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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 computationWed, 25 Jan 2017 11:04:25 +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/25/t1485338715mvn2q7szvfilipe.htm/, Retrieved Tue, 14 May 2024 11:38:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306551, Retrieved Tue, 14 May 2024 11:38:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2017-01-25 10:04:25] [3e1967f5210cd535e22e7f22478b75ce] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&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]7 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306551&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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 time7 seconds
R ServerBig Analytics Cloud Computing Center






Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 14.418 -0.0723318ITHSUM[t] + 0.480727SKEOUSUM[t] -0.0555452Bevr_Leeftijd[t] -0.22524`TVDC(t-1)`[t] + 0.0575471`TVDC(t-2)`[t] + 0.0653889`TVDC(t-3)`[t] -0.098749`TVDC(t-4)`[t] -0.067819`TVDC(t-5)`[t] -0.0658449`TVDC(t-6)`[t] + 0.0101674`TVDC(t-7)`[t] + 0.112194`TVDC(t-8)`[t] -0.11866`TVDC(t-9)`[t] -0.0810897`TVDC(t-10)`[t] -0.156746`TVDC(t-11)`[t] + 0.0146598`TVDC(t-12)`[t] + 0.00314663t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  14.418 -0.0723318ITHSUM[t] +  0.480727SKEOUSUM[t] -0.0555452Bevr_Leeftijd[t] -0.22524`TVDC(t-1)`[t] +  0.0575471`TVDC(t-2)`[t] +  0.0653889`TVDC(t-3)`[t] -0.098749`TVDC(t-4)`[t] -0.067819`TVDC(t-5)`[t] -0.0658449`TVDC(t-6)`[t] +  0.0101674`TVDC(t-7)`[t] +  0.112194`TVDC(t-8)`[t] -0.11866`TVDC(t-9)`[t] -0.0810897`TVDC(t-10)`[t] -0.156746`TVDC(t-11)`[t] +  0.0146598`TVDC(t-12)`[t] +  0.00314663t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  14.418 -0.0723318ITHSUM[t] +  0.480727SKEOUSUM[t] -0.0555452Bevr_Leeftijd[t] -0.22524`TVDC(t-1)`[t] +  0.0575471`TVDC(t-2)`[t] +  0.0653889`TVDC(t-3)`[t] -0.098749`TVDC(t-4)`[t] -0.067819`TVDC(t-5)`[t] -0.0658449`TVDC(t-6)`[t] +  0.0101674`TVDC(t-7)`[t] +  0.112194`TVDC(t-8)`[t] -0.11866`TVDC(t-9)`[t] -0.0810897`TVDC(t-10)`[t] -0.156746`TVDC(t-11)`[t] +  0.0146598`TVDC(t-12)`[t] +  0.00314663t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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
TVDC[t] = + 14.418 -0.0723318ITHSUM[t] + 0.480727SKEOUSUM[t] -0.0555452Bevr_Leeftijd[t] -0.22524`TVDC(t-1)`[t] + 0.0575471`TVDC(t-2)`[t] + 0.0653889`TVDC(t-3)`[t] -0.098749`TVDC(t-4)`[t] -0.067819`TVDC(t-5)`[t] -0.0658449`TVDC(t-6)`[t] + 0.0101674`TVDC(t-7)`[t] + 0.112194`TVDC(t-8)`[t] -0.11866`TVDC(t-9)`[t] -0.0810897`TVDC(t-10)`[t] -0.156746`TVDC(t-11)`[t] + 0.0146598`TVDC(t-12)`[t] + 0.00314663t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+14.42 6.715+2.1470e+00 0.03526 0.01763
ITHSUM-0.07233 0.09461-7.6450e-01 0.4471 0.2236
SKEOUSUM+0.4807 0.1148+4.1890e+00 8.038e-05 4.019e-05
Bevr_Leeftijd-0.05554 0.1139-4.8780e-01 0.6272 0.3136
`TVDC(t-1)`-0.2252 0.1105-2.0380e+00 0.04531 0.02265
`TVDC(t-2)`+0.05755 0.11+5.2320e-01 0.6025 0.3012
`TVDC(t-3)`+0.06539 0.1075+6.0840e-01 0.5449 0.2724
`TVDC(t-4)`-0.09875 0.1049-9.4170e-01 0.3496 0.1748
`TVDC(t-5)`-0.06782 0.1073-6.3180e-01 0.5295 0.2648
`TVDC(t-6)`-0.06585 0.106-6.2100e-01 0.5366 0.2683
`TVDC(t-7)`+0.01017 0.106+9.5920e-02 0.9239 0.4619
`TVDC(t-8)`+0.1122 0.1106+1.0140e+00 0.314 0.157
`TVDC(t-9)`-0.1187 0.1099-1.0800e+00 0.284 0.142
`TVDC(t-10)`-0.08109 0.1121-7.2330e-01 0.4719 0.236
`TVDC(t-11)`-0.1568 0.1098-1.4270e+00 0.158 0.07901
`TVDC(t-12)`+0.01466 0.1125+1.3030e-01 0.8967 0.4484
t+0.003147 0.007506+4.1920e-01 0.6763 0.3382

