W7: Multiple regression (1)

*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, 21 Nov 2009 06:40:01 -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/21/t1258810949e3hdw2bje6ce5s8.htm/, Retrieved Sat, 21 Nov 2009 14:42:41 +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/21/t1258810949e3hdw2bje6ce5s8.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:

cvm

Dataseries X:

» Textbox « » Textfile « » CSV «

6,3 2 6,2 1,8 6,1 2,7 6,3 2,3 6,5 1,9 6,6 2 6,5 2,3 6,2 2,8 6,2 2,4 5,9 2,3 6,1 2,7 6,1 2,7 6,1 2,9 6,1 3 6,1 2,2 6,4 2,3 6,7 2,8 6,9 2,8 7 2,8 7 2,2 6,8 2,6 6,4 2,8 5,9 2,5 5,5 2,4 5,5 2,3 5,6 1,9 5,8 1,7 5,9 2 6,1 2,1 6,1 1,7 6 1,8 6 1,8 5,9 1,8 5,5 1,3 5,6 1,3 5,4 1,3 5,2 1,2 5,2 1,4 5,2 2,2 5,5 2,9 5,8 3,1 5,8 3,5 5,5 3,6 5,3 4,4 5,1 4,1 5,2 5,1 5,8 5,8 5,8 5,9 5,5 5,4 5 5,5 4,9 4,8 5,3 3,2 6,1 2,7 6,5 2,1 6,8 1,9 6,6 0,6 6,4 0,7 6,4 -0,2 6,6 -1 6,7 -1,7

