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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 computationMon, 23 Jan 2017 10:51:05 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/23/t14851650833l8kvcuojcironp.htm/, Retrieved Wed, 15 May 2024 12:17:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304676, Retrieved Wed, 15 May 2024 12:17:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact46

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
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Histogram
Boxplots 
14 22
19 24
17 21
17 21
15 24
20 20
15 22
19 20
15 19
15 23
19 21
NA 19
20 19
18 21
15 21
14 22
20 22
NA 19
16 21
16 21
16 21
10 20
19 22
19 22
16 24
15 21
18 19
17 19
19 23
17 21
NA 21
19 19
20 21
5 19
19 21
16 21
15 23
16 19
18 19
16 19
15 18
17 22
NA 18
20 22
19 18
7 22
13 22
16 19
16 22
NA 25
18 19
18 19
16 19
17 19
19 21
16 21
19 20
13 19
16 19
13 22
12 26
17 19
17 21
17 21
16 20
16 23
14 22
16 22
13 22
16 21
14 21
20 22
12 23
13 18
18 24
14 22
19 21
18 21
14 21
18 23
19 21
15 23
14 21
17 19
19 21
13 21
19 21
18 23
20 23
15 20
15 20
15 19
20 23
15 22
19 19
18 23
18 22
15 22
20 21
17 21
12 21
18 21
19 22
20 25
NA 21
17 23
15 19
16 22
18 20
18 21
14 25
15 21
12 19
17 23
14 22
18 21
17 24
17 21
20 19
16 18
14 19
15 20
18 19
20 22
17 21
17 22
17 24
17 28
15 19
17 18
18 23
17 19
20 23
15 19
16 22
15 21
18 19
11 22
15 21
18 23
20 22
19 19
14 19
16 21
15 22
17 21
18 20
20 23
17 22
18 23
15 22
16 21
11 20
15 18
18 18
17 20
16 19
12 21
19 24
18 19
15 20
17 19
19 23
18 22
19 21
16 24
16 21
16 21
14 22

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304676&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]8 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304676&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center






Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 21.7536 + 0.0209138Bevr_Leeftijd[t] -0.42953`ITHSUM(t-1s)`[t] -0.364899`ITHSUM(t-2s)`[t] + 0.289674`ITHSUM(t-3s)`[t] -0.205331`ITHSUM(t-4s)`[t] + 0.163583`ITHSUM(t-5s)`[t] -0.491844`ITHSUM(t-6s)`[t] + 0.238047`ITHSUM(t-7s)`[t] -0.0282742`ITHSUM(t-8s)`[t] + 0.0698727`ITHSUM(t-9s)`[t] -0.0778675`ITHSUM(t-10s)`[t] + 0.352357`ITHSUM(t-11s)`[t] + 