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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 09:58:01 +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/t14851618919c2l84vzmbnksyb.htm/, Retrieved Wed, 15 May 2024 03:11:39 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 15 May 2024 03:11:39 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0

Tree of Dependent Computations

Dataset

Dataseries X:
Download CSV
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Boxplots 
14 22 13 22
19 24 16 24
17 21 17 26
17 21 NA 21
15 24 NA 26
20 20 16 25
15 22 NA 21
19 20 NA 24
15 19 NA 27
15 23 17 28
19 21 17 23
NA 19 15 25
20 19 16 24
18 21 14 24
15 21 16 24
14 22 17 25
20 22 NA 25
NA 19 NA NA
16 21 NA 25
16 21 NA 25
16 21 16 24
10 20 NA 26
19 22 16 26
19 22 NA 25
16 24 NA 26
15 21 NA 23
18 19 16 24
17 19 15 24
19 23 16 25
17 21 16 25
NA 21 13 24
19 19 15 28
20 21 17 27
5 19 NA NA
19 21 13 23
16 21 17 23
15 23 NA 24
16 19 14 24
18 19 14 22
16 19 18 25
15 18 NA 25
17 22 17 28
NA 18 13 22
20 22 16 28
19 18 15 25
7 22 15 24
13 22 NA 24
16 19 15 23
16 22 13 25
NA 25 NA NA
18 19 17 26
18 19 NA 25
16 19 NA 27
17 19 11 26
19 21 14 23
16 21 13 25
19 20 NA 21
13 19 17 22
16 19 16 24
13 22 NA 25
12 26 17 27
17 19 16 24
17 21 16 26
17 21 16 21
16 20 15 27
16 23 12 22
14 22 17 23
16 22 14 24
13 22 14 25
16 21 16 24
14 21 NA 23
20 22 NA 28
12 23 NA NA
13 18 NA 24
18 24 NA 26
14 22 15 22
19 21 16 25
18 21 14 25
14 21 15 24
18 23 17 24
19 21 NA 26
15 23 10 21
14 21 NA 25
17 19 17 25
19 21 NA 26
13 21 20 25
19 21 17 26
18 23 18 27
20 23 NA 25
15 20 17 NA
15 20 14 20
15 19 NA 24
20 23 17 26
15 22 NA 25
19 19 17 25
18 23 NA 24
18 22 16 26
15 22 18 25
20 21 18 28
17 21 16 27
12 21 NA 25
18 21 NA 26
19 22 15 26
20 25 13 26
NA 21 NA NA
17 23 NA 28
15 19 NA NA
16 22 NA 21
18 20 NA 25
18 21 16 25
14 25 NA 24
15 21 NA 24
12 19 NA 24
17 23 12 23
14 22 NA 23
18 21 16 24
17 24 16 24
17 21 NA 25
20 19 16 28
16 18 14 23
14 19 15 24
15 20 14 23
18 19 NA 24
20 22 15 25
17 21 NA 24
17 22 15 23
17 24 16 23
17 28 NA 25
15 19 NA 21
17 18 NA 22
18 23 11 19
17 19 NA 24
20 23 18 25
15 19 NA 21
16 22 11 22
15 21 NA 23
18 19 18 27
11 22 NA NA
15 21 15 26
18 23 19 29
20 22 17 28
19 19 NA 24
14 19 14 25
16 21 NA 25
15 22 13 22
17 21 17 25
18 20 14 26
20 23 19 26
17 22 14 24
18 23 NA 25
15 22 NA 19
16 21 16 25
11 20 16 23
15 18 15 25
18 18 12 25
17 20 NA 26
16 19 17 27
12 21 NA 24
19 24 NA 22
18 19 18 25
15 20 15 24
17 19 18 23
19 23 15 27
18 22 NA 24
19 21 NA 24
16 24 NA 21
16 21 16 25
16 21 NA 25
14 22 16 23

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=&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=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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] = -4.43531 + 0.141745Bevr_Leeftijd[t] -0.0191008TVDC[t] + 0.529657SKEOUSUM[t] + 0.0109061`ITHSUM(t-1)`[t] -0.00981637`ITHSUM(t-2)`[t] -0.0692083`ITHSUM(t-3)`[t] + 0.0533177`ITHSUM(t-4)`[t] -0.119872`ITHSUM(t-5)`[t] + 0.0546353`ITHSUM(t-6)`[t] + 0.219308`ITHSUM(t-7)`[t] -0.0423992`ITHSUM(t-8)`[t] + 0.2383`ITHSUM(t-9)`[t] + 0.0290157`ITHSUM(t-10)`[t] + 0.0124315`ITHSUM(t-11)`[t] -0.0518774`ITHSUM(t-12)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  -4.43531 +  0.141745Bevr_Leeftijd[t] -0.0191008TVDC[t] +  0.529657SKEOUSUM[t] +  0.0109061`ITHSUM(t-1)`[t] -0.00981637`ITHSUM(t-2)`[t] -0.0692083`ITHSUM(t-3)`[t] +  0.0533177`ITHSUM(t-4)`[t] -0.119872`ITHSUM(t-5)`[t] +  0.0546353`ITHSUM(t-6)`[t] +  0.219308`ITHSUM(t-7)`[t] -0.0423992`ITHSUM(t-8)`[t] +  0.2383`ITHSUM(t-9)`[t] +  0.0290157`ITHSUM(t-10)`[t] +  0.0124315`ITHSUM(t-11)`[t] -0.0518774`ITHSUM(t-12)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  -4.43531 +  0.141745Bevr_Leeftijd[t] -0.0191008TVDC[t] +  0.529657SKEOUSUM[t] +  0.0109061`ITHSUM(t-1)`[t] -0.00981637`ITHSUM(t-2)`[t] -0.0692083`ITHSUM(t-3)`[t] +  0.0533177`ITHSUM(t-4)`[t] -0.119872`ITHSUM(t-5)`[t] +  0.0546353`ITHSUM(t-6)`[t] +  0.219308`ITHSUM(t-7)`[t] -0.0423992`ITHSUM(t-8)`[t] +  0.2383`ITHSUM(t-9)`[t] +  0.0290157`ITHSUM(t-10)`[t] +  0.0124315`ITHSUM(t-11)`[t] -0.0518774`ITHSUM(t-12)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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] = -4.43531 + 0.141745Bevr_Leeftijd[t] -0.0191008TVDC[t] + 0.529657SKEOUSUM[t] + 0.0109061`ITHSUM(t-1)`[t] -0.00981637`ITHSUM(t-2)`[t] -0.0692083`ITHSUM(t-3)`[t] + 0.0533177`ITHSUM(t-4)`[t] -0.119872`ITHSUM(t-5)`[t] + 0.0546353`ITHSUM(t-6)`[t] + 0.219308`ITHSUM(t-7)`[t] -0.0423992`ITHSUM(t-8)`[t] + 0.2383`ITHSUM(t-9)`[t] + 0.0290157`ITHSUM(t-10)`[t] + 0.0124315`ITHSUM(t-11)`[t] -0.0518774`ITHSUM(t-12)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.435 6.783-6.5390e-01 0.5153 0.2576
Bevr_Leeftijd+0.1417 0.1488+9.5270e-01 0.344 0.172
TVDC-0.0191 0.149-1.2820e-01 0.8983 0.4492
SKEOUSUM+0.5297 0.1537+3.4460e+00 0.0009597 0.0004798
`ITHSUM(t-1)`+0.01091 0.1108+9.8400e-02 0.9219 0.4609
`ITHSUM(t-2)`-0.009816 0.1118-8.7770e-02 0.9303 0.4652
`ITHSUM(t-3)`-0.06921 0.112-6.1790e-01 0.5386 0.2693
`ITHSUM(t-4)`+0.05332 0.1082+4.9260e-01 0.6238 0.3119
`ITHSUM(t-5)`-0.1199 0.1121-1.0690e+00 0.2885 0.1442
`ITHSUM(t-6)`+0.05464 0.1043+5.2360e-01 0.6022 0.3011
`ITHSUM(t-7)`+0.2193 0.1041+2.1070e+00 0.03863 0.01932
`ITHSUM(t-8)`-0.0424 0.1067-3.9720e-01 0.6924 0.3462
`ITHSUM(t-9)`+0.2383 0.1111+2.1450e+00 0.03538 0.01769
`ITHSUM(t-10)`+0.02902 0.1102+2.6330e-01 0.7931 0.3966
`ITHSUM(t-11)`+0.01243 0.111+1.1190e-01 0.9112 0.4556
`ITHSUM(t-12)`-0.05188 0.1099-4.7190e-01 0.6384 0.3192

