WS7 Multiple Regression3

*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: Fri, 20 Nov 2009 10:09:06 -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/t1258737013evsiiv59mtv015p.htm/, Retrieved Fri, 20 Nov 2009 18:10:25 +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/t1258737013evsiiv59mtv015p.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 «

2.05 1.00 2.11 1.00 2.09 1.00 2.05 1.00 2.08 1.00 2.06 1.00 2.06 1.00 2.08 1.00 2.07 1.00 2.06 1.00 2.07 1.00 2.06 1.00 2.09 1.00 2.07 1.00 2.09 1.00 2.28 1.25 2.33 1.25 2.35 1.25 2.52 1.50 2.63 1.50 2.58 1.50 2.70 1.75 2.81 1.75 2.97 2.00 3.04 2.00 3.28 2.25 3.33 2.25 3.50 2.50 3.56 2.50 3.57 2.50 3.69 2.75 3.82 2.75 3.79 2.75 3.96 3.00 4.06 3.00 4.05 3.00 4.03 3.00 3.94 3.00 4.02 3.00 3.88 3.00 4.02 3.00 4.03 3.00 4.09 3.00 3.99 3.00 4.01 3.00 4.01 3.00 4.19 3.25 4.30 3.25 4.27 3.25 3.82 3.25 3.15 2.75 2.49 2.00 1.81 1.00 1.26 1.00 1.06 0.50 0.84 0.25 0.78 0.25 0.70 0.25 0.36 0.25 0.35 0.25

