17.7644 & -0.914401 \tabularnewline
97 & 18.95 & 16.7973 & 2.15271 \tabularnewline
98 & 15.6 & 13.615 & 1.98504 \tabularnewline
99 & 17.1 & 17.6669 & -0.566888 \tabularnewline
100 & 15.4 & 16.0856 & -0.68565 \tabularnewline
101 & 15.4 & 15.9914 & -0.591412 \tabularnewline
102 & 13.35 & 14.2652 & -0.915216 \tabularnewline
103 & 19.1 & 17.0763 & 2.02373 \tabularnewline
104 & 7.6 & 6.79957 & 0.800434 \tabularnewline
105 & 19.1 & 17.0363 & 2.06367 \tabularnewline
106 & 14.75 & 16.4138 & -1.66381 \tabularnewline
107 & 19.25 & 16.9008 & 2.34922 \tabularnewline
108 & 13.6 & 16.0308 & -2.43077 \tabularnewline
109 & 12.75 & 15.506 & -2.75603 \tabularnewline
110 & 9.85 & 8.11388 & 1.73612 \tabularnewline
111 & 15.25 & 15.7798 & -0.529842 \tabularnewline
112 & 11.9 & 13.3287 & -1.42873 \tabularnewline
113 & 16.35 & 17.402 & -1.05202 \tabularnewline
114 & 12.4 & 13.8899 & -1.48989 \tabularnewline
115 & 18.15 & 15.94 & 2.21004 \tabularnewline
116 & 17.75 & 15.2127 & 2.53727 \tabularnewline
117 & 12.35 & 12.7267 & -0.37671 \tabularnewline
118 & 15.6 & 15.3361 & 0.263873 \tabularnewline
119 & 19.3 & 16.6938 & 2.60623 \tabularnewline
120 & 17.1 & 16.5342 & 0.565762 \tabularnewline
121 & 18.4 & 15.4016 & 2.99844 \tabularnewline
122 & 19.05 & 16.8681 & 2.18194 \tabularnewline
123 & 18.55 & 14.5361 & 4.01391 \tabularnewline
124 & 19.1 & 18.1238 & 0.976186 \tabularnewline
125 & 12.85 & 15.7337 & -2.88366 \tabularnewline
126 & 9.5 & 10.8447 & -1.34466 \tabularnewline
127 & 4.5 & 7.42833 & -2.92833 \tabularnewline
128 & 13.6 & 15.039 & -1.43902 \tabularnewline
129 & 11.7 & 12.3912 & -0.691153 \tabularnewline
130 & 13.35 & 13.961 & -0.611046 \tabularnewline
131 & 17.6 & 18.8321 & -1.23209 \tabularnewline
132 & 14.05 & 13.8058 & 0.24421 \tabularnewline
133 & 16.1 & 17.5503 & -1.45027 \tabularnewline
134 & 13.35 & 15.5306 & -2.18057 \tabularnewline
135 & 11.85 & 15.1885 & -3.33852 \tabularnewline
136 & 11.95 & 11.8165 & 0.133493 \tabularnewline
137 & 13.2 & 16.392 & -3.19199 \tabularnewline
138 & 7.7 & 9.48971 & -1.78971 \tabularnewline
139 & 14.6 & 13.6329 & 0.967146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271049&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]13.0092[/C][C]-0.10923[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.394[/C][C]1.80602[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.7003[/C][C]1.09972[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6562[/C][C]-4.25617[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.382[/C][C]-4.68196[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9343[/C][C]0.665719[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.9116[/C][C]3.88839[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.3774[/C][C]-0.0774385[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4169[/C][C]-1.31689[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.8342[/C][C]-2.63417[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.267[/C][C]0.132977[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.9428[/C][C]-4.54284[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1679[/C][C]0.432071[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3407[/C][C]-1.3407[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.75929[/C][C]-2.45929[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.2657[/C][C]-1.36566[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9007[/C][C]-1.60075[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.69503[/C][C]0.304965[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.2832[/C][C]-3.88319[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.6055[/C][C]1.19454[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.1951[/C][C]0.604868[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.0185[/C][C]1.78152[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.5297[/C][C]1.17034[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.9516[/C][C]-1.05162[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.1453[/C][C]-1.24528[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5054[/C][C]0.994633[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]10.9723[/C][C]-2.67229[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.0166[/C][C]0.683442[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3566[/C][C]-1.3566[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.9455[/C][C]-4.24546[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.2269[/C][C]-0.426894[