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 WMan>25[t] = + 6.38588796845063 -0.160470000726172Infl[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) 6.38588796845063 0.12663 50.4296 0 0 Infl -0.160470000726172 0.044702 -3.5897 0.000682 0.000341 Multiple Linear Regression - Regression Statistics Multiple R 0.426365369418309 R-squared 0.181787428239211 Adjusted R-squared 0.167680314932991 F-TEST (value) 12.8862244382097 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.000681690348453667 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.48835694565493 Sum Squared Residuals 13.8325653694259 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.3 6.06494796699829 0.235052033001706 2 6.2 6.09704196714352 0.102958032856481 3 6.1 5.95261896648996 0.147381033510035 4 6.3 6.01680696678043 0.283193033219566 5 6.5 6.0809949670709 0.419005032929098 6 6.6 6.06494796699829 0.535052033001715 7 6.5 6.01680696678043 0.483193033219567 8 6.2 5.93657196641735 0.263428033582653 9 6.2 6.00075996670782 0.199240033292184 10 5.9 6.01680696678043 -0.116806966780433 11 6.1 5.95261896648996 0.147381033510035 12 6.1 5.95261896648996 0.147381033510035 13 6.1 5.92052496634473 0.179475033655269 14 6.1 5.90447796627211 0.195522033727886 15 6.1 6.03285396685305 0.0671460331469491 16 6.4 6.01680696678043 0.383193033219567 17 6.7 5.93657196641735 0.763428033582653 18 6.9 5.93657196641735 0.963428033582653 19 7 5.93657196641735 1.06342803358265 20 7 6.03285396685305 0.96714603314695 21 6.8 5.96866596656258 0.831334033437418 22 6.4 5.93657196641735 0.463428033582653 23 5.9 5.9847129666352 -0.0847129666351987 24 5.5 6.00075996670782 -0.500759966707816 25 5.5 6.01680696678043 -0.516806966780433 26 5.6 6.0809949670709 -0.480994967070903 27 5.8 6.11308896721614 -0.313088967216137 28 5.9 6.06494796699829 -0.164947966998285 29 6.1 6.04890096692567 0.0510990330743319 30 6.1 6.11308896721614 -0.0130889672161368 31 6 6.09704196714352 -0.0970419671435193 32 6 6.09704196714352 -0.0970419671435193 33 5.9 6.09704196714352 -0.197041967143519 34 5.5 6.1772769675066 -0.677276967506605 35 5.6 6.1772769675066 -0.577276967506606 36 5.4 6.1772769675066 -0.777276967506605 37 5.2 6.19332396757922 -0.993323967579222 38 5.2 6.16122996743399 -0.961229967433988 39 5.2 6.03285396685305 -0.83285396685305 40 5.5 5.92052496634473 -0.42052496634473 41 5.8 5.88843096619950 -0.0884309661994961 42 5.8 5.82424296590903 -0.0242429659090275 43 5.5 5.80819596583641 -0.30819596583641 44 5.3 5.67981996525547 -0.379819965255473 45 5.1 5.72796096547332 -0.627960965473325 46 5.2 5.56749096474715 -0.367490964747152 47 5.8 5.45516196423883 0.344838035761168 48 5.8 5.43911496416622 0.360885035833785 49 5.5 5.5193499645293 -0.0193499645293008 50 5 5.50330296445668 -0.503302964456684 51 4.9 5.615631964965 -0.715631964965004 52 5.3 5.87238396612688 -0.572383966126879 53 6.1 5.95261896648996 0.147381033510035 54 6.5 6.04890096692567 0.451099033074332 55 6.8 6.0809949670709 0.719005032929098 56 6.6 6.28960596801493 0.310394031985074 57 6.4 6.27355896794231 0.126441032057692 58 6.4 6.41798196859586 -0.0179819685958625 59 6.6 6.5463579691768 0.0536420308231993 60 6.7 6.65868696968512 0.0413130303148796 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0274980816320827 0.0549961632641654 0.972501918367917 6 0.0265279601236305 0.0530559202472611 0.97347203987637 7 0.0152526478162500 0.0305052956325001 0.98474735218375 8 0.00478475311789227 0.00956950623578454 0.995215246882108 9 0.00161378932011810 0.00322757864023619 0.998386210679882 10 0.00414849135594805 0.0082969827118961 0.995851508644052 11 0.00150220832250578 0.00300441664501156 0.998497791677494 12 0.00051212153298762 0.00102424306597524 0.999487878467012 13 0.000165069933210901 0.000330139866421801 0.999834930066789 14 5.22067028762867e-05 0.000104413405752573 0.999947793297124 15 2.6221709576041e-05 5.2443419152082e-05 0.999973778290424 16 1.21096151177987e-05 2.42192302355973e-05 0.999987890384882 17 0.000204370546039924 0.000408741092079849 0.99979562945396 18 0.00329605166570514 0.00659210333141027 0.996703948334295 19 0.0256585484139815 0.051317096827963 0.974341451586018 20 0.0942678977668756 0.188535795533751 0.905732102233124 21 0.162926019432227 0.325852038864455 0.837073980567773 22 0.155546742565706 0.311093485131412 0.844453257434294 23 0.165352530691414 0.330705061382829 0.834647469308586 24 0.307506000135365 0.615012000270729 0.692493999864635 25 0.426320720573305 0.85264144114661 0.573679279426695 26 0.458209578796707 0.916419157593415 0.541790421203293 27 0.406262300800039 0.812524601600077 0.593737699199961 28 0.346410221939443 0.692820443878886 0.653589778060557 29 0.289374193247156 0.578748386494313 0.710625806752844 30 0.233584100678006 0.467168201356012 0.766415899321994 31 0.181716619756316 0.363433239512633 0.818283380243684 32 0.137745753148958 0.275491506297916 0.862254246851042 33 0.102489204013804 0.204978408027608 0.897510795986196 34 0.0993922756983946 0.198784551396789 0.900607724301605 35 0.0834254156259888 0.166850831251978 0.916574584374011 36 0.0949671354706091 0.189934270941218 0.90503286452939 37 0.171196124386296 0.342392248772592 0.828803875613704 38 0.321566705291908 0.643133410583815 0.678433294708092 39 0.581474842908859 0.837050314182281 0.418525157091141 40 0.672202294987515 0.65559541002497 0.327797705012485 41 0.648405104813611 0.703189790372779 0.351594895186389 42 0.63118988088586 0.73762023822828 0.36881011911414 43 0.656070136640316 0.687859726719367 0.343929863359684 44 0.700070305428296 0.599859389143407 0.299929694571704 45 0.787181528410556 0.425636943178888 0.212818471589444 46 0.771453676349413 0.457092647301173 0.228546323650587 47 0.754947221662361 0.490105556675279 0.245052778337639 48 0.792300567589804 0.415398864820391 0.207699432410196 49 0.746119320249597 0.507761359500806 0.253880679750403 50 0.684234581079164 0.631530837841672 0.315765418920836 51 0.781163866207255 0.43767226758549 0.218836133792745 52 0.982531294436327 0.0349374111273456 0.0174687055636728 53 0.990419384277556 0.0191612314448882 0.0095806157224441 54 0.972584606461741 0.0548307870765179 0.0274153935382590 55 0.98399671564356 0.0320065687128818 0.0160032843564409 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.215686274509804 NOK 5% type I error level 15 0.294117647058824 NOK 10% type I error level 19 0.372549019607843 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/107uex1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/107uex1258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/1hcv51258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/1hcv51258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/23cir1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/23cir1258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/35sfb1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/35sfb1258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/4iruh1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/4iruh1258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/5n4ue1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/5n4ue1258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/6munp1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/6munp1258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/7ay7c1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/7ay7c1258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/8kbo21258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/8kbo21258810796.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/9v68k1258810796.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810949e3hdw2bje6ce5s8/9v68k1258810796.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') }

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