\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) & +14.42 &  6.715 & +2.1470e+00 &  0.03526 &  0.01763 \tabularnewline
ITHSUM & -0.07233 &  0.09461 & -7.6450e-01 &  0.4471 &  0.2236 \tabularnewline
SKEOUSUM & +0.4807 &  0.1148 & +4.1890e+00 &  8.038e-05 &  4.019e-05 \tabularnewline
Bevr_Leeftijd & -0.05554 &  0.1139 & -4.8780e-01 &  0.6272 &  0.3136 \tabularnewline
`TVDC(t-1)` & -0.2252 &  0.1105 & -2.0380e+00 &  0.04531 &  0.02265 \tabularnewline
`TVDC(t-2)` & +0.05755 &  0.11 & +5.2320e-01 &  0.6025 &  0.3012 \tabularnewline
`TVDC(t-3)` & +0.06539 &  0.1075 & +6.0840e-01 &  0.5449 &  0.2724 \tabularnewline
`TVDC(t-4)` & -0.09875 &  0.1049 & -9.4170e-01 &  0.3496 &  0.1748 \tabularnewline
`TVDC(t-5)` & -0.06782 &  0.1073 & -6.3180e-01 &  0.5295 &  0.2648 \tabularnewline
`TVDC(t-6)` & -0.06585 &  0.106 & -6.2100e-01 &  0.5366 &  0.2683 \tabularnewline
`TVDC(t-7)` & +0.01017 &  0.106 & +9.5920e-02 &  0.9239 &  0.4619 \tabularnewline
`TVDC(t-8)` & +0.1122 &  0.1106 & +1.0140e+00 &  0.314 &  0.157 \tabularnewline
`TVDC(t-9)` & -0.1187 &  0.1099 & -1.0800e+00 &  0.284 &  0.142 \tabularnewline
`TVDC(t-10)` & -0.08109 &  0.1121 & -7.2330e-01 &  0.4719 &  0.236 \tabularnewline
`TVDC(t-11)` & -0.1568 &  0.1098 & -1.4270e+00 &  0.158 &  0.07901 \tabularnewline
`TVDC(t-12)` & +0.01466 &  0.1125 & +1.3030e-01 &  0.8967 &  0.4484 \tabularnewline
t & +0.003147 &  0.007506 & +4.1920e-01 &  0.6763 &  0.3382 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&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]+14.42[/C][C] 6.715[/C][C]+2.1470e+00[/C][C] 0.03526[/C][C] 0.01763[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-0.07233[/C][C] 0.09461[/C][C]-7.6450e-01[/C][C] 0.4471[/C][C] 0.2236[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.4807[/C][C] 0.1148[/C][C]+4.1890e+00[/C][C] 8.038e-05[/C][C] 4.019e-05[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.05554[/C][C] 0.1139[/C][C]-4.8780e-01[/C][C] 0.6272[/C][C] 0.3136[/C][/ROW]
[ROW][C]`TVDC(t-1)`[/C][C]-0.2252[/C][C] 0.1105[/C][C]-2.0380e+00[/C][C] 0.04531[/C][C] 0.02265[/C][/ROW]
[ROW][C]`TVDC(t-2)`[/C][C]+0.05755[/C][C] 0.11[/C][C]+5.2320e-01[/C][C] 0.6025[/C][C] 0.3012[/C][/ROW]
[ROW][C]`TVDC(t-3)`[/C][C]+0.06539[/C][C] 0.1075[/C][C]+6.0840e-01[/C][C] 0.5449[/C][C] 0.2724[/C][/ROW]
[ROW][C]`TVDC(t-4)`[/C][C]-0.09875[/C][C] 0.1049[/C][C]-9.4170e-01[/C][C] 0.3496[/C][C] 0.1748[/C][/ROW]
[ROW][C]`TVDC(t-5)`[/C][C]-0.06782[/C][C] 0.1073[/C][C]-6.3180e-01[/C][C] 0.5295[/C][C] 0.2648[/C][/ROW]
[ROW][C]`TVDC(t-6)`[/C][C]-0.06585[/C][C] 0.106[/C][C]-6.2100e-01[/C][C] 0.5366[/C][C] 0.2683[/C][/ROW]
[ROW][C]`TVDC(t-7)`[/C][C]+0.01017[/C][C] 0.106[/C][C]+9.5920e-02[/C][C] 0.9239[/C][C] 0.4619[/C][/ROW]
[ROW][C]`TVDC(t-8)`[/C][C]+0.1122[/C][C] 0.1106[/C][C]+1.0140e+00[/C][C] 0.314[/C][C] 0.157[/C][/ROW]
[ROW][C]`TVDC(t-9)`[/C][C]-0.1187[/C][C] 0.1099[/C][C]-1.0800e+00[/C][C] 0.284[/C][C] 0.142[/C][/ROW]
[ROW][C]`TVDC(t-10)`[/C][C]-0.08109[/C][C] 0.1121[/C][C]-7.2330e-01[/C][C] 0.4719[/C][C] 0.236[/C][/ROW]
[ROW][C]`TVDC(t-11)`[/C][C]-0.1568[/C][C] 0.1098[/C][C]-1.4270e+00[/C][C] 0.158[/C][C] 0.07901[/C][/ROW]
[ROW][C]`TVDC(t-12)`[/C][C]+0.01466[/C][C] 0.1125[/C][C]+1.3030e-01[/C][C] 0.8967[/C][C] 0.4484[/C][/ROW]
[ROW][C]t[/C][C]+0.003147[/C][C] 0.007506[/C][C]+4.1920e-01[/C][C] 0.6763[/C][C] 0.3382[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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)+14.42 6.715+2.1470e+00 0.03526 0.01763
ITHSUM-0.07233 0.09461-7.6450e-01 0.4471 0.2236
SKEOUSUM+0.4807 0.1148+4.1890e+00 8.038e-05 4.019e-05
Bevr_Leeftijd-0.05554 0.1139-4.8780e-01 0.6272 0.3136
`TVDC(t-1)`-0.2252 0.1105-2.0380e+00 0.04531 0.02265
`TVDC(t-2)`+0.05755 0.11+5.2320e-01 0.6025 0.3012
`TVDC(t-3)`+0.06539 0.1075+6.0840e-01 0.5449 0.2724
`TVDC(t-4)`-0.09875 0.1049-9.4170e-01 0.3496 0.1748
`TVDC(t-5)`-0.06782 0.1073-6.3180e-01 0.5295 0.2648
`TVDC(t-6)`-0.06585 0.106-6.2100e-01 0.5366 0.2683
`TVDC(t-7)`+0.01017 0.106+9.5920e-02 0.9239 0.4619
`TVDC(t-8)`+0.1122 0.1106+1.0140e+00 0.314 0.157
`TVDC(t-9)`-0.1187 0.1099-1.0800e+00 0.284 0.142
`TVDC(t-10)`-0.08109 0.1121-7.2330e-01 0.4719 0.236
`TVDC(t-11)`-0.1568 0.1098-1.4270e+00 0.158 0.07901
`TVDC(t-12)`+0.01466 0.1125+1.3030e-01 0.8967 0.4484
t+0.003147 0.007506+4.1920e-01 0.6763 0.3382