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.8271[/C][C]-0.527095[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.71971[/C][C]0.680291[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.3004[/C][C]-3.0004[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.9617[/C][C]0.838292[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.333[/C][C]-5.43302[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.9295[/C][C]-0.529547[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.7533[/C][C]2.24669[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.0489[/C][C]-0.248937[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7767[/C][C]0.52332[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.6374[/C][C]0.162611[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.48303[/C][C]3.21697[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.6238[/C][C]0.276196[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.6742[/C][C]1.62576[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.5516[/C][C]-0.451619[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.4731[/C][C]2.82694[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6889[/C][C]-2.38892[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.4723[/C][C]2.02772[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.2132[/C][C]-2.61325[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.5105[/C][C]3.38949[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.3865[/C][C]-1.18648[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.4327[/C][C]-1.33267[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.4147[/C][C]0.585292[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.472[/C][C]3.02801[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0259[/C][C]-0.725932[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.4212[/C][C]0.978756[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.2364[/C][C]0.763582[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]11.0101[/C][C]2.18986[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.4872[/C][C]-3.78716[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.19173[/C][C]-2.84173[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.2726[/C][C]2.42742[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.1163[/C][C]1.98372[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.1245[/C][C]1.72547[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.5701[/C][C]-0.470067[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.7494[/C][C]2.35062[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.6636[/C][C]-2.56359[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.918[/C][C]2.43195[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.8931[/C][C]0.506933[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.61985[/C][C]7.08015[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.2447[/C][C]-2.64466[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.7785[/C][C]-1.17853[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.6662[/C][C]0.933812[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.112[/C][C]0.987995[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.3737[/C][C]1.12634[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.4161[/C][C]-0.266104[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.579[/C][C]2.17097[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.1235[/C][C]2.67647[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.317[/C][C]0.133008[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.3336[/C][C]2.31641[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.9403[/C][C]3.40974[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.43631[/C][C]0.163689[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.8694[/C][C]1.53064[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.4052[/C][C]1.69484[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.6503[/C][C]2.09966[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.3275[/C][C]-0.0774851[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.2763[/C][C]1.37367[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.6487[/C][C]-0.298663[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.3935[/C][C]-0.743482[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]13.0183[/C][C]0.581746[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.8509[/C][C]0.499134[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.1481[/C][C]-2.39815[