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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: Mon, 23 Nov 2009 12:34:54 -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/23/t1259005813zy0qd8tak4hgde5.htm/, Retrieved Mon, 23 Nov 2009 20:50:25 +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/23/t1259005813zy0qd8tak4hgde5.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 «

93.3 121.8 97.3 127.6 127 129.9 111.7 128 96.4 123.5 133 124 72.2 127.4 95.8 127.6 124.1 128.4 127.6 131.4 110.7 135.1 104.6 134 112.7 144.5 115.3 147.3 139.4 150.9 119 148.7 97.4 141.4 154 138.9 81.5 139.8 88.8 145.6 127.7 147.9 105.1 148.5 114.9 151.1 106.4 157.5 104.5 167.5 121.6 172.3 141.4 173.5 99 187.5 126.7 205.5 134.1 195.1 81.3 204.5 88.6 204.5 132.7 201.7 132.9 207 134.4 206.6 103.7 210.6 119.7 211.1 115 215 132.9 223.9 108.5 238.2 113.9 238.9 142 229.6 97.7 232.2 92.2 222.1 128.8 221.6 134.9 227.3 128.2 221 114.8 213.6 117.9 243.4 119.1 253.8 120.7 265.3 129.1 268.2 117.6 268.5 129.2 266.9 100 268.4 87 250.8 128 231.2 127.7 192 93.4 171.4 84.1 160 71.7 148.1

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

IPtran[t] = + 87.4011660986683 + 0.087466220745299IGPic[t] + 0.790502037927038M1[t] + 10.2350222607929M2[t] + 28.3739580466937M3[t] + 9.07989113025423M4[t] + 5.893939772381M5[t] + 34.3615323610541M6[t] -17.8698473847992M7[t] -13.5502439867646M8[t] + 24.5761222473868M9[t] + 22.3864560534537M10[t] + 13.4338141805839M11[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)	87.4011660986683	7.242784	12.0673	0	0
IGPic	0.087466220745299	0.030569	2.8613	0.006232	0.003116
M1	0.790502037927038	6.605164	0.1197	0.905237	0.452618
M2	10.2350222607929	6.90284	1.4827	0.144683	0.072342
M3	28.3739580466937	6.910883	4.1057	0.000156	7.8e-05
M4	9.07989113025423	6.922797	1.3116	0.195897	0.097948
M5	5.893939772381	6.926625	0.8509	0.399044	0.199522
M6	34.3615323610541	6.915243	4.969	9e-06	4e-06
M7	-17.8698473847992	6.923676	-2.581	0.012964	0.006482
M8	-13.5502439867646	6.913623	-1.9599	0.055823	0.027912
M9	24.5761222473868	6.906661	3.5583	0.000853	0.000426
M10	22.3864560534537	6.900961	3.244	0.002149	0.001074
M11	13.4338141805839	6.898685	1.9473	0.057362	0.028681

Multiple Linear Regression - Regression Statistics
Multiple R	0.852991722337246
R-squared	0.727594878375862
Adjusted R-squared	0.659493597969827
F-TEST (value)	10.6840117254446
F-TEST (DF numerator)	12
F-TEST (DF denominator)	48
p-value	7.28515914261152e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.9073928806020
Sum Squared Residuals	5710.61853368671