Multiple Linear Regression - Regression Statistics
Multiple R 0.5904
R-squared 0.3486
Adjusted R-squared 0.1997
F-TEST (value) 2.341
F-TEST (DF numerator)16
F-TEST (DF denominator)70
p-value 0.007764
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.735
Sum Squared Residuals 210.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5904 \tabularnewline
R-squared &  0.3486 \tabularnewline
Adjusted R-squared &  0.1997 \tabularnewline
F-TEST (value) &  2.341 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value &  0.007764 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.735 \tabularnewline
Sum Squared Residuals &  210.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5904[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3486[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1997[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.341[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/C][/ROW]
[ROW][C]p-value[/C][C] 0.007764[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.735[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 210.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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 0.5904
R-squared 0.3486
Adjusted R-squared 0.1997
F-TEST (value) 2.341
F-TEST (DF numerator)16
F-TEST (DF denominator)70
p-value 0.007764
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.735
Sum Squared Residuals 210.8






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 16 14.93 1.067
2 15 14.68 0.3206
3 16 14.88 1.121
4 16 14.6 1.4
5 15 16.64-1.637
6 17 16.55 0.4506
7 13 14.07-1.069
8 17 14.93 2.068
9 14 14.5-0.5034
10 14 13.98 0.01553
11 18 16.2 1.795
12 17 16.01 0.9891
13 16 16.52-0.5246
14 15 15.88-0.8804
15 15 15.4-0.4037
16 15 15.03-0.03384
17 13 14.85-1.855
18 17 16.77 0.2281
19 11 15.96-4.961
20 14 15.71-1.715
21 13 15.96-2.963
22 17 14.06 2.942
23 16 14.91 1.088
24 17 16.7 0.3022
25 16 15.51 0.4866
26 16 16.67-0.67
27 16 13.22 2.784
28 15 16.9-1.899
29 12 14.06-2.056
30 17 16.67 0.3263
31 14 14.66-0.6608
32 14 16.32-2.325
33 16 15.41 0.5873
34 15 13.93 1.075
35 16 15.46 0.5409
36 14 15.29-1.286
37 15 15.36-0.3582
38 17 15.62 1.383
39 10 13.18-3.178
40 17 17.56-0.5611
41 20 15.49 4.505
42 17 14.88 2.116
43 18 17.13 0.8703
44 14 13.04 0.9638
45 17 16.32 0.6775
46 17 14.96 2.044
47 16 14.46 1.54
48 18 16.44 1.557
49 18 16.73 1.272
50 16 16.45-0.4535
51 15 15.24-0.2426
52 13 14.33-1.326
53 16 15.65 0.3481
54 12 13.62-1.622
55 16 15.63 0.3717
56 16 14.81 1.187
57 16 16.54-0.5414
58 14 14.83-0.8306
59 15 15.31-0.3094
60 14 14.47-0.472
61 15 15.78-0.7803
62 15 14.54 0.4554
63 16 15.44 0.5553
64 11 12.86-1.861
65 18 17.02 0.9828
66 11 13.22-2.222
67 18 17.54 0.4631
68 15 16.01-1.011
69 19 17.79 1.206
70 17 16.94 0.05615
71 14 16.06-2.064
72 13 14.71-1.711
73 17 16.58 0.4224
74 14 14.72-0.7176
75 19 17.23 1.765
76 14 14.22-0.2162
77 16 17.33-1.333
78 16 14.79 1.215
79 15 14.94 0.05507
80 12 15.21-3.215
81 17 17.85-0.8516
82 18 15.34 2.66
83 15 15.51-0.5113
84 18 14.84 3.163
85 15 16.33-1.33
86 16 15.44 0.5586
87 16 14.82 1.178

