*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: Tue, 17 Nov 2009 12:24:47 -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/17/t1258485990hq1cbrad8j3cc62.htm/, Retrieved Tue, 17 Nov 2009 20:26:43 +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/17/t1258485990hq1cbrad8j3cc62.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 «

344744 492865 338653 480961 327532 461935 326225 456608 318672 441977 317756 439148 337302 488180 349420 520564 336923 501492 330758 485025 321002 464196 320820 460170 327032 467037 324047 460070 316735 447988 315710 442867 313427 436087 310527 431328 330962 484015 339015 509673 341332 512927 339092 502831 323308 470984 325849 471067 330675 476049 332225 474605 331735 470439 328047 461251 326165 454724 327081 455626 346764 516847 344190 525192 343333 522975 345777 518585 344094 509239 348609 512238 354846 519164 356427 517009 353467 509933 355996 509127 352487 500857 355178 506971 374556 569323 375021 579714 375787 577992 372720 565464 364431 547344 370490 554788 376974 562325 377632 560854 378205 555332 370861 543599 369167 536662 371551 542722 382842 593530 381903 610763 384502 612613 392058 611324 384359 594167 388884 595454 386586 590865 387495 589379 385705 584428 378670 573100 377367 567456 376911 569028 389827 620735 387820 628884 3872 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 72515.1331006383 + 0.548453531164433X[t] + 3444.62240244408M1[t] + 5252.44902743883M2[t] + 6444.18114239702M3[t] + 7655.64877227978M4[t] + 9291.3039631184M5[t] + 9145.69561028996M6[t] -3397.38353593821M7[t] -10003.1470243896M8[t] -9464.2835414011M9[t] -5046.2793650683M10[t] -2975.32673037337M11[t] -213.238635508375t + 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) 72515.1331006383 12756.933278 5.6844 0 0 X 0.548453531164433 0.029096 18.8496 0 0 M1 3444.62240244408 2219.682776 1.5519 0.126137 0.063069 M2 5252.44902743883 2195.918196 2.3919 0.020023 0.010011 M3 6444.18114239702 2195.152478 2.9356 0.004765 0.002382 M4 7655.64877227978 2232.349855 3.4294 0.00112 0.00056 M5 9291.3039631184 2311.339667 4.0199 0.00017 8.5e-05 M6 9145.69561028996 2321.142191 3.9402 0.000221 0.000111 M7 -3397.38353593821 2302.920102 -1.4753 0.145553 0.072777 M8 -10003.1470243896 2472.023054 -4.0465 0.000156 7.8e-05 M9 -9464.2835414011 2401.19951 -3.9415 0.00022 0.00011 M10 -5046.2793650683 2272.792059 -2.2203 0.03032 0.01516 M11 -2975.32673037337 2179.989875 -1.3648 0.177576 0.088788 t -213.238635508375 71.089081 -2.9996 0.003979 0.001989 Multiple Linear Regression - Regression Statistics Multiple R 0.990415764972113 R-squared 0.980923387505297 Adjusted R-squared 0.976647595049587 F-TEST (value) 229.413236883256 F-TEST (DF numerator) 13 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3775.4821923672 Sum Squared Residuals 826747415.523148 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 344744 346060.066504933 -1316.06650493254 2 338653 341125.863659437 -2472.86365943729 3 327532 331669.480254953 -4137.48025495261 4 326225 329746.097288814 -3521.09728881405 5 318672 323144.090229678 -4472.0902296775 6 317756 321233.668201677 -3477.66820167649 7 337302 335369.123959994 1932.87604000558 8 349420 346311.240989264 3108.75901073634 9 336923 336176.760090376 746.239909624273 10 330758 331350.141333515 -592.141333515424 11 321002 321784.116732078 -782.116732078 12 320820 322338.130910475 -1518.13091047498 13 327032 329335.745075917 -2303.74507591685 14 324047 327109.257313781 -3062.25731378062 15 316735 321461.335229702 -4726.33522970174 16 315710 319650.933690983 -3940.93369098307 17 313427 317354.835305018 -3927.83530501847 18 310527 314385.89796187 -3858.89796187012 19 330962 330525.951376594 436.04862340595 20 339015 337779.169955251 1235.83004474867 21 341332 339889.462593140 1442.53740685949 22 339092 338557.041283329 534.958716671184 23 323308 322948.155675522 359.844324478328 24 325849 325755.765413473 93.2345865266907 25 330675 331719.54467267 -1044.54467267022 26 332225 332522.165763155 -297.165763155156 27 331735 331215.801831774 519.198168226073 28 328047 327174.839781809 872.160218190492 29 326165 325017.500139230 1147.49986077049 30 327081 325153.358236003 1927.64176399699 31 346764 345973.914085684 790.085914315782 32 344190 343731.756679292 458.243320708333 33 343333 342841.46004818 491.539951819767 34 345777 344638.514587193 1138.48541280721 35 344094 341370.381884117 2723.61811588344 36 348609 345777.282118944 2831.71788105631 37 354846 352807.255042724 2038.74495727574 38 356427 353219.925672551 3207.07432744872 39 353467 350317.561965482 3149.43803451844 40 355996 350873.737413737 5122.26258626258 41 352487 347760.443266338 4726.5567336622 42 355178 350754.84116754 4423.15883245967 43 374556 372195.697960969 2360.30203903149 44 375021 371075.676479338 3945.32352066161 45 375787 370456.864346153 5330.13565384664 46 372720 367790.60404855 4929.39595145024 47 364431 359710.340063037 4720.65993696321 48 370490 366555.11624389 3934.88375611019 49 376974 