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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 14:29:45 +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/t1418913046c76mupexqxh6omf.htm/, Retrieved Fri, 17 May 2024 19:57:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270998, Retrieved Fri, 17 May 2024 19:57:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

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 14:29:45] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 11 8 7 18 12 20 21 12 149 18 68 1.8
12.2 19 18 20 23 20 19 22 8 139 31 39 2.1
12.8 16 12 9 22 14 18 21 11 148 39 32 2.2
7.4 24 24 19 22 25 24 21 13 158 46 62 2.3
6.7 15 16 12 19 15 20 21 11 128 31 33 2.1
12.6 17 19 16 25 20 20 21 10 224 67 52 2.7
14.8 19 16 17 28 21 24 21 7 159 35 62 2.1
13.3 19 15 9 16 15 21 23 10 105 52 77 2.4
11.1 28 28 28 28 28 28 22 15 159 77 76 2.9
8.2 26 21 20 21 11 10 25 12 167 37 41 2.2
11.4 15 18 16 22 22 22 21 12 165 32 48 2.1
6.4 26 22 22 24 22 19 23 10 159 36 63 2.2
10.6 16 19 17 24 27 27 22 10 119 38 30 2.2
12 24 22 12 26 24 23 21 14 176 69 78 2.7
6.3 25 25 18 28 23 24 21 6 54 21 19 1.9
11.9 15 16 12 20 21 25 21 14 163 54 66 2.5
9.3 21 19 16 26 20 24 21 11 124 36 35 2.2
10 27 26 21 28 25 28 24 12 121 23 45 1.9
6.4 26 24 15 27 16 28 23 15 153 34 21 2.1
13.8 26 20 17 23 24 22 21 13 148 112 25 3.5
10.8 22 19 17 24 21 26 24 11 221 35 44 2.1
13.8 21 19 17 24 22 26 23 12 188 47 69 2.3
11.7 22 23 18 22 25 21 21 7 149 47 54 2.3
10.9 20 18 15 21 23 26 22 11 244 37 74 2.2
9.9 22 21 21 21 22 24 21 12 150 20 61 1.9
11.5 21 20 12 26 25 25 22 13 153 22 41 1.9
8.3 8 15 6 23 23 24 22 9 94 23 46 1.9
11.7 22 19 13 21 19 20 21 11 156 32 39 2.1
9 20 19 19 27 21 24 21 12 132 30 34 2
9.7 24 7 12 25 19 25 25 15 161 92 51 3.2
10.8 17 20 14 23 25 23 22 12 105 43 42 2.3
10.3 20 20 13 25 16 21 22 6 97 55 31 2.5
10.4 23 19 12 23 24 23 20 5 151 16 39 1.8
9.3 22 20 19 22 18 18 21 11 166 71 49 2.8
11.8 19 18 10 24 28 24 21 6 157 43 53 2.3
5.9 15 14 10 19 15 18 22 12 111 29 31 2
11.4 20 17 11 21 17 21 21 10 145 56 39 2.5
13 22 17 11 27 18 23 24 6 162 46 54 2.3
10.8 17 8 10 25 26 25 22 12 163 19 49 1.8
11.3 24 22 22 23 22 22 21 6 187 59 46 2.6
11.8 17 20 12 17 19 23 22 12 109 30 55 2
12.7 25 22 20 25 26 25 22 8 105 7 50 1.6
10.9 18 14 11 24 12 24 23 12 148 19 30 1.8
13.3 24 21 17 20 20 23 23 14 125 48 45 2.4
10.1 23 20 14 19 24 27 21 12 116 23 35 1.9
14.3 20 18 16 21 22 23 21 14 138 33 41 2.1
9.3 22 24 15 18 23 23 22 11 164 34 73 2.1
12.5 22 19 15 27 19 24 21 10 162 48 17 2.4
7.6 15 16 10 25 24 26 21 7 99 18 40 1.8
15.9 17 16 10 20 21 20 21 12 202 43 64 2.3
9.2 19 16 18 21 16 23 21 7 186 33 37 2.1
11.1 22 22 22 27 23 23 21 12 183 71 65 2.8
13 21 21 16 24 20 17 22 10 214 26 100 2
14.5 21 15 10 27 19 20 22 10 188 67 28 2.7
12.3 20 15 16 23 18 18 21 12 177 80 56 2.9
11.4 21 14 16 24 21 19 23 12 126 29 29 2
12.6 18 14 5 27 25 26 21 5 139 32 50 2.1
NA 22 19 18 24 15 14 21 10 78 47 3 2.3
13 16 16 10 25 17 25 21 10 162 43 59 2.3
13.2 24 26 16 24 24 18 20 11 159 29 61 2
7.7 19 18 16 23 22 26 21 12 110 32 51 2.1
4.35 20 17 15 22 14 15 22 9 48 23 12 1
12.7 6 6 4 24 5 27 22 11 50 16 45 1
18.1 15 22 9 19 25 23 22 12 150 33 37 4
17.85 18 20 18 25 21 23 20 12 154 32 37 4
17.1 21 17 12 24 9 22 22 12 194 52 68 4
19.1 23 20 16 28 15 20 21 12 158 75 72 4
16.1 20 23 17 23 23 21 21 10 159 72 143 4
13.35 20 18 14 19 21 25 21 15 67 15 9 2
18.4 18 13 13 19 9 19 21 10 147 29 55 4
14.7 25 22 20 27 24 25 21 15 39 13 17 1
10.6 16 20 16 24 16 24 21 10 100 40 37 3
12.6 20 20 15 26 20 22 21 15 111 19 27 3
16.2 14 13 10 21 15 28 22 9 138 24 37 4
13.6 22 16 16 25 18 22 24 15 101 121 58 3
14.1 20 16 15 19 21 23 22 13 101 36 21 3
14.5 17 15 16 20 21 19 20 12 114 23 19 3
16.15 22 19 19 26 21 21 21 12 165 85 78 4
14.75 22 19 9 27 20 25 24 8 114 41 35 3
14.8 20 24 19 23 24 23 25 9 111 46 48 3
12.45 17 9 7 18 15 28 22 15 75 18 27 2
12.65 22 22 23 23 24 14 21 12 82 35 43 2
17.35 17 15 14 21 18 23 21 12 121 17 30 3
8.6 22 22 10 23 24 24 22 15 32 4 25 1
18.4 21 22 16 22 24 25 23 11 150 28 69 4
16.1 25 24 12 21 15 15 24 12 117 44 72 3
17.75 19 21 7 24 20 26 22 14 165 38 13 4
15.25 24 25 20 26 26 21 25 12 154 57 61 4
17.65 17 26 9 24 26 26 22 12 126 23 43 4
16.35 22 21 12 22 23 23 21 12 149 36 51 4
17.65 17 14 10 20 13 15 21 11 145 22 67 4
13.6 26 28 19 20 16 16 21 12 120 40 36 3
14.35 20 21 11 18 22 20 22 12 109 31 44 3
14.75 19 16 15 18 21 20 22 12 132 11 45 4
18.25 21 16 14 25 11 21 21 12 172 38 34 4
9.9 24 25 11 28 23 28 22 8 169 24 36 4
16 21 21 14 23 18 19 23 8 114 37 72 3
18.25 19 22 15 20 19 21 21 12 156 37 39 4
16.85 13 9 7 22 15 22 21 12 172 22 43 4
18.95 27 24 22 23 21 17 21 11 167 43 80 4
15.6 22 22 11 20 25 26 21 12 113 31 40 3
17.1 21 10 12 24 12 22 22 10 173 31 61 4
16.1 22 22 17 18 24 17 22 11 2 -4 23 1
15.4 22 21 13 23 19 16 21 11 165 21 29 4
15.4 21 20 15 21 21 18 21 11 165 21 29 4
13.35 19 17 11 19 19 17 25 13 118 32 54 3
19.1 11 7 7 19 18 25 21 7 158 26 43 4
7.6 19 14 13 25 23 21 25 8 49 32 20 1
19.1 21 23 7 18 23 27 22 11 155 33 61 4
14.75 19 18 11 22 27 23 21 8 151 30 57 4
19.25 8 17 22 5 6 8 23 14 220 67 54 4
13.6 23 20 15 24 22 22 20 9 141 22 36 4
12.75 17 19 15 28 23 28 22 13 122 33 16 4
9.85 25 19 11 27 20 24 25 13 44 24 40 1
15.25 24 23 10 23 23 25 20 11 152 28 27 4
11.9 22 20 18 24 27 23 21 9 107 41 61 3
16.35 23 19 14 25 24 26 21 12 154 31 69 4
12.4 17 16 16 19 12 22 23 12 103 33 34 3
18.15 22 21 16 24 24 22 22 13 175 21 34 4
17.75 21 20 17 28 24 26 21 11 143 52 34 4
12.35 19 20 14 19 19 21 21 11 110 29 13 3
15.6 19 19 10 23 28 21 21 9 131 11 12 4
19.3 16 19 16 23 23 24 21 12 167 26 51 4
17.1 23 20 16 26 19 18 21 15 137 7 19 4
18.4 23 22 17 25 23 26 21 14 121 13 81 3
19.05 20 19 12 24 20 23 21 12 149 20 42 4
18.55 24 23 17 23 18 25 22 9 168 52 22 4
19.1 25 16 11 22 20 20 21 9 140 28 85 4
12.85 20 18 12 26 21 26 22 13 168 39 25 4
9.5 23 23 8 23 25 19 22 15 94 9 22 2
4.5 21 20 17 22 18 21 22 11 51 19 19 1
13.6 23 23 17 22 28 24 22 10 145 60 45 4
11.7 11 13 7 17 9 6 23 11 66 19 45 2
13.35 27 26 18 22 26 21 22 14 109 14 51 3
17.6 16 13 14 26 12 19 21 12 164 -2 73 4
14.05 18 10 13 24 12 24 21 13 119 51 24 3
16.1 23 21 19 27 20 21 20 11 126 2 61 4
13.35 24 24 15 22 25 21 20 11 132 24 23 4
11.85 20 21 15 23 24 26 21 13 142 40 14 4
11.95 20 23 8 22 23 24 21 12 83 20 54 2
13.2 14 16 11 20 22 23 21 9 166 20 36 4
7.7 23 26 17 27 28 26 24 13 93 25 26 2
