Multiple regression zonder

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 10 Dec 2008 02:19:48 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis.htm/, Retrieved Wed, 10 Dec 2008 09:32:40 +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/Dec/10/t12289015479qi3apt7753bcis.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)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

6,4 12,5 6,8 14,8 7,5 15,9 7,5 14,8 7,6 12,9 7,6 14,3 7,4 14,2 7,3 15,9 7,1 15,3 6,9 15,5 6,8 15,1 7,5 15 7,6 12,1 7,8 15,8 8 16,9 8,1 15,1 8,2 13,7 8,3 14,8 8,2 14,7 8 16 7,9 15,4 7,6 15 7,6 15,5 8,2 15,1 8,3 11,7 8,4 16,3 8,4 16,7 8,4 15 8,6 14,9 8,9 14,6 8,8 15,3 8,3 17,9 7,5 16,4 7,2 15,4 7,5 17,9 8,8 15,9 9,3 13,9 9,3 17,8 8,7 17,9 8,2 17,4 8,3 16,7 8,5 16 8,6 16,6 8,6 19,1 8,2 17,8 8,1 17,2 8 18,6 8,6 16,3 8,7 15,1 8,8 19,2 8,5 17,7 8,4 19,1 8,5 18 8,7 17,5 8,7 17,8 8,6 21,1 8,5 17,2 8,3 19,4 8,1 19,8 8,2 17,6 8,1 16,2 8,1 19,5 7,9 19,9 7,9 20 7,9 17,3 8 18,9 8 18,6 7,9 21,4 8 18,6 7,7 19,8 7,2 20,8 7,5 19,6 7,3 17,7 7 19,8 7 22,2 7 20,7 7,2 17,9 7,3 21,2 7,1 21,4 6,8 21,7 6,6 23,2 6,2 21,5 6,2 22,9 6,8 23,2 6,9 18,6

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkloosheid[t] = + 9.15164512364859 -0.0739891523477135export[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) 9.15164512364859 0.492017 18.6003 0 0 export -0.0739891523477135 0.028102 -2.6329 0.010094 0.005047 Multiple Linear Regression - Regression Statistics Multiple R 0.277637689013702 R-squared 0.0770826863608693 Adjusted R-squared 0.0659632006543739 F-TEST (value) 6.9322168664546 F-TEST (DF numerator) 1 F-TEST (DF denominator) 83 p-value 0.0100942975113051 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.6740199854038 Sum Squared Residuals 37.7071440800703 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.4 8.22678071930217 -1.82678071930217 2 6.8 8.05660566890243 -1.25660566890243 3 7.5 7.97521760131994 -0.475217601319943 4 7.5 8.05660566890243 -0.556605668902428 5 7.6 8.19718505836308 -0.597185058363084 6 7.6 8.09360024507628 -0.493600245076285 7 7.4 8.10099916031106 -0.700999160311056 8 7.3 7.97521760131994 -0.675217601319943 9 7.1 8.01961109272857 -0.919611092728572 10 6.9 8.00481326225903 -1.10481326225903 11 6.8 8.03440892319811 -1.23440892319811 12 7.5 8.04180783843289 -0.541807838432885 13 7.6 8.25637638024125 -0.656376380241255 14 7.8 7.98261651655471 -0.182616516554715 15 8 7.90122844897223 0.0987715510277703 16 8.1 8.03440892319811 0.0655910768018856 17 8.2 8.13799373648491 0.0620062635150863 18 8.3 8.05660566890243 0.243394331097573 19 8.2 8.0640045841372 0.135995415862800 20 8 7.96781868608517 0.0321813139148282 21 7.9 8.0122121774938 -0.112212177493800 22 7.6 8.04180783843289 -0.441807838432886 23 7.6 8.00481326225903 -0.404813262259029 24 8.2 8.03440892319811 0.165591076801885 25 8.3 8.28597204118034 0.0140279588196606 26 8.4 7.94562194038086 0.454378059619143 27 8.4 7.91602627944177 0.483973720558228 28 8.4 8.04180783843289 0.358192161567115 29 8.6 8.04920675366766 0.550793246332343 30 8.9 8.07140349937197 0.82859650062803 31 8.8 8.01961109272857 0.78038890727143 32 8.3 7.82723929662452 0.472760703375484 33 7.5 7.93822302514609 -0.438223025146086 34 7.2 8.0122121774938 -0.8122121774938 35 7.5 7.82723929662452 -0.327239296624516 36 8.8 7.97521760131994 0.824782398680058 37 9.3 8.12319590601537 1.17680409398463 38 9.3 7.83463821185929 1.46536178814071 39 8.7 7.82723929662452 0.872760703375483 40 8.2 7.86423387279837 0.335766127201626 41 8.3 7.91602627944177 0.383973720558228 42 8.5 7.96781868608517 0.532181313914828 43 8.6 7.92342519467654 0.676574805323456 44 8.6 7.73845231380726 0.86154768619274 45 8.2 7.83463821185929 0.365361788140712 46 8.1 7.87903170326792 0.220968296732084 47 8 