0.130543`ITHSUM(t-12s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  21.7536 +  0.0209138Bevr_Leeftijd[t] -0.42953`ITHSUM(t-1s)`[t] -0.364899`ITHSUM(t-2s)`[t] +  0.289674`ITHSUM(t-3s)`[t] -0.205331`ITHSUM(t-4s)`[t] +  0.163583`ITHSUM(t-5s)`[t] -0.491844`ITHSUM(t-6s)`[t] +  0.238047`ITHSUM(t-7s)`[t] -0.0282742`ITHSUM(t-8s)`[t] +  0.0698727`ITHSUM(t-9s)`[t] -0.0778675`ITHSUM(t-10s)`[t] +  0.352357`ITHSUM(t-11s)`[t] +  0.130543`ITHSUM(t-12s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304676&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  21.7536 +  0.0209138Bevr_Leeftijd[t] -0.42953`ITHSUM(t-1s)`[t] -0.364899`ITHSUM(t-2s)`[t] +  0.289674`ITHSUM(t-3s)`[t] -0.205331`ITHSUM(t-4s)`[t] +  0.163583`ITHSUM(t-5s)`[t] -0.491844`ITHSUM(t-6s)`[t] +  0.238047`ITHSUM(t-7s)`[t] -0.0282742`ITHSUM(t-8s)`[t] +  0.0698727`ITHSUM(t-9s)`[t] -0.0778675`ITHSUM(t-10s)`[t] +  0.352357`ITHSUM(t-11s)`[t] +  0.130543`ITHSUM(t-12s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304676&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
ITHSUM[t] = + 21.7536 + 0.0209138Bevr_Leeftijd[t] -0.42953`ITHSUM(t-1s)`[t] -0.364899`ITHSUM(t-2s)`[t] + 0.289674`ITHSUM(t-3s)`[t] -0.205331`ITHSUM(t-4s)`[t] + 0.163583`ITHSUM(t-5s)`[t] -0.491844`ITHSUM(t-6s)`[t] + 0.238047`ITHSUM(t-7s)`[t] -0.0282742`ITHSUM(t-8s)`[t] + 0.0698727`ITHSUM(t-9s)`[t] -0.0778675`ITHSUM(t-10s)`[t] + 0.352357`ITHSUM(t-11s)`[t] + 0.130543`ITHSUM(t-12s)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+21.75 13.72+1.5850e+00 0.1738 0.08691
Bevr_Leeftijd+0.02091 0.2192+9.5410e-02 0.9277 0.4638
`ITHSUM(t-1s)`-0.4295 0.3121-1.3760e+00 0.2272 0.1136
`ITHSUM(t-2s)`-0.3649 0.269-1.3570e+00 0.2329 0.1164
`ITHSUM(t-3s)`+0.2897 0.3117+9.2930e-01 0.3954 0.1977
`ITHSUM(t-4s)`-0.2053 0.2253-9.1150e-01 0.4038 0.2019
`ITHSUM(t-5s)`+0.1636 0.1986+8.2350e-01 0.4477 0.2239
`ITHSUM(t-6s)`-0.4918 0.2577-1.9080e+00 0.1146 0.05731
`ITHSUM(t-7s)`+0.2381 0.1967+1.2100e+00 0.2804 0.1402
`ITHSUM(t-8s)`-0.02827 0.1963-1.4410e-01 0.8911 0.4455
`ITHSUM(t-9s)`+0.06987 0.1755+3.9810e-01 0.707 0.3535
`ITHSUM(t-10s)`-0.07787 0.1206-6.4570e-01 0.5469 0.2735
`ITHSUM(t-11s)`+0.3524 0.1232+2.8600e+00 0.03539 0.0177
`ITHSUM(t-12s)`+0.1305 0.2351+5.5520e-01 0.6027 0.3013