\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) & -4.435 &  6.783 & -6.5390e-01 &  0.5153 &  0.2576 \tabularnewline
Bevr_Leeftijd & +0.1417 &  0.1488 & +9.5270e-01 &  0.344 &  0.172 \tabularnewline
TVDC & -0.0191 &  0.149 & -1.2820e-01 &  0.8983 &  0.4492 \tabularnewline
SKEOUSUM & +0.5297 &  0.1537 & +3.4460e+00 &  0.0009597 &  0.0004798 \tabularnewline
`ITHSUM(t-1)` & +0.01091 &  0.1108 & +9.8400e-02 &  0.9219 &  0.4609 \tabularnewline
`ITHSUM(t-2)` & -0.009816 &  0.1118 & -8.7770e-02 &  0.9303 &  0.4652 \tabularnewline
`ITHSUM(t-3)` & -0.06921 &  0.112 & -6.1790e-01 &  0.5386 &  0.2693 \tabularnewline
`ITHSUM(t-4)` & +0.05332 &  0.1082 & +4.9260e-01 &  0.6238 &  0.3119 \tabularnewline
`ITHSUM(t-5)` & -0.1199 &  0.1121 & -1.0690e+00 &  0.2885 &  0.1442 \tabularnewline
`ITHSUM(t-6)` & +0.05464 &  0.1043 & +5.2360e-01 &  0.6022 &  0.3011 \tabularnewline
`ITHSUM(t-7)` & +0.2193 &  0.1041 & +2.1070e+00 &  0.03863 &  0.01932 \tabularnewline
`ITHSUM(t-8)` & -0.0424 &  0.1067 & -3.9720e-01 &  0.6924 &  0.3462 \tabularnewline
`ITHSUM(t-9)` & +0.2383 &  0.1111 & +2.1450e+00 &  0.03538 &  0.01769 \tabularnewline
`ITHSUM(t-10)` & +0.02902 &  0.1102 & +2.6330e-01 &  0.7931 &  0.3966 \tabularnewline
`ITHSUM(t-11)` & +0.01243 &  0.111 & +1.1190e-01 &  0.9112 &  0.4556 \tabularnewline
`ITHSUM(t-12)` & -0.05188 &  0.1099 & -4.7190e-01 &  0.6384 &  0.3192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]-4.435[/C][C] 6.783[/C][C]-6.5390e-01[/C][C] 0.5153[/C][C] 0.2576[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.1417[/C][C] 0.1488[/C][C]+9.5270e-01[/C][C] 0.344[/C][C] 0.172[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.0191[/C][C] 0.149[/C][C]-1.2820e-01[/C][C] 0.8983[/C][C] 0.4492[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.5297[/C][C] 0.1537[/C][C]+3.4460e+00[/C][C] 0.0009597[/C][C] 0.0004798[/C][/ROW]
[ROW][C]`ITHSUM(t-1)`[/C][C]+0.01091[/C][C] 0.1108[/C][C]+9.8400e-02[/C][C] 0.9219[/C][C] 0.4609[/C][/ROW]
[ROW][C]`ITHSUM(t-2)`[/C][C]-0.009816[/C][C] 0.1118[/C][C]-8.7770e-02[/C][C] 0.9303[/C][C] 0.4652[/C][/ROW]
[ROW][C]`ITHSUM(t-3)`[/C][C]-0.06921[/C][C] 0.112[/C][C]-6.1790e-01[/C][C] 0.5386[/C][C] 0.2693[/C][/ROW]
[ROW][C]`ITHSUM(t-4)`[/C][C]+0.05332[/C][C] 0.1082[/C][C]+4.9260e-01[/C][C] 0.6238[/C][C] 0.3119[/C][/ROW]
[ROW][C]`ITHSUM(t-5)`[/C][C]-0.1199[/C][C] 0.1121[/C][C]-1.0690e+00[/C][C] 0.2885[/C][C] 0.1442[/C][/ROW]
[ROW][C]`ITHSUM(t-6)`[/C][C]+0.05464[/C][C] 0.1043[/C][C]+5.2360e-01[/C][C] 0.6022[/C][C] 0.3011[/C][/ROW]
[ROW][C]`ITHSUM(t-7)`[/C][C]+0.2193[/C][C] 0.1041[/C][C]+2.1070e+00[/C][C] 0.03863[/C][C] 0.01932[/C][/ROW]
[ROW][C]`ITHSUM(t-8)`[/C][C]-0.0424[/C][C] 0.1067[/C][C]-3.9720e-01[/C][C] 0.6924[/C][C] 0.3462[/C][/ROW]
[ROW][C]`ITHSUM(t-9)`[/C][C]+0.2383[/C][C] 0.1111[/C][C]+2.1450e+00[/C][C] 0.03538[/C][C] 0.01769[/C][/ROW]
[ROW][C]`ITHSUM(t-10)`[/C][C]+0.02902[/C][C] 0.1102[/C][C]+2.6330e-01[/C][C] 0.7931[/C][C] 0.3966[/C][/ROW]
[ROW][C]`ITHSUM(t-11)`[/C][C]+0.01243[/C][C] 0.111[/C][C]+1.1190e-01[/C][C] 0.9112[/C][C] 0.4556[/C][/ROW]
[ROW][C]`ITHSUM(t-12)`[/C][C]-0.05188[/C][C] 0.1099[/C][C]-4.7190e-01[/C][C] 0.6384[/C][C] 0.3192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)-4.435 6.783-6.5390e-01 0.5153 0.2576
Bevr_Leeftijd+0.1417 0.1488+9.5270e-01 0.344 0.172
TVDC-0.0191 0.149-1.2820e-01 0.8983 0.4492
SKEOUSUM+0.5297 0.1537+3.4460e+00 0.0009597 0.0004798
`ITHSUM(t-1)`+0.01091 0.1108+9.8400e-02 0.9219 0.4609
`ITHSUM(t-2)`-0.009816 0.1118-8.7770e-02 0.9303 0.4652
`ITHSUM(t-3)`-0.06921 0.112-6.1790e-01 0.5386 0.2693
`ITHSUM(t-4)`+0.05332 0.1082+4.9260e-01 0.6238 0.3119
`ITHSUM(t-5)`-0.1199 0.1121-1.0690e+00 0.2885 0.1442
`ITHSUM(t-6)`+0.05464 0.1043+5.2360e-01 0.6022 0.3011
`ITHSUM(t-7)`+0.2193 0.1041+2.1070e+00 0.03863 0.01932
`ITHSUM(t-8)`-0.0424 0.1067-3.9720e-01 0.6924 0.3462
`ITHSUM(t-9)`+0.2383 0.1111+2.1450e+00 0.03538 0.01769
`ITHSUM(t-10)`+0.02902 0.1102+2.6330e-01 0.7931 0.3966
`ITHSUM(t-11)`+0.01243 0.111+1.1190e-01 0.9112 0.4556
`ITHSUM(t-12)`-0.05188 0.1099-4.7190e-01 0.6384 0.3192