14.6 16 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'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270998&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270998&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270998&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.7468 + 0.0140849AMS.I1[t] -0.0632515AMS.I2[t] -0.0355815AMS.I3[t] -0.0489016AMS.E1[t] -0.0227896AMS.E2[t] -0.0215454AMS.E3[t] -0.143336age[t] + 0.115144CONFSOFTTOT[t] -0.00695443LFM[t] -0.0230001PRH[t] + 0.0372279CH[t] + 2.76232PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.7468 +  0.0140849AMS.I1[t] -0.0632515AMS.I2[t] -0.0355815AMS.I3[t] -0.0489016AMS.E1[t] -0.0227896AMS.E2[t] -0.0215454AMS.E3[t] -0.143336age[t] +  0.115144CONFSOFTTOT[t] -0.00695443LFM[t] -0.0230001PRH[t] +  0.0372279CH[t] +  2.76232PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270998&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.7468 +  0.0140849AMS.I1[t] -0.0632515AMS.I2[t] -0.0355815AMS.I3[t] -0.0489016AMS.E1[t] -0.0227896AMS.E2[t] -0.0215454AMS.E3[t] -0.143336age[t] +  0.115144CONFSOFTTOT[t] -0.00695443LFM[t] -0.0230001PRH[t] +  0.0372279CH[t] +  2.76232PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270998&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270998&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] = + 10.7468 + 0.0140849AMS.I1[t] -0.0632515AMS.I2[t] -0.0355815AMS.I3[t] -0.0489016AMS.E1[t] -0.0227896AMS.E2[t] -0.0215454AMS.E3[t] -0.143336age[t] + 0.115144CONFSOFTTOT[t] -0.00695443LFM[t] -0.0230001PRH[t] + 0.0372279CH[t] + 2.76232PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.74684.502342.3870.01844150.00922076
AMS.I10.01408490.07576580.18590.8528150.426407
AMS.I2-0.06325150.0713529-0.88650.3770190.18851
AMS.I3-0.03558150.0604003-0.58910.5568280.278414
AMS.E1-0.04890160.0751482-0.65070.5163750.258188
AMS.E2-0.02278960.0573622-0.39730.6918090.345904
AMS.E3-0.02154540.0654115-0.32940.7424010.371201
age-0.1433360.179518-0.79840.4260780.213039
CONFSOFTTOT0.1151440.09020131.2770.2040630.102032
LFM-0.006954430.0062612-1.1110.2687560.134378
PRH-0.02300010.0113251-2.0310.0443210.0221605
CH0.03722790.01083693.4350.0007966810.000398341
PR2.762320.24362411.344.21477e-212.10738e-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) & 10.7468 & 4.50234 & 2.387 & 0.0184415 & 0.00922076 \tabularnewline
AMS.I1 & 0.0140849 & 0.0757658 & 0.1859 & 0.852815 & 0.426407 \tabularnewline
AMS.I2 & -0.0632515 & 0.0713529 & -0.8865 & 0.377019 & 0.18851 \tabularnewline
AMS.I3 & -0.0355815 & 0.0604003 & -0.5891 & 0.556828 & 0.278414 \tabularnewline
AMS.E1 & -0.0489016 & 0.0751482 & -0.6507 & 0.516375 & 0.258188 \tabularnewline
AMS.E2 & -0.0227896 & 0.0573622 & -0.3973 & 0.691809 & 0.345904 \tabularnewline
AMS.E3 & -0.0215454 & 0.0654115 & -0.3294 & 0.742401 & 0.371201 \tabularnewline
age & -0.143336 & 0.179518 & -0.7984 & 0.426078 & 0.213039 \tabularnewline
CONFSOFTTOT & 0.115144 & 0.0902013 & 1.277 & 0.204063 & 0.102032 \tabularnewline
LFM & -0.00695443 & 0.0062612 & -1.111 & 0.268756 & 0.134378 \tabularnewline
PRH & -0.0230001 & 0.0113251 & -2.031 & 0.044321 & 0.0221605 \tabularnewline
CH & 0.0372279 & 0.0108369 & 3.435 & 0.000796681 & 0.000398341 \tabularnewline
PR & 2.76232 & 0.243624 & 11.34 & 4.21477e-21 & 2.10738e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270998&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]10.7468[/C][C]4.50234[/C][C]2.387[/C][C]0.0184415[/C][C]0.00922076[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0140849[/C][C]0.0757658[/C][C]0.1859[/C][C]0.852815[/C][C]0.426407[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0632515[/C][C]0.0713529[/C][C]-0.8865[/C][C]0.377019[/C][C]0.18851[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0355815[/C][C]0.0604003[/C][C]-0.5891[/C][C]0.556828[/C][C]0.278414[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0489016[/C][C]0.0751482[/C][C]-0.6507[/C][C]0.516375[/C][C]0.258188[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0227896[/C][C]0.0573622[/C][C]-0.3973[/C][C]0.691809[/C][C]0.345904[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0215454[/C][C]0.0654115[/C][C]-0.3294[/C][C]0.742401[/C][C]0.371201[/C][/ROW]
[ROW][C]age[/C][C]-0.143336[/C][C]0.179518[/C][C]-0.7984[/C][C]0.426078[/C][C]0.213039[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.115144[/C][C]0.0902013[/C][C]1.277[/C][C]0.204063[/C][C]0.102032[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00695443[/C][C]0.0062612[/C][C]-1.111[/C][C]0.268756[/C][C]0.134378[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0230001[/C][C]0.0113251[/C][C]-2.031[/C][C]0.044321[/C][C]0.0221605[/C][/ROW]
[ROW][C]CH[/C][C]0.0372279[/C][C]0.0108369[/C][C]3.435[/C][C]0.000796681[/C][C]0.000398341[/C][/ROW]
[ROW][C]PR[/C][C]2.76232[/C][C]0.243624[/C][C]11.34[/C][C]4.21477e-21[/C][C]2.10738e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270998&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270998&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)10.74684.502342.3870.01844150.00922076
AMS.I10.01408490.07576580.18590.8528150.426407
AMS.I2-0.06325150.0713529-0.88650.3770190.18851
AMS.I3-0.03558150.0604003-0.58910.5568280.278414
AMS.E1-0.04890160.0751482-0.65070.5163750.258188
AMS.E2-0.02278960.0573622-0.39730.6918090.345904
AMS.E3-0.02154540.0654115-0.32940.7424010.371201
age-0.1433360.179518-0.79840.4260780.213039
CONFSOFTTOT0.1151440.09020131.2770.2040630.102032
LFM-0.006954430.0062612-1.1110.2687560.134378
PRH-0.02300010.0113251-2.0310.0443210.0221605
CH0.03722790.01083693.4350.0007966810.000398341
PR2.762320.24362411.344.21477e-212.10738e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.7731
R-squared0.597683
Adjusted R-squared0.560258
F-TEST (value)15.9702
F-TEST (DF numerator)12
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.31084
Sum Squared Residuals688.855

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.7731 \tabularnewline
R-squared & 0.597683 \tabularnewline
Adjusted R-squared & 0.560258 \tabularnewline
F-TEST (value) & 15.9702 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.31084 \tabularnewline
Sum Squared Residuals & 688.855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270998&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.7731[/C][/ROW]
[ROW][C]R-squared[/C][C]0.597683[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.560258[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.9702[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.31084[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]688.855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270998&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270998&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 R0.7731
R-squared0.597683
Adjusted R-squared0.560258
F-TEST (value)15.9702
F-TEST (DF numerator)12
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.31084
Sum Squared Residuals688.855






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9872-0.0871536
212.210.51521.6848
312.811.70881.09115
47.411.7195-4.31955
56.711.4999-4.79992
612.611.54261.0574
714.811.02683.77324
813.313.5933-0.293272
911.112.3003-1.20026
108.210.9694-2.76939
1111.411.2750.124983
126.411.1977-4.79771
1310.610.28520.314801
141213.1054-1.10538
156.39.24466-2.94466
1611.913.113-1.21303
179.310.9734-1.67341
18109.687910.312093
196.49.94004-3.54004
2013.812.57781.22225
2110.89.961070.838928
2213.811.61932.18067
2311.710.90570.794254
2410.911.6422-0.742231
259.911.3236-1.42358
2611.510.51890.981058
278.311.1923-2.89229
2811.711.18990.51014
29910.3887-1.38871
309.713.667-3.96704
3110.811.4591-0.659056
3210.310.919-0.619011
3310.49.989690.410313
349.312.2695-2.96945
3511.811.11760.682414
365.911.526-5.62602
3711.411.8977-0.497669
381310.79372.2063
3910.811.2248-0.424799