\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) & +21.75 &  13.72 & +1.5850e+00 &  0.1738 &  0.08691 \tabularnewline
Bevr_Leeftijd & +0.02091 &  0.2192 & +9.5410e-02 &  0.9277 &  0.4638 \tabularnewline
`ITHSUM(t-1s)` & -0.4295 &  0.3121 & -1.3760e+00 &  0.2272 &  0.1136 \tabularnewline
`ITHSUM(t-2s)` & -0.3649 &  0.269 & -1.3570e+00 &  0.2329 &  0.1164 \tabularnewline
`ITHSUM(t-3s)` & +0.2897 &  0.3117 & +9.2930e-01 &  0.3954 &  0.1977 \tabularnewline
`ITHSUM(t-4s)` & -0.2053 &  0.2253 & -9.1150e-01 &  0.4038 &  0.2019 \tabularnewline
`ITHSUM(t-5s)` & +0.1636 &  0.1986 & +8.2350e-01 &  0.4477 &  0.2239 \tabularnewline
`ITHSUM(t-6s)` & -0.4918 &  0.2577 & -1.9080e+00 &  0.1146 &  0.05731 \tabularnewline
`ITHSUM(t-7s)` & +0.2381 &  0.1967 & +1.2100e+00 &  0.2804 &  0.1402 \tabularnewline
`ITHSUM(t-8s)` & -0.02827 &  0.1963 & -1.4410e-01 &  0.8911 &  0.4455 \tabularnewline
`ITHSUM(t-9s)` & +0.06987 &  0.1755 & +3.9810e-01 &  0.707 &  0.3535 \tabularnewline
`ITHSUM(t-10s)` & -0.07787 &  0.1206 & -6.4570e-01 &  0.5469 &  0.2735 \tabularnewline
`ITHSUM(t-11s)` & +0.3524 &  0.1232 & +2.8600e+00 &  0.03539 &  0.0177 \tabularnewline
`ITHSUM(t-12s)` & +0.1305 &  0.2351 & +5.5520e-01 &  0.6027 &  0.3013 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304676&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]+21.75[/C][C] 13.72[/C][C]+1.5850e+00[/C][C] 0.1738[/C][C] 0.08691[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.02091[/C][C] 0.2192[/C][C]+9.5410e-02[/C][C] 0.9277[/C][C] 0.4638[/C][/ROW]
[ROW][C]`ITHSUM(t-1s)`[/C][C]-0.4295[/C][C] 0.3121[/C][C]-1.3760e+00[/C][C] 0.2272[/C][C] 0.1136[/C][/ROW]
[ROW][C]`ITHSUM(t-2s)`[/C][C]-0.3649[/C][C] 0.269[/C][C]-1.3570e+00[/C][C] 0.2329[/C][C] 0.1164[/C][/ROW]
[ROW][C]`ITHSUM(t-3s)`[/C][C]+0.2897[/C][C] 0.3117[/C][C]+9.2930e-01[/C][C] 0.3954[/C][C] 0.1977[/C][/ROW]
[ROW][C]`ITHSUM(t-4s)`[/C][C]-0.2053[/C][C] 0.2253[/C][C]-9.1150e-01[/C][C] 0.4038[/C][C] 0.2019[/C][/ROW]
[ROW][C]`ITHSUM(t-5s)`[/C][C]+0.1636[/C][C] 0.1986[/C][C]+8.2350e-01[/C][C] 0.4477[/C][C] 0.2239[/C][/ROW]
[ROW][C]`ITHSUM(t-6s)`[/C][C]-0.4918[/C][C] 0.2577[/C][C]-1.9080e+00[/C][C] 0.1146[/C][C] 0.05731[/C][/ROW]
[ROW][C]`ITHSUM(t-7s)`[/C][C]+0.2381[/C][C] 0.1967[/C][C]+1.2100e+00[/C][C] 0.2804[/C][C] 0.1402[/C][/ROW]
[ROW][C]`ITHSUM(t-8s)`[/C][C]-0.02827[/C][C] 0.1963[/C][C]-1.4410e-01[/C][C] 0.8911[/C][C] 0.4455[/C][/ROW]
[ROW][C]`ITHSUM(t-9s)`[/C][C]+0.06987[/C][C] 0.1755[/C][C]+3.9810e-01[/C][C] 0.707[/C][C] 0.3535[/C][/ROW]
[ROW][C]`ITHSUM(t-10s)`[/C][C]-0.07787[/C][C] 0.1206[/C][C]-6.4570e-01[/C][C] 0.5469[/C][C] 0.2735[/C][/ROW]
[ROW][C]`ITHSUM(t-11s)`[/C][C]+0.3524[/C][C] 0.1232[/C][C]+2.8600e+00[/C][C] 0.03539[/C][C] 0.0177[/C][/ROW]
[ROW][C]`ITHSUM(t-12s)`[/C][C]+0.1305[/C][C] 0.2351[/C][C]+5.5520e-01[/C][C] 0.6027[/C][C] 0.3013[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304676&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304676&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)+21.75 13.72+1.5850e+00 0.1738 0.08691
Bevr_Leeftijd+0.02091 0.2192+9.5410e-02 0.9277 0.4638
`ITHSUM(t-1s)`-0.4295 0.3121-1.3760e+00 0.2272 0.1136
`ITHSUM(t-2s)`-0.3649 0.269-1.3570e+00 0.2329 0.1164
`ITHSUM(t-3s)`+0.2897 0.3117+9.2930e-01 0.3954 0.1977
`ITHSUM(t-4s)`-0.2053 0.2253-9.1150e-01 0.4038 0.2019
`ITHSUM(t-5s)`+0.1636 0.1986+8.2350e-01 0.4477 0.2239
`ITHSUM(t-6s)`-0.4918 0.2577-1.9080e+00 0.1146 0.05731
`ITHSUM(t-7s)`+0.2381 0.1967+1.2100e+00 0.2804 0.1402
`ITHSUM(t-8s)`-0.02827 0.1963-1.4410e-01 0.8911 0.4455
`ITHSUM(t-9s)`+0.06987 0.1755+3.9810e-01 0.707 0.3535
`ITHSUM(t-10s)`-0.07787 0.1206-6.4570e-01 0.5469 0.2735
`ITHSUM(t-11s)`+0.3524 0.1232+2.8600e+00 0.03539 0.0177
`ITHSUM(t-12s)`+0.1305 0.2351+5.5520e-01 0.6027 0.3013






