Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2010 » Dec » 03 »

*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, 03 Dec 2010 10:05:27 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara.htm/, Retrieved Fri, 03 Dec 2010 11:03:27 +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/2010/Dec/03/t1291370607ivjxehr2irqkara.htm/},
    year = {2010},
}
@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 = {2010},
    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 «

2284 41 76403 194493 3160 90 108094 530670 4150 136 134759 518365 7285 97 188873 491303 1134 63 146216 527021 4658 114 156608 233773 2384 77 61348 405972 3748 6 50350 652925 5371 47 87720 446211 1285 51 99489 341340 9327 85 87419 387699 5565 43 94355 493408 1528 32 60326 146494 3122 25 94670 414462 7561 77 82425 364304 2675 54 59017 355178 13253 251 90829 357760 880 15 80791 261216 2053 44 100423 397144 1424 73 131116 374943 4036 85 100269 424898 3045 49 27330 202055 5119 38 39039 378525 1431 35 106885 310768 554 9 79285 325738 1975 34 118881 394510 1765 20 77623 247060 1012 29 114768 368078 810 11 74015 236761 1280 52 69465 312378 666 13 117869 339836 1380 29 60982 347385 4677 66 90131 426280 876 33 138971 352850 814 15 39625 301881 514 15 102725 377516 5692 68 64239 357312 3642 100 90262 458343 540 13 103960 354228 2099 45 106611 308636 567 14 103345 386212 2001 36 95551 393343 2949 40 82903 378509 2253 68 63593 452469 6533 29 126910 364839 1889 43 37527 358649 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Wealth[t] = + 294370.551448375 + 9.09965160204044Costs[t] -180.944089053398Orders[t] + 0.738242849391378Dividends[t] -346.303558627737t + 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)	294370.551448375	53080.636479	5.5457	1e-06	1e-06
Costs	9.09965160204044	7.40132	1.2295	0.22515	0.112575
Orders	-180.944089053398	474.895146	-0.381	0.704943	0.352472
Dividends	0.738242849391378	0.407513	1.8116	0.076583	0.038291
t	-346.303558627737	936.761194	-0.3697	0.713316	0.356658

Multiple Linear Regression - Regression Statistics
Multiple R	0.350414092233016
R-squared	0.122790036035489
Adjusted R-squared	0.046510908734227
F-TEST (value)	1.60974620947790
F-TEST (DF numerator)	4
F-TEST (DF denominator)	46
p-value	0.187896665754246
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	89158.422019695
Sum Squared Residuals	365664313983.934

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	194493	363793.112919668	-169300.112919668
2	530670	385947.497940873	144722.502059127
3	518365	405971.66695083	112393.333049170
4	491303	481158.864189646	10144.1358103537
5	527021	399501.477428195	127519.522571805
6	233773	429666.01726431	-195893.01726431
7	405972	344997.023424595	60974.9765754047
8	652925	361790.480116336	291134.519883664
9	446211	396382.338738386	49828.6612616139
10	341340	366819.462472095	-25479.4624720946
11	387699	424589.866877107	-36890.8668771066
12	493408	402730.778135224	90677.221864776
13	146494	342517.900116807	-196023.900116807
14	414462	383297.262254703	31164.7377452967
15	364304	404895.435835959	-40591.4358359589
16	355178	346969.159979436	8208.84002056363
17	357760	430717.967048511	-72957.9670485114
18	261216	353073.997512249	-91857.9975122487
19	397144	372647.390319517	24496.6096804826
20	374943	383988.915097027	-9045.91509702724
21	424898	382466.995279112	42431.0047208875
22	202055	325770.228997027	-123715.228997027
23	378525	354931.073364142	23593.9266358575
24	310768	371654.911324157	-60886.9113241572
25	325738	347657.256982726	-21919.2569827264
26	394510	384949.419988764	9560.58001123585
27	247060	354766.983360266	-107706.983360266
28	368078	373362.175984464	-5284.17598446397
29	236761	344349.125563938	-107588.125563938
30	312378	337501.945642350	-25123.9456423496
31	339836	374359.182355092	-34523.1823550918
32	347385	335618.503642139	11766.4963578608
33	426280	380097.860937372	46182.1390626277
34	352850	387190.717342426	-34340.717342426
35	301881	316195.754871797	-14314.7548717971
36	377516	359702.679629153	17813.3203708468
37	357312	368472.321044384	-11160.3210443841
38	458343	362892.814521577	95450.1854784235
39	354228	360173.977992028	-5945.97799202815
40	308636	370180.902225009	-61544.9022250092
41	386212	359092.098026599	27119.9019734013
42	393343	362060.060137966	31282.9398620343
43	378509	360279.154382757	18229.8456172434
44	452469	334277.589393866	118191.410606134
45	364839	426677.936659968	-61838.9366599678
46	358649	315553.273207567	43095.7267924328
47	376641	344942.314112785	31698.6858872151
48	429112	362012.339835496	67099.6601645036
49	330546	338100.650623410	-7554.65062341034
50	403560	393023.866952399	10536.1330476012
51	317892	341720.270997787	-23828.2709977874

