*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 05:00:00 -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/t1258545678zfc4lofmk1821mo.htm/, Retrieved Wed, 18 Nov 2009 13:01:31 +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/t1258545678zfc4lofmk1821mo.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

149657 0 142773 0 133639 0 128332 0 120297 0 118632 0 155276 0 169316 0 167395 0 157939 0 149601 0 146310 0 141579 0 136473 0 129818 0 124226 0 116428 0 116440 0 147747 0 160069 0 163129 0 151108 0 141481 0 139174 0 134066 0 130104 0 123090 0 116598 0 109627 0 105428 0 137272 0 159836 0 155283 0 141514 0 131852 0 130691 0 128461 0 123066 0 117599 0 111599 0 105395 0 102334 0 131305 0 149033 0 144954 0 132404 0 122104 0 118755 0 116222 1 110924 1 103753 1 99983 1 93302 1 91496 1 119321 1 139261 1 133739 1 123913 1 113438 1 109416 1 109406 1 105645 1 101328 1 97686 1 93093 1 91382 1 122257 1 139183 1 139887 1 131822 1 116805 1 113706 1 113012 1 110452 1 107005 1 102841 1 98173 1 98181 1 137277 1 147579 1 146571 1 138920 1 130340 1 128140 1 127059 1 122860 1 117702 1 113537 1 108366 1 111078 1 150739 1 159129 1 157928 1 147768 1 137507 1 136919 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 134775.187500000 -14649.1250000000X[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) 134775.187500000 2581.726546 52.2035 0 0 X -14649.1250000000 3651.112696 -4.0122 0.000121 6e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.382381313631161 R-squared 0.146215469014292 Adjusted R-squared 0.137132654854870 F-TEST (value) 16.0980359663761 F-TEST (DF numerator) 1 F-TEST (DF denominator) 94 p-value 0.000120720523048345 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 17886.7261957388 Sum Squared Residuals 30073887556.1250 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 149657 134775.187499999 14881.8125000010 2 142773 134775.1875 7997.8125 3 133639 134775.1875 -1136.18750000002 4 128332 134775.1875 -6443.18750000002 5 120297 134775.1875 -14478.1875000000 6 118632 134775.1875 -16143.1875000000 7 155276 134775.1875 20500.8125000000 8 169316 134775.1875 34540.8125 9 167395 134775.1875 32619.8125 10 157939 134775.1875 23163.8125 11 149601 134775.1875 14825.8125000000 12 146310 134775.1875 11534.8125000000 13 141579 134775.1875 6803.81249999998 14 136473 134775.1875 1697.81249999998 15 129818 134775.1875 -4957.18750000002 16 124226 134775.1875 -10549.1875000000 17 116428 134775.1875 -18347.1875 18 116440 134775.1875 -18335.1875 19 147747 134775.1875 12971.8125000000 20 160069 134775.1875 25293.8125 21 163129 134775.1875 28353.8125 22 151108 134775.1875 16332.8125000000 23 141481 134775.1875 6705.81249999998 24 139174 134775.1875 4398.81249999998 25 134066 134775.1875 -709.18750000002 26 130104 134775.1875 -4671.18750000002 27 123090 134775.1875 -11685.1875000000 28 116598 134775.1875 -18177.1875 29 109627 134775.1875 -25148.1875 30 105428 134775.1875 -29347.1875 31 137272 134775.1875 2496.81249999998 32 159836 134775.1875 25060.8125 33 155283 134775.1875 20507.8125 34 141514 134775.1875 6738.81249999998 35 131852 134775.1875 -2923.18750000002 36 130691 134775.1875 -4084.18750000002 37 128461 134775.1875 -6314.18750000002 38 123066 134775.1875 -11709.1875000000 39 117599 134775.1875 -17176.1875000000 40 111599 134775.1875 -23176.1875 41 105395 134775.1875 -29380.1875 42 102334 134775.1875 -32441.1875 43 131305 134775.1875 -3470.18750000002 44 149033 134775.1875 14257.8125000000 45 144954 134775.1875 10178.8125000000 46 132404 134775.1875 -2371.18750000002 47 122104 134775.1875 -12671.1875000000 48 118755 134775.1875 -16020.1875000000 49 116222 120126.0625 -3904.0625 50 110924 120126.0625 -9202.0625 51 103753 120126.0625 -16373.0625 52 99983 120126.0625 -20143.0625 53 93302 120126.0625 -26824.0625 54 91496 120126.0625 -28630.0625 55 119321 120126.0625 -805.0625 