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*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 17 Nov 2009 10:31:01 -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/17/t12584797064wgqija4ca1i29p.htm/, Retrieved Tue, 17 Nov 2009 18:41:59 +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/17/t12584797064wgqija4ca1i29p.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 «

15836.8 89.1 17570.4 82.6 18252.1 102.7 16196.7 91.8 16643 94.1 17729 103.1 16446.1 93.2 15993.8 91 16373.5 94.3 17842.2 99.4 22321.5 115.7 22786.7 116.8 18274.1 99.8 22392.9 96 23899.3 115.9 21343.5 109.1 22952.3 117.3 21374.4 109.8 21164.1 112.8 20906.5 110.7 17877.4 100 20664.3 113.3 22160 122.4 19813.6 112.5 17735.4 104.2 19640.2 92.5 20844.4 117.2 19823.1 109.3 18594.6 106.1 21350.6 118.8 18574.1 105.3 18924.2 106 17343.4 102 19961.2 112.9 19932.1 116.5 19464.6 114.8 16165.4 100.5 17574.9 85.4 19795.4 114.6 19439.5 109.9 17170 100.7 21072.4 115.5 17751.8 100.7 17515.5 99 18040.3 102.3 19090.1 108.8 17746.5 105.9 19202.1 113.2 15141.6 95.7 16258.1 80.9 18586.5 113.9 17209.4 98.1 17838.7 102.8 19123.5 104.7 16583.6 95.9 15991.2 94.6 16704.4 101.6 17420.4 103.9 17872 110.3 17823.2 114.1

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

uitvoer[t] = -8805.83279053197 + 250.471410487679indproc[t] + 925.360560207709M1[t] + 5581.8938010698M2[t] + 813.169402892505M3[t] + 1649.41580758891M4[t] + 1346.43181771581M5[t] + 1288.77850090195M6[t] + 1466.88691319352M7[t] + 1559.80917503726M8[t] + 1016.47288534455M9[t] + 835.720737428436M10[t] + 218.436569258521M11[t] + 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)	-8805.83279053197	2662.573029	-3.3073	0.001811	0.000906
indproc	250.471410487679	22.942298	10.9175	0	0
M1	925.360560207709	756.539044	1.2231	0.227371	0.113685
M2	5581.8938010698	899.157345	6.2079	0	0
M3	813.169402892505	656.886839	1.2379	0.221896	0.110948
M4	1649.41580758891	700.01911	2.3562	0.022682	0.011341
M5	1346.43181771581	695.643171	1.9355	0.058955	0.029478
M6	1288.77850090195	662.151613	1.9463	0.057605	0.028802
M7	1466.88691319352	717.867556	2.0434	0.046642	0.023321
M8	1559.80917503726	730.683494	2.1347	0.038028	0.019014
M9	1016.47288534455	732.919359	1.3869	0.17202	0.08601
M10	835.720737428436	673.428482	1.241	0.220767	0.110383
M11	218.436569258521	656.084269	0.3329	0.74066	0.37033

Multiple Linear Regression - Regression Statistics
Multiple R	0.894656098130333
R-squared	0.800409533921793
Adjusted R-squared	0.749450265986931
F-TEST (value)	15.7068491436124
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	1.33715261085854e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1037.35118094877
Sum Squared Residuals	50576581.2129433

