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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 17:50:26 +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/t1292176271cbqpt4p727b7ze6.htm/, Retrieved Tue, 07 May 2024 17:28:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108579, Retrieved Tue, 07 May 2024 17:28:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118

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 17:50:26] [c9b1b69acb8f4b2b921fdfd5091a94b7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.0	4.5	1000	6600	42.0	3	1	3
1.8	69.0	2547000	4603000	624.0	3	5	4
.7	27.0	10550	179500	180.0	4	4	4
3.9	19.0	0,023	0,3	35.0	1	1	1
1.0	30.4	160000	169000	392.0	4	5	4
3.6	28.0	3300	25600	63.0	1	2	1
1.4	50.0	52160	440000	230.0	1	1	1
1.5	7.0	0,425	6400	112.0	5	4	4
.7	30.0	465000	423000	281.0	5	5	5
2.1	3.5	0,075	1200	42.0	1	1	1
4.1	6.0	0,785	3500	42.0	2	2	2
1.2	10.4	0,2	5000	120.0	2	2	2
.5	20.0	27660	115000	148.0	5	5	5
3.4	3.9	0,12	1000	16.0	3	1	2
1.5	41.0	85000	325000	310.0	1	3	1
3.4	9.0	0,101	4000	28.0	5	1	3
.8	7.6	1040	5500	68.0	5	3	4
.8	46.0	521000	655000	336.0	5	5	5
2.0	24.0	0,01	0,25	50.0	1	1	1
1.9	100.0	62000	1320000	267.0	1	1	1
1.3	3.2	0,023	0,4	19.0	4	1	3
5.6	5.0	1700	6300	12.0	2	1	1
3.1	6.5	3500	10800	120.0	2	1	1
1.8	12.0	0,48	15500	140.0	2	2	2
.9	20.2	10000	115000	170.0	4	4	4
1.8	13.0	1620	11400	17.0	2	1	2
1.9	27.0	192000	180000	115.0	4	4	4
.9	18.0	2500	12100	31.0	5	5	5
2.6	4.7	0,28	1900	21.0	3	1	3
2.4	9.8	4235	50400	52.0	1	1	1
1.2	29.0	6800	179000	164.0	2	3	2
.9	7.0	0,75	12300	225.0	2	2	2
.5	6.0	3600	21000	225.0	3	2	3
.6	20.0	55500	175000	151.0	5	5	5
2.3	4.5	0,9	2600	60.0	2	1	2
.5	7.5	2000	12300	200.0	3	1	3
2.6	2.3	0,104	2500	46.0	3	2	2
.6	24.0	4190	58000	210.0	4	3	4
6.6	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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132
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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \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=108579&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]'George Udny Yule' @ 72.249.76.132[/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=108579&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108579&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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132
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] = + 3.80087752167087 + 0.0116286267514754L[t] + 3.56230226124492e-06Wb[t] -9.87715046978052e-07Wbr[t] -0.00728026813938717Tg[t] + 0.904135193634048P[t] + 0.261340559481030S[t] -1.67549920357273`D `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.80087752167087 +  0.0116286267514754L[t] +  3.56230226124492e-06Wb[t] -9.87715046978052e-07Wbr[t] -0.00728026813938717Tg[t] +  0.904135193634048P[t] +  0.261340559481030S[t] -1.67549920357273`D
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108579&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.80087752167087 +  0.0116286267514754L[t] +  3.56230226124492e-06Wb[t] -9.87715046978052e-07Wbr[t] -0.00728026813938717Tg[t] +  0.904135193634048P[t] +  0.261340559481030S[t] -1.67549920357273`D
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108579&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108579&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] = + 3.80087752167087 + 0.0116286267514754L[t] + 3.56230226124492e-06Wb[t] -9.87715046978052e-07Wbr[t] -0.00728026813938717Tg[t] + 0.904135193634048P[t] + 0.261340559481030S[t] -1.67549920357273`D `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.800877521670870.3819069.952400
L0.01162862675147540.0157480.73840.4658130.232907
Wb3.56230226124492e-062e-061.92230.0637970.031898
Wbr-9.87715046978052e-071e-06-0.88830.381240.19062
Tg-0.007280268139387170.00213-3.41840.0017820.000891