56 139261 120126.0625 19134.9375 57 133739 120126.0625 13612.9375 58 123913 120126.0625 3786.9375 59 113438 120126.0625 -6688.0625 60 109416 120126.0625 -10710.0625 61 109406 120126.0625 -10720.0625 62 105645 120126.0625 -14481.0625 63 101328 120126.0625 -18798.0625 64 97686 120126.0625 -22440.0625 65 93093 120126.0625 -27033.0625 66 91382 120126.0625 -28744.0625 67 122257 120126.0625 2130.9375 68 139183 120126.0625 19056.9375 69 139887 120126.0625 19760.9375 70 131822 120126.0625 11695.9375 71 116805 120126.0625 -3321.0625 72 113706 120126.0625 -6420.0625 73 113012 120126.0625 -7114.0625 74 110452 120126.0625 -9674.0625 75 107005 120126.0625 -13121.0625 76 102841 120126.0625 -17285.0625 77 98173 120126.0625 -21953.0625 78 98181 120126.0625 -21945.0625 79 137277 120126.0625 17150.9375 80 147579 120126.0625 27452.9375 81 146571 120126.0625 26444.9375 82 138920 120126.0625 18793.9375 83 130340 120126.0625 10213.9375 84 128140 120126.0625 8013.9375 85 127059 120126.0625 6932.9375 86 122860 120126.0625 2733.9375 87 117702 120126.0625 -2424.0625 88 113537 120126.0625 -6589.0625 89 108366 120126.0625 -11760.0625 90 111078 120126.0625 -9048.0625 91 150739 120126.0625 30612.9375 92 159129 120126.0625 39002.9375 93 157928 120126.0625 37801.9375 94 147768 120126.0625 27641.9375 95 137507 120126.0625 17380.9375 96 136919 120126.0625 16792.9375 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.343937236885549 0.687874473771097 0.656062763114452 6 0.315451300827236 0.630902601654472 0.684548699172764 7 0.399078493821086 0.798156987642172 0.600921506178914 8 0.658765203563454 0.682469592873091 0.341234796436546 9 0.752861602489454 0.494276795021093 0.247138397510546 10 0.724893345286758 0.550213309426483 0.275106654713242 11 0.648589248498718 0.702821503002565 0.351410751501282 12 0.560876898118251 0.878246203763497 0.439123101881748 13 0.472004296583476 0.944008593166951 0.527995703416524 14 0.398426649222114 0.796853298444229 0.601573350777886 15 0.361188738007383 0.722377476014767 0.638811261992617 16 0.361456542045590 0.722913084091179 0.63854345795441 17 0.427651050917385 0.855302101834771 0.572348949082615 18 0.472575490740041 0.945150981480082 0.527424509259959 19 0.419232258978994 0.838464517957988 0.580767741021006 20 0.458194887546083 0.916389775092166 0.541805112453917 21 0.526732874216239 0.946534251567523 0.473267125783761 22 0.493506959749907 0.987013919499815 0.506493040250093 23 0.430588327598861 0.861176655197722 0.569411672401139 24 0.369571558494586 0.739143116989173 0.630428441505414 25 0.317245641121963 0.634491282243927 0.682754358878037 26 0.278074851417122 0.556149702834245 0.721925148582878 27 0.269698343402920 0.539396686805839 0.73030165659708 28 0.299365994742848 0.598731989485696 0.700634005257152 29 0.384972588330976 0.769945176661953 0.615027411669024 30 0.507494185946129 0.985011628107742 0.492505814053871 31 0.446421817651733 0.892843635303465 0.553578182348267 32 0.508344687046541 0.983310625906918 0.491655312953459 33 0.535677373721197 0.928645252557606 0.464322626278803 34 0.491619192490497 0.983238384980993 0.508380807509504 35 0.439328877862121 0.878657755724242 0.560671122137879 36 0.389571195316557 0.779142390633114 0.610428804683443 37 0.344752981002034 0.689505962004069 0.655247018997966 38 0.315001601202545 0.63000320240509 0.684998398797455 39 0.307479231884154 0.614958463768309 0.692520768115846 40 0.333851357167581 0.667702714335162 0.666148642832419 41 0.411742590562301 0.823485181124603 0.588257409437699 42 0.528576670176164 0.942846659647672 0.471423329823836 43 0.470490300307782 0.940980600615564 0.529509699692218 44 0.454728553527178 0.909457107054356 0.545271446472822 45 0.430435693518917 0.860871387037834 0.569564306481083 46 0.381229573489709 0.762459146979417 0.618770426510291 47 0.341912073821751 0.683824147643503 0.658087926178249 48 