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  16 &  14.93 &  1.067 \tabularnewline
2 &  15 &  14.68 &  0.3206 \tabularnewline
3 &  16 &  14.88 &  1.121 \tabularnewline
4 &  16 &  14.6 &  1.4 \tabularnewline
5 &  15 &  16.64 & -1.637 \tabularnewline
6 &  17 &  16.55 &  0.4506 \tabularnewline
7 &  13 &  14.07 & -1.069 \tabularnewline
8 &  17 &  14.93 &  2.068 \tabularnewline
9 &  14 &  14.5 & -0.5034 \tabularnewline
10 &  14 &  13.98 &  0.01553 \tabularnewline
11 &  18 &  16.2 &  1.795 \tabularnewline
12 &  17 &  16.01 &  0.9891 \tabularnewline
13 &  16 &  16.52 & -0.5246 \tabularnewline
14 &  15 &  15.88 & -0.8804 \tabularnewline
15 &  15 &  15.4 & -0.4037 \tabularnewline
16 &  15 &  15.03 & -0.03384 \tabularnewline
17 &  13 &  14.85 & -1.855 \tabularnewline
18 &  17 &  16.77 &  0.2281 \tabularnewline
19 &  11 &  15.96 & -4.961 \tabularnewline
20 &  14 &  15.71 & -1.715 \tabularnewline
21 &  13 &  15.96 & -2.963 \tabularnewline
22 &  17 &  14.06 &  2.942 \tabularnewline
23 &  16 &  14.91 &  1.088 \tabularnewline
24 &  17 &  16.7 &  0.3022 \tabularnewline
25 &  16 &  15.51 &  0.4866 \tabularnewline
26 &  16 &  16.67 & -0.67 \tabularnewline
27 &  16 &  13.22 &  2.784 \tabularnewline
28 &  15 &  16.9 & -1.899 \tabularnewline
29 &  12 &  14.06 & -2.056 \tabularnewline
30 &  17 &  16.67 &  0.3263 \tabularnewline
31 &  14 &  14.66 & -0.6608 \tabularnewline
32 &  14 &  16.32 & -2.325 \tabularnewline
33 &  16 &  15.41 &  0.5873 \tabularnewline
34 &  15 &  13.93 &  1.075 \tabularnewline
35 &  16 &  15.46 &  0.5409 \tabularnewline
36 &  14 &  15.29 & -1.286 \tabularnewline
37 &  15 &  15.36 & -0.3582 \tabularnewline
38 &  17 &  15.62 &  1.383 \tabularnewline
39 &  10 &  13.18 & -3.178 \tabularnewline
40 &  17 &  17.56 & -0.5611 \tabularnewline
41 &  20 &  15.49 &  4.505 \tabularnewline
42 &  17 &  14.88 &  2.116 \tabularnewline
43 &  18 &  17.13 &  0.8703 \tabularnewline
44 &  14 &  13.04 &  0.9638 \tabularnewline
45 &  17 &  16.32 &  0.6775 \tabularnewline
46 &  17 &  14.96 &  2.044 \tabularnewline
47 &  16 &  14.46 &  1.54 \tabularnewline
48 &  18 &  16.44 &  1.557 \tabularnewline
49 &  18 &  16.73 &  1.272 \tabularnewline
50 &  16 &  16.45 & -0.4535 \tabularnewline
51 &  15 &  15.24 & -0.2426 \tabularnewline
52 &  13 &  14.33 & -1.326 \tabularnewline
53 &  16 &  15.65 &  0.3481 \tabularnewline
54 &  12 &  13.62 & -1.622 \tabularnewline
55 &  16 &  15.63 &  0.3717 \tabularnewline
56 &  16 &  14.81 &  1.187 \tabularnewline
57 &  16 &  16.54 & -0.5414 \tabularnewline
58 &  14 &  14.83 & -0.8306 \tabularnewline
59 &  15 &  15.31 & -0.3094 \tabularnewline
60 &  14 &  14.47 & -0.472 \tabularnewline
61 &  15 &  15.78 & -0.7803 \tabularnewline
62 &  15 &  14.54 &  0.4554 \tabularnewline
63 &  16 &  15.44 &  0.5553 \tabularnewline
64 &  11 &  12.86 & -1.861 \tabularnewline
65 &  18 &  17.02 &  0.9828 \tabularnewline
66 &  11 &  13.22 & -2.222 \tabularnewline
67 &  18 &  17.54 &  0.4631 \tabularnewline
68 &  15 &  16.01 & -1.011 \tabularnewline
69 &  19 &  17.79 &  1.206 \tabularnewline
70 &  17 &  16.94 &  0.05615 \tabularnewline
71 &  14 &  16.06 & -2.064 \tabularnewline
72 &  13 &  14.71 & -1.711 \tabularnewline
73 &  17 &  16.58 &  0.4224 \tabularnewline
74 &  14 &  14.72 & -0.7176 \tabularnewline
75 &  19 &  17.23 &  1.765 \tabularnewline
76 &  14 &  14.22 & -0.2162 \tabularnewline
77 &  16 &  17.33 & -1.333 \tabularnewline
78 &  16 &  14.79 &  1.215 \tabularnewline
79 &  15 &  14.94 &  0.05507 \tabularnewline
80 &  12 &  15.21 & -3.215 \tabularnewline
81 &  17 &  17.85 & -0.8516 \tabularnewline
82 &  18 &  15.34 &  2.66 \tabularnewline
83 &  15 &  15.51 & -0.5113 \tabularnewline
84 &  18 &  14.84 &  3.163 \tabularnewline
85 &  15 &  16.33 & -1.33 \tabularnewline
86 &  16 &  15.44 &  0.5586 \tabularnewline
87 &  16 &  14.82 &  1.178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&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] 16[/C][C] 14.93[/C][C] 1.067[/C][/ROW]
[ROW][C]2[/C][C] 15[/C][C] 14.68[/C][C] 0.3206[/C][/ROW]
[ROW][C]3[/C][C] 16[/C][C] 14.88[/C][C] 1.121[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 14.6[/C][C] 1.4[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 16.64[/C][C]-1.637[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 16.55[/C][C] 0.4506[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 14.07[/C][C]-1.069[/C][/ROW]
[ROW][C]8[/C][C] 17[/C][C] 14.93[/C][C] 2.068[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 14.5[/C][C]-0.5034[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 13.98[/C][C] 0.01553[/C][/ROW]
[ROW][C]11[/C][C] 18[/C][C] 16.2[/C][C] 1.795[/C][/ROW]
[ROW][C]12[/C][C] 17[/C][C] 16.01[/C][C] 0.9891[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 16.52[/C][C]-0.5246[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.88[/C][C]-0.8804[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.4[/C][C]-0.4037[/C][/ROW]
[ROW][C]16[/C][C] 15[/C][C] 15.03[/C][C]-0.03384[/C][/ROW]
[ROW][C]17[/C][C] 13[/C][C] 14.85[/C][C]-1.855[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.77[/C][C] 0.2281[/C][/ROW]
[ROW][C]19[/C][C] 11[/C][C] 15.96[/C][C]-4.961[/C][/ROW]
[ROW][C]20[/C][C] 14[/C][C] 15.71[/C][C]-1.715[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 15.96[/C][C]-2.963[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 14.06[/C][C] 2.942[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 14.91[/C][C] 1.088[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 16.7[/C][C] 0.3022[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 15.51[/C][C] 0.4866[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 16.67[/C][C]-0.67[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 13.22[/C][C] 2.784[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 16.9[/C][C]-1.899[/C][/ROW]