P0.9041351936340480.316772.85420.0076240.003812
S0.2613405594810300.201641.29610.2045160.102258
`D `-1.675499203572730.386851-4.33110.0001447.2e-05

\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) & 3.80087752167087 & 0.381906 & 9.9524 & 0 & 0 \tabularnewline
L & 0.0116286267514754 & 0.015748 & 0.7384 & 0.465813 & 0.232907 \tabularnewline
Wb & 3.56230226124492e-06 & 2e-06 & 1.9223 & 0.063797 & 0.031898 \tabularnewline
Wbr & -9.87715046978052e-07 & 1e-06 & -0.8883 & 0.38124 & 0.19062 \tabularnewline
Tg & -0.00728026813938717 & 0.00213 & -3.4184 & 0.001782 & 0.000891 \tabularnewline
P & 0.904135193634048 & 0.31677 & 2.8542 & 0.007624 & 0.003812 \tabularnewline
S & 0.261340559481030 & 0.20164 & 1.2961 & 0.204516 & 0.102258 \tabularnewline
`D
` & -1.67549920357273 & 0.386851 & -4.3311 & 0.000144 & 7.2e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108579&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]3.80087752167087[/C][C]0.381906[/C][C]9.9524[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]0.0116286267514754[/C][C]0.015748[/C][C]0.7384[/C][C]0.465813[/C][C]0.232907[/C][/ROW]
[ROW][C]Wb[/C][C]3.56230226124492e-06[/C][C]2e-06[/C][C]1.9223[/C][C]0.063797[/C][C]0.031898[/C][/ROW]
[ROW][C]Wbr[/C][C]-9.87715046978052e-07[/C][C]1e-06[/C][C]-0.8883[/C][C]0.38124[/C][C]0.19062[/C][/ROW]
[ROW][C]Tg[/C][C]-0.00728026813938717[/C][C]0.00213[/C][C]-3.4184[/C][C]0.001782[/C][C]0.000891[/C][/ROW]
[ROW][C]P[/C][C]0.904135193634048[/C][C]0.31677[/C][C]2.8542[/C][C]0.007624[/C][C]0.003812[/C][/ROW]
[ROW][C]S[/C][C]0.261340559481030[/C][C]0.20164[/C][C]1.2961[/C][C]0.204516[/C][C]0.102258[/C][/ROW]
[ROW][C]`D
`[/C][C]-1.67549920357273[/C][C]0.386851[/C][C]-4.3311[/C][C]0.000144[/C][C]7.2e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108579&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108579&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)3.800877521670870.3819069.952400
L0.01162862675147540.0157480.73840.4658130.232907
Wb3.56230226124492e-062e-061.92230.0637970.031898
Wbr-9.87715046978052e-071e-06-0.88830.381240.19062
Tg-0.007280268139387170.00213-3.41840.0017820.000891
P0.9041351936340480.316772.85420.0076240.003812
S0.2613405594810300.201641.29610.2045160.102258
`D `-1.675499203572730.386851-4.33110.0001447.2e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.831306208882482
R-squared0.691070012926565
Adjusted R-squared0.621311628748692
F-TEST (value)9.9066229969497
F-TEST (DF numerator)7
F-TEST (DF denominator)31
p-value1.95327288599056e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.864957965555126
Sum Squared Residuals23.1927207474952

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.831306208882482 \tabularnewline
R-squared & 0.691070012926565 \tabularnewline
Adjusted R-squared & 0.621311628748692 \tabularnewline
F-TEST (value) & 9.9066229969497 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 31 \tabularnewline
p-value & 1.95327288599056e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.864957965555126 \tabularnewline
Sum Squared Residuals & 23.1927207474952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108579&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.831306208882482[/C][/ROW]
[ROW][C]R-squared[/C][C]0.691070012926565[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.621311628748692[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.9066229969497[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]31[/C][/ROW]
[ROW][C]p-value[/C][C]1.95327288599056e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.864957965555126[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]23.1927207474952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108579&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108579&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 R0.831306208882482