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	93.3	98.8450538233728	-5.54505382337281
2	97.3	108.796878126561	-11.4968781265614
3	127	127.136986220176	-0.136986220176470
4	111.7	107.676733484321	4.02326651567917
5	96.4	104.097184133094	-7.69718413309374
6	133	132.608509832140	0.391490167860499
7	72.2	80.6745152368203	-8.47451523682025
8	95.8	85.011611879004	10.7883881209961
9	124.1	123.207951089752	0.892048910248477
10	127.6	121.280683558054	6.3193164419457
11	110.7	112.651666701942	-1.95166670194216
12	104.6	99.1216396785384	5.47836032146158
13	112.7	100.830537034291	11.8694629657090
14	115.3	110.519962675244	4.78003732475623
15	139.4	128.973776855828	10.4262231441723
16	119	109.487284253749	9.51271574625149
17	97.4	105.662829484435	-8.2628294844346
18	154	133.911756521244	20.0882434787556
19	81.5	81.759096374062	-0.259096374061959
20	88.8	86.5860038524193	2.21399614758071
21	127.7	124.913542394285	2.78645760571514
22	105.1	122.776355932799	-17.6763559327989
23	114.9	114.051126233867	0.84887376613306
24	106.4	101.177095866053	5.22290413394708
25	104.5	102.842260111433	1.65773988856707
26	121.6	112.706618193876	8.89338180612376
27	141.4	130.950513444671	10.4494865553285
28	99	112.880973618666	-13.8809736186661
29	126.7	111.269414234208	15.4305857657917
30	134.1	138.827358127130	-4.72735812713025
31	81.3	87.4181608562828	-6.1181608562828
32	88.6	91.7377642543174	-3.13776425431741
33	132.7	129.619225070382	3.08077492961804
34	132.9	127.893129846399	5.0068701536011
35	134.4	118.905501485231	15.4944985147690
36	103.7	105.821552187628	-2.12155218762829
37	119.7	106.655787335928	13.0442126640720
38	115	116.441425819700	-1.4414258197005
39	132.9	135.358810970235	-2.45881097023451
40	108.5	117.315511010453	-8.81551101045278
41	113.9	114.190786007101	-0.290786007101257
42	142	141.844942742843	0.155057257156931
43	97.7	89.8409751709276	7.85902482907242
44	92.2	93.2771697394347	-1.07716973943465
45	128.8	131.359802863213	-2.55980286321339
46	134.9	129.668694127528	5.23130587247152
47	128.2	120.165015063963	8.03498493603665
48	114.8	106.083950849864	8.7160491501358
49	117.9	109.480946266001	8.41905373399888
50	119.1	119.835115184618	-0.735115184618108
51	120.7	138.97991250909	-18.2799125090899
52	129.1	119.939497632812	9.16050236718825
53	117.6	116.779786141162	0.82021385883788
54	129.2	145.107432776643	-15.9074327766427
55	100	93.0072523619074	6.99274763809259
56	87	95.7874502748247	-8.78745027482474
57	128	132.199478582368	-4.19947858236827
58	127.7	126.581136535219	1.11886346478058
59	93.4	115.826690514997	-22.4266905149965
60	84.1	101.395761417916	-17.2957614179162
61	71.7	101.145415428974	-29.4454154289741

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0405629452439553	0.0811258904879105	0.959437054756045
17	0.0457317933981643	0.0914635867963285	0.954268206601836
18	0.0668179818620993	0.133635963724199	0.9331820181379
19	0.0283436021801218	0.0566872043602435	0.971656397819878
20	0.0753423959116287	0.150684791823257	0.924657604088371
21	0.0478930334097292	0.0957860668194583	0.95210696659027
22	0.263341112088031	0.526682224176062	0.736658887911969
23	0.179842604898954	0.359685209797908	0.820157395101046
24	0.133099105475322	0.266198210950645	0.866900894524677
25	0.100037268202798	0.200074536405597	0.899962731797202
26	0.0800430871882476	0.160086174376495	0.919956912811752
27	0.103236038688412	0.206472077376824	0.896763961311588
28	0.234901056958083	0.469802113916165	0.765098943041917
29	0.356419467793963	0.712838935587926	0.643580532206037
30	0.455839253075368	0.911678506150735	0.544160746924632
31	0.370298638958584	0.740597277917169	0.629701361041416
32	0.335809579131641	0.671619158263283	0.664190420868359
33	0.290003647060046	0.580007294120093	0.709996352939954
34	0.231884212807089	0.463768425614178	0.768115787192911
35	0.295871291406427	0.591742582812855	0.704128708593573
36	0.238668512842434	0.477337025684867	0.761331487157566
37	0.286869876580787	0.573739753161575	0.713130123419213
38	0.226271874847247	0.452543749694495	0.773728125152753
39	0.389754038053957	0.779508076107915	0.610245961946043
40	0.336757657571445	0.673515315142889	0.663242342428555
41	0.255954469746259	0.511908939492518	0.744045530253741
42	0.551020824296644	0.897958351406712	0.448979175703356
43	0.593141344251715	0.81371731149657	0.406858655748285
44	0.813221236343794	0.373557527312412	0.186778763656206
45	0.707753488427331	0.584493023145337	0.292246511572669

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	4	0.133333333333333	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/10jyrs1259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/10jyrs1259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/1ktnq1259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/1ktnq1259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/2cvf81259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/2cvf81259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/3npwv1259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/3npwv1259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/4lwbq1259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/4lwbq1259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/57d6g1259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/57d6g1259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/6b9s01259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/6b9s01259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/7nre91259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/7nre91259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/8qc5j1259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/8qc5j1259004889.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/9qjgc1259004889.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005813zy0qd8tak4hgde5/9qjgc1259004889.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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