[ROW][C]29[/C][C] 12[/C][C] 14.06[/C][C]-2.056[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 16.67[/C][C] 0.3263[/C][/ROW]
[ROW][C]31[/C][C] 14[/C][C] 14.66[/C][C]-0.6608[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 16.32[/C][C]-2.325[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 15.41[/C][C] 0.5873[/C][/ROW]
[ROW][C]34[/C][C] 15[/C][C] 13.93[/C][C] 1.075[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.46[/C][C] 0.5409[/C][/ROW]
[ROW][C]36[/C][C] 14[/C][C] 15.29[/C][C]-1.286[/C][/ROW]
[ROW][C]37[/C][C] 15[/C][C] 15.36[/C][C]-0.3582[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 15.62[/C][C] 1.383[/C][/ROW]
[ROW][C]39[/C][C] 10[/C][C] 13.18[/C][C]-3.178[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 17.56[/C][C]-0.5611[/C][/ROW]
[ROW][C]41[/C][C] 20[/C][C] 15.49[/C][C] 4.505[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 14.88[/C][C] 2.116[/C][/ROW]
[ROW][C]43[/C][C] 18[/C][C] 17.13[/C][C] 0.8703[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 13.04[/C][C] 0.9638[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 16.32[/C][C] 0.6775[/C][/ROW]
[ROW][C]46[/C][C] 17[/C][C] 14.96[/C][C] 2.044[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 14.46[/C][C] 1.54[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 16.44[/C][C] 1.557[/C][/ROW]
[ROW][C]49[/C][C] 18[/C][C] 16.73[/C][C] 1.272[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 16.45[/C][C]-0.4535[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 15.24[/C][C]-0.2426[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 14.33[/C][C]-1.326[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 15.65[/C][C] 0.3481[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 13.62[/C][C]-1.622[/C][/ROW]
[ROW][C]55[/C][C] 16[/C][C] 15.63[/C][C] 0.3717[/C][/ROW]
[ROW][C]56[/C][C] 16[/C][C] 14.81[/C][C] 1.187[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 16.54[/C][C]-0.5414[/C][/ROW]
[ROW][C]58[/C][C] 14[/C][C] 14.83[/C][C]-0.8306[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 15.31[/C][C]-0.3094[/C][/ROW]
[ROW][C]60[/C][C] 14[/C][C] 14.47[/C][C]-0.472[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 15.78[/C][C]-0.7803[/C][/ROW]
[ROW][C]62[/C][C] 15[/C][C] 14.54[/C][C] 0.4554[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 15.44[/C][C] 0.5553[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 12.86[/C][C]-1.861[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 17.02[/C][C] 0.9828[/C][/ROW]
[ROW][C]66[/C][C] 11[/C][C] 13.22[/C][C]-2.222[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 17.54[/C][C] 0.4631[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 16.01[/C][C]-1.011[/C][/ROW]
[ROW][C]69[/C][C] 19[/C][C] 17.79[/C][C] 1.206[/C][/ROW]
[ROW][C]70[/C][C] 17[/C][C] 16.94[/C][C] 0.05615[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 16.06[/C][C]-2.064[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 14.71[/C][C]-1.711[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.58[/C][C] 0.4224[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 14.72[/C][C]-0.7176[/C][/ROW]
[ROW][C]75[/C][C] 19[/C][C] 17.23[/C][C] 1.765[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.22[/C][C]-0.2162[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 17.33[/C][C]-1.333[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 14.79[/C][C] 1.215[/C][/ROW]
[ROW][C]79[/C][C] 15[/C][C] 14.94[/C][C] 0.05507[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 15.21[/C][C]-3.215[/C][/ROW]
[ROW][C]81[/C][C] 17[/C][C] 17.85[/C][C]-0.8516[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 15.34[/C][C] 2.66[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 15.51[/C][C]-0.5113[/C][/ROW]
[ROW][C]84[/C][C] 18[/C][C] 14.84[/C][C] 3.163[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 16.33[/C][C]-1.33[/C][/ROW]
[ROW][C]86[/C][C] 16[/C][C] 15.44[/C][C] 0.5586[/C][/ROW]
[ROW][C]87[/C][C] 16[/C][C] 14.82[/C][C] 1.178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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 16 14.93 1.067
2 15 14.68 0.3206
3 16 14.88 1.121
4 16 14.6 1.4
5 15 16.64-1.637
6 17 16.55 0.4506
7 13 14.07-1.069
8 17 14.93 2.068
9 14 14.5-0.5034
10 14 13.98 0.01553
11 18 16.2 1.795
12 17 16.01 0.9891
13 16 16.52-0.5246
14 15 15.88-0.8804
15 15 15.4-0.4037
16 15 15.03-0.03384
17 13 14.85-1.855
18 17 16.77 0.2281
19 11 15.96-4.961
20 14 15.71-1.715
21 13 15.96-2.963
22 17 14.06 2.942
23 16 14.91 1.088
24 17 16.7 0.3022
25 16 15.51 0.4866
26 16 16.67-0.67
27 16 13.22 2.784
28 15 16.9-1.899
29 12 14.06-2.056
30 17 16.67 0.3263
31 14 14.66-0.6608
32 14 16.32-2.325
33 16 15.41 0.5873
34 15 13.93 1.075
35 16 15.46 0.5409
36 14 15.29-1.286
37 15 15.36-0.3582
38 17 15.62 1.383
39 10 13.18-3.178
40 17 17.56-0.5611
41 20 15.49 4.505
42 17 14.88 2.116
43 18 17.13 0.8703
44 14 13.04 0.9638
45 17 16.32 0.6775
46 17 14.96 2.044
47 16 14.46 1.54
48 18 16.44 1.557
49 18 16.73 1.272
50 16 16.45-0.4535
51 15 15.24-0.2426
52 13 14.33-1.326
53 16 15.65 0.3481
54 12 13.62-1.622
55 16 15.63 0.3717
56 16 14.81 1.187
57 16 16.54-0.5414
58 14 14.83-0.8306
59 15 15.31-0.3094
60 14 14.47-0.472
61 15 15.78-0.7803
62 15 14.54 0.4554
63 16 15.44 0.5553
64 11 12.86-1.861
65 18 17.02 0.9828
66 11 13.22-2.222
67 18 17.54 0.4631
68 15 16.01-1.011
69 19 17.79 1.206
70 17 16.94 0.05615
71 14 16.06-2.064
72 13 14.71-1.711
73 17 16.58 0.4224
74 14 14.72-0.7176
75 19 17.23 1.765
76 14 14.22-0.2162
77 16 17.33-1.333
78 16 14.79 1.215
79 15 14.94 0.05507
80 12 15.21-3.215
81 17 17.85-0.8516
82 18 15.34 2.66
83 15 15.51-0.5113
84 18 14.84 3.163
85 15 16.33-1.33
86 16 15.44 0.5586
87 16 14.82 1.178






