FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 09:52:28 +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/t1485334373j74j3vsqczj4ewz.htm/, Retrieved Mon, 13 May 2024 23:19:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305838, Retrieved Mon, 13 May 2024 23:19:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact22

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 08:52:28] [741065c5f9ea70737cde495521166004] [Current]
Feedback Forum

Post a new message

Dataset

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

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=305838&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=305838&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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] = + 14.6101 -0.0523243Bevr_Leeftijd[t] -0.072981ITHSUM[t] + 0.48211SKEOUSUM[t] -0.223857`TVDC(t-1)`[t] + 0.0589709`TVDC(t-2)`[t] + 0.0664208`TVDC(t-3)`[t] -0.0989983`TVDC(t-4)`[t] -0.0670883`TVDC(t-5)`[t] -0.0672937`TVDC(t-6)`[t] + 0.00698124`TVDC(t-7)`[t] + 0.110494`TVDC(t-8)`[t] -0.119718`TVDC(t-9)`[t] -0.0836087`TVDC(t-10)`[t] -0.159748`TVDC(t-11)`[t] + 0.0139292`TVDC(t-12)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  14.6101 -0.0523243Bevr_Leeftijd[t] -0.072981ITHSUM[t] +  0.48211SKEOUSUM[t] -0.223857`TVDC(t-1)`[t] +  0.0589709`TVDC(t-2)`[t] +  0.0664208`TVDC(t-3)`[t] -0.0989983`TVDC(t-4)`[t] -0.0670883`TVDC(t-5)`[t] -0.0672937`TVDC(t-6)`[t] +  0.00698124`TVDC(t-7)`[t] +  0.110494`TVDC(t-8)`[t] -0.119718`TVDC(t-9)`[t] -0.0836087`TVDC(t-10)`[t] -0.159748`TVDC(t-11)`[t] +  0.0139292`TVDC(t-12)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305838&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  14.6101 -0.0523243Bevr_Leeftijd[t] -0.072981ITHSUM[t] +  0.48211SKEOUSUM[t] -0.223857`TVDC(t-1)`[t] +  0.0589709`TVDC(t-2)`[t] +  0.0664208`TVDC(t-3)`[t] -0.0989983`TVDC(t-4)`[t] -0.0670883`TVDC(t-5)`[t] -0.0672937`TVDC(t-6)`[t] +  0.00698124`TVDC(t-7)`[t] +  0.110494`TVDC(t-8)`[t] -0.119718`TVDC(t-9)`[t] -0.0836087`TVDC(t-10)`[t] -0.159748`TVDC(t-11)`[t] +  0.0139292`TVDC(t-12)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305838&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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] = + 14.6101 -0.0523243Bevr_Leeftijd[t] -0.072981ITHSUM[t] + 0.48211SKEOUSUM[t] -0.223857`TVDC(t-1)`[t] + 0.0589709`TVDC(t-2)`[t] + 0.0664208`TVDC(t-3)`[t] -0.0989983`TVDC(t-4)`[t] -0.0670883`TVDC(t-5)`[t] -0.0672937`TVDC(t-6)`[t] + 0.00698124`TVDC(t-7)`[t] + 0.110494`TVDC(t-8)`[t] -0.119718`TVDC(t-9)`[t] -0.0836087`TVDC(t-10)`[t] -0.159748`TVDC(t-11)`[t] + 0.0139292`TVDC(t-12)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+14.61 6.661+2.1930e+00 0.03155 0.01578
Bevr_Leeftijd-0.05232 0.1129-4.6330e-01 0.6446 0.3223
ITHSUM-0.07298 0.09405-7.7600e-01 0.4403 0.2202
SKEOUSUM+0.4821 0.114+4.2270e+00 6.928e-05 3.464e-05
`TVDC(t-1)`-0.2239 0.1098-2.0380e+00 0.04523 0.02261
`TVDC(t-2)`+0.05897 0.1093+5.3950e-01 0.5912 0.2956
`TVDC(t-3)`+0.06642 0.1068+6.2180e-01 0.536 0.268
`TVDC(t-4)`-0.099 0.1042-9.4960e-01 0.3455 0.1728
`TVDC(t-5)`-0.06709 0.1067-6.2880e-01 0.5315 0.2658
`TVDC(t-6)`-0.06729 0.1053-6.3870e-01 0.525 0.2625
`TVDC(t-7)`+0.006981 0.1051+6.6420e-02 0.9472 0.4736
`TVDC(t-8)`+0.1105 0.1099+1.0050e+00 0.3181 0.1591
`TVDC(t-9)`-0.1197 0.1092-1.0960e+00 0.2768 0.1384
`TVDC(t-10)`-0.08361 0.1113-7.5120e-01 0.455 0.2275
`TVDC(t-11)`-0.1598 0.109-1.4660e+00 0.1471 0.07354
`TVDC(t-12)`+0.01393 0.1119+1.2450e-01 0.9013 0.4506