4011.310.72820.571778
4111.811.8868-0.0868167
4212.79.799692.90031
4310.910.46690.433056
4413.311.86911.43091
4510.110.8371-0.737096
4614.311.50732.79274
479.311.8141-2.51413
4812.510.22392.27609
497.610.4153-2.8153
5015.912.44473.45534
519.210.3965-1.19652
5211.112.1626-1.06259
531312.30850.691497
5414.511.2043.29603
5512.312.9839-0.683854
5611.410.67210.727856
5712.611.01191.58812
58NANA0.968755
591310.98522.01478
6013.217.1905-3.99045
617.711.2806-3.58056
624.351.925192.42481
6312.710.70471.99525
6418.116.28291.81713
6517.8517.70150.148491
6617.114.3722.72805
6719.121.6199-2.51994
6816.113.96132.13869
6913.3512.67080.679233
7018.411.83196.56811
7114.717.3728-2.67277
7210.611.8288-1.22876
7312.612.9917-0.391731
7416.215.21640.983565
7513.612.96130.638702
7614.113.38970.710342
7714.514.5486-0.048579
7816.1513.95512.19492
7914.7512.3142.43599
8014.814.8605-0.0605234
8112.4510.79531.65474
8212.659.44963.2004
8317.3517.8228-0.47277
848.67.020991.57901
8518.416.84531.55471
8616.113.56782.53219
8717.7517.7120.0380066
8815.2513.76831.48175
8917.6517.9612-0.311203
9016.3517.1331-0.783085
9117.6517.27280.377197
9213.613.42860.171423
9314.3517.0608-2.71081
9414.7512.72322.02675
9518.2523.3954-5.14539
969.98.119811.78019
971613.91262.08741
9818.2518.9395-0.689453
9916.8514.89031.95971
10018.9517.16471.78534
10115.615.9641-0.364062
10217.110.07667.02337
10316.116.8183-0.718268
10415.416.1054-0.705405
10515.416.5234-1.12343
10613.3511.33112.01887
10719.118.7350.364978
1087.65.449022.15098
10919.120.99-1.89001
11014.7512.25172.49833
11119.2521.8454-2.59545
11213.615.985-2.385
11312.7511.41941.33058
1149.8510.4399-0.5899
11515.2517.1136-1.86362
11611.912.7968-0.896828
11716.3517.8407-1.49069
11812.410.17292.22705
11918.1515.47732.67267
12017.7518.4321-0.682093
12112.3512.34970.000284086
12215.612.89522.70476
12319.318.70410.595913
12417.114.23132.86868
12518.416.11312.28695
12619.0514.83844.2116
12718.5517.80520.744791
12819.121.6609-2.56087
12912.8514.6342-1.78416
1309.513.0253-3.52531
1314.56.01421-1.51421
13213.614.8384-1.23842
13311.712.6354-0.935418
13413.3514.4915-1.1415
13517.617.15770.442308
13614.0515.5856-1.53559
13716.118.5204-2.42042
13813.3516.5844-3.23435
13911.8511.9414-0.091355
14011.9515.1169-3.16688
14113.215.1303-1.93029
1427.76.826810.87319
14314.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9872 & -0.0871536 \tabularnewline
2 & 12.2 & 10.5152 & 1.6848 \tabularnewline
3 & 12.8 & 11.7088 & 1.09115 \tabularnewline
4 & 7.4 & 11.7195 & -4.31955 \tabularnewline
5 & 6.7 & 11.4999 & -4.79992 \tabularnewline
6 & 12.6 & 11.5426 & 1.0574 \tabularnewline
7 & 14.8 & 11.0268 & 3.77324 \tabularnewline
8 & 13.3 & 13.5933 & -0.293272 \tabularnewline
9 & 11.1 & 12.3003 & -1.20026 \tabularnewline
10 & 8.2 & 10.9694 & -2.76939 \tabularnewline
11 & 11.4 & 11.275 & 0.124983 \tabularnewline
12 & 6.4 & 11.1977 & -4.79771 \tabularnewline
13 & 10.6 & 10.2852 & 0.314801 \tabularnewline
14 & 12 & 13.1054 & -1.10538 \tabularnewline
15 & 6.3 & 9.24466 & -2.94466 \tabularnewline
16 & 11.9 & 13.113 & -1.21303 \tabularnewline
17 & 9.3 & 10.9734 & -1.67341 \tabularnewline
18 & 10 & 9.68791 & 0.312093 \tabularnewline
19 & 6.4 & 9.94004 & -3.54004 \tabularnewline
20 & 13.8 & 12.5778 & 1.22225 \tabularnewline
21 & 10.8 & 9.96107 & 0.838928 \tabularnewline
22 & 13.8 & 11.6193 & 2.18067 \tabularnewline
23 & 11.7 & 10.9057 & 0.794254 \tabularnewline
24 & 10.9 & 11.6422 & -0.742231 \tabularnewline
25 & 9.9 & 11.3236 & -1.42358 \tabularnewline
26 & 11.5 & 10.5189 & 0.981058 \tabularnewline
27 & 8.3 & 11.1923 & -2.89229 \tabularnewline
28 & 11.7 & 11.1899 & 0.51014 \tabularnewline
29 & 9 & 10.3887 & -1.38871 \tabularnewline
30 & 9.7 & 13.667 & -3.96704 \tabularnewline
31 & 10.8 & 11.4591 & -0.659056 \tabularnewline
32 & 10.3 & 10.919 & -0.619011 \tabularnewline
33 & 10.4 & 9.98969 & 0.410313 \tabularnewline
34 & 9.3 & 12.2695 & -2.96945 \tabularnewline
35 & 11.8 & 11.1176 & 0.682414 \tabularnewline
36 & 5.9 & 11.526 & -5.62602 \tabularnewline
37 & 11.4 & 11.8977 & -0.497669 \tabularnewline
38 & 13 & 10.7937 & 2.2063 \tabularnewline
39 & 10.8 & 11.2248 & -0.424799 \tabularnewline
40 & 11.3 & 10.7282 & 0.571778 \tabularnewline
41 & 11.8 & 11.8868 & -0.0868167 \tabularnewline
42 & 12.7 & 9.79969 & 2.90031 \tabularnewline
43 & 10.9 & 10.4669 & 0.433056 \tabularnewline
44 & 13.3 & 11.8691 & 1.43091 \tabularnewline
45 & 10.1 & 10.8371 & -0.737096 \tabularnewline
46 & 14.3 & 11.5073 & 2.79274 \tabularnewline
47 & 9.3 & 11.8141 & -2.51413 \tabularnewline
48 & 12.5 & 10.2239 & 2.27609 \tabularnewline
49 & 7.6 & 10.4153 & -2.8153 \tabularnewline
50 & 15.9 & 12.4447 & 3.45534 \tabularnewline
51 & 9.2 & 10.3965 & -1.19652 \tabularnewline
52 & 11.1 & 12.1626 & -1.06259 \tabularnewline
53 & 13 & 12.3085 & 0.691497 \tabularnewline
54 & 14.5 & 11.204 & 3.29603 \tabularnewline
55 & 12.3 & 12.9839 & -0.683854 \tabularnewline
56 & 11.4 & 10.6721 & 0.727856 \tabularnewline
57 & 12.6 & 11.0119 & 1.58812 \tabularnewline
58 & NA & NA & 0.968755 \tabularnewline
59 & 13 & 10.9852 & 2.01478 \tabularnewline
60 & 13.2 & 17.1905 & -3.99045 \tabularnewline
61 & 7.7 & 11.2806 & -3.58056 \tabularnewline
62 & 4.35 & 1.92519 & 2.42481 \tabularnewline
63 & 12.7 & 10.7047 & 1.99525 \tabularnewline
64 & 18.1 & 16.2829 & 1.81713 \tabularnewline
65 & 17.85 & 17.7015 & 0.148491 \tabularnewline
66 & 17.1 & 14.372 & 2.72805 \tabularnewline
67 & 19.1 & 21.6199 & -2.51994 \tabularnewline
68 & 16.1 & 13.9613 & 2.13869 \tabularnewline
69 & 13.35 & 12.6708 & 0.679233 \tabularnewline
70 & 18.4 & 11.8319 & 6.56811 \tabularnewline
71 & 14.7 & 17.3728 & -2.67277 \tabularnewline
72 & 10.6 & 11.8288 & -1.22876 \tabularnewline
73 & 12.6 & 12.9917 & -0.391731 \tabularnewline
74 & 16.2 & 15.2164 & 0.983565 \tabularnewline
75 & 13.6 & 12.9613 & 0.638702 \tabularnewline
76 & 14.1 & 13.3897 & 0.710342 \tabularnewline
77 & 14.5 & 14.5486 & -0.048579 \tabularnewline
78 & 16.15 & 13.9551 & 2.19492 \tabularnewline
79 & 14.75 & 12.314 & 2.43599 \tabularnewline
80 & 14.8 & 14.8605 & -0.0605234 \tabularnewline
81 & 12.45 & 10.7953 & 1.65474 \tabularnewline
82 & 12.65 & 9.4496 & 3.2004 \tabularnewline
83 & 17.35 & 17.8228 & -0.47277 \tabularnewline
84 & 8.6 & 7.02099 & 1.57901 \tabularnewline
85 & 18.4 & 16.8453 & 1.55471 \tabularnewline
86 & 16.1 & 13.5678 & 2.53219 \tabularnewline
87 & 17.75 & 17.712 & 0.0380066 \tabularnewline
88 & 15.25 & 13.7683 & 1.48175 \tabularnewline
89 & 17.65 & 17.9612 & -0.311203 \tabularnewline
90 & 16.35 & 17.1331 & -0.783085 \tabularnewline
91 & 17.65 & 17.2728 & 0.377197 \tabularnewline
92 & 13.6 & 13.4286 & 0.171423 \tabularnewline
93 & 14.35 & 17.0608 & -2.71081 \tabularnewline
94 & 14.75 & 12.7232 & 2.02675 \tabularnewline
95 & 18.25 & 23.3954 & -5.14539 \tabularnewline
96 & 9.9 & 8.11981 & 1.78019 \tabularnewline
97 & 16 & 13.9126 & 2.08741 \tabularnewline
98 & 18.25 & 18.9395 & -0.689453 \tabularnewline
99 & 16.85 & 14.8903 & 1.95971 \tabularnewline
100 & 18.95 & 17.1647 & 1.78534 \tabularnewline
101 & 15.6 & 15.9641 & -0.364062 \tabularnewline
102 & 17.1 & 10.0766 & 7.02337 \tabularnewline
