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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 09:13:36 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258733685vowvbwn3vzililt.htm/, Retrieved Fri, 29 Mar 2024 11:28:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58299, Retrieved Fri, 29 Mar 2024 11:28:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSDHW, DSHW
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [workshop 7 bereke...] [2009-11-19 18:08:31] [eaf42bcf5162b5692bb3c7f9d4636222]
-    D        [Multiple Regression] [DSHW-WS7-MiltReg.T] [2009-11-20 16:13:36] [36295456a56d4c7dcc9b9537ce63463b] [Current]
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Dataseries X:
2,0	0,0	2,0	1,7	1,6	1,4
2,1	0,0	2,0	2,0	1,7	1,6
2,5	0,0	2,1	2,0	2,0	1,7
2,5	0,0	2,5	2,1	2,0	2,0
2,6	0,0	2,5	2,5	2,1	2,0
2,7	0,0	2,6	2,5	2,5	2,1
3,7	0,0	2,7	2,6	2,5	2,5
4,0	0,0	3,7	2,7	2,6	2,5
5,0	0,0	4,0	3,7	2,7	2,6
5,1	0,0	5,0	4,0	3,7	2,7
5,1	0,0	5,1	5,0	4,0	3,7
5,0	0,0	5,1	5,1	5,0	4,0
5,1	0,0	5,0	5,1	5,1	5,0
4,7	0,0	5,1	5,0	5,1	5,1
4,5	0,0	4,7	5,1	5,0	5,1
4,5	0,0	4,5	4,7	5,1	5,0
4,6	0,0	4,5	4,5	4,7	5,1
4,6	0,0	4,6	4,5	4,5	4,7
4,6	0,0	4,6	4,6	4,5	4,5
4,6	0,0	4,6	4,6	4,6	4,5
5,3	0,0	4,6	4,6	4,6	4,6
5,4	0,0	5,3	4,6	4,6	4,6
5,3	0,0	5,4	5,3	4,6	4,6
5,2	0,0	5,3	5,4	5,3	4,6
5,0	0,0	5,2	5,3	5,4	5,3
4,2	0,0	5,0	5,2	5,3	5,4
4,3	0,0	4,2	5,0	5,2	5,3
4,3	0,0	4,3	4,2	5,0	5,2
4,3	0,0	4,3	4,3	4,2	5,0
4,0	0,0	4,3	4,3	4,3	4,2
4,0	0,0	4,0	4,3	4,3	4,3
4,1	0,0	4,0	4,0	4,3	4,3
4,4	0,0	4,1	4,0	4,0	4,3
3,6	0,0	4,4	4,1	4,0	4,0
3,7	0,0	3,6	4,4	4,1	4,0
3,8	0,0	3,7	3,6	4,4	4,1
3,3	0,0	3,8	3,7	3,6	4,4
3,3	0,0	3,3	3,8	3,7	3,6
3,3	0,0	3,3	3,3	3,8	3,7
3,5	0,0	3,3	3,3	3,3	3,8
3,3	0,0	3,5	3,3	3,3	3,3
3,3	0,0	3,3	3,5	3,3	3,3
3,4	0,0	3,3	3,3	3,5	3,3
3,4	0,0	3,4	3,3	3,3	3,5
5,2	0,0	3,4	3,4	3,3	3,3
5,3	0,0	5,2	3,4	3,4	3,3
4,8	1,0	5,3	5,2	3,4	3,4
5,0	1,0	4,8	5,3	5,2	3,4
4,6	1,0	5,0	4,8	5,3	5,2
4,6	1,0	4,6	5,0	4,8	5,3
3,5	1,0	4,6	4,6	5,0	4,8
3,5	1,0	3,5	4,6	4,6	5,0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58299&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58299&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58299&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
IndGez[t] = + 0.680001490026424 -0.109916882560442InvlMex[t] + 0.829083916174935`Yt-1`[t] + 0.0406279848806237`Yt-2`[t] + 0.102037061011512`Yt-3`[t] -0.109824058538842`Yt-4`[t] -0.199444656948999M1[t] -0.252551856372627M2[t] -0.2362239526388M3[t] -0.0226862977284185M4[t] -0.0745510928749468M5[t] -0.161684153029778M6[t] + 0.160657728882051M7[t] + 0.0429349888681616M8[t] + 0.906708489222317M9[t] -0.040782689573798M10[t] -0.0505139202881374M11[t] -0.00275273476324471t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IndGez[t] =  +  0.680001490026424 -0.109916882560442InvlMex[t] +  0.829083916174935`Yt-1`[t] +  0.0406279848806237`Yt-2`[t] +  0.102037061011512`Yt-3`[t] -0.109824058538842`Yt-4`[t] -0.199444656948999M1[t] -0.252551856372627M2[t] -0.2362239526388M3[t] -0.0226862977284185M4[t] -0.0745510928749468M5[t] -0.161684153029778M6[t] +  0.160657728882051M7[t] +  0.0429349888681616M8[t] +  0.906708489222317M9[t] -0.040782689573798M10[t] -0.0505139202881374M11[t] -0.00275273476324471t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58299&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IndGez[t] =  +  0.680001490026424 -0.109916882560442InvlMex[t] +  0.829083916174935`Yt-1`[t] +  0.0406279848806237`Yt-2`[t] +  0.102037061011512`Yt-3`[t] -0.109824058538842`Yt-4`[t] -0.199444656948999M1[t] -0.252551856372627M2[t] -0.2362239526388M3[t] -0.0226862977284185M4[t] -0.0745510928749468M5[t] -0.161684153029778M6[t] +  0.160657728882051M7[t] +  0.0429349888681616M8[t] +  0.906708489222317M9[t] -0.040782689573798M10[t] -0.0505139202881374M11[t] -0.00275273476324471t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58299&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58299&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
