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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 12 Dec 2010 17:57:21 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/12/t12921766443x2k4gbsdm4xdj0.htm/, Retrieved Tue, 07 May 2024 11:00:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108583, Retrieved Tue, 07 May 2024 11:00:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 10 part ...] [2010-12-12 17:57:21] [c9b1b69acb8f4b2b921fdfd5091a94b7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.0	6.3	4.5	1000	6600	42.0	3	1	3
1.8	2.1	69.0	2547000	4603000	624.0	3	5	4
.7	9.1	27.0	10550	179500	180.0	4	4	4
3.9	15.8	19.0	0,023	0,3	35.0	1	1	1
1.0	5.2	30.4	160000	169000	392.0	4	5	4
3.6	10.9	28.0	3300	25600	63.0	1	2	1
1.4	8.3	50.0	52160	440000	230.0	1	1	1
1.5	11.0	7.0	0,425	6400	112.0	5	4	4
.7	3.2	30.0	465000	423000	281.0	5	5	5
2.1	6.3	3.5	0,075	1200	42.0	1	1	1
4.1	6.6	6.0	0,785	3500	42.0	2	2	2
1.2	9.5	10.4	0,2	5000	120.0	2	2	2
.5	3.3	20.0	27660	115000	148.0	5	5	5
3.4	11.0	3.9	0,12	1000	16.0	3	1	2
1.5	4.7	41.0	85000	325000	310.0	1	3	1
3.4	10.4	9.0	0,101	4000	28.0	5	1	3
.8	7.4	7.6	1040	5500	68.0	5	3	4
.8	2.1	46.0	521000	655000	336.0	5	5	5
2.0	17.9	24.0	0,01	0,25	50.0	1	1	1
1.9	6.1	100.0	62000	1320000	267.0	1	1	1
1.3	11.9	3.2	0,023	0,4	19.0	4	1	3
5.6	13.8	5.0	1700	6300	12.0	2	1	1
3.1	14.3	6.5	3500	10800	120.0	2	1	1
1.8	15.2	12.0	0,48	15500	140.0	2	2	2
.9	10.0	20.2	10000	115000	170.0	4	4	4
1.8	11.9	13.0	1620	11400	17.0	2	1	2
1.9	6.5	27.0	192000	180000	115.0	4	4	4
.9	7.5	18.0	2500	12100	31.0	5	5	5
2.6	10.6	4.7	0,28	1900	21.0	3	1	3
2.4	7.4	9.8	4235	50400	52.0	1	1	1
1.2	8.4	29.0	6800	179000	164.0	2	3	2
.9	5.7	7.0	0,75	12300	225.0	2	2	2
.5	4.9	6.0	3600	21000	225.0	3	2	3
.6	3.2	20.0	55500	175000	151.0	5	5	5
2.3	11.0	4.5	0,9	2600	60.0	2	1	2
.5	4.9	7.5	2000	12300	200.0	3	1	3
2.6	13.2	2.3	0,104	2500	46.0	3	2	2
.6	9.7	24.0	4190	58000	210.0	4	3	4
6.6	12.8	3.0	3500	3900	14.0	2	1	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108583&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108583&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108583&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.98678631006288 -0.0139630483503009SWS[t] + 0.0119435681086067L[t] + 3.63660450881043e-06Wb[t] -1.02179702553102e-06Wbr[t] -0.00750615624554444Tg[t] + 0.924263159311339P[t] + 0.2630780264437S[t] -1.71362938051807`D `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.98678631006288 -0.0139630483503009SWS[t] +  0.0119435681086067L[t] +  3.63660450881043e-06Wb[t] -1.02179702553102e-06Wbr[t] -0.00750615624554444Tg[t] +  0.924263159311339P[t] +  0.2630780264437S[t] -1.71362938051807`D
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108583&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.98678631006288 -0.0139630483503009SWS[t] +  0.0119435681086067L[t] +  3.63660450881043e-06Wb[t] -1.02179702553102e-06Wbr[t] -0.00750615624554444Tg[t] +  0.924263159311339P[t] +  0.2630780264437S[t] -1.71362938051807`D
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108583&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108583&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
PS[t] = + 3.98678631006288 -0.0139630483503009SWS[t] + 0.0119435681086067L[t] + 3.63660450881043e-06Wb[t] -1.02179702553102e-06Wbr[t] -0.00750615624554444Tg[t] + 0.924263159311339P[t] + 0.2630780264437S[t] -1.71362938051807`D `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.986786310062880.8122414.90843e-051.5e-05
SWS-0.01396304835030090.053604-0.26050.7962680.398134
L0.01194356810860670.0160360.74480.4621860.231093
Wb3.63660450881043e-062e-061.91090.0656210.03281
Wbr-1.02179702553102e-061e-06-0.8990.3758250.187912
Tg-0.007506156245544440.00233-3.22170.0030620.001531
P0.9242631593113390.3307942.79410.0089820.004491