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
20 0.1124 0.2248 0.8876
21 0.4152 0.8304 0.5848
22 0.4897 0.9794 0.5103
23 0.3672 0.7345 0.6328
24 0.3983 0.7966 0.6017
25 0.2908 0.5816 0.7092
26 0.2358 0.4716 0.7642
27 0.2858 0.5715 0.7142
28 0.2508 0.5017 0.7492
29 0.2277 0.4554 0.7723
30 0.4415 0.8831 0.5585
31 0.6598 0.6805 0.3402
32 0.6629 0.6743 0.3372
33 0.749 0.502 0.251
34 0.6933 0.6135 0.3067
35 0.6265 0.7469 0.3735
36 0.5778 0.8445 0.4222
37 0.5162 0.9675 0.4838
38 0.4691 0.9381 0.5309
39 0.5935 0.813 0.4065
40 0.5802 0.8397 0.4198
41 0.8807 0.2386 0.1193
42 0.9462 0.1076 0.05382
43 0.9368 0.1264 0.0632
44 0.9255 0.149 0.07452
45 0.9097 0.1807 0.09033
46 0.9245 0.151 0.07549
47 0.9155 0.169 0.08452
48 0.9234 0.1531 0.07657
49 0.9244 0.1512 0.07558
50 0.8925 0.2149 0.1075
51 0.8659 0.2682 0.1341
52 0.8574 0.2852 0.1426
53 0.8663 0.2674 0.1337
54 0.8408 0.3183 0.1592
55 0.7893 0.4214 0.2107
56 0.739 0.5221 0.261
57 0.6656 0.6688 0.3344
58 0.5902 0.8195 0.4098
59 0.5293 0.9414 0.4707
60 0.6605 0.679 0.3395
61 0.5955 0.8091 0.4045
62 0.5356 0.9287 0.4644
63 0.6407 0.7185 0.3593
64 0.5839 0.8323 0.4161
65 0.664 0.6719 0.336
66 0.7107 0.5786 0.2893
67 0.7254 0.5493 0.2746