IndGez[t] = + 0.680001490026424 -0.109916882560442InvlMex[t] + 0.829083916174935`Yt-1`[t] + 0.0406279848806237`Yt-2`[t] + 0.102037061011512`Yt-3`[t] -0.109824058538842`Yt-4`[t] -0.199444656948999M1[t] -0.252551856372627M2[t] -0.2362239526388M3[t] -0.0226862977284185M4[t] -0.0745510928749468M5[t] -0.161684153029778M6[t] + 0.160657728882051M7[t] + 0.0429349888681616M8[t] + 0.906708489222317M9[t] -0.040782689573798M10[t] -0.0505139202881374M11[t] -0.00275273476324471t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6800014900264240.4192061.62210.1140160.057008
InvlMex-0.1099168825604420.225445-0.48760.6289940.314497
`Yt-1`0.8290839161749350.1694894.89172.4e-051.2e-05
`Yt-2`0.04062798488062370.2478350.16390.8707560.435378
`Yt-3`0.1020370610115120.2589430.39410.6960030.348001
`Yt-4`-0.1098240585388420.189244-0.58030.5655190.28276
M1-0.1994446569489990.307899-0.64780.5214910.260745
M2-0.2525518563726270.310744-0.81270.4220260.211013
M3-0.23622395263880.295468-0.79950.4295570.214778
M4-0.02268629772841850.330934-0.06860.9457470.472874
M5-0.07455109287494680.344289-0.21650.8298640.414932
M6-0.1616841530297780.302225-0.5350.5961460.298073
M70.1606577288820510.3039480.52860.6005370.300269
M80.04293498886816160.3097320.13860.8905680.445284
M90.9067084892223170.3180132.85120.0073540.003677
M10-0.0407826895737980.32261-0.12640.9001480.450074
M11-0.05051392028813740.349013-0.14470.8857760.442888
t-0.002752734763244710.004832-0.56970.5726340.286317

\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) & 0.680001490026424 & 0.419206 & 1.6221 & 0.114016 & 0.057008 \tabularnewline
InvlMex & -0.109916882560442 & 0.225445 & -0.4876 & 0.628994 & 0.314497 \tabularnewline
`Yt-1` & 0.829083916174935 & 0.169489 & 4.8917 & 2.4e-05 & 1.2e-05 \tabularnewline
`Yt-2` & 0.0406279848806237 & 0.247835 & 0.1639 & 0.870756 & 0.435378 \tabularnewline
`Yt-3` & 0.102037061011512 & 0.258943 & 0.3941 & 0.696003 & 0.348001 \tabularnewline
`Yt-4` & -0.109824058538842 & 0.189244 & -0.5803 & 0.565519 & 0.28276 \tabularnewline
M1 & -0.199444656948999 & 0.307899 & -0.6478 & 0.521491 & 0.260745 \tabularnewline
M2 & -0.252551856372627 & 0.310744 & -0.8127 & 0.422026 & 0.211013 \tabularnewline
M3 & -0.2362239526388 & 0.295468 & -0.7995 & 0.429557 & 0.214778 \tabularnewline
M4 & -0.0226862977284185 & 0.330934 & -0.0686 & 0.945747 & 0.472874 \tabularnewline
M5 & -0.0745510928749468 & 0.344289 & -0.2165 & 0.829864 & 0.414932 \tabularnewline
M6 & -0.161684153029778 & 0.302225 & -0.535 & 0.596146 & 0.298073 \tabularnewline
M7 & 0.160657728882051 & 0.303948 & 0.5286 & 0.600537 & 0.300269 \tabularnewline
M8 & 0.0429349888681616 & 0.309732 & 0.1386 & 0.890568 & 0.445284 \tabularnewline
M9 & 0.906708489222317 & 0.318013 & 2.8512 & 0.007354 & 0.003677 \tabularnewline
M10 & -0.040782689573798 & 0.32261 & -0.1264 & 0.900148 & 0.450074 \tabularnewline
M11 & -0.0505139202881374 & 0.349013 & -0.1447 & 0.885776 & 0.442888 \tabularnewline
t & -0.00275273476324471 & 0.004832 & -0.5697 & 0.572634 & 0.286317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58299&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]0.680001490026424[/C][C]0.419206[/C][C]1.6221[/C][C]0.114016[/C][C]0.057008[/C][/ROW]