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.6165[/C][C]1.63353[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.3054[/C][C]-5.40542[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.9424[/C][C]2.05763[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.2123[/C][C]2.03774[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.7644[/C][C]-0.914401[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.7973[/C][C]2.15271[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.615[/C][C]1.98504[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.6669[/C][C]-0.566888[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.0856[/C][C]-0.68565[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]15.9914[/C][C]-0.591412[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.2652[/C][C]-0.915216[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0763[/C][C]2.02373[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.79957[/C][C]0.800434[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]17.0363[/C][C]2.06367[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.4138[/C][C]-1.66381[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]16.9008[/C][C]2.34922[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.0308[/C][C]-2.43077[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.506[/C][C]-2.75603[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.11388[/C][C]1.73612[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.7798[/C][C]-0.529842[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.3287[/C][C]-1.42873[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.402[/C][C]-1.05202[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.8899[/C][C]-1.48989[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]15.94[/C][C]2.21004[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.2127[/C][C]2.53727[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.7267[/C][C]-0.37671[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.3361[/C][C]0.263873[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.6938[/C][C]2.60623[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.5342[/C][C]0.565762[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.4016[/C][C]2.99844[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8681[/C][C]2.18194[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.5361[/C][C]4.01391[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.1238[/C][C]0.976186[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.7337[/C][C]-2.88366[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.8447[/C][C]-1.34466[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.42833[/C][C]-2.92833[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]15.039[/C][C]-1.43902[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.3912[/C][C]-0.691153[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.961[/C][C]-0.611046[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]18.8321[/C][C]-1.23209[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.8058[/C][C]0.24421[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.5503[/C][C]-1.45027[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.5306[/C][C]-2.18057[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1885[/C][C]-3.33852[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.8165[/C][C]0.133493[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.392[/C][C]-3.19199[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.48971[/C][C]-1.78971[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.6329[/C][C]0.967146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271049&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271049&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
112.913.0092-0.10923
212.210.3941.80602
312.811.70031.09972
47.411.6562-4.25617
56.711.382-4.68196
612.611.93430.665719
714.810.91163.88839
813.313.3774-0.0774385
911.112.4169-1.31689
108.210.8342-2.63417
1111.411.2670.132977
126.410.9428-4.54284
1310.610.16790.432071
141213.3407-1.3407
156.38.75929-2.45929
1611.913.2657-1.36566
179.310.9007-1.60075
18109.695030.304965
196.410.2832-3.88319
2013.812.60551.19454
2110.810.19510.604868
2213.812.01851.78152
2311.710.52971.17034
2410.911.9516-1.05162
259.911.1453-1.24528
2611.510.50540.994633
278.310.9723-2.67229
2811.711.01660.683442
29910.3566-1.3566
309.713.9455-4.24546
3110.811.2269-0.426894
3210.310.8271-0.527095
3310.49.719710.680291
349.312.3004-3.0004