Multiple Linear Regression - Regression Statistics
Multiple R 0.9318
R-squared 0.8682
Adjusted R-squared 0.5257
F-TEST (value) 2.534
F-TEST (DF numerator)13
F-TEST (DF denominator)5
p-value 0.1561
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.527
Sum Squared Residuals 11.66

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9318 \tabularnewline
R-squared &  0.8682 \tabularnewline
Adjusted R-squared &  0.5257 \tabularnewline
F-TEST (value) &  2.534 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 5 \tabularnewline
p-value &  0.1561 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.527 \tabularnewline
Sum Squared Residuals &  11.66 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304676&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9318[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8682[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5257[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.534[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]5[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1561[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.527[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 11.66[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304676&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304676&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 R 0.9318
R-squared 0.8682
Adjusted R-squared 0.5257
F-TEST (value) 2.534
F-TEST (DF numerator)13
F-TEST (DF denominator)5
p-value 0.1561
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.527
Sum Squared Residuals 11.66






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 15 14.74 0.2581
2 16 15.42 0.5758
3 11 12.74-1.743
4 15 15.02-0.02361
5 18 18.22-0.2232
6 17 16.73 0.2716
7 16 15.86 0.1391
8 12 11.83 0.1745
9 19 18.22 0.7762
10 18 17.27 0.7262
11 15 15.67-0.6717
12 17 15.65 1.351
13 19 20.4-1.401
14 18 16.78 1.223
15 19 19.07-0.0675
16 16 15.78 0.2162
17 16 16.87-0.8669
18 16 16.61-0.6094
19 14 14.11-0.1057

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  15 &  14.74 &  0.2581 \tabularnewline
2 &  16 &  15.42 &  0.5758 \tabularnewline
3 &  11 &  12.74 & -1.743 \tabularnewline
4 &  15 &  15.02 & -0.02361 \tabularnewline
5 &  18 &  18.22 & -0.2232 \tabularnewline
6 &  17 &  16.73 &  0.2716 \tabularnewline
7 &  16 &  15.86 &  0.1391 \tabularnewline
8 &  12 &  11.83 &  0.1745 \tabularnewline
9 &  19 &  18.22 &  0.7762 \tabularnewline
10 &  18 &  17.27 &  0.7262 \tabularnewline
11 &  15 &  15.67 & -0.6717 \tabularnewline
12 &  17 &  15.65 &  1.351 \tabularnewline
13 &  19 &  20.4 & -1.401 \tabularnewline
14 &  18 &  16.78 &  1.223 \tabularnewline
15 &  19 &  19.07 & -0.0675 \tabularnewline
16 &  16 &  15.78 &  0.2162 \tabularnewline
17 &  16 &  16.87 & -0.8669 \tabularnewline
18 &  16 &  16.61 & -0.6094 \tabularnewline
19 &  14 &  14.11 & -0.1057 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304676&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] 15[/C][C] 14.74[/C][C] 0.2581[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.42[/C][C] 0.5758[/C][/ROW]
[ROW][C]3[/C][C] 11[/C][C] 12.74[/C][C]-1.743[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 15.02[/C][C]-0.02361[/C][/ROW]
[ROW][C]5[/C][C] 18[/C][C] 18.22[/C][C]-0.2232[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 16.73[/C][C] 0.2716[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.86[/C][C] 0.1391[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 11.83[/C][C] 0.1745[/C][/ROW]
[ROW][C]9[/C][C] 19[/C][C] 18.22[/C][C] 0.7762[/C][/ROW]
[ROW][C]10[/C][C] 18[/C][C] 17.27[/C][C] 0.7262[/C][/ROW]
[ROW][C]11[/C][C] 15[/C][C] 15.67[/C][C]-0.6717[/C][/ROW]
[ROW][C]12[/C][C] 17[/C][C] 15.65[/C][C] 1.351[/C][/ROW]
[ROW][C]13[/C][C] 19[/C][C] 20.4[/C][C]-1.401[/C][/ROW]
[ROW][C]14[/C][C] 18[/C][C] 16.78[/C][C] 1.223[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 19.07[/C][C]-0.0675[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 15.78[/C][C] 0.2162[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.87[/C][C]-0.8669[/C][/ROW]
[ROW][C]18[/C][C] 16[/C][C] 16.61[/C][C]-0.6094[/C][/ROW]
[ROW][C]19[/C][C] 14[/C][C] 14.11[/C][C]-0.1057[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304676&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304676&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
1 15 14.74 0.2581
2 16 15.42 0.5758
3 11 12.74-1.743
4 15 15.02-0.02361
5 18 18.22-0.2232
6 17 16.73 0.2716
7 16 15.86 0.1391
8 12 11.83 0.1745
9 19 18.22 0.7762
10 18 17.27 0.7262
11 15 15.67-0.6717
12 17 15.65 1.351
13 19 20.4-1.401
14 18 16.78 1.223
15 19 19.07-0.0675
16 16 15.78 0.2162
17 16 16.87-0.8669
18 16 16.61-0.6094
19 14 14.11-0.1057