0.311278378589644 0.622556757179287 0.688721621410356 49 0.261796777504448 0.523593555008897 0.738203222495552 50 0.222197786162915 0.444395572325831 0.777802213837085 51 0.201018790070814 0.402037580141627 0.798981209929186 52 0.191679861288996 0.383359722577992 0.808320138711004 53 0.211728012307881 0.423456024615762 0.788271987692119 54 0.245824919821779 0.491649839643558 0.754175080178221 55 0.217916038456109 0.435832076912219 0.78208396154389 56 0.263780835282639 0.527561670565277 0.736219164717361 57 0.262115638567464 0.524231277134929 0.737884361432536 58 0.223361494371700 0.446722988743399 0.7766385056283 59 0.185764532283185 0.37152906456637 0.814235467716815 60 0.158608302083798 0.317216604167596 0.841391697916202 61 0.134629245545420 0.269258491090841 0.86537075445458 62 0.121345651436084 0.242691302872168 0.878654348563916 63 0.12210807210515 0.2442161442103 0.87789192789485 64 0.139820445912363 0.279640891824727 0.860179554087637 65 0.193630330508105 0.387260661016211 0.806369669491895 66 0.290809348798827 0.581618697597653 0.709190651201173 67 0.251128799632512 0.502257599265024 0.748871200367488 68 0.263705949596084 0.527411899192169 0.736294050403916 69 0.274484515714420 0.548969031428839 0.72551548428558 70 0.243301701471934 0.486603402943868 0.756698298528066 71 0.203524502319271 0.407049004638542 0.796475497680729 72 0.174381224174759 0.348762448349517 0.825618775825241 73 0.150737465593021 0.301474931186043 0.849262534406979 74 0.137861903360971 0.275723806721942 0.862138096639029 75 0.141502407010684 0.283004814021369 0.858497592989316 76 0.176225571071859 0.352451142143717 0.823774428928141 77 0.285958585832375 0.57191717166475 0.714041414167625 78 0.481463148312783 0.962926296625566 0.518536851687217 79 0.435509555647147 0.871019111294294 0.564490444352853 80 0.454380250003157 0.908760500006314 0.545619749996843 81 0.459947922067074 0.919895844134148 0.540052077932926 82 0.407068799806603 0.814137599613206 0.592931200193397 83 0.330931560813147 0.661863121626294 0.669068439186853 84 0.259593262483818 0.519186524967636 0.740406737516182 85 0.1965230261901 0.3930460523802 0.8034769738099 86 0.15111330759393 0.30222661518786 0.84888669240607 87 0.133252611992876 0.266505223985752 0.866747388007124 88 0.154340615239040 0.308681230478079 0.84565938476096 89 0.320239297346626 0.640478594693252 0.679760702653374 90 0.793132942576127 0.413734114847745 0.206867057423873 91 0.660678446399579 0.678643107200842 0.339321553600421 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/10iwmv1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/10iwmv1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/1ltus1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/1ltus1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/2u2fk1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/2u2fk1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/3taxq1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/3taxq1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/4kgmz1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/4kgmz1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/5ojs61258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/5ojs61258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/6z7xv1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/6z7xv1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/7t1ar1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/7t1ar1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/8dh8n1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/8dh8n1258545594.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/9n6gx1258545594.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258545678zfc4lofmk1821mo/9n6gx1258545594.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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