3511.810.96170.838292
365.911.333-5.43302
3711.411.9295-0.529547
381310.75332.24669
3910.811.0489-0.248937
4011.310.77670.52332
4111.811.63740.162611
4212.79.483033.21697
4310.910.62380.276196
4413.311.67421.62576
4510.110.5516-0.451619
4614.311.47312.82694
479.311.6889-2.38892
4812.510.47232.02772
497.610.2132-2.61325
5015.912.51053.38949
519.210.3865-1.18648
5211.112.4327-1.33267
531312.41470.585292
5414.511.4723.02801
5512.313.0259-0.725932
5611.410.42120.978756
571312.23640.763582
5813.211.01012.18986
597.711.4872-3.78716
604.357.19173-2.84173
6112.710.27262.42742
6218.116.11631.98372
6317.8516.12451.72547
6417.117.5701-0.470067
6519.116.74942.35062
6616.118.6636-2.56359
6713.3510.9182.43195
6818.417.89310.506933
6914.77.619857.08015
7010.613.2447-2.64466
7112.613.7785-1.17853
7213.612.66620.933812
7314.113.1120.987995
7414.513.37371.12634
7516.1516.4161-0.266104
7614.7512.5792.17097
7714.812.12352.67647
7812.4512.3170.133008
7912.6510.33362.31641
8017.3513.94033.40974
818.68.436310.163689
8218.416.86941.53064
8316.114.40521.69484
8417.7515.65032.09966
8515.2515.3275-0.0774851
8617.6516.27631.37367
8716.3516.6487-0.298663
8817.6518.3935-0.743482
8913.613.01830.581746
9014.3513.85090.499134
9114.7517.1481-2.39815
9218.2516.61651.63353
939.915.3054-5.40542
941613.94242.05763
9518.2516.21232.03774
9616.8517.7644-0.914401
9718.9516.79732.15271
9815.613.6151.98504
9917.117.6669-0.566888
10015.416.0856-0.68565
10115.415.9914-0.591412
10213.3514.2652-0.915216
10319.117.07632.02373
1047.66.799570.800434
10519.117.03632.06367
10614.7516.4138-1.66381
10719.2516.90082.34922
10813.616.0308-2.43077
10912.7515.506-2.75603
1109.858.113881.73612
11115.2515.7798-0.529842
11211.913.3287-1.42873
11316.3517.402-1.05202
11412.413.8899-1.48989
11518.1515.942.21004
11617.7515.21272.53727
11712.3512.7267-0.37671
11815.615.33610.263873
11919.316.69382.60623
12017.116.53420.565762
12118.415.40162.99844
12219.0516.86812.18194
12318.5514.53614.01391
12419.118.12380.976186
12512.8515.7337-2.88366
1269.510.8447-1.34466
1274.57.42833-2.92833
12813.615.039-1.43902
12911.712.3912-0.691153
13013.3513.961-0.611046
13117.618.8321-1.23209
13214.0513.80580.24421
13316.117.5503-1.45027
13413.3515.5306-2.18057
13511.8515.1885-3.33852
13611.9511.81650.133493
13713.216.392-3.19199
1387.79.48971-1.78971
13914.613.63290.967146






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.5594690.8810630.440531
160.4320260.8640510.567974
170.2914120.5828240.708588
180.1972130.3944270.802787
190.139530.2790590.86047
200.09349380.1869880.906506
210.05314610.1062920.946854
220.04671860.09343720.953281
230.06741150.1348230.932589
240.04549070.09098150.954509
250.05206080.1041220.947939
260.06502870.1300570.934971
270.1031980.2063970.896802
280.1869540.3739090.813046
290.2260540.4521080.773946
300.3901530.7803060.609847
310.3767130.7534260.623287
320.3100920.6201850.689908
330.2673590.5347180.732641
340.2803540.5607070.719646
350.2281890.4563790.771811
360.4446420.8892840.555358
370.3841550.7683090.615845
380.3517950.703590.648205
390.3248420.6496840.675158
400.2767990.5535970.723201
410.2668550.533710.733145
420.2879480.5758950.712052
430.2500220.5000440.749978
440.4261980.8523950.573802
450.3719770.7439550.628023
460.4698340.9396670.530166
470.4824030.9648070.517597
480.4876410.9752820.512359
490.5505790.8988420.449421
500.6425420.7149160.357458
510.6257570.7484850.374243
520.5994580.8010850.400542
530.5541750.8916490.445825
540.5590680.8818640.440932
550.5227880.9544240.477212
560.4788340.9576670.521166
570.4409530.8819060.559047
580.4248140.8496270.575186
590.4946280.9892550.505372
600.5801140.8397720.419886
610.6120220.7759560.387978
620.6568350.6863310.343165
630.6219130.7561750.378087
640.5881420.8237160.411858
650.5783370.8433270.421663
660.6192850.761430.380715
670.6545570.6908870.345443
680.611720.7765610.38828
690.9390060.1219880.0609941
700.9445750.110850.055425
710.93580.1284010.0642005
720.9403270.1193450.0596727
730.9281330.1437340.0718672
740.9144550.1710910.0855454
750.912430.1751390.0875697
760.909020.1819590.0909796
770.9198910.1602170.0801087
780.8980960.2038090.101904
790.896980.2060410.10302
800.9319010.1361970.0680985
810.9129360.1741270.0870636
820.899110.2017810.10089
830.8852520.2294970.114748
840.8717020.2565960.128298
850.8465570.3068860.153443
860.8326160.3347690.167384
870.8014350.3971290.198565
880.7694420.4611160.230558
890.7281370.5437250.271863