\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) & +14.61 &  6.661 & +2.1930e+00 &  0.03155 &  0.01578 \tabularnewline
Bevr_Leeftijd & -0.05232 &  0.1129 & -4.6330e-01 &  0.6446 &  0.3223 \tabularnewline
ITHSUM & -0.07298 &  0.09405 & -7.7600e-01 &  0.4403 &  0.2202 \tabularnewline
SKEOUSUM & +0.4821 &  0.114 & +4.2270e+00 &  6.928e-05 &  3.464e-05 \tabularnewline
`TVDC(t-1)` & -0.2239 &  0.1098 & -2.0380e+00 &  0.04523 &  0.02261 \tabularnewline
`TVDC(t-2)` & +0.05897 &  0.1093 & +5.3950e-01 &  0.5912 &  0.2956 \tabularnewline
`TVDC(t-3)` & +0.06642 &  0.1068 & +6.2180e-01 &  0.536 &  0.268 \tabularnewline
`TVDC(t-4)` & -0.099 &  0.1042 & -9.4960e-01 &  0.3455 &  0.1728 \tabularnewline
`TVDC(t-5)` & -0.06709 &  0.1067 & -6.2880e-01 &  0.5315 &  0.2658 \tabularnewline
`TVDC(t-6)` & -0.06729 &  0.1053 & -6.3870e-01 &  0.525 &  0.2625 \tabularnewline
`TVDC(t-7)` & +0.006981 &  0.1051 & +6.6420e-02 &  0.9472 &  0.4736 \tabularnewline
`TVDC(t-8)` & +0.1105 &  0.1099 & +1.0050e+00 &  0.3181 &  0.1591 \tabularnewline
`TVDC(t-9)` & -0.1197 &  0.1092 & -1.0960e+00 &  0.2768 &  0.1384 \tabularnewline
`TVDC(t-10)` & -0.08361 &  0.1113 & -7.5120e-01 &  0.455 &  0.2275 \tabularnewline
`TVDC(t-11)` & -0.1598 &  0.109 & -1.4660e+00 &  0.1471 &  0.07354 \tabularnewline
`TVDC(t-12)` & +0.01393 &  0.1119 & +1.2450e-01 &  0.9013 &  0.4506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305838&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]+14.61[/C][C] 6.661[/C][C]+2.1930e+00[/C][C] 0.03155[/C][C] 0.01578[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]-0.05232[/C][C] 0.1129[/C][C]-4.6330e-01[/C][C] 0.6446[/C][C] 0.3223[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-0.07298[/C][C] 0.09405[/C][C]-7.7600e-01[/C][C] 0.4403[/C][C] 0.2202[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.4821[/C][C] 0.114[/C][C]+4.2270e+00[/C][C] 6.928e-05[/C][C] 3.464e-05[/C][/ROW]
[ROW][C]`TVDC(t-1)`[/C][C]-0.2239[/C][C] 0.1098[/C][C]-2.0380e+00[/C][C] 0.04523[/C][C] 0.02261[/C][/ROW]
[ROW][C]`TVDC(t-2)`[/C][C]+0.05897[/C][C] 0.1093[/C][C]+5.3950e-01[/C][C] 0.5912[/C][C] 0.2956[/C][/ROW]
[ROW][C]`TVDC(t-3)`[/C][C]+0.06642[/C][C] 0.1068[/C][C]+6.2180e-01[/C][C] 0.536[/C][C] 0.268[/C][/ROW]
[ROW][C]`TVDC(t-4)`[/C][C]-0.099[/C][C] 0.1042[/C][C]-9.4960e-01[/C][C] 0.3455[/C][C] 0.1728[/C][/ROW]
[ROW][C]`TVDC(t-5)`[/C][C]-0.06709[/C][C] 0.1067[/C][C]-6.2880e-01[/C][C] 0.5315[/C][C] 0.2658[/C][/ROW]
[ROW][C]`TVDC(t-6)`[/C][C]-0.06729[/C][C] 0.1053[/C][C]-6.3870e-01[/C][C] 0.525[/C][C] 0.2625[/C][/ROW]
[ROW][C]`TVDC(t-7)`[/C][C]+0.006981[/C][C] 0.1051[/C][C]+6.6420e-02[/C][C] 0.9472[/C][C] 0.4736[/C][/ROW]
[ROW][C]`TVDC(t-8)`[/C][C]+0.1105[/C][C] 0.1099[/C][C]+1.0050e+00[/C][C] 0.3181[/C][C] 0.1591[/C][/ROW]
[ROW][C]`TVDC(t-9)`[/C][C]-0.1197[/C][C] 0.1092[/C][C]-1.0960e+00[/C][C] 0.2768[/C][C] 0.1384[/C][/ROW]
[ROW][C]`TVDC(t-10)`[/C][C]-0.08361[/C][C] 0.1113[/C][C]-7.5120e-01[/C][C] 0.455[/C][C] 0.2275[/C][/ROW]
[ROW][C]`TVDC(t-11)`[/C][C]-0.1598[/C][C] 0.109[/C][C]-1.4660e+00[/C][C] 0.1471[/C][C] 0.07354[/C][/ROW]
[ROW][C]`TVDC(t-12)`[/C][C]+0.01393[/C][C] 0.1119[/C][C]+1.2450e-01[/C][C] 0.9013[/C][C] 0.4506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305838&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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)+14.61 6.661+2.1930e+00 0.03155 0.01578
Bevr_Leeftijd-0.05232 0.1129-4.6330e-01 0.6446 0.3223
ITHSUM-0.07298 0.09405-7.7600e-01 0.4403 0.2202
SKEOUSUM+0.4821 0.114+4.2270e+00 6.928e-05 3.464e-05
`TVDC(t-1)`-0.2239 0.1098-2.0380e+00 0.04523 0.02261
`TVDC(t-2)`+0.05897 0.1093+5.3950e-01 0.5912 0.2956
`TVDC(t-3)`+0.06642 0.1068+6.2180e-01 0.536 0.268
`TVDC(t-4)`-0.099 0.1042-9.4960e-01 0.3455 0.1728
`TVDC(t-5)`-0.06709 0.1067-6.2880e-01 0.5315 0.2658
`TVDC(t-6)`-0.06729 0.1053-6.3870e-01 0.525 0.2625
`TVDC(t-7)`+0.006981 0.1051+6.6420e-02 0.9472 0.4736
`TVDC(t-8)`+0.1105 0.1099+1.0050e+00 0.3181 0.1591
`TVDC(t-9)`-0.1197 0.1092-1.0960e+00 0.2768 0.1384
`TVDC(t-10)`-0.08361 0.1113-7.5120e-01 0.455 0.2275
`TVDC(t-11)`-0.1598 0.109-1.4660e+00 0.1471 0.07354
`TVDC(t-12)`+0.01393 0.1119+1.2450e-01 0.9013 0.4506