Multiple Linear Regression - Regression Statistics
Multiple R 0.5123
R-squared 0.2624
Adjusted R-squared 0.1066
F-TEST (value) 1.684
F-TEST (DF numerator)15
F-TEST (DF denominator)71
p-value 0.07399
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.168
Sum Squared Residuals 333.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5123 \tabularnewline
R-squared &  0.2624 \tabularnewline
Adjusted R-squared &  0.1066 \tabularnewline
F-TEST (value) &  1.684 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value &  0.07399 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.168 \tabularnewline
Sum Squared Residuals &  333.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5123[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2624[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1066[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.684[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]71[/C][/ROW]
[ROW][C]p-value[/C][C] 0.07399[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.168[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 333.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 0.5123
R-squared 0.2624
Adjusted R-squared 0.1066
F-TEST (value) 1.684
F-TEST (DF numerator)15
F-TEST (DF denominator)71
p-value 0.07399
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.168
Sum Squared Residuals 333.6






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 18 17.78 0.2199
2 17 16.48 0.5216
3 19 17.88 1.122
4 17 17.12-0.1244
5 19 17.94 1.058
6 20 17.3 2.695
7 19 15.73 3.265
8 16 15.27 0.734
9 16 16.67-0.6722
10 18 15.82 2.178
11 16 16.64-0.6432
12 17 19.45-2.453
13 20 19.42 0.5758
14 19 17.62 1.375
15 7 16.76-9.759
16 16 15.92 0.07617
17 16 17.27-1.266
18 18 17 0.997
19 17 16.96 0.04417
20 19 17.78 1.221
21 16 16.87-0.8746
22 13 13.57-0.5669
23 16 16.66-0.6581
24 12 16.54-4.541
25 17 15.73 1.274
26 17 17.12-0.1238
27 17 16.75 0.2491
28 16 17.33-1.334
29 16 16.01-0.008562
30 14 15.51-1.505
31 16 14.68 1.317
32 13 16.95-3.954
33 16 15.49 0.5099
34 14 15.62-1.622
35 19 17.27 1.726
36 18 16.98 1.018
37 14 16.2-2.204
38 18 15.93 2.067
39 15 14.07 0.9312
40 17 16.19 0.8148
41 13 15.12-2.118
42 19 18.43 0.5747
43 18 17.37 0.6342
44 15 14.69 0.3073
45 20 17.96 2.043
46 19 16.28 2.722
47 18 17.54 0.4603
48 15 15.35-0.3537
49 20 19.55 0.449
50 17 16.78 0.2175
51 19 17.76 1.242
52 20 18.84 1.162
53 18 17.56 0.4386
54 17 16.37 0.6275
55 18 16.53 1.473
56 17 17.94-0.9353
57 20 17.39 2.609
58 16 16.56-0.5631
59 14 16.85-2.855
60 15 16.19-1.194
61 20 18.02 1.975
62 17 16.52 0.4842
63 17 16.52 0.4834
64 18 14.94 3.057
65 20 17.12 2.88
66 16 15.31 0.6882
67 18 17.28 0.7248
68 15 17.45-2.449
69 18 18.56-0.5613
70 20 18.98 1.016
71 14 17.69-3.688
72 15 15.95-0.9519
73 17 16.86 0.1375
74 18 18.59-0.5875
75 20 16.82 3.182
76 17 17.59-0.5866
77 16 16.97-0.9748
78 11 14.98-3.982
79 15 16.93-1.932
80 18 16.02 1.977
81 16 17.95-1.952
82 18 16.96 1.042
83 15 17.1-2.097
84 17 16.25 0.7521
85 19 16.85 2.146
86 16 16.95-0.9487
87 14 14.46-0.4582