900.6798720.6402560.320128
910.6722670.6554650.327733
920.6460660.7078680.353934
930.8864140.2271720.113586
940.8660920.2678170.133908
950.8548170.2903660.145183
960.8260630.3478740.173937
970.7986990.4026020.201301
980.8021950.3956090.197805
990.79120.41760.2088
1000.755880.488240.24412
1010.7057530.5884930.294247
1020.6769650.646070.323035
1030.6978270.6043470.302173
1040.7325830.5348330.267417
1050.6853940.6292130.314606
1060.6425360.7149270.357464
1070.597630.8047390.40237
1080.5798390.8403220.420161
1090.5527390.8945210.447261
1100.5198280.9603430.480172
1110.4883880.9767760.511612
1120.4249140.8498280.575086
1130.368450.73690.63155
1140.3009810.6019630.699019
1150.2926360.5852710.707364
1160.2819160.5638320.718084
1170.214190.4283790.78581
1180.596470.8070590.40353
1190.6448750.7102490.355125
1200.6462050.707590.353795
1210.6096020.7807960.390398
1220.7325150.5349710.267485
1230.8161960.3676080.183804
1240.6910480.6179030.308952

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.559469 & 0.881063 & 0.440531 \tabularnewline
16 & 0.432026 & 0.864051 & 0.567974 \tabularnewline
17 & 0.291412 & 0.582824 & 0.708588 \tabularnewline
18 & 0.197213 & 0.394427 & 0.802787 \tabularnewline
19 & 0.13953 & 0.279059 & 0.86047 \tabularnewline
20 & 0.0934938 & 0.186988 & 0.906506 \tabularnewline
21 & 0.0531461 & 0.106292 & 0.946854 \tabularnewline
22 & 0.0467186 & 0.0934372 & 0.953281 \tabularnewline
23 & 0.0674115 & 0.134823 & 0.932589 \tabularnewline
24 & 0.0454907 & 0.0909815 & 0.954509 \tabularnewline
25 & 0.0520608 & 0.104122 & 0.947939 \tabularnewline
26 & 0.0650287 & 0.130057 & 0.934971 \tabularnewline
27 & 0.103198 & 0.206397 & 0.896802 \tabularnewline
28 & 0.186954 & 0.373909 & 0.813046 \tabularnewline
29 & 0.226054 & 0.452108 & 0.773946 \tabularnewline
30 & 0.390153 & 0.780306 & 0.609847 \tabularnewline
31 & 0.376713 & 0.753426 & 0.623287 \tabularnewline
32 & 0.310092 & 0.620185 & 0.689908 \tabularnewline
33 & 0.267359 & 0.534718 & 0.732641 \tabularnewline
34 & 0.280354 & 0.560707 & 0.719646 \tabularnewline
35 & 0.228189 & 0.456379 & 0.771811 \tabularnewline
36 & 0.444642 & 0.889284 & 0.555358 \tabularnewline
37 & 0.384155 & 0.768309 & 0.615845 \tabularnewline
38 & 0.351795 & 0.70359 & 0.648205 \tabularnewline
39 & 0.324842 & 0.649684 & 0.675158 \tabularnewline
40 & 0.276799 & 0.553597 & 0.723201 \tabularnewline
41 & 0.266855 & 0.53371 & 0.733145 \tabularnewline
42 & 0.287948 & 0.575895 & 0.712052 \tabularnewline
43 & 0.250022 & 0.500044 & 0.749978 \tabularnewline
44 & 0.426198 & 0.852395 & 0.573802 \tabularnewline
45 & 0.371977 & 0.743955 & 0.628023 \tabularnewline
46 & 0.469834 & 0.939667 & 0.530166 \tabularnewline
47 & 0.482403 & 0.964807 & 0.517597 \tabularnewline
48 & 0.487641 & 0.975282 & 0.512359 \tabularnewline
49 & 0.550579 & 0.898842 & 0.449421 \tabularnewline
50 & 0.642542 & 0.714916 & 0.357458 \tabularnewline
51 & 0.625757 & 0.748485 & 0.374243 \tabularnewline
52 & 0.599458 & 0.801085 & 0.400542 \tabularnewline
53 & 0.554175 & 0.891649 & 0.445825 \tabularnewline
54 & 0.559068 & 0.881864 & 0.440932 \tabularnewline
55 & 0.522788 & 0.954424 & 0.477212 \tabularnewline
56 & 0.478834 & 0.957667 & 0.521166 \tabularnewline
57 & 0.440953 & 0.881906 & 0.559047 \tabularnewline
58 & 0.424814 & 0.849627 & 0.575186 \tabularnewline
59 & 0.494628 & 0.989255 & 0.505372 \tabularnewline
60 & 0.580114 & 0.839772 & 0.419886 \tabularnewline
61 & 0.612022 & 0.775956 & 0.387978 \tabularnewline
62 & 0.656835 & 0.686331 & 0.343165 \tabularnewline
63 & 0.621913 & 0.756175 & 0.378087 \tabularnewline
64 & 0.588142 & 0.823716 & 0.411858 \tabularnewline
65 & 0.578337 & 0.843327 & 0.421663 \tabularnewline
66 & 0.619285 & 0.76143 & 0.380715 \tabularnewline
67 & 0.654557 & 0.690887 & 0.345443 \tabularnewline
68 & 0.61172 & 0.776561 & 0.38828 \tabularnewline
69 & 0.939006 & 0.121988 & 0.0609941 \tabularnewline
70 & 0.944575 & 0.11085 & 0.055425 \tabularnewline
71 & 0.9358 & 0.128401 & 0.0642005 \tabularnewline
72 & 0.940327 & 0.119345 & 0.0596727 \tabularnewline
73 & 0.928133 & 0.143734 & 0.0718672 \tabularnewline
74 & 0.914455 & 0.171091 & 0.0855454 \tabularnewline
75 & 0.91243 & 0.175139 & 0.0875697 \tabularnewline
76 & 0.90902 & 0.181959 & 0.0909796 \tabularnewline
77 & 0.919891 & 0.160217 & 0.0801087 \tabularnewline
78 & 0.898096 & 0.203809 & 0.101904 \tabularnewline
79 & 0.89698 & 0.206041 & 0.10302 \tabularnewline
80 & 0.931901 & 0.136197 & 0.0680985 \tabularnewline
