Model 1

*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, 27 Dec 2010 20:33:39 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772.htm/, Retrieved Mon, 27 Dec 2010 21:31:42 +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/2010/Dec/27/t1293481891fd1bqbrkdxxn772.htm/}, year = {2010}, } @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 = {2010}, 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 «

4.24 3.353 4.15 3.186 3.93 3.902 3.7 4.164 3.7 3.499 3.65 4.145 3.55 3.796 3.43 3.711 3.47 3.949 3.58 3.74 3.67 3.243 3.72 4.407 3.8 4.814 3.76 3.908 3.63 5.25 3.48 3.937 3.41 4.004 3.43 5.56 3.5 3.922 3.62 3.759 3.58 4.138 3.52 4.634 3.45 3.996 3.36 4.308 3.27 4.143 3.21 4.429 3.19 5.219 3.16 4.929 3.12 5.761 3.06 5.592 3.01 4.163 2.98 4.962 2.97 5.208 3.02 4.755 3.07 4.491 3.18 5.732 3.29 5.731 3.43 5.04 3.61 6.102 3.74 4.904 3.87 5.369 3.88 5.578 4.09 4.619 4.19 4.731 4.2 5.011 4.29 5.299 4.37 4.146 4.47 4.625 4.61 4.736 4.65 4.219 4.69 5.116 4.82 4.205 4.86 4.121 4.87 5.103 5.01 4.3 5.03 4.578 5.13 3.809 5.18 5.657 5.21 4.248 5.26 3.83 5.25 4.736 5.2 4.839 5.16 4.411 5.19 4.57 5.39 4.104 5.58 4.801 5.76 3.953 5.89 3.828 5.98 4.44 6.02 4.026 5.62 4.109 4.87 4.785

