Workshop 7: 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: Fri, 25 Dec 2009 08:14:42 -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/Dec/25/t12617541962etgjknzyktsw4e.htm/, Retrieved Fri, 25 Dec 2009 16:16:48 +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/Dec/25/t12617541962etgjknzyktsw4e.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:

No seasonal dummies No lineair Trend

Dataseries X:

» Textbox « » Textfile « » CSV «

25.6 8.1 23.7 7.7 22 7.5 21.3 7.6 20.7 7.8 20.4 7.8 20.3 7.8 20.4 7.5 19.8 7.5 19.5 7.1 23.1 7.5 23.5 7.5 23.5 7.6 22.9 7.7 21.9 7.7 21.5 7.9 20.5 8.1 20.2 8.2 19.4 8.2 19.2 8.2 18.8 7.9 18.8 7.3 22.6 6.9 23.3 6.6 23 6.7 21.4 6.9 19.9 7 18.8 7.1 18.6 7.2 18.4 7.1 18.6 6.9 19.9 7 19.2 6.8 18.4 6.4 21.1 6.7 20.5 6.6 19.1 6.4 18.1 6.3 17 6.2 17.1 6.5 17.4 6.8 16.8 6.8 15.3 6.4 14.3 6.1 13.4 5.8 15.3 6.1 22.1 7.2 23.7 7.3 22.2 6.9 19.5 6.1 16.6 5.8 17.3 6.2 19.8 7.1 21.2 7.7 21.5 7.9 20.6 7.7 19.1 7.4 19.6 7.5 23.5 8 24 8.1

