Workshop 7

*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: Fri, 20 Nov 2009 08:54:20 -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/20/t12587326867upo1di34d03l93.htm/, Retrieved Fri, 20 Nov 2009 16:58:18 +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/20/t12587326867upo1di34d03l93.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 «

33 62 39 64 45 62 46 64 45 64 45 69 49 69 50 65 54 56 59 58 58 53 56 62 48 55 50 60 52 59 53 58 55 53 43 57 42 57 38 53 41 54 41 53 39 57 34 57 27 55 15 49 14 50 31 49 41 54 43 58 46 58 42 52 45 56 45 52 40 59 35 53 36 52 38 53 39 51 32 50 24 56 21 52 12 46 29 48 36 46 31 48 28 48 30 49 38 53 27 48 40 51 40 48 44 50 47 55 45 52 42 53 38 52 46 55 37 53 41 53 40 56 33 54 34 52 36 55 36 54 38 59 42 56 35 56 25 51 24 53 22 52 27 51 17 46 30 49 30 46 34 55 37 57 36 53 33 52 33 53 33 50 37 54 40 53 35 50 37 51 43 52 42 47 33 51 39 49 40 53 37 52 44 45 42 53 43 51 40 48 30 48 30 48 31 48 18 40 24 43 22 40 26 39 28 39 23 36 17 41 12 39 9 40 19 39 21 46 18 40 18 37 15 37 24 44 18 41 19 40 30 36 33 38 35 43 36 42 47 45 46 46

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 Spaar[t] = + 50.6298826025243 + 0.224827909425653Alg_E[t] + 0.85574414100791M1[t] + 0.479212425823561M2[t] -1.78112277147162M3[t] -0.431389132537066M4[t] + 0.271033806029273M5[t] + 2.11538884570027M6[t] + 0.824847139715868M7[t] -1.74790201229214M8[t] -1.58340930016167M9[t] -0.766227288399419M10[t] -0.207113175532512M11[t] -0.11952713024534t + 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) 50.6298826025243 2.228464 22.7196 0 0 Alg_E 0.224827909425653 0.03841 5.8534 0 0 M1 0.85574414100791 1.648074 0.5192 0.604666 0.302333 M2 0.479212425823561 1.695835 0.2826 0.778043 0.389022 M3 -1.78112277147162 1.691833 -1.0528 0.294815 0.147407 M4 -0.431389132537066 1.687817 -0.2556 0.798758 0.399379 M5 0.271033806029273 1.685106 0.1608 0.872522 0.436261 M6 2.11538884570027 1.684915 1.2555 0.212037 0.106018 M7 0.824847139715868 1.684764 0.4896 0.625425 0.312712 M8 -1.74790201229214 1.684281 -1.0378 0.301715 0.150858 M9 -1.58340930016167 1.684061 -0.9402 0.349216 0.174608 M10 -0.766227288399419 1.684891 -0.4548 0.650201 0.3251 M11 -0.207113175532512 1.683906 -0.123 0.902341 0.451171 t -0.11952713024534 0.011623 -10.2841 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.857292104916475 R-squared 0.73494975315212 Adjusted R-squared 0.702747386712659 F-TEST (value) 22.8228492006564 F-TEST (DF numerator) 13 F-TEST (DF denominator) 107 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.76483670875441 Sum Squared Residuals 1516.61751246357 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 62 58.7854206243334 3.21457937566656 2 64 59.6383292354577 4.36167076454234 3 62 58.607434364471 3.39256563552894 4 64 60.062468782586 3.93753121741406 5 64 60.4205366814813 3.57946331851871 6 69 62.145364590907 6.85463540909306 7 69 61.6346073923798 7.36539260762019 8 65 59.1671590195521 5.83284098044788 9 56 60.1114362391399 -4.11143623913986 10 58 61.933230667785 -3.93323066778503 11 53 62.1479897409809 -9.14798974098094 12 62 61.7859199674168 0.214080032583189 13 55 60.7235137027742 -5.72351370277416 14 60 60.6771106761958 -0.677110676195773 15 59 58.7469041675066 0.253095832493444 16 58 