7.77544688998112 0.224553110018883 48 8.6 7.94562194038086 0.654378059619142 49 8.7 8.03440892319811 0.665591076801885 50 8.8 7.73105339857249 1.06894660142751 51 8.5 7.84203712709406 0.657962872905941 52 8.4 7.73845231380726 0.66154768619274 53 8.5 7.81984038138974 0.680159618610255 54 8.7 7.8568349575636 0.843165042436398 55 8.7 7.83463821185929 0.865361788140712 56 8.6 7.59047400911183 1.00952599088817 57 8.5 7.87903170326792 0.620968296732084 58 8.3 7.71625556810295 0.583744431897055 59 8.1 7.68665990716386 0.413340092836139 60 8.2 7.84943604232883 0.350563957671169 61 8.1 7.95302085561563 0.146979144384371 62 8.1 7.70885665286817 0.391143347131825 63 7.9 7.67926099192909 0.220739008070911 64 7.9 7.67186207669432 0.228137923305683 65 7.9 7.87163278803314 0.0283672119668562 66 8 7.7532501442768 0.246749855723197 67 8 7.77544688998112 0.224553110018883 68 7.9 7.56827726340752 0.331722736592482 69 8 7.77544688998112 0.224553110018883 70 7.7 7.68665990716386 0.0133400928361399 71 7.2 7.61267075481615 -0.412670754816147 72 7.5 7.7014577376334 -0.201457737633403 73 7.3 7.84203712709406 -0.542037127094059 74 7 7.68665990716386 -0.68665990716386 75 7 7.50908594152935 -0.509085941529348 76 7 7.62006967005092 -0.620069670050918 77 7.2 7.82723929662452 -0.627239296624516 78 7.3 7.58307509387706 -0.283075093877062 79 7.1 7.56827726340752 -0.468277263407519 80 6.8 7.5460805177032 -0.746080517703205 81 6.6 7.43509678918163 -0.835096789181635 82 6.2 7.56087834817275 -1.36087834817275 83 6.2 7.45729353488595 -1.25729353488595 84 6.8 7.43509678918163 -0.635096789181635 85 6.9 7.77544688998112 -0.875446889981116 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.49106843893636 0.98213687787272 0.50893156106364 6 0.391152800242184 0.782305600484367 0.608847199757816 7 0.271997257679144 0.543994515358289 0.728002742320856 8 0.185908976709426 0.371817953418852 0.814091023290574 9 0.138128532137337 0.276257064274675 0.861871467862663 10 0.135148314038513 0.270296628077026 0.864851685961487 11 0.148173059741485 0.296346119482969 0.851826940258515 12 0.120721935201824 0.241443870403648 0.879278064798176 13 0.13879303883963 0.27758607767926 0.86120696116037 14 0.150082223681594 0.300164447363188 0.849917776318406 15 0.162314338566064 0.324628677132128 0.837685661433936 16 0.215194508515336 0.430389017030671 0.784805491484664 17 0.327960087791765 0.655920175583531 0.672039912208235 18 0.411049278898575 0.82209855779715 0.588950721101425 19 0.443731884606021 0.887463769212042 0.556268115393979 20 0.402512854922008 0.805025709844016 0.597487145077992 21 0.362785680081348 0.725571360162697 0.637214319918652 22 0.339857012599224 0.679714025198448 0.660142987400776 23 0.315735950808791 0.631471901617582 0.684264049191209 24 0.326027373620932 0.652054747241865 0.673972626379068 25 0.465372871739616 0.930745743479233 0.534627128260384 26 0.486902044743954 0.973804089487908 0.513097955256046 27 0.48382866183534 0.96765732367068 0.51617133816466 28 0.502092449939663 0.995815100120674 0.497907550060337 29 0.551086651153792 0.897826697692416 0.448913348846208 30 0.659094441404034 0.681811117191932 0.340905558595966 31 0.702418453842544 0.595163092314911 0.297581546157456 32 0.651964688688261 0.696070622623477 0.348035311311739 33 0.67620500075931 0.647589998481381 0.323794999240690 34 0.827228661533091 0.345542676933817 0.172771338466909 35 0.830278251182814 0.339443497634372 0.169721748817186 36 0.849331400234533 0.301337199530934 0.150668599765467 37 0.921060791242638 0.157878417514725 0.0789392087573624 38 0.970340812000272 0.0593183759994554 0.0296591879997277 39 0.968937645067372 0.0621247098652564 0.0310623549326282 40 0.956210997867124 0.0875780042657514 0.0437890021328757 41 0.94125426081573 0.117491478368541 