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R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 24 Nov 2008 10:49:22 -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/Nov/24/t1227549055sm03g05q59ar3jn.htm/, Retrieved Mon, 24 Nov 2008 17:50:55 +0000

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/Nov/24/t1227549055sm03g05q59ar3jn.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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7,3 0 7,1 0 6,9 0 6,8 0 7,5 0 7,6 0 7,8 0 8 0 8,1 0 8,2 0 8,3 0 8,2 0 8 0 7,9 0 7,6 0 7,6 0 8,2 0 8,3 0 8,4 0 8,4 0 8,4 0 8,6 0 8,9 0 8,8 0 8,3 0 7,5 0 7,2 0 7,5 0 8,8 0 9,3 0 9,3 0 8,7 0 8,2 0 8,3 0 8,5 0 8,6 0 8,6 0 8,2 0 8,1 0 8 0 8,6 0 8,7 0 8,8 0 8,5 0 8,4 0 8,5 0 8,7 0 8,7 0 8,6 0 8,5 0 8,3 0 8,1 0 8,2 0 8,1 0 8,1 0 7,9 0 7,9 0 7,9 0 8 0 8 0 7,9 0 8 0 7,7 1 7,2 1 7,5 1 7,3 1 7 1 7 1 7 1 7,2 1 7,3 1 7,1 1 6,8 1 6,6 1 6,2 1 6,2 1 6,8 1 6,9 1 6,8 1

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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation y[t] = + 8.16774193548387 -1.19127134724858x[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) 8.16774193548387 0.063063 129.5167 0 0 x -1.19127134724858 0.135946 -8.7628 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.706618073433049 R-squared 0.499309101702234 Adjusted R-squared 0.492806622503562 F-TEST (value) 76.7874969602648 F-TEST (DF numerator) 1 F-TEST (DF denominator) 77 p-value 3.43725048423948e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.496560535097573 Sum Squared Residuals 18.9860721062619 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.3 8.16774193548387 -0.867741935483866 2 7.1 8.16774193548387 -1.06774193548387 3 6.9 8.16774193548387 -1.26774193548387 4 6.8 8.16774193548387 -1.36774193548387 5 7.5 8.16774193548387 -0.667741935483871 6 7.6 8.16774193548387 -0.567741935483871 7 7.8 8.16774193548387 -0.367741935483871 8 8 8.16774193548387 -0.167741935483871 9 8.1 8.16774193548387 -0.0677419354838714 10 8.2 8.16774193548387 0.0322580645161282 11 8.3 8.16774193548387 0.132258064516130 12 8.2 8.16774193548387 0.0322580645161282 13 8 8.16774193548387 -0.167741935483871 14 7.9 8.16774193548387 -0.267741935483871 15 7.6 8.16774193548387 -0.567741935483871 16 7.6 8.16774193548387 -0.567741935483871 17 8.2 8.16774193548387 0.0322580645161282 18 8.3 8.16774193548387 0.132258064516130 19 8.4 8.16774193548387 0.232258064516129 20 8.4 8.16774193548387 0.232258064516129 21 8.4 8.16774193548387 0.232258064516129 22 8.6 8.16774193548387 0.432258064516129 23 8.9 8.16774193548387 0.73225806451613 24 8.8 8.16774193548387 0.63225806451613 25 8.3 8.16774193548387 0.132258064516130 26 7.5 8.16774193548387 -0.667741935483871 27 7.2 8.16774193548387 -0.96774193548387 28 7.5 8.16774193548387 -0.667741935483871 29 8.8 8.16774193548387 0.63225806451613 30 9.3 8.16774193548387 1.13225806451613 31 9.3 8.16774193548387 1.13225806451613 32 8.7 8.16774193548387 0.532258064516128 33 8.2 8.16774193548387 0.0322580645161282 34 8.3 8.16774193548387 0.132258064516130 35 8.5 8.16774193548387 0.332258064516129 36 8.6 8.16774193548387 0.432258064516129 37 8.6 8.16774193548387 0.432258064516129 38 8.2 8.16774193548387 0.0322580645161282 39 8.1 8.16774193548387 -0.0677419354838714 40 8 8.16774193548387 -0.167741935483871 41 8.6 8.16774193548387 0.432258064516129 42 8.7 8.16774193548387 0.532258064516128 43 8.8 8.16774193548387 0.63225806451613 44 8.5 8.16774193548387 0.332258064516129 45 8.4 8.16774193548387 0.232258064516129 46 8.5 8.16774193548387 0.332258064516129 47 8.7 8.16774193548387 