R-squared0.691070012926565
Adjusted R-squared0.621311628748692
F-TEST (value)9.9066229969497
F-TEST (DF numerator)7
F-TEST (DF denominator)31
p-value1.95327288599056e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.864957965555126
Sum Squared Residuals23.1927207474952






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.491726992814420.508273007185578
21.81.90420851071227-0.104208510712273
30.70.6245958149639620.0754041850360376
43.93.256988380231140.643011619768864
51-0.07518605921374541.07518605921375
63.63.405609379213630.194390620786369
71.41.94903880200413-0.549038802004129
81.51.92460940680106-0.424609406801063
90.70.792530811366229-0.0925308113662289
102.13.02459801210541-0.924598012105413
114.12.5413769131531.55862308684700
121.22.02319829947000-0.823198299470005
130.50.3907991699263560.109200830073644
143.43.351505321438430.0484946785615689
151.52.01521406571369-0.515214065713694
163.43.45325607106899-0.053256071068995
170.81.99517004541947-1.19517004541947
180.80.5485131274543480.251486872545652
1923.20592749497353-1.20592749497353
201.91.426964031330540.473035968669461
211.32.55114744277319-1.25114744277319
225.64.165602489980151.43439751001985
233.13.39874389741239-0.298743897412388
241.81.88582872893599-0.0858287289359854
250.90.6800721887342040.219927811265796
261.82.54140862880179-0.741408628801795
271.91.743699131803530.156300868196467
280.91.23134164217282-0.331341642172823
292.61.648019304996020.951980695003978
302.42.99154618183822-0.59154618183822
311.22.03286004313727-0.832860043137274
320.91.21202445330264-0.312024453302640
330.50.4332603121175540.0667396878824456
340.60.4088699576425710.191130042357429
352.32.132437940242830.167562059757169
360.50.3742628335391350.125737166460865
372.63.37435040436818-0.774350404368182
380.60.207312466873210.39268753312679
396.64.136567360381412.46343263961859

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.49172699281442 & 0.508273007185578 \tabularnewline
2 & 1.8 & 1.90420851071227 & -0.104208510712273 \tabularnewline
3 & 0.7 & 0.624595814963962 & 0.0754041850360376 \tabularnewline
4 & 3.9 & 3.25698838023114 & 0.643011619768864 \tabularnewline
5 & 1 & -0.0751860592137454 & 1.07518605921375 \tabularnewline
6 & 3.6 & 3.40560937921363 & 0.194390620786369 \tabularnewline
7 & 1.4 & 1.94903880200413 & -0.549038802004129 \tabularnewline
8 & 1.5 & 1.92460940680106 & -0.424609406801063 \tabularnewline
9 & 0.7 & 0.792530811366229 & -0.0925308113662289 \tabularnewline
10 & 2.1 & 3.02459801210541 & -0.924598012105413 \tabularnewline
11 & 4.1 & 2.541376913153 & 1.55862308684700 \tabularnewline
12 & 1.2 & 2.02319829947000 & -0.823198299470005 \tabularnewline
13 & 0.5 & 0.390799169926356 & 0.109200830073644 \tabularnewline
14 & 3.4 & 3.35150532143843 & 0.0484946785615689 \tabularnewline
15 & 1.5 & 2.01521406571369 & -0.515214065713694 \tabularnewline
16 & 3.4 & 3.45325607106899 & -0.053256071068995 \tabularnewline
17 & 0.8 & 1.99517004541947 & -1.19517004541947 \tabularnewline
18 & 0.8 & 0.548513127454348 & 0.251486872545652 \tabularnewline
19 & 2 & 3.20592749497353 & -1.20592749497353 \tabularnewline
20 & 1.9 & 1.42696403133054 & 0.473035968669461 \tabularnewline
21 & 1.3 & 2.55114744277319 & -1.25114744277319 \tabularnewline
22 & 5.6 & 4.16560248998015 & 1.43439751001985 \tabularnewline
23 & 3.1 & 3.39874389741239 & -0.298743897412388 \tabularnewline
24 & 1.8 & 1.88582872893599 & -0.0858287289359854 \tabularnewline
25 & 0.9 & 0.680072188734204 & 0.219927811265796 \tabularnewline
26 & 1.8 & 2.54140862880179 & -0.741408628801795 \tabularnewline
27 & 1.9 & 1.74369913180353 & 0.156300868196467 \tabularnewline
28 & 0.9 & 1.23134164217282 & -0.331341642172823 \tabularnewline
29 & 2.6 & 1.64801930499602 & 0.951980695003978 \tabularnewline