81 & 0.912936 & 0.174127 & 0.0870636 \tabularnewline
82 & 0.89911 & 0.201781 & 0.10089 \tabularnewline
83 & 0.885252 & 0.229497 & 0.114748 \tabularnewline
84 & 0.871702 & 0.256596 & 0.128298 \tabularnewline
85 & 0.846557 & 0.306886 & 0.153443 \tabularnewline
86 & 0.832616 & 0.334769 & 0.167384 \tabularnewline
87 & 0.801435 & 0.397129 & 0.198565 \tabularnewline
88 & 0.769442 & 0.461116 & 0.230558 \tabularnewline
89 & 0.728137 & 0.543725 & 0.271863 \tabularnewline
90 & 0.679872 & 0.640256 & 0.320128 \tabularnewline
91 & 0.672267 & 0.655465 & 0.327733 \tabularnewline
92 & 0.646066 & 0.707868 & 0.353934 \tabularnewline
93 & 0.886414 & 0.227172 & 0.113586 \tabularnewline
94 & 0.866092 & 0.267817 & 0.133908 \tabularnewline
95 & 0.854817 & 0.290366 & 0.145183 \tabularnewline
96 & 0.826063 & 0.347874 & 0.173937 \tabularnewline
97 & 0.798699 & 0.402602 & 0.201301 \tabularnewline
98 & 0.802195 & 0.395609 & 0.197805 \tabularnewline
99 & 0.7912 & 0.4176 & 0.2088 \tabularnewline
100 & 0.75588 & 0.48824 & 0.24412 \tabularnewline
101 & 0.705753 & 0.588493 & 0.294247 \tabularnewline
102 & 0.676965 & 0.64607 & 0.323035 \tabularnewline
103 & 0.697827 & 0.604347 & 0.302173 \tabularnewline
104 & 0.732583 & 0.534833 & 0.267417 \tabularnewline
105 & 0.685394 & 0.629213 & 0.314606 \tabularnewline
106 & 0.642536 & 0.714927 & 0.357464 \tabularnewline
107 & 0.59763 & 0.804739 & 0.40237 \tabularnewline
108 & 0.579839 & 0.840322 & 0.420161 \tabularnewline
109 & 0.552739 & 0.894521 & 0.447261 \tabularnewline
110 & 0.519828 & 0.960343 & 0.480172 \tabularnewline
111 & 0.488388 & 0.976776 & 0.511612 \tabularnewline
112 & 0.424914 & 0.849828 & 0.575086 \tabularnewline
113 & 0.36845 & 0.7369 & 0.63155 \tabularnewline
114 & 0.300981 & 0.601963 & 0.699019 \tabularnewline
115 & 0.292636 & 0.585271 & 0.707364 \tabularnewline
116 & 0.281916 & 0.563832 & 0.718084 \tabularnewline
117 & 0.21419 & 0.428379 & 0.78581 \tabularnewline
118 & 0.59647 & 0.807059 & 0.40353 \tabularnewline
119 & 0.644875 & 0.710249 & 0.355125 \tabularnewline
120 & 0.646205 & 0.70759 & 0.353795 \tabularnewline
121 & 0.609602 & 0.780796 & 0.390398 \tabularnewline
122 & 0.732515 & 0.534971 & 0.267485 \tabularnewline
123 & 0.816196 & 0.367608 & 0.183804 \tabularnewline
124 & 0.691048 & 0.617903 & 0.308952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271049&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]0.559469[/C][C]0.881063[/C][C]0.440531[/C][/ROW]
[ROW][C]16[/C][C]0.432026[/C][C]0.864051[/C][C]0.567974[/C][/ROW]
[ROW][C]17[/C][C]0.291412[/C][C]0.582824[/C][C]0.708588[/C][/ROW]
[ROW][C]18[/C][C]0.197213[/C][C]0.394427[/C][C]0.802787[/C][/ROW]
[ROW][C]19[/C][C]0.13953[/C][C]0.279059[/C][C]0.86047[/C][/ROW]
[ROW][C]20[/C][C]0.0934938[/C][C]0.186988[/C][C]0.906506[/C][/ROW]
[ROW][C]21[/C][C]0.0531461[/C][C]0.106292[/C][C]0.946854[/C][/ROW]
[ROW][C]22[/C][C]0.0467186[/C][C]0.0934372[/C][C]0.953281[/C][/ROW]
[ROW][C]23[/C][C]0.0674115[/C][C]0.134823[/C][C]0.932589[/C][/ROW]
[ROW][C]24[/C][C]0.0454907[/C][C]0.0909815[/C][C]0.954509[/C][/ROW]
[ROW][C]25[/C][C]0.0520608[/C][C]0.104122[/C][C]0.947939[/C][/ROW]
[ROW][C]26[/C][C]0.0650287[/C][C]0.130057[/C][C]0.934971[/C][/ROW]
[ROW][C]27[/C][C]0.103198[/C][C]0.206397[/C][C]0.896802[/C][/ROW]
[ROW][C]28[/C][C]0.186954[/C][C]0.373909[/C][C]0.813046[/C][/ROW]
[ROW][C]29[/C][C]0.226054[/C][C]0.452108[/C][C]0.773946[/C][/ROW]
[ROW][C]30[/C][C]0.390153[/C][C]0.780306[/C][C]0.609847[/C][/ROW]
[ROW][C]31[/C][C]0.376713[/C][C]0.753426[/C][C]0.623287[/C][/ROW]
[ROW][C]32[/C][C]0.310092[/C][C]0.620185[/C][C]0.689908[/C][/ROW]
[ROW][C]33[/C][C]0.267359[/C][C]0.534718[/C][C]0.732641[/C][/ROW]
[ROW][C]34[/C][C]0.280354[/C][C]0.560707[/C][C]0.719646[/C][/ROW]
[ROW][C]35[/C][C]0.228189[/C][C]0.456379[/C][C]0.771811[/C][/ROW]
[ROW][C]36[/C][C]0.444642[/C][C]0.889284[/C][C]0.555358[/C][/ROW]
[ROW][C]37[/C][C]0.384155[/C][C]0.768309[/C][C]0.615845[/C][/ROW]
[ROW][C]38[/C][C]0.351795[/C][C]0.70359[/C][C]0.648205[/C][/ROW]
[ROW][C]39[/C][C]0.324842[/C][C]0.649684[/C][C]0.675158[/C][/ROW]
[ROW][C]40[/C][C]0.276799[/C][C]0.553597[/C][C]0.723201[/C][/ROW]
[ROW][C]41[/C][C]0.266855[/C][C]0.53371[/C][C]0.733145[/C][/ROW]
[ROW][C]42[/C][C]0.287948[/C][C]0.575895[/C][C]0.712052[/C][/ROW]
[ROW][C]43[/C][C]0.250022[/C][C]0.500044[/C][C]0.749978[/C][/ROW]
[ROW][C]44[/C][C]0.426198[/C][C]0.852395[/C][C]0.573802[/C][/ROW]
[ROW][C]45[/C][C]0.371977[/C][C]0.743955[/C][C]0.628023[/C][/ROW]