S0.26307802644370.204851.28420.2088850.104443
`D `-1.713629380518070.419191-4.08793e-040.00015

\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) & 3.98678631006288 & 0.812241 & 4.9084 & 3e-05 & 1.5e-05 \tabularnewline
SWS & -0.0139630483503009 & 0.053604 & -0.2605 & 0.796268 & 0.398134 \tabularnewline
L & 0.0119435681086067 & 0.016036 & 0.7448 & 0.462186 & 0.231093 \tabularnewline
Wb & 3.63660450881043e-06 & 2e-06 & 1.9109 & 0.065621 & 0.03281 \tabularnewline
Wbr & -1.02179702553102e-06 & 1e-06 & -0.899 & 0.375825 & 0.187912 \tabularnewline
Tg & -0.00750615624554444 & 0.00233 & -3.2217 & 0.003062 & 0.001531 \tabularnewline
P & 0.924263159311339 & 0.330794 & 2.7941 & 0.008982 & 0.004491 \tabularnewline
S & 0.2630780264437 & 0.20485 & 1.2842 & 0.208885 & 0.104443 \tabularnewline
`D
` & -1.71362938051807 & 0.419191 & -4.0879 & 3e-04 & 0.00015 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108583&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]3.98678631006288[/C][C]0.812241[/C][C]4.9084[/C][C]3e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]SWS[/C][C]-0.0139630483503009[/C][C]0.053604[/C][C]-0.2605[/C][C]0.796268[/C][C]0.398134[/C][/ROW]
[ROW][C]L[/C][C]0.0119435681086067[/C][C]0.016036[/C][C]0.7448[/C][C]0.462186[/C][C]0.231093[/C][/ROW]
[ROW][C]Wb[/C][C]3.63660450881043e-06[/C][C]2e-06[/C][C]1.9109[/C][C]0.065621[/C][C]0.03281[/C][/ROW]
[ROW][C]Wbr[/C][C]-1.02179702553102e-06[/C][C]1e-06[/C][C]-0.899[/C][C]0.375825[/C][C]0.187912[/C][/ROW]
[ROW][C]Tg[/C][C]-0.00750615624554444[/C][C]0.00233[/C][C]-3.2217[/C][C]0.003062[/C][C]0.001531[/C][/ROW]
[ROW][C]P[/C][C]0.924263159311339[/C][C]0.330794[/C][C]2.7941[/C][C]0.008982[/C][C]0.004491[/C][/ROW]
[ROW][C]S[/C][C]0.2630780264437[/C][C]0.20485[/C][C]1.2842[/C][C]0.208885[/C][C]0.104443[/C][/ROW]
[ROW][C]`D
`[/C][C]-1.71362938051807[/C][C]0.419191[/C][C]-4.0879[/C][C]3e-04[/C][C]0.00015[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108583&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108583&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)3.986786310062880.8122414.90843e-051.5e-05
SWS-0.01396304835030090.053604-0.26050.7962680.398134
L0.01194356810860670.0160360.74480.4621860.231093
Wb3.63660450881043e-062e-061.91090.0656210.03281
Wbr-1.02179702553102e-061e-06-0.8990.3758250.187912
Tg-0.007506156245544440.00233-3.22170.0030620.001531
P0.9242631593113390.3307942.79410.0089820.004491
S0.26307802644370.204851.28420.2088850.104443
`D `-1.713629380518070.419191-4.08793e-040.00015






Multiple Linear Regression - Regression Statistics
Multiple R0.831725417344077
R-squared0.691767169856179
Adjusted R-squared0.609571748484493
F-TEST (value)8.41612778804339
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value6.32987895032855e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.878263099112862
Sum Squared Residuals23.1403821378998

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.831725417344077 \tabularnewline
R-squared & 0.691767169856179 \tabularnewline
Adjusted R-squared & 0.609571748484493 \tabularnewline
F-TEST (value) & 8.41612778804339 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 6.32987895032855e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.878263099112862 \tabularnewline
Sum Squared Residuals & 23.1403821378998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108583&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.831725417344077[/C][/ROW]
[ROW][C]R-squared[/C][C]0.691767169856179[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.609571748484493[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.41612778804339[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]30[/C][/ROW]