30 & 2.4 & 2.99154618183822 & -0.59154618183822 \tabularnewline
31 & 1.2 & 2.03286004313727 & -0.832860043137274 \tabularnewline
32 & 0.9 & 1.21202445330264 & -0.312024453302640 \tabularnewline
33 & 0.5 & 0.433260312117554 & 0.0667396878824456 \tabularnewline
34 & 0.6 & 0.408869957642571 & 0.191130042357429 \tabularnewline
35 & 2.3 & 2.13243794024283 & 0.167562059757169 \tabularnewline
36 & 0.5 & 0.374262833539135 & 0.125737166460865 \tabularnewline
37 & 2.6 & 3.37435040436818 & -0.774350404368182 \tabularnewline
38 & 0.6 & 0.20731246687321 & 0.39268753312679 \tabularnewline
39 & 6.6 & 4.13656736038141 & 2.46343263961859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108579&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]1.49172699281442[/C][C]0.508273007185578[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]1.90420851071227[/C][C]-0.104208510712273[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.624595814963962[/C][C]0.0754041850360376[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]3.25698838023114[/C][C]0.643011619768864[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]-0.0751860592137454[/C][C]1.07518605921375[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]3.40560937921363[/C][C]0.194390620786369[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]1.94903880200413[/C][C]-0.549038802004129[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]1.92460940680106[/C][C]-0.424609406801063[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.792530811366229[/C][C]-0.0925308113662289[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]3.02459801210541[/C][C]-0.924598012105413[/C][/ROW]
[ROW][C]11[/C][C]4.1[/C][C]2.541376913153[/C][C]1.55862308684700[/C][/ROW]
[ROW][C]12[/C][C]1.2[/C][C]2.02319829947000[/C][C]-0.823198299470005[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]0.390799169926356[/C][C]0.109200830073644[/C][/ROW]
[ROW][C]14[/C][C]3.4[/C][C]3.35150532143843[/C][C]0.0484946785615689[/C][/ROW]
[ROW][C]15[/C][C]1.5[/C][C]2.01521406571369[/C][C]-0.515214065713694[/C][/ROW]
[ROW][C]16[/C][C]3.4[/C][C]3.45325607106899[/C][C]-0.053256071068995[/C][/ROW]
[ROW][C]17[/C][C]0.8[/C][C]1.99517004541947[/C][C]-1.19517004541947[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.548513127454348[/C][C]0.251486872545652[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]3.20592749497353[/C][C]-1.20592749497353[/C][/ROW]
[ROW][C]20[/C][C]1.9[/C][C]1.42696403133054[/C][C]0.473035968669461[/C][/ROW]
[ROW][C]21[/C][C]1.3[/C][C]2.55114744277319[/C][C]-1.25114744277319[/C][/ROW]
[ROW][C]22[/C][C]5.6[/C][C]4.16560248998015[/C][C]1.43439751001985[/C][/ROW]
[ROW][C]23[/C][C]3.1[/C][C]3.39874389741239[/C][C]-0.298743897412388[/C][/ROW]
[ROW][C]24[/C][C]1.8[/C][C]1.88582872893599[/C][C]-0.0858287289359854[/C][/ROW]
[ROW][C]25[/C][C]0.9[/C][C]0.680072188734204[/C][C]0.219927811265796[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]2.54140862880179[/C][C]-0.741408628801795[/C][/ROW]
[ROW][C]27[/C][C]1.9[/C][C]1.74369913180353[/C][C]0.156300868196467[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]1.23134164217282[/C][C]-0.331341642172823[/C][/ROW]
[ROW][C]29[/C][C]2.6[/C][C]1.64801930499602[/C][C]0.951980695003978[/C][/ROW]
[ROW][C]30[/C][C]2.4[/C][C]2.99154618183822[/C][C]-0.59154618183822[/C][/ROW]
[ROW][C]31[/C][C]1.2[/C][C]2.03286004313727[/C][C]-0.832860043137274[/C][/ROW]
[ROW][C]32[/C][C]0.9[/C][C]1.21202445330264[/C][C]-0.312024453302640[/C][/ROW]
[ROW][C]33[/C][C]0.5[/C][C]0.433260312117554[/C][C]0.0667396878824456[/C][/ROW]
[ROW][C]34[/C][C]0.6[/C][C]0.408869957642571[/C][C]0.191130042357429[/C][/ROW]
[ROW][C]35[/C][C]2.3[/C][C]2.13243794024283[/C][C]0.167562059757169[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.374262833539135[/C][C]0.125737166460865[/C][/ROW]