[ROW][C]InvlMex[/C][C]-0.109916882560442[/C][C]0.225445[/C][C]-0.4876[/C][C]0.628994[/C][C]0.314497[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]0.829083916174935[/C][C]0.169489[/C][C]4.8917[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.0406279848806237[/C][C]0.247835[/C][C]0.1639[/C][C]0.870756[/C][C]0.435378[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]0.102037061011512[/C][C]0.258943[/C][C]0.3941[/C][C]0.696003[/C][C]0.348001[/C][/ROW]
[ROW][C]`Yt-4`[/C][C]-0.109824058538842[/C][C]0.189244[/C][C]-0.5803[/C][C]0.565519[/C][C]0.28276[/C][/ROW]
[ROW][C]M1[/C][C]-0.199444656948999[/C][C]0.307899[/C][C]-0.6478[/C][C]0.521491[/C][C]0.260745[/C][/ROW]
[ROW][C]M2[/C][C]-0.252551856372627[/C][C]0.310744[/C][C]-0.8127[/C][C]0.422026[/C][C]0.211013[/C][/ROW]
[ROW][C]M3[/C][C]-0.2362239526388[/C][C]0.295468[/C][C]-0.7995[/C][C]0.429557[/C][C]0.214778[/C][/ROW]
[ROW][C]M4[/C][C]-0.0226862977284185[/C][C]0.330934[/C][C]-0.0686[/C][C]0.945747[/C][C]0.472874[/C][/ROW]
[ROW][C]M5[/C][C]-0.0745510928749468[/C][C]0.344289[/C][C]-0.2165[/C][C]0.829864[/C][C]0.414932[/C][/ROW]
[ROW][C]M6[/C][C]-0.161684153029778[/C][C]0.302225[/C][C]-0.535[/C][C]0.596146[/C][C]0.298073[/C][/ROW]
[ROW][C]M7[/C][C]0.160657728882051[/C][C]0.303948[/C][C]0.5286[/C][C]0.600537[/C][C]0.300269[/C][/ROW]
[ROW][C]M8[/C][C]0.0429349888681616[/C][C]0.309732[/C][C]0.1386[/C][C]0.890568[/C][C]0.445284[/C][/ROW]
[ROW][C]M9[/C][C]0.906708489222317[/C][C]0.318013[/C][C]2.8512[/C][C]0.007354[/C][C]0.003677[/C][/ROW]
[ROW][C]M10[/C][C]-0.040782689573798[/C][C]0.32261[/C][C]-0.1264[/C][C]0.900148[/C][C]0.450074[/C][/ROW]
[ROW][C]M11[/C][C]-0.0505139202881374[/C][C]0.349013[/C][C]-0.1447[/C][C]0.885776[/C][C]0.442888[/C][/ROW]
[ROW][C]t[/C][C]-0.00275273476324471[/C][C]0.004832[/C][C]-0.5697[/C][C]0.572634[/C][C]0.286317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58299&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58299&T=2

As an alternative you can also use a 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)0.6800014900264240.4192061.62210.1140160.057008
InvlMex-0.1099168825604420.225445-0.48760.6289940.314497
`Yt-1`0.8290839161749350.1694894.89172.4e-051.2e-05
`Yt-2`0.04062798488062370.2478350.16390.8707560.435378
`Yt-3`0.1020370610115120.2589430.39410.6960030.348001
`Yt-4`-0.1098240585388420.189244-0.58030.5655190.28276
M1-0.1994446569489990.307899-0.64780.5214910.260745
M2-0.2525518563726270.310744-0.81270.4220260.211013
M3-0.23622395263880.295468-0.79950.4295570.214778
M4-0.02268629772841850.330934-0.06860.9457470.472874
M5-0.07455109287494680.344289-0.21650.8298640.414932
M6-0.1616841530297780.302225-0.5350.5961460.298073
M70.1606577288820510.3039480.52860.6005370.300269
M80.04293498886816160.3097320.13860.8905680.445284
M90.9067084892223170.3180132.85120.0073540.003677
M10-0.0407826895737980.32261-0.12640.9001480.450074
M11-0.05051392028813740.349013-0.14470.8857760.442888
t-0.002752734763244710.004832-0.56970.5726340.286317







Multiple Linear Regression - Regression Statistics
Multiple R0.950462850624025
R-squared0.903379630416347
Adjusted R-squared0.85506944562452
F-TEST (value)18.6995689275274
F-TEST (DF numerator)17
F-TEST (DF denominator)34
p-value1.68287606072681e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.342358847011439
Sum Squared Residuals3.98512572431807

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.950462850624025 \tabularnewline
