textiel

*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, 20 Dec 2008 02:03:22 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf.htm/, Retrieved Sat, 20 Dec 2008 10:04:40 +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/2008/Dec/20/t1229763879zyj5423vph7k9lf.htm/}, year = {2008}, } @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 = {2008}, 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 «

101,3 11554,5 102 13182,1 109,2 14800,1 88,6 12150,7 94,3 14478,2 98,3 13253,9 86,4 12036,8 80,6 12653,2 104,1 14035,4 108,2 14571,4 93,4 15400,9 71,9 14283,2 94,1 14485,3 94,9 14196,3 96,4 15559,1 91,1 13767,4 84,4 14634 86,4 14381,1 88 12509,9 75,1 12122,3 109,7 13122,3 103 13908,7 82,1 13456,5 68 12441,6 96,4 12953 94,3 13057,2 90 14350,1 88 13830,2 76,1 13755,5 82,5 13574,4 81,4 12802,6 66,5 11737,3 97,2 13850,2 94,1 15081,8 80,7 13653,3 70,5 14019,1 87,8 13962 89,5 13768,7 99,6 14747,1 84,2 13858,1 75,1 13188 92 13693,1 80,8 12970 73,1 11392,8 99,8 13985,2 90 14994,7 83,1 13584,7 72,4 14257,8 78,8 13553,4 87,3 14007,3 91 16535,8 80,1 14721,4 73,6 13664,6 86,4 16405,9 74,5 13829,4 71,2 13735,6 92,4 15870,5 81,5 15962,4 85,3 15744,1 69,9 16083,7 84,2 14863,9 90,7 15533,1 100,3 17473,1 79,4 15925,5 84,8 15573,7 92,9 17495 81,6 14155,8 76 14913,9 98,7 17250,4 89,1 15879,8 88,7 17647,8 67,1 17749,9

