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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: Wed, 18 Nov 2009 11:21:31 -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/t12585686965sl9fm2wxjyik4g.htm/, Retrieved Wed, 18 Nov 2009 19:25:08 +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/t12585686965sl9fm2wxjyik4g.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 «

90398 562000 90269 561000 90390 555000 88219 544000 87032 537000 87175 543000 92603 594000 93571 611000 94118 613000 92159 611000 89528 594000 89955 595000 89587 591000 89488 589000 88521 584000 86587 573000 85159 567000 84915 569000 91378 621000 92729 629000 92194 628000 89664 612000 86285 595000 86858 597000 87184 593000 86629 590000 85220 580000 84816 574000 84831 573000 84957 573000 90951 620000 92134 626000 91790 620000 86625 588000 83324 566000 82719 557000 83614 561000 81640 549000 78665 532000 77828 526000 75728 511000 72187 499000 79357 555000 81329 565000 77304 542000 75576 527000 72932 510000 74291 514000 74988 517000 73302 508000 70483 493000 69848 490000 66466 469000 67610 478000 75091 528000 76207 534000 73454 518000 72008 506000 71362 502000 74250 516000

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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = -12072.7365437522 + 0.170011796713566X[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)	-12072.7365437522	5062.021257	-2.385	0.02037	0.010185
X	0.170011796713566	0.009018	18.8515	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.927196406321419
R-squared	0.859693175895354
Adjusted R-squared	0.857274092721136
F-TEST (value)	355.37975091462
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2907.35079218225
Sum Squared Residuals	490255940.470561

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	90398	83473.8932092723	6924.1067907277
2	90269	83303.8814125586	6965.11858744136
3	90390	82283.8106322772	8106.18936772278
4	88219	80413.680868428	7805.31913157201
5	87032	79223.598291433	7808.40170856697
6	87175	80243.6690717144	6931.33092828557
7	92603	88914.2707041063	3688.72929589368
8	93571	91804.471248237	1766.52875176305
9	94118	92144.4948416641	1973.50515833592
10	92159	91804.471248237	354.528751763054
11	89528	88914.2707041063	613.729295893684
12	89955	89084.2825008199	870.717499180117
13	89587	88404.2353139656	1182.76468603438
14	89488	88064.2117205385	1423.78827946152
15	88521	87214.1527369706	1306.84726302935
16	86587	85344.0229731214	1242.97702687858
17	85159	84323.95219284	835.047807159978
18	84915	84663.9757862672	251.024213732845
19	91378	93504.5892153726	-2126.58921537261
20	92729	94864.6835890811	-2135.68358908114
21	92194	94694.6717923676	-2500.67179236758
22	89664	91974.4830449505	-2310.48304495051
23	86285	89084.2825008199	-2799.28250081988
24	86858	89424.306094247	-2566.30609424702
25	87184	88744.2589073928	-1560.25890739275
26	86629	88234.223517252	-1605.22351725205
27	85220	86534.1055501164	-1314.10555011639
28	84816	85514.034769835	-698.034769834987
29	84831	85344.0229731214	-513.022973121421
30	84957	85344.0229731214	-387.022973121421
31	90951	93334.577418659	-2383.57741865904
32	92134	94354.6481989404	-2220.64819894044
33	91790	93334.577418659	-1544.57741865904
34	86625	87894.1999238249	-1269.19992382492
35	83324	84153.9403961265	-829.940396126456
36	82719	82623.8342257044	95.1657742956428
37	83614	83303.8814125586	310.118587441377
38	81640	81263.7398519958	376.260148004174
39	78665	78373.5393078652	291.460692134804
40	77828	77353.4685275838	474.531472416203
41	75728	74803.2915768803	924.7084231197
42	72187	72763.1500163175	-576.150016317504
43	79357	82283.8106322772	-2926.81063227722
44	81329	83983.9285994129	-2654.92859941289
45	77304	80073.6572750009	-2769.65727500086
46	75576	77523.4803242974	-1947.48032429736
47	72932	74633.2797801667	-1701.27978016673
48	74291	75313.326967021	-1022.326967021
49	74988	75823.3623571617	-835.362357161698
50	73302	74293.2561867396	-991.2561867396
51	70483	71743.0792360361	-1260.07923603610
52	69848	71233.0438458954	-1385.04384589540
53	66466	67662.7961149105	-1196.79611491051
54	67610	69192.9022853326	-1582.90228533261
55	75091	77693.4921210109	-2602.49212101093
56	76207	78713.5629012923	-2506.56290129233
57	73454	75993.3741538753	-2539.37415387527
58	72008	73953.2325933125	-1945.23259331247
59	71362	73273.1854064582	-1911.1854064582
60	74250	75653.3505604481	-1403.35056044813