[ROW][C]46[/C][C]0.469834[/C][C]0.939667[/C][C]0.530166[/C][/ROW]
[ROW][C]47[/C][C]0.482403[/C][C]0.964807[/C][C]0.517597[/C][/ROW]
[ROW][C]48[/C][C]0.487641[/C][C]0.975282[/C][C]0.512359[/C][/ROW]
[ROW][C]49[/C][C]0.550579[/C][C]0.898842[/C][C]0.449421[/C][/ROW]
[ROW][C]50[/C][C]0.642542[/C][C]0.714916[/C][C]0.357458[/C][/ROW]
[ROW][C]51[/C][C]0.625757[/C][C]0.748485[/C][C]0.374243[/C][/ROW]
[ROW][C]52[/C][C]0.599458[/C][C]0.801085[/C][C]0.400542[/C][/ROW]
[ROW][C]53[/C][C]0.554175[/C][C]0.891649[/C][C]0.445825[/C][/ROW]
[ROW][C]54[/C][C]0.559068[/C][C]0.881864[/C][C]0.440932[/C][/ROW]
[ROW][C]55[/C][C]0.522788[/C][C]0.954424[/C][C]0.477212[/C][/ROW]
[ROW][C]56[/C][C]0.478834[/C][C]0.957667[/C][C]0.521166[/C][/ROW]
[ROW][C]57[/C][C]0.440953[/C][C]0.881906[/C][C]0.559047[/C][/ROW]
[ROW][C]58[/C][C]0.424814[/C][C]0.849627[/C][C]0.575186[/C][/ROW]
[ROW][C]59[/C][C]0.494628[/C][C]0.989255[/C][C]0.505372[/C][/ROW]
[ROW][C]60[/C][C]0.580114[/C][C]0.839772[/C][C]0.419886[/C][/ROW]
[ROW][C]61[/C][C]0.612022[/C][C]0.775956[/C][C]0.387978[/C][/ROW]
[ROW][C]62[/C][C]0.656835[/C][C]0.686331[/C][C]0.343165[/C][/ROW]
[ROW][C]63[/C][C]0.621913[/C][C]0.756175[/C][C]0.378087[/C][/ROW]
[ROW][C]64[/C][C]0.588142[/C][C]0.823716[/C][C]0.411858[/C][/ROW]
[ROW][C]65[/C][C]0.578337[/C][C]0.843327[/C][C]0.421663[/C][/ROW]
[ROW][C]66[/C][C]0.619285[/C][C]0.76143[/C][C]0.380715[/C][/ROW]
[ROW][C]67[/C][C]0.654557[/C][C]0.690887[/C][C]0.345443[/C][/ROW]
[ROW][C]68[/C][C]0.61172[/C][C]0.776561[/C][C]0.38828[/C][/ROW]
[ROW][C]69[/C][C]0.939006[/C][C]0.121988[/C][C]0.0609941[/C][/ROW]
[ROW][C]70[/C][C]0.944575[/C][C]0.11085[/C][C]0.055425[/C][/ROW]
[ROW][C]71[/C][C]0.9358[/C][C]0.128401[/C][C]0.0642005[/C][/ROW]
[ROW][C]72[/C][C]0.940327[/C][C]0.119345[/C][C]0.0596727[/C][/ROW]
[ROW][C]73[/C][C]0.928133[/C][C]0.143734[/C][C]0.0718672[/C][/ROW]
[ROW][C]74[/C][C]0.914455[/C][C]0.171091[/C][C]0.0855454[/C][/ROW]
[ROW][C]75[/C][C]0.91243[/C][C]0.175139[/C][C]0.0875697[/C][/ROW]
[ROW][C]76[/C][C]0.90902[/C][C]0.181959[/C][C]0.0909796[/C][/ROW]
[ROW][C]77[/C][C]0.919891[/C][C]0.160217[/C][C]0.0801087[/C][/ROW]
[ROW][C]78[/C][C]0.898096[/C][C]0.203809[/C][C]0.101904[/C][/ROW]
[ROW][C]79[/C][C]0.89698[/C][C]0.206041[/C][C]0.10302[/C][/ROW]
[ROW][C]80[/C][C]0.931901[/C][C]0.136197[/C][C]0.0680985[/C][/ROW]
[ROW][C]81[/C][C]0.912936[/C][C]0.174127[/C][C]0.0870636[/C][/ROW]
[ROW][C]82[/C][C]0.89911[/C][C]0.201781[/C][C]0.10089[/C][/ROW]
[ROW][C]83[/C][C]0.885252[/C][C]0.229497[/C][C]0.114748[/C][/ROW]
[ROW][C]84[/C][C]0.871702[/C][C]0.256596[/C][C]0.128298[/C][/ROW]
[ROW][C]85[/C][C]0.846557[/C][C]0.306886[/C][C]0.153443[/C][/ROW]
[ROW][C]86[/C][C]0.832616[/C][C]0.334769[/C][C]0.167384[/C][/ROW]
[ROW][C]87[/C][C]0.801435[/C][C]0.397129[/C][C]0.198565[/C][/ROW]
[ROW][C]88[/C][C]0.769442[/C][C]0.461116[/C][C]0.230558[/C][/ROW]
[ROW][C]89[/C][C]0.728137[/C][C]0.543725[/C][C]0.271863[/C][/ROW]
[ROW][C]90[/C][C]0.679872[/C][C]0.640256[/C][C]0.320128[/C][/ROW]
[ROW][C]91[/C][C]0.672267[/C][C]0.655465[/C][C]0.327733[/C][/ROW]
[ROW][C]92[/C][C]0.646066[/C][C]0.707868[/C][C]0.353934[/C][/ROW]
[ROW][C]93[/C][C]0.886414[/C][C]0.227172[/C][C]0.113586[/C][/ROW]
[ROW][C]94[/C][C]0.866092[/C][C]0.267817[/C][C]0.133908[/C][/ROW]
[ROW][C]95[/C][C]0.854817[/C][C]0.290366[/C][C]0.145183[/C][/ROW]
[ROW][C]96[/C][C]0.826063[/C][C]0.347874[/C][C]0.173937[/C][/ROW]
[ROW][C]97[/C][C]0.798699[/C][C]0.402602[/C][C]0.201301[/C][/ROW]
[ROW][C]98[/C][C]0.802195[/C][C]0.395609[/C][C]0.197805[/C][/ROW]
[ROW][C]99[/C][C]0.7912[/C][C]0.4176[/C][C]0.2088[/C][/ROW]
[ROW][C]100[/C][C]0.75588[/C][C]0.48824[/C][C]0.24412[/C][/ROW]
[ROW][C]101[/C][C]0.705753[/C][C]0.588493[/C][C]0.294247[/C][/ROW]
[ROW][C]102[/C][C]0.676965[/C][C]0.64607[/C][C]0.323035[/C][/ROW]
[ROW][C]103[/C][C]0.697827[/C][C]0.604347[/C][C]0.302173[/C][/ROW]
[ROW][C]104[/C][C]0.732583[/C][C]0.534833[/C][C]0.267417[/C][/ROW]
[ROW][C]105[/C][C]0.685394[/C][C]0.629213[/C][C]0.314606[/C][/ROW]
[ROW][C]106[/C][C]0.642536[/C][C]0.714927[/C][C]0.357464[/C][/ROW]
[ROW][C]107[/C][C]0.59763[/C][C]0.804739[/C][C]0.40237[/C][/ROW]
[ROW][C]108[/C][C]0.579839[/C][C]0.840322[/C][C]0.420161[/C][/ROW]
[ROW][C]109[/C][C]0.552739[/C][C]0.894521[/C][C]0.447261[/C][/ROW]
[ROW][C]110[/C][C]0.519828[/C][C]0.960343[/C][C]0.480172[/C][/ROW]
[ROW][C]111[/C][C]0.488388[/C][C]0.976776[/C][C]0.511612[/C][/ROW]
[ROW][C]112[/C][C]0.424914[/C][C]0.849828[/C][C]0.575086[/C][/ROW]