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 56 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3.04790639755097 + 2.37529185907747X[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) 3.04790639755097 2.858161 1.0664 0.290668 0.145334 X 2.37529185907747 0.396795 5.9862 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.617973267289145 R-squared 0.381890959084021 Adjusted R-squared 0.371233906654436 F-TEST (value) 35.8345763622055 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.43626900372951e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.01145292020237 Sum Squared Residuals 234.664685311058 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.6 22.2877704560783 3.31222954392165 2 23.7 21.3376537124475 2.36234628755254 3 22 20.8625953406320 1.13740465936803 4 21.3 21.1001245265397 0.19987547346028 5 20.7 21.5751828983552 -0.875182898355215 6 20.4 21.5751828983552 -1.17518289835522 7 20.3 21.5751828983552 -1.27518289835521 8 20.4 20.8625953406320 -0.462595340631976 9 19.8 20.8625953406320 -1.06259534063197 10 19.5 19.912478597001 -0.412478597000987 11 23.1 20.8625953406320 2.23740465936803 12 23.5 20.8625953406320 2.63740465936803 13 23.5 21.1001245265397 2.39987547346028 14 22.9 21.3376537124475 1.56234628755253 15 21.9 21.3376537124475 0.56234628755253 16 21.5 21.8127120842630 -0.312712084262963 17 20.5 22.2877704560785 -1.78777045607845 18 20.2 22.5252996419862 -2.3252996419862 19 19.4 22.5252996419862 -3.1252996419862 20 19.2 22.5252996419862 -3.3252996419862 21 18.8 21.8127120842630 -3.01271208426296 22 18.8 20.3875369688165 -1.58753696881648 23 22.6 19.4374202251855 3.16257977481451 24 23.3 18.7248326674623 4.57516733253775 25 23 18.96236185337 4.03763814663 26 21.4 19.4374202251855 1.96257977481450 27 19.9 19.6749494110932 0.225050588906757 28 18.8 19.912478597001 -1.11247859700099 29 18.6 20.1500077829087 -1.55000778290873 30 18.4 19.912478597001 -1.51247859700099 31 18.6 19.4374202251855 -0.837420225185494 32 19.9 19.6749494110932 0.225050588906757 33 19.2 19.1998910392777 0.000108960722252083 34 18.4 18.2497742956468 0.150225704353237 35 21.1 18.96236185337 2.13763814663 36 20.5 18.7248326674623 1.77516733253775 37 19.1 18.2497742956468 0.85022570435324 38 18.1 18.012245109739 0.087754890260988 39 17 17.7747159238313 -0.774715923831268 40 17.1 18.4873034815545 -1.38730348155451 41 17.4 19.1998910392777 -1.79989103927775 42 16.8 19.1998910392777 -2.39989103927775 43 15.3 18.2497742956468 -2.94977429564676 44 14.3 17.5371867379235 -3.23718673792352 45 13.4 16.8245991802003 -3.42459918020028 46 15.3 17.5371867379235 -2.23718673792352 47 22.1 20.1500077829087 1.94999221709127 48 23.7 20.3875369688165 3.31246303118352 49 22.2 19.4374202251855 2.76257977481450 50 19.5 17.5371867379235 1.96281326207648 51 16.6 16.8245991802003 -0.224599180200279 52 17.3 17.7747159238313 -0.474715923831267 53 19.8 19.912478597001 -0.112478597000986 54 21.2 21.3376537124475 -0.137653712447470 55 21.5 21.8127120842630 -0.312712084262963 56 20.6 21.3376537124475 -0.737653712447468 57 19.1 20.6250661547242 -1.52506615472423 58 19.6 20.8625953406320 -1.26259534063197 59 23.5 22.0502412701707 1.44975872982929 60 24 22.2877704560785 1.71222954392155 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.444140583716565 0.88828116743313 0.555859416283435 6 0.505445092314943 0.989109815370114 0.494554907685057 7 0.501280925839052 0.997438148321897 0.498719074160948 8 0.369275884468075 0.738551768936151 0.630724115531925 9 0.271799823259579 0.543599646519158 0.728200176740421 10 0.197300137184628 0.394600274369256 0.802699862815372 11 0.233596025401782 0.467192050803564 0.766403974598218 12 0.286175704181974 0.572351408363948 0.713824295818026 13 0.287762307830112 0.575524615660225 0.712237692169888 14 0.230410167178467 0.460820334356933 0.769589832821533 15 0.167563370573107 0.335126741146213 0.832436629426893 16 0.135335605341768 0.270671210683536 0.864664394658232 17 0.161191653101876 0.322383306203752 0.838808346898124 18 0.177616429391225 0.35523285878245 0.822383570608775 19 0.221755061736983 0.443510123473966 0.778244938263017 20 0.271542622433319 0.543085244866637 0.728457377566681 21 0.353903042020665 0.70780608404133 0.646096957979335 22 0.404639192334911 0.809278384669823 0.595360807665089 23 0.405679602110445 0.81135920422089 0.594320397889555 24 0.532781993694914 0.934436012610172 0.467218006305086 25 0.645453665718217 0.709092668563565 0.354546334281783 26 0.631733939919919 0.736532120160161 0.368266060080081 27 0.61298262216841 0.774034755663179 0.387017377831589 28 0.648774329515914 0.702451340968171 0.351225670484086 29 0.690232384384012 0.619535231231977 0.309767615615988 30 0.725183141771282 0.549633716457437 0.274816858228718 31 0.717610788033118 0.564778423933763 0.282389211966882 32 0.659293651552935 0.68141269689413 0.340706348447065 33 0.609397367471996 0.781205265056009 0.390602632528004 34 0.575881482041339 0.848237035917322 0.424118517958661 35 0.584881875200679 0.830236249598642 0.415118124799321 36 0.584615972824476 0.830768054351047 0.415384027175524 37 0.561695837313698 0.876608325372604 0.438304162686302 38 0.532447506669088 0.935104986661825 0.467552493330912 39 0.509192008844566 0.981615982310868 0.490807991155434 40 0.480235032188201 0.960470064376402 0.519764967811799 41 0.466408929634958 0.932817859269916 0.533591070365042 42 0.501495573720318 0.997008852559364 0.498504426279682 43 0.578464951012131 0.843070097975738 0.421535048987869 44 0.672951747963994 0.654096504072011 0.327048252036006 45 0.795793145143717 0.408413709712567 0.204206854856283 46 0.846766313090841 0.306467373818318 0.153233686909159 47 0.82507729379695 0.349845412406099 0.174922706203049 48 0.917389251035764 0.165221497928472 0.082610748964236 49 0.96254977148984 0.0749004570203201 0.0374502285101600 50 0.983683890980784 0.0326322180384316 0.0163161090192158 51 0.972964067047318 0.0540718659053639 0.0270359329526820 52 0.976544870457003 0.046910259085994 0.023455129542997 53 0.997871246407635 0.00425750718472997 0.00212875359236498 54 0.991403680207685 0.0171926395846294 0.0085963197923147 55 0.994383435775055 0.0112331284498899 0.00561656422494494 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0196078431372549 NOK 5% type I error level 5 0.0980392156862745 NOK 10% type I error level 7 0.137254901960784 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/10xbn41261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/10xbn41261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/1atjs1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/1atjs1261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/2k0bn1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/2k0bn1261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/3xszr1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/3xszr1261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/4lt2k1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/4lt2k1261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/50o6x1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/50o6x1261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/606tz1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/606tz1261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/70gkk1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/70gkk1261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/82b571261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/82b571261754025.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/9588s1261754025.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/25/t12617541962etgjknzyktsw4e/9588s1261754025.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