Multiple Linear Regression - Regression Statistics
Multiple R 0.589
R-squared 0.347
Adjusted R-squared 0.209
F-TEST (value) 2.515
F-TEST (DF numerator)15
F-TEST (DF denominator)71
p-value 0.004885
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.725
Sum Squared Residuals 211.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.589 \tabularnewline
R-squared &  0.347 \tabularnewline
Adjusted R-squared &  0.209 \tabularnewline
F-TEST (value) &  2.515 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value &  0.004885 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.725 \tabularnewline
Sum Squared Residuals &  211.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305838&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.589[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.347[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.209[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.515[/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] 0.004885[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.725[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 211.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305838&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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 0.589
R-squared 0.347
Adjusted R-squared 0.209
F-TEST (value) 2.515
F-TEST (DF numerator)15
F-TEST (DF denominator)71
p-value 0.004885
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.725
Sum Squared Residuals 211.3






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 16 15.05 0.9507
2 15 14.8 0.2038
3 16 15.01 0.9901
4 16 14.72 1.284
5 15 16.75-1.746
6 17 16.67 0.3332
7 13 14.18-1.181
8 17 15.04 1.964
9 14 14.6-0.6005
10 14 14.07-0.07135
11 18 16.3 1.702
12 17 16.11 0.8858
13 16 16.63-0.6324
14 15 15.97-0.9701
15 15 15.5-0.5029
16 15 15.12-0.1186
17 13 14.95-1.947
18 17 16.84 0.1593
19 11 16.03-5.026
20 14 15.78-1.781
21 13 16.03-3.028
22 17 14.1 2.9
23 16 14.97 1.031
24 17 16.79 0.2138
25 16 15.58 0.4216
26 16 16.75-0.7492
27 16 13.29 2.71
28 15 16.97-1.969
29 12 14.12-2.117
30 17 16.73 0.2718
31 14 14.71-0.7074
32 14 16.36-2.365
33 16 15.44 0.564
34 15 13.94 1.056
35 16 15.49 0.5145
36 14 15.32-1.315
37 15 15.38-0.3787
38 17 15.65 1.354
39 10 13.21-3.211
40 17 17.57-0.5726
41 20 15.51 4.495
42 17 14.9 2.099
43 18 17.16 0.8363
44 14 13.04 0.9642
45 17 16.33 0.666
46 17 14.96 2.036
47 16 14.46 1.541
48 18 16.43 1.572
49 18 16.71 1.289
50 16 16.44-0.4444
51 15 15.22-0.2168
52 13 14.29-1.29
53 16 15.6 0.3998
54 12 13.58-1.579
55 16 15.57 0.4295
56 16 14.76 1.24
57 16 16.48-0.4777
58 14 14.77-0.771
59 15 15.25-0.2465
60 14 14.41-0.4128
61 15 15.73-0.7344
62 15 14.5 0.5034
63 16 15.4 0.6047
64 11 12.8-1.804
65 18 16.96 1.038
66 11 13.16-2.156
67 18 17.46 0.5426
68 15 15.95-0.9467
69 19 17.73 1.27
70 17 16.89 0.11
71 14 15.99-1.994
72 13 14.64-1.639
73 17 16.49 0.5069
74 14 14.63-0.6281
75 19 17.15 1.854
76 14 14.11-0.1133
77 16 17.23-1.226
78 16 14.68 1.321
79 15 14.82 0.1778
80 12 15.08-3.084
81 17 17.72-0.7226
82 18 15.21 2.788
83 15 15.39-0.3925
84 18 14.7 3.296
85 15 16.21-1.206
86 16 15.31 0.6874
87 16 14.7 1.296