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 &  0.1124 &  0.2248 &  0.8876 \tabularnewline
21 &  0.4152 &  0.8304 &  0.5848 \tabularnewline
22 &  0.4897 &  0.9794 &  0.5103 \tabularnewline
23 &  0.3672 &  0.7345 &  0.6328 \tabularnewline
24 &  0.3983 &  0.7966 &  0.6017 \tabularnewline
25 &  0.2908 &  0.5816 &  0.7092 \tabularnewline
26 &  0.2358 &  0.4716 &  0.7642 \tabularnewline
27 &  0.2858 &  0.5715 &  0.7142 \tabularnewline
28 &  0.2508 &  0.5017 &  0.7492 \tabularnewline
29 &  0.2277 &  0.4554 &  0.7723 \tabularnewline
30 &  0.4415 &  0.8831 &  0.5585 \tabularnewline
31 &  0.6598 &  0.6805 &  0.3402 \tabularnewline
32 &  0.6629 &  0.6743 &  0.3372 \tabularnewline
33 &  0.749 &  0.502 &  0.251 \tabularnewline
34 &  0.6933 &  0.6135 &  0.3067 \tabularnewline
35 &  0.6265 &  0.7469 &  0.3735 \tabularnewline
36 &  0.5778 &  0.8445 &  0.4222 \tabularnewline
37 &  0.5162 &  0.9675 &  0.4838 \tabularnewline
38 &  0.4691 &  0.9381 &  0.5309 \tabularnewline
39 &  0.5935 &  0.813 &  0.4065 \tabularnewline
40 &  0.5802 &  0.8397 &  0.4198 \tabularnewline
41 &  0.8807 &  0.2386 &  0.1193 \tabularnewline
42 &  0.9462 &  0.1076 &  0.05382 \tabularnewline
43 &  0.9368 &  0.1264 &  0.0632 \tabularnewline
44 &  0.9255 &  0.149 &  0.07452 \tabularnewline
45 &  0.9097 &  0.1807 &  0.09033 \tabularnewline
46 &  0.9245 &  0.151 &  0.07549 \tabularnewline
47 &  0.9155 &  0.169 &  0.08452 \tabularnewline
48 &  0.9234 &  0.1531 &  0.07657 \tabularnewline
49 &  0.9244 &  0.1512 &  0.07558 \tabularnewline
50 &  0.8925 &  0.2149 &  0.1075 \tabularnewline
51 &  0.8659 &  0.2682 &  0.1341 \tabularnewline
52 &  0.8574 &  0.2852 &  0.1426 \tabularnewline
53 &  0.8663 &  0.2674 &  0.1337 \tabularnewline
54 &  0.8408 &  0.3183 &  0.1592 \tabularnewline
55 &  0.7893 &  0.4214 &  0.2107 \tabularnewline
56 &  0.739 &  0.5221 &  0.261 \tabularnewline
57 &  0.6656 &  0.6688 &  0.3344 \tabularnewline
58 &  0.5902 &  0.8195 &  0.4098 \tabularnewline
59 &  0.5293 &  0.9414 &  0.4707 \tabularnewline
60 &  0.6605 &  0.679 &  0.3395 \tabularnewline
61 &  0.5955 &  0.8091 &  0.4045 \tabularnewline
62 &  0.5356 &  0.9287 &  0.4644 \tabularnewline
63 &  0.6407 &  0.7185 &  0.3593 \tabularnewline
64 &  0.5839 &  0.8323 &  0.4161 \tabularnewline
65 &  0.664 &  0.6719 &  0.336 \tabularnewline
66 &  0.7107 &  0.5786 &  0.2893 \tabularnewline
67 &  0.7254 &  0.5493 &  0.2746 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&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]20[/C][C] 0.1124[/C][C] 0.2248[/C][C] 0.8876[/C][/ROW]
[ROW][C]21[/C][C] 0.4152[/C][C] 0.8304[/C][C] 0.5848[/C][/ROW]
[ROW][C]22[/C][C] 0.4897[/C][C] 0.9794[/C][C] 0.5103[/C][/ROW]
[ROW][C]23[/C][C] 0.3672[/C][C] 0.7345[/C][C] 0.6328[/C][/ROW]
[ROW][C]24[/C][C] 0.3983[/C][C] 0.7966[/C][C] 0.6017[/C][/ROW]
[ROW][C]25[/C][C] 0.2908[/C][C] 0.5816[/C][C] 0.7092[/C][/ROW]
[ROW][C]26[/C][C] 0.2358[/C][C] 0.4716[/C][C] 0.7642[/C][/ROW]
[ROW][C]27[/C][C] 0.2858[/C][C] 0.5715[/C][C] 0.7142[/C][/ROW]
[ROW][C]28[/C][C] 0.2508[/C][C] 0.5017[/C][C] 0.7492[/C][/ROW]
[ROW][C]29[/C][C] 0.2277[/C][C] 0.4554[/C][C] 0.7723[/C][/ROW]
[ROW][C]30[/C][C] 0.4415[/C][C] 0.8831[/C][C] 0.5585[/C][/ROW]
[ROW][C]31[/C][C] 0.6598[/C][C] 0.6805[/C][C] 0.3402[/C][/ROW]
[ROW][C]32[/C][C] 0.6629[/C][C] 0.6743[/C][C] 0.3372[/C][/ROW]
[ROW][C]33[/C][C] 0.749[/C][C] 0.502[/C][C] 0.251[/C][/ROW]
[ROW][C]34[/C][C] 0.6933[/C][C] 0.6135[/C][C] 0.3067[/C][/ROW]
[ROW][C]35[/C][C] 0.6265[/C][C] 0.7469[/C][C] 0.3735[/C][/ROW]
[ROW][C]36[/C][C] 0.5778[/C][C] 0.8445[/C][C] 0.4222[/C][/ROW]
[ROW][C]37[/C][C] 0.5162[/C][C] 0.9675[/C][C] 0.4838[/C][/ROW]
[ROW][C]38[/C][C] 0.4691[/C][C] 0.9381[/C][C] 0.5309[/C][/ROW]
[ROW][C]39[/C][C] 0.5935[/C][C] 0.813[/C][C] 0.4065[/C][/ROW]
[ROW][C]40[/C][C] 0.5802[/C][C] 0.8397[/C][C] 0.4198[/C][/ROW]
[ROW][C]41[/C][C] 0.8807[/C][C] 0.2386[/C][C] 0.1193[/C][/ROW]
[ROW][C]42[/C][C] 0.9462[/C][C] 0.1076[/C][C] 0.05382[/C][/ROW]
[ROW][C]43[/C][C] 0.9368[/C][C] 0.1264[/C][C] 0.0632[/C][/ROW]
[ROW][C]44[/C][C] 0.9255[/C][C] 0.149[/C][C] 0.07452[/C][/ROW]
[ROW][C]45[/C][C] 0.9097[/C][C] 0.1807[/C][C] 0.09033[/C][/ROW]
[ROW][C]46[/C][C] 0.9245[/C][C] 0.151[/C][C] 0.07549[/C][/ROW]
[ROW][C]47[/C][C] 0.9155[/C][C] 0.169[/C][C] 0.08452[/C][/ROW]
[ROW][C]48[/C][C] 0.9234[/C][C] 0.1531[/C][C] 0.07657[/C][/ROW]
[ROW][C]49[/C][C] 0.9244[/C][C] 0.1512[/C][C] 0.07558[/C][/ROW]
[ROW][C]50[/C][C] 0.8925[/C][C] 0.2149[/C][C] 0.1075[/C][/ROW]
[ROW][C]51[/C][C] 0.8659[/C][C] 0.2682[/C][C] 0.1341[/C][/ROW]
[ROW][C]52[/C][C] 0.8574[/C][C] 0.2852[/C][C] 0.1426[/C][/ROW]
[ROW][C]53[/C][C] 0.8663[/C][C] 0.2674[/C][C] 0.1337[/C][/ROW]
[ROW][C]54[/C][C] 0.8408[/C][C] 0.3183[/C][C] 0.1592[/C][/ROW]
[ROW][C]55[/C][C] 0.7893[/C][C] 0.4214[/C][C] 0.2107[/C][/ROW]
[ROW][C]56[/C][C] 0.739[/C][C] 0.5221[/C][C] 0.261[/C][/ROW]
[ROW][C]57[/C][C] 0.6656[/C][C] 0.6688[/C][C] 0.3344[/C][/ROW]
[ROW][C]58[/C][C] 0.5902[/C][C] 0.8195[/C][C] 0.4098[/C][/ROW]
[ROW][C]59[/C][C] 0.5293[/C][C] 0.9414[/C][C] 0.4707[/C][/ROW]
[ROW][C]60[/C][C] 0.6605[/C][C] 0.679[/C][C] 0.3395[/C][/ROW]
[ROW][C]61[/C][C] 0.5955[/C][C] 0.8091[/C][C] 0.4045[/C][/ROW]
[ROW][C]62[/C][C] 0.5356[/C][C] 0.9287[/C][C] 0.4644[/C][/ROW]
[ROW][C]63[/C][C] 0.6407[/C][C] 0.7185[/C][C] 0.3593[/C][/ROW]
[ROW][C]64[/C][C] 0.5839[/C][C] 0.8323[/C][C] 0.4161[/C][/ROW]
[ROW][C]65[/C][C] 0.664[/C][C] 0.6719[/C][C] 0.336[/C][/ROW]
[ROW][C]66[/C][C] 0.7107[/C][C] 0.5786[/C][C] 0.2893[/C][/ROW]
[ROW][C]67[/C][C] 0.7254[/C][C] 0.5493[/C][C] 0.2746[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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
20 0.1124 0.2248 0.8876
21 0.4152 0.8304 0.5848
22 0.4897 0.9794 0.5103
23 0.3672 0.7345 0.6328
24 0.3983 0.7966 0.6017
25 0.2908 0.5816 0.7092
26 0.2358 0.4716 0.7642
27 0.2858 0.5715 0.7142
28 0.2508 0.5017 0.7492
29 0.2277 0.4554 0.7723
30 0.4415 0.8831 0.5585
31 0.6598 0.6805 0.3402
32 0.6629 0.6743 0.3372
33 0.749 0.502 0.251
34 0.6933 0.6135 0.3067
35 0.6265 0.7469 0.3735
36 0.5778 0.8445 0.4222
37 0.5162 0.9675 0.4838
38 0.4691 0.9381 0.5309
39 0.5935 0.813 0.4065
40 0.5802 0.8397 0.4198
41 0.8807 0.2386 0.1193
42 0.9462 0.1076 0.05382
43 0.9368 0.1264 0.0632
44 0.9255 0.149 0.07452
45 0.9097 0.1807 0.09033
46 0.9245 0.151 0.07549
47 0.9155 0.169 0.08452
48 0.9234 0.1531 0.07657
49 0.9244 0.1512 0.07558
50 0.8925 0.2149 0.1075
51 0.8659 0.2682 0.1341
52 0.8574 0.2852 0.1426
53 0.8663 0.2674 0.1337
54 0.8408 0.3183 0.1592
55 0.7893 0.4214 0.2107
56 0.739 0.5221 0.261
57 0.6656 0.6688 0.3344
58 0.5902 0.8195 0.4098
59 0.5293 0.9414 0.4707
60 0.6605 0.679 0.3395
61 0.5955 0.8091 0.4045
62 0.5356 0.9287 0.4644
63 0.6407 0.7185 0.3593
64 0.5839 0.8323 0.4161
65 0.664 0.6719 0.336
66 0.7107 0.5786 0.2893
67 0.7254 0.5493 0.2746