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  18 &  17.78 &  0.2199 \tabularnewline
2 &  17 &  16.48 &  0.5216 \tabularnewline
3 &  19 &  17.88 &  1.122 \tabularnewline
4 &  17 &  17.12 & -0.1244 \tabularnewline
5 &  19 &  17.94 &  1.058 \tabularnewline
6 &  20 &  17.3 &  2.695 \tabularnewline
7 &  19 &  15.73 &  3.265 \tabularnewline
8 &  16 &  15.27 &  0.734 \tabularnewline
9 &  16 &  16.67 & -0.6722 \tabularnewline
10 &  18 &  15.82 &  2.178 \tabularnewline
11 &  16 &  16.64 & -0.6432 \tabularnewline
12 &  17 &  19.45 & -2.453 \tabularnewline
13 &  20 &  19.42 &  0.5758 \tabularnewline
14 &  19 &  17.62 &  1.375 \tabularnewline
15 &  7 &  16.76 & -9.759 \tabularnewline
16 &  16 &  15.92 &  0.07617 \tabularnewline
17 &  16 &  17.27 & -1.266 \tabularnewline
18 &  18 &  17 &  0.997 \tabularnewline
19 &  17 &  16.96 &  0.04417 \tabularnewline
20 &  19 &  17.78 &  1.221 \tabularnewline
21 &  16 &  16.87 & -0.8746 \tabularnewline
22 &  13 &  13.57 & -0.5669 \tabularnewline
23 &  16 &  16.66 & -0.6581 \tabularnewline
24 &  12 &  16.54 & -4.541 \tabularnewline
25 &  17 &  15.73 &  1.274 \tabularnewline
26 &  17 &  17.12 & -0.1238 \tabularnewline
27 &  17 &  16.75 &  0.2491 \tabularnewline
28 &  16 &  17.33 & -1.334 \tabularnewline
29 &  16 &  16.01 & -0.008562 \tabularnewline
30 &  14 &  15.51 & -1.505 \tabularnewline
31 &  16 &  14.68 &  1.317 \tabularnewline
32 &  13 &  16.95 & -3.954 \tabularnewline
33 &  16 &  15.49 &  0.5099 \tabularnewline
34 &  14 &  15.62 & -1.622 \tabularnewline
35 &  19 &  17.27 &  1.726 \tabularnewline
36 &  18 &  16.98 &  1.018 \tabularnewline
37 &  14 &  16.2 & -2.204 \tabularnewline
38 &  18 &  15.93 &  2.067 \tabularnewline
39 &  15 &  14.07 &  0.9312 \tabularnewline
40 &  17 &  16.19 &  0.8148 \tabularnewline
41 &  13 &  15.12 & -2.118 \tabularnewline
42 &  19 &  18.43 &  0.5747 \tabularnewline
43 &  18 &  17.37 &  0.6342 \tabularnewline
44 &  15 &  14.69 &  0.3073 \tabularnewline
45 &  20 &  17.96 &  2.043 \tabularnewline
46 &  19 &  16.28 &  2.722 \tabularnewline
47 &  18 &  17.54 &  0.4603 \tabularnewline
48 &  15 &  15.35 & -0.3537 \tabularnewline
49 &  20 &  19.55 &  0.449 \tabularnewline
50 &  17 &  16.78 &  0.2175 \tabularnewline
51 &  19 &  17.76 &  1.242 \tabularnewline
52 &  20 &  18.84 &  1.162 \tabularnewline
53 &  18 &  17.56 &  0.4386 \tabularnewline
54 &  17 &  16.37 &  0.6275 \tabularnewline
55 &  18 &  16.53 &  1.473 \tabularnewline
56 &  17 &  17.94 & -0.9353 \tabularnewline
57 &  20 &  17.39 &  2.609 \tabularnewline
58 &  16 &  16.56 & -0.5631 \tabularnewline
59 &  14 &  16.85 & -2.855 \tabularnewline
60 &  15 &  16.19 & -1.194 \tabularnewline
61 &  20 &  18.02 &  1.975 \tabularnewline
62 &  17 &  16.52 &  0.4842 \tabularnewline
63 &  17 &  16.52 &  0.4834 \tabularnewline
64 &  18 &  14.94 &  3.057 \tabularnewline
65 &  20 &  17.12 &  2.88 \tabularnewline
66 &  16 &  15.31 &  0.6882 \tabularnewline
67 &  18 &  17.28 &  0.7248 \tabularnewline
68 &  15 &  17.45 & -2.449 \tabularnewline
69 &  18 &  18.56 & -0.5613 \tabularnewline
70 &  20 &  18.98 &  1.016 \tabularnewline
71 &  14 &  17.69 & -3.688 \tabularnewline
72 &  15 &  15.95 & -0.9519 \tabularnewline
73 &  17 &  16.86 &  0.1375 \tabularnewline
74 &  18 &  18.59 & -0.5875 \tabularnewline
75 &  20 &  16.82 &  3.182 \tabularnewline
76 &  17 &  17.59 & -0.5866 \tabularnewline
77 &  16 &  16.97 & -0.9748 \tabularnewline
78 &  11 &  14.98 & -3.982 \tabularnewline
79 &  15 &  16.93 & -1.932 \tabularnewline
80 &  18 &  16.02 &  1.977 \tabularnewline
81 &  16 &  17.95 & -1.952 \tabularnewline
82 &  18 &  16.96 &  1.042 \tabularnewline
83 &  15 &  17.1 & -2.097 \tabularnewline
84 &  17 &  16.25 &  0.7521 \tabularnewline
85 &  19 &  16.85 &  2.146 \tabularnewline
86 &  16 &  16.95 & -0.9487 \tabularnewline
87 &  14 &  14.46 & -0.4582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 18[/C][C] 17.78[/C][C] 0.2199[/C][/ROW]
[ROW][C]2[/C][C] 17[/C][C] 16.48[/C][C] 0.5216[/C][/ROW]
[ROW][C]3[/C][C] 19[/C][C] 17.88[/C][C] 1.122[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 17.12[/C][C]-0.1244[/C][/ROW]
[ROW][C]5[/C][C] 19[/C][C] 17.94[/C][C] 1.058[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 17.3[/C][C] 2.695[/C][/ROW]
[ROW][C]7[/C][C] 19[/C][C] 15.73[/C][C] 3.265[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.27[/C][C] 0.734[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 16.67[/C][C]-0.6722[/C][/ROW]
[ROW][C]10[/C][C] 18[/C][C] 15.82[/C][C] 2.178[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.64[/C][C]-0.6432[/C][/ROW]
[ROW][C]12[/C][C] 17[/C][C] 19.45[/C][C]-2.453[/C][/ROW]
[ROW][C]13[/C][C] 20[/C][C] 19.42[/C][C] 0.5758[/C][/ROW]
[ROW][C]14[/C][C] 19[/C][C] 17.62[/C][C] 1.375[/C][/ROW]