[ROW][C]p-value[/C][C]6.32987895032855e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.878263099112862[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]23.1403821378998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108583&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108583&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 R0.831725417344077
R-squared0.691767169856179
Adjusted R-squared0.609571748484493
F-TEST (value)8.41612778804339
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value6.32987895032855e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.878263099112862
Sum Squared Residuals23.1403821378998






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.529178706595660.470821293404342
21.81.89049067430249-0.0904906743024908
30.70.5808916172425210.119108382757479
43.93.204094053937370.695905946062635
51-0.09805204762396861.09805204762397
63.63.418753769322440.181246230677561
71.41.95546188289337-0.555461882893371
81.51.88868068032156-0.388680680321562
90.70.818541674584192-0.118541674584192
102.13.09784895307491-0.997848953074913
114.12.594883212908781.50511678709122
121.22.01992706226638-0.81992706226638
130.50.4203093373009040.0796906626990957
143.43.367261576613010.0327384233869904
151.52.06083304722624-0.56083304722624
163.43.47830920596308-0.0783092059630768
170.82.01800678406329-1.21800678406329
180.80.5787524665724740.221247533427526
1923.12189715307559-1.12189715307559
201.91.442234019508620.45776598049138
211.32.53547068103443-1.23547068103443
225.64.161460079377671.43853992062233
233.13.3636768343476-0.263676834347599
241.81.798596420213730.00140357978626561
250.90.6260759487110750.273924051288925
261.82.52687616117539-0.726876161175386
271.91.764446688524580.13555331147542
280.91.2296436232266-0.329643623226598
292.61.630322453227960.969677546772037
302.43.04780085021171-0.647800850211707
311.22.03717943480815-0.837179434808153
320.91.2367749904922-0.336774990492199
330.50.4508350545133140.0491649454866858
340.60.4391224213927220.160877578607278
352.32.118261644673550.181738355326453
360.50.3963973532791590.103602646720841
372.63.35379374662198-0.753793746621976
380.60.1494399040224730.450560095977527
396.64.14552187999682.4544781200032

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.52917870659566 & 0.470821293404342 \tabularnewline
2 & 1.8 & 1.89049067430249 & -0.0904906743024908 \tabularnewline
3 & 0.7 & 0.580891617242521 & 0.119108382757479 \tabularnewline
4 & 3.9 & 3.20409405393737 & 0.695905946062635 \tabularnewline
5 & 1 & -0.0980520476239686 & 1.09805204762397 \tabularnewline
6 & 3.6 & 3.41875376932244 & 0.181246230677561 \tabularnewline
7 & 1.4 & 1.95546188289337 & -0.555461882893371 \tabularnewline
8 & 1.5 & 1.88868068032156 & -0.388680680321562 \tabularnewline
9 & 0.7 & 0.818541674584192 & -0.118541674584192 \tabularnewline
10 & 2.1 & 3.09784895307491 & -0.997848953074913 \tabularnewline
11 & 4.1 & 2.59488321290878 & 1.50511678709122 \tabularnewline
12 & 1.2 & 2.01992706226638 & -0.81992706226638 \tabularnewline
13 & 0.5 & 0.420309337300904 & 0.0796906626990957 \tabularnewline
14 & 3.4 & 3.36726157661301 & 0.0327384233869904 \tabularnewline
15 & 1.5 & 2.06083304722624 & -0.56083304722624 \tabularnewline
16 & 3.4 & 3.47830920596308 & -0.0783092059630768 \tabularnewline
17 & 0.8 & 2.01800678406329 & -1.21800678406329 \tabularnewline
18 & 0.8 & 0.578752466572474 & 0.221247533427526 \tabularnewline
19 & 2 & 3.12189715307559 & -1.12189715307559 \tabularnewline
20 & 1.9 & 1.44223401950862 & 0.45776598049138 \tabularnewline
21 & 1.3 & 2.53547068103443 & -1.23547068103443 \tabularnewline
22 & 5.6 & 4.16146007937767 & 1.43853992062233 \tabularnewline
23 & 3.1 & 3.3636768343476 & -0.263676834347599 \tabularnewline
24 & 1.8 & 1.79859642021373 & 0.00140357978626561 \tabularnewline
