Multiple regression analysis

*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: Wed, 18 Nov 2009 08:18:10 -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/18/t1258557778013k385phxinnx1.htm/, Retrieved Wed, 18 Nov 2009 16:23:11 +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/18/t1258557778013k385phxinnx1.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:

Er worden in deze berekening 2 exogene reeksen opgenomen. Met deze berekenign wil ik controleren of de totale industriële productie een groter effect heeft op de werkloosheid dan de afzetprijzen.

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

8.4 99 100.5 8.4 98.6 100.5 8.4 98.6 100.7 8.6 98.5 101.1 8.9 98.9 101.5 8.8 99.4 101.9 8.3 99.8 102.1 7.5 99.9 102.1 7.2 100 102.1 7.4 100.1 102.4 8.8 100.1 102.8 9.3 100.2 103.1 9.3 100.3 103.1 8.7 100 102.9 8.2 99.9 102.4 8.3 99.4 101.9 8.5 99.8 101.3 8.6 99.6 100.7 8.5 100 100.6 8.2 99.9 101 8.1 100.3 101.5 7.9 100.6 101.9 8.6 100.7 102.1 8.7 100.8 102.3 8.7 100.8 102.5 8.5 100.6 102.9 8.4 101.1 103.6 8.5 101.1 104.3 8.7 100.9 104.8 8.7 101.1 105.2 8.6 101.2 105.7 8.5 101.4 106.1 8.3 101.9 106.4 8 102.1 106.3 8.2 102.1 106.3 8.1 103 106.5 8.1 103.4 107.1 8 103.2 107.7 7.9 103.1 108.3 7.9 103 109 8 103.7 109.3 8 103.4 109.6 7.9 103.5 109.7 8 103.8 109.7 7.7 104 109.5 7.2 104.2 109.3 7.5 104.4 109 7.3 104.4 108.8 7 104.9 108.8 7 105.3 108.8 7 105.2 108.7 7.2 105.4 108.3 7.3 105.4 107.8 7.1 105.5 107.2 6.8 105.7 106.4 6.4 105.6 105.7 6.1 105.8 105.3 6.5 105.4 105 7.7 105.5 104.6 7.9 105.8 104 7.5 106.1 103 6.9 106 101.6 6.6 105.5 100.2 6.9 105.4 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 werkl[t] = + 29.8377683055636 -0.188151055754045afzetp[t] -0.0249996487779284iprod[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) 29.8377683055636 2.671935 11.1671 0 0 afzetp -0.188151055754045 0.022575 -8.3344 0 0 iprod -0.0249996487779284 0.014836 -1.6851 0.096489 0.048245 Multiple Linear Regression - Regression Statistics Multiple R 0.722190580035731 R-squared 0.521559233892346 Adjusted R-squared 0.50769138559937 F-TEST (value) 37.6092399393013 F-TEST (DF numerator) 2 F-TEST (DF denominator) 69 p-value 8.99647023544503e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.490516263581652 Sum Squared Residuals 16.6018281338292 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.4 8.69834908373133 -0.298349083731329 2 8.4 8.773609506033 -0.373609506032996 3 8.4 8.7686095762774 -0.368609576277408 4 8.6 8.77742482234164 -0.177424822341641 5 8.9 8.69216454052885 0.207835459471150 6 8.8 8.58808915314066 0.211910846859345 7 8.3 8.50782880108345 -0.207828801083453 8 7.5 8.48901369550805 -0.989013695508048 9 7.2 8.47019858993264 -1.27019858993264 10 7.4 8.44388358972386 -1.04388358972386 11 8.8 8.43388373021269 0.366116269787310 12 9.3 8.4075687300039 0.892431269996094 13 9.3 8.3887536244285 0.911246375571498 14 8.7 8.4501988709103 0.249801129089698 15 8.2 8.48151380087467 -0.28151380087467 16 8.3 8.58808915314066 -0.288089153140655 17 8.5 8.5278285201058 -0.0278285201057966 18 8.6 8.58045852052336 0.0195414794766365 19 8.5 8.50769806309954 -0.00769806309953693 20 8.2 8.51651330916377 -0.31651330916377 21 8.1 8.42875306247319 -0.328753062473189 22 7.9 8.36230788623580 -0.462307886235803 23 8.6 8.33849285090481 0.261507149095188 24 8.7 8.31467781557382 0.385322184426176 25 8.7 8.30967788581824 0.390322114181762 26 8.5 8.33730823745788 0.162691762542125 27 8.4 8.2257329554363 0.174267044563697 28 8.5 8.20823320129175 0.291766798708247 29 8.7 8.2333635880536 0.466636411946403 30 8.7 8.18573351739162 0.514266482608382 31 8.6 8.15441858742725 0.445581412572752 32 8.5 8.10678851676527 0.393211483234733 33 8.3 8.00521309425486 0.294786905745136 34 8 7.97008284798185 0.0299171520181488 35 8.2 7.97008284798185 0.229917152018148 36 8.1 7.79574696804762 0.304253031952376 37 8.1 7.70548675647925 0.394513243520752 38 8 7.7281171783633 0.2718828216367 39 7.9 7.73193249467195 0.168067505328051 40 7.9 7.7332478461028 0.166752153897198 41 8 7.59404221244159 0.405957787558408 42 8 7.64298763453443 0.357012365465573 43 7.9 7.62167256408123 0.278327435918770 44 8 7.56522724735502 0.434772752644983 45 7.7 7.5325969659598 0.167403034040207 46 7.2 7.49996668456457 -0.299966684564569 47 7.5 7.46983636804714 0.0301636319528618 48 7.3 7.47483629780272 -0.174836297802724 49 7 7.3807607699257 -0.380760769925701 50 7 7.30550034762408 -0.305500347624085 51 7 7.32681541807728 -0.326815418077281 52 7.2 7.29918506643764 -0.0991850664376426 53 7.3 7.3116848908266 -0.0116848908266071 54 7.1 7.30786957451796 -0.207869574517961 55 6.8 7.29023908238949 -0.490239082389494 56 6.4 7.32655394210945 -0.926553942109449 57 6.1 7.29892359046981 -1.19892359046981 58 6.5 7.3816839074048 -0.881683907404806 59 7.7 7.37286866134057 0.327131338659426 60 7.9 7.33142313388112 0.568576866118882 61 7.5 7.29997746593283 0.200022534067167 62 6.9 7.35379207979734 -0.453792079797336 63 6.6 7.48286711596346 -0.88286711596346 64 6.9 7.53168180007238 -0.631681800072376 65 7.7 7.44129085052009 0.258709149479914 66 8 7.43997549908923 0.560024500910768 67 8 7.39603000675198 0.603969993248019 68 7.7 7.48629021832036 0.213709781679643 69 7.3 