R-squared & 0.903379630416347 \tabularnewline
Adjusted R-squared & 0.85506944562452 \tabularnewline
F-TEST (value) & 18.6995689275274 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 1.68287606072681e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.342358847011439 \tabularnewline
Sum Squared Residuals & 3.98512572431807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58299&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.950462850624025[/C][/ROW]
[ROW][C]R-squared[/C][C]0.903379630416347[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.85506944562452[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.6995689275274[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]1.68287606072681e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.342358847011439[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.98512572431807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58299&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58299&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.950462850624025
R-squared0.903379630416347
Adjusted R-squared0.85506944562452
F-TEST (value)18.6995689275274
F-TEST (DF numerator)17
F-TEST (DF denominator)34
p-value1.68287606072681e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.342358847011439
Sum Squared Residuals3.98512572431807







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
122.21454512062515-0.214545120625151
22.12.15911247629585-0.0591124762958488
32.52.275224749333490.224775250666508
42.52.78875881687701-0.288758816877015
52.62.76059618702064-0.160596187020643
62.72.78345120227078-0.0834512022707805
73.73.146081916109380.553918083890616
843.86895686209640.131043137903601
955.01855208766768-0.0185520876676811
105.15.000635140905070.0993648590949268
115.15.032474611690220.0675253883097832
1255.15338843915303-0.153388439153031
135.14.76866230338560.331337696614397
144.74.78066555647428-0.0806655564742768
154.54.45646625136180.043533748638203
164.54.50636930627673-0.00636930627673205
174.64.391828949132350.208171050867654
184.64.4083737570450.191626242955003
194.64.75399051438941-0.153990514389412
204.64.64371874571343-0.0437187457134295
215.35.49375710545046-0.193757105450456
225.45.123871933213550.276128066786449
235.35.22273594876990.0772640512301022
245.25.26307748387342-0.0630774838734171
2554.907235767179580.0927642328204208
264.24.66031013931462-0.460310139314622
274.34.003271278121860.296728721878136
284.34.255037195633580.0449628043664231
294.34.144817627110420.155182372889575
3044.15299478512457-0.152994785124574
3144.21287635156679-0.212876351566793
324.14.080212481325470.0197875186745276
334.44.99353052023042-0.593530520230422
343.64.32902179757326-0.729021797573259
353.73.675662800721060.0243371992789359
363.83.793458702408520.00654129759147848
373.33.56365563443097-0.26365563443097
383.33.195379493576920.104620506423082
393.33.187861970354460.112138029645545
403.53.336645954141950.163354045858049
413.33.50275723673659-0.202757236736587
423.33.255180255559650.0448197444403515
433.43.58705121793441-0.187051217934410
443.43.5071119108647-0.107111910864699
455.24.394160286651440.80583971334856
465.34.946471128308120.353528871691883
474.84.96912663881882-0.169126638818821
4854.790075374565030.209924625434969
494.64.54590117437870.0540988256213028
504.64.104532334338330.495467665661665
513.54.17717575082839-0.677175750828391
523.53.413188727070730.0868112729292748