25 & 0.9 & 0.626075948711075 & 0.273924051288925 \tabularnewline
26 & 1.8 & 2.52687616117539 & -0.726876161175386 \tabularnewline
27 & 1.9 & 1.76444668852458 & 0.13555331147542 \tabularnewline
28 & 0.9 & 1.2296436232266 & -0.329643623226598 \tabularnewline
29 & 2.6 & 1.63032245322796 & 0.969677546772037 \tabularnewline
30 & 2.4 & 3.04780085021171 & -0.647800850211707 \tabularnewline
31 & 1.2 & 2.03717943480815 & -0.837179434808153 \tabularnewline
32 & 0.9 & 1.2367749904922 & -0.336774990492199 \tabularnewline
33 & 0.5 & 0.450835054513314 & 0.0491649454866858 \tabularnewline
34 & 0.6 & 0.439122421392722 & 0.160877578607278 \tabularnewline
35 & 2.3 & 2.11826164467355 & 0.181738355326453 \tabularnewline
36 & 0.5 & 0.396397353279159 & 0.103602646720841 \tabularnewline
37 & 2.6 & 3.35379374662198 & -0.753793746621976 \tabularnewline
38 & 0.6 & 0.149439904022473 & 0.450560095977527 \tabularnewline
39 & 6.6 & 4.1455218799968 & 2.4544781200032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108583&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]1.52917870659566[/C][C]0.470821293404342[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]1.89049067430249[/C][C]-0.0904906743024908[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.580891617242521[/C][C]0.119108382757479[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]3.20409405393737[/C][C]0.695905946062635[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]-0.0980520476239686[/C][C]1.09805204762397[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]3.41875376932244[/C][C]0.181246230677561[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]1.95546188289337[/C][C]-0.555461882893371[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]1.88868068032156[/C][C]-0.388680680321562[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.818541674584192[/C][C]-0.118541674584192[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]3.09784895307491[/C][C]-0.997848953074913[/C][/ROW]
[ROW][C]11[/C][C]4.1[/C][C]2.59488321290878[/C][C]1.50511678709122[/C][/ROW]
[ROW][C]12[/C][C]1.2[/C][C]2.01992706226638[/C][C]-0.81992706226638[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]0.420309337300904[/C][C]0.0796906626990957[/C][/ROW]
[ROW][C]14[/C][C]3.4[/C][C]3.36726157661301[/C][C]0.0327384233869904[/C][/ROW]
[ROW][C]15[/C][C]1.5[/C][C]2.06083304722624[/C][C]-0.56083304722624[/C][/ROW]
[ROW][C]16[/C][C]3.4[/C][C]3.47830920596308[/C][C]-0.0783092059630768[/C][/ROW]
[ROW][C]17[/C][C]0.8[/C][C]2.01800678406329[/C][C]-1.21800678406329[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.578752466572474[/C][C]0.221247533427526[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]3.12189715307559[/C][C]-1.12189715307559[/C][/ROW]
[ROW][C]20[/C][C]1.9[/C][C]1.44223401950862[/C][C]0.45776598049138[/C][/ROW]
[ROW][C]21[/C][C]1.3[/C][C]2.53547068103443[/C][C]-1.23547068103443[/C][/ROW]
[ROW][C]22[/C][C]5.6[/C][C]4.16146007937767[/C][C]1.43853992062233[/C][/ROW]
[ROW][C]23[/C][C]3.1[/C][C]3.3636768343476[/C][C]-0.263676834347599[/C][/ROW]
[ROW][C]24[/C][C]1.8[/C][C]1.79859642021373[/C][C]0.00140357978626561[/C][/ROW]
[ROW][C]25[/C][C]0.9[/C][C]0.626075948711075[/C][C]0.273924051288925[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]2.52687616117539[/C][C]-0.726876161175386[/C][/ROW]
[ROW][C]27[/C][C]1.9[/C][C]1.76444668852458[/C][C]0.13555331147542[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]1.2296436232266[/C][C]-0.329643623226598[/C][/ROW]
[ROW][C]29[/C][C]2.6[/C][C]1.63032245322796[/C][C]0.969677546772037[/C][/ROW]
[ROW][C]30[/C][C]2.4[/C][C]3.04780085021171[/C][C]-0.647800850211707[/C][/ROW]
[ROW][C]31[/C][C]1.2[/C][C]2.03717943480815[/C][C]-0.837179434808153[/C][/ROW]