7.49629007783153 -0.196290077831528 70 7.4 7.50129000758711 -0.101290007587114 71 8.1 7.5062899373427 0.5937100626573 72 8.3 7.49247476152288 0.80752523847712 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0122092883998943 0.0244185767997886 0.987790711600106 7 0.0452850518383918 0.0905701036767836 0.954714948161608 8 0.286980345958293 0.573960691916585 0.713019654041707 9 0.459740867666909 0.919481735333818 0.540259132333091 10 0.463084444205076 0.926168888410151 0.536915555794924 11 0.655532946521112 0.688934106957777 0.344467053478888 12 0.821125833372913 0.357748333254174 0.178874166627087 13 0.891900424983415 0.216199150033169 0.108099575016585 14 0.848459089294904 0.303081821410192 0.151540910705096 15 0.81098310451689 0.378033790966220 0.189016895483110 16 0.784836533209321 0.430326933581358 0.215163466790679 17 0.893229643768662 0.213540712462676 0.106770356231338 18 0.930572724388262 0.138854551223477 0.0694272756117385 19 0.933401705667794 0.133196588664413 0.0665982943322065 20 0.924786182178157 0.150427635643687 0.0752138178218433 21 0.9210300560635 0.157939887873000 0.0789699439364998 22 0.937496628676343 0.125006742647314 0.062503371323657 23 0.929332294606131 0.141335410787738 0.070667705393869 24 0.9180260616828 0.163947876634399 0.0819739383171995 25 0.898583738313502 0.202832523372995 0.101416261686498 26 0.876036711335697 0.247926577328607 0.123963288664303 27 0.849331878461741 0.301336243076518 0.150668121538259 28 0.813050569565847 0.373898860868306 0.186949430434153 29 0.76364387327354 0.472712253452919 0.236356126726460 30 0.70609177512306 0.58781644975388 0.29390822487694 31 0.649475839007717 0.701048321984567 0.350524160992283 32 0.595903103019503 0.808193793960995 0.404096896980497 33 0.551491216107696 0.897017567784608 0.448508783892304 34 0.564001698963691 0.871996602072617 0.435998301036309 35 0.530133260291388 0.939733479417224 0.469866739708612 36 0.46666992424761 0.93333984849522 0.53333007575239 37 0.40034050456856 0.80068100913712 0.59965949543144 38 0.345254758716307 0.690509517432615 0.654745241283693 39 0.309026886640270 0.618053773280541 0.69097311335973 40 0.276864438764569 0.553728877529138 0.723135561235431 41 0.232891146922964 0.465782293845927 0.767108853077036 42 0.190644968291570 0.381289936583140 0.80935503170843 43 0.156451571645127 0.312903143290254 0.843548428354873 44 0.150533767183703 0.301067534367407 0.849466232816297 45 0.144363171335860 0.288726342671721 0.85563682866414 46 0.145506218580627 0.291012437161254 0.854493781419373 47 0.145680111331681 0.291360222663361 0.85431988866832 48 0.167343536578548 0.334687073157096 0.832656463421452 49 0.164031700789208 0.328063401578417 0.835968299210792 50 0.134116910046079 0.268233820092157 0.865883089953921 51 0.115601491335328 0.231202982670657 0.884398508664672 52 0.108037141144502 0.216074282289004 0.891962858855498 53 0.133676044145268 0.267352088290536 0.866323955854732 54 0.127420573747580 0.254841147495161 0.87257942625242 55 0.0895621360953577 0.179124272190715 0.910437863904642 56 0.078225773277814 0.156451546555628 0.921774226722186 57 0.255295623095381 0.510591246190761 0.744704376904619 58 0.243171455668576 0.486342911337152 0.756828544331424 59 0.465209480659011 0.930418961318022 0.534790519340989 60 0.8541065203091 0.291786959381801 0.145893479690900 61 0.870704470985266 0.258591058029468 0.129295529014734 62 0.814930415412502 0.370139169174996 0.185069584587498 63 0.75435520253841 0.49128959492318 0.24564479746159 64 0.631736180406399 0.736527639187201 0.368263819593601 65 0.532596593631382 0.934806812737236 0.467403406368618 66 0.640527303342784 0.718945393314432 0.359472696657216 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 1 0.0163934426229508 OK 10% type I error level 2 0.0327868852459016 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/101kq51258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/101kq51258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/1oew61258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/1oew61258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/2dxlz1258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/2dxlz1258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/32jnq1258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/32jnq1258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/4tngt1258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/4tngt1258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/5xnb31258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/5xnb31258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/6vwp81258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/6vwp81258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/73i2z1258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/73i2z1258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/8iwuo1258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/8iwuo1258557485.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/9h0gl1258557485.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258557778013k385phxinnx1/9h0gl1258557485.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') }

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