Multiple Regression Model 2

*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, 19 Nov 2009 13:24:21 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0.htm/, Retrieved Thu, 19 Nov 2009 21:27:06 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0.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 «

10 24.1 9.2 24.1 9.2 24.1 9.5 21.3 9.6 21.3 9.5 21.3 9.1 19.1 8.9 19.1 9 19.1 10.1 26.2 10.3 26.2 10.2 26.2 9.6 21.7 9.2 21.7 9.3 21.7 9.4 19.4 9.4 19.4 9.2 19.4 9 19.5 9 19.5 9 19.5 9.8 28.7 10 28.7 9.8 28.7 9.3 21.8 9 21.8 9 21.8 9.1 20 9.1 20 9.1 20 9.2 22.6 8.8 22.6 8.3 22.6 8.4 22.4 8.1 22.4 7.7 22.4 7.9 18.6 7.9 18.6 8 18.6 7.9 16.2 7.6 16.2 7.1 16.2 6.8 13.8 6.5 13.8 6.9 13.8 8.2 24.1 8.7 24.1 8.3 24.1 7.9 19.9 7.5 19.9 7.8 19.9 8.3 22.3 8.4 22.3 8.2 22.3 7.7 20.9 7.2 20.9 7.3 20.9 8.1 25.5 8.5 25.5 8.4 25.5

