*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: Mon, 23 Nov 2009 12:21:39 -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/23/t12590041309q583go5cb2x2l7.htm/, Retrieved Mon, 23 Nov 2009 20:22:22 +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/23/t12590041309q583go5cb2x2l7.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 «

267413 294912 267366 293488 264777 290555 258863 284736 254844 281818 254868 287854 277267 316263 285351 325412 286602 326011 283042 328282 276687 317480 277915 317539 277128 313737 277103 312276 275037 309391 270150 302950 267140 300316 264993 304035 287259 333476 291186 337698 292300 335932 288186 323931 281477 313927 282656 314485 280190 313218 280408 309664 276836 302963 275216 298989 274352 298423 271311 301631 289802 329765 290726 335083 292300 327616 278506 309119 269826 295916 265861 291413 269034 291542 264176 284678 255198 276475 253353 272566 246057 264981 235372 263290 258556 296806 260993 303598 254663 286994 250643 276427 243422 266424 247105 267153 248541 268381 245039 262522 237080 255542 237085 253158 225554 243803 226839 250741 247934 280445 248333 285257 246969 270976 245098 261076 246263 255603 255765 260376 264319 263903 268347 264291 273046 263276 273963 262572 267430 256167 271993 264221 292710 293860

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] = + 100661.943324773 + 0.56639088912659X[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) 100661.943324773 13014.646505 7.7345 0 0 X 0.56639088912659 0.044444 12.7439 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.845084893761692 R-squared 0.714168477664211 Adjusted R-squared 0.709771069628276 F-TEST (value) 162.406688628413 F-TEST (DF numerator) 1 F-TEST (DF denominator) 65 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9138.03088849535 Sum Squared Residuals 5427734553.74118 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 267413 267697.413218874 -284.413218873647 2 267366 266890.872592757 475.127407242538 3 264777 265229.648114949 -452.648114949221 4 258863 261933.819531122 -3070.81953112159 5 254844 260281.09091665 -5437.09091665021 6 254868 263699.826323418 -8831.8263234183 7 277267 279790.425092616 -2523.42509261559 8 285351 284972.335337235 378.664662765244 9 286602 285311.603479822 1290.39652017842 10 283042 286597.877189028 -3555.87718902807 11 276687 280479.722804683 -3792.72280468265 12 277915 280513.139867141 -2598.13986714112 13 277128 278359.721706682 -1231.72170668182 14 277103 277532.224617668 -429.224617667875 15 275037 275898.186902538 -861.186902537663 16 270150 272250.063185673 -2100.0631856733 17 267140 270758.189583714 -3618.18958371386 18 264993 272864.597300376 -7871.59730037565 19 287259 289539.711467152 -2280.71146715158 20 291186 291931.013801044 -745.01380104404 21 292300 290930.767490846 1369.23250915352 22 288186 284133.510430438 4052.48956956172 23 281477 278467.335975616 3009.66402438412 24 282656 278783.382091749 3872.61790825149 25 280190 278065.764835225 2124.23516477488 26 280408 276052.811615269 4355.18838473078 27 276836 272257.426267232 4578.57373276806 28 275216 270006.588873843 5209.41112615712 29 274352 269686.011630597 4665.98836940277 30 271311 271502.993602915 -191.993602915328 31 289802 287437.834877603 2364.16512239720 32 290726 290449.901625978 276.098374021991 33 292300 286220.66085687 6079.33914313024 34 278506 275744.128580695 2761.87141930477 35 269826 268266.069671557 1559.93032844313 36 265861 265715.61149782 145.388502180165 37 269034 265788.675922517 3245.32407748284 38 264176 261900.968859552 2275.03114044775 39 255198 257254.864396047 -2056.86439604684 40 253353 255040.842410451 -1687.842410451 41 246057 250744.767516426 -4687.76751642582 42 235372 249787.000522913 -14415.0005229128 43 258556 268770.157562880 -10214.1575628795 44 260993 272617.084481827 -11624.0844818273 45 254663 263212.730158769 -8549.73015876943 46 250643 257227.677633369 -6584.67763336876 47 243422 251562.069569435 -8140.06956943548 48 247105 251974.968527609 -4869.96852760877 49 248541 252670.496539456 -4129.49653945622 50 245039 249352.012320064 -4313.01232006353 51 237080 245398.60391396 -8318.60391395993 52 237085 244048.328034282 -6963.32803428215 53 225554 238749.741266503 -13195.7412665029 54 226839 242679.361255263 -15840.3612552632 55 247934 259503.436225879 -11569.4362258794 56 248333 262228.909184357 -13895.9091843565 57 246969 254140.28089674 -7171.28089673972 58 245098 248533.011094386 -3435.01109438648 59 246263 245433.153758197 829.84624180334 60 255765 248136.537471998 7628.46252800213 61 264319 250134.198137947 14184.8018620526 62 268347 250353.957802928 17993.0421970715 63 273046 249779.071050465 23266.928949535 64 273963 249380.33186452 24582.6681354801 65 267430 245752.598219664 21677.4017803359 66 271993 250314.310440690 21678.6895593104 67 292710 267101.570003513 25608.4299964874 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00104059966562289 0.00208119933124579 0.998959400334377 6 0.0196288992398975 0.0392577984797951 