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R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 18 Nov 2009 09:21:49 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb.htm/, Retrieved Wed, 18 Nov 2009 17:23:02 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb.htm/},
    year = {2009},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

17823.2 0 17872 0 17420.4 0 16704.4 0 15991.2 0 15583.6 0 19123.5 0 17838.7 0 17209.4 0 18586.5 0 16258.1 0 15141.6 0 19202.1 0 17746.5 0 19090.1 1 18040.3 1 17515.5 1 17751.8 1 21072.4 1 17170 1 19439.5 1 19795.4 1 17574.9 1 16165.4 1 19464.6 1 19932.1 1 19961.2 1 17343.4 1 18924.2 1 18574.1 1 21350.6 1 18594.6 1 19832.1 1 20844.4 1 19640.2 1 17735.4 1 19813.6 1 22160 1 20664.3 1 17877.4 1 20906.5 1 21164.1 1 21374.4 1 22952.3 1 21343.5 1 23899.3 1 22392.9 1 18274.1 1 22786.7 1 22321.5 1 17842.2 1 16373.5 1 15933.8 0 16446.1 0 17729 0 16643 0 16196.7 0 18252.1 0 17570.4 0 15836.8 0

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 14512.3328981723 + 2551.15600522193X[t] + 3366.93468015666M1[t] + 3538.99152741514M2[t] + 2001.65717362924M3[t] + 257.494020887727M4[t] + 1337.84206919060M5[t] + 1371.21891644909M6[t] + 3580.93576370757M7[t] + 2074.35261096606M8[t] + 2222.54945822454M9[t] + 3677.52630548303M10[t] + 2072.96315274152M11[t] + 16.3231527415143t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	14512.3328981723	702.696071	20.6524	0	0
X	2551.15600522193	368.650565	6.9203	0	0
M1	3366.93468015666	840.806046	4.0044	0.000225	0.000112
M2	3538.99152741514	839.469254	4.2157	0.000115	5.8e-05
M3	2001.65717362924	843.51057	2.373	0.021879	0.010939
M4	257.494020887727	842.207954	0.3057	0.761184	0.380592
M5	1337.84206919060	836.213862	1.5999	0.116472	0.058236
M6	1371.21891644909	835.382027	1.6414	0.107529	0.053765
M7	3580.93576370757	834.67752	4.2902	9.1e-05	4.5e-05
M8	2074.35261096606	834.100662	2.4869	0.016575	0.008287
M9	2222.54945822454	833.651719	2.666	0.010555	0.005277
M10	3677.52630548303	833.330897	4.413	6.1e-05	3.1e-05
M11	2072.96315274152	833.138344	2.4881	0.016525	0.008263
t	16.3231527415143	10.342189	1.5783	0.121348	0.060674

Multiple Linear Regression - Regression Statistics
Multiple R	0.83185183405605
R-squared	0.691977473822413
Adjusted R-squared	0.604927629467877
F-TEST (value)	7.9492097769197
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	5.56225816517042e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1317.20588736988
Sum Squared Residuals	79811442.0872062

