workshop7

*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 12:17:04 -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/t1258658281701rkyjyg23soy0.htm/, Retrieved Thu, 19 Nov 2009 20:18:13 +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/t1258658281701rkyjyg23soy0.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 «

5.4 2.7 5.4 2.5 5.6 2.2 5.7 2.9 5.8 3.1 5.8 3 5.8 2.8 5.9 2.5 6.1 1.9 6.4 1.9 6.4 1.8 6.3 2 6.2 2.6 6.2 2.5 6.3 2.5 6.4 1.6 6.5 1.4 6.6 0.8 6.6 1.1 6.6 1.3 6.8 1.2 7 1.3 7.2 1.1 7.3 1.3 7.5 1.2 7.6 1.6 7.6 1.7 7.7 1.5 7.7 0.9 7.7 1.5 7.7 1.4 7.6 1.6 7.7 1.7 7.9 1.4 7.9 1.8 7.9 1.7 7.8 1.4 7.6 1.2 7.4 1 7 1.7 7 2.4 7.2 2 7.5 2.1 7.8 2 7.8 1.8 7.7 2.7 7.6 2.3 7.6 1.9 7.5 2 7.5 2.3 7.6 2.8 7.6 2.4 7.9 2.3 7.6 2.7 7.5 2.7 7.5 2.9 7.6 3 7.7 2.2 7.8 2.3 7.9 2.8 7.9 2.8

