Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2009 » Dec » 19 »

*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: Sat, 19 Dec 2009 12:39:40 -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/19/t1261251637glkqpai8687xadt.htm/, Retrieved Sat, 19 Dec 2009 20:40:49 +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/19/t1261251637glkqpai8687xadt.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 «

19915 23322 19843 22558 19761 19185 20858 17869 21968 21515 23061 17686 22661 18044 22269 20398 21857 22894 21568 22016 21274 25325 20987 27683 19683 17333 19381 20190 19071 22589 20772 14588 22485 14296 24181 12237 23479 7607 22782 9303 22067 9226 21489 9351 20903 21266 20330 21377 19736 22034 19483 22483 19242 15122 20334 18982 21423 19653 22523 16653 21986 23528 21462 24612 20908 24733 20575 21839 20237 22421 19904 26543 19610 27067 19251 31403 18941 25762 20450 29359 21946 34174 23409 20163 22741 25226 22069 25077 21539 29764 21189 21372 20960 34136 20704 29126 19697 17279 19598 16163 19456 8058 20316 17888 21083 7642 22158 7458 21469 4639 20892 10276 20578 3129 20233 20023 19947 3744 20049 7848

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

X[t] = + 20936.5059672832 + 0.00318662866511115Y[t] -850.512275499433M1[t] -1054.14415230839M2[t] -1240.03096873281M3[t] + 23.7299392397103M4[t] + 1276.66641328505M5[t] + 2593.81819724512M6[t] + 2008.56947348187M7[t] + 1446.44019361042M8[t] + 958.429601616493M9[t] + 593.375779247383M10[t] + 255.982802727519M11[t] -17.0403940647126t + 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)	20936.5059672832	310.309195	67.4698	0	0
Y	0.00318662866511115	0.008182	0.3895	0.698729	0.349365
M1	-850.512275499433	280.800945	-3.0289	0.004017	0.002008
M2	-1054.14415230839	279.978203	-3.7651	0.000471	0.000236
M3	-1240.03096873281	282.619878	-4.3876	6.6e-05	3.3e-05
M4	23.7299392397103	280.588504	0.0846	0.932969	0.466484
M5	1276.66641328505	280.45865	4.5521	3.9e-05	1.9e-05
M6	2593.81819724512	286.51104	9.0531	0	0
M7	2008.56947348187	284.417183	7.0621	0	0
M8	1446.44019361042	281.105004	5.1456	5e-06	3e-06
M9	958.429601616493	280.854594	3.4125	0.001352	0.000676
M10	593.375779247383	279.680005	2.1216	0.039288	0.019644
M11	255.982802727519	278.104947	0.9205	0.362138	0.181069
t	-17.0403940647126	3.406432	-5.0024	9e-06	4e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.950017497702683
R-squared	0.902533245941267
Adjusted R-squared	0.874988293707278
F-TEST (value)	32.7658308598397
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	439.408719526264
Sum Squared Residuals	8881681.0486027