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 2.21454512062515 & -0.214545120625151 \tabularnewline
2 & 2.1 & 2.15911247629585 & -0.0591124762958488 \tabularnewline
3 & 2.5 & 2.27522474933349 & 0.224775250666508 \tabularnewline
4 & 2.5 & 2.78875881687701 & -0.288758816877015 \tabularnewline
5 & 2.6 & 2.76059618702064 & -0.160596187020643 \tabularnewline
6 & 2.7 & 2.78345120227078 & -0.0834512022707805 \tabularnewline
7 & 3.7 & 3.14608191610938 & 0.553918083890616 \tabularnewline
8 & 4 & 3.8689568620964 & 0.131043137903601 \tabularnewline
9 & 5 & 5.01855208766768 & -0.0185520876676811 \tabularnewline
10 & 5.1 & 5.00063514090507 & 0.0993648590949268 \tabularnewline
11 & 5.1 & 5.03247461169022 & 0.0675253883097832 \tabularnewline
12 & 5 & 5.15338843915303 & -0.153388439153031 \tabularnewline
13 & 5.1 & 4.7686623033856 & 0.331337696614397 \tabularnewline
14 & 4.7 & 4.78066555647428 & -0.0806655564742768 \tabularnewline
15 & 4.5 & 4.4564662513618 & 0.043533748638203 \tabularnewline
16 & 4.5 & 4.50636930627673 & -0.00636930627673205 \tabularnewline
17 & 4.6 & 4.39182894913235 & 0.208171050867654 \tabularnewline
18 & 4.6 & 4.408373757045 & 0.191626242955003 \tabularnewline
19 & 4.6 & 4.75399051438941 & -0.153990514389412 \tabularnewline
20 & 4.6 & 4.64371874571343 & -0.0437187457134295 \tabularnewline
21 & 5.3 & 5.49375710545046 & -0.193757105450456 \tabularnewline
22 & 5.4 & 5.12387193321355 & 0.276128066786449 \tabularnewline
23 & 5.3 & 5.2227359487699 & 0.0772640512301022 \tabularnewline
24 & 5.2 & 5.26307748387342 & -0.0630774838734171 \tabularnewline
25 & 5 & 4.90723576717958 & 0.0927642328204208 \tabularnewline
26 & 4.2 & 4.66031013931462 & -0.460310139314622 \tabularnewline
27 & 4.3 & 4.00327127812186 & 0.296728721878136 \tabularnewline
28 & 4.3 & 4.25503719563358 & 0.0449628043664231 \tabularnewline
29 & 4.3 & 4.14481762711042 & 0.155182372889575 \tabularnewline
30 & 4 & 4.15299478512457 & -0.152994785124574 \tabularnewline
31 & 4 & 4.21287635156679 & -0.212876351566793 \tabularnewline
32 & 4.1 & 4.08021248132547 & 0.0197875186745276 \tabularnewline
33 & 4.4 & 4.99353052023042 & -0.593530520230422 \tabularnewline
34 & 3.6 & 4.32902179757326 & -0.729021797573259 \tabularnewline
35 & 3.7 & 3.67566280072106 & 0.0243371992789359 \tabularnewline
36 & 3.8 & 3.79345870240852 & 0.00654129759147848 \tabularnewline
37 & 3.3 & 3.56365563443097 & -0.26365563443097 \tabularnewline
38 & 3.3 & 3.19537949357692 & 0.104620506423082 \tabularnewline
39 & 3.3 & 3.18786197035446 & 0.112138029645545 \tabularnewline
40 & 3.5 & 3.33664595414195 & 0.163354045858049 \tabularnewline
41 & 3.3 & 3.50275723673659 & -0.202757236736587 \tabularnewline
42 & 3.3 & 3.25518025555965 & 0.0448197444403515 \tabularnewline
43 & 3.4 & 3.58705121793441 & -0.187051217934410 \tabularnewline
44 & 3.4 & 3.5071119108647 & -0.107111910864699 \tabularnewline
45 & 5.2 & 4.39416028665144 & 0.80583971334856 \tabularnewline
46 & 5.3 & 4.94647112830812 & 0.353528871691883 \tabularnewline
47 & 4.8 & 4.96912663881882 & -0.169126638818821 \tabularnewline
48 & 5 & 4.79007537456503 & 0.209924625434969 \tabularnewline
49 & 4.6 & 4.5459011743787 & 0.0540988256213028 \tabularnewline
50 & 4.6 & 4.10453233433833 & 0.495467665661665 \tabularnewline
51 & 3.5 & 4.17717575082839 & -0.677175750828391 \tabularnewline
52 & 3.5 & 3.41318872707073 & 0.0868112729292748 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58299&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]2[/C][C]2.21454512062515[/C][C]-0.214545120625151[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]2.15911247629585[/C][C]-0.0591124762958488[/C][/ROW]