103 & 16.1 & 16.8183 & -0.718268 \tabularnewline
104 & 15.4 & 16.1054 & -0.705405 \tabularnewline
105 & 15.4 & 16.5234 & -1.12343 \tabularnewline
106 & 13.35 & 11.3311 & 2.01887 \tabularnewline
107 & 19.1 & 18.735 & 0.364978 \tabularnewline
108 & 7.6 & 5.44902 & 2.15098 \tabularnewline
109 & 19.1 & 20.99 & -1.89001 \tabularnewline
110 & 14.75 & 12.2517 & 2.49833 \tabularnewline
111 & 19.25 & 21.8454 & -2.59545 \tabularnewline
112 & 13.6 & 15.985 & -2.385 \tabularnewline
113 & 12.75 & 11.4194 & 1.33058 \tabularnewline
114 & 9.85 & 10.4399 & -0.5899 \tabularnewline
115 & 15.25 & 17.1136 & -1.86362 \tabularnewline
116 & 11.9 & 12.7968 & -0.896828 \tabularnewline
117 & 16.35 & 17.8407 & -1.49069 \tabularnewline
118 & 12.4 & 10.1729 & 2.22705 \tabularnewline
119 & 18.15 & 15.4773 & 2.67267 \tabularnewline
120 & 17.75 & 18.4321 & -0.682093 \tabularnewline
121 & 12.35 & 12.3497 & 0.000284086 \tabularnewline
122 & 15.6 & 12.8952 & 2.70476 \tabularnewline
123 & 19.3 & 18.7041 & 0.595913 \tabularnewline
124 & 17.1 & 14.2313 & 2.86868 \tabularnewline
125 & 18.4 & 16.1131 & 2.28695 \tabularnewline
126 & 19.05 & 14.8384 & 4.2116 \tabularnewline
127 & 18.55 & 17.8052 & 0.744791 \tabularnewline
128 & 19.1 & 21.6609 & -2.56087 \tabularnewline
129 & 12.85 & 14.6342 & -1.78416 \tabularnewline
130 & 9.5 & 13.0253 & -3.52531 \tabularnewline
131 & 4.5 & 6.01421 & -1.51421 \tabularnewline
132 & 13.6 & 14.8384 & -1.23842 \tabularnewline
133 & 11.7 & 12.6354 & -0.935418 \tabularnewline
134 & 13.35 & 14.4915 & -1.1415 \tabularnewline
135 & 17.6 & 17.1577 & 0.442308 \tabularnewline
136 & 14.05 & 15.5856 & -1.53559 \tabularnewline
137 & 16.1 & 18.5204 & -2.42042 \tabularnewline
138 & 13.35 & 16.5844 & -3.23435 \tabularnewline
139 & 11.85 & 11.9414 & -0.091355 \tabularnewline
140 & 11.95 & 15.1169 & -3.16688 \tabularnewline
141 & 13.2 & 15.1303 & -1.93029 \tabularnewline
142 & 7.7 & 6.82681 & 0.87319 \tabularnewline
143 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270998&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12.9[/C][C]12.9872[/C][C]-0.0871536[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5152[/C][C]1.6848[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.7088[/C][C]1.09115[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.7195[/C][C]-4.31955[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.4999[/C][C]-4.79992[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.5426[/C][C]1.0574[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.0268[/C][C]3.77324[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.5933[/C][C]-0.293272[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.3003[/C][C]-1.20026[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.9694[/C][C]-2.76939[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.275[/C][C]0.124983[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.1977[/C][C]-4.79771[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.2852[/C][C]0.314801[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.1054[/C][C]-1.10538[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.24466[/C][C]-2.94466[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.113[/C][C]-1.21303[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9734[/C][C]-1.67341[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.68791[/C][C]0.312093[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]9.94004[/C][C]-3.54004[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.5778[/C][C]1.22225[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]9.96107[/C][C]0.838928[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.6193[/C][C]2.18067[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.9057[/C][C]0.794254[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.6422[/C][C]-0.742231[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.3236[/C][C]-1.42358[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5189[/C][C]0.981058[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.1923[/C][C]-2.89229[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1899[/C][C]0.51014[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3887[/C][C]-1.38871[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.667[/C][C]-3.96704[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.4591[/C][C]-0.659056[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.919[/C][C]-0.619011[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.98969[/C][C]0.410313[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2695[/C][C]-2.96945[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.1176[/C][C]0.682414[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.526[/C][C]-5.62602[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8977[/C][C]-0.497669[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.7937[/C][C]2.2063[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.2248[/C][C]-0.424799[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7282[/C][C]0.571778[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.8868[/C][C]-0.0868167[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.79969[/C][C]2.90031[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.4669[/C][C]0.433056[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.8691[/C][C]1.43091[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.8371[/C][C]-0.737096[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.5073[/C][C]2.79274[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.8141[/C][C]-2.51413[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.2239[/C][C]2.27609[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.4153[/C][C]-2.8153[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4447[/C][C]3.45534[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.3965[/C][C]-1.19652[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.1626[/C][C]-1.06259[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.3085[/C][C]0.691497[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.204[/C][C]3.29603[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]12.9839[/C][C]-0.683854[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.6721[/C][C]0.727856[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]11.0119[/C][C]1.58812[/C][/ROW]
[ROW][C]58[/C][C]NA[/C][C]NA[/C][C]0.968755[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.9852[/C][C]2.01478[/C][/ROW]
[ROW][C]60[/C][C]13.2[/C][C]17.1905[/C][C]-3.99045[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]11.2806[/C][C]-3.58056[/C][/ROW]
[ROW][C]62[/C][C]4.35[/C][C]1.92519[/C][C]2.42481[/C][/ROW]
[ROW][C]63[/C][C]12.7[/C][C]10.7047[/C][C]1.99525[/C][/ROW]
[ROW][C]64[/C][C]18.1[/C][C]16.2829[/C][C]1.81713[/C][/ROW]
[ROW][C]65[/C][C]17.85[/C][C]17.7015[/C][C]0.148491[/C][/ROW]
[ROW][C]66[/C][C]17.1[/C][C]14.372[/C][C]2.72805[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]21.6199[/C][C]-2.51994[/C][/ROW]
[ROW][C]68[/C][C]16.1[/C][C]13.9613[/C][C]2.13869[/C][/ROW]
[ROW][C]69[/C][C]13.35[/C][C]12.6708[/C][C]0.679233[/C][/ROW]
[ROW][C]70[/C][C]18.4[/C][C]11.8319[/C][C]6.56811[/C][/ROW]