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  16 &  15.05 &  0.9507 \tabularnewline
2 &  15 &  14.8 &  0.2038 \tabularnewline
3 &  16 &  15.01 &  0.9901 \tabularnewline
4 &  16 &  14.72 &  1.284 \tabularnewline
5 &  15 &  16.75 & -1.746 \tabularnewline
6 &  17 &  16.67 &  0.3332 \tabularnewline
7 &  13 &  14.18 & -1.181 \tabularnewline
8 &  17 &  15.04 &  1.964 \tabularnewline
9 &  14 &  14.6 & -0.6005 \tabularnewline
10 &  14 &  14.07 & -0.07135 \tabularnewline
11 &  18 &  16.3 &  1.702 \tabularnewline
12 &  17 &  16.11 &  0.8858 \tabularnewline
13 &  16 &  16.63 & -0.6324 \tabularnewline
14 &  15 &  15.97 & -0.9701 \tabularnewline
15 &  15 &  15.5 & -0.5029 \tabularnewline
16 &  15 &  15.12 & -0.1186 \tabularnewline
17 &  13 &  14.95 & -1.947 \tabularnewline
18 &  17 &  16.84 &  0.1593 \tabularnewline
19 &  11 &  16.03 & -5.026 \tabularnewline
20 &  14 &  15.78 & -1.781 \tabularnewline
21 &  13 &  16.03 & -3.028 \tabularnewline
22 &  17 &  14.1 &  2.9 \tabularnewline
23 &  16 &  14.97 &  1.031 \tabularnewline
24 &  17 &  16.79 &  0.2138 \tabularnewline
25 &  16 &  15.58 &  0.4216 \tabularnewline
26 &  16 &  16.75 & -0.7492 \tabularnewline
27 &  16 &  13.29 &  2.71 \tabularnewline
28 &  15 &  16.97 & -1.969 \tabularnewline
29 &  12 &  14.12 & -2.117 \tabularnewline
30 &  17 &  16.73 &  0.2718 \tabularnewline
31 &  14 &  14.71 & -0.7074 \tabularnewline
32 &  14 &  16.36 & -2.365 \tabularnewline
33 &  16 &  15.44 &  0.564 \tabularnewline
34 &  15 &  13.94 &  1.056 \tabularnewline
35 &  16 &  15.49 &  0.5145 \tabularnewline
36 &  14 &  15.32 & -1.315 \tabularnewline
37 &  15 &  15.38 & -0.3787 \tabularnewline
38 &  17 &  15.65 &  1.354 \tabularnewline
39 &  10 &  13.21 & -3.211 \tabularnewline
40 &  17 &  17.57 & -0.5726 \tabularnewline
41 &  20 &  15.51 &  4.495 \tabularnewline
42 &  17 &  14.9 &  2.099 \tabularnewline
43 &  18 &  17.16 &  0.8363 \tabularnewline
44 &  14 &  13.04 &  0.9642 \tabularnewline
45 &  17 &  16.33 &  0.666 \tabularnewline
46 &  17 &  14.96 &  2.036 \tabularnewline
47 &  16 &  14.46 &  1.541 \tabularnewline
48 &  18 &  16.43 &  1.572 \tabularnewline
49 &  18 &  16.71 &  1.289 \tabularnewline
50 &  16 &  16.44 & -0.4444 \tabularnewline
51 &  15 &  15.22 & -0.2168 \tabularnewline
52 &  13 &  14.29 & -1.29 \tabularnewline
53 &  16 &  15.6 &  0.3998 \tabularnewline
54 &  12 &  13.58 & -1.579 \tabularnewline
55 &  16 &  15.57 &  0.4295 \tabularnewline
56 &  16 &  14.76 &  1.24 \tabularnewline
57 &  16 &  16.48 & -0.4777 \tabularnewline
58 &  14 &  14.77 & -0.771 \tabularnewline
59 &  15 &  15.25 & -0.2465 \tabularnewline
60 &  14 &  14.41 & -0.4128 \tabularnewline
61 &  15 &  15.73 & -0.7344 \tabularnewline
62 &  15 &  14.5 &  0.5034 \tabularnewline
63 &  16 &  15.4 &  0.6047 \tabularnewline
64 &  11 &  12.8 & -1.804 \tabularnewline
65 &  18 &  16.96 &  1.038 \tabularnewline
66 &  11 &  13.16 & -2.156 \tabularnewline
67 &  18 &  17.46 &  0.5426 \tabularnewline
68 &  15 &  15.95 & -0.9467 \tabularnewline
69 &  19 &  17.73 &  1.27 \tabularnewline
70 &  17 &  16.89 &  0.11 \tabularnewline
71 &  14 &  15.99 & -1.994 \tabularnewline
72 &  13 &  14.64 & -1.639 \tabularnewline
73 &  17 &  16.49 &  0.5069 \tabularnewline
74 &  14 &  14.63 & -0.6281 \tabularnewline
75 &  19 &  17.15 &  1.854 \tabularnewline
76 &  14 &  14.11 & -0.1133 \tabularnewline
77 &  16 &  17.23 & -1.226 \tabularnewline
78 &  16 &  14.68 &  1.321 \tabularnewline
79 &  15 &  14.82 &  0.1778 \tabularnewline
80 &  12 &  15.08 & -3.084 \tabularnewline
81 &  17 &  17.72 & -0.7226 \tabularnewline
82 &  18 &  15.21 &  2.788 \tabularnewline
83 &  15 &  15.39 & -0.3925 \tabularnewline
84 &  18 &  14.7 &  3.296 \tabularnewline
85 &  15 &  16.21 & -1.206 \tabularnewline
86 &  16 &  15.31 &  0.6874 \tabularnewline
87 &  16 &  14.7 &  1.296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305838&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] 15.05[/C][C] 0.9507[/C][/ROW]
[ROW][C]2[/C][C] 15[/C][C] 14.8[/C][C] 0.2038[/C][/ROW]
[ROW][C]3[/C][C] 16[/C][C] 15.01[/C][C] 0.9901[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 14.72[/C][C] 1.284[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 16.75[/C][C]-1.746[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 16.67[/C][C] 0.3332[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 14.18[/C][C]-1.181[/C][/ROW]
[ROW][C]8[/C][C] 17[/C][C] 15.04[/C][C] 1.964[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 14.6[/C][C]-0.6005[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 14.07[/C][C]-0.07135[/C][/ROW]
[ROW][C]11[/C][C] 18[/C][C] 16.3[/C][C] 1.702[/C][/ROW]
[ROW][C]12[/C][C] 17[/C][C] 16.11[/C][C] 0.8858[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 16.63[/C][C]-0.6324[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.97[/C][C]-0.9701[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.5[/C][C]-0.5029[/C][/ROW]
[ROW][C]16[/C][C] 15[/C][C] 15.12[/C][C]-0.1186[/C][/ROW]
[ROW][C]17[/C][C] 13[/C][C] 14.95[/C][C]-1.947[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.84[/C][C] 0.1593[/C][/ROW]
[ROW][C]19[/C][C] 11[/C][C] 16.03[/C][C]-5.026[/C][/ROW]
[ROW][C]20[/C][C] 14[/C][C] 15.78[/C][C]-1.781[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 16.03[/C][C]-3.028[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 14.1[/C][C] 2.9[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 14.97[/C][C] 1.031[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 16.79[/C][C] 0.2138[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 15.58[/C][C] 0.4216[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 16.75[/C][C]-0.7492[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 13.29[/C][C] 2.71[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 16.97[/C][C]-1.969[/C][/ROW]
