Model 4

*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, 19 Dec 2009 18:20:37 -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/20/t126127211713t3ylxeb1qcvjd.htm/, Retrieved Sun, 20 Dec 2009 02:22:10 +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/20/t126127211713t3ylxeb1qcvjd.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 «

2172 2155 3016 0 2150 2172 2155 0 2533 2150 2172 0 2058 2533 2150 0 2160 2058 2533 0 2260 2160 2058 0 2498 2260 2160 0 2695 2498 2260 0 2799 2695 2498 0 2946 2799 2695 0 2930 2946 2799 0 2318 2930 2946 0 2540 2318 2930 0 2570 2540 2318 0 2669 2570 2540 0 2450 2669 2570 0 2842 2450 2669 0 3440 2842 2450 0 2678 3440 2842 0 2981 2678 3440 0 2260 2981 2678 0 2844 2260 2981 0 2546 2844 2260 0 2456 2546 2844 0 2295 2456 2546 0 2379 2295 2456 0 2479 2379 2295 0 2057 2479 2379 0 2280 2057 2479 0 2351 2280 2057 0 2276 2351 2280 0 2548 2276 2351 0 2311 2548 2276 0 2201 2311 2548 1 2725 2201 2311 1 2408 2725 2201 1 2139 2408 2725 1 1898 2139 2408 1 2537 1898 2139 1 2068 2537 1898 1 2063 2068 2537 1 2520 2063 2068 1 2434 2520 2063 1 2190 2434 2520 1 2794 2190 2434 1 2070 2794 2190 1 2615 2070 2794 1 2265 2615 2070 1 2139 2265 2615 1 2428 2139 2265 1 2137 2428 2139 1 1823 2137 2428 1 2063 1823 2137 1 1806 2063 1823 1 1758 1806 2063 1 2243 1758 1806 1 1993 2243 1758 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 1134.96781186213 + 0.218332264908275`y(t-1)`[t] + 0.311376317013316`y(t-2)`[t] -16.8622480344403x[t] -123.943865589962M1[t] + 61.3837845106491M2[t] + 265.623286709349M3[t] -158.893504322475M4[t] + 61.1246420426986M5[t] + 336.226451123308M6[t] + 92.6391545422365M7[t] + 271.513481458097M8[t] + 177.538679779320M9[t] + 111.471061167625M10[t] + 411.981538275436M11[t] -4.61154711748763t + 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) 1134.96781186213 498.072338 2.2787 0.027709 0.013855 `y(t-1)` 0.218332264908275 0.14134 1.5447 0.129739 0.06487 `y(t-2)` 0.311376317013316 0.143207 2.1743 0.035231 0.017615 x -16.8622480344403 138.659443 -0.1216 0.903775 0.451888 M1 -123.943865589962 187.113645 -0.6624 0.511251 0.255625 M2 61.3837845106491 183.662884 0.3342 0.739838 0.369919 M3 265.623286709349 183.03314 1.4512 0.153973 0.076986 M4 -158.893504322475 176.700822 -0.8992 0.373542 0.186771 M5 61.1246420426986 192.370155 0.3177 0.752216 0.376108 M6 336.226451123308 186.991295 1.7981 0.079187 0.039593 M7 92.6391545422365 176.810552 0.5239 0.603007 0.301503 M8 271.513481458097 180.154126 1.5071 0.139093 0.069546 M9 177.538679779320 175.627581 1.0109 0.317728 0.158864 M10 111.471061167625 175.087306 0.6367 0.527721 0.263861 M11 411.981538275436 175.165311 2.352 0.023324 0.011662 t -4.61154711748763 4.034457 -1.143 0.259346 0.129673 Multiple Linear Regression - Regression Statistics Multiple R 0.743705558183632 R-squared 0.553097957273228 Adjusted R-squared 0.397201895856912 F-TEST (value) 3.54786357171780 F-TEST (DF numerator) 15 F-TEST (DF denominator) 43 p-value 0.000564374728205697 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 255.067585916146 Sum Squared Residuals 2797557.3555589 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2172 2416.02940214417 -244.029402144172 2 2150 2332.36214468227 -182.362144682273 3 2533 2532.48018732473 0.519812675270018 4 2058 2180.12282766099 -122.122827660995 5 2160 2411.07873049335 -251.07873049335 6 2260 2555.93513289579 -295.935132895791 7 2498 2361.32990002342 136.670099976583 8 2695 2618.69339057129 76.3066094287091 9 2799 2637.22606141113 161.773938588874 10 2946 2650.59458568403 295.405414315972 11 2930 3010.97149558525 -80.9714955852525 12 2318 2636.65741255475 -318.657412554754 13 2540 2369.50063265123 170.499367348773 14 2570 2408.12419243184 161.875807568162 15 2669 2683.42765783726 -14.4276578372551 16 2450 2285.25550342426 164.744496575739 17 2842 2483.67359204135 358.326407958646 18 3440 2771.55868842260 668.441311577397 19 2678 2775.98205540841 -97.9820554084126 20 2981 2970.07868692064 10.9213130793577 21 2260 2700.37826082744 -440.378260827438 22 2844 2566.62855615442 277.371443845576 23 2546 2765.53120428458 -219.531204284579 24 2456 2465.71887308477 -9.7188730847663 25 2295 2224.72341406560 70.276585934396 26 2379 