*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 09:24:55 -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/20/t12587343553flez7cmbd4vodk.htm/, Retrieved Fri, 20 Nov 2009 17:26:07 +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/20/t12587343553flez7cmbd4vodk.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 «

17823.2 0 16704.4 17823.2 17872 0 15991.2 16704.4 17420.4 0 15583.6 15991.2 16704.4 0 19123.5 15583.6 15991.2 0 17838.7 19123.5 15583.6 0 17209.4 17838.7 19123.5 0 18586.5 17209.4 17838.7 0 16258.1 18586.5 17209.4 0 15141.6 16258.1 18586.5 0 19202.1 15141.6 16258.1 0 17746.5 19202.1 15141.6 0 19090.1 17746.5 19202.1 0 18040.3 19090.1 17746.5 0 17515.5 18040.3 19090.1 1 17751.8 17515.5 18040.3 1 21072.4 17751.8 17515.5 1 17170 21072.4 17751.8 1 19439.5 17170 21072.4 1 19795.4 19439.5 17170 1 17574.9 19795.4 19439.5 1 16165.4 17574.9 19795.4 1 19464.6 16165.4 17574.9 1 19932.1 19464.6 16165.4 1 19961.2 19932.1 19464.6 1 17343.4 19961.2 19932.1 1 18924.2 17343.4 19961.2 1 18574.1 18924.2 17343.4 1 21350.6 18574.1 18924.2 1 18594.6 21350.6 18574.1 1 19832.1 18594.6 21350.6 1 20844.4 19832.1 18594.6 1 19640.2 20844.4 19832.1 1 17735.4 19640.2 20844.4 1 19813.6 17735.4 19640.2 1 22160 19813.6 17735.4 1 20664.3 22160 19813.6 1 17877.4 20664.3 22160 1 20906.5 17877.4 20664.3 1 21 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 4191.72492513178 + 1080.24288788584X[t] + 0.328116813601246Y1[t] + 0.234391904330329Y2[t] + 4298.67549723807M1[t] + 4696.81573343637M2[t] + 3294.52970242339M3[t] + 802.946013604901M4[t] + 1550.95493967962M5[t] + 1874.28478142891M6[t] + 3557.31109805262M7[t] + 2221.62527782109M8[t] + 3900.98112052044M9[t] + 4561.20371537569M10[t] + 1833.90410905446M11[t] + 21.6932594243532t + 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) 4191.72492513178 2236.758638 1.874 0.068246 0.034123 X 1080.24288788584 441.704928 2.4456 0.018956 0.009478 Y1 0.328116813601246 0.126612 2.5915 0.013275 0.006637 Y2 0.234391904330329 0.132355 1.7709 0.084192 0.042096 M1 4298.67549723807 755.421367 5.6904 1e-06 1e-06 M2 4696.81573343637 799.379855 5.8756 1e-06 0 M3 3294.52970242339 790.282701 4.1688 0.000159 8e-05 M4 802.946013604901 841.078294 0.9547 0.345482 0.172741 M5 1550.95493967962 734.097018 2.1127 0.040913 0.020456 M6 1874.28478142891 756.645511 2.4771 0.01757 0.008785 M7 3557.31109805262 792.723135 4.4875 6e-05 3e-05 M8 2221.62527782109 735.701324 3.0197 0.004392 0.002196 M9 3900.98112052044 814.087469 4.7918 2.3e-05 1.1e-05 M10 4561.20371537569 956.807491 4.7671 2.5e-05 1.2e-05 M11 1833.90410905446 787.703641 2.3282 0.025045 0.012523 t 21.6932594243532 10.276002 2.1111 0.041065 0.020533 Multiple Linear Regression - Regression Statistics Multiple R 0.896380203979196 R-squared 0.803497470085785 Adjusted R-squared 0.729809021367954 F-TEST (value) 10.9039813439764 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 8.79917361196192e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1089.29160730302 Sum Squared Residuals 47462248.2296315 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 17823.2 18170.7019721751 -347.501972175150 2 17872 18094.2848937726 -222.28489377263 3 17420.4 16412.7834027918 1007.61659720824 4 16704.4 15008.8555416596 1695.54445834037 5 15991.2 16186.7171471828 -195.517147182758 6 15583.6 16024.1096188735 -440.509618873523 7 19123.5 18033.1760335368 1090.32396646321 8 17838.7 16277.9773753938 1560.72262460623 9 17209.4 17066.9259450889 142.474054911056 10 18586.5 18819.4615598116 -232.961559811590 11 16258.1 16587.9967065700 -329.896706570037 12 15141.6 14875.4627517513 266.137248248658 13 19202.1 19166.3034401534 35.7965598465856 14 17746.5 19167.8766108322 -1421.37661083215 15 19090.1 18822.0518587908 268.048141209216 16 18040.3 17497.0929276342 543.207072365798 17 17515.5 17764.6738172551 -249.173817255067 18 17751.8 17939.6670594381 -187.867059438056 19 21072.4 20293.1158363245 779.284163675518 20 17170 18333.9599696669 -1163.95996966691 21 19439.5 19052.0611994542 387.438800545839 22 19795.4 20486.1246560134 -690.724656013393 23 17574.9 18707.2186902417 -1132.31869024171 