[ROW][C]29[/C][C] 12[/C][C] 14.12[/C][C]-2.117[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 16.73[/C][C] 0.2718[/C][/ROW]
[ROW][C]31[/C][C] 14[/C][C] 14.71[/C][C]-0.7074[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 16.36[/C][C]-2.365[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 15.44[/C][C] 0.564[/C][/ROW]
[ROW][C]34[/C][C] 15[/C][C] 13.94[/C][C] 1.056[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.49[/C][C] 0.5145[/C][/ROW]
[ROW][C]36[/C][C] 14[/C][C] 15.32[/C][C]-1.315[/C][/ROW]
[ROW][C]37[/C][C] 15[/C][C] 15.38[/C][C]-0.3787[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 15.65[/C][C] 1.354[/C][/ROW]
[ROW][C]39[/C][C] 10[/C][C] 13.21[/C][C]-3.211[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 17.57[/C][C]-0.5726[/C][/ROW]
[ROW][C]41[/C][C] 20[/C][C] 15.51[/C][C] 4.495[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 14.9[/C][C] 2.099[/C][/ROW]
[ROW][C]43[/C][C] 18[/C][C] 17.16[/C][C] 0.8363[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 13.04[/C][C] 0.9642[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 16.33[/C][C] 0.666[/C][/ROW]
[ROW][C]46[/C][C] 17[/C][C] 14.96[/C][C] 2.036[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 14.46[/C][C] 1.541[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 16.43[/C][C] 1.572[/C][/ROW]
[ROW][C]49[/C][C] 18[/C][C] 16.71[/C][C] 1.289[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 16.44[/C][C]-0.4444[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 15.22[/C][C]-0.2168[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 14.29[/C][C]-1.29[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 15.6[/C][C] 0.3998[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 13.58[/C][C]-1.579[/C][/ROW]
[ROW][C]55[/C][C] 16[/C][C] 15.57[/C][C] 0.4295[/C][/ROW]
[ROW][C]56[/C][C] 16[/C][C] 14.76[/C][C] 1.24[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 16.48[/C][C]-0.4777[/C][/ROW]
[ROW][C]58[/C][C] 14[/C][C] 14.77[/C][C]-0.771[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 15.25[/C][C]-0.2465[/C][/ROW]
[ROW][C]60[/C][C] 14[/C][C] 14.41[/C][C]-0.4128[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 15.73[/C][C]-0.7344[/C][/ROW]
[ROW][C]62[/C][C] 15[/C][C] 14.5[/C][C] 0.5034[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 15.4[/C][C] 0.6047[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 12.8[/C][C]-1.804[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 16.96[/C][C] 1.038[/C][/ROW]
[ROW][C]66[/C][C] 11[/C][C] 13.16[/C][C]-2.156[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 17.46[/C][C] 0.5426[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 15.95[/C][C]-0.9467[/C][/ROW]
[ROW][C]69[/C][C] 19[/C][C] 17.73[/C][C] 1.27[/C][/ROW]
[ROW][C]70[/C][C] 17[/C][C] 16.89[/C][C] 0.11[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 15.99[/C][C]-1.994[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 14.64[/C][C]-1.639[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.49[/C][C] 0.5069[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 14.63[/C][C]-0.6281[/C][/ROW]
[ROW][C]75[/C][C] 19[/C][C] 17.15[/C][C] 1.854[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.11[/C][C]-0.1133[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 17.23[/C][C]-1.226[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 14.68[/C][C] 1.321[/C][/ROW]
[ROW][C]79[/C][C] 15[/C][C] 14.82[/C][C] 0.1778[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 15.08[/C][C]-3.084[/C][/ROW]
[ROW][C]81[/C][C] 17[/C][C] 17.72[/C][C]-0.7226[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 15.21[/C][C] 2.788[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 15.39[/C][C]-0.3925[/C][/ROW]
[ROW][C]84[/C][C] 18[/C][C] 14.7[/C][C] 3.296[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 16.21[/C][C]-1.206[/C][/ROW]
[ROW][C]86[/C][C] 16[/C][C] 15.31[/C][C] 0.6874[/C][/ROW]
[ROW][C]87[/C][C] 16[/C][C] 14.7[/C][C] 1.296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305838&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 16 15.05 0.9507
2 15 14.8 0.2038
3 16 15.01 0.9901
4 16 14.72 1.284
5 15 16.75-1.746
6 17 16.67 0.3332
7 13 14.18-1.181
8 17 15.04 1.964
9 14 14.6-0.6005
10 14 14.07-0.07135
11 18 16.3 1.702
12 17 16.11 0.8858
13 16 16.63-0.6324
14 15 15.97-0.9701
15 15 15.5-0.5029
16 15 15.12-0.1186
17 13 14.95-1.947
18 17 16.84 0.1593
19 11 16.03-5.026
20 14 15.78-1.781
21 13 16.03-3.028
22 17 14.1 2.9
23 16 14.97 1.031
24 17 16.79 0.2138
25 16 15.58 0.4216
26 16 16.75-0.7492
27 16 13.29 2.71
28 15 16.97-1.969
29 12 14.12-2.117
30 17 16.73 0.2718
31 14 14.71-0.7074
32 14 16.36-2.365
33 16 15.44 0.564
34 15 13.94 1.056
35 16 15.49 0.5145
36 14 15.32-1.315
37 15 15.38-0.3787
38 17 15.65 1.354
39 10 13.21-3.211
40 17 17.57-0.5726
41 20 15.51 4.495
42 17 14.9 2.099
43 18 17.16 0.8363
44 14 13.04 0.9642
45 17 16.33 0.666
46 17 14.96 2.036
47 16 14.46 1.541
48 18 16.43 1.572
49 18 16.71 1.289
50 16 16.44-0.4444
51 15 15.22-0.2168
52 13 14.29-1.29
53 16 15.6 0.3998
54 12 13.58-1.579
55 16 15.57 0.4295
56 16 14.76 1.24
57 16 16.48-0.4777
58 14 14.77-0.771
59 15 15.25-0.2465
60 14 14.41-0.4128
61 15 15.73-0.7344
62 15 14.5 0.5034
63 16 15.4 0.6047
64 11 12.8-1.804
65 18 16.96 1.038
66 11 13.16-2.156
67 18 17.46 0.5426
68 15 15.95-0.9467
69 19 17.73 1.27
70 17 16.89 0.11
71 14 15.99-1.994
72 13 14.64-1.639
73 17 16.49 0.5069
74 14 14.63-0.6281
75 19 17.15 1.854
76 14 14.11-0.1133
77 16 17.23-1.226
78 16 14.68 1.321
79 15 14.82 0.1778
80 12 15.08-3.084
81 17 17.72-0.7226
82 18 15.21 2.788
83 15 15.39-0.3925
84 18 14.7 3.296
85 15 16.21-1.206
86 16 15.31 0.6874
87 16 14.7 1.296