373920.194275212 3053.80572478814 50 377632 374708.007120355 2923.99287964465 51 378205 372657.940200715 5547.05979928484 52 370861 367221.163913937 3639.83608606275 53 369167 364838.95832358 4328.04167642017 54 371551 367803.739734099 3747.26026590051 55 382842 382913.248963765 -71.2489637654439 56 381903 385545.746542362 -3642.74654236238 57 384502 386886.010422497 -2384.0104224967 58 392058 390383.81936165 1674.18063834984 59 384359 382831.716126649 1527.28387335146 60 388884 386299.663916122 2584.33608387784 61 386586 387014.194428544 -428.194428544277 62 387495 387793.78047072 -298.780470720308 63 385705 386056.880517375 -351.880517375007 64 378670 380842.227910719 -2172.22791071870 65 377367 379169.172736157 -1802.17273615689 66 376911 379672.494698811 -2761.49469881056 67 389827 395275.063652993 -5448.06365299336 68 387820 392925.409354493 -5105.40935449257 69 387267 392893.442499653 -5626.44249965347 70 380575 388259.879385763 -7684.87938576305 71 372402 380951.289518598 -8549.28951859844 72 376740 384666.041397096 -7926.04139709605 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.000715244630606708 0.00143048926121342 0.999284755369393 18 0.000256446628734183 0.000512893257468366 0.999743553371266 19 0.00136604566100699 0.00273209132201399 0.998633954338993 20 0.00135259579626021 0.00270519159252042 0.99864740420374 21 0.000733076101352404 0.00146615220270481 0.999266923898648 22 0.00061054369279736 0.00122108738559472 0.999389456307203 23 0.00035166344861589 0.00070332689723178 0.999648336551384 24 0.000227566479395257 0.000455132958790514 0.999772433520605 25 0.000141415055394137 0.000282830110788275 0.999858584944606 26 0.000528592904969662 0.00105718580993932 0.99947140709503 27 0.00925994561397192 0.0185198912279438 0.990740054386028 28 0.0205767321429387 0.0411534642858774 0.979423267857061 29 0.0392326677074898 0.0784653354149796 0.96076733229251 30 0.0323537396924909 0.0647074793849819 0.96764626030751 31 0.363550013792811 0.727100027585623 0.636449986207189 32 0.508563535344978 0.982872929310043 0.491436464655022 33 0.532583598310036 0.934832803379928 0.467416401689964 34 0.499729116670677 0.999458233341353 0.500270883329323 35 0.447567802943103 0.895135605886206 0.552432197056897 36 0.407860773670209 0.815721547340418 0.592139226329791 37 0.391910658881617 0.783821317763233 0.608089341118383 38 0.34962322353403 0.69924644706806 0.65037677646597 39 0.393577743415675 0.78715548683135 0.606422256584325 40 0.379986770788135 0.75997354157627 0.620013229211865 41 0.385414909535114 0.770829819070229 0.614585090464886 42 0.39162253587892 0.78324507175784 0.60837746412108 43 0.709390312355814 0.581219375288373 0.290609687644186 44 0.678977254194079 0.642045491611842 0.321022745805921 45 0.642027778694131 0.715944442611739 0.357972221305869 46 0.56854840896203 0.86290318207594 0.43145159103797 47 0.548172197061764 0.903655605876471 0.451827802938236 48 0.468697527360517 0.937395054721035 0.531302472639483 49 0.404039106979094 0.808078213958188 0.595960893020906 50 0.358217182804715 0.71643436560943 0.641782817195285 51 0.279974679103702 0.559949358207404 0.720025320896298 52 0.215627874055253 0.431255748110506 0.784372125944747 53 0.201374910461055 0.402749820922109 0.798625089538945 54 0.256857622584289 0.513715245168578 0.743142377415711 55 0.973738457607703 0.0525230847845944 0.0262615423922972 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.256410256410256 NOK 5% type I error level 12 0.307692307692308 NOK 10% type I error level 15 0.384615384615385 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/10ypdc1258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/10ypdc1258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/1nhnj1258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/1nhnj1258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/27tc61258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/27tc61258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/36kfd1258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/36kfd1258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/4b3c61258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/4b3c61258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/5yph51258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/5yph51258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/6x9401258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/6x9401258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/787ra1258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/787ra1258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/8le3i1258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/8le3i1258485883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/9uj2w1258485883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485990hq1cbrad8j3cc62/9uj2w1258485883.ps (open in new window)

Parameters (Session):

par2 = Include Monthly Dummies ; par3 = No 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