[ROW][C]3[/C][C]2.5[/C][C]2.27522474933349[/C][C]0.224775250666508[/C][/ROW]
[ROW][C]4[/C][C]2.5[/C][C]2.78875881687701[/C][C]-0.288758816877015[/C][/ROW]
[ROW][C]5[/C][C]2.6[/C][C]2.76059618702064[/C][C]-0.160596187020643[/C][/ROW]
[ROW][C]6[/C][C]2.7[/C][C]2.78345120227078[/C][C]-0.0834512022707805[/C][/ROW]
[ROW][C]7[/C][C]3.7[/C][C]3.14608191610938[/C][C]0.553918083890616[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]3.8689568620964[/C][C]0.131043137903601[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]5.01855208766768[/C][C]-0.0185520876676811[/C][/ROW]
[ROW][C]10[/C][C]5.1[/C][C]5.00063514090507[/C][C]0.0993648590949268[/C][/ROW]
[ROW][C]11[/C][C]5.1[/C][C]5.03247461169022[/C][C]0.0675253883097832[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]5.15338843915303[/C][C]-0.153388439153031[/C][/ROW]
[ROW][C]13[/C][C]5.1[/C][C]4.7686623033856[/C][C]0.331337696614397[/C][/ROW]
[ROW][C]14[/C][C]4.7[/C][C]4.78066555647428[/C][C]-0.0806655564742768[/C][/ROW]
[ROW][C]15[/C][C]4.5[/C][C]4.4564662513618[/C][C]0.043533748638203[/C][/ROW]
[ROW][C]16[/C][C]4.5[/C][C]4.50636930627673[/C][C]-0.00636930627673205[/C][/ROW]
[ROW][C]17[/C][C]4.6[/C][C]4.39182894913235[/C][C]0.208171050867654[/C][/ROW]
[ROW][C]18[/C][C]4.6[/C][C]4.408373757045[/C][C]0.191626242955003[/C][/ROW]
[ROW][C]19[/C][C]4.6[/C][C]4.75399051438941[/C][C]-0.153990514389412[/C][/ROW]
[ROW][C]20[/C][C]4.6[/C][C]4.64371874571343[/C][C]-0.0437187457134295[/C][/ROW]
[ROW][C]21[/C][C]5.3[/C][C]5.49375710545046[/C][C]-0.193757105450456[/C][/ROW]
[ROW][C]22[/C][C]5.4[/C][C]5.12387193321355[/C][C]0.276128066786449[/C][/ROW]
[ROW][C]23[/C][C]5.3[/C][C]5.2227359487699[/C][C]0.0772640512301022[/C][/ROW]
[ROW][C]24[/C][C]5.2[/C][C]5.26307748387342[/C][C]-0.0630774838734171[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]4.90723576717958[/C][C]0.0927642328204208[/C][/ROW]
[ROW][C]26[/C][C]4.2[/C][C]4.66031013931462[/C][C]-0.460310139314622[/C][/ROW]
[ROW][C]27[/C][C]4.3[/C][C]4.00327127812186[/C][C]0.296728721878136[/C][/ROW]
[ROW][C]28[/C][C]4.3[/C][C]4.25503719563358[/C][C]0.0449628043664231[/C][/ROW]
[ROW][C]29[/C][C]4.3[/C][C]4.14481762711042[/C][C]0.155182372889575[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.15299478512457[/C][C]-0.152994785124574[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.21287635156679[/C][C]-0.212876351566793[/C][/ROW]
[ROW][C]32[/C][C]4.1[/C][C]4.08021248132547[/C][C]0.0197875186745276[/C][/ROW]
[ROW][C]33[/C][C]4.4[/C][C]4.99353052023042[/C][C]-0.593530520230422[/C][/ROW]
[ROW][C]34[/C][C]3.6[/C][C]4.32902179757326[/C][C]-0.729021797573259[/C][/ROW]
[ROW][C]35[/C][C]3.7[/C][C]3.67566280072106[/C][C]0.0243371992789359[/C][/ROW]
[ROW][C]36[/C][C]3.8[/C][C]3.79345870240852[/C][C]0.00654129759147848[/C][/ROW]
[ROW][C]37[/C][C]3.3[/C][C]3.56365563443097[/C][C]-0.26365563443097[/C][/ROW]
[ROW][C]38[/C][C]3.3[/C][C]3.19537949357692[/C][C]0.104620506423082[/C][/ROW]
[ROW][C]39[/C][C]3.3[/C][C]3.18786197035446[/C][C]0.112138029645545[/C][/ROW]
[ROW][C]40[/C][C]3.5[/C][C]3.33664595414195[/C][C]0.163354045858049[/C][/ROW]
[ROW][C]41[/C][C]3.3[/C][C]3.50275723673659[/C][C]-0.202757236736587[/C][/ROW]
[ROW][C]42[/C][C]3.3[/C][C]3.25518025555965[/C][C]0.0448197444403515[/C][/ROW]
[ROW][C]43[/C][C]3.4[/C][C]3.58705121793441[/C][C]-0.187051217934410[/C][/ROW]
[ROW][C]44[/C][C]3.4[/C][C]3.5071119108647[/C][C]-0.107111910864699[/C][/ROW]
[ROW][C]45[/C][C]5.2[/C][C]4.39416028665144[/C][C]0.80583971334856[/C][/ROW]