[ROW][C]32[/C][C]0.9[/C][C]1.2367749904922[/C][C]-0.336774990492199[/C][/ROW]
[ROW][C]33[/C][C]0.5[/C][C]0.450835054513314[/C][C]0.0491649454866858[/C][/ROW]
[ROW][C]34[/C][C]0.6[/C][C]0.439122421392722[/C][C]0.160877578607278[/C][/ROW]
[ROW][C]35[/C][C]2.3[/C][C]2.11826164467355[/C][C]0.181738355326453[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.396397353279159[/C][C]0.103602646720841[/C][/ROW]
[ROW][C]37[/C][C]2.6[/C][C]3.35379374662198[/C][C]-0.753793746621976[/C][/ROW]
[ROW][C]38[/C][C]0.6[/C][C]0.149439904022473[/C][C]0.450560095977527[/C][/ROW]
[ROW][C]39[/C][C]6.6[/C][C]4.1455218799968[/C][C]2.4544781200032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108583&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108583&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
121.529178706595660.470821293404342
21.81.89049067430249-0.0904906743024908
30.70.5808916172425210.119108382757479
43.93.204094053937370.695905946062635
51-0.09805204762396861.09805204762397
63.63.418753769322440.181246230677561
71.41.95546188289337-0.555461882893371
81.51.88868068032156-0.388680680321562
90.70.818541674584192-0.118541674584192
102.13.09784895307491-0.997848953074913
114.12.594883212908781.50511678709122
121.22.01992706226638-0.81992706226638
130.50.4203093373009040.0796906626990957
143.43.367261576613010.0327384233869904
151.52.06083304722624-0.56083304722624
163.43.47830920596308-0.0783092059630768
170.82.01800678406329-1.21800678406329
180.80.5787524665724740.221247533427526
1923.12189715307559-1.12189715307559
201.91.442234019508620.45776598049138
211.32.53547068103443-1.23547068103443
225.64.161460079377671.43853992062233
233.13.3636768343476-0.263676834347599
241.81.798596420213730.00140357978626561
250.90.6260759487110750.273924051288925
261.82.52687616117539-0.726876161175386
271.91.764446688524580.13555331147542
280.91.2296436232266-0.329643623226598
292.61.630322453227960.969677546772037
302.43.04780085021171-0.647800850211707
311.22.03717943480815-0.837179434808153
320.91.2367749904922-0.336774990492199
330.50.4508350545133140.0491649454866858
340.60.4391224213927220.160877578607278
352.32.118261644673550.181738355326453
360.50.3963973532791590.103602646720841
372.63.35379374662198-0.753793746621976
380.60.1494399040224730.450560095977527
396.64.14552187999682.4544781200032






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8557193631108780.2885612737782440.144280636889122
130.7512212563093480.4975574873813040.248778743690652
140.6484310022280970.7031379955438070.351568997771903
150.5061490248453790.9877019503092430.493850975154621
160.3959754488249480.7919508976498960.604024551175052
170.4181708373497310.8363416746994630.581829162650269
180.3060698944450730.6121397888901470.693930105554927
190.3223019191847930.6446038383695860.677698080815207
200.2412377213184460.4824754426368920.758762278681554
210.4073069629791140.8146139259582290.592693037020886
220.5092642550468490.9814714899063030.490735744953151
230.4401367514745350.880273502949070.559863248525465
240.3163270601446050.632654120289210.683672939855395
250.2203667684222870.4407335368445740.779633231577713
260.1769803068006020.3539606136012030.823019693199398
270.1661278163444830.3322556326889670.833872183655517

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.855719363110878 & 0.288561273778244 & 0.144280636889122 \tabularnewline
13 & 0.751221256309348 & 0.497557487381304 & 0.248778743690652 \tabularnewline
14 & 0.648431002228097 & 0.703137995543807 & 0.351568997771903 \tabularnewline
15 & 0.506149024845379 & 0.987701950309243 & 0.493850975154621 \tabularnewline
16 & 0.395975448824948 & 0.791950897649896 & 0.604024551175052 \tabularnewline
17 & 0.418170837349731 & 0.836341674699463 & 0.581829162650269 \tabularnewline