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.999996159342638	7.68131472472353e-06	3.84065736236176e-06
9	0.999994285198886	1.14296022270545e-05	5.71480111352725e-06
10	0.999990776478215	1.84470435705724e-05	9.22352178528618e-06
11	0.999979140232783	4.1719534434222e-05	2.0859767217111e-05
12	0.999990373367297	1.92532654051587e-05	9.62663270257937e-06
13	0.999999733846074	5.32307851399562e-07	2.66153925699781e-07
14	0.99999974233983	5.15320340034468e-07	2.57660170017234e-07
15	0.999999313022337	1.37395532618426e-06	6.86977663092131e-07
16	0.99999874601937	2.50796125806381e-06	1.25398062903190e-06
17	0.999999717822024	5.6435595116163e-07	2.82177975580815e-07
18	0.999999206701693	1.58659661321778e-06	7.93298306608889e-07
19	0.999999104189153	1.79162169421509e-06	8.95810847107547e-07
20	0.99999783305948	4.33388104127622e-06	2.16694052063811e-06
21	0.9999959631167	8.07376659947206e-06	4.03688329973603e-06
22	0.999998607614706	2.78477058867004e-06	1.39238529433502e-06
23	0.999999460426316	1.07914736875013e-06	5.39573684375067e-07
24	0.999998580430546	2.83913890789466e-06	1.41956945394733e-06
25	0.999997404198352	5.19160329527971e-06	2.59580164763985e-06
26	0.999996589741198	6.82051760401477e-06	3.41025880200738e-06
27	0.999994196206582	1.16075868362915e-05	5.80379341814576e-06
28	0.999986208514185	2.75829716298629e-05	1.37914858149315e-05
29	0.999984453355163	3.10932896743656e-05	1.55466448371828e-05
30	0.99998014015295	3.97196940991508e-05	1.98598470495754e-05
31	0.999939321255208	0.000121357489583447	6.06787447917233e-05
32	0.999848979139503	0.000302041720994272	0.000151020860497136
33	0.999819758172448	0.000360483655104420	0.000180241827552210
34	0.9996807114911	0.00063857701780151	0.000319288508900755
35	0.99909573212842	0.00180853574315841	0.000904267871579207
36	0.998049630423837	0.00390073915232605	0.00195036957616302
37	0.994888806832002	0.0102223863359967	0.00511119316799836
38	0.989725603615988	0.0205487927680246	0.0102743963840123
39	0.97542735691884	0.0491452861623218	0.0245726430811609
40	0.998460280351207	0.0030794392975852	0.0015397196487926
41	0.993960854926569	0.0120782901468629	0.00603914507343144
42	0.983927377583853	0.0321452448322938	0.0160726224161469
43	0.970153382428467	0.0596932351430666	0.0298466175715333

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	30	0.833333333333333	NOK
5% type I error level	35	0.972222222222222	NOK
10% type I error level	36	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/10s77x1291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/10s77x1291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/1exao1291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/1exao1291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/2exao1291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/2exao1291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/3exao1291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/3exao1291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/47or91291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/47or91291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/57or91291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/57or91291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/67or91291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/67or91291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/7hxqb1291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/7hxqb1291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/8s77x1291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/8s77x1291370719.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/9s77x1291370719.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291370607ivjxehr2irqkara/9s77x1291370719.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal 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