[ROW][C]46[/C][C]5.3[/C][C]4.94647112830812[/C][C]0.353528871691883[/C][/ROW]
[ROW][C]47[/C][C]4.8[/C][C]4.96912663881882[/C][C]-0.169126638818821[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]4.79007537456503[/C][C]0.209924625434969[/C][/ROW]
[ROW][C]49[/C][C]4.6[/C][C]4.5459011743787[/C][C]0.0540988256213028[/C][/ROW]
[ROW][C]50[/C][C]4.6[/C][C]4.10453233433833[/C][C]0.495467665661665[/C][/ROW]
[ROW][C]51[/C][C]3.5[/C][C]4.17717575082839[/C][C]-0.677175750828391[/C][/ROW]
[ROW][C]52[/C][C]3.5[/C][C]3.41318872707073[/C][C]0.0868112729292748[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58299&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58299&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
122.21454512062515-0.214545120625151
22.12.15911247629585-0.0591124762958488
32.52.275224749333490.224775250666508
42.52.78875881687701-0.288758816877015
52.62.76059618702064-0.160596187020643
62.72.78345120227078-0.0834512022707805
73.73.146081916109380.553918083890616
843.86895686209640.131043137903601
955.01855208766768-0.0185520876676811
105.15.000635140905070.0993648590949268
115.15.032474611690220.0675253883097832
1255.15338843915303-0.153388439153031
135.14.76866230338560.331337696614397
144.74.78066555647428-0.0806655564742768
154.54.45646625136180.043533748638203
164.54.50636930627673-0.00636930627673205
174.64.391828949132350.208171050867654
184.64.4083737570450.191626242955003
194.64.75399051438941-0.153990514389412
204.64.64371874571343-0.0437187457134295
215.35.49375710545046-0.193757105450456
225.45.123871933213550.276128066786449
235.35.22273594876990.0772640512301022
245.25.26307748387342-0.0630774838734171
2554.907235767179580.0927642328204208
264.24.66031013931462-0.460310139314622
274.34.003271278121860.296728721878136
284.34.255037195633580.0449628043664231
294.34.144817627110420.155182372889575
3044.15299478512457-0.152994785124574
3144.21287635156679-0.212876351566793
324.14.080212481325470.0197875186745276
334.44.99353052023042-0.593530520230422
343.64.32902179757326-0.729021797573259
353.73.675662800721060.0243371992789359
363.83.793458702408520.00654129759147848
373.33.56365563443097-0.26365563443097
383.33.195379493576920.104620506423082
393.33.187861970354460.112138029645545
403.53.336645954141950.163354045858049
413.33.50275723673659-0.202757236736587
423.33.255180255559650.0448197444403515
433.43.58705121793441-0.187051217934410
443.43.5071119108647-0.107111910864699
455.24.394160286651440.80583971334856
465.34.946471128308120.353528871691883
474.84.96912663881882-0.169126638818821
4854.790075374565030.209924625434969
494.64.54590117437870.0540988256213028
504.64.104532334338330.495467665661665
513.54.17717575082839-0.677175750828391
523.53.413188727070730.0868112729292748







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.2368187704999090.4736375409998170.763181229500091
220.1635362218494350.3270724436988690.836463778150565
230.1579969645646090.3159939291292180.842003035435391
240.1212131008065500.2424262016131010.87878689919345
250.07088612486992190.1417722497398440.929113875130078
260.1214263732782150.2428527465564300.878573626721785
270.07906983222259950.1581396644451990.9209301677774
280.04477722141729690.08955444283459390.955222778582703
290.02884258550023100.05768517100046210.97115741449977
300.01115138035580300.02230276071160600.988848619644197
310.006911011430463160.01382202286092630.993088988569537