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] = + 1.02531885999819 + 1.14700986268801X[t] + 0.0378092542034817M1[t] -0.058801305622438M2[t] -0.0393603860451528M3[t] -0.0652699596022685M4[t] + 0.096871946243817M5[t] + 0.00361187955230062M6[t] + 0.0463518128607841M7[t] + 0.104442239303668M8[t] + 0.0911821726121515M9[t] + 0.0292211196518342M10[t] -0.00338944017408287M11[t] -0.0127399333084835t + 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) 1.02531885999819 0.068224 15.0287 0 0 X 1.14700986268801 0.017166 66.8186 0 0 M1 0.0378092542034817 0.078967 0.4788 0.634352 0.317176 M2 -0.058801305622438 0.078894 -0.7453 0.459868 0.229934 M3 -0.0393603860451528 0.078646 -0.5005 0.619128 0.309564 M4 -0.0652699596022685 0.078493 -0.8315 0.409966 0.204983 M5 0.096871946243817 0.07834 1.2366 0.222527 0.111264 M6 0.00361187955230062 0.078269 0.0461 0.963393 0.481696 M7 0.0463518128607841 0.078211 0.5927 0.556315 0.278157 M8 0.104442239303668 0.078184 1.3359 0.188169 0.094085 M9 0.0911821726121515 0.078154 1.1667 0.249344 0.124672 M10 0.0292211196518342 0.078093 0.3742 0.709987 0.354993 M11 -0.00338944017408287 0.07807 -0.0434 0.965558 0.482779 t -0.0127399333084835 0.000988 -12.8966 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.995059924204524 R-squared 0.990144252757913 Adjusted R-squared 0.98735893288515 F-TEST (value) 355.486729707429 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.123427738668546 Sum Squared Residuals 0.70078270695022 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.05 2.19739804358119 -0.147398043581186 2 2.11 2.08804755044680 0.0219524495532044 3 2.09 2.09474853671559 -0.00474853671559424 4 2.05 2.05609902985000 -0.00609902984999507 5 2.08 2.20550100238760 -0.125501002387597 6 2.06 2.09950100238760 -0.039501002387597 7 2.06 2.12950100238760 -0.0695010023875966 8 2.08 2.17485149552200 -0.0948514955219974 9 2.07 2.14885149552200 -0.0788514955219975 10 2.06 2.07415050925320 -0.0141505092531963 11 2.07 2.02880001611880 0.0411999838812042 12 2.06 2.01944952298440 0.0405504770156049 13 2.09 2.04451884387939 0.0454811561206067 14 2.07 1.93516835074499 0.134831649255009 15 2.09 1.94186933701379 0.148130662986208 16 2.28 2.18997229582019 0.090027704179805 17 2.33 2.33937426835780 -0.00937426835779695 18 2.35 2.23337426835780 0.116625731642203 19 2.52 2.5501267340298 -0.0301267340297997 20 2.63 2.5954772271642 0.0345227728357997 21 2.58 2.5694772271642 0.0105227728357999 22 2.7 2.7815287065674 -0.0815287065674017 23 2.81 2.736178213433 0.0738217865669988 24 2.97 3.01358018597060 -0.0435801859706031 25 3.04 3.0386495068656 0.00135049313439872 26 3.28 3.2160514794032 0.0639485205967992 27 3.33 3.222752465672 0.107247534327998 28 3.5 3.47085542447841 0.0291445755215942 29 3.56 3.62025739701601 -0.0602573970160078 30 3.57 3.51425739701601 0.0557426029839919 31 3.69 3.83100986268801 -0.141009862688011 32 3.82 3.87636035582241 -0.0563603558224113 33 3.79 3.85036035582241 -0.060360355822411 34 3.96 4.06241183522561 -0.102411835225613 35 4.06 4.01706134209121 0.0429386579087876 36 4.05 4.00771084895681 0.0422891510431884 37 4.03 4.03278016985181 -0.00278016985180923 38 3.94 3.92342967671741 0.0165703232825938 39 4.02 3.93013066298621 0.0898693370137916 40 3.88 3.89148115612061 -0.0114811561206087 41 4.02 4.04088312865821 -0.0208831286582112 42 4.03 3.93488312865821 0.0951168713417895 43 4.09 3.96488312865821 0.125116871341789 44 3.99 4.01023362179261 -0.0202336217926110 45 4.01 3.98423362179261 0.0257663782073885 46 4.01 3.90953263552381 0.100467364476189 47 4.19 4.15093460806141 0.0390653919385879 48 4.3 4.14158411492701 0.158415885072988 49 4.27 4.16665343582201 0.103346564177990 50 3.82 4.05730294268761 -0.237302942687607 51 3.15 3.4904989976124 -0.340498997612403 52 2.49 2.59159209373080 -0.101592093730795 53 1.81 1.59398420358039 0.216015796419613 54 1.26 1.48798420358039 -0.227984203580388 55 1.06 0.944479272236382 0.115520727763618 56 0.84 0.70307729969878 0.13692270030122 57 0.78 0.67707729969878 0.102922700301220 58 0.7 0.602376313429979 0.0976236865700213 59 0.36 0.557025820295578 -0.197025820295578 60 0.35 0.547675327161178 -0.197675327161178 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00713835350722497 0.0142767070144499 0.992861646492775 18 0.00222830067598725 0.00445660135197451 0.997771699324013 19 0.000543770866821013 0.00108754173364203 0.999456229133179 20 0.000261231990809338 0.000522463981618676 0.99973876800919 21 4.39060857313712e-05 8.78121714627423e-05 0.999956093914269 22 8.97928776559758e-05 0.000179585755311952 0.999910207122344 23 2.19361789408474e-05 4.38723578816948e-05 0.99997806382106 24 5.77702443458515e-06 1.15540488691703e-05 0.999994222975565 25 1.31741492290457e-06 2.63482984580914e-06 0.999998682585077 26 2.66549760711689e-07 5.33099521423377e-07 0.99999973345024 27 1.64503545518321e-07 3.29007091036641e-07 0.999999835496455 28 3.52479716541826e-08 7.04959433083652e-08 0.999999964752028 29 1.03452813189296e-08 2.06905626378592e-08 0.999999989654719 30 3.28529159324842e-09 6.57058318649684e-09 0.999999996714708 31 3.54568571908232e-09 7.09137143816464e-09 0.999999996454314 32 9.63104737599223e-10 1.92620947519845e-09 0.999999999036895 33 3.18236964335805e-10 6.3647392867161e-10 0.999999999681763 34 3.83574746934388e-10 7.67149493868776e-10 0.999999999616425 35 1.50585916153623e-10 3.01171832307245e-10 0.999999999849414 36 2.44480367735108e-10 4.88960735470215e-10 0.99999999975552 37 3.68956398894838e-10 7.37912797789676e-10 0.999999999631044 38 1.04919676085456e-09 2.09839352170912e-09 0.999999998950803 39 1.80643518488933e-09 3.61287036977866e-09 0.999999998193565 40 8.30657425768722e-09 1.66131485153744e-08 0.999999991693426 41 3.14811410889581e-08 6.29622821779161e-08 0.99999996851886 42 4.43310060738692e-05 8.86620121477385e-05 0.999955668993926 43 0.119346217303749 0.238692434607498 0.880653782696251 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.925925925925926 NOK 5% type I error level 26 0.962962962962963 NOK 10% type I error level 26 0.962962962962963 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/10wa4g1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/10wa4g1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/1k1qj1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/1k1qj1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/265bp1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/265bp1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/3sk1v1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/3sk1v1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/4kgnp1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/4kgnp1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/5b1p51258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/5b1p51258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/6cjfh1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/6cjfh1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/7rief1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/7rief1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/8fgly1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/8fgly1258736941.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/9q3to1258736941.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258737013evsiiv59mtv015p/9q3to1258736941.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