*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, 14 Dec 2009 03:56:51 -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/14/t1260788395ubjavm3t3h6f8s6.htm/, Retrieved Mon, 14 Dec 2009 12:00:08 +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/14/t1260788395ubjavm3t3h6f8s6.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 «

106.2 431 436 443 448 460 467 81 484 431 436 443 448 460 94.7 510 484 431 436 443 448 101 513 510 484 431 436 443 109.4 503 513 510 484 431 436 102.3 471 503 513 510 484 431 90.7 471 471 503 513 510 484 96.2 476 471 471 503 513 510 96.1 475 476 471 471 503 513 106 470 475 476 471 471 503 103.1 461 470 475 476 471 471 102 455 461 470 475 476 471 104.7 456 455 461 470 475 476 86 517 456 455 461 470 475 92.1 525 517 456 455 461 470 106.9 523 525 517 456 455 461 112.6 519 523 525 517 456 455 101.7 509 519 523 525 517 456 92 512 509 519 523 525 517 97.4 519 512 509 519 523 525 97 517 519 512 509 519 523 105.4 510 517 519 512 509 519 102.7 509 510 517 519 512 509 98.1 501 509 510 517 519 512 104.5 507 501 509 510 517 519 87.4 569 507 501 509 510 517 89.9 580 569 507 501 509 510 109.8 578 580 569 507 501 509 111.7 565 578 580 569 507 501 98.6 547 565 578 580 569 507 96.9 555 547 565 578 580 569 95.1 562 555 547 565 578 580 97 561 562 555 547 565 578 112.7 555 561 562 555 547 565 102 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] = -103.597529140216 + 0.456638020998805X[t] + 1.15233154517053`Y(t-1)`[t] -0.132883985021822`Y(t-2)`[t] -0.136717309166669`Y(t-3)`[t] + 0.288076677389240`Y(t-4)`[t] -0.0444267752463386`Y(t-5)`[t] + 2.60941417635306M1[t] + 66.9697686993288M2[t] + 13.9511567062971M3[t] + 0.76530488075559M4[t] -2.58819717511082M5[t] -17.7476852617300M6[t] + 5.99420155049773M7[t] + 3.11341674291098M8[t] -0.771493007730226M9[t] -4.40903117293207M10[t] -2.59469677920587M11[t] -0.457581264982037t + 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) -103.597529140216 42.1143 -2.4599 0.017549 0.008775 X 0.456638020998805 0.253008 1.8048 0.077375 0.038688 `Y(t-1)` 1.15233154517053 0.140525 8.2002 0 0 `Y(t-2)` -0.132883985021822 0.215596 -0.6164 0.540572 0.270286 `Y(t-3)` -0.136717309166669 0.245302 -0.5573 0.579884 0.289942 `Y(t-4)` 0.288076677389240 0.258323 1.1152 0.270325 0.135162 `Y(t-5)` -0.0444267752463386 0.16776 -0.2648 0.792279 0.39614 M1 2.60941417635306 4.094573 0.6373 0.526967 0.263483 M2 66.9697686993288 5.740297 11.6666 0 0 M3 13.9511567062971 9.436584 1.4784 0.14583 0.072915 M4 0.76530488075559 10.061094 0.0761 0.939683 0.469841 M5 -2.58819717511082 9.545484 -0.2711 0.787443 0.393722 M6 -17.7476852617300 9.149096 -1.9398 0.058289 0.029144 M7 5.99420155049773 4.263845 1.4058 0.166218 0.083109 M8 3.11341674291098 4.723967 0.6591 0.513001 0.256501 M9 -0.771493007730226 5.013167 -0.1539 0.878339 0.43917 M10 -4.40903117293207 5.186876 -0.85 0.399525 0.199763 M11 -2.59469677920587 3.527936 -0.7355 0.465631 0.232816 t -0.457581264982037 0.172888 -2.6467 0.010961 0.00548 Multiple Linear Regression - Regression Statistics Multiple R 0.995879165152403 R-squared 0.991775311584647 Adjusted R-squared 0.98869105342889 F-TEST (value) 321.560408208147 F-TEST (DF numerator) 18 F-TEST (DF denominator) 48 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.39725135046519 Sum Squared Residuals 1398.25546272472 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 431 441.116822983256 -10.1168229832557 2 484 486.218502125266 -2.21850212526644 3 510 500.78600065426 9.21399934574013 4 513 511.826339744647 1.17366025535347 5 503 503.477613479077 -0.477613479076982 6 471 484.631994511141 -13.6319945111414 7 471 471.798752016127 -0.798752016126502 8 476 476.300489547179 -0.300489547179060 9 475 479.034899249011 -4.03489924901063 10 470 468.869558832443 1.13044116755710 11 461 464.011358221509 -3.01135822150921 12 455 457.516708627322 -2.51670862732155 13 456 455.356806781775 0.643193218225054 14 517 512.504583673193 4.49541632680735 15 525 530.422970248381 -5.42297024838079 16 523 523.185172747388 -0.185172747387612 17 519 510.824072645417 8.17592735458284 18 509 502.320602726454 6.67939727354561 19 512 510.051754705814 1.94824529418566 20 519 513.580370112286 5.41962988771363 21 517 517.026612681994 -0.0266126819942674 22 510 510.419190042299 -0.419190042299460 23 509 503.093944288661 5.9060557113388 24 501 505.065072290592 -4.06507229059177 25 507 501.123500542677 5.87649945732348 26 569 563.403858910723 5.59614108927686 27 580 582.833161818317 -2.83316181831692 28 578 579.633174772325 -1.63317477232487 29 565 