[ROW][C]37[/C][C]2.6[/C][C]3.37435040436818[/C][C]-0.774350404368182[/C][/ROW]
[ROW][C]38[/C][C]0.6[/C][C]0.20731246687321[/C][C]0.39268753312679[/C][/ROW]
[ROW][C]39[/C][C]6.6[/C][C]4.13656736038141[/C][C]2.46343263961859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108579&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108579&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
121.491726992814420.508273007185578
21.81.90420851071227-0.104208510712273
30.70.6245958149639620.0754041850360376
43.93.256988380231140.643011619768864
51-0.07518605921374541.07518605921375
63.63.405609379213630.194390620786369
71.41.94903880200413-0.549038802004129
81.51.92460940680106-0.424609406801063
90.70.792530811366229-0.0925308113662289
102.13.02459801210541-0.924598012105413
114.12.5413769131531.55862308684700
121.22.02319829947000-0.823198299470005
130.50.3907991699263560.109200830073644
143.43.351505321438430.0484946785615689
151.52.01521406571369-0.515214065713694
163.43.45325607106899-0.053256071068995
170.81.99517004541947-1.19517004541947
180.80.5485131274543480.251486872545652
1923.20592749497353-1.20592749497353
201.91.426964031330540.473035968669461
211.32.55114744277319-1.25114744277319
225.64.165602489980151.43439751001985
233.13.39874389741239-0.298743897412388
241.81.88582872893599-0.0858287289359854
250.90.6800721887342040.219927811265796
261.82.54140862880179-0.741408628801795
271.91.743699131803530.156300868196467
280.91.23134164217282-0.331341642172823
292.61.648019304996020.951980695003978
302.42.99154618183822-0.59154618183822
311.22.03286004313727-0.832860043137274
320.91.21202445330264-0.312024453302640
330.50.4332603121175540.0667396878824456
340.60.4088699576425710.191130042357429
352.32.132437940242830.167562059757169
360.50.3742628335391350.125737166460865
372.63.37435040436818-0.774350404368182
380.60.207312466873210.39268753312679
396.64.136567360381412.46343263961859






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7898814746083410.4202370507833180.210118525391659
120.7942504375387760.4114991249224480.205749562461224
130.6974965887140720.6050068225718560.302503411285928
140.5914139125664380.8171721748671250.408586087433562
150.4664452678858620.9328905357717240.533554732114138
160.3539976786086080.7079953572172160.646002321391392
170.4075016578672890.8150033157345790.592498342132711
180.3015904797506710.6031809595013420.698409520249329
190.3086953478726480.6173906957452960.691304652127352
200.2413223212604270.4826446425208550.758677678739573
210.4395565644232030.8791131288464050.560443435576797
220.5493263158822390.9013473682355230.450673684117761
230.4868740122199950.973748024439990.513125987780005
240.3727540059052140.7455080118104290.627245994094786
250.2675478187316570.5350956374633130.732452181268343
260.2324641878104000.4649283756207990.7675358121896
270.2473124155004850.4946248310009690.752687584499515
280.1530491118286720.3060982236573430.846950888171328

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.789881474608341 & 0.420237050783318 & 0.210118525391659 \tabularnewline
12 & 0.794250437538776 & 0.411499124922448 & 0.205749562461224 \tabularnewline
13 & 0.697496588714072 & 0.605006822571856 & 0.302503411285928 \tabularnewline
14 & 0.591413912566438 & 0.817172174867125 & 0.408586087433562 \tabularnewline
15 & 0.466445267885862 & 0.932890535771724 & 0.533554732114138 \tabularnewline
16 & 0.353997678608608 & 0.707995357217216 & 0.646002321391392 \tabularnewline
17 & 0.407501657867289 & 0.815003315734579 & 0.592498342132711 \tabularnewline
18 & 0.301590479750671 & 0.603180959501342 & 0.698409520249329 \tabularnewline
19 & 0.308695347872648 & 0.617390695745296 & 0.691304652127352 \tabularnewline
20 & 0.241322321260427 & 0.482644642520855 & 0.758677678739573 \tabularnewline