18 & 0.306069894445073 & 0.612139788890147 & 0.693930105554927 \tabularnewline
19 & 0.322301919184793 & 0.644603838369586 & 0.677698080815207 \tabularnewline
20 & 0.241237721318446 & 0.482475442636892 & 0.758762278681554 \tabularnewline
21 & 0.407306962979114 & 0.814613925958229 & 0.592693037020886 \tabularnewline
22 & 0.509264255046849 & 0.981471489906303 & 0.490735744953151 \tabularnewline
23 & 0.440136751474535 & 0.88027350294907 & 0.559863248525465 \tabularnewline
24 & 0.316327060144605 & 0.63265412028921 & 0.683672939855395 \tabularnewline
25 & 0.220366768422287 & 0.440733536844574 & 0.779633231577713 \tabularnewline
26 & 0.176980306800602 & 0.353960613601203 & 0.823019693199398 \tabularnewline
27 & 0.166127816344483 & 0.332255632688967 & 0.833872183655517 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108583&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]12[/C][C]0.855719363110878[/C][C]0.288561273778244[/C][C]0.144280636889122[/C][/ROW]
[ROW][C]13[/C][C]0.751221256309348[/C][C]0.497557487381304[/C][C]0.248778743690652[/C][/ROW]
[ROW][C]14[/C][C]0.648431002228097[/C][C]0.703137995543807[/C][C]0.351568997771903[/C][/ROW]
[ROW][C]15[/C][C]0.506149024845379[/C][C]0.987701950309243[/C][C]0.493850975154621[/C][/ROW]
[ROW][C]16[/C][C]0.395975448824948[/C][C]0.791950897649896[/C][C]0.604024551175052[/C][/ROW]
[ROW][C]17[/C][C]0.418170837349731[/C][C]0.836341674699463[/C][C]0.581829162650269[/C][/ROW]
[ROW][C]18[/C][C]0.306069894445073[/C][C]0.612139788890147[/C][C]0.693930105554927[/C][/ROW]
[ROW][C]19[/C][C]0.322301919184793[/C][C]0.644603838369586[/C][C]0.677698080815207[/C][/ROW]
[ROW][C]20[/C][C]0.241237721318446[/C][C]0.482475442636892[/C][C]0.758762278681554[/C][/ROW]
[ROW][C]21[/C][C]0.407306962979114[/C][C]0.814613925958229[/C][C]0.592693037020886[/C][/ROW]
[ROW][C]22[/C][C]0.509264255046849[/C][C]0.981471489906303[/C][C]0.490735744953151[/C][/ROW]
[ROW][C]23[/C][C]0.440136751474535[/C][C]0.88027350294907[/C][C]0.559863248525465[/C][/ROW]
[ROW][C]24[/C][C]0.316327060144605[/C][C]0.63265412028921[/C][C]0.683672939855395[/C][/ROW]
[ROW][C]25[/C][C]0.220366768422287[/C][C]0.440733536844574[/C][C]0.779633231577713[/C][/ROW]
[ROW][C]26[/C][C]0.176980306800602[/C][C]0.353960613601203[/C][C]0.823019693199398[/C][/ROW]
[ROW][C]27[/C][C]0.166127816344483[/C][C]0.332255632688967[/C][C]0.833872183655517[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108583&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108583&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
120.8557193631108780.2885612737782440.144280636889122
130.7512212563093480.4975574873813040.248778743690652
140.6484310022280970.7031379955438070.351568997771903
150.5061490248453790.9877019503092430.493850975154621
160.3959754488249480.7919508976498960.604024551175052
170.4181708373497310.8363416746994630.581829162650269
180.3060698944450730.6121397888901470.693930105554927
190.3223019191847930.6446038383695860.677698080815207
200.2412377213184460.4824754426368920.758762278681554
210.4073069629791140.8146139259582290.592693037020886
220.5092642550468490.9814714899063030.490735744953151
230.4401367514745350.880273502949070.559863248525465
240.3163270601446050.632654120289210.683672939855395
250.2203667684222870.4407335368445740.779633231577713
260.1769803068006020.3539606136012030.823019693199398
270.1661278163444830.3322556326889670.833872183655517






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
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=108583&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=108583&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108583&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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}