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 Y[t] = + 6.8925548273832 -0.450048120480993X[t] -0.078750036721978M1[t] -0.120513226375429M2[t] -0.109860556364419M3[t] -0.177209811172647M4[t] -0.115558103571256M5[t] -0.162907358379485M6[t] -0.152254688368474M7[t] -0.112601055947844M8[t] -0.0939560852137923M9[t] -0.00130534002202076M10[t] -0.0176555572398699M11[t] + 0.0383482923986091t + 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) 6.8925548273832 0.201041 34.2843 0 0 X -0.450048120480993 0.066701 -6.7473 0 0 M1 -0.078750036721978 0.191341 -0.4116 0.682527 0.341263 M2 -0.120513226375429 0.200525 -0.601 0.550735 0.275367 M3 -0.109860556364419 0.200315 -0.5484 0.585986 0.292993 M4 -0.177209811172647 0.20001 -0.886 0.380127 0.190063 M5 -0.115558103571256 0.199794 -0.5784 0.565765 0.282882 M6 -0.162907358379485 0.19956 -0.8163 0.418428 0.209214 M7 -0.152254688368474 0.199444 -0.7634 0.449042 0.224521 M8 -0.112601055947844 0.199418 -0.5646 0.574998 0.287499 M9 -0.0939560852137923 0.199101 -0.4719 0.639181 0.319591 M10 -0.00130534002202076 0.199043 -0.0066 0.994795 0.497398 M11 -0.0176555572398699 0.199055 -0.0887 0.9297 0.46485 t 0.0383482923986091 0.002348 16.3305 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.931216785308685 R-squared 0.867164701240642 Adjusted R-squared 0.830423022860394 F-TEST (value) 23.6016627293439 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 2.22044604925031e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.314614031618701 Sum Squared Residuals 4.65215347789455 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5.4 5.63702315776115 -0.237023157761147 2 5.4 5.7236178846025 -0.323617884602503 3 5.6 5.90763328315642 -0.307633283156423 4 5.7 5.56359863641011 0.136401363589894 5 5.8 5.57358901231391 0.226410987686092 6 5.8 5.60959286195239 0.190407138047613 7 5.8 5.7486034484582 0.0513965515417942 8 5.9 5.96161980942174 -0.0616198094217432 9 6.1 6.288641944843 -0.188641944843001 10 6.4 6.41964098243338 -0.0196409824333802 11 6.4 6.48664386966224 -0.08664386966224 12 6.3 6.45263809520452 -0.152638095204521 13 6.2 6.14220747859255 0.0577925214074454 14 6.2 6.18379739338581 0.0162026066141876 15 6.3 6.23279835579543 0.067201644204567 16 6.4 6.60884070181871 -0.208840701818707 17 6.5 6.79885032591491 -0.298850325914906 18 6.6 7.05987823579388 -0.459878235793882 19 6.6 6.9738647620592 -0.373864762059204 20 6.6 6.96185706278225 -0.361857062782245 21 6.8 7.063855137963 -0.263855137963005 22 7 7.14984936350529 -0.149849363505286 23 7.2 7.26185706278225 -0.0618570627822449 24 7.3 7.22785128832453 0.0721487116754742 25 7.5 7.23245435604926 0.267545643950744 26 7.6 7.04902021060202 0.550979789397982 27 7.6 7.05301636096354 0.546983639036462 28 7.7 7.11402502265012 0.585974977349883 29 7.7 7.48405389493871 0.215946105061288 30 7.7 7.2050240602405 0.494975939759504 31 7.7 7.29902983469822 0.400970165301784 32 7.6 7.28702213542126 0.312977864578743 33 7.7 7.29901058650582 0.400989413494182 34 7.9 7.5650240602405 0.334975939759503 35 7.9 7.40700288722886 0.492997112771141 36 7.9 7.50801154891544 0.391988451084562 37 7.8 7.60262424073637 0.197375759263633 38 7.6 7.68921896757772 -0.0892189675777252 39 7.4 7.82822955408354 -0.428229554083542 40 7 7.48419490733723 -0.484194907337228 41 7 7.26916122300053 -0.269161223000532 42 7.2 7.44017950878331 -0.240179508783310 43 7.5 7.44417565914483 0.0558243408551698 44 7.8 7.56718239601217 0.23281760398783 45 7.8 7.71418528324103 0.0858147167589713 46 7.7 7.44014101239852 0.259858987601485 47 7.6 7.64215833577167 -0.0421583357716732 48 7.6 7.87818143360255 -0.278181433602549 49 7.5 7.79277487723108 -0.292774877231081 50 7.5 7.65434554383194 -0.154345543831942 51 7.6 7.47832244600106 0.121677553998935 52 7.6 7.62934073178384 -0.0293407317838426 53 7.9 7.77434554383194 0.125654456168059 54 7.6 7.58532533322992 0.0146746667700755 55 7.5 7.63432629563954 -0.134326295639544 56 7.5 7.62231859636259 -0.122318596362585 57 7.6 7.63430704744715 -0.0343070474471465 58 7.7 8.12534458142232 -0.425344581422321 59 7.8 8.10233784455498 -0.302337844554983 60 7.9 7.93331763395297 -0.0333176339529647 61 7.9 7.8929158896296 0.00708411037040462 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00198464167413135 0.00396928334826271 0.998015358325869 18 0.000617576502372809 0.00123515300474562 0.999382423497627 19 0.000125603039414816 0.000251206078829633 0.999874396960585 20 3.82999762714679e-05 7.65999525429358e-05 0.999961700023729 21 1.26262254316843e-05 2.52524508633686e-05 0.999987373774568 22 3.25860107898704e-05 6.51720215797408e-05 0.99996741398921 23 1.61640286270093e-05 3.23280572540187e-05 0.999983835971373 24 0.000330199504044968 0.000660399008089937 0.999669800495955 25 0.0472012981135872 0.0944025962271745 0.952798701886413 26 0.159315392088676 0.318630784177352 0.840684607911324 27 0.145592163332535 0.291184326665070 0.854407836667465 28 0.158773372309474 0.317546744618949 0.841226627690526 29 0.122792973376730 0.245585946753459 0.87720702662327 30 0.0998057929354351 0.199611585870870 0.900194207064565 31 0.0721978302314872 0.144395660462974 0.927802169768513 32 0.0482364357179944 0.0964728714359887 0.951763564282006 33 0.0378871303796205 0.075774260759241 0.96211286962038 34 0.0362705351806255 0.072541070361251 0.963729464819374 35 0.0495148974697147 0.0990297949394293 0.950485102530285 36 0.0646274518780188 0.129254903756038 0.935372548121981 37 0.106592736410558 0.213185472821116 0.893407263589442 38 0.183895202370513 0.367790404741025 0.816104797629488 39 0.335591986572442 0.671183973144884 0.664408013427558 40 0.662911044070297 0.674177911859406 0.337088955929703 41 0.895943402009774 0.208113195980453 0.104056597990226 42 0.897221859586564 0.205556280826873 0.102778140413436 43 0.804656957208392 0.390686085583217 0.195343042791608 44 0.799529133474322 0.400941733051356 0.200470866525678 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.285714285714286 NOK 5% type I error level 8 0.285714285714286 NOK 10% type I error level 13 0.464285714285714 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/10rvwf1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/10rvwf1258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/19ffs1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/19ffs1258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/22ctf1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/22ctf1258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/3d0ot1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/3d0ot1258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/4pfm01258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/4pfm01258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/5wt6m1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/5wt6m1258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/6amq81258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/6amq81258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/7g0xn1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/7g0xn1258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/8z3yk1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/8z3yk1258658219.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/98j0y1258658219.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258658281701rkyjyg23soy0/98j0y1258658219.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