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	15836.8	14436.5304441279	1400.26955587214
2	17570.4	17464.9995168201	105.400483179876
3	18252.1	17730.7504694452	521.349530554822
4	16196.7	15836.8584998259	359.841500174122
5	16643	16109.9587540744	533.04124592556
6	17729	18306.5481316497	-577.548131649693
7	16446.1	16004.9895801132	441.110419886751
8	15993.8	15546.8747388841	446.925261115908
9	16373.5	15830.0941038007	543.405896199278
10	17842.2	16926.7461493718	915.45385062823
11	22321.5	20392.1459721510	1929.35402784897
12	22786.7	20449.2279544289	2337.47204557105
13	18274.1	17116.5745363461	1157.52546365388
14	22392.9	20821.3164173550	1571.58358264498
15	23899.3	21036.9730878825	2862.32691211745
16	21343.5	20170.0139012627	1173.48609873727
17	22952.3	21920.8954773886	1031.40452261140
18	21374.4	19984.7065819171	1389.69341808286
19	21164.1	20914.2292256718	249.870774328241
20	20906.5	20481.1615254914	425.338474508629
21	17877.4	17257.7811435805	619.618856419507
22	20664.3	20408.2987551505	256.001244849490
23	22160	22070.3044224185	89.6955775815234
24	19813.6	19372.2008893319	441.399110668067
25	17735.4	18218.6487424919	-483.248742491903
26	19640.2	19944.6664806481	-304.46648064815
27	20844.4	21362.5859215165	-518.185921516527
28	19823.1	20220.1081833603	-397.008183360266
29	18594.6	19115.6156799266	-521.015679926591
30	21350.6	22238.9492763063	-888.34927630626
31	18574.1	19035.6936470142	-461.593647014166
32	18924.2	19303.9458961993	-379.745896199278
33	17343.4	17758.7239645559	-415.323964555851
34	19961.2	20308.1101909554	-346.91019095544
35	19932.1	20592.5231005412	-660.42310054117
36	19464.6	19948.2851334536	-483.685133453594
37	16165.4	17291.9045236875	-1126.50452368749
38	17574.9	18166.3194661856	-591.419466185628
39	19795.4	20711.3602542486	-915.96025424856
40	19439.5	20370.3910296529	-930.891029652874
41	17170	17763.0700632931	-593.070063293124
42	21072.4	21412.3936216969	-339.993621696917
43	17751.8	17883.5251587708	-131.725158770842
44	17515.5	17550.6460227855	-35.1460227855242
45	18040.3	17833.8653877022	206.434612297844
46	19090.1	19281.1774079560	-191.077407955955
47	17746.5	17937.5261493718	-191.02614937177
48	19202.1	19547.5308766733	-345.430876673309
49	15141.6	16089.6417533466	-948.041753346631
50	16258.1	17039.1981189911	-781.098118991072
51	18586.5	20536.0302669072	-1949.53026690719
52	17209.4	17414.8283858983	-205.428385898255
53	17838.7	18289.0600253172	-450.360025317248
54	19123.5	18707.30238843	416.197611570016
55	16583.6	16681.2623884300	-97.6623884299837
56	15991.2	16448.5718166397	-457.371816639734
57	16704.4	17658.5354003608	-954.135400360777
58	17420.4	18053.8674965663	-633.467496566326
59	17872	19039.6003555176	-1167.60035551756
60	17823.2	19772.9551461122	-1949.75514611222

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.731576336064083	0.536847327871833	0.268423663935917
17	0.773402242629986	0.453195514740029	0.226597757370014
18	0.921134770707196	0.157730458585608	0.0788652292928041
19	0.931768549019709	0.136462901960582	0.0682314509802912
20	0.922853111770384	0.154293776459232	0.0771468882296159
21	0.905946774935137	0.188106450129726	0.0940532250648631
22	0.915679172651197	0.168641654697605	0.0843208273488026
23	0.973032850871218	0.0539342982575639	0.0269671491287819
24	0.993860219476668	0.0122795610466649	0.00613978052333243
25	0.99799334736961	0.00401330526078233	0.00200665263039116
26	0.997999814011375	0.00400037197724943	0.00200018598862471
27	0.99964109557491	0.000717808850181451	0.000358904425090725
28	0.99946793583862	0.00106412832276122	0.000532064161380609
29	0.999160827822054	0.00167834435589265	0.000839172177946327
30	0.9990628170303	0.00187436593940103	0.000937182969700517
31	0.998094814598406	0.00381037080318729	0.00190518540159364
32	0.996178180149852	0.00764363970029602	0.00382181985014801
33	0.992860994776085	0.0142780104478292	0.0071390052239146
34	0.987341580810623	0.0253168383787539	0.0126584191893770
35	0.983452881836206	0.0330942363275877	0.0165471181637938
36	0.984039530489073	0.0319209390218534	0.0159604695109267
37	0.976316203304894	0.0473675933902112	0.0236837966951056
38	0.960466415708962	0.0790671685820767	0.0395335842910383
39	0.96496582680299	0.070068346394019	0.0350341731970095
40	0.93582799888035	0.128344002239300	0.0641720011196499
41	0.88543481442961	0.229130371140781	0.114565185570391
42	0.80455697748618	0.390886045027641	0.195443022513821
43	0.672204434099957	0.655591131800086	0.327795565900043
44	0.525082300459745	0.94983539908051	0.474917699540255

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.275862068965517	NOK
5% type I error level	14	0.482758620689655	NOK
10% type I error level	17	0.586206896551724	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/10yedf1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/10yedf1258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/1fiqt1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/1fiqt1258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/24fet1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/24fet1258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/3hfog1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/3hfog1258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/46cb51258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/46cb51258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/52psz1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/52psz1258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/660761258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/660761258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/7s3ed1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/7s3ed1258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/8fkxf1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/8fkxf1258479056.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/9wl5k1258479056.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584797064wgqija4ca1i29p/9wl5k1258479056.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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('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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