60.2019385856214 -2.20193858562142 17 53 61.2344902127937 -8.23449021279373 18 57 60.2613832091115 -3.26138320911155 19 57 58.6264864634562 -1.62648646345616 20 53 55.0348985435002 -2.0348985435002 21 54 55.7543478536623 -1.75434785366229 22 53 56.4520027351792 -3.4520027351792 23 57 56.4419338989495 0.55806610105054 24 57 55.4053803971084 1.59461960289163 25 55 54.5678020418914 0.43219795810863 26 49 51.3738082833538 -2.37380828335385 27 50 48.7691180463877 1.23088195361233 28 49 53.821399015313 -4.82139901531298 29 54 56.6525739178905 -2.65257391789051 30 58 58.8270576461675 -0.827057646167467 31 58 58.0914725382147 -0.0914725382146861 32 52 54.4998846182587 -2.49988461825873 33 56 55.2193339284208 0.780666071579181 34 52 55.9169888099377 -3.91698880993773 35 59 55.232436245431 3.76756375456897 36 53 54.19588274359 -1.19588274358994 37 52 55.1569276637782 -3.15692766377816 38 53 55.1105246371998 -2.11052463719978 39 51 52.9554902190849 -1.95549021908491 40 50 52.6119013617946 -2.61190136179455 41 56 51.3961738947103 4.60382610528967 42 52 52.446518075859 -0.446518075859024 43 46 49.0129980547984 -3.01299805479841 44 48 50.1427962327812 -2.14279623278116 45 46 51.7615571806459 -5.76155718064586 46 48 51.3350725150345 -3.33507251503451 47 48 51.1001757693791 -3.10017576937912 48 49 51.6374176335176 -2.63741763351759 49 53 54.1722579196854 -1.17225791968539 50 48 51.2030920705735 -3.20309207057352 51 51 51.7459925655665 -0.74599256556648 52 48 52.9761990742557 -4.9761990742557 53 50 54.4584065202793 -4.45840652027931 54 55 56.8577181579819 -1.85771815798191 55 52 54.9979935029009 -2.99799350290087 56 53 51.6312334923706 1.36876650762943 57 52 50.7768874365531 1.22311256344691 58 55 53.2731655934752 1.72683440652478 59 53 51.6893013912659 1.31069860873409 60 53 52.6761990742557 0.323800925744307 61 56 53.1875881755926 2.81241182440739 62 54 51.1177339641834 2.88226603581665 63 52 48.9626995460685 3.03730045393152 64 55 50.642561873609 4.357438126391 65 54 51.22545768193 2.77454231807000 66 59 53.399941410207 5.60005858979304 67 56 52.8891842116798 3.11081578832017 68 56 48.6231125634469 7.37688743655308 69 51 46.4197990510755 4.58020094892448 70 53 46.8926260231668 6.10737397683322 71 52 46.882557186937 5.11744281306296 72 51 48.0942827793525 2.90571722064753 73 46 46.5822206958585 -0.582220695858516 74 49 49.0089246729623 -0.00892467296231159 75 46 46.6290623454218 -0.62906234542179 76 55 48.7585804918136 6.24141950818638 77 57 50.0159600284116 6.98403997158843 78 53 51.5159600284116 1.48403997158843 79 52 49.4314074639049 2.56859253609512 80 53 46.7391311816515 6.26086881834847 81 50 46.7840967635367 3.21590323646334 82 54 48.3810632827562 5.61893671724382 83 53 49.4951339936547 3.50486600634529 84 50 48.4585804918136 1.54141950818638 85 51 49.6444533214275 1.35554667857251 86 52 50.4973619325517 1.50263806744828 87 47 47.8926716955855 -0.892671695585544 88 51 47.0994270194439 3.90057298055612 89 49 49.0312902843188 -0.0312902843187983 90 53 50.9809461031701 2.01905389682990 91 52 48.8963935386634 3.10360646133659 92 45 47.7779126223896 -2.77791262238963 93 53 47.3732223854235 5.62677761457654 94 51 48.295705176366 2.70429482363398 95 48 48.0608084307106 -0.0608084307106281 96 48 45.9001153817413 