eigen reeks 1

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 01:37:21 -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/27/t12277753077ax0oalguk2g3ar.htm/, Retrieved Thu, 27 Nov 2008 08:41:47 +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/27/t12277753077ax0oalguk2g3ar.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}, }

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Original text written by user:

IsPrivate?

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User-defined keywords:

Dataseries X:

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97,8 0 107,4 0 117,5 0 105,6 0 97,4 0 99,5 0 98,0 0 104,3 0 100,6 0 101,1 0 103,9 0 96,9 0 95,5 0 108,4 0 117,0 0 103,8 0 100,8 0 110,6 0 104,0 0 112,6 0 107,3 0 98,9 0 109,8 0 104,9 0 102,2 0 123,9 0 124,9 0 112,7 0 121,9 0 100,6 0 104,3 0 120,4 0 107,5 0 102,9 0 125,6 0 107,5 0 108,8 0 128,4 0 121,1 0 119,5 0 128,7 0 108,7 0 105,5 0 119,8 0 111,3 0 110,6 0 120,1 0 97,5 0 107,7 0 127,3 0 117,2 0 119,8 0 116,2 0 111,0 0 112,4 0 130,6 0 109,1 0 118,8 0 123,9 0 101,6 0 112,8 0 128,0 0 129,6 0 125,8 0 119,5 0 115,7 0 113,6 0 129,7 0 112,0 0 116,8 0 127,0 0 112,1 1 114,2 1 121,1 1 131,6 1 125,0 1 120,4 1 117,7 1 117,5 1 120,6 1 127,5 1 112,3 1 124,5 1 115,2 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 C[t] = + 112.170422535211 + 7.8065005417118D[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) 112.170422535211 1.117096 100.4125 0 0 D 7.8065005417118 2.839608 2.7491 0.007348 0.003674 Multiple Linear Regression - Regression Statistics Multiple R 0.290500100047263 R-squared 0.08439030812747 Adjusted R-squared 0.073224336275366 F-TEST (value) 7.55781128998353 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 0.00734780665014612 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.4128162268306 Sum Squared Residuals 7265.29096424702 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.8 112.170422535211 -14.3704225352113 2 107.4 112.170422535211 -4.77042253521125 3 117.5 112.170422535211 5.32957746478873 4 105.6 112.170422535211 -6.57042253521127 5 97.4 112.170422535211 -14.7704225352113 6 99.5 112.170422535211 -12.6704225352113 7 98 112.170422535211 -14.1704225352113 8 104.3 112.170422535211 -7.87042253521127 9 100.6 112.170422535211 -11.5704225352113 10 101.1 112.170422535211 -11.0704225352113 11 103.9 112.170422535211 -8.27042253521126 12 96.9 112.170422535211 -15.2704225352113 13 95.5 112.170422535211 -16.6704225352113 14 108.4 112.170422535211 -3.77042253521126 15 117 112.170422535211 4.82957746478873 16 103.8 112.170422535211 -8.37042253521127 17 100.8 112.170422535211 -11.3704225352113 18 110.6 112.170422535211 -1.57042253521127 19 104 112.170422535211 -8.17042253521127 20 112.6 112.170422535211 0.429577464788728 21 107.3 112.170422535211 -4.87042253521127 22 98.9 112.170422535211 -13.2704225352113 23 109.8 112.170422535211 -2.37042253521127 24 104.9 112.170422535211 -7.27042253521126 25 102.2 112.170422535211 -9.97042253521126 26 123.9 112.170422535211 11.7295774647887 27 124.9 112.170422535211 12.7295774647887 28 112.7 112.170422535211 0.529577464788736 29 121.9 112.170422535211 9.72957746478874 30 100.6 112.170422535211 -11.5704225352113 31 104.3 112.170422535211 -7.87042253521127 32 120.4 112.170422535211 8.22957746478874 33 107.5 112.170422535211 -4.67042253521127 34 102.9 112.170422535211 -9.27042253521126 35 125.6 112.170422535211 13.4295774647887 36 107.5 112.170422535211 -4.67042253521127 37 108.8 112.170422535211 -3.37042253521127 38 128.4 112.170422535211 16.2295774647887 39 121.1 112.170422535211 8.92957746478873 40 119.5 112.170422535211 7.32957746478873 41 128.7 112.170422535211 16.5295774647887 42 108.7 112.170422535211 -3.47042253521126 43 105.5 112.170422535211 -6.67042253521127 44 119.8 112.170422535211 