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
19 0.2453 0.4905 0.7547
20 0.5103 0.9793 0.4897
21 0.8441 0.3118 0.1559
22 0.8807 0.2385 0.1193
23 0.8188 0.3624 0.1812
24 0.788 0.424 0.212
25 0.7067 0.5865 0.2933
26 0.6184 0.7633 0.3816
27 0.6766 0.6467 0.3234
28 0.6354 0.7293 0.3646
29 0.6046 0.7909 0.3954
30 0.712 0.5761 0.288
31 0.7626 0.4748 0.2374
32 0.7684 0.4631 0.2316
33 0.7145 0.5711 0.2855
34 0.6733 0.6534 0.3267
35 0.6086 0.7828 0.3914
36 0.5833 0.8334 0.4167
37 0.5505 0.8989 0.4495
38 0.5071 0.9859 0.4929
39 0.6222 0.7556 0.3778
40 0.5716 0.8568 0.4284
41 0.8861 0.2278 0.1139
42 0.9466 0.1067 0.05337
43 0.9389 0.1222 0.06112
44 0.9309 0.1383 0.06913
45 0.9152 0.1695 0.08477
46 0.9311 0.1378 0.06888
47 0.9235 0.1529 0.07647
48 0.9142 0.1716 0.08581
49 0.9135 0.173 0.0865
50 0.8964 0.2072 0.1036
51 0.8594 0.2813 0.1406
52 0.8371 0.3258 0.1629
53 0.7973 0.4053 0.2027
54 0.8003 0.3995 0.1997
55 0.7779 0.4443 0.2221
56 0.7149 0.5701 0.2851
57 0.6714 0.6573 0.3286
58 0.6051 0.7898 0.3949
59 0.5162 0.9676 0.4838
60 0.4991 0.9983 0.5009
61 0.4089 0.8178 0.5911
62 0.3192 0.6385 0.6808
63 0.3333 0.6665 0.6667
64 0.2671 0.5341 0.7329
65 0.3827 0.7655 0.6173
66 0.6028 0.7944 0.3972
67 0.7336 0.5327 0.2664
68 0.6992 0.6015 0.3008