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	17823.2	17895.5907310705	-72.3907310704674
2	17872	18083.9707310705	-211.970731070499
3	17420.4	16562.9595300261	857.440469973893
4	16704.4	14835.1195300261	1869.28046997389
5	15991.2	15931.7907310705	59.4092689295067
6	15583.6	15981.4907310705	-397.890731070495
7	19123.5	18207.5307310705	915.969268929495
8	17838.7	16717.2707310705	1121.42926892950
9	17209.4	16881.7907310705	327.609268929503
10	18586.5	18353.0907310705	233.409268929503
11	16258.1	16764.8507310705	-506.750731070498
12	15141.6	14708.2107310705	433.3892689295
13	19202.1	18091.4685639687	1110.63143603132
14	17746.5	18279.8485639687	-533.348563968669
15	19090.1	19309.9933681462	-219.893368146218
16	18040.3	17582.1533681462	458.146631853785
17	17515.5	18678.8245691906	-1163.32456919060
18	17751.8	18728.5245691906	-976.7245691906
19	21072.4	20954.5645691906	117.835430809402
20	17170	19464.3045691906	-2294.3045691906
21	19439.5	19628.8245691906	-189.324569190601
22	19795.4	21100.1245691906	-1304.7245691906
23	17574.9	19511.8845691906	-1936.9845691906
24	16165.4	17455.2445691906	-1289.8445691906
25	19464.6	20838.5024020888	-1373.90240208878
26	19932.1	21026.8824020888	-1094.78240208877
27	19961.2	19505.8712010444	455.328798955613
28	17343.4	17778.0312010444	-434.631201044384
29	18924.2	18874.7024020888	49.497597911227
30	18574.1	18924.4024020888	-350.302402088773
31	21350.6	21150.4424020888	200.157597911227
32	18594.6	19660.1824020888	-1065.58240208877
33	19832.1	19824.7024020888	7.39759791122636
34	20844.4	21296.0024020888	-451.602402088772
35	19640.2	19707.7624020888	-67.562402088774
36	17735.4	17651.1224020888	84.2775979112295
37	19813.6	21034.3802349870	-1220.78023498695
38	22160	21222.7602349869	937.239765013057
39	20664.3	19701.7490339426	962.55096605744
40	17877.4	17973.9090339426	-96.5090339425561
41	20906.5	19070.5802349869	1835.91976501305
42	21164.1	19120.2802349869	2043.81976501306
43	21374.4	21346.3202349869	28.0797650130584
44	22952.3	19856.0602349869	3096.23976501306
45	21343.5	20020.5802349869	1322.91976501306
46	23899.3	21491.8802349869	2407.41976501305
47	22392.9	19903.6402349869	2489.25976501306
48	18274.1	17847.0002349869	427.099765013055
49	22786.7	21230.2580678851	1556.44193211488
50	22321.5	21418.6380678851	902.861932114885
51	17842.2	19897.6268668407	-2055.42686684073
52	16373.5	18169.7868668407	-1796.28686684073
53	15933.8	16715.3020626632	-781.502062663187
54	16446.1	16765.0020626632	-318.902062663186
55	17729	18991.0420626632	-1262.04206266318
56	16643	17500.7820626632	-857.782062663184
57	16196.7	17665.3020626632	-1468.60206266318
58	18252.1	19136.6020626632	-884.502062663186
59	17570.4	17548.3620626632	22.0379373368146
60	15836.8	15491.7220626632	345.077937336815

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0660899384932542	0.132179876986508	0.933910061506746
18	0.0304357823023166	0.0608715646046332	0.969564217697683
19	0.0108817506720086	0.0217635013440173	0.989118249327991
20	0.113456246288780	0.226912492577560	0.88654375371122
21	0.0780344224541093	0.156068844908219	0.92196557754589
22	0.0409738005694912	0.0819476011389825	0.959026199430509
23	0.0250422425144145	0.0500844850288290	0.974957757485586
24	0.0131405753478562	0.0262811506957123	0.986859424652144
25	0.0079250004531467	0.0158500009062934	0.992074999546853
26	0.00468647063824343	0.00937294127648686	0.995313529361757
27	0.00338550783343792	0.00677101566687584	0.996614492166562
28	0.00647016009039945	0.0129403201807989	0.9935298399096
29	0.00552181257263299	0.0110436251452660	0.994478187427367
30	0.00369195036564684	0.00738390073129369	0.996308049634353
31	0.00180956416334466	0.00361912832668933	0.998190435836655
32	0.00193097771466711	0.00386195542933423	0.998069022285333
33	0.000836537372002766	0.00167307474400553	0.999163462627997
34	0.000637825954814779	0.00127565190962956	0.999362174045185
35	0.00226726193886405	0.0045345238777281	0.997732738061136
36	0.00248560632452332	0.00497121264904664	0.997514393675477
37	0.0589222789590876	0.117844557918175	0.941077721040912
38	0.181847906367140	0.363695812734281	0.81815209363286
39	0.146583816137901	0.293167632275803	0.853416183862099
40	0.125103962004295	0.250207924008590	0.874896037995705
41	0.112838541445117	0.225677082890235	0.887161458554882
42	0.0962951778238156	0.192590355647631	0.903704822176184
43	0.0829801962123895	0.165960392424779	0.91701980378761

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.333333333333333	NOK
5% type I error level	14	0.518518518518518	NOK
10% type I error level	17	0.62962962962963	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/10zfib1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/10zfib1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/1943r1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/1943r1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/2jouq1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/2jouq1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/3gmgd1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/3gmgd1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/4xft71258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/4xft71258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/58kju1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/58kju1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/6lhzp1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/6lhzp1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/7zpdg1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/7zpdg1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/898jv1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/898jv1258561300.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/9if0t1258561300.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561369aj3q0ghomeeytbb/9if0t1258561300.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal 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('http://www.xycoon.com/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<br />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<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />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')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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