Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2009 » Dec » 17 »

*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: Thu, 17 Dec 2009 08:20:44 -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/Dec/17/t1261063341zar8zcje0mnnvtg.htm/, Retrieved Thu, 17 Dec 2009 16:22:33 +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/Dec/17/t1261063341zar8zcje0mnnvtg.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 «

104.2 97.4 103.2 97 112.7 105.4 106.4 102.7 102.6 98.1 110.6 104.5 95.2 87.4 89 89.9 112.5 109.8 116.8 111.7 107.2 98.6 113.6 96.9 101.8 95.1 102.6 97 122.7 112.7 110.3 102.9 110.5 97.4 121.6 111.4 100.3 87.4 100.7 96.8 123.4 114.1 127.1 110.3 124.1 103.9 131.2 101.6 111.6 94.6 114.2 95.9 130.1 104.7 125.9 102.8 119 98.1 133.8 113.9 107.5 80.9 113.5 95.7 134.4 113.2 126.8 105.9 135.6 108.8 139.9 102.3 129.8 99 131 100.7 153.1 115.5 134.1 100.7 144.1 109.9 155.9 114.6 123.3 85.4 128.1 100.5 144.3 114.8 153 116.5 149.9 112.9 150.9 102 141 106 138.9 105.3 157.4 118.8 142.9 106.1 151.7 109.3 161 117.2 138.5 92.5 135.9 104.2 151.5 112.5 164 122.4 159.1 113.3 157 100 142.1 110.7 144.8 112.8 152.1 109.8 154.9 117.3 148.4 109.1 157.3 115.9 145.7 96 133.8 99.8 156.8 116.8

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

Omzet[t] = + 29.6444465901532 + 0.832814320948403Productie[t] -13.2023060390343M1[t] -14.0192330461690M2[t] -7.22885821757064M3[t] -12.0853823690019M4[t] -11.0120699935285M5[t] -8.77747562585243M6[t] -10.5632622060097M7[t] -20.7979652292691M8[t] -14.2683565650436M9[t] -10.2440374584020M10[t] -8.42173846251315M11[t] + 0.697992924868762t + 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)	29.6444465901532	14.96341	1.9811	0.052585	0.026293
Productie	0.832814320948403	0.157747	5.2794	2e-06	1e-06
M1	-13.2023060390343	2.641013	-4.999	6e-06	3e-06
M2	-14.0192330461690	2.649091	-5.2921	2e-06	1e-06
M3	-7.22885821757064	3.168455	-2.2815	0.026412	0.013206
M4	-12.0853823690019	2.761009	-4.3772	5.4e-05	2.7e-05
M5	-11.0120699935285	2.684727	-4.1017	0.000137	6.8e-05
M6	-8.77747562585243	3.277109	-2.6784	0.009735	0.004868
M7	-10.5632622060097	3.288779	-3.2119	0.002205	0.001102
M8	-20.7979652292691	2.680361	-7.7594	0	0
M9	-14.2683565650436	3.283533	-4.3454	6e-05	3e-05
M10	-10.2440374584020	3.448381	-2.9707	0.004399	0.002199
M11	-8.42173846251315	2.972414	-2.8333	0.006428	0.003214
t	0.697992924868762	0.039542	17.6521	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.979694556876288
R-squared	0.959801424773025
Adjusted R-squared	0.95029994335574
F-TEST (value)	101.015976627284
F-TEST (DF numerator)	13
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.35053862743482
Sum Squared Residuals	1040.99524918413