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 TWV[t] = + 2.53000723423698 + 0.250196720479237`WV-25`[t] + 1.10081835719362M1[t] + 0.720818357193624M2[t] + 0.820818357193624M3[t] + 1.34608983145497M4[t] + 1.32608983145497M5[t] + 1.12608983145497M6[t] + 1.03121966697127M7[t] + 0.751219666971267M8[t] + 0.771219666971266M9[t] + 0.0400000000000001M10[t] + 0.24M11[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) 2.53000723423698 1.041451 2.4293 0.019004 0.009502 `WV-25` 0.250196720479237 0.039163 6.3886 0 0 M1 1.10081835719362 0.46893 2.3475 0.023162 0.011581 M2 0.720818357193624 0.46893 1.5372 0.130961 0.06548 M3 0.820818357193624 0.46893 1.7504 0.086574 0.043287 M4 1.34608983145497 0.490332 2.7453 0.008539 0.004269 M5 1.32608983145497 0.490332 2.7045 0.009497 0.004748 M6 1.12608983145497 0.490332 2.2966 0.026145 0.013073 M7 1.03121966697127 0.502304 2.053 0.045665 0.022832 M8 0.751219666971267 0.502304 1.4955 0.141457 0.070728 M9 0.771219666971266 0.502304 1.5354 0.1314 0.0657 M10 0.0400000000000001 0.439719 0.091 0.927905 0.463953 M11 0.24 0.439719 0.5458 0.587783 0.293891 Multiple Linear Regression - Regression Statistics Multiple R 0.729257593587251 R-squared 0.531816637804669 Adjusted R-squared 0.412280460222882 F-TEST (value) 4.44900153713548 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 9.98343333580287e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.695257304326818 Sum Squared Residuals 22.7189878033303 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 9.6605665549802 0.339433445019795 2 9.2 9.2805665549802 -0.0805665549802007 3 9.2 9.3805665549802 -0.180566554980201 4 9.5 9.20528721189968 0.294712788100315 5 9.6 9.18528721189968 0.414712788100316 6 9.5 8.98528721189968 0.514712788100314 7 9.1 8.33998426236166 0.760015737638337 8 8.9 8.05998426236166 0.840015737638338 9 9 8.07998426236166 0.920015737638338 10 10.1 9.12516131079297 0.974838689207026 11 10.3 9.32516131079297 0.974838689207027 12 10.2 9.08516131079297 1.11483868920703 13 9.6 9.06009442583003 0.539905574169967 14 9.2 8.68009442583003 0.519905574169966 15 9.3 8.78009442583003 0.519905574169967 16 9.4 8.72991344298914 0.670086557010864 17 9.4 8.70991344298914 0.690086557010864 18 9.2 8.50991344298914 0.690086557010864 19 9 8.44006295055335 0.559937049446645 20 9 8.16006295055336 0.839937049446644 21 9 8.18006295055335 0.819937049446645 22 9.8 9.75065311199107 0.0493468880089356 23 10 9.95065311199106 0.0493468880089351 24 9.8 9.71065311199106 0.089346888008936 25 9.3 9.08511409787796 0.214885902122044 26 9 8.70511409787796 0.294885902122043 27 9 8.80511409787796 0.194885902122043 28 9.1 8.88003147527668 0.219968524723321 29 9.1 8.86003147527668 0.239968524723322 30 9.1 8.66003147527668 0.439968524723322 31 9.2 9.21567278403899 -0.0156727840389892 32 8.8 8.93567278403899 -0.135672784038989 33 8.3 8.95567278403899 -0.655672784038989 34 8.4 8.17441377297187 0.225586227028125 35 8.1 8.37441377297188 -0.274413772971875 36 7.7 8.13441377297188 -0.434413772971875 37 7.9 8.2844845923444 -0.384484592344400 38 7.9 7.9044845923444 -0.00448459234440062 39 8 8.0044845923444 -0.00448459234440128 40 7.9 7.92928393745558 -0.0292839374555795 41 7.6 7.90928393745558 -0.30928393745558 42 7.1 7.70928393745558 -0.609283937455579 43 6.8 7.01394164382171 -0.213941643821708 44 6.5 6.73394164382171 -0.233941643821708 45 6.9 6.75394164382171 0.146058356178292 46 8.2 8.59974819778658 -0.399748197786578 47 8.7 8.79974819778658 -0.0997481977865782 48 8.3 8.55974819778658 -0.259748197786576 49 7.9 8.6097403289674 -0.709740328967406 50 7.5 8.2297403289674 -0.729740328967407 51 7.8 8.3297403289674 -0.529740328967408 52 8.3 9.45548393237892 -1.15548393237892 53 8.4 9.43548393237892 -1.03548393237892 54 8.2 9.23548393237892 -1.03548393237892 55 7.7 8.79033835922429 -1.09033835922429 56 7.2 8.51033835922429 -1.31033835922429 57 7.3 8.53033835922429 -1.23033835922429 58 8.1 8.9500236064575 -0.850023606457509 59 8.5 9.1500236064575 -0.650023606457508 60 8.4 8.9100236064575 -0.510023606457508 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0173508021579016 0.0347016043158033 0.982649197842098 17 0.0042640607898098 0.0085281215796196 0.99573593921019 18 0.00149328090501298 0.00298656181002595 0.998506719094987 19 0.00042708878972079 0.00085417757944158 0.99957291121028 20 0.000141613478035156 0.000283226956070313 0.999858386521965 21 5.21703675492622e-05 0.000104340735098524 0.99994782963245 22 0.000147913742829223 0.000295827485658446 0.99985208625717 23 0.000148655999713856 0.000297311999427711 0.999851344000286 24 0.000194323995958999 0.000388647991917999 0.99980567600404 25 0.000750795786805677 0.00150159157361135 0.999249204213194 26 0.000520047153302895 0.00104009430660579 0.999479952846697 27 0.00036161537450658 0.00072323074901316 0.999638384625493 28 0.000494936536980767 0.000989873073961535 0.999505063463019 29 0.00103198085576886 0.00206396171153772 0.99896801914423 30 0.00372871101092813 0.00745742202185626 0.996271288989072 31 0.0112518788958429 0.0225037577916858 0.988748121104157 32 0.137872887350192 0.275745774700383 0.862127112649808 33 0.452274161329804 0.904548322659608 0.547725838670196 34 0.937038298740902 0.125923402518196 0.0629617012590978 35 0.985844290709973 0.0283114185800541 0.0141557092900271 36 0.9960855905881 0.00782881882379962 0.00391440941189981 37 0.994332777581581 0.0113344448368376 0.00566722241841879 38 0.996069448343571 0.00786110331285731 0.00393055165642866 39 0.994217190384286 0.0115656192314270 0.00578280961571351 40 0.989691177254486 0.0206176454910274 0.0103088227455137 41 0.97906205515759 0.0418758896848188 0.0209379448424094 42 0.9898316232196 0.0203367535608011 0.0101683767804006 43 0.986452468584297 0.027095062831405 0.0135475314157025 44 0.977251668920627 0.0454966621587467 0.0227483310793733 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.551724137931034 NOK 5% type I error level 26 0.896551724137931 NOK 10% type I error level 26 0.896551724137931 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/102sik1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/102sik1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/1d2jv1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/1d2jv1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/23l4a1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/23l4a1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/3w4gq1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/3w4gq1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/4sxrh1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/4sxrh1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/5723t1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/5723t1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/6yol01258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/6yol01258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/7cp2o1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/7cp2o1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/83z2y1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/83z2y1258662256.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/90b9b1258662256.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258662414kn86nnphoao4py0/90b9b1258662256.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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') }

Copyright

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.

Privacy Policy

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