Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2010 » Dec » 29 »

*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: Wed, 29 Dec 2010 13:51:42 +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/29/t1293630556dp2hp501dar7dql.htm/, Retrieved Wed, 29 Dec 2010 14:49:27 +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/29/t1293630556dp2hp501dar7dql.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 «

597141 25 593408 24 590072 21 579799 22 574205 20 572775 24 572942 24 619567 24 625809 24 619916 28 587625 27 565742 18 557274 25 560576 27 548854 25 531673 28 525919 28 511038 27 498662 25 555362 24 564591 24 541657 25 527070 18 509846 22 514258 20 516922 23 507561 23 492622 19 490243 17 469357 15 477580 13 528379 15 533590 17 517945 9 506174 4 501866 1 516141 6 528222 2 532638 2 536322 4 536535 7 523597 8 536214 9 586570 15 596594 15 580523 14 564478 16 557560 11 575093 11 580112 11 574761 13 563250 18 551531 13 537034 17 544686 19 600991 22 604378 22 586111 24 563668 26 548604 24

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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Werkloos[t] = + 520417.636254683 + 1649.97677557105cv[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)	520417.636254683	11819.645501	44.0299	0	0
cv	1649.97677557105	605.088971	2.7268	0.008444	0.004222

Multiple Linear Regression - Regression Statistics
Multiple R	0.337094357596456
R-squared	0.113632605923367
Adjusted R-squared	0.0983504094737703
F-TEST (value)	7.43562002347922
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	0.0084443171655817
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	34810.9390710286
Sum Squared Residuals	70284485782.3982

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	597141	561667.055643959	35473.9443560411
2	593408	560017.078868388	33390.9211316119
3	590072	555067.148541675	35004.8514583249
4	579799	556717.125317246	23081.8746827539
5	574205	553417.171766104	20787.8282338960
6	572775	560017.078868388	12757.9211316118
7	572942	560017.078868388	12924.9211316118
8	619567	560017.078868388	59549.9211316118
9	625809	560017.078868388	65791.9211316118
10	619916	566616.985970672	53299.0140293276
11	587625	564967.009195101	22657.9908048987
12	565742	550117.218214962	15624.7817850381
13	557274	561667.055643959	-4393.05564395925
14	560576	564967.009195101	-4391.00919510133
15	548854	561667.055643959	-12813.0556439593
16	531673	566616.985970672	-34943.9859706724
17	525919	566616.985970672	-40697.9859706724
18	511038	564967.009195101	-53929.0091951013
19	498662	561667.055643959	-63005.0556439592
20	555362	560017.078868388	-4655.07886838820
21	564591	560017.078868388	4573.9211316118
22	541657	561667.055643959	-20010.0556439593
23	527070	550117.218214962	-23047.2182149619
24	509846	556717.125317246	-46871.1253172461
25	514258	553417.171766104	-39159.171766104
26	516922	558367.102092817	-41445.1020928172
27	507561	558367.102092817	-50806.1020928172
28	492622	551767.194990533	-59145.194990533
29	490243	548467.241439391	-58224.2414393909
30	469357	545167.287888249	-75810.2878882488
31	477580	541867.334337107	-64287.3343371067
32	528379	545167.287888249	-16788.2878882488
33	533590	548467.241439391	-14877.2414393909
34	517945	535267.427234823	-17322.4272348225
35	506174	527017.543356967	-20843.5433569673
36	501866	522067.613030254	-20201.6130302542
37	516141	530317.496908109	-14176.4969081094
38	528222	523717.589805825	4504.41019417476
39	532638	523717.589805825	8920.41019417476
40	536322	527017.543356967	9304.45664303268
41	536535	531967.47368368	4567.52631631954
42	523597	533617.450459252	-10020.4504592515
43	536214	535267.427234823	946.572765177457
44	586570	545167.287888249	41402.7121117512
45	596594	545167.287888249	51426.7121117512
46	580523	543517.311112678	37005.6888873222
47	564478	546817.26466382	17660.7353361801
48	557560	538567.380785965	18992.6192140354
49	575093	538567.380785965	36525.6192140354
50	580112	538567.380785965	41544.6192140354
51	574761	541867.334337107	32893.6656628933
52	563250	550117.218214962	13132.7817850381
53	551531	541867.334337107	9663.66566289328
54	537034	548467.241439391	-11433.2414393909
55	544686	551767.194990533	-7081.19499053298
56	600991	556717.125317246	44273.8746827539
57	604378	556717.125317246	47660.8746827539
58	586111	560017.078868388	26093.9211316118
59	563668	563317.03241953	350.967580469709
60	548604	560017.078868388	-11413.0788683882