[ROW][C]71[/C][C]14.7[/C][C]17.3728[/C][C]-2.67277[/C][/ROW]
[ROW][C]72[/C][C]10.6[/C][C]11.8288[/C][C]-1.22876[/C][/ROW]
[ROW][C]73[/C][C]12.6[/C][C]12.9917[/C][C]-0.391731[/C][/ROW]
[ROW][C]74[/C][C]16.2[/C][C]15.2164[/C][C]0.983565[/C][/ROW]
[ROW][C]75[/C][C]13.6[/C][C]12.9613[/C][C]0.638702[/C][/ROW]
[ROW][C]76[/C][C]14.1[/C][C]13.3897[/C][C]0.710342[/C][/ROW]
[ROW][C]77[/C][C]14.5[/C][C]14.5486[/C][C]-0.048579[/C][/ROW]
[ROW][C]78[/C][C]16.15[/C][C]13.9551[/C][C]2.19492[/C][/ROW]
[ROW][C]79[/C][C]14.75[/C][C]12.314[/C][C]2.43599[/C][/ROW]
[ROW][C]80[/C][C]14.8[/C][C]14.8605[/C][C]-0.0605234[/C][/ROW]
[ROW][C]81[/C][C]12.45[/C][C]10.7953[/C][C]1.65474[/C][/ROW]
[ROW][C]82[/C][C]12.65[/C][C]9.4496[/C][C]3.2004[/C][/ROW]
[ROW][C]83[/C][C]17.35[/C][C]17.8228[/C][C]-0.47277[/C][/ROW]
[ROW][C]84[/C][C]8.6[/C][C]7.02099[/C][C]1.57901[/C][/ROW]
[ROW][C]85[/C][C]18.4[/C][C]16.8453[/C][C]1.55471[/C][/ROW]
[ROW][C]86[/C][C]16.1[/C][C]13.5678[/C][C]2.53219[/C][/ROW]
[ROW][C]87[/C][C]17.75[/C][C]17.712[/C][C]0.0380066[/C][/ROW]
[ROW][C]88[/C][C]15.25[/C][C]13.7683[/C][C]1.48175[/C][/ROW]
[ROW][C]89[/C][C]17.65[/C][C]17.9612[/C][C]-0.311203[/C][/ROW]
[ROW][C]90[/C][C]16.35[/C][C]17.1331[/C][C]-0.783085[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]17.2728[/C][C]0.377197[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.4286[/C][C]0.171423[/C][/ROW]
[ROW][C]93[/C][C]14.35[/C][C]17.0608[/C][C]-2.71081[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]12.7232[/C][C]2.02675[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]23.3954[/C][C]-5.14539[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]8.11981[/C][C]1.78019[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.9126[/C][C]2.08741[/C][/ROW]
[ROW][C]98[/C][C]18.25[/C][C]18.9395[/C][C]-0.689453[/C][/ROW]
[ROW][C]99[/C][C]16.85[/C][C]14.8903[/C][C]1.95971[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]17.1647[/C][C]1.78534[/C][/ROW]
[ROW][C]101[/C][C]15.6[/C][C]15.9641[/C][C]-0.364062[/C][/ROW]
[ROW][C]102[/C][C]17.1[/C][C]10.0766[/C][C]7.02337[/C][/ROW]
[ROW][C]103[/C][C]16.1[/C][C]16.8183[/C][C]-0.718268[/C][/ROW]
[ROW][C]104[/C][C]15.4[/C][C]16.1054[/C][C]-0.705405[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]16.5234[/C][C]-1.12343[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]11.3311[/C][C]2.01887[/C][/ROW]
[ROW][C]107[/C][C]19.1[/C][C]18.735[/C][C]0.364978[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]5.44902[/C][C]2.15098[/C][/ROW]
[ROW][C]109[/C][C]19.1[/C][C]20.99[/C][C]-1.89001[/C][/ROW]
[ROW][C]110[/C][C]14.75[/C][C]12.2517[/C][C]2.49833[/C][/ROW]
[ROW][C]111[/C][C]19.25[/C][C]21.8454[/C][C]-2.59545[/C][/ROW]
[ROW][C]112[/C][C]13.6[/C][C]15.985[/C][C]-2.385[/C][/ROW]
[ROW][C]113[/C][C]12.75[/C][C]11.4194[/C][C]1.33058[/C][/ROW]
[ROW][C]114[/C][C]9.85[/C][C]10.4399[/C][C]-0.5899[/C][/ROW]
[ROW][C]115[/C][C]15.25[/C][C]17.1136[/C][C]-1.86362[/C][/ROW]
[ROW][C]116[/C][C]11.9[/C][C]12.7968[/C][C]-0.896828[/C][/ROW]
[ROW][C]117[/C][C]16.35[/C][C]17.8407[/C][C]-1.49069[/C][/ROW]
[ROW][C]118[/C][C]12.4[/C][C]10.1729[/C][C]2.22705[/C][/ROW]
[ROW][C]119[/C][C]18.15[/C][C]15.4773[/C][C]2.67267[/C][/ROW]
[ROW][C]120[/C][C]17.75[/C][C]18.4321[/C][C]-0.682093[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]12.3497[/C][C]0.000284086[/C][/ROW]
[ROW][C]122[/C][C]15.6[/C][C]12.8952[/C][C]2.70476[/C][/ROW]
[ROW][C]123[/C][C]19.3[/C][C]18.7041[/C][C]0.595913[/C][/ROW]
[ROW][C]124[/C][C]17.1[/C][C]14.2313[/C][C]2.86868[/C][/ROW]
[ROW][C]125[/C][C]18.4[/C][C]16.1131[/C][C]2.28695[/C][/ROW]
[ROW][C]126[/C][C]19.05[/C][C]14.8384[/C][C]4.2116[/C][/ROW]
[ROW][C]127[/C][C]18.55[/C][C]17.8052[/C][C]0.744791[/C][/ROW]
[ROW][C]128[/C][C]19.1[/C][C]21.6609[/C][C]-2.56087[/C][/ROW]
[ROW][C]129[/C][C]12.85[/C][C]14.6342[/C][C]-1.78416[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]13.0253[/C][C]-3.52531[/C][/ROW]
[ROW][C]131[/C][C]4.5[/C][C]6.01421[/C][C]-1.51421[/C][/ROW]
[ROW][C]132[/C][C]13.6[/C][C]14.8384[/C][C]-1.23842[/C][/ROW]
[ROW][C]133[/C][C]11.7[/C][C]12.6354[/C][C]-0.935418[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]14.4915[/C][C]-1.1415[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.1577[/C][C]0.442308[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]15.5856[/C][C]-1.53559[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]18.5204[/C][C]-2.42042[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]16.5844[/C][C]-3.23435[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]11.9414[/C][C]-0.091355[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]15.1169[/C][C]-3.16688[/C][/ROW]
[ROW][C]141[/C][C]13.2[/C][C]15.1303[/C][C]-1.93029[/C][/ROW]
[ROW][C]142[/C][C]7.7[/C][C]6.82681[/C][C]0.87319[/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=270998&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270998&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9872-0.0871536
212.210.51521.6848
312.811.70881.09115
47.411.7195-4.31955
56.711.4999-4.79992
612.611.54261.0574
714.811.02683.77324
813.313.5933-0.293272
911.112.3003-1.20026
108.210.9694-2.76939
1111.411.2750.124983
126.411.1977-4.79771
1310.610.28520.314801
141213.1054-1.10538
156.39.24466-2.94466
1611.913.113-1.21303
179.310.9734-1.67341
18109.687910.312093
196.49.94004-3.54004
2013.812.57781.22225
2110.89.961070.838928
2213.811.61932.18067
2311.710.90570.794254
2410.911.6422-0.742231
259.911.3236-1.42358
2611.510.51890.981058
278.311.1923-2.89229
2811.711.18990.51014
29910.3887-1.38871
309.713.667-3.96704
3110.811.4591-0.659056
3210.310.919-0.619011
3310.49.989690.410313
349.312.2695-2.96945
3511.811.11760.682414
365.911.526-5.62602
3711.411.8977-0.497669
381310.79372.2063
3910.811.2248-0.424799
4011.310.72820.571778
4111.811.8868-0.0868167
4212.79.799692.90031
4310.910.46690.433056
4413.311.86911.43091
4510.110.8371-0.737096
4614.311.50732.79274
479.311.8141-2.51413
4812.510.22392.27609
497.610.4153-2.8153
5015.912.44473.45534
519.210.3965-1.19652
5211.112.1626-1.06259
531312.30850.691497
5414.511.2043.29603
5512.312.9839-0.683854
5611.410.67210.727856
5712.611.01191.58812
58NANA0.968755
591310.98522.01478
6013.217.1905-3.99045
617.711.2806-3.58056
624.351.925192.42481
6312.710.70471.99525
6418.116.28291.81713
6517.8517.70150.148491
6617.114.3722.72805
6719.121.6199-2.51994
6816.113.96132.13869
6913.3512.67080.679233
7018.411.83196.56811
7114.717.3728-2.67277
7210.611.8288-1.22876
7312.612.9917-0.391731
7416.215.21640.983565
7513.612.96130.638702
7614.113.38970.710342
7714.514.5486-0.048579
7816.1513.95512.19492
7914.7512.3142.43599
8014.814.8605-0.0605234
8112.4510.79531.65474
8212.659.44963.2004
8317.3517.8228-0.47277
848.67.020991.57901
8518.416.84531.55471
8616.113.56782.53219
8717.7517.7120.0380066
8815.2513.76831.48175
8917.6517.9612-0.311203
9016.3517.1331-0.783085
9117.6517.27280.377197
9213.613.42860.171423
9314.3517.0608-2.71081
9414.7512.72322.02675
9518.2523.3954-5.14539
969.98.119811.78019
971613.91262.08741
9818.2518.9395-0.689453
9916.8514.89031.95971
10018.9517.16471.78534
10115.615.9641-0.364062
10217.110.07667.02337
10316.116.8183-0.718268