2.09988461825873 97 48 46.6363323925038 1.36366760749616 98 48 46.3651014564998 1.63489854350020 99 40 41.0624763064258 -1.06247630642579 100 43 43.6416502716689 -0.641650271668926 101 40 43.7748902611386 -3.77489026113862 102 39 46.3990298082669 -7.39902980826689 103 39 45.4386167908885 -6.43861679088845 104 36 41.6222009615068 -5.62220096150684 105 41 40.3181990868381 0.681800913161947 106 39 39.8917144212267 -0.8917144212267 107 40 39.6568176755713 0.343182324428691 108 39 41.992682815115 -2.99268281511501 109 46 43.1785556447289 2.82144435527112 110 40 42.0080130710222 -2.00801307102224 111 37 39.6281507434817 -2.62815074348171 112 37 40.1838735238940 -3.18387352389397 113 44 42.7902205170458 1.20977948295416 114 41 43.1660809699176 -2.16608096991758 115 40 41.9808400431135 -1.98084004311349 116 36 41.7616707645423 -5.76167076454233 117 38 42.4811200747044 -4.48112007470442 118 43 43.6284307750726 -0.628430775072634 119 42 44.2928456671198 -2.29284566711985 120 45 46.8535387160892 -1.85353871608921 121 46 47.3649278174261 -1.36492781742612 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.325234881047865 0.650469762095729 0.674765118952135 18 0.322521920833765 0.645043841667531 0.677478079166235 19 0.195657895024613 0.391315790049225 0.804342104975387 20 0.113710167967367 0.227420335934734 0.886289832032633 21 0.397790152341236 0.795580304682471 0.602209847658764 22 0.358468691135862 0.716937382271725 0.641531308864138 23 0.652876302691013 0.694247394617973 0.347123697308987 24 0.562158998972255 0.87568200205549 0.437841001027745 25 0.555503486212317 0.888993027575365 0.444496513787683 26 0.612790180223478 0.774419639553044 0.387209819776522 27 0.551892349250934 0.896215301498131 0.448107650749066 28 0.516677823621695 0.96664435275661 0.483322176378305 29 0.53580038550908 0.92839922898184 0.46419961449092 30 0.507570857514545 0.98485828497091 0.492429142485455 31 0.465079388310612 0.930158776621224 0.534920611689388 32 0.399965404430136 0.799930808860272 0.600034595569864 33 0.553131811461384 0.893736377077232 0.446868188538616 34 0.55993039139897 0.880139217202061 0.440069608601031 35 0.769204357222667 0.461591285554665 0.230795642777333 36 0.715547788304659 0.568904423390683 0.284452211695341 37 0.688307191774671 0.623385616450657 0.311692808225329 38 0.650071206187309 0.699857587625382 0.349928793812691 39 0.59539431196973 0.80921137606054 0.40460568803027 40 0.5547641428917 0.8904717142166 0.4452358571083 41 0.64258147389741 0.71483705220518 0.35741852610259 42 0.58565512702801 0.82868974594398 0.41434487297199 43 0.612535914273895 0.77492817145221 0.387464085726105 44 0.561570623483743 0.876858753032513 0.438429376516257 45 0.610486680249302 0.779026639501396 0.389513319750698 46 0.632477276397816 0.735045447204368 0.367522723602184 47 0.630720903487861 0.738558193024277 0.369279096512139 48 0.613497705151923 0.773004589696153 0.386502294848077 49 0.646126220866446 0.707747558267108 0.353873779133554 50 0.655351757096599 0.689296485806802 0.344648242903401 51 0.629124621509907 0.741750756980185 0.370875378490093 52 0.74715698533434 0.505686029331318 0.252843014665659 53 0.839824378826056 0.320351242347889 0.160175621173944 54 0.854138110491453 0.291723779017094 0.145861889508547 55 