[ROW][C]113[/C][C]0.36845[/C][C]0.7369[/C][C]0.63155[/C][/ROW]
[ROW][C]114[/C][C]0.300981[/C][C]0.601963[/C][C]0.699019[/C][/ROW]
[ROW][C]115[/C][C]0.292636[/C][C]0.585271[/C][C]0.707364[/C][/ROW]
[ROW][C]116[/C][C]0.281916[/C][C]0.563832[/C][C]0.718084[/C][/ROW]
[ROW][C]117[/C][C]0.21419[/C][C]0.428379[/C][C]0.78581[/C][/ROW]
[ROW][C]118[/C][C]0.59647[/C][C]0.807059[/C][C]0.40353[/C][/ROW]
[ROW][C]119[/C][C]0.644875[/C][C]0.710249[/C][C]0.355125[/C][/ROW]
[ROW][C]120[/C][C]0.646205[/C][C]0.70759[/C][C]0.353795[/C][/ROW]
[ROW][C]121[/C][C]0.609602[/C][C]0.780796[/C][C]0.390398[/C][/ROW]
[ROW][C]122[/C][C]0.732515[/C][C]0.534971[/C][C]0.267485[/C][/ROW]
[ROW][C]123[/C][C]0.816196[/C][C]0.367608[/C][C]0.183804[/C][/ROW]
[ROW][C]124[/C][C]0.691048[/C][C]0.617903[/C][C]0.308952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271049&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271049&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
150.5594690.8810630.440531
160.4320260.8640510.567974
170.2914120.5828240.708588
180.1972130.3944270.802787
190.139530.2790590.86047
200.09349380.1869880.906506
210.05314610.1062920.946854
220.04671860.09343720.953281
230.06741150.1348230.932589
240.04549070.09098150.954509
250.05206080.1041220.947939
260.06502870.1300570.934971
270.1031980.2063970.896802
280.1869540.3739090.813046
290.2260540.4521080.773946
300.3901530.7803060.609847
310.3767130.7534260.623287
320.3100920.6201850.689908
330.2673590.5347180.732641
340.2803540.5607070.719646
350.2281890.4563790.771811
360.4446420.8892840.555358
370.3841550.7683090.615845
380.3517950.703590.648205
390.3248420.6496840.675158
400.2767990.5535970.723201
410.2668550.533710.733145
420.2879480.5758950.712052
430.2500220.5000440.749978
440.4261980.8523950.573802
450.3719770.7439550.628023
460.4698340.9396670.530166
470.4824030.9648070.517597
480.4876410.9752820.512359
490.5505790.8988420.449421
500.6425420.7149160.357458
510.6257570.7484850.374243
520.5994580.8010850.400542
530.5541750.8916490.445825
540.5590680.8818640.440932
550.5227880.9544240.477212
560.4788340.9576670.521166
570.4409530.8819060.559047
580.4248140.8496270.575186
590.4946280.9892550.505372
600.5801140.8397720.419886
610.6120220.7759560.387978
620.6568350.6863310.343165
630.6219130.7561750.378087
640.5881420.8237160.411858
650.5783370.8433270.421663
660.6192850.761430.380715
670.6545570.6908870.345443
680.611720.7765610.38828
690.9390060.1219880.0609941
700.9445750.110850.055425
710.93580.1284010.0642005
720.9403270.1193450.0596727
730.9281330.1437340.0718672
740.9144550.1710910.0855454
750.912430.1751390.0875697
760.909020.1819590.0909796
770.9198910.1602170.0801087
780.8980960.2038090.101904
790.896980.2060410.10302
800.9319010.1361970.0680985
810.9129360.1741270.0870636
820.899110.2017810.10089
830.8852520.2294970.114748
840.8717020.2565960.128298
850.8465570.3068860.153443
860.8326160.3347690.167384
870.8014350.3971290.198565
880.7694420.4611160.230558
890.7281370.5437250.271863
900.6798720.6402560.320128
910.6722670.6554650.327733
920.6460660.7078680.353934
930.8864140.2271720.113586
940.8660920.2678170.133908
950.8548170.2903660.145183
960.8260630.3478740.173937
970.7986990.4026020.201301
980.8021950.3956090.197805
990.79120.41760.2088
1000.755880.488240.24412
1010.7057530.5884930.294247
1020.6769650.646070.323035
1030.6978270.6043470.302173
1040.7325830.5348330.267417
1050.6853940.6292130.314606
1060.6425360.7149270.357464
1070.597630.8047390.40237
1080.5798390.8403220.420161
1090.5527390.8945210.447261
1100.5198280.9603430.480172
1110.4883880.9767760.511612
1120.4249140.8498280.575086
1130.368450.73690.63155
1140.3009810.6019630.699019
1150.2926360.5852710.707364
1160.2819160.5638320.718084
1170.214190.4283790.78581
1180.596470.8070590.40353
1190.6448750.7102490.355125
1200.6462050.707590.353795
1210.6096020.7807960.390398
1220.7325150.5349710.267485
1230.8161960.3676080.183804
1240.6910480.6179030.308952






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 level20.0181818OK

\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 & 0.0181818 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271049&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]0.0181818[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271049&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271049&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 level20.0181818OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}