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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation textiel[t] = + 83.1762254399543 -0.000892183092078553Invoer[t] + 19.3569098578666M1[t] + 22.3930421252442M2[t] + 28.4718012860539M3[t] + 14.5853361786827M4[t] + 10.8900853360037M5[t] + 19.7785898932271M6[t] + 10.5840997156565M7[t] + 1.95730219877615M8[t] + 30.2427447226140M9[t] + 24.5824880440775M10[t] + 15.6802838960059M11[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) 83.1762254399543 9.374567 8.8725 0 0 Invoer -0.000892183092078553 0.00061 -1.4633 0.148695 0.074348 M1 19.3569098578666 3.654582 5.2966 2e-06 1e-06 M2 22.3930421252442 3.612257 6.1992 0 0 M3 28.4718012860539 3.605852 7.896 0 0 M4 14.5853361786827 3.605219 4.0456 0.000154 7.7e-05 M5 10.8900853360037 3.593091 3.0308 0.003618 0.001809 M6 19.7785898932271 3.575026 5.5324 1e-06 0 M7 10.5840997156565 3.731751 2.8362 0.006245 0.003123 M8 1.95730219877615 3.786561 0.5169 0.607153 0.303576 M9 30.2427447226140 3.575776 8.4577 0 0 M10 24.5824880440775 3.578554 6.8694 0 0 M11 15.6802838960059 3.575639 4.3853 4.8e-05 2.4e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.84523090617935 R-squared 0.714415284760764 Adjusted R-squared 0.656330257932445 F-TEST (value) 12.2994741290617 F-TEST (DF numerator) 12 F-TEST (DF denominator) 59 p-value 5.26112486909369e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.19212480324673 Sum Squared Residuals 2262.20216516002 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.3 92.2244057603991 9.07559423960088 2 102 93.8084208271098 8.19157917289022 3 109.2 98.4436277449365 10.7563722550635 4 88.6 86.9209125217181 1.67908747828187 5 94.3 81.1491055322263 13.1508944677737 6 98.3 91.1299098490815 7.17009015091849 7 86.4 83.0212957128797 3.37870428712030 8 80.6 73.8445565380421 6.75544346195787 9 104.1 100.896823592009 3.20317640799105 10 108.2 94.7583567761183 13.4416432238817 11 93.4 85.1160867531676 8.2839132468324 12 71.9 70.4329958991779 1.46700410082208 13 94.1 89.6095955541354 4.49040444586457 14 94.9 92.9035687351237 1.9964312648763 15 96.4 97.7664607780489 -1.36646077804884 16 91.1 85.4785201167547 5.62147988324527 17 84.4 81.0101034064805 3.38989659351954 18 86.4 90.1242410676905 -3.72424106769055 19 88 82.5992038920174 5.40079610798264 20 75.1 74.3182165416266 0.781783458373367 21 109.7 101.711475973386 7.98852402661413 22 103 95.3496065112388 7.65039348876119 23 82.1 86.8508475574051 -4.75084755740513 24 68 72.0760402815498 -4.07604028154979 25 96.4 90.9766877061274 5.42331229387261 26 94.3 93.9198544953104 0.380145504689613 27 90 98.8451101363718 -8.84511013637182 28 88 85.4224910185722 2.57750898142782 29 76.1 81.7938862528715 -5.69388625287149 30 82.5 90.8439651680703 -8.34396516807032 31 81.4 82.338061900966 -0.938061900965958 32 66.5 74.6617070320769 -8.16170703207686 33 97.2 101.062055900662 -3.86205590066189 34 94.1 94.3029865259215 -0.202986525921468 35 80.7 86.675265924884 -5.97526592488407 36 70.5 70.6686214537959 -0.168621453795869 37 87.8 90.0764749662201 -2.27647496622014 38 89.5 93.2850662252965 -3.78506622529649 39 99.6 98.4909134488166 1.10908655118336 40 84.2 85.3975991103032 -1.19759911030319 41 75.1 82.300200157626 -7.20020015762606 42 92 90.7380630350406 1.2619369649594 43 80.8 82.188710451352 -1.38871045135201 44 73.1 74.969064107298 -1.86906410729794 45 99.8 100.941611183231 -1.14161118323129 46 90 94.3806956732415 -4.38069567324151 47 83.1 86.7364696850007 -3.63646968500067 48 72.4 70.4556573497167 1.94434265028328 49 78.8 90.4410209776434 -11.6410209776434 50 87.3 93.0721913395266 -5.77219133952655 51 91 96.8950655520157 -5.89506555201573 52 80.1 84.6273774469118 -4.52737744691179 53 73.6 81.8749856959414 -8.27498569594142 54 86.4 88.3177487428499 -1.91774874284989 55 74.5 81.4219683020197 -6.9219683020197 56 71.2 72.8788575591763 -1.67885755917629 57 92.4 99.2595783997356 -6.8595783997356 58 81.5 93.517330095037 -12.0173300950371 59 85.3 84.8098895159662 0.490110484033764 60 69.9 68.8266202418905 1.07337975810952 61 84.2 89.2718150354745 -5.07181503547449 62 90.7 91.710898377633 -1.01089837763309 63 100.3 96.0588223398105 4.24117766018949 64 79.4 83.55309978574 -4.15309978573999 65 84.8 80.1717189548543 4.62828104514575 66 92.9 87.3460721372671 5.55392786273286 67 81.6 81.1307597407653 0.46924025923473 68 76 71.8275982217801 4.17240177821986 69 98.7 98.0284549509764 0.671545049023605 70 89.1 93.5910244184428 -4.49102441844278 71 88.7 83.1114405635763 5.58855943642371 72 67.1 67.3400647738692 -0.240064773869212 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.662666436146828 0.674667127706345 0.337333563853172 17 0.718740964431832 0.562518071136336 0.281259035568168 18 0.714441831364224 0.571116337271552 0.285558168635776 19 0.643082168855687 0.713835662288625 0.356917831144313 20 0.619752777468639 0.760494445062722 0.380247222531361 21 0.613456818388897 0.773086363222206 0.386543181611103 22 0.745374649992104 0.509250700015793 0.254625350007896 23 0.895456265247089 0.209087469505823 0.104543734752911 24 0.867298517119042 0.265402965761917 0.132701482880958 25 0.90951264076818 0.180974718463639 0.0904873592318196 26 0.902311284029019 0.195377431941963 0.0976887159709815 27 0.962713619032208 0.0745727619355831 0.0372863809677915 28 0.960371579987751 0.0792568400244974 0.0396284200122487 29 0.978961447148564 0.042077105702871 0.0210385528514355 30 0.987042131635738 0.0259157367285237 0.0129578683642618 31 0.983264810618646 0.0334703787627084 0.0167351893813542 32 0.990239548135471 0.0195209037290578 0.00976045186452892 33 0.989569270749261 0.0208614585014771 0.0104307292507386 34 0.994988694973841 0.0100226100523175 0.00501130502615875 35 0.994243396280654 0.0115132074386913 0.00575660371934565 36 0.989734215078025 0.0205315698439496 0.0102657849219748 37 0.992372496286951 0.0152550074260972 0.00762750371304858 38 0.989185195658533 0.0216296086829338 0.0108148043414669 39 0.985194762881432 0.0296104742371368 0.0148052371185684 40 0.982505765048924 0.0349884699021524 0.0174942349510762 41 0.9807988133487 0.038402373302599 0.0192011866512995 42 0.97360657586164 0.0527868482767195 0.0263934241383597 43 0.963207866594357 0.0735842668112861 0.0367921334056431 44 0.941950034164081 0.116099931671838 0.0580499658359189 45 0.947028785830997 0.105942428338006 0.052971214169003 46 0.958030560911111 0.0839388781777777 0.0419694390888889 47 0.931172892668364 0.137654214663273 0.0688271073316363 48 0.972509620283222 0.0549807594335555 0.0274903797167777 49 0.969482815172761 0.0610343696544776 0.0305171848272388 50 0.948801531608643 0.102396936782714 0.0511984683913569 51 0.94483004386335 0.1103399122733 0.05516995613665 52 0.920517535020005 0.158964929959990 0.0794824649799951 53 0.909851043565391 0.180297912869217 0.0901489564346087 54 0.860841736108943 0.278316527782114 0.139158263891057 55 0.847766055445305 0.304467889109391 0.152233944554695 56 0.734544172302492 0.530911655395015 0.265455827697508 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 13 0.317073170731707 NOK 10% type I error level 20 0.48780487804878 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/10jtwy1229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/10jtwy1229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/19yis1229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/19yis1229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/2qlt31229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/2qlt31229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/3aks11229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/3aks11229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/4oqz71229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/4oqz71229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/55dc11229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/55dc11229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/60c0b1229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/60c0b1229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/7w9wo1229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/7w9wo1229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/8fzoz1229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/8fzoz1229763789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/9613y1229763789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t1229763879zyj5423vph7k9lf/9613y1229763789.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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