2342.26415386730 36.7358461327037 27 2479 2510.10043216166 -31.10043216166 28 2057 2128.96093113229 -71.9609311322941 29 2280 2283.36894629002 -3.36894629001971 30 2351 2471.14649754807 -120.146497548067 31 2276 2307.88616335197 -31.8861633519653 32 2548 2487.88174179016 60.1182582098372 33 2311 2425.32854527295 -114.328545272950 34 2201 2370.73674295369 -169.736742953689 35 2725 2568.82293667195 156.177063328055 36 2408 2232.38456321949 175.615436780507 37 2139 2197.7790126511 -58.7790126510984 38 1898 2221.05744388067 -323.057443880674 39 2537 2284.30709384241 252.69290615759 40 2068 1919.65138056928 148.348619430723 41 2063 2231.62961414649 -168.629614146491 42 2520 2354.99272210583 165.007277894174 43 2434 2205.01484188528 228.985158114718 44 2190 2502.80002377663 -312.800023776629 45 2794 2324.1622390796 469.8377609204 46 2070 2309.37994000377 -239.379940003767 47 2615 2635.27760567654 -20.2776056765413 48 2265 2112.23915114099 152.760848859013 49 2139 2076.9675384879 62.0324615121014 50 2428 2121.19206513792 306.807934862082 51 2137 2344.68462883394 -207.684628833945 52 1823 1942.00935721317 -119.009357213173 53 2063 1998.24911702879 64.7508829712143 54 1806 2223.36695902771 -417.366959027712 55 1758 1993.78703933092 -235.787039330922 56 2243 2077.54615694128 165.453843058725 57 1993 2069.90489340889 -76.9048934088851 58 1932 2095.66017520409 -163.660175204092 59 2465 2300.39675778168 164.603242218319 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.913139458707411 0.173721082585178 0.086860541292589 20 0.854840983633045 0.29031803273391 0.145159016366955 21 0.980838557513603 0.0383228849727935 0.0191614424863968 22 0.978033301247529 0.0439333975049421 0.0219666987524711 23 0.975781318210026 0.0484373635799474 0.0242186817899737 24 0.957350307825548 0.0852993843489043 0.0426496921744522 25 0.928675355931751 0.142649288136497 0.0713246440682487 26 0.884412605573297 0.231174788853406 0.115587394426703 27 0.829456693189296 0.341086613621408 0.170543306810704 28 0.763285910651033 0.473428178697934 0.236714089348967 29 0.675783126205895 0.648433747588211 0.324216873794105 30 0.61742893349789 0.76514213300422 0.38257106650211 31 0.507656743181303 0.984686513637394 0.492343256818697 32 0.44188878156739 0.88377756313478 0.55811121843261 33 0.332815105596054 0.665630211192108 0.667184894403946 34 0.241437605501304 0.482875211002607 0.758562394498696 35 0.262087191526620 0.524174383053241 0.73791280847338 36 0.193284321425304 0.386568642850608 0.806715678574696 37 0.137980754560365 0.27596150912073 0.862019245439635 38 0.413516857845543 0.827033715691085 0.586483142154457 39 0.365474140726633 0.730948281453267 0.634525859273367 40 0.35044714797364 0.70089429594728 0.64955285202636 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 3 0.136363636363636 NOK 10% type I error level 4 0.181818181818182 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/10s1v71261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/10s1v71261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/1lr8e1261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/1lr8e1261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/2o0h01261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/2o0h01261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/3szi21261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/3szi21261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/4qbdh1261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/4qbdh1261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/5kx3v1261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/5kx3v1261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/665ax1261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/665ax1261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/70nsq1261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/70nsq1261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/8f3ym1261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/8f3ym1261272031.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/9peaq1261272031.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126127211713t3ylxeb1qcvjd/9peaq1261272031.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