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.2568, df1 = 2, df2 = 3, p-value = 0.133
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = -Inf, df1 = 26, df2 = -21, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.046458, df1 = 2, df2 = 3, p-value = 0.9553


\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline

> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.2568, df1 = 2, df2 = 3, p-value = 0.133

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = -Inf, df1 = 26, df2 = -21, p-value = NA

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.046458, df1 = 2, df2 = 3, p-value = 0.9553

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304676&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.2568, df1 = 2, df2 = 3, p-value = 0.133

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = -Inf, df1 = 26, df2 = -21, p-value = NA

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.046458, df1 = 2, df2 = 3, p-value = 0.9553

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304676&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304676&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:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 4.2568, df1 = 2, df2 = 3, p-value = 0.133
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = -Inf, df1 = 26, df2 = -21, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.046458, df1 = 2, df2 = 3, p-value = 0.9553






Variance Inflation Factors (Multicollinearity)
> vif
  Bevr_Leeftijd  `ITHSUM(t-1s)`  `ITHSUM(t-2s)`  `ITHSUM(t-3s)`  `ITHSUM(t-4s)` 
       1.177147        3.618018        2.689441        2.371966        1.841049 
 `ITHSUM(t-5s)`  `ITHSUM(t-6s)`  `ITHSUM(t-7s)`  `ITHSUM(t-8s)`  `ITHSUM(t-9s)` 
       1.504522        2.613584        1.678365        1.461630        2.138865 
`ITHSUM(t-10s)` `ITHSUM(t-11s)` `ITHSUM(t-12s)` 
       1.818923        1.559147        1.908536 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
  Bevr_Leeftijd  `ITHSUM(t-1s)`  `ITHSUM(t-2s)`  `ITHSUM(t-3s)`  `ITHSUM(t-4s)` 
       1.177147        3.618018        2.689441        2.371966        1.841049 
 `ITHSUM(t-5s)`  `ITHSUM(t-6s)`  `ITHSUM(t-7s)`  `ITHSUM(t-8s)`  `ITHSUM(t-9s)` 
       1.504522        2.613584        1.678365        1.461630        2.138865 
`ITHSUM(t-10s)` `ITHSUM(t-11s)` `ITHSUM(t-12s)` 
       1.818923        1.559147        1.908536 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304676&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
  Bevr_Leeftijd  `ITHSUM(t-1s)`  `ITHSUM(t-2s)`  `ITHSUM(t-3s)`  `ITHSUM(t-4s)` 
       1.177147        3.618018        2.689441        2.371966        1.841049 
 `ITHSUM(t-5s)`  `ITHSUM(t-6s)`  `ITHSUM(t-7s)`  `ITHSUM(t-8s)`  `ITHSUM(t-9s)` 
       1.504522        2.613584        1.678365        1.461630        2.138865 
`ITHSUM(t-10s)` `ITHSUM(t-11s)` `ITHSUM(t-12s)` 
       1.818923        1.559147        1.908536 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304676&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304676&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:

Variance Inflation Factors (Multicollinearity)
> vif
  Bevr_Leeftijd  `ITHSUM(t-1s)`  `ITHSUM(t-2s)`  `ITHSUM(t-3s)`  `ITHSUM(t-4s)` 
       1.177147        3.618018        2.689441        2.371966        1.841049 
 `ITHSUM(t-5s)`  `ITHSUM(t-6s)`  `ITHSUM(t-7s)`  `ITHSUM(t-8s)`  `ITHSUM(t-9s)` 
       1.504522        2.613584        1.678365        1.461630        2.138865 
`ITHSUM(t-10s)` `ITHSUM(t-11s)` `ITHSUM(t-12s)` 
       1.818923        1.559147        1.908536


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
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,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')