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	104.2	98.256248336362	5.94375166363801
2	103.2	97.8041885257167	5.39581147428328
3	112.7	112.288196575150	0.411803424849511
4	106.4	105.881066682027	0.518933317972653
5	102.6	103.821426106007	-1.22142610600683
6	110.6	112.084025052621	-1.48402505262144
7	95.2	96.7551065091152	-1.5551065091152
8	89	89.3004322130956	-0.300432213095634
9	112.5	113.101038789063	-0.601038789063119
10	116.8	119.405698030375	-2.60569803037536
11	107.2	111.016122346709	-3.8161223467089
12	113.6	118.720069388479	-5.12006938847858
13	101.8	104.716690496606	-2.91669049660587
14	102.6	106.180103624142	-3.58010362414194
15	122.7	126.743656216499	-4.04365621649898
16	110.3	114.423544644642	-4.12354464464214
17	110.5	111.614371179768	-1.11437117976810
18	121.6	126.206358965591	-4.60635896559055
19	100.3	105.131021607540	-4.83102160754035
20	100.7	103.422766126065	-2.72276612606474
21	123.4	125.058055467566	-1.65805546756638
22	127.1	126.615673079473	0.484326920527255
23	124.1	123.805953346161	0.294046653839383
24	131.2	131.010211795361	0.189788204638797
25	111.6	112.676198434557	-1.0761984345568
26	114.2	113.639922969524	0.560077030476217
27	130.1	128.457056747337	1.64294325266311
28	125.9	122.716178310972	3.18382168902757
29	119	120.573256302857	-1.57325630285711
30	133.8	136.664309866387	-2.86430986638669
31	107.5	108.093643619801	-0.593643619800858
32	113.5	110.882585471447	2.61741452855336
33	134.4	132.684437677138	1.71556232286203
34	126.8	131.327205165725	-4.52720516572491
35	135.6	136.262658617233	-0.662658617232932
36	139.9	139.969096918450	-0.0690969184502082
37	129.8	124.716496545155	5.08350345484509
38	131	126.013346808501	4.98665319149873
39	153.1	145.827366512005	7.2726334879952
40	134.1	129.343183335406	4.75681666459406
41	144.1	138.776380388473	5.32361961152656
42	155.9	145.623194989476	10.2768050105243
43	123.3	120.217223162494	3.0827768375062
44	128.1	123.256009310424	4.84399068957587
45	144.3	142.392855689081	1.90714431091945
46	153	148.530952066203	4.46904793379688
47	149.9	148.053112431547	1.84688756845348
48	150.9	148.095167720591	2.80483227940916
49	141	138.922111890219	2.07788810978111
50	138.9	138.220207783289	0.679792216710943
51	157.4	156.951568869560	0.448431130440338
52	142.9	142.216295766952	0.683704233047565
53	151.7	146.652606894330	5.04739310567046
54	161	156.164427322367	4.83557267763329
55	138.5	134.506119939653	3.99388006034739
56	135.9	134.713337396358	1.18666260364164
57	151.5	148.853297849324	2.64670215067564
58	164	161.820471658224	2.17952834177614
59	159.1	156.762153258351	2.33784674164897
60	157	154.805454177119	2.19454582288084
61	142.1	151.212254297102	-9.11225429710153
62	144.8	152.842230288827	-8.04223028882722
63	152.1	157.832155079449	-5.73215507944917
64	154.9	159.919731260000	-5.01973125999971
65	148.4	154.861959128565	-6.46195912856498
66	157.3	163.457683803559	-6.15768380355891
67	145.7	145.796885161397	-0.0968851613971864
68	133.8	139.424869482611	-5.6248694826105
69	156.8	160.810314527828	-4.01031452782762

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.259945051993976	0.519890103987952	0.740054948006024
18	0.143106340057154	0.286212680114308	0.856893659942846
19	0.08970239208989	0.17940478417978	0.91029760791011
20	0.0474590012790256	0.0949180025580512	0.952540998720974
21	0.0321730768510723	0.0643461537021446	0.967826923148928
22	0.087889503365567	0.175779006731134	0.912110496634433
23	0.119454256318114	0.238908512636227	0.880545743681886
24	0.166685892396935	0.333371784793871	0.833314107603065
25	0.117224748326337	0.234449496652674	0.882775251673663
26	0.0885162024594634	0.177032404918927	0.911483797540537
27	0.102605703861198	0.205211407722396	0.897394296138802
28	0.114937948974310	0.229875897948621	0.88506205102569
29	0.0873774004344976	0.174754800868995	0.912622599565502
30	0.113423158115165	0.226846316230330	0.886576841884835
31	0.116819857577433	0.233639715154867	0.883180142422567
32	0.112933024658486	0.225866049316972	0.887066975341514
33	0.107362192002191	0.214724384004382	0.89263780799781
34	0.266792899036205	0.53358579807241	0.733207100963795
35	0.377435545936608	0.754871091873216	0.622564454063392
36	0.604421526369547	0.791156947260906	0.395578473630453
37	0.569622662565253	0.860754674869495	0.430377337434748
38	0.518732960497468	0.962534079005063	0.481267039502532
39	0.543851462876067	0.912297074247866	0.456148537123933
40	0.485999150434547	0.971998300869093	0.514000849565453
41	0.422467289444829	0.844934578889658	0.577532710555171
42	0.593815496428087	0.812369007143826	0.406184503571913
43	0.667379860996874	0.665240278006252	0.332620139003126
44	0.574079177232747	0.851841645534507	0.425920822767253
45	0.639966496273185	0.72006700745363	0.360033503726815
46	0.617702650600037	0.764594698799926	0.382297349399963
47	0.757239919331551	0.485520161336897	0.242760080668449
48	0.896769080255322	0.206461839489357	0.103230919744678
49	0.900320233543296	0.199359532913408	0.099679766456704
50	0.853382271653906	0.293235456692188	0.146617728346094
51	0.829217059141855	0.34156588171629	0.170782940858145
52	0.696986177116865	0.60602764576627	0.303013822883135

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	2	0.0555555555555556	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/10tsr11261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/10tsr11261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/1m96z1261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/1m96z1261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/24kby1261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/24kby1261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/367kt1261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/367kt1261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/4hg9p1261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/4hg9p1261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/5k2z81261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/5k2z81261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/6nmpj1261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/6nmpj1261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/7992y1261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/7992y1261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/86qa71261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/86qa71261063239.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/9mwyg1261063239.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261063341zar8zcje0mnnvtg/9mwyg1261063239.ps (open in new window)

Parameters (Session):

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

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