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 &  0.2453 &  0.4905 &  0.7547 \tabularnewline
20 &  0.5103 &  0.9793 &  0.4897 \tabularnewline
21 &  0.8441 &  0.3118 &  0.1559 \tabularnewline
22 &  0.8807 &  0.2385 &  0.1193 \tabularnewline
23 &  0.8188 &  0.3624 &  0.1812 \tabularnewline
24 &  0.788 &  0.424 &  0.212 \tabularnewline
25 &  0.7067 &  0.5865 &  0.2933 \tabularnewline
26 &  0.6184 &  0.7633 &  0.3816 \tabularnewline
27 &  0.6766 &  0.6467 &  0.3234 \tabularnewline
28 &  0.6354 &  0.7293 &  0.3646 \tabularnewline
29 &  0.6046 &  0.7909 &  0.3954 \tabularnewline
30 &  0.712 &  0.5761 &  0.288 \tabularnewline
31 &  0.7626 &  0.4748 &  0.2374 \tabularnewline
32 &  0.7684 &  0.4631 &  0.2316 \tabularnewline
33 &  0.7145 &  0.5711 &  0.2855 \tabularnewline
34 &  0.6733 &  0.6534 &  0.3267 \tabularnewline
35 &  0.6086 &  0.7828 &  0.3914 \tabularnewline
36 &  0.5833 &  0.8334 &  0.4167 \tabularnewline
37 &  0.5505 &  0.8989 &  0.4495 \tabularnewline
38 &  0.5071 &  0.9859 &  0.4929 \tabularnewline
39 &  0.6222 &  0.7556 &  0.3778 \tabularnewline
40 &  0.5716 &  0.8568 &  0.4284 \tabularnewline
41 &  0.8861 &  0.2278 &  0.1139 \tabularnewline
42 &  0.9466 &  0.1067 &  0.05337 \tabularnewline
43 &  0.9389 &  0.1222 &  0.06112 \tabularnewline
44 &  0.9309 &  0.1383 &  0.06913 \tabularnewline
45 &  0.9152 &  0.1695 &  0.08477 \tabularnewline
46 &  0.9311 &  0.1378 &  0.06888 \tabularnewline
47 &  0.9235 &  0.1529 &  0.07647 \tabularnewline
48 &  0.9142 &  0.1716 &  0.08581 \tabularnewline
49 &  0.9135 &  0.173 &  0.0865 \tabularnewline
50 &  0.8964 &  0.2072 &  0.1036 \tabularnewline
51 &  0.8594 &  0.2813 &  0.1406 \tabularnewline
52 &  0.8371 &  0.3258 &  0.1629 \tabularnewline
53 &  0.7973 &  0.4053 &  0.2027 \tabularnewline
54 &  0.8003 &  0.3995 &  0.1997 \tabularnewline
55 &  0.7779 &  0.4443 &  0.2221 \tabularnewline
56 &  0.7149 &  0.5701 &  0.2851 \tabularnewline
57 &  0.6714 &  0.6573 &  0.3286 \tabularnewline
58 &  0.6051 &  0.7898 &  0.3949 \tabularnewline
59 &  0.5162 &  0.9676 &  0.4838 \tabularnewline
60 &  0.4991 &  0.9983 &  0.5009 \tabularnewline
61 &  0.4089 &  0.8178 &  0.5911 \tabularnewline
62 &  0.3192 &  0.6385 &  0.6808 \tabularnewline
63 &  0.3333 &  0.6665 &  0.6667 \tabularnewline
64 &  0.2671 &  0.5341 &  0.7329 \tabularnewline
65 &  0.3827 &  0.7655 &  0.6173 \tabularnewline
66 &  0.6028 &  0.7944 &  0.3972 \tabularnewline
67 &  0.7336 &  0.5327 &  0.2664 \tabularnewline
68 &  0.6992 &  0.6015 &  0.3008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305838&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] 0.2453[/C][C] 0.4905[/C][C] 0.7547[/C][/ROW]
[ROW][C]20[/C][C] 0.5103[/C][C] 0.9793[/C][C] 0.4897[/C][/ROW]
[ROW][C]21[/C][C] 0.8441[/C][C] 0.3118[/C][C] 0.1559[/C][/ROW]
[ROW][C]22[/C][C] 0.8807[/C][C] 0.2385[/C][C] 0.1193[/C][/ROW]
[ROW][C]23[/C][C] 0.8188[/C][C] 0.3624[/C][C] 0.1812[/C][/ROW]
[ROW][C]24[/C][C] 0.788[/C][C] 0.424[/C][C] 0.212[/C][/ROW]
[ROW][C]25[/C][C] 0.7067[/C][C] 0.5865[/C][C] 0.2933[/C][/ROW]
[ROW][C]26[/C][C] 0.6184[/C][C] 0.7633[/C][C] 0.3816[/C][/ROW]
[ROW][C]27[/C][C] 0.6766[/C][C] 0.6467[/C][C] 0.3234[/C][/ROW]
[ROW][C]28[/C][C] 0.6354[/C][C] 0.7293[/C][C] 0.3646[/C][/ROW]
[ROW][C]29[/C][C] 0.6046[/C][C] 0.7909[/C][C] 0.3954[/C][/ROW]
[ROW][C]30[/C][C] 0.712[/C][C] 0.5761[/C][C] 0.288[/C][/ROW]
[ROW][C]31[/C][C] 0.7626[/C][C] 0.4748[/C][C] 0.2374[/C][/ROW]
[ROW][C]32[/C][C] 0.7684[/C][C] 0.4631[/C][C] 0.2316[/C][/ROW]
[ROW][C]33[/C][C] 0.7145[/C][C] 0.5711[/C][C] 0.2855[/C][/ROW]
[ROW][C]34[/C][C] 0.6733[/C][C] 0.6534[/C][C] 0.3267[/C][/ROW]
[ROW][C]35[/C][C] 0.6086[/C][C] 0.7828[/C][C] 0.3914[/C][/ROW]
[ROW][C]36[/C][C] 0.5833[/C][C] 0.8334[/C][C] 0.4167[/C][/ROW]
[ROW][C]37[/C][C] 0.5505[/C][C] 0.8989[/C][C] 0.4495[/C][/ROW]
[ROW][C]38[/C][C] 0.5071[/C][C] 0.9859[/C][C] 0.4929[/C][/ROW]
[ROW][C]39[/C][C] 0.6222[/C][C] 0.7556[/C][C] 0.3778[/C][/ROW]
[ROW][C]40[/C][C] 0.5716[/C][C] 0.8568[/C][C] 0.4284[/C][/ROW]
[ROW][C]41[/C][C] 0.8861[/C][C] 0.2278[/C][C] 0.1139[/C][/ROW]
[ROW][C]42[/C][C] 0.9466[/C][C] 0.1067[/C][C] 0.05337[/C][/ROW]
[ROW][C]43[/C][C] 0.9389[/C][C] 0.1222[/C][C] 0.06112[/C][/ROW]
[ROW][C]44[/C][C] 0.9309[/C][C] 0.1383[/C][C] 0.06913[/C][/ROW]
[ROW][C]45[/C][C] 0.9152[/C][C] 0.1695[/C][C] 0.08477[/C][/ROW]
[ROW][C]46[/C][C] 0.9311[/C][C] 0.1378[/C][C] 0.06888[/C][/ROW]
[ROW][C]47[/C][C] 0.9235[/C][C] 0.1529[/C][C] 0.07647[/C][/ROW]
[ROW][C]48[/C][C] 0.9142[/C][C] 0.1716[/C][C] 0.08581[/C][/ROW]
[ROW][C]49[/C][C] 0.9135[/C][C] 0.173[/C][C] 0.0865[/C][/ROW]
[ROW][C]50[/C][C] 0.8964[/C][C] 0.2072[/C][C] 0.1036[/C][/ROW]
[ROW][C]51[/C][C] 0.8594[/C][C] 0.2813[/C][C] 0.1406[/C][/ROW]
[ROW][C]52[/C][C] 0.8371[/C][C] 0.3258[/C][C] 0.1629[/C][/ROW]
[ROW][C]53[/C][C] 0.7973[/C][C] 0.4053[/C][C] 0.2027[/C][/ROW]
[ROW][C]54[/C][C] 0.8003[/C][C] 0.3995[/C][C] 0.1997[/C][/ROW]
[ROW][C]55[/C][C] 0.7779[/C][C] 0.4443[/C][C] 0.2221[/C][/ROW]
[ROW][C]56[/C][C] 0.7149[/C][C] 0.5701[/C][C] 0.2851[/C][/ROW]
[ROW][C]57[/C][C] 0.6714[/C][C] 0.6573[/C][C] 0.3286[/C][/ROW]
[ROW][C]58[/C][C] 0.6051[/C][C] 0.7898[/C][C] 0.3949[/C][/ROW]
[ROW][C]59[/C][C] 0.5162[/C][C] 0.9676[/C][C] 0.4838[/C][/ROW]
[ROW][C]60[/C][C] 0.4991[/C][C] 0.9983[/C][C] 0.5009[/C][/ROW]
[ROW][C]61[/C][C] 0.4089[/C][C] 0.8178[/C][C] 0.5911[/C][/ROW]
[ROW][C]62[/C][C] 0.3192[/C][C] 0.6385[/C][C] 0.6808[/C][/ROW]
[ROW][C]63[/C][C] 0.3333[/C][C] 0.6665[/C][C] 0.6667[/C][/ROW]
[ROW][C]64[/C][C] 0.2671[/C][C] 0.5341[/C][C] 0.7329[/C][/ROW]
[ROW][C]65[/C][C] 0.3827[/C][C] 0.7655[/C][C] 0.6173[/C][/ROW]
[ROW][C]66[/C][C] 0.6028[/C][C] 0.7944[/C][C] 0.3972[/C][/ROW]
[ROW][C]67[/C][C] 0.7336[/C][C] 0.5327[/C][C] 0.2664[/C][/ROW]
[ROW][C]68[/C][C] 0.6992[/C][C] 0.6015[/C][C] 0.3008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305838&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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 0.2453 0.4905 0.7547
20 0.5103 0.9793 0.4897
21 0.8441 0.3118 0.1559
22 0.8807 0.2385 0.1193
23 0.8188 0.3624 0.1812
24 0.788 0.424 0.212
25 0.7067 0.5865 0.2933
26 0.6184 0.7633 0.3816
27 0.6766 0.6467 0.3234
28 0.6354 0.7293 0.3646
29 0.6046 0.7909 0.3954
30 0.712 0.5761 0.288
31 0.7626 0.4748 0.2374
32 0.7684 0.4631 0.2316
33 0.7145 0.5711 0.2855
34 0.6733 0.6534 0.3267
35 0.6086 0.7828 0.3914
36 0.5833 0.8334 0.4167
37 0.5505 0.8989 0.4495
38 0.5071 0.9859 0.4929
39 0.6222 0.7556 0.3778
40 0.5716 0.8568 0.4284
41 0.8861 0.2278 0.1139
42 0.9466 0.1067 0.05337
43 0.9389 0.1222 0.06112
44 0.9309 0.1383 0.06913
45 0.9152 0.1695 0.08477
46 0.9311 0.1378 0.06888
47 0.9235 0.1529 0.07647
48 0.9142 0.1716 0.08581
49 0.9135 0.173 0.0865
50 0.8964 0.2072 0.1036
51 0.8594 0.2813 0.1406
52 0.8371 0.3258 0.1629
53 0.7973 0.4053 0.2027
54 0.8003 0.3995 0.1997
55 0.7779 0.4443 0.2221
56 0.7149 0.5701 0.2851
57 0.6714 0.6573 0.3286
58 0.6051 0.7898 0.3949
59 0.5162 0.9676 0.4838
60 0.4991 0.9983 0.5009
61 0.4089 0.8178 0.5911
62 0.3192 0.6385 0.6808
63 0.3333 0.6665 0.6667
64 0.2671 0.5341 0.7329
65 0.3827 0.7655 0.6173
66 0.6028 0.7944 0.3972
67 0.7336 0.5327 0.2664
68 0.6992 0.6015 0.3008