10415.416.1054-0.705405
10515.416.5234-1.12343
10613.3511.33112.01887
10719.118.7350.364978
1087.65.449022.15098
10919.120.99-1.89001
11014.7512.25172.49833
11119.2521.8454-2.59545
11213.615.985-2.385
11312.7511.41941.33058
1149.8510.4399-0.5899
11515.2517.1136-1.86362
11611.912.7968-0.896828
11716.3517.8407-1.49069
11812.410.17292.22705
11918.1515.47732.67267
12017.7518.4321-0.682093
12112.3512.34970.000284086
12215.612.89522.70476
12319.318.70410.595913
12417.114.23132.86868
12518.416.11312.28695
12619.0514.83844.2116
12718.5517.80520.744791
12819.121.6609-2.56087
12912.8514.6342-1.78416
1309.513.0253-3.52531
1314.56.01421-1.51421
13213.614.8384-1.23842
13311.712.6354-0.935418
13413.3514.4915-1.1415
13517.617.15770.442308
13614.0515.5856-1.53559
13716.118.5204-2.42042
13813.3516.5844-3.23435
13911.8511.9414-0.091355
14011.9515.1169-3.16688
14113.215.1303-1.93029
1427.76.826810.87319
14314.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.2722670.5445330.727733
170.2726820.5453640.727318
180.2945030.5890070.705497
190.1999750.3999510.800025
200.123660.2473190.87634
210.07147870.1429570.928521
220.03988120.07976240.960119
230.03547470.07094950.964525
240.04911340.09822670.950887
250.07223570.1444710.927764
260.07196950.1439390.92803
270.1164040.2328080.883596
280.1454040.2908080.854596
290.1749140.3498290.825086
300.4015240.8030490.598476
310.3683850.7367710.631615
320.3050910.6101820.694909
330.2467810.4935630.753219
340.2494640.4989290.750536
350.1984040.3968080.801596
360.3822390.7644780.617761
370.3250610.6501210.674939
380.2794180.5588360.720582
390.2501750.5003510.749825
400.2049480.4098960.795052
410.1873070.3746140.812693
420.2135080.4270160.786492
430.1760040.3520070.823996
440.2882840.5765670.711716
450.2447310.4894620.755269
460.3430530.6861070.656947
470.3567550.7135110.643245
480.3655830.7311670.634417
490.4179240.8358470.582076
500.513470.9730590.48653
510.5113250.977350.488675
520.4733460.9466920.526654
530.4273310.8546620.572669
540.4407180.8814370.559282
550.4002620.8005250.599738
560.3547590.7095180.645241
570.3252310.6504620.674769
580.2933580.5867150.706642
590.2785570.5571150.721443
600.3383360.6766720.661664
610.4268680.8537360.573132
620.4795120.9590230.520488
630.5136180.9727650.486382
640.4797740.9595480.520226
650.436140.8722810.56386
660.4441520.8883050.555848
670.4633340.9266680.536666
680.4914660.9829310.508534
690.4392930.8785860.560707
700.7848990.4302020.215101
710.799750.4004990.20025
720.7822770.4354450.217723
730.7456310.5087380.254369
740.7344340.5311330.265566
750.6954060.6091880.304594
760.6538330.6923330.346167
770.6074850.785030.392515
780.6039260.7921490.396074
790.6240130.7519750.375987
800.5807760.8384480.419224
810.5600720.8798560.439928
820.5920620.8158760.407938
830.5442570.9114860.455743
840.5016220.9967550.498378
850.4669660.9339310.533034
860.4781390.9562770.521861
870.4266310.8532630.573369
880.4165770.8331540.583423
890.3716880.7433760.628312
900.3313950.662790.668605
910.2892410.5784820.710759
920.2439160.4878320.756084
930.2928910.5857820.707109
940.2859220.5718440.714078
950.4579930.9159860.542007
960.4263070.8526130.573693
970.4106450.8212910.589355
980.36020.72040.6398
990.3320230.6640450.667977
1000.2953060.5906120.704694
1010.2527150.5054310.747285
1020.8270940.3458110.172906
1030.7944890.4110220.205511
1040.7498920.5002160.250108
1050.7278750.544250.272125
1060.7459030.5081950.254097
1070.7429440.5141120.257056
1080.7006780.5986440.299322
1090.6557010.6885970.344299
1100.6109980.7780050.389002
1110.597790.804420.40221
1120.5513160.8973680.448684
1130.5392270.9215470.460773
1140.4860040.9720080.513996
1150.420270.8405410.57973
1160.3724250.744850.627575
1170.2986670.5973330.701333
1180.2778820.5557640.722118
1190.2780390.5560780.721961
1200.2056660.4113310.794334
1210.5101630.9796740.489837
1220.6643160.6713670.335684
1230.6765490.6469010.323451
1240.6749410.6501180.325059
1250.7513130.4973750.248687
1260.729030.541940.27097
1270.5456640.9086720.454336

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.272267 & 0.544533 & 0.727733 \tabularnewline
17 & 0.272682 & 0.545364 & 0.727318 \tabularnewline
18 & 0.294503 & 0.589007 & 0.705497 \tabularnewline
19 & 0.199975 & 0.399951 & 0.800025 \tabularnewline
20 & 0.12366 & 0.247319 & 0.87634 \tabularnewline
21 & 0.0714787 & 0.142957 & 0.928521 \tabularnewline
22 & 0.0398812 & 0.0797624 & 0.960119 \tabularnewline
23 & 0.0354747 & 0.0709495 & 0.964525 \tabularnewline
24 & 0.0491134 & 0.0982267 & 0.950887 \tabularnewline
25 & 0.0722357 & 0.144471 & 0.927764 \tabularnewline
26 & 0.0719695 & 0.143939 & 0.92803 \tabularnewline
27 & 0.116404 & 0.232808 & 0.883596 \tabularnewline
28 & 0.145404 & 0.290808 & 0.854596 \tabularnewline
29 & 0.174914 & 0.349829 & 0.825086 \tabularnewline
30 & 0.401524 & 0.803049 & 0.598476 \tabularnewline
31 & 0.368385 & 0.736771 & 0.631615 \tabularnewline
32 & 0.305091 & 0.610182 & 0.694909 \tabularnewline
33 & 0.246781 & 0.493563 & 0.753219 \tabularnewline
34 & 0.249464 & 0.498929 & 0.750536 \tabularnewline
35 & 0.198404 & 0.396808 & 0.801596 \tabularnewline
36 & 0.382239 & 0.764478 & 0.617761 \tabularnewline
37 & 0.325061 & 0.650121 & 0.674939 \tabularnewline
38 & 0.279418 & 0.558836 & 0.720582 \tabularnewline
39 & 0.250175 & 0.500351 & 0.749825 \tabularnewline
40 & 0.204948 & 0.409896 & 0.795052 \tabularnewline
41 & 0.187307 & 0.374614 & 0.812693 \tabularnewline
42 & 0.213508 & 0.427016 & 0.786492 \tabularnewline
43 & 0.176004 & 0.352007 & 0.823996 \tabularnewline
44 & 0.288284 & 0.576567 & 0.711716 \tabularnewline
45 & 0.244731 & 0.489462 & 0.755269 \tabularnewline
46 & 0.343053 & 0.686107 & 0.656947 \tabularnewline
47 & 0.356755 & 0.713511 & 0.643245 \tabularnewline
48 & 0.365583 & 0.731167 & 0.634417 \tabularnewline
49 & 0.417924 & 0.835847 & 0.582076 \tabularnewline
50 & 0.51347 & 0.973059 & 0.48653 \tabularnewline
51 & 0.511325 & 0.97735 & 0.488675 \tabularnewline
52 & 0.473346 & 0.946692 & 0.526654 \tabularnewline
53 & 0.427331 & 0.854662 & 0.572669 \tabularnewline
54 & 0.440718 & 0.881437 & 0.559282 \tabularnewline
55 & 0.400262 & 0.800525 & 0.599738 \tabularnewline
56 & 0.354759 & 0.709518 & 0.645241 \tabularnewline
57 & 0.325231 & 0.650462 & 0.674769 \tabularnewline
58 & 0.293358 & 0.586715 & 0.706642 \tabularnewline
59 & 0.278557 & 0.557115 & 0.721443 \tabularnewline
60 & 0.338336 & 0.676672 & 0.661664 \tabularnewline
61 & 0.426868 & 0.853736 & 0.573132 \tabularnewline
62 & 0.479512 & 0.959023 & 0.520488 \tabularnewline
63 & 0.513618 & 0.972765 & 0.486382 \tabularnewline
64 & 0.479774 & 0.959548 & 0.520226 \tabularnewline
65 & 0.43614 & 0.872281 & 0.56386 \tabularnewline
66 & 0.444152 & 0.888305 & 0.555848 \tabularnewline
67 & 0.463334 & 0.926668 & 0.536666 \tabularnewline
68 & 0.491466 & 0.982931 & 0.508534 \tabularnewline
69 & 0.439293 & 0.878586 & 0.560707 \tabularnewline
70 & 0.784899 & 0.430202 & 0.215101 \tabularnewline
71 & 0.79975 & 0.400499 & 0.20025 \tabularnewline
72 & 0.782277 & 0.435445 & 0.217723 \tabularnewline
73 & 0.745631 & 0.508738 & 0.254369 \tabularnewline