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0213602780569695	0.042720556113939	0.97863972194303
6	0.0304479556670555	0.060895911334111	0.969552044332945
7	0.0882982565517542	0.176596513103508	0.911701743448246
8	0.0794596004503398	0.158919200900680	0.92054039954966
9	0.0503735065618639	0.100747013123728	0.949626493438136
10	0.118235199707565	0.236470399415131	0.881764800292435
11	0.400823606916035	0.801647213832071	0.599176393083965
12	0.521704967548121	0.956590064903758	0.478295032451879
13	0.63073296801609	0.73853406396782	0.36926703198391
14	0.729682562177601	0.540634875644798	0.270317437822399
15	0.863044305415585	0.273911389168829	0.136955694584415
16	0.977592170343965	0.0448156593120696	0.0224078296560348
17	0.998703915301636	0.00259216939672797	0.00129608469836398
18	0.99990158433348	0.000196831333040504	9.84156665202522e-05
19	0.99982422246492	0.00035155507015999	0.000175777535079995
20	0.99963978788604	0.00072042422791841	0.000360212113959205
21	0.999374380069021	0.00125123986195816	0.00062561993097908
22	0.999285956792261	0.00142808641547715	0.000714043207738576
23	0.999851872615103	0.000296254769793597	0.000148127384896799
24	0.999932376900621	0.000135246198757569	6.76230993787846e-05
25	0.999926567569677	0.000146864860646294	7.34324303231468e-05
26	0.999926705808908	0.000146588382184324	7.3294191092162e-05
27	0.999944622708767	0.000110754582465147	5.53772912325734e-05
28	0.999955620646658	8.87587066831386e-05	4.43793533415693e-05
29	0.999960645227119	7.8709545762861e-05	3.93547728814305e-05
30	0.99996272962717	7.45407456610125e-05	3.72703728305063e-05
31	0.999928794474006	0.000142411051988832	7.12055259944158e-05
32	0.999871421632382	0.000257156735235792	0.000128578367617896
33	0.99973504051172	0.000529918976558683	0.000264959488279341
34	0.99956883757234	0.0008623248553214	0.0004311624276607
35	0.999565732080277	0.00086853583944546	0.00043426791972273
36	0.999678199477581	0.00064360104483781	0.000321800522418905
37	0.999812201487592	0.000375597024815407	0.000187798512407704
38	0.999944729025623	0.000110541948754389	5.52709743771945e-05
39	0.9999886233462	2.27533076003314e-05	1.13766538001657e-05
40	0.999999043581472	1.91283705627616e-06	9.56418528138079e-07
41	0.999999997656164	4.68767198989386e-09	2.34383599494693e-09
42	0.999999998736831	2.52633755957247e-09	1.26316877978624e-09
43	0.999999996521704	6.9565920639158e-09	3.4782960319579e-09
44	0.999999985323998	2.93520047830502e-08	1.46760023915251e-08
45	0.999999964619195	7.0761610515383e-08	3.53808052576915e-08
46	0.999999845946691	3.08106617089157e-07	1.54053308544578e-07
47	0.999999318033528	1.36393294404151e-06	6.81966472020756e-07
48	0.999998609466834	2.78106633259546e-06	1.39053316629773e-06
49	0.999999285636586	1.42872682806034e-06	7.1436341403017e-07
50	0.999999563137057	8.73725886122776e-07	4.36862943061388e-07
51	0.999997923482138	4.15303572440047e-06	2.07651786220023e-06
52	0.99998564800035	2.87039992992561e-05	1.43519996496280e-05
53	0.999880928981202	0.000238142037596606	0.000119071018798303
54	0.999163640809245	0.00167271838151089	0.000836359190755444
55	0.994606763775966	0.0107864724480672	0.00539323622403359

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	38	0.745098039215686	NOK
5% type I error level	41	0.80392156862745	NOK
10% type I error level	42	0.823529411764706	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/108qnk1258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/108qnk1258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/1fhw21258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/1fhw21258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/2ymop1258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/2ymop1258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/36fl61258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/36fl61258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/4qz1m1258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/4qz1m1258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/5r17e1258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/5r17e1258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/6r9mh1258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/6r9mh1258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/72i3o1258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/72i3o1258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/8ts061258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/8ts061258568487.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/953nd1258568487.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585686965sl9fm2wxjyik4g/953nd1258568487.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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('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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