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=305838&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=305838&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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 = 2.4786, df1 = 2, df2 = 69, p-value = 0.0913
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.82653, df1 = 30, df2 = 41, p-value = 0.704
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.28671, df1 = 2, df2 = 69, p-value = 0.7516


\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 = 2.4786, df1 = 2, df2 = 69, p-value = 0.0913

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.82653, df1 = 30, df2 = 41, p-value = 0.704

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.28671, df1 = 2, df2 = 69, p-value = 0.7516

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305838&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 = 2.4786, df1 = 2, df2 = 69, p-value = 0.0913

[/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 = 0.82653, df1 = 30, df2 = 41, p-value = 0.704

[/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 = 0.28671, df1 = 2, df2 = 69, p-value = 0.7516

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305838&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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 = 2.4786, df1 = 2, df2 = 69, p-value = 0.0913
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.82653, df1 = 30, df2 = 41, p-value = 0.704
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.28671, df1 = 2, df2 = 69, p-value = 0.7516






Variance Inflation Factors (Multicollinearity)
> vif
Bevr_Leeftijd        ITHSUM      SKEOUSUM   `TVDC(t-1)`   `TVDC(t-2)` 
     1.075125      1.344026      1.386577      1.311116      1.298886 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.248573      1.167041      1.229696      1.175620      1.170195 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.240636      1.225512      1.280920      1.227760      1.310717 


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

> vif
Bevr_Leeftijd        ITHSUM      SKEOUSUM   `TVDC(t-1)`   `TVDC(t-2)` 
     1.075125      1.344026      1.386577      1.311116      1.298886 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.248573      1.167041      1.229696      1.175620      1.170195 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.240636      1.225512      1.280920      1.227760      1.310717 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305838&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
Bevr_Leeftijd        ITHSUM      SKEOUSUM   `TVDC(t-1)`   `TVDC(t-2)` 
     1.075125      1.344026      1.386577      1.311116      1.298886 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.248573      1.167041      1.229696      1.175620      1.170195 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.240636      1.225512      1.280920      1.227760      1.310717 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305838&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305838&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        ITHSUM      SKEOUSUM   `TVDC(t-1)`   `TVDC(t-2)` 
     1.075125      1.344026      1.386577      1.311116      1.298886 
  `TVDC(t-3)`   `TVDC(t-4)`   `TVDC(t-5)`   `TVDC(t-6)`   `TVDC(t-7)` 
     1.248573      1.167041      1.229696      1.175620      1.170195 
  `TVDC(t-8)`   `TVDC(t-9)`  `TVDC(t-10)`  `TVDC(t-11)`  `TVDC(t-12)` 
     1.240636      1.225512      1.280920      1.227760      1.310717


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = Double ; par3 = additive ; par4 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = additive ; 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')