[ROW][C]15[/C][C] 7[/C][C] 16.76[/C][C]-9.759[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 15.92[/C][C] 0.07617[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 17.27[/C][C]-1.266[/C][/ROW]
[ROW][C]18[/C][C] 18[/C][C] 17[/C][C] 0.997[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 16.96[/C][C] 0.04417[/C][/ROW]
[ROW][C]20[/C][C] 19[/C][C] 17.78[/C][C] 1.221[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 16.87[/C][C]-0.8746[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 13.57[/C][C]-0.5669[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.66[/C][C]-0.6581[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 16.54[/C][C]-4.541[/C][/ROW]
[ROW][C]25[/C][C] 17[/C][C] 15.73[/C][C] 1.274[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 17.12[/C][C]-0.1238[/C][/ROW]
[ROW][C]27[/C][C] 17[/C][C] 16.75[/C][C] 0.2491[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.33[/C][C]-1.334[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.01[/C][C]-0.008562[/C][/ROW]
[ROW][C]30[/C][C] 14[/C][C] 15.51[/C][C]-1.505[/C][/ROW]
[ROW][C]31[/C][C] 16[/C][C] 14.68[/C][C] 1.317[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 16.95[/C][C]-3.954[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 15.49[/C][C] 0.5099[/C][/ROW]
[ROW][C]34[/C][C] 14[/C][C] 15.62[/C][C]-1.622[/C][/ROW]
[ROW][C]35[/C][C] 19[/C][C] 17.27[/C][C] 1.726[/C][/ROW]
[ROW][C]36[/C][C] 18[/C][C] 16.98[/C][C] 1.018[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 16.2[/C][C]-2.204[/C][/ROW]
[ROW][C]38[/C][C] 18[/C][C] 15.93[/C][C] 2.067[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 14.07[/C][C] 0.9312[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 16.19[/C][C] 0.8148[/C][/ROW]
[ROW][C]41[/C][C] 13[/C][C] 15.12[/C][C]-2.118[/C][/ROW]
[ROW][C]42[/C][C] 19[/C][C] 18.43[/C][C] 0.5747[/C][/ROW]
[ROW][C]43[/C][C] 18[/C][C] 17.37[/C][C] 0.6342[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 14.69[/C][C] 0.3073[/C][/ROW]
[ROW][C]45[/C][C] 20[/C][C] 17.96[/C][C] 2.043[/C][/ROW]
[ROW][C]46[/C][C] 19[/C][C] 16.28[/C][C] 2.722[/C][/ROW]
[ROW][C]47[/C][C] 18[/C][C] 17.54[/C][C] 0.4603[/C][/ROW]
[ROW][C]48[/C][C] 15[/C][C] 15.35[/C][C]-0.3537[/C][/ROW]
[ROW][C]49[/C][C] 20[/C][C] 19.55[/C][C] 0.449[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 16.78[/C][C] 0.2175[/C][/ROW]
[ROW][C]51[/C][C] 19[/C][C] 17.76[/C][C] 1.242[/C][/ROW]
[ROW][C]52[/C][C] 20[/C][C] 18.84[/C][C] 1.162[/C][/ROW]
[ROW][C]53[/C][C] 18[/C][C] 17.56[/C][C] 0.4386[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.37[/C][C] 0.6275[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 16.53[/C][C] 1.473[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 17.94[/C][C]-0.9353[/C][/ROW]
[ROW][C]57[/C][C] 20[/C][C] 17.39[/C][C] 2.609[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 16.56[/C][C]-0.5631[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 16.85[/C][C]-2.855[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 16.19[/C][C]-1.194[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 18.02[/C][C] 1.975[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 16.52[/C][C] 0.4842[/C][/ROW]
[ROW][C]63[/C][C] 17[/C][C] 16.52[/C][C] 0.4834[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 14.94[/C][C] 3.057[/C][/ROW]
[ROW][C]65[/C][C] 20[/C][C] 17.12[/C][C] 2.88[/C][/ROW]
[ROW][C]66[/C][C] 16[/C][C] 15.31[/C][C] 0.6882[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 17.28[/C][C] 0.7248[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 17.45[/C][C]-2.449[/C][/ROW]
[ROW][C]69[/C][C] 18[/C][C] 18.56[/C][C]-0.5613[/C][/ROW]
[ROW][C]70[/C][C] 20[/C][C] 18.98[/C][C] 1.016[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 17.69[/C][C]-3.688[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 15.95[/C][C]-0.9519[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.86[/C][C] 0.1375[/C][/ROW]
[ROW][C]74[/C][C] 18[/C][C] 18.59[/C][C]-0.5875[/C][/ROW]
[ROW][C]75[/C][C] 20[/C][C] 16.82[/C][C] 3.182[/C][/ROW]
[ROW][C]76[/C][C] 17[/C][C] 17.59[/C][C]-0.5866[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 16.97[/C][C]-0.9748[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 14.98[/C][C]-3.982[/C][/ROW]
[ROW][C]79[/C][C] 15[/C][C] 16.93[/C][C]-1.932[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 16.02[/C][C] 1.977[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 17.95[/C][C]-1.952[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 16.96[/C][C] 1.042[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 17.1[/C][C]-2.097[/C][/ROW]
[ROW][C]84[/C][C] 17[/C][C] 16.25[/C][C] 0.7521[/C][/ROW]
[ROW][C]85[/C][C] 19[/C][C] 16.85[/C][C] 2.146[/C][/ROW]
[ROW][C]86[/C][C] 16[/C][C] 16.95[/C][C]-0.9487[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 14.46[/C][C]-0.4582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