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	19915	20143.2718514468	-228.271851446805
2	19843	19920.1649962730	-77.1649962729715
3	19761	19706.4892872964	54.5107127035835
4	20858	20949.0161978809	-91.0161978809427
5	21968	22196.5307259746	-228.530725974568
6	23061	23484.4405147112	-423.440514711208
7	22661	22883.2922099454	-222.29220994536
8	22269	22311.6238598869	-42.6238598868656
9	21857	21814.5266989763	42.4733010236551
10	21568	21429.6346225746	138.365377425446
11	21274	21085.7458062428	188.254193757169
12	20987	20820.2366798429	166.763320157069
13	19683	19919.7024035949	-236.702403594885
14	19381	19708.1343308174	-327.134330817438
15	19071	19512.8518424959	-441.851842495903
16	20772	20734.0761404542	37.9238595458387
17	22485	21969.0417248646	515.958275135422
18	24181	23262.5918463385	918.408153661534
19	23479	22645.5486377910	833.451362208957
20	22782	22071.7834860709	710.21651392909
21	22067	21566.4871296051	500.512870394946
22	21489	21184.7912417544	304.20875824563
23	20903	20868.3265517146	34.6734482854068
24	20330	20595.6570707042	-265.657070704189
25	19736	19730.198016173	5.80198382697873
26	19483	19510.95654157	-27.9565415699858
27	19242	19284.5725574770	-42.5725574769665
28	20334	20543.5934580321	-209.593458032108
29	21423	21781.6277658470	-358.627765847027
30	22523	23072.1792697470	-549.179269747046
31	21986	22491.7982239917	-505.798223991726
32	21462	21916.0828555285	-454.082855528545
33	20908	21411.4174515384	-503.417451538381
34	20575	21020.1011317477	-445.101131747727
35	20237	20667.5223790462	-430.522379046245
36	19904	20407.6344656116	-503.634465611602
37	19610	19541.7515894680	68.2484105320258
38	19251	19334.8965404862	-83.8965404862252
39	18941	19113.9935576972	-172.993557697197
40	20450	20372.1763749134	77.8236250865852
41	21946	21623.4160719166	322.583928083446
42	23409	22878.8796075850	530.120392414965
43	22741	22292.7243906885	448.275609311467
44	22069	21713.0799090813	355.920090918730
45	21539	21222.964651576	316.035348423996
46	21189	20814.1282473846	374.871752615432
47	20960	20500.3690050815	459.630994918529
48	20704	20211.3807986770	492.619201322968
49	19697	19306.0761393173	390.923860682685
50	19598	19081.8475908534	516.15240914662
51	19456	18853.0927550335	602.907244966482
52	20316	20131.1378287194	184.862171280627
53	21083	21334.3837113973	-251.383711397273
54	22158	22633.9087616182	-475.908761618246
55	21469	22022.6365375833	-553.636537583338
56	20892	21461.4298894324	-569.429889432408
57	20578	20933.6040683042	-355.604068304216
58	20233	20605.3447565388	-372.344756538781
59	19947	20199.0362579149	-252.036257914860
60	20049	19939.0909851642	109.909014835754

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0857694817914128	0.171538963582826	0.914230518208587
18	0.357084835895921	0.714169671791841	0.642915164104079
19	0.270555296717200	0.541110593434401	0.7294447032828
20	0.26149309969991	0.52298619939982	0.73850690030009
21	0.386431369844891	0.772862739689781	0.613568630155109
22	0.553465155937333	0.893069688125334	0.446534844062667
23	0.529157192204368	0.941685615591263	0.470842807795632
24	0.573398451425715	0.85320309714857	0.426601548574285
25	0.529557545612146	0.940884908775708	0.470442454387854
26	0.443429634996382	0.886859269992763	0.556570365003618
27	0.364627522757988	0.729255045515977	0.635372477242012
28	0.294847074077614	0.589694148155228	0.705152925922386
29	0.262253282111674	0.524506564223349	0.737746717888325
30	0.266926484048293	0.533852968096587	0.733073515951707
31	0.193796341451772	0.387592682903544	0.806203658548228
32	0.131651507759406	0.263303015518812	0.868348492240594
33	0.0890646779258764	0.178129355851753	0.910935322074124
34	0.0554938014788977	0.110987602957795	0.944506198521102
35	0.0413116006490333	0.0826232012980666	0.958688399350967
36	0.050521735502292	0.101043471004584	0.949478264497708
37	0.0933509712496436	0.186701942499287	0.906649028750356
38	0.230601329881035	0.46120265976207	0.769398670118965
39	0.768729640724172	0.462540718551656	0.231270359275828
40	0.97972476495639	0.0405504700872183	0.0202752350436091
41	0.968109821239798	0.0637803575204045	0.0318901787602023
42	0.947347394225798	0.105305211548405	0.0526526057742023
43	0.932329120317087	0.135341759365826	0.0676708796829131

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	1	0.0370370370370370	OK
10% type I error level	3	0.111111111111111	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/10e1xy1261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/10e1xy1261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/1p76o1261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/1p76o1261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/2ekl11261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/2ekl11261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/3m6tc1261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/3m6tc1261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/4eu041261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/4eu041261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/5bt301261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/5bt301261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/6ko3s1261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/6ko3s1261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/700r61261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/700r61261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/8t6j71261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/8t6j71261251575.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/9l4yy1261251575.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251637glkqpai8687xadt/9l4yy1261251575.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

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