566.530717863766 -1.53071786376572 30 547 546.307451265728 0.692548734271657 31 555 550.488814173832 4.51118582616778 32 562 558.651540891901 3.34845910809889 33 561 560.984679361627 0.0153206383726432 34 555 556.274686832664 -1.2746868326641 35 544 548.522556279 -4.52255627899997 36 537 538.067659439906 -1.06765943990612 37 543 540.229067415193 2.77093258480693 38 594 600.836562447136 -6.83656244713572 39 611 607.67910984444 3.32089015556 40 613 612.399921479967 0.600078520033101 41 611 601.966113747853 9.0338862521472 42 594 592.957285641014 1.04271435898568 43 595 598.225558801828 -3.22555880182787 44 591 595.196418666733 -4.19641866673305 45 589 588.364534263266 0.635465736733481 46 584 581.569534993327 2.43046500667275 47 573 578.152987219608 -5.15298721960834 48 567 565.209378096901 1.79062190309889 49 569 569.40896659631 -0.408966596310248 50 621 621.497012726195 -0.497012726195316 51 629 632.308128837738 -3.30812883773794 52 628 627.451346099964 0.548653900036087 53 612 611.824815730069 0.175184269931155 54 595 593.024971102072 1.97502889792818 55 597 596.008775420746 0.991224579253566 56 593 597.2711807819 -4.27118078190041 57 590 586.589274444101 3.41072555589877 58 580 581.867029299266 -1.86702929926630 59 574 567.219153991221 6.78084600877873 60 573 567.141181545279 5.85881845472055 61 573 571.76483568079 1.23516431921043 62 620 620.539480117487 -0.539480117486715 63 626 626.970628596864 -0.970628596864477 64 620 620.50404515571 -0.504045155710183 65 588 603.376666533818 -15.3766665338185 66 566 562.75769475359 3.24230524641030 67 557 560.426344881653 -3.42634488165263 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 22 0.945301696466527 0.109396607066946 0.0546983035334728 23 0.956371062638483 0.0872578747230338 0.0436289373615169 24 0.927267176650401 0.145465646699198 0.072732823349599 25 0.902887334753056 0.194225330493888 0.097112665246944 26 0.880663608179335 0.238672783641329 0.119336391820665 27 0.861203841146589 0.277592317706822 0.138796158853411 28 0.833442320305853 0.333115359388293 0.166557679694147 29 0.809159597422938 0.381680805154124 0.190840402577062 30 0.741986873356598 0.516026253286804 0.258013126643402 31 0.657522689955267 0.684954620089466 0.342477310044733 32 0.597794387766836 0.804411224466328 0.402205612233164 33 0.533268714942308 0.933462570115384 0.466731285057692 34 0.451812766068561 0.903625532137122 0.548187233931439 35 0.405685566085889 0.811371132171777 0.594314433914111 36 0.418256357205204 0.836512714410408 0.581743642794796 37 0.315662939189519 0.631325878379038 0.68433706081048 38 0.39241196249685 0.7848239249937 0.60758803750315 39 0.288906748316459 0.577813496632917 0.711093251683541 40 0.20884992618528 0.41769985237056 0.79115007381472 41 0.637717457485602 0.724565085028796 0.362282542514398 42 0.521713239301276 0.956573521397448 0.478286760698724 43 0.444948705538771 0.889897411077541 0.555051294461229 44 0.346447843028887 0.692895686057774 0.653552156971113 45 0.224277307459137 0.448554614918274 0.775722692540863 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 0 0 OK 10% type I error level 1 0.0416666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/10lwm71260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/10lwm71260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/1y2qx1260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/1y2qx1260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/2yyot1260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/2yyot1260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/3ad181260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/3ad181260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/4x5os1260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/4x5os1260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/57wdk1260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/57wdk1260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/6z1ol1260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/6z1ol1260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/7jmfg1260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/7jmfg1260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/8tgs71260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/8tgs71260788205.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/9ez8k1260788205.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t1260788395ubjavm3t3h6f8s6/9ez8k1260788205.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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