7.62957746478873 45 111.3 112.170422535211 -0.87042253521127 46 110.6 112.170422535211 -1.57042253521127 47 120.1 112.170422535211 7.92957746478873 48 97.5 112.170422535211 -14.6704225352113 49 107.7 112.170422535211 -4.47042253521126 50 127.3 112.170422535211 15.1295774647887 51 117.2 112.170422535211 5.02957746478874 52 119.8 112.170422535211 7.62957746478873 53 116.2 112.170422535211 4.02957746478874 54 111 112.170422535211 -1.17042253521127 55 112.4 112.170422535211 0.229577464788739 56 130.6 112.170422535211 18.4295774647887 57 109.1 112.170422535211 -3.07042253521127 58 118.8 112.170422535211 6.62957746478873 59 123.9 112.170422535211 11.7295774647887 60 101.6 112.170422535211 -10.5704225352113 61 112.8 112.170422535211 0.62957746478873 62 128 112.170422535211 15.8295774647887 63 129.6 112.170422535211 17.4295774647887 64 125.8 112.170422535211 13.6295774647887 65 119.5 112.170422535211 7.32957746478873 66 115.7 112.170422535211 3.52957746478874 67 113.6 112.170422535211 1.42957746478873 68 129.7 112.170422535211 17.5295774647887 69 112 112.170422535211 -0.170422535211267 70 116.8 112.170422535211 4.62957746478873 71 127 112.170422535211 14.8295774647887 72 112.1 119.976923076923 -7.87692307692308 73 114.2 119.976923076923 -5.77692307692307 74 121.1 119.976923076923 1.12307692307692 75 131.6 119.976923076923 11.6230769230769 76 125 119.976923076923 5.02307692307692 77 120.4 119.976923076923 0.423076923076930 78 117.7 119.976923076923 -2.27692307692307 79 117.5 119.976923076923 -2.47692307692308 80 120.6 119.976923076923 0.623076923076918 81 127.5 119.976923076923 7.52307692307692 82 112.3 119.976923076923 -7.67692307692308 83 124.5 119.976923076923 4.52307692307692 84 115.2 119.976923076923 -4.77692307692307 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.619057733894845 0.76188453221031 0.380942266105155 6 0.511303328688545 0.97739334262291 0.488696671311455 7 0.436840246162958 0.873680492325916 0.563159753837042 8 0.31479825205385 0.6295965041077 0.68520174794615 9 0.231462062204015 0.46292412440803 0.768537937795985 10 0.163002335977983 0.326004671955966 0.836997664022017 11 0.107029320203569 0.214058640407139 0.89297067979643 12 0.101164285079601 0.202328570159202 0.8988357149204 13 0.111281380473955 0.222562760947911 0.888718619526045 14 0.0977969233296642 0.195593846659328 0.902203076670336 15 0.207900157285545 0.415800314571091 0.792099842714455 16 0.160810508440079 0.321621016880159 0.83918949155992 17 0.134715746601031 0.269431493202062 0.865284253398969 18 0.125581753273238 0.251163506546476 0.874418246726762 19 0.0971767252067868 0.194353450413574 0.902823274793213 20 0.102477826354404 0.204955652708807 0.897522173645596 21 0.079779754471417 0.159559508942834 0.920220245528583 22 0.085478236603791 0.170956473207582 0.914521763396209 23 0.0737937648572766 0.147587529714553 0.926206235142723 24 0.0590114786939713 0.118022957387943 0.940988521306029 25 0.0534251875733034 0.106850375146607 0.946574812426697 26 0.210200907945298 0.420401815890595 0.789799092054702 27 0.432902870072745 0.86580574014549 0.567097129927255 28 0.401293964596334 0.802587929192669 0.598706035403666 29 0.503059425452708 0.993881149094583 0.496940574547292 30 0.536403654543304 0.927192690913391 0.463596345456696 31 0.524708876705068 0.950582246589863 0.475291123294932 32 0.577906620748069 0.844186758503862 0.422093379251931 33 0.544597773591008 0.910804452817984 0.455402226408992 34 0.564729119122996 0.870541761754009 0.435270880877004 35 0.705298770083146 0.589402459833708 0.294701229916854 36 0.68325579052327 0.633488418953459 0.316744209476730 37 0.655411243860276 0.689177512279448 0.344588756139724 38 0.81027576621398 0.379448467572041 0.189724233786021 39 0.820127948737497 0.359744102525006 0.179872051262503 40 0.812570070775747 