0.980371100760102 7 0.0152493622307652 0.0304987244615305 0.984750637769235 8 0.00471179519729631 0.00942359039459262 0.995288204802704 9 0.00138970547707386 0.00277941095414771 0.998610294522926 10 0.000813670050581358 0.00162734010116272 0.999186329949419 11 0.000314823017378798 0.000629646034757596 0.99968517698262 12 9.18930629443465e-05 0.000183786125888693 0.999908106937056 13 2.47106487751642e-05 4.94212975503284e-05 0.999975289351225 14 7.07656147277191e-06 1.41531229455438e-05 0.999992923438527 15 1.85432410759590e-06 3.70864821519179e-06 0.999998145675892 16 4.34760639019229e-07 8.69521278038459e-07 0.99999956523936 17 1.10406242674004e-07 2.20812485348008e-07 0.999999889593757 18 2.40585492371650e-07 4.81170984743301e-07 0.999999759414508 19 6.83934173886577e-08 1.36786834777315e-07 0.999999931606583 20 1.62846223681723e-08 3.25692447363447e-08 0.999999983715378 21 5.03093332144809e-09 1.00618666428962e-08 0.999999994969067 22 6.96324192289415e-09 1.39264838457883e-08 0.999999993036758 23 5.89022359943381e-09 1.17804471988676e-08 0.999999994109776 24 6.29746946809062e-09 1.25949389361812e-08 0.99999999370253 25 2.84534478016425e-09 5.69068956032851e-09 0.999999997154655 26 3.49567558664325e-09 6.9913511732865e-09 0.999999996504324 27 4.90647524734652e-09 9.81295049469304e-09 0.999999995093525 28 7.97585378841928e-09 1.59517075768386e-08 0.999999992024146 29 7.809777220857e-09 1.5619554441714e-08 0.999999992190223 30 2.3231610064275e-09 4.646322012855e-09 0.99999999767684 31 7.38897032803957e-10 1.47779406560791e-09 0.999999999261103 32 2.06882451630177e-10 4.13764903260354e-10 0.999999999793117 33 1.75128942818334e-10 3.50257885636668e-10 0.99999999982487 34 6.86504671135594e-11 1.37300934227119e-10 0.99999999993135 35 2.35075430045574e-11 4.70150860091147e-11 0.999999999976492 36 6.51215297246021e-12 1.30243059449204e-11 0.999999999993488 37 3.3320703752254e-12 6.6641407504508e-12 0.999999999996668 38 1.26817395071544e-12 2.53634790143088e-12 0.999999999998732 39 3.21086264875726e-13 6.42172529751452e-13 0.999999999999679 40 7.67833682532071e-14 1.53566736506414e-13 0.999999999999923 41 2.55917850321551e-14 5.11835700643103e-14 0.999999999999974 42 9.74484560012574e-13 1.94896912002515e-12 0.999999999999025 43 2.94769181198869e-12 5.89538362397737e-12 0.999999999997052 44 2.22322743825744e-11 4.44645487651487e-11 0.999999999977768 45 2.49186815864726e-11 4.98373631729452e-11 0.999999999975081 46 1.34962952069873e-11 2.69925904139746e-11 0.999999999986504 47 7.87604645213027e-12 1.57520929042605e-11 0.999999999992124 48 3.04170971944714e-12 6.08341943889428e-12 0.999999999996958 49 1.18215998599462e-12 2.36431997198923e-12 0.999999999998818 50 4.31934692960309e-13 8.63869385920619e-13 0.999999999999568 51 2.22110175816047e-13 4.44220351632094e-13 0.999999999999778 52 9.66742863667377e-14 1.93348572733475e-13 0.999999999999903 53 3.06730803002057e-13 6.13461606004114e-13 0.999999999999693 54 3.42639894093472e-11 6.85279788186943e-11 0.999999999965736 55 3.64310520151234e-10 7.28621040302467e-10 0.99999999963569 56 4.22703230456607e-07 8.45406460913214e-07 0.99999957729677 57 0.000100030612360649 0.000200061224721298 0.99989996938764 58 0.00573643137261794 0.0114728627452359 0.994263568627382 59 0.117214327166318 0.234428654332636 0.882785672833682 60 0.640875888403288 0.718248223193423 0.359124111596712 61 0.925295512748117 0.149408974503766 0.0747044872518831 62 0.98654613189888 0.0269077362022381 0.0134538681011190 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 51 0.879310344827586 NOK 5% type I error level 55 0.948275862068966 NOK 10% type I error level 55 0.948275862068966 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/1042bq1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/1042bq1259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/1gckv1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/1gckv1259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/2pfte1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/2pfte1259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/396781259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/396781259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/4fq8r1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/4fq8r1259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/5b78v1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/5b78v1259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/6n1ps1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/6n1ps1259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/77wfc1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/77wfc1259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/84oe31259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/84oe31259004094.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/9x0ju1259004094.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041309q583go5cb2x2l7/9x0ju1259004094.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