0.0587457391842705 42 0.92736756403548 0.145264871929041 0.0726324359645203 43 0.910954927115871 0.178090145768258 0.0890450728841289 44 0.913244446877084 0.173511106245832 0.0867555531229159 45 0.885381096621727 0.229237806756547 0.114618903378273 46 0.854714787685668 0.290570424628664 0.145285212314332 47 0.820519177452764 0.358961645094471 0.179480822547235 48 0.790081044187236 0.419837911625528 0.209918955812764 49 0.774049896180617 0.451900207638765 0.225950103819383 50 0.821392040197214 0.357215919605573 0.178607959802786 51 0.78888085067617 0.422238298647662 0.211119149323831 52 0.777846826436721 0.444306347126558 0.222153173563279 53 0.751159587958331 0.497680824083337 0.248840412041669 54 0.744827446307777 0.510345107384447 0.255172553692223 55 0.754887644515502 0.490224710968996 0.245112355484498 56 0.904621978668618 0.190756042662763 0.0953780213313815 57 0.890500007079941 0.218999985840118 0.109499992920059 58 0.909924105526075 0.180151788947851 0.0900758944739255 59 0.923107447995692 0.153785104008616 0.0768925520043082 60 0.903731821360107 0.192536357279786 0.096268178639893 61 0.868617575351309 0.262764849297382 0.131382424648691 62 0.883195877647676 0.233608244704648 0.116804122352324 63 0.891195133008394 0.217609733983211 0.108804866991606 64 0.904865897183226 0.190268205633548 0.095134102816774 65 0.87001953857991 0.259960922840179 0.129980461420090 66 0.87181120951674 0.25637758096652 0.12818879048326 67 0.876125045690832 0.247749908618335 0.123874954309168 68 0.95843253941242 0.0831349211751598 0.0415674605875799 69 0.975599051870363 0.0488018962592746 0.0244009481296373 70 0.986683691868254 0.0266326162634914 0.0133163081317457 71 0.984640745158478 0.0307185096830443 0.0153592548415221 72 0.986976071301994 0.0260478573960128 0.0130239286980064 73 0.977201516819755 0.0455969663604891 0.0227984831802445 74 0.963532778269653 0.072934443460693 0.0364672217303465 75 0.953367822243496 0.0932643555130086 0.0466321777565043 76 0.924992369022276 0.150015261955449 0.0750076309777244 77 0.871070154214772 0.257859691570456 0.128929845785228 78 0.894929399875206 0.210141200249588 0.105070600124794 79 0.893972804471745 0.212054391056511 0.106027195528255 80 0.816435125675211 0.367129748649577 0.183564874324789 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 5 0.0657894736842105 NOK 10% type I error level 11 0.144736842105263 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/10pn8a1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/10pn8a1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/1j7yj1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/1j7yj1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/2odki1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/2odki1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/3k5yz1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/3k5yz1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/4shzc1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/4shzc1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/5xvwe1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/5xvwe1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/6bhn51228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/6bhn51228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/7s4lt1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/7s4lt1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/8nwku1228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/8nwku1228900779.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/9k8241228900779.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/10/t12289015479qi3apt7753bcis/9k8241228900779.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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