Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2009 » Nov » 20 »

*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: Fri, 20 Nov 2009 03:54:12 -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/20/t12587147121912iw8ofe7pelf.htm/, Retrieved Fri, 20 Nov 2009 11:58:44 +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/20/t12587147121912iw8ofe7pelf.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 «

8.1 10.9 115.6 92.9 7.7 10 127.1 107.7 7.5 9.2 123 103.5 7.6 9.2 122.2 91.1 7.8 9.5 126.4 79.8 7.8 9.6 112.7 71.9 7.8 9.5 105.8 82.9 7.5 9.1 120.9 90.1 7.5 8.9 116.3 100.7 7.1 9 115.7 90.7 7.5 10.1 127.9 108.8 7.5 10.3 108.3 44.1 7.6 10.2 121.1 93.6 7.7 9.6 128.6 107.4 7.7 9.2 123.1 96.5 7.9 9.3 127.7 93.6 8.1 9.4 126.6 76.5 8.2 9.4 118.4 76.7 8.2 9.2 110 84 8.2 9 129.6 103.3 7.9 9 115.8 88.5 7.3 9 125.9 99 6.9 9.8 128.4 105.9 6.6 10 114 44.7 6.7 9.8 125.6 94 6.9 9.3 128.5 107.1 7 9 136.6 104.8 7.1 9 133.1 102.5 7.2 9.1 124.6 77.7 7.1 9.1 123.5 85.2 6.9 9.1 117.2 91.3 7 9.2 135.5 106.5 6.8 8.8 124.8 92.4 6.4 8.3 127.8 97.5 6.7 8.4 133.1 107 6.6 8.1 125.7 51.1 6.4 7.7 128.4 98.6 6.3 7.9 131.9 102.2 6.2 7.9 146.3 114.3 6.5 8 140.6 99.4 6.8 7.9 129.5 72.5 6.8 7.6 132.4 92.3 6.4 7.1 125.9 99.4 6.1 6.8 126.9 85.9 5.8 6.5 135.8 109.4 6.1 6.9 129.5 97.6 7.2 8.2 130.2 104.7 7.3 8.7 133.8 56.9 6.9 8.3 123.3 86.7 6.1 7.9 140.7 108.5 5.8 7.5 145.9 103.4 6.2 7.8 128.5 86 etc...

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

bouw[t] = -39.6100382738278 -1.37967687409676mannen[t] + 2.15587514738497vrouwen[t] + 0.958850147549073voeding[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)	-39.6100382738278	54.883119	-0.7217	0.473419	0.23671
mannen	-1.37967687409676	3.639731	-0.3791	0.706052	0.353026
vrouwen	2.15587514738497	2.600637	0.829	0.410574	0.205287
voeding	0.958850147549073	0.279193	3.4344	0.001114	0.000557

Multiple Linear Regression - Regression Statistics
Multiple R	0.464567423905489
R-squared	0.215822891354183
Adjusted R-squared	0.174550411951771
F-TEST (value)	5.22922040253227
F-TEST (DF numerator)	3
F-TEST (DF denominator)	57
p-value	0.00293789281644452
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.7866480509924
Sum Squared Residuals	12462.7627532833

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	92.9	83.5566952091573	9.3433047908427
2	107.7	93.195055022964	14.504944977036
3	103.5	87.8150046749241	15.6849953250759
4	91.1	86.9099568694752	4.19004313052477
5	79.8	91.3079546585775	-11.5079546585775
6	71.9	78.3872951518937	-6.48729515189367
7	82.9	71.5556416190666	11.3443583809334
8	90.1	85.5858318503326	4.51416814966737
9	100.7	80.7439461421299	19.9560538578701
10	90.7	80.9360943179776	9.76390568202236
11	108.8	94.453658030561	14.3463419694389
12	44.1	76.0913701680762	-31.9913701680762
13	93.6	88.0110968545562	5.58890314544378
14	107.4	93.7709801853336	13.6290198146664
15	96.5	87.6349543148597	8.86504568514028
16	93.6	91.9853171335046	1.61468286649538
17	76.5	90.8702341111198	-14.3702341111198
18	76.7	82.8696952138077	-6.1696952138077
19	84	74.3841789449185	9.61582105508151
20	103.3	92.7464668074033	10.5535331925967
21	88.5	79.9282378334551	8.57176216654486
22	99	90.4404304481588	8.55956955184115
23	105.9	95.1141266845782	10.7858733154218
24	44.7	82.1517626515776	-37.4517626515776
25	94	92.7052816462601	1.29471835373987
26	107.1	94.1320741256406	12.9679258743594
27	104.8	101.114030089163	3.68596991083705
28	102.5	97.6200868853315	4.87991311466849
29	77.7	89.5474804584932	-11.8474804584932
30	85.2	88.630712983599	-3.43071298359891
31	91.3	82.865892428859	8.4341075711409
32	106.5	100.490469956336	6.00953004366404
33	92.4	89.6443586934262	2.75564130657377
34	97.5	91.9948423120197	5.50515768798032
35	107	96.8784325465392	10.1215674534608
36	51.1	89.2741465978703	-38.1741465978703
37	98.6	91.2766273121182	7.32337268788184
38	102.2	95.2017455454266	6.99825445457343
39	114.3	109.147155357543	5.15284464245709
40	99.4	103.483393969023	-4.08339396902264
41	72.5	92.2106667542604	-19.7106667542604
42	92.3	94.3445696379372	-2.04456963793725
43	99.4	87.5859768548145	11.8140231451855
44	85.9	88.311967520377	-2.41196752037709
45	109.4	96.6128743515774	12.7871256484226
46	97.6	91.0205654187432	6.57943458125681
47	104.7	92.9767536521215	11.7232463478785
48	56.9	97.368584069581	-40.4685840695810
49	86.7	86.9901782110005	-0.290178211000469
50	108.5	103.915562218678	4.58443778132224
51	103.4	108.453135989208	-5.05313598920799
52	86.2	91.8640352164309	-5.6640352164309
53	71	98.7957546952995	-27.7957546952995
54	75.9	83.1295887690594	-7.22958876905944
55	87.1	81.463628217214	5.63637178278597
56	102	93.4143329807436	8.58566701925643
57	88.5	90.832598174182	-2.33259817418197
58	87.8	85.444772160481	2.35522783951893
59	100.8	97.8899505971746	2.91004940282537
60	50.6	91.4229568621009	-40.8229568621009
61	85.9	90.0988318429583	-4.19883184295828