0.532258064516128 48 8.7 8.16774193548387 0.532258064516128 49 8.6 8.16774193548387 0.432258064516129 50 8.5 8.16774193548387 0.332258064516129 51 8.3 8.16774193548387 0.132258064516130 52 8.1 8.16774193548387 -0.0677419354838714 53 8.2 8.16774193548387 0.0322580645161282 54 8.1 8.16774193548387 -0.0677419354838714 55 8.1 8.16774193548387 -0.0677419354838714 56 7.9 8.16774193548387 -0.267741935483871 57 7.9 8.16774193548387 -0.267741935483871 58 7.9 8.16774193548387 -0.267741935483871 59 8 8.16774193548387 -0.167741935483871 60 8 8.16774193548387 -0.167741935483871 61 7.9 8.16774193548387 -0.267741935483871 62 8 8.16774193548387 -0.167741935483871 63 7.7 6.9764705882353 0.723529411764706 64 7.2 6.9764705882353 0.223529411764706 65 7.5 6.9764705882353 0.523529411764706 66 7.3 6.9764705882353 0.323529411764706 67 7 6.9764705882353 0.0235294117647059 68 7 6.9764705882353 0.0235294117647059 69 7 6.9764705882353 0.0235294117647059 70 7.2 6.9764705882353 0.223529411764706 71 7.3 6.9764705882353 0.323529411764706 72 7.1 6.9764705882353 0.123529411764706 73 6.8 6.9764705882353 -0.176470588235294 74 6.6 6.9764705882353 -0.376470588235294 75 6.2 6.9764705882353 -0.776470588235294 76 6.2 6.9764705882353 -0.776470588235294 77 6.8 6.9764705882353 -0.176470588235294 78 6.9 6.9764705882353 -0.0764705882352937 79 6.8 6.9764705882353 -0.176470588235294 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.395955102726488 0.791910205452975 0.604044897273512 6 0.441175234689827 0.882350469379654 0.558824765310173 7 0.554824598054538 0.890350803890924 0.445175401945462 8 0.697310931154521 0.605378137690958 0.302689068845479 9 0.788981353958602 0.422037292082796 0.211018646041398 10 0.851809752189731 0.296380495620538 0.148190247810269 11 0.896664960681785 0.206670078636431 0.103335039318215 12 0.900481737073434 0.199036525853132 0.0995182629265662 13 0.876519275776076 0.246961448447848 0.123480724223924 14 0.842227413133083 0.315545173733833 0.157772586866917 15 0.816865693295386 0.366268613409227 0.183134306704614 16 0.794065381251388 0.411869237497225 0.205934618748612 17 0.79074986653231 0.418500266935378 0.209250133467689 18 0.799266611160343 0.401466777679313 0.200733388839657 19 0.818789653349909 0.362420693300182 0.181210346650091 20 0.827357007904703 0.345285984190595 0.172642992095297 21 0.828310545496894 0.343378909006211 0.171689454503106 22 0.859597063622025 0.280805872755949 0.140402936377975 23 0.929426081826538 0.141147836346924 0.0705739181734622 24 0.953597104618272 0.0928057907634558 0.0464028953817279 25 0.93965220925359 0.120695581492820 0.0603477907464102 26 0.952229532723482 0.095540934553036 0.047770467276518 27 0.984469728423957 0.0310605431520861 0.0155302715760431 28 0.990648153694503 0.0187036926109934 0.0093518463054967 29 0.99425226161704 0.0114954767659201 0.00574773838296006 30 0.999539232063384 0.000921535873232669 0.000460767936616334 31 0.99997683547463 4.63290507383082e-05 2.31645253691541e-05 32 0.999979126993642 4.17460127160139e-05 2.08730063580070e-05 33 0.9999594496754 8.11006492007647e-05 4.05503246003823e-05 34 0.999923866122347 0.00015226775530509 7.6133877652545e-05 35 0.999886068751256 0.000227862497487217 0.000113931248743609 36 0.999860850147329 0.000278299705342127 0.000139149852671064 37 0.999830868277015 0.000338263445969156 0.000169131722984578 38 0.9996878282471 0.000624343505798738 0.000312171752899369 39 0.999463435528095 0.00107312894380937 0.000536564471904685 40 0.999191561115275 0.00161687776944975 0.000808438884724877 41 0.99901324771014 0.00197350457971870 0.000986752289859349 