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

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 18 17.78 0.2199
2 17 16.48 0.5216
3 19 17.88 1.122
4 17 17.12-0.1244
5 19 17.94 1.058
6 20 17.3 2.695
7 19 15.73 3.265
8 16 15.27 0.734
9 16 16.67-0.6722
10 18 15.82 2.178
11 16 16.64-0.6432
12 17 19.45-2.453
13 20 19.42 0.5758
14 19 17.62 1.375
15 7 16.76-9.759
16 16 15.92 0.07617
17 16 17.27-1.266
18 18 17 0.997
19 17 16.96 0.04417
20 19 17.78 1.221
21 16 16.87-0.8746
22 13 13.57-0.5669
23 16 16.66-0.6581
24 12 16.54-4.541
25 17 15.73 1.274
26 17 17.12-0.1238
27 17 16.75 0.2491
28 16 17.33-1.334
29 16 16.01-0.008562
30 14 15.51-1.505
31 16 14.68 1.317
32 13 16.95-3.954
33 16 15.49 0.5099
34 14 15.62-1.622
35 19 17.27 1.726
36 18 16.98 1.018
37 14 16.2-2.204
38 18 15.93 2.067
39 15 14.07 0.9312
40 17 16.19 0.8148
41 13 15.12-2.118
42 19 18.43 0.5747
43 18 17.37 0.6342
44 15 14.69 0.3073
45 20 17.96 2.043
46 19 16.28 2.722
47 18 17.54 0.4603
48 15 15.35-0.3537
49 20 19.55 0.449
50 17 16.78 0.2175
51 19 17.76 1.242
52 20 18.84 1.162
53 18 17.56 0.4386
54 17 16.37 0.6275
55 18 16.53 1.473
56 17 17.94-0.9353
57 20 17.39 2.609
58 16 16.56-0.5631
59 14 16.85-2.855
60 15 16.19-1.194
61 20 18.02 1.975
62 17 16.52 0.4842
63 17 16.52 0.4834
64 18 14.94 3.057
65 20 17.12 2.88
66 16 15.31 0.6882
67 18 17.28 0.7248
68 15 17.45-2.449
69 18 18.56-0.5613
70 20 18.98 1.016
71 14 17.69-3.688
72 15 15.95-0.9519
73 17 16.86 0.1375
74 18 18.59-0.5875
75 20 16.82 3.182
76 17 17.59-0.5866
77 16 16.97-0.9748
78 11 14.98-3.982
79 15 16.93-1.932
80 18 16.02 1.977
81 16 17.95-1.952
82 18 16.96 1.042
83 15 17.1-2.097
84 17 16.25 0.7521
85 19 16.85 2.146
86 16 16.95-0.9487
87 14 14.46-0.4582






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
19 0.866 0.2679 0.134
20 0.8174 0.3651 0.1826
21 0.9442 0.1115 0.05577
22 0.9193 0.1614 0.08069
23 0.9219 0.1562 0.07809
24 0.9488 0.1024 0.0512
25 0.9773 0.04547 0.02274
26 0.9626 0.0748 0.0374
27 0.9543 0.09143 0.04572
28 0.9416 0.1167 0.05836
29 0.9357 0.1285 0.06427
30 0.9227 0.1545 0.07727
31 0.9056 0.1889 0.09445
32 0.9655 0.06896 0.03448
33 0.9482 0.1035 0.05177
34 0.9318 0.1364 0.06821
35 0.9287 0.1426 0.07129
36 0.9059 0.1881 0.09406
37 0.9269 0.1462 0.07308
38 0.9441 0.1118 0.05589
39 0.926 0.148 0.07402
40 0.9085 0.183 0.09148
41 0.9136 0.1728 0.08639
42 0.898 0.204 0.102
43 0.8787 0.2425 0.1213
44 0.8597 0.2806 0.1403
45 0.8825 0.2349 0.1175
46 0.9145 0.1709 0.08545
47 0.8841 0.2318 0.1159
48 0.8499 0.3003 0.1501
49 0.8684 0.2633 0.1316
50 0.8207 0.3586 0.1793
51 0.8812 0.2376 0.1188
52 0.8565 0.2871 0.1435
53 0.9298 0.1404 0.07018
54 0.9002 0.1995 0.09977
55 0.89 0.22 0.11
56 0.8422 0.3156 0.1578
57 0.8876 0.2247 0.1124
58 0.8919 0.2163 0.1081
59 0.8785 0.243 0.1215
60 0.8256 0.3487 0.1744
61 0.776 0.448 0.224
62 0.8328 0.3345 0.1672
63 0.795 0.4099 0.205
64 0.7659 0.4682 0.2341
65 0.8409 0.3183 0.1591
66 0.8681 0.2638 0.1319
67 0.8666 0.2667 0.1334
68 0.8685 0.263 0.1315