74 & 0.734434 & 0.531133 & 0.265566 \tabularnewline
75 & 0.695406 & 0.609188 & 0.304594 \tabularnewline
76 & 0.653833 & 0.692333 & 0.346167 \tabularnewline
77 & 0.607485 & 0.78503 & 0.392515 \tabularnewline
78 & 0.603926 & 0.792149 & 0.396074 \tabularnewline
79 & 0.624013 & 0.751975 & 0.375987 \tabularnewline
80 & 0.580776 & 0.838448 & 0.419224 \tabularnewline
81 & 0.560072 & 0.879856 & 0.439928 \tabularnewline
82 & 0.592062 & 0.815876 & 0.407938 \tabularnewline
83 & 0.544257 & 0.911486 & 0.455743 \tabularnewline
84 & 0.501622 & 0.996755 & 0.498378 \tabularnewline
85 & 0.466966 & 0.933931 & 0.533034 \tabularnewline
86 & 0.478139 & 0.956277 & 0.521861 \tabularnewline
87 & 0.426631 & 0.853263 & 0.573369 \tabularnewline
88 & 0.416577 & 0.833154 & 0.583423 \tabularnewline
89 & 0.371688 & 0.743376 & 0.628312 \tabularnewline
90 & 0.331395 & 0.66279 & 0.668605 \tabularnewline
91 & 0.289241 & 0.578482 & 0.710759 \tabularnewline
92 & 0.243916 & 0.487832 & 0.756084 \tabularnewline
93 & 0.292891 & 0.585782 & 0.707109 \tabularnewline
94 & 0.285922 & 0.571844 & 0.714078 \tabularnewline
95 & 0.457993 & 0.915986 & 0.542007 \tabularnewline
96 & 0.426307 & 0.852613 & 0.573693 \tabularnewline
97 & 0.410645 & 0.821291 & 0.589355 \tabularnewline
98 & 0.3602 & 0.7204 & 0.6398 \tabularnewline
99 & 0.332023 & 0.664045 & 0.667977 \tabularnewline
100 & 0.295306 & 0.590612 & 0.704694 \tabularnewline
101 & 0.252715 & 0.505431 & 0.747285 \tabularnewline
102 & 0.827094 & 0.345811 & 0.172906 \tabularnewline
103 & 0.794489 & 0.411022 & 0.205511 \tabularnewline
104 & 0.749892 & 0.500216 & 0.250108 \tabularnewline
105 & 0.727875 & 0.54425 & 0.272125 \tabularnewline
106 & 0.745903 & 0.508195 & 0.254097 \tabularnewline
107 & 0.742944 & 0.514112 & 0.257056 \tabularnewline
108 & 0.700678 & 0.598644 & 0.299322 \tabularnewline
109 & 0.655701 & 0.688597 & 0.344299 \tabularnewline
110 & 0.610998 & 0.778005 & 0.389002 \tabularnewline
111 & 0.59779 & 0.80442 & 0.40221 \tabularnewline
112 & 0.551316 & 0.897368 & 0.448684 \tabularnewline
113 & 0.539227 & 0.921547 & 0.460773 \tabularnewline
114 & 0.486004 & 0.972008 & 0.513996 \tabularnewline
115 & 0.42027 & 0.840541 & 0.57973 \tabularnewline
116 & 0.372425 & 0.74485 & 0.627575 \tabularnewline
117 & 0.298667 & 0.597333 & 0.701333 \tabularnewline
118 & 0.277882 & 0.555764 & 0.722118 \tabularnewline
119 & 0.278039 & 0.556078 & 0.721961 \tabularnewline
120 & 0.205666 & 0.411331 & 0.794334 \tabularnewline
121 & 0.510163 & 0.979674 & 0.489837 \tabularnewline
122 & 0.664316 & 0.671367 & 0.335684 \tabularnewline
123 & 0.676549 & 0.646901 & 0.323451 \tabularnewline
124 & 0.674941 & 0.650118 & 0.325059 \tabularnewline
125 & 0.751313 & 0.497375 & 0.248687 \tabularnewline
126 & 0.72903 & 0.54194 & 0.27097 \tabularnewline
127 & 0.545664 & 0.908672 & 0.454336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270998&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]16[/C][C]0.272267[/C][C]0.544533[/C][C]0.727733[/C][/ROW]
[ROW][C]17[/C][C]0.272682[/C][C]0.545364[/C][C]0.727318[/C][/ROW]
[ROW][C]18[/C][C]0.294503[/C][C]0.589007[/C][C]0.705497[/C][/ROW]
[ROW][C]19[/C][C]0.199975[/C][C]0.399951[/C][C]0.800025[/C][/ROW]
[ROW][C]20[/C][C]0.12366[/C][C]0.247319[/C][C]0.87634[/C][/ROW]
[ROW][C]21[/C][C]0.0714787[/C][C]0.142957[/C][C]0.928521[/C][/ROW]
[ROW][C]22[/C][C]0.0398812[/C][C]0.0797624[/C][C]0.960119[/C][/ROW]
[ROW][C]23[/C][C]0.0354747[/C][C]0.0709495[/C][C]0.964525[/C][/ROW]
[ROW][C]24[/C][C]0.0491134[/C][C]0.0982267[/C][C]0.950887[/C][/ROW]
[ROW][C]25[/C][C]0.0722357[/C][C]0.144471[/C][C]0.927764[/C][/ROW]
[ROW][C]26[/C][C]0.0719695[/C][C]0.143939[/C][C]0.92803[/C][/ROW]
[ROW][C]27[/C][C]0.116404[/C][C]0.232808[/C][C]0.883596[/C][/ROW]
[ROW][C]28[/C][C]0.145404[/C][C]0.290808[/C][C]0.854596[/C][/ROW]
[ROW][C]29[/C][C]0.174914[/C][C]0.349829[/C][C]0.825086[/C][/ROW]
[ROW][C]30[/C][C]0.401524[/C][C]0.803049[/C][C]0.598476[/C][/ROW]
[ROW][C]31[/C][C]0.368385[/C][C]0.736771[/C][C]0.631615[/C][/ROW]
[ROW][C]32[/C][C]0.305091[/C][C]0.610182[/C][C]0.694909[/C][/ROW]
[ROW][C]33[/C][C]0.246781[/C][C]0.493563[/C][C]0.753219[/C][/ROW]
[ROW][C]34[/C][C]0.249464[/C][C]0.498929[/C][C]0.750536[/C][/ROW]
[ROW][C]35[/C][C]0.198404[/C][C]0.396808[/C][C]0.801596[/C][/ROW]
[ROW][C]36[/C][C]0.382239[/C][C]0.764478[/C][C]0.617761[/C][/ROW]
[ROW][C]37[/C][C]0.325061[/C][C]0.650121[/C][C]0.674939[/C][/ROW]
[ROW][C]38[/C][C]0.279418[/C][C]0.558836[/C][C]0.720582[/C][/ROW]
[ROW][C]39[/C][C]0.250175[/C][C]0.500351[/C][C]0.749825[/C][/ROW]
[ROW][C]40[/C][C]0.204948[/C][C]0.409896[/C][C]0.795052[/C][/ROW]
[ROW][C]41[/C][C]0.187307[/C][C]0.374614[/C][C]0.812693[/C][/ROW]
[ROW][C]42[/C][C]0.213508[/C][C]0.427016[/C][C]0.786492[/C][/ROW]
[ROW][C]43[/C][C]0.176004[/C][C]0.352007[/C][C]0.823996[/C][/ROW]
[ROW][C]44[/C][C]0.288284[/C][C]0.576567[/C][C]0.711716[/C][/ROW]
[ROW][C]45[/C][C]0.244731[/C][C]0.489462[/C][C]0.755269[/C][/ROW]
[ROW][C]46[/C][C]0.343053[/C][C]0.686107[/C][C]0.656947[/C][/ROW]
[ROW][C]47[/C][C]0.356755[/C][C]0.713511[/C][C]0.643245[/C][/ROW]
[ROW][C]48[/C][C]0.365583[/C][C]0.731167[/C][C]0.634417[/C][/ROW]
[ROW][C]49[/C][C]0.417924[/C][C]0.835847[/C][C]0.582076[/C][/ROW]
[ROW][C]50[/C][C]0.51347[/C][C]0.973059[/C][C]0.48653[/C][/ROW]
[ROW][C]51[/C][C]0.511325[/C][C]0.97735[/C][C]0.488675[/C][/ROW]
[ROW][C]52[/C][C]0.473346[/C][C]0.946692[/C][C]0.526654[/C][/ROW]
[ROW][C]53[/C][C]0.427331[/C][C]0.854662[/C][C]0.572669[/C][/ROW]
[ROW][C]54[/C][C]0.440718[/C][C]0.881437[/C][C]0.559282[/C][/ROW]
[ROW][C]55[/C][C]0.400262[/C][C]0.800525[/C][C]0.599738[/C][/ROW]
[ROW][C]56[/C][C]0.354759[/C][C]0.709518[/C][C]0.645241[/C][/ROW]
[ROW][C]57[/C][C]0.325231[/C][C]0.650462[/C][C]0.674769[/C][/ROW]
[ROW][C]58[/C][C]0.293358[/C][C]0.586715[/C][C]0.706642[/C][/ROW]
[ROW][C]59[/C][C]0.278557[/C][C]0.557115[/C][C]0.721443[/C][/ROW]
[ROW][C]60[/C][C]0.338336[/C][C]0.676672[/C][C]0.661664[/C][/ROW]
[ROW][C]61[/C][C]0.426868[/C][C]0.853736[/C][C]0.573132[/C][/ROW]
[ROW][C]62[/C][C]0.479512[/C][C]0.959023[/C][C]0.520488[/C][/ROW]
[ROW][C]63[/C][C]0.513618[/C][C]0.972765[/C][C]0.486382[/C][/ROW]
[ROW][C]64[/C][C]0.479774[/C][C]0.959548[/C][C]0.520226[/C][/ROW]
[ROW][C]65[/C][C]0.43614[/C][C]0.872281[/C][C]0.56386[/C][/ROW]
[ROW][C]66[/C][C]0.444152[/C][C]0.888305[/C][C]0.555848[/C][/ROW]
[ROW][C]67[/C][C]0.463334[/C][C]0.926668[/C][C]0.536666[/C][/ROW]
[ROW][C]68[/C][C]0.491466[/C][C]0.982931[/C][C]0.508534[/C][/ROW]
[ROW][C]69[/C][C]0.439293[/C][C]0.878586[/C][C]0.560707[/C][/ROW]
[ROW][C]70[/C][C]0.784899[/C][C]0.430202[/C][C]0.215101[/C][/ROW]
[ROW][C]71[/C][C]0.79975[/C][C]0.400499[/C][C]0.20025[/C][/ROW]
[ROW][C]72[/C][C]0.782277[/C][C]0.435445[/C][C]0.217723[/C][/ROW]
[ROW][C]73[/C][C]0.745631[/C][C]0.508738[/C][C]0.254369[/C][/ROW]
[ROW][C]74[/C][C]0.734434[/C][C]0.531133[/C][C]0.265566[/C][/ROW]
[ROW][C]75[/C][C]0.695406[/C][C]0.609188[/C][C]0.304594[/C][/ROW]
[ROW][C]76[/C][C]0.653833[/C][C]0.692333[/C][C]0.346167[/C][/ROW]
[ROW][C]77[/C][C]0.607485[/C][C]0.78503[/C][C]0.392515[/C][/ROW]
[ROW][C]78[/C][C]0.603926[/C][C]0.792149[/C][C]0.396074[/C][/ROW]