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.236818770499909 & 0.473637540999817 & 0.763181229500091 \tabularnewline
22 & 0.163536221849435 & 0.327072443698869 & 0.836463778150565 \tabularnewline
23 & 0.157996964564609 & 0.315993929129218 & 0.842003035435391 \tabularnewline
24 & 0.121213100806550 & 0.242426201613101 & 0.87878689919345 \tabularnewline
25 & 0.0708861248699219 & 0.141772249739844 & 0.929113875130078 \tabularnewline
26 & 0.121426373278215 & 0.242852746556430 & 0.878573626721785 \tabularnewline
27 & 0.0790698322225995 & 0.158139664445199 & 0.9209301677774 \tabularnewline
28 & 0.0447772214172969 & 0.0895544428345939 & 0.955222778582703 \tabularnewline
29 & 0.0288425855002310 & 0.0576851710004621 & 0.97115741449977 \tabularnewline
30 & 0.0111513803558030 & 0.0223027607116060 & 0.988848619644197 \tabularnewline
31 & 0.00691101143046316 & 0.0138220228609263 & 0.993088988569537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58299&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]21[/C][C]0.236818770499909[/C][C]0.473637540999817[/C][C]0.763181229500091[/C][/ROW]
[ROW][C]22[/C][C]0.163536221849435[/C][C]0.327072443698869[/C][C]0.836463778150565[/C][/ROW]
[ROW][C]23[/C][C]0.157996964564609[/C][C]0.315993929129218[/C][C]0.842003035435391[/C][/ROW]
[ROW][C]24[/C][C]0.121213100806550[/C][C]0.242426201613101[/C][C]0.87878689919345[/C][/ROW]
[ROW][C]25[/C][C]0.0708861248699219[/C][C]0.141772249739844[/C][C]0.929113875130078[/C][/ROW]
[ROW][C]26[/C][C]0.121426373278215[/C][C]0.242852746556430[/C][C]0.878573626721785[/C][/ROW]
[ROW][C]27[/C][C]0.0790698322225995[/C][C]0.158139664445199[/C][C]0.9209301677774[/C][/ROW]
[ROW][C]28[/C][C]0.0447772214172969[/C][C]0.0895544428345939[/C][C]0.955222778582703[/C][/ROW]
[ROW][C]29[/C][C]0.0288425855002310[/C][C]0.0576851710004621[/C][C]0.97115741449977[/C][/ROW]
[ROW][C]30[/C][C]0.0111513803558030[/C][C]0.0223027607116060[/C][C]0.988848619644197[/C][/ROW]
[ROW][C]31[/C][C]0.00691101143046316[/C][C]0.0138220228609263[/C][C]0.993088988569537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58299&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58299&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.2368187704999090.4736375409998170.763181229500091
220.1635362218494350.3270724436988690.836463778150565
230.1579969645646090.3159939291292180.842003035435391
240.1212131008065500.2424262016131010.87878689919345
250.07088612486992190.1417722497398440.929113875130078
260.1214263732782150.2428527465564300.878573626721785
270.07906983222259950.1581396644451990.9209301677774
280.04477722141729690.08955444283459390.955222778582703
290.02884258550023100.05768517100046210.97115741449977
300.01115138035580300.02230276071160600.988848619644197
310.006911011430463160.01382202286092630.993088988569537







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.181818181818182NOK
10% type I error level40.363636363636364NOK

\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 & 2 & 0.181818181818182 & NOK \tabularnewline
10% type I error level & 4 & 0.363636363636364 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58299&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]2[/C][C]0.181818181818182[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.363636363636364[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58299&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58299&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.181818181818182NOK
10% type I error level40.363636363636364NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}