24 16165.4 17014.1342551618 -848.734255161838 25 19464.6 20482.3796215949 -1017.77962159494 26 19932.1 20807.3090490025 -875.209049002504 27 19961.2 19682.3693033375 278.830696662542 28 17343.4 18041.4346012011 -698.034601201134 29 18924.2 18557.6359707883 366.564029211665 30 18574.1 18662.7195404591 -88.6195404591304 31 21350.6 20989.6517485245 360.948251475484 32 18594.6 19517.8158455323 -923.215845532318 33 19832.1 20311.6133099138 -479.513309913786 34 20844.4 21228.9518268511 -384.551826851083 35 19640.2 19780.3520269675 -140.152026967454 36 17735.4 18027.3540235547 -291.954023554651 37 19813.6 21082.7140610849 -1269.11406108490 38 22160 21843.2193986089 316.780601391113 39 20664.3 21257.1460356109 -592.846035610935 40 17877.4 18916.6379266726 -1039.23792667264 41 20906.5 20253.3682498338 653.131750166216 42 21164.1 20440.3640071286 723.735992871433 43 21374.4 22606.5948396921 -1232.19483969206 44 22952.3 21397.3859399634 1554.91406003657 45 21343.5 21393.8995455431 -50.3995455431093 46 23899.3 22591.0619573239 1308.23804267607 47 22392.9 20790.5325762208 1602.36742377921 48 18274.1 17399.5489695322 874.551030467834 49 22786.7 20188.1009049916 2598.5990950084 50 22321.5 20119.4100477838 2202.08995221617 51 17842.2 18803.8493994691 -961.649399469068 52 16373.5 16874.9790028324 -501.479002832401 53 15933.8 16508.8048149401 -575.004814940057 54 16446.1 16452.8397741007 -6.73977410072358 55 17729 18727.3615419222 -998.361541922158 56 16643 17671.4608694436 -1028.46086944358 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.131981202616841 0.263962405233681 0.86801879738316 20 0.469700458369108 0.939400916738215 0.530299541630892 21 0.417270927127853 0.834541854255705 0.582729072872147 22 0.284124388918874 0.568248777837749 0.715875611081126 23 0.187941646677064 0.375883293354129 0.812058353322936 24 0.112716885645726 0.225433771291452 0.887283114354274 25 0.073101269463748 0.146202538927496 0.926898730536252 26 0.0481855877627475 0.096371175525495 0.951814412237252 27 0.0504713410483262 0.100942682096652 0.949528658951674 28 0.0816475237624091 0.163295047524818 0.91835247623759 29 0.075181087839783 0.150362175679566 0.924818912160217 30 0.045861495020572 0.091722990041144 0.954138504979428 31 0.0848556718616897 0.169711343723379 0.91514432813831 32 0.0493300321116367 0.0986600642232734 0.950669967888363 33 0.0432099818321633 0.0864199636643266 0.956790018167837 34 0.0285951430887788 0.0571902861775576 0.971404856911221 35 0.0374459355431966 0.0748918710863932 0.962554064456803 36 0.116009014605537 0.232018029211073 0.883990985394463 37 0.0957659415761587 0.191531883152317 0.904234058423841 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 6 0.315789473684211 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/10wkxi1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/10wkxi1258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/189ok1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/189ok1258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/2niya1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/2niya1258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/3thmv1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/3thmv1258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/4fl2p1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/4fl2p1258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/5jr7x1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/5jr7x1258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/62dva1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/62dva1258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/7apo31258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/7apo31258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/8yab81258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/8yab81258734291.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/9xmup1258734291.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587343553flez7cmbd4vodk/9xmup1258734291.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') }

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