0.892828500252739 0.214342999494522 0.107171499747261 56 0.901779483247096 0.196441033505808 0.0982205167529041 57 0.939667482887394 0.120665034225213 0.0603325171126064 58 0.976278555727244 0.047442888545513 0.0237214442727565 59 0.984183443420348 0.0316331131593049 0.0158165565796525 60 0.988347182523573 0.0233056349528534 0.0116528174764267 61 0.99168395119863 0.0166320976027419 0.00831604880137097 62 0.991546486859949 0.0169070262801024 0.0084535131400512 63 0.989213155645487 0.0215736887090264 0.0107868443545132 64 0.991747084685636 0.0165058306287283 0.00825291531436416 65 0.99204080733085 0.015918385338301 0.0079591926691505 66 0.992470332404372 0.0150593351912565 0.00752966759562824 67 0.99010753555239 0.0197849288952201 0.00989246444761007 68 0.995029595452334 0.00994080909533178 0.00497040454766589 69 0.994369016535492 0.0112619669290158 0.00563098346450792 70 0.994697527885314 0.0106049442293726 0.00530247211468631 71 0.993487050577588 0.0130258988448237 0.00651294942241186 72 0.990275846072576 0.0194483078548474 0.0097241539274237 73 0.993148073662142 0.0137038526757163 0.00685192633785813 74 0.9935367811999 0.0129264376001995 0.00646321880009973 75 0.992941808885715 0.0141163822285694 0.00705819111428468 76 0.9923859255612 0.0152281488776015 0.00761407443880074 77 0.993889316045119 0.0122213679097628 0.00611068395488138 78 0.990334272744525 0.0193314545109498 0.0096657272554749 79 0.985020704996184 0.0299585900076322 0.0149792950038161 80 0.996004313161733 0.00799137367653452 0.00399568683826726 81 0.99369172500906 0.0126165499818787 0.00630827499093935 82 0.992040093688891 0.0159198126222174 0.00795990631110871 83 0.987607177341122 0.024785645317756 0.012392822658878 84 0.980721762349027 0.0385564753019467 0.0192782376509734 85 0.977409162445373 0.0451816751092544 0.0225908375546272 86 0.966967672191184 0.0660646556176317 0.0330323278088158 87 0.959579153393047 0.0808416932139068 0.0404208466069534 88 0.948969045570097 0.102061908859807 0.0510309544299035 89 0.93270913822412 0.134581723551761 0.0672908617758806 90 0.926214052827828 0.147571894344343 0.0737859471721716 91 0.936693985254029 0.126612029491943 0.0633060147459715 92 0.917662178859019 0.164675642281963 0.0823378211409813 93 0.955798075304036 0.0884038493919276 0.0442019246959638 94 0.947002560762715 0.105994878474570 0.0529974392372848 95 0.919031187795338 0.161937624409325 0.0809688122046623 96 0.923611357331691 0.152777285336618 0.0763886426683088 97 0.880141638459926 0.239716723080149 0.119858361540074 98 0.909521090287238 0.180957819425524 0.090478909712762 99 0.88440653945586 0.231186921088279 0.115593460544140 100 0.9464241909892 0.107151618021600 0.0535758090108002 101 0.93095633987471 0.138087320250579 0.0690436601252893 102 0.901347431648554 0.197305136702893 0.0986525683514464 103 0.886921610836934 0.226156778326131 0.113078389163066 104 0.887589796320515 0.224820407358970 0.112410203679485 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0227272727272727 NOK 5% type I error level 28 0.318181818181818 NOK 10% type I error level 31 0.352272727272727 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = 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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