42 0.999060071301757 0.00187985739648539 0.000939928698242696 43 0.999371961949283 0.00125607610143381 0.000628038050716907 44 0.999124040850926 0.00175191829814816 0.000875959149074079 45 0.99861470437743 0.00277059124513937 0.00138529562256969 46 0.99815607875223 0.00368784249554079 0.00184392124777040 47 0.998567754794872 0.00286449041025527 0.00143224520512763 48 0.99903080588921 0.0019383882215793 0.00096919411078965 49 0.999210501192013 0.00157899761597458 0.00078949880798729 50 0.999223836486384 0.00155232702723164 0.00077616351361582 51 0.998853612424584 0.00229277515083125 0.00114638757541563 52 0.997951517343248 0.00409696531350329 0.00204848265675164 53 0.996740004576554 0.00651999084689116 0.00325999542344558 54 0.994527870064121 0.0109442598717571 0.00547212993587853 55 0.991120010590522 0.017759978818956 0.008879989409478 56 0.985656827902691 0.0286863441946172 0.0143431720973086 57 0.977385644857502 0.0452287102849962 0.0226143551424981 58 0.965254536928423 0.0694909261431538 0.0347454630715769 59 0.9468157786535 0.106368442693000 0.0531842213465002 60 0.920959878329638 0.158080243340724 0.0790401216703622 61 0.88754264404094 0.22491471191812 0.11245735595906 62 0.841587088512096 0.316825822975808 0.158412911487904 63 0.902693999374947 0.194612001250106 0.097306000625053 64 0.87695383285383 0.246092334292341 0.123046167146170 65 0.908381003013252 0.183237993973497 0.0916189969867483 66 0.908076623382748 0.183846753234503 0.0919233766172517 67 0.868335489969141 0.263329020061718 0.131664510030859 68 0.815749277574162 0.368501444851676 0.184250722425838 69 0.749826670257863 0.500346659484275 0.250173329742137 70 0.735120415372506 0.529759169254988 0.264879584627494 71 0.80565053688831 0.388698926223379 0.194349463111689 72 0.820552707733605 0.35889458453279 0.179447292266395 73 0.730017249530212 0.539965500939575 0.269982750469788 74 0.570959887132702 0.858080225734597 0.429040112867298 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.342857142857143 NOK 5% type I error level 31 0.442857142857143 NOK 10% type I error level 34 0.485714285714286 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/10mfan1227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/10mfan1227548951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/1spjt1227548950.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/1spjt1227548950.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/2spxv1227548950.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/2spxv1227548950.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/32lki1227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/32lki1227548951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/4m6ta1227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/4m6ta1227548951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/5arsw1227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/5arsw1227548951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/6yczw1227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/6yczw1227548951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/765i71227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/765i71227548951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/82y651227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/82y651227548951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/9l7qi1227548951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549055sm03g05q59ar3jn/9l7qi1227548951.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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