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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Lening[t] = + 5.21136043574033 -0.240415648462039Huis[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) 5.21136043574033 0.706047 7.381 0 0 Huis -0.240415648462039 0.155077 -1.5503 0.125581 0.06279 Multiple Linear Regression - Regression Statistics Multiple R 0.182194206333795 R-squared 0.0331947288216015 Adjusted R-squared 0.0193832249476245 F-TEST (value) 2.40341161429533 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0.125580634006007 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.862933170964766 Sum Squared Residuals 52.1257560285915 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.24 4.40524676644709 -0.165246766447087 2 4.15 4.44539617974028 -0.295396179740276 3 3.93 4.27325857544146 -0.343258575441456 4 3.7 4.2102696755444 -0.510269675544402 5 3.7 4.37014608177166 -0.670146081771658 6 3.65 4.21483757286518 -0.564837572865181 7 3.55 4.29874263417843 -0.748742634178433 8 3.43 4.31917796429771 -0.889177964297706 9 3.47 4.26195903996374 -0.79195903996374 10 3.58 4.31220591049231 -0.732205910492306 11 3.67 4.43169248777794 -0.76169248777794 12 3.72 4.15184867296813 -0.431848672968126 13 3.8 4.05399950404408 -0.253999504044077 14 3.76 4.27181608155068 -0.511816081550684 15 3.63 3.94917828131463 -0.319178281314628 16 3.48 4.26484402774529 -0.784844027745285 17 3.41 4.24873617929833 -0.838736179298328 18 3.43 3.8746494302914 -0.444649430291396 19 3.5 4.26845026247222 -0.768450262472215 20 3.62 4.30763801317153 -0.687638013171528 21 3.58 4.21652048240442 -0.636520482404415 22 3.52 4.09727432076724 -0.577274320767244 23 3.45 4.25065950448602 -0.800659504486024 24 3.36 4.17564982216587 -0.815649822165869 25 3.27 4.21531840416210 -0.945318404162105 26 3.21 4.14655952870196 -0.936559528701962 27 3.19 3.95663116641695 -0.766631166416951 28 3.16 4.02635170447094 -0.866351704470942 29 3.12 3.82632588495053 -0.706325884950526 30 3.06 3.86695612954061 -0.80695612954061 31 3.01 4.21051009119286 -1.20051009119286 32 2.98 4.01841798807170 -1.03841798807170 33 2.97 3.95927573855003 -0.989275738550033 34 3.02 4.06818402730334 -1.04818402730334 35 3.07 4.13165375849732 -1.06165375849732 36 3.18 3.83329793875592 -0.653297938755925 37 3.29 3.83353835440439 -0.543538354404387 38 3.43 3.99966556749166 -0.569665567491656 39 3.61 3.74434414882497 -0.134344148824971 40 3.74 4.03236209568249 -0.292362095682493 41 3.87 3.92056881914765 -0.0505688191476452 42 3.88 3.87032194861908 0.00967805138092084 43 4.09 4.10088055549417 -0.0108805554941746 44 4.19 4.07395400286643 0.116045997133574 45 4.2 4.00663762129706 0.193362378702945 46 4.29 3.93739791453999 0.352602085460012 47 4.37 4.21459715721672 0.155402842783281 48 4.47 4.0994380616034 0.370561938396598 49 4.61 4.07275192462412 0.537248075375884 50 4.65 4.19704681487899 0.452953185121010 51 4.69 3.98139397820854 0.708606021791459 52 4.82 4.20041263395746 0.619587366042542 53 4.86 4.22060754842827 0.63939245157173 54 4.87 3.98451938163855 0.885480618361452 55 5.01 4.17757314735356 0.832426852646435 56 5.03 4.11073759708112 0.919262402918882 57 5.13 4.29561723074843 0.834382769251574 58 5.18 3.85132911239058 1.32867088760942 59 5.21 4.19007476107359 1.01992523892641 60 5.26 4.29056850213072 0.969431497869277 61 5.25 4.07275192462412 1.17724807537588 62 5.2 4.04798911283253 1.15201088716747 63 5.16 4.15088701037428 1.00911298962572 64 5.19 4.11266092226881 1.07733907773119 65 5.39 4.22469461445212 1.16530538554788 66 5.58 4.05712490747408 1.52287509252592 67 5.76 4.26099737736989 1.49900262263011 68 5.89 4.29104933342765 1.59895066657235 69 5.98 4.14391495656888 1.83608504343112 70 6.02 4.24344703503216 1.77655296496784 71 5.62 4.22349253620981 1.39650746379019 72 4.87 4.06097155784948 0.809028442150524 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0176930363770394 0.0353860727540789 0.98230696362296 6 0.00356759214362445 0.0071351842872489 0.996432407856376 7 0.00194429412344306 0.00388858824688611 0.998055705876557 8 0.00195776915964717 0.00391553831929435 0.998042230840353 9 0.000708187536073354 0.00141637507214671 0.999291812463927 10 0.000240715686850729 0.000481431373701458 0.99975928431315 11 0.000134659429859371 0.000269318859718742 0.99986534057014 12 5.20234986537778e-05 0.000104046997307556 0.999947976501346 13 2.52990842040531e-05 5.05981684081061e-05 0.999974700915796 14 7.02630076004432e-06 1.40526015200886e-05 0.99999297369924 15 1.81111026660676e-06 3.62222053321353e-06 0.999998188889733 16 9.17608129057805e-07 1.83521625811561e-06 0.99999908239187 17 6.11638068401631e-07 1.22327613680326e-06 0.999999388361932 18 1.55291801321844e-07 3.10583602643689e-07 0.999999844708199 19 7.19418306917439e-08 1.43883661383488e-07 0.99999992805817 20 2.69940392075777e-08 5.39880784151553e-08 0.99999997300596 21 9.21388219769308e-09 1.84277643953862e-08 0.999999990786118 22 2.69106547109863e-09 5.38213094219726e-09 0.999999997308935 23 1.86764659327223e-09 3.73529318654446e-09 0.999999998132353 24 1.51815449442456e-09 3.03630898884912e-09 0.999999998481846 25 3.29231936680435e-09 6.58463873360871e-09 0.99999999670768 26 6.41058397086951e-09 1.28211679417390e-08 0.999999993589416 27 3.34603448203515e-09 6.6920689640703e-09 0.999999996653965 28 2.96661950766719e-09 5.93323901533438e-09 0.99999999703338 29 9.68561963087527e-10 1.93712392617505e-09 0.999999999031438 30 4.26640623129398e-10 8.53281246258796e-10 0.99999999957336 31 1.48594287055889e-08 2.97188574111778e-08 0.999999985140571 32 4.14501654468861e-08 8.29003308937723e-08 0.999999958549834 33 6.87848371831897e-08 1.37569674366379e-07 0.999999931215163 34 4.12373247227694e-07 8.24746494455389e-07 0.999999587626753 35 8.54757958023726e-06 1.70951591604745e-05 0.99999145242042 36 6.75692434428835e-06 1.35138486885767e-05 0.999993243075656 37 6.2217921691828e-06 1.24435843383656e-05 0.99999377820783 38 1.54814742884605e-05 3.0962948576921e-05 0.999984518525711 39 3.55336859995537e-05 7.10673719991074e-05 0.999964466314 40 0.000117788016928198 0.000235576033856395 0.999882211983072 41 0.000358724143286684 0.000717448286573369 0.999641275856713 42 0.000909426331443361 0.00181885266288672 0.999090573668557 43 0.00429960581167416 0.00859921162334832 0.995700394188326 44 0.0168600337562273 0.0337200675124547 0.983139966243773 45 0.0470367834138312 0.0940735668276624 0.952963216586169 46 0.104072274091047 0.208144548182094 0.895927725908953 47 0.271234614327616 0.542469228655232 0.728765385672384 48 0.461979172144362 0.923958344288725 0.538020827855638 49 0.631677709244029 0.736644581511942 0.368322290755971 50 0.802532421682072 0.394935156635855 0.197467578317928 51 0.868752037328291 0.262495925343417 0.131247962671709 52 0.931821733800724 0.136356532398552 0.0681782661992761 53 0.968614098691346 0.0627718026173082 0.0313859013086541 54 0.975434898537601 0.0491302029247971 0.0245651014623985 55 0.983682184177218 0.0326356316455642 0.0163178158227821 56 0.986550044393423 0.0268999112131546 0.0134499556065773 57 0.992158299901714 0.0156834001965727 0.00784170009828635 58 0.992515418602058 0.0149691627958848 0.0074845813979424 59 0.992068081292068 0.0158638374158635 0.00793191870793176 60 0.996049677412279 0.00790064517544259 0.00395032258772129 61 0.992883986200957 0.0142320275980860 0.00711601379904299 62 0.986524511761123 0.0269509764777543 0.0134754882388771 63 0.982238914750448 0.0355221704991046 0.0177610852495523 64 0.970265849121553 0.0594683017568946 0.0297341508784473 65 0.961562050518156 0.0768758989636878 0.0384379494818439 66 0.942522786379072 0.114954427241855 0.0574772136209276 67 0.878056178473347 0.243887643053306 0.121943821526653 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 39 0.619047619047619 NOK 5% type I error level 50 0.793650793650794 NOK 10% type I error level 54 0.857142857142857 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/10f9q1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/10f9q1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/10ofo21293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/10ofo21293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/2toqt1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/2toqt1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/3toqt1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/3toqt1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/4toqt1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/4toqt1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/53x8w1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/53x8w1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/63x8w1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/63x8w1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/7e67z1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/7e67z1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/8e67z1293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/8e67z1293482010.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/9ofo21293482010.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293481891fd1bqbrkdxxn772/9ofo21293482010.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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