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.00762031156406058	0.0152406231281212	0.99237968843594
6	0.0131329847170379	0.0262659694340759	0.986867015282962
7	0.00727379645494426	0.0145475929098885	0.992726203545056
8	0.0320555080530021	0.0641110161060043	0.967944491946998
9	0.0686000112100343	0.137200022420069	0.931399988789966
10	0.0455827055775144	0.0911654111550288	0.954417294422486
11	0.0412704255431219	0.0825408510862437	0.958729574456878
12	0.0226075670916323	0.0452151341832646	0.977392432908368
13	0.0499086017907774	0.0998172035815548	0.950091398209223
14	0.0736538574480682	0.147307714896136	0.926346142551932
15	0.102388612111330	0.204777224222661	0.89761138788867
16	0.201800961366007	0.403601922732014	0.798199038633993
17	0.280382103963126	0.560764207926252	0.719617896036874
18	0.426110863784098	0.852221727568196	0.573889136215902
19	0.658531290775079	0.682937418449843	0.341468709224921
20	0.591878705511451	0.816242588977098	0.408121294488549
21	0.51765234376849	0.96469531246302	0.48234765623151
22	0.468134778510139	0.936269557020277	0.531865221489861
23	0.51287250380214	0.97425499239572	0.48712749619786
24	0.596189095508737	0.807621808982525	0.403810904491263
25	0.630105382748712	0.739789234502575	0.369894617251288
26	0.656239043586023	0.687521912827955	0.343760956413977
27	0.731186494439545	0.537627011120909	0.268813505560455
28	0.837443143430243	0.325113713139514	0.162556856569757
29	0.90662310064856	0.186753798702881	0.0933768993514405
30	0.981075604957046	0.0378487900859072	0.0189243950429536
31	0.99682654145475	0.00634691709049919	0.00317345854524960
32	0.996702597175046	0.00659480564990824	0.00329740282495412
33	0.996693004605666	0.00661399078866887	0.00330699539433444
34	0.996577316003043	0.00684536799391451	0.00342268399695726
35	0.996446550631867	0.00710689873626679	0.00355344936813339
36	0.99626637458753	0.00746725082494203	0.00373362541247101
37	0.996048230194288	0.00790353961142368	0.00395176980571184
38	0.994220506276974	0.0115589874460517	0.00577949372302586
39	0.991137755133636	0.0177244897327283	0.00886224486636415
40	0.986230196039054	0.027539607921891	0.0137698039609455
41	0.9801821780168	0.0396356439663981	0.0198178219831991
42	0.984131214018425	0.0317375719631504	0.0158687859815752
43	0.985250814848923	0.0294983703021548	0.0147491851510774
44	0.982451036900928	0.0350979261981448	0.0175489630990724
45	0.98665332908226	0.0266933418354806	0.0133466709177403
46	0.981167227999972	0.0376655440000552	0.0188327720000276
47	0.966203539962677	0.0675929200746464	0.0337964600373232
48	0.943464399929616	0.113071200140768	0.0565356000703839
49	0.916144243751538	0.167711512496924	0.083855756248462
50	0.899597036458727	0.200805927082545	0.100402963541273
51	0.876668967417152	0.246662065165695	0.123331032582848
52	0.796264683300262	0.407470633399476	0.203735316699738
53	0.689821308878666	0.620357382242668	0.310178691121334
54	0.608907735225779	0.782184529548442	0.391092264774221
55	0.846266692709683	0.307466614580633	0.153733307290317

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.137254901960784	NOK
5% type I error level	21	0.411764705882353	NOK
10% type I error level	26	0.509803921568627	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/106t321293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/106t321293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/1zso81293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/1zso81293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/2zso81293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/2zso81293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/3a1ob1293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/3a1ob1293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/4a1ob1293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/4a1ob1293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/5a1ob1293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/5a1ob1293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/63s5e1293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/63s5e1293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/7w2mh1293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/7w2mh1293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/8w2mh1293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/8w2mh1293630694.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/9w2mh1293630694.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293630556dp2hp501dar7dql/9w2mh1293630694.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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