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 &  0.866 &  0.2679 &  0.134 \tabularnewline
20 &  0.8174 &  0.3651 &  0.1826 \tabularnewline
21 &  0.9442 &  0.1115 &  0.05577 \tabularnewline
22 &  0.9193 &  0.1614 &  0.08069 \tabularnewline
23 &  0.9219 &  0.1562 &  0.07809 \tabularnewline
24 &  0.9488 &  0.1024 &  0.0512 \tabularnewline
25 &  0.9773 &  0.04547 &  0.02274 \tabularnewline
26 &  0.9626 &  0.0748 &  0.0374 \tabularnewline
27 &  0.9543 &  0.09143 &  0.04572 \tabularnewline
28 &  0.9416 &  0.1167 &  0.05836 \tabularnewline
29 &  0.9357 &  0.1285 &  0.06427 \tabularnewline
30 &  0.9227 &  0.1545 &  0.07727 \tabularnewline
31 &  0.9056 &  0.1889 &  0.09445 \tabularnewline
32 &  0.9655 &  0.06896 &  0.03448 \tabularnewline
33 &  0.9482 &  0.1035 &  0.05177 \tabularnewline
34 &  0.9318 &  0.1364 &  0.06821 \tabularnewline
35 &  0.9287 &  0.1426 &  0.07129 \tabularnewline
36 &  0.9059 &  0.1881 &  0.09406 \tabularnewline
37 &  0.9269 &  0.1462 &  0.07308 \tabularnewline
38 &  0.9441 &  0.1118 &  0.05589 \tabularnewline
39 &  0.926 &  0.148 &  0.07402 \tabularnewline
40 &  0.9085 &  0.183 &  0.09148 \tabularnewline
41 &  0.9136 &  0.1728 &  0.08639 \tabularnewline
42 &  0.898 &  0.204 &  0.102 \tabularnewline
43 &  0.8787 &  0.2425 &  0.1213 \tabularnewline
44 &  0.8597 &  0.2806 &  0.1403 \tabularnewline
45 &  0.8825 &  0.2349 &  0.1175 \tabularnewline
46 &  0.9145 &  0.1709 &  0.08545 \tabularnewline
47 &  0.8841 &  0.2318 &  0.1159 \tabularnewline
48 &  0.8499 &  0.3003 &  0.1501 \tabularnewline
49 &  0.8684 &  0.2633 &  0.1316 \tabularnewline
50 &  0.8207 &  0.3586 &  0.1793 \tabularnewline
51 &  0.8812 &  0.2376 &  0.1188 \tabularnewline
52 &  0.8565 &  0.2871 &  0.1435 \tabularnewline
53 &  0.9298 &  0.1404 &  0.07018 \tabularnewline
54 &  0.9002 &  0.1995 &  0.09977 \tabularnewline
55 &  0.89 &  0.22 &  0.11 \tabularnewline
56 &  0.8422 &  0.3156 &  0.1578 \tabularnewline
57 &  0.8876 &  0.2247 &  0.1124 \tabularnewline
58 &  0.8919 &  0.2163 &  0.1081 \tabularnewline
59 &  0.8785 &  0.243 &  0.1215 \tabularnewline
60 &  0.8256 &  0.3487 &  0.1744 \tabularnewline
61 &  0.776 &  0.448 &  0.224 \tabularnewline
62 &  0.8328 &  0.3345 &  0.1672 \tabularnewline
63 &  0.795 &  0.4099 &  0.205 \tabularnewline
64 &  0.7659 &  0.4682 &  0.2341 \tabularnewline
65 &  0.8409 &  0.3183 &  0.1591 \tabularnewline
66 &  0.8681 &  0.2638 &  0.1319 \tabularnewline
67 &  0.8666 &  0.2667 &  0.1334 \tabularnewline
68 &  0.8685 &  0.263 &  0.1315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]19[/C][C] 0.866[/C][C] 0.2679[/C][C] 0.134[/C][/ROW]
[ROW][C]20[/C][C] 0.8174[/C][C] 0.3651[/C][C] 0.1826[/C][/ROW]
[ROW][C]21[/C][C] 0.9442[/C][C] 0.1115[/C][C] 0.05577[/C][/ROW]
[ROW][C]22[/C][C] 0.9193[/C][C] 0.1614[/C][C] 0.08069[/C][/ROW]
[ROW][C]23[/C][C] 0.9219[/C][C] 0.1562[/C][C] 0.07809[/C][/ROW]
[ROW][C]24[/C][C] 0.9488[/C][C] 0.1024[/C][C] 0.0512[/C][/ROW]
[ROW][C]25[/C][C] 0.9773[/C][C] 0.04547[/C][C] 0.02274[/C][/ROW]
[ROW][C]26[/C][C] 0.9626[/C][C] 0.0748[/C][C] 0.0374[/C][/ROW]
[ROW][C]27[/C][C] 0.9543[/C][C] 0.09143[/C][C] 0.04572[/C][/ROW]
[ROW][C]28[/C][C] 0.9416[/C][C] 0.1167[/C][C] 0.05836[/C][/ROW]
[ROW][C]29[/C][C] 0.9357[/C][C] 0.1285[/C][C] 0.06427[/C][/ROW]
[ROW][C]30[/C][C] 0.9227[/C][C] 0.1545[/C][C] 0.07727[/C][/ROW]
[ROW][C]31[/C][C] 0.9056[/C][C] 0.1889[/C][C] 0.09445[/C][/ROW]
[ROW][C]32[/C][C] 0.9655[/C][C] 0.06896[/C][C] 0.03448[/C][/ROW]
[ROW][C]33[/C][C] 0.9482[/C][C] 0.1035[/C][C] 0.05177[/C][/ROW]
[ROW][C]34[/C][C] 0.9318[/C][C] 0.1364[/C][C] 0.06821[/C][/ROW]
[ROW][C]35[/C][C] 0.9287[/C][C] 0.1426[/C][C] 0.07129[/C][/ROW]
[ROW][C]36[/C][C] 0.9059[/C][C] 0.1881[/C][C] 0.09406[/C][/ROW]
[ROW][C]37[/C][C] 0.9269[/C][C] 0.1462[/C][C] 0.07308[/C][/ROW]
[ROW][C]38[/C][C] 0.9441[/C][C] 0.1118[/C][C] 0.05589[/C][/ROW]
[ROW][C]39[/C][C] 0.926[/C][C] 0.148[/C][C] 0.07402[/C][/ROW]
[ROW][C]40[/C][C] 0.9085[/C][C] 0.183[/C][C] 0.09148[/C][/ROW]
[ROW][C]41[/C][C] 0.9136[/C][C] 0.1728[/C][C] 0.08639[/C][/ROW]
[ROW][C]42[/C][C] 0.898[/C][C] 0.204[/C][C] 0.102[/C][/ROW]
[ROW][C]43[/C][C] 0.8787[/C][C] 0.2425[/C][C] 0.1213[/C][/ROW]
[ROW][C]44[/C][C] 0.8597[/C][C] 0.2806[/C][C] 0.1403[/C][/ROW]
[ROW][C]45[/C][C] 0.8825[/C][C] 0.2349[/C][C] 0.1175[/C][/ROW]
[ROW][C]46[/C][C] 0.9145[/C][C] 0.1709[/C][C] 0.08545[/C][/ROW]
[ROW][C]47[/C][C] 0.8841[/C][C] 0.2318[/C][C] 0.1159[/C][/ROW]
[ROW][C]48[/C][C] 0.8499[/C][C] 0.3003[/C][C] 0.1501[/C][/ROW]
[ROW][C]49[/C][C] 0.8684[/C][C] 0.2633[/C][C] 0.1316[/C][/ROW]
[ROW][C]50[/C][C] 0.8207[/C][C] 0.3586[/C][C] 0.1793[/C][/ROW]
[ROW][C]51[/C][C] 0.8812[/C][C] 0.2376[/C][C] 0.1188[/C][/ROW]
[ROW][C]52[/C][C] 0.8565[/C][C] 0.2871[/C][C] 0.1435[/C][/ROW]
[ROW][C]53[/C][C] 0.9298[/C][C] 0.1404[/C][C] 0.07018[/C][/ROW]
[ROW][C]54[/C][C] 0.9002[/C][C] 0.1995[/C][C] 0.09977[/C][/ROW]
[ROW][C]55[/C][C] 0.89[/C][C] 0.22[/C][C] 0.11[/C][/ROW]
[ROW][C]56[/C][C] 0.8422[/C][C] 0.3156[/C][C] 0.1578[/C][/ROW]
[ROW][C]57[/C][C] 0.8876[/C][C] 0.2247[/C][C] 0.1124[/C][/ROW]
[ROW][C]58[/C][C] 0.8919[/C][C] 0.2163[/C][C] 0.1081[/C][/ROW]
[ROW][C]59[/C][C] 0.8785[/C][C] 0.243[/C][C] 0.1215[/C][/ROW]
[ROW][C]60[/C][C] 0.8256[/C][C] 0.3487[/C][C] 0.1744[/C][/ROW]
[ROW][C]61[/C][C] 0.776[/C][C] 0.448[/C][C] 0.224[/C][/ROW]
[ROW][C]62[/C][C] 0.8328[/C][C] 0.3345[/C][C] 0.1672[/C][/ROW]
[ROW][C]63[/C][C] 0.795[/C][C] 0.4099[/C][C] 0.205[/C][/ROW]
[ROW][C]64[/C][C] 0.7659[/C][C] 0.4682[/C][C] 0.2341[/C][/ROW]
[ROW][C]65[/C][C] 0.8409[/C][C] 0.3183[/C][C] 0.1591[/C][/ROW]
[ROW][C]66[/C][C] 0.8681[/C][C] 0.2638[/C][C] 0.1319[/C][/ROW]
[ROW][C]67[/C][C] 0.8666[/C][C] 0.2667[/C][C] 0.1334[/C][/ROW]
[ROW][C]68[/C][C] 0.8685[/C][C] 0.263[/C][C] 0.1315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
19 0.866 0.2679 0.134
20 0.8174 0.3651 0.1826
21 0.9442 0.1115 0.05577
22 0.9193 0.1614 0.08069
23 0.9219 0.1562 0.07809
24 0.9488 0.1024 0.0512
25 0.9773 0.04547 0.02274
26 0.9626 0.0748 0.0374
27 0.9543 0.09143 0.04572
28 0.9416 0.1167 0.05836
29 0.9357 0.1285 0.06427
30 0.9227 0.1545 0.07727
31 0.9056 0.1889 0.09445
32 0.9655 0.06896 0.03448
33 0.9482 0.1035 0.05177
34 0.9318 0.1364 0.06821
35 0.9287 0.1426 0.07129
36 0.9059 0.1881 0.09406
37 0.9269 0.1462 0.07308
38 0.9441 0.1118 0.05589
39 0.926 0.148 0.07402
40 0.9085 0.183 0.09148
41 0.9136 0.1728 0.08639
42 0.898 0.204 0.102
43 0.8787 0.2425 0.1213
44 0.8597 0.2806 0.1403
45 0.8825 0.2349 0.1175
46 0.9145 0.1709 0.08545
47 0.8841 0.2318 0.1159
48 0.8499 0.3003 0.1501
49 0.8684 0.2633 0.1316
50 0.8207 0.3586 0.1793
51 0.8812 0.2376 0.1188
52 0.8565 0.2871 0.1435
53 0.9298 0.1404 0.07018
54 0.9002 0.1995 0.09977
55 0.89 0.22 0.11
56 0.8422 0.3156 0.1578
57 0.8876 0.2247 0.1124
58 0.8919 0.2163 0.1081
59 0.8785 0.243 0.1215
60 0.8256 0.3487 0.1744
61 0.776 0.448 0.224
62 0.8328 0.3345 0.1672
63 0.795 0.4099 0.205
64 0.7659 0.4682 0.2341
65 0.8409 0.3183 0.1591
66 0.8681 0.2638 0.1319
67 0.8666 0.2667 0.1334
68 0.8685 0.263 0.1315