21 & 0.439556564423203 & 0.879113128846405 & 0.560443435576797 \tabularnewline
22 & 0.549326315882239 & 0.901347368235523 & 0.450673684117761 \tabularnewline
23 & 0.486874012219995 & 0.97374802443999 & 0.513125987780005 \tabularnewline
24 & 0.372754005905214 & 0.745508011810429 & 0.627245994094786 \tabularnewline
25 & 0.267547818731657 & 0.535095637463313 & 0.732452181268343 \tabularnewline
26 & 0.232464187810400 & 0.464928375620799 & 0.7675358121896 \tabularnewline
27 & 0.247312415500485 & 0.494624831000969 & 0.752687584499515 \tabularnewline
28 & 0.153049111828672 & 0.306098223657343 & 0.846950888171328 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108579&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]11[/C][C]0.789881474608341[/C][C]0.420237050783318[/C][C]0.210118525391659[/C][/ROW]
[ROW][C]12[/C][C]0.794250437538776[/C][C]0.411499124922448[/C][C]0.205749562461224[/C][/ROW]
[ROW][C]13[/C][C]0.697496588714072[/C][C]0.605006822571856[/C][C]0.302503411285928[/C][/ROW]
[ROW][C]14[/C][C]0.591413912566438[/C][C]0.817172174867125[/C][C]0.408586087433562[/C][/ROW]
[ROW][C]15[/C][C]0.466445267885862[/C][C]0.932890535771724[/C][C]0.533554732114138[/C][/ROW]
[ROW][C]16[/C][C]0.353997678608608[/C][C]0.707995357217216[/C][C]0.646002321391392[/C][/ROW]
[ROW][C]17[/C][C]0.407501657867289[/C][C]0.815003315734579[/C][C]0.592498342132711[/C][/ROW]
[ROW][C]18[/C][C]0.301590479750671[/C][C]0.603180959501342[/C][C]0.698409520249329[/C][/ROW]
[ROW][C]19[/C][C]0.308695347872648[/C][C]0.617390695745296[/C][C]0.691304652127352[/C][/ROW]
[ROW][C]20[/C][C]0.241322321260427[/C][C]0.482644642520855[/C][C]0.758677678739573[/C][/ROW]
[ROW][C]21[/C][C]0.439556564423203[/C][C]0.879113128846405[/C][C]0.560443435576797[/C][/ROW]
[ROW][C]22[/C][C]0.549326315882239[/C][C]0.901347368235523[/C][C]0.450673684117761[/C][/ROW]
[ROW][C]23[/C][C]0.486874012219995[/C][C]0.97374802443999[/C][C]0.513125987780005[/C][/ROW]
[ROW][C]24[/C][C]0.372754005905214[/C][C]0.745508011810429[/C][C]0.627245994094786[/C][/ROW]
[ROW][C]25[/C][C]0.267547818731657[/C][C]0.535095637463313[/C][C]0.732452181268343[/C][/ROW]
[ROW][C]26[/C][C]0.232464187810400[/C][C]0.464928375620799[/C][C]0.7675358121896[/C][/ROW]
[ROW][C]27[/C][C]0.247312415500485[/C][C]0.494624831000969[/C][C]0.752687584499515[/C][/ROW]
[ROW][C]28[/C][C]0.153049111828672[/C][C]0.306098223657343[/C][C]0.846950888171328[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108579&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108579&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
110.7898814746083410.4202370507833180.210118525391659
120.7942504375387760.4114991249224480.205749562461224
130.6974965887140720.6050068225718560.302503411285928
140.5914139125664380.8171721748671250.408586087433562
150.4664452678858620.9328905357717240.533554732114138
160.3539976786086080.7079953572172160.646002321391392
170.4075016578672890.8150033157345790.592498342132711
180.3015904797506710.6031809595013420.698409520249329
190.3086953478726480.6173906957452960.691304652127352
200.2413223212604270.4826446425208550.758677678739573
210.4395565644232030.8791131288464050.560443435576797
220.5493263158822390.9013473682355230.450673684117761
230.4868740122199950.973748024439990.513125987780005
240.3727540059052140.7455080118104290.627245994094786
250.2675478187316570.5350956374633130.732452181268343
260.2324641878104000.4649283756207990.7675358121896
270.2473124155004850.4946248310009690.752687584499515
280.1530491118286720.3060982236573430.846950888171328






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=108579&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=108579&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108579&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')
}