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.0869808899232209	0.173961779846442	0.913019110076779
8	0.0558306425119368	0.111661285023874	0.944169357488063
9	0.0569387957591162	0.113877591518232	0.943061204240884
10	0.200363044767714	0.400726089535428	0.799636955232286
11	0.127524030766648	0.255048061533296	0.872475969233352
12	0.620118892048027	0.759762215903946	0.379881107951973
13	0.525781175447762	0.948437649104476	0.474218824552238
14	0.445277267452509	0.890554534905018	0.554722732547491
15	0.362213513604335	0.72442702720867	0.637786486395665
16	0.322298421856369	0.644596843712739	0.67770157814363
17	0.378364208124817	0.756728416249633	0.621635791875183
18	0.29612140390084	0.59224280780168	0.70387859609916
19	0.290657870639409	0.581315741278819	0.70934212936059
20	0.245072980558099	0.490145961116198	0.754927019441901
21	0.204769807535363	0.409539615070726	0.795230192464637
22	0.164336344048412	0.328672688096824	0.835663655951588
23	0.137108288480980	0.274216576961961	0.86289171151902
24	0.464602834579341	0.929205669158681	0.535397165420659
25	0.388696224614482	0.777392449228964	0.611303775385518
26	0.367107863411229	0.734215726822459	0.632892136588771
27	0.327059598876713	0.654119197753427	0.672940401123287
28	0.286219999488337	0.572439998976674	0.713780000511663
29	0.276488780495954	0.552977560991908	0.723511219504046
30	0.221251859143485	0.442503718286971	0.778748140856515
31	0.206080184678516	0.412160369357032	0.793919815321484
32	0.200719900121131	0.401439800242262	0.799280099878869
33	0.176046370206925	0.35209274041385	0.823953629793075
34	0.153043384522139	0.306086769044278	0.84695661547786
35	0.172988353220343	0.345976706440685	0.827011646779657
36	0.589592866693148	0.820814266613703	0.410407133306852
37	0.536348607394194	0.927302785211611	0.463651392605806
38	0.499652970003485	0.99930594000697	0.500347029996515
39	0.476912937733721	0.953825875467442	0.523087062266279
40	0.426701758743855	0.85340351748771	0.573298241256145
41	0.471264986311278	0.942529972622556	0.528735013688722
42	0.388636539145957	0.777273078291914	0.611363460854043
43	0.336504987317002	0.673009974634003	0.663495012682998
44	0.326365361188351	0.652730722376703	0.673634638811649
45	0.267407231637268	0.534814463274537	0.732592768362732
46	0.222784318676479	0.445568637352958	0.77721568132352
47	0.292143130563751	0.584286261127502	0.707856869436249
48	0.523557199822189	0.952885600355622	0.476442800177811
49	0.439338775700721	0.878677551401443	0.560661224299279
50	0.43299535379113	0.86599070758226	0.56700464620887
51	0.343808093950182	0.687616187900364	0.656191906049818
52	0.269737797077808	0.539475594155615	0.730262202922192
53	0.210513214293832	0.421026428587664	0.789486785706168
54	0.131333076512837	0.262666153025675	0.868666923487162

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	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/10w2ca1258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/10w2ca1258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/1yka21258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/1yka21258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/2dpco1258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/2dpco1258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/3f8271258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/3f8271258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/4sl6u1258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/4sl6u1258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/525r81258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/525r81258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/6i8tb1258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/6i8tb1258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/73rj21258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/73rj21258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/8td4h1258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/8td4h1258714448.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/9auas1258714448.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587147121912iw8ofe7pelf/9auas1258714448.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by