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level10.02OK
10% type I error level40.08OK

\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 & 1 & 0.02 & OK \tabularnewline
10% type I error level & 4 & 0.08 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]1[/C][C]0.02[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.08[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 level0 0OK
5% type I error level10.02OK
10% type I error level40.08OK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.028764, df1 = 2, df2 = 69, p-value = 0.9717
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.86497, df1 = 30, df2 = 41, p-value = 0.6571
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.3589, df1 = 2, df2 = 69, p-value = 0.01649


\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 = 0.028764, df1 = 2, df2 = 69, p-value = 0.9717

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.86497, df1 = 30, df2 = 41, p-value = 0.6571

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.3589, df1 = 2, df2 = 69, p-value = 0.01649

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=7

[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 = 0.028764, df1 = 2, df2 = 69, p-value = 0.9717

[/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 = 0.86497, df1 = 30, df2 = 41, p-value = 0.6571

[/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 = 4.3589, df1 = 2, df2 = 69, p-value = 0.01649

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=7

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 = 0.028764, df1 = 2, df2 = 69, p-value = 0.9717
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.86497, df1 = 30, df2 = 41, p-value = 0.6571
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.3589, df1 = 2, df2 = 69, p-value = 0.01649






Variance Inflation Factors (Multicollinearity)
> vif
 Bevr_Leeftijd           TVDC       SKEOUSUM  `ITHSUM(t-1)`  `ITHSUM(t-2)` 
      1.182209       1.528647       1.595694       1.174005       1.195563 
 `ITHSUM(t-3)`  `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)` 
      1.207578       1.135659       1.213046       1.071455       1.075107 
 `ITHSUM(t-8)`  `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.136009       1.247425       1.137146       1.162648       1.162089 


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

> vif
 Bevr_Leeftijd           TVDC       SKEOUSUM  `ITHSUM(t-1)`  `ITHSUM(t-2)` 
      1.182209       1.528647       1.595694       1.174005       1.195563 
 `ITHSUM(t-3)`  `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)` 
      1.207578       1.135659       1.213046       1.071455       1.075107 
 `ITHSUM(t-8)`  `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.136009       1.247425       1.137146       1.162648       1.162089 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
 Bevr_Leeftijd           TVDC       SKEOUSUM  `ITHSUM(t-1)`  `ITHSUM(t-2)` 
      1.182209       1.528647       1.595694       1.174005       1.195563 
 `ITHSUM(t-3)`  `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)` 
      1.207578       1.135659       1.213046       1.071455       1.075107 
 `ITHSUM(t-8)`  `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.136009       1.247425       1.137146       1.162648       1.162089 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=8

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           TVDC       SKEOUSUM  `ITHSUM(t-1)`  `ITHSUM(t-2)` 
      1.182209       1.528647       1.595694       1.174005       1.195563 
 `ITHSUM(t-3)`  `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)` 
      1.207578       1.135659       1.213046       1.071455       1.075107 
 `ITHSUM(t-8)`  `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.136009       1.247425       1.137146       1.162648       1.162089


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 363611212121212128111111111 ; par2 = 112SingleDoubleDoubleSingleSingleSingle02222222Do not include Seasonal DummiesDo not include Seasonal Dummies ; par3 = 00Exact Pearson Chi-Squared by SimulationadditiveadditiveadditiveadditiveadditiveadditiveTRUE0.990.990.990.990.990.99No Linear TrendNo Linear Trend ; par4 = 0112121212121212two.sidedtwo.sidedtwo.sidedtwo.sidedtwo.sidedtwo.sided1212 ; par5 = 1212unpairedpairedpairedunpairedpairedunpaired ; par6 = White NoiseWhite Noise0.00.00.00.000 ; par7 = 0.950.95 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 12 ; par5 = ;
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')