[ROW][C]79[/C][C]0.624013[/C][C]0.751975[/C][C]0.375987[/C][/ROW]
[ROW][C]80[/C][C]0.580776[/C][C]0.838448[/C][C]0.419224[/C][/ROW]
[ROW][C]81[/C][C]0.560072[/C][C]0.879856[/C][C]0.439928[/C][/ROW]
[ROW][C]82[/C][C]0.592062[/C][C]0.815876[/C][C]0.407938[/C][/ROW]
[ROW][C]83[/C][C]0.544257[/C][C]0.911486[/C][C]0.455743[/C][/ROW]
[ROW][C]84[/C][C]0.501622[/C][C]0.996755[/C][C]0.498378[/C][/ROW]
[ROW][C]85[/C][C]0.466966[/C][C]0.933931[/C][C]0.533034[/C][/ROW]
[ROW][C]86[/C][C]0.478139[/C][C]0.956277[/C][C]0.521861[/C][/ROW]
[ROW][C]87[/C][C]0.426631[/C][C]0.853263[/C][C]0.573369[/C][/ROW]
[ROW][C]88[/C][C]0.416577[/C][C]0.833154[/C][C]0.583423[/C][/ROW]
[ROW][C]89[/C][C]0.371688[/C][C]0.743376[/C][C]0.628312[/C][/ROW]
[ROW][C]90[/C][C]0.331395[/C][C]0.66279[/C][C]0.668605[/C][/ROW]
[ROW][C]91[/C][C]0.289241[/C][C]0.578482[/C][C]0.710759[/C][/ROW]
[ROW][C]92[/C][C]0.243916[/C][C]0.487832[/C][C]0.756084[/C][/ROW]
[ROW][C]93[/C][C]0.292891[/C][C]0.585782[/C][C]0.707109[/C][/ROW]
[ROW][C]94[/C][C]0.285922[/C][C]0.571844[/C][C]0.714078[/C][/ROW]
[ROW][C]95[/C][C]0.457993[/C][C]0.915986[/C][C]0.542007[/C][/ROW]
[ROW][C]96[/C][C]0.426307[/C][C]0.852613[/C][C]0.573693[/C][/ROW]
[ROW][C]97[/C][C]0.410645[/C][C]0.821291[/C][C]0.589355[/C][/ROW]
[ROW][C]98[/C][C]0.3602[/C][C]0.7204[/C][C]0.6398[/C][/ROW]
[ROW][C]99[/C][C]0.332023[/C][C]0.664045[/C][C]0.667977[/C][/ROW]
[ROW][C]100[/C][C]0.295306[/C][C]0.590612[/C][C]0.704694[/C][/ROW]
[ROW][C]101[/C][C]0.252715[/C][C]0.505431[/C][C]0.747285[/C][/ROW]
[ROW][C]102[/C][C]0.827094[/C][C]0.345811[/C][C]0.172906[/C][/ROW]
[ROW][C]103[/C][C]0.794489[/C][C]0.411022[/C][C]0.205511[/C][/ROW]
[ROW][C]104[/C][C]0.749892[/C][C]0.500216[/C][C]0.250108[/C][/ROW]
[ROW][C]105[/C][C]0.727875[/C][C]0.54425[/C][C]0.272125[/C][/ROW]
[ROW][C]106[/C][C]0.745903[/C][C]0.508195[/C][C]0.254097[/C][/ROW]
[ROW][C]107[/C][C]0.742944[/C][C]0.514112[/C][C]0.257056[/C][/ROW]
[ROW][C]108[/C][C]0.700678[/C][C]0.598644[/C][C]0.299322[/C][/ROW]
[ROW][C]109[/C][C]0.655701[/C][C]0.688597[/C][C]0.344299[/C][/ROW]
[ROW][C]110[/C][C]0.610998[/C][C]0.778005[/C][C]0.389002[/C][/ROW]
[ROW][C]111[/C][C]0.59779[/C][C]0.80442[/C][C]0.40221[/C][/ROW]
[ROW][C]112[/C][C]0.551316[/C][C]0.897368[/C][C]0.448684[/C][/ROW]
[ROW][C]113[/C][C]0.539227[/C][C]0.921547[/C][C]0.460773[/C][/ROW]
[ROW][C]114[/C][C]0.486004[/C][C]0.972008[/C][C]0.513996[/C][/ROW]
[ROW][C]115[/C][C]0.42027[/C][C]0.840541[/C][C]0.57973[/C][/ROW]
[ROW][C]116[/C][C]0.372425[/C][C]0.74485[/C][C]0.627575[/C][/ROW]
[ROW][C]117[/C][C]0.298667[/C][C]0.597333[/C][C]0.701333[/C][/ROW]
[ROW][C]118[/C][C]0.277882[/C][C]0.555764[/C][C]0.722118[/C][/ROW]
[ROW][C]119[/C][C]0.278039[/C][C]0.556078[/C][C]0.721961[/C][/ROW]
[ROW][C]120[/C][C]0.205666[/C][C]0.411331[/C][C]0.794334[/C][/ROW]
[ROW][C]121[/C][C]0.510163[/C][C]0.979674[/C][C]0.489837[/C][/ROW]
[ROW][C]122[/C][C]0.664316[/C][C]0.671367[/C][C]0.335684[/C][/ROW]
[ROW][C]123[/C][C]0.676549[/C][C]0.646901[/C][C]0.323451[/C][/ROW]
[ROW][C]124[/C][C]0.674941[/C][C]0.650118[/C][C]0.325059[/C][/ROW]
[ROW][C]125[/C][C]0.751313[/C][C]0.497375[/C][C]0.248687[/C][/ROW]
[ROW][C]126[/C][C]0.72903[/C][C]0.54194[/C][C]0.27097[/C][/ROW]
[ROW][C]127[/C][C]0.545664[/C][C]0.908672[/C][C]0.454336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270998&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270998&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
160.2722670.5445330.727733
170.2726820.5453640.727318
180.2945030.5890070.705497
190.1999750.3999510.800025
200.123660.2473190.87634
210.07147870.1429570.928521
220.03988120.07976240.960119
230.03547470.07094950.964525
240.04911340.09822670.950887
250.07223570.1444710.927764
260.07196950.1439390.92803
270.1164040.2328080.883596
280.1454040.2908080.854596
290.1749140.3498290.825086
300.4015240.8030490.598476
310.3683850.7367710.631615
320.3050910.6101820.694909
330.2467810.4935630.753219
340.2494640.4989290.750536
350.1984040.3968080.801596
360.3822390.7644780.617761
370.3250610.6501210.674939
380.2794180.5588360.720582
390.2501750.5003510.749825
400.2049480.4098960.795052
410.1873070.3746140.812693
420.2135080.4270160.786492
430.1760040.3520070.823996
440.2882840.5765670.711716
450.2447310.4894620.755269
460.3430530.6861070.656947
470.3567550.7135110.643245
480.3655830.7311670.634417
490.4179240.8358470.582076
500.513470.9730590.48653
510.5113250.977350.488675
520.4733460.9466920.526654
530.4273310.8546620.572669
540.4407180.8814370.559282
550.4002620.8005250.599738
560.3547590.7095180.645241
570.3252310.6504620.674769
580.2933580.5867150.706642
590.2785570.5571150.721443
600.3383360.6766720.661664
610.4268680.8537360.573132
620.4795120.9590230.520488
630.5136180.9727650.486382
640.4797740.9595480.520226
650.436140.8722810.56386
660.4441520.8883050.555848
670.4633340.9266680.536666
680.4914660.9829310.508534
690.4392930.8785860.560707
700.7848990.4302020.215101
710.799750.4004990.20025
720.7822770.4354450.217723
730.7456310.5087380.254369
740.7344340.5311330.265566
750.6954060.6091880.304594
760.6538330.6923330.346167
770.6074850.785030.392515
780.6039260.7921490.396074
790.6240130.7519750.375987
800.5807760.8384480.419224
810.5600720.8798560.439928
820.5920620.8158760.407938
830.5442570.9114860.455743
840.5016220.9967550.498378
850.4669660.9339310.533034
860.4781390.9562770.521861
870.4266310.8532630.573369
880.4165770.8331540.583423
890.3716880.7433760.628312
900.3313950.662790.668605
910.2892410.5784820.710759
920.2439160.4878320.756084
930.2928910.5857820.707109
940.2859220.5718440.714078
950.4579930.9159860.542007
960.4263070.8526130.573693
970.4106450.8212910.589355
980.36020.72040.6398
990.3320230.6640450.667977
1000.2953060.5906120.704694
1010.2527150.5054310.747285
1020.8270940.3458110.172906
1030.7944890.4110220.205511
1040.7498920.5002160.250108
1050.7278750.544250.272125
1060.7459030.5081950.254097
1070.7429440.5141120.257056
1080.7006780.5986440.299322
1090.6557010.6885970.344299
1100.6109980.7780050.389002
1110.597790.804420.40221
1120.5513160.8973680.448684
1130.5392270.9215470.460773
1140.4860040.9720080.513996
1150.420270.8405410.57973
1160.3724250.744850.627575
1170.2986670.5973330.701333
1180.2778820.5557640.722118
1190.2780390.5560780.721961
1200.2056660.4113310.794334
1210.5101630.9796740.489837
1220.6643160.6713670.335684
1230.6765490.6469010.323451
1240.6749410.6501180.325059
1250.7513130.4973750.248687
1260.729030.541940.27097
1270.5456640.9086720.454336






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 level30.0267857OK

\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 & 3 & 0.0267857 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270998&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]3[/C][C]0.0267857[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270998&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270998&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 level30.0267857OK


Figures (Output of Computation)

Figure 1
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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')
}