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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 level00OK
10% type I error level00OK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.0509, df1 = 2, df2 = 68, p-value = 0.1365
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.79115, df1 = 32, df2 = 38, p-value = 0.7494
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.054574, df1 = 2, df2 = 68, p-value = 0.9469


\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 = 2.0509, df1 = 2, df2 = 68, p-value = 0.1365

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.79115, df1 = 32, df2 = 38, p-value = 0.7494

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.054574, df1 = 2, df2 = 68, p-value = 0.9469

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&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 = 2.0509, df1 = 2, df2 = 68, p-value = 0.1365

[/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 = 0.79115, df1 = 32, df2 = 38, p-value = 0.7494

[/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 = 0.054574, df1 = 2, df2 = 68, p-value = 0.9469

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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 = 2.0509, df1 = 2, df2 = 68, p-value = 0.1365
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.79115, df1 = 32, df2 = 38, p-value = 0.7494
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.054574, df1 = 2, df2 = 68, p-value = 0.9469






Variance Inflation Factors (Multicollinearity)
> vif
       ITHSUM      SKEOUSUM Bevr_Leeftijd   `TVDC(t-1)`   `TVDC(t-2)` 
     1.344386      1.387724      1.080042      1.312285      1.300125 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.249229      1.167078      1.230020      1.176870      1.176242 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.242305      1.226159      1.284610      1.233002      1.311032 
            t 
     1.026411 


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

> vif
       ITHSUM      SKEOUSUM Bevr_Leeftijd   `TVDC(t-1)`   `TVDC(t-2)` 
     1.344386      1.387724      1.080042      1.312285      1.300125 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.249229      1.167078      1.230020      1.176870      1.176242 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.242305      1.226159      1.284610      1.233002      1.311032 
            t 
     1.026411 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306551&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
       ITHSUM      SKEOUSUM Bevr_Leeftijd   `TVDC(t-1)`   `TVDC(t-2)` 
     1.344386      1.387724      1.080042      1.312285      1.300125 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.249229      1.167078      1.230020      1.176870      1.176242 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.242305      1.226159      1.284610      1.233002      1.311032 
            t 
     1.026411 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306551&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306551&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
       ITHSUM      SKEOUSUM Bevr_Leeftijd   `TVDC(t-1)`   `TVDC(t-2)` 
     1.344386      1.387724      1.080042      1.312285      1.300125 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.249229      1.167078      1.230020      1.176870      1.176242 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.242305      1.226159      1.284610      1.233002      1.311032 
            t 
     1.026411


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 = 12 ; par2 = Double ; par3 = additive ; par4 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Double ; par3 = Linear Trend ; par4 = 12 ; par5 = ;
R code (references can be found in the software module):
par5 <- ''
par4 <- '12'
par3 <- 'additive'
par2 <- 'Double'
par1 <- '1'
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')