0.374859858448506 0.187429929224253 41 0.896065900400317 0.207868199199365 0.103934099599683 42 0.880945000172782 0.238109999654435 0.119054999827218 43 0.885207226627493 0.229585546745014 0.114792773372507 44 0.874369565469279 0.251260869061443 0.125630434530721 45 0.850496517217273 0.299006965565454 0.149503482782727 46 0.827569267218232 0.344861465563537 0.172430732781768 47 0.811726093294145 0.37654781341171 0.188273906705855 48 0.926721646725177 0.146556706549645 0.0732783532748226 49 0.932241177614055 0.13551764477189 0.067758822385945 50 0.953362995229967 0.0932740095400654 0.0466370047700327 51 0.939812931808538 0.120374136382924 0.0601870681914622 52 0.926799949195806 0.146400101608387 0.0732000508041935 53 0.906362680365823 0.187274639268354 0.0936373196341771 54 0.896401483657727 0.207197032684545 0.103598516342273 55 0.881438723460461 0.237122553079077 0.118561276539539 56 0.931905403871233 0.136189192257534 0.0680945961287672 57 0.935708583972155 0.128582832055690 0.0642914160278448 58 0.915893416532203 0.168213166935594 0.084106583467797 59 0.907429001991106 0.185141996017788 0.092570998008894 60 0.975511925041512 0.0489761499169756 0.0244880749584878 61 0.975841759101456 0.048316481797088 0.024158240898544 62 0.978621793284126 0.042756413431748 0.021378206715874 63 0.98598888754206 0.0280222249158784 0.0140111124579392 64 0.98541202579981 0.0291759484003803 0.0145879742001902 65 0.976743368675064 0.0465132626498718 0.0232566313249359 66 0.965812865032502 0.0683742699349965 0.0341871349674982 67 0.960180826805537 0.079638346388927 0.0398191731944635 68 0.97330032259839 0.0533993548032188 0.0266996774016094 69 0.97327558858436 0.0534488228312802 0.0267244114156401 70 0.970895621707344 0.0582087565853128 0.0291043782926564 71 0.954706862676832 0.090586274646335 0.0452931373231675 72 0.955985456661036 0.0880290866779272 0.0440145433389636 73 0.948528084337123 0.102943831325753 0.0514719156628767 74 0.909926128917052 0.180147742165896 0.090073871082948 75 0.956312903226205 0.0873741935475904 0.0436870967737952 76 0.938364468031988 0.123271063936025 0.0616355319680124 77 0.877840787531312 0.244318424937377 0.122159212468688 78 0.779162870229689 0.441674259540623 0.220837129770311 79 0.633186104392175 0.73362779121565 0.366813895607825 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 6 0.08 NOK 10% type I error level 15 0.2 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/10w8rn1227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/10w8rn1227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/1ayo41227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/1ayo41227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/2gp351227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/2gp351227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/3xc4u1227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/3xc4u1227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/4d71o1227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/4d71o1227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/5ek561227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/5ek561227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/6sk1a1227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/6sk1a1227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/7w4f11227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/7w4f11227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/81a3k1227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/81a3k1227775030.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/9kkhx1227775030.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277753077ax0oalguk2g3ar/9kkhx1227775030.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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