WS 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: Tue, 24 Nov 2009 16:14:58 -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/25/t1259104578nzi0ai2ucvf64lx.htm/, Retrieved Wed, 25 Nov 2009 00:16:31 +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/25/t1259104578nzi0ai2ucvf64lx.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:

WS 7

Dataseries X:

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13.7 15 15.3 14.3 14.2 15.5 14.4 15.3 13.5 15.1 13.7 14.4 11.9 11.7 14.2 13.7 14.6 16.3 13.5 14.2 15.6 16.7 11.9 13.5 14.1 15 14.6 11.9 14.9 14.9 15.6 14.6 14.2 14.6 14.1 15.6 14.6 15.3 14.9 14.1 17.2 17.9 14.2 14.9 15.4 16.4 14.6 14.2 14.3 15.4 17.2 14.6 17.5 17.9 15.4 17.2 14.5 15.9 14.3 15.4 14.4 13.9 17.5 14.3 16.6 17.8 14.5 17.5 16.7 17.9 14.4 14.5 16.6 17.4 16.6 14.4 16.9 16.7 16.7 16.6 15.7 16 16.6 16.7 16.4 16.6 16.9 16.6 18.4 19.1 15.7 16.9 16.9 17.8 16.4 15.7 16.5 17.2 18.4 16.4 18.3 18.6 16.9 18.4 15.1 16.3 16.5 16.9 15.7 15.1 18.3 16.5 18.1 19.2 15.1 18.3 16.8 17.7 15.7 15.1 18.9 19.1 18.1 15.7 19 18 16.8 18.1 18.1 17.5 18.9 16.8 17.8 17.8 19 18.9 21.5 21.1 18.1 19 17.1 17.2 17.8 18.1 18.7 19.4 21.5 17.8 19 19.8 17.1 21.5 16.4 17.6 18.7 17.1 16.9 16.2 19 18.7 18.6 19.5 16.4 19 19.3 19.9 16.9 16.4 19.4 20 18.6 16.9 17.6 17.3 19.3 18.6 18.6 18.9 19.4 19.3 18.1 18.6 17.6 19.4 20.4 21.4 18.6 17.6 18.1 18.6 18.1 18.6 19.6 19.8 20.4 18.1 19.9 20.8 18.1 20 etc...

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] = -2.98974717065154 + 0.887683960481875X[t] + 0.144018071360225Y2[t] + 0.109167909345324Y3[t] -0.402453762506518M1[t] -0.116179718351996M2[t] -0.52698664707924M3[t] + 0.594705637013487M4[t] -0.321163975730627M5[t] + 0.284771208347894M6[t] + 0.354028397539171M7[t] + 0.695979012366943M8[t] + 0.367144520312719M9[t] -0.0207176829835056M10[t] + 0.375399406309265M11[t] + 0.00595760122729083t + 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) -2.98974717065154 0.522119 -5.7262 0 0 X 0.887683960481875 0.03888 22.8313 0 0 Y2 0.144018071360225 0.042187 3.4138 0.001174 0.000587 Y3 0.109167909345324 0.045262 2.4119 0.019055 0.009527 M1 -0.402453762506518 0.231647 -1.7374 0.087632 0.043816 M2 -0.116179718351996 0.221844 -0.5237 0.602483 0.301242 M3 -0.52698664707924 0.222852 -2.3647 0.02141 0.010705 M4 0.594705637013487 0.267052 2.2269 0.029849 0.014924 M5 -0.321163975730627 0.233584 -1.3749 0.174438 0.087219 M6 0.284771208347894 0.232749 1.2235 0.226083 0.113041 M7 0.354028397539171 0.238318 1.4855 0.142819 0.071409 M8 0.695979012366943 0.245412 2.836 0.006281 0.003141 M9 0.367144520312719 0.23667 1.5513 0.126271 0.063136 M10 -0.0207176829835056 0.22416 -0.0924 0.92668 0.46334 M11 0.375399406309265 0.226067 1.6606 0.102197 0.051099 t 0.00595760122729083 0.004171 1.4284 0.158549 0.079275 Multiple Linear Regression - Regression Statistics Multiple R 0.992105899409369 R-squared 0.984274115642872 Adjusted R-squared 0.980207076584995 F-TEST (value) 242.012457130536 F-TEST (DF numerator) 15 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.379575850739525 Sum Squared Residuals 8.3565139349488 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13.7 13.6935936707469 0.00640632925307883 2 14.2 14.4092189414908 -0.209218941490816 3 13.5 13.4502322614352 0.0497677385648431 4 11.9 11.5553481802552 0.344651819744814 5 14.6 14.6825536916755 -0.0825536916754956 6 15.6 15.3426736104560 0.257326389544032 7 14.1 14.1230058057754 -0.0230058057754395 8 14.9 14.8209170523749 0.0790829476250858 9 14.2 14.1248757757084 0.075124224291595 10 14.6 14.3158125390470 0.284187460953021 11 17.2 17.012387204344 0.187612795655985 12 15.4 15.2926091505416 0.107390849458411 13 14.3 14.4265431780552 -0.126543178055204 14 17.5 16.9625887604911 0.537411239508858 15 14.5 14.4274493967096 0.0725506032903916 16 14.4 14.1205044891387 0.27949551086126 17 16.6 16.5898430193256 0.0101569806744103 18 16.7 16.9485986655076 -0.248598665507595 19 16.6 16.8858944417432 -0.285894441743187 20 16.9 16.8669950931567 0.0330049068433241 21 15.7 15.9192544137909 -0.219254413790940 22 16.4 16.1022488184847 0.297751181515332 23 18.4 18.5834620973807 -0.183462097380744 24 16.9 17.0298423024101 -0.129842302410100 25 16.5 16.4651894441039 0.034810555896078 26 18.3 18.0024873458107 0.297512654189328 27 15.1 15.3346058166403 -0.23460581664033 28 15.7 15.6126003140924 0.0873996859076265 29 18.1 18.0778369490201 0.0221630509799018 30 16.8 17.0952773265142 -0.295277326514196 31 18.9 18.8243937784791 0.0756062215208716 32 19 18.2706291276646 0.729370872335388 33 18.1 17.6644299243043 0.435570075695710 34 17.8 17.7924849271411 0.00751507285887725 35 21.5 21.0052172139617 0.494782786038296 36 17.1 17.0323514231816 0.067648576818446 37 18.7 19.0888764661917 -0.388876466191689 38 19 19.5064234463590 -0.506423446358962 39 16.4 16.8987595188558 -0.498759518855819 40 16.9 17.0015259358618 -0.101525935861796 41 18.6 18.6792743812022 -0.0792743812021703 42 19.3 19.434413222083 -0.134413222083002 43 19.4 19.8978110845348 -0.497811084534806 44 17.6 18.135370703128 -0.535370703128013 45 18.6 19.3236074927498 -0.723607492749826 46 18.1 18.4270819650225 -0.327081965022460 47 20.4 21.2621875794304 -0.862187579430414 48 18.1 18.4443895586644 -0.344389558664401 49 19.6 19.3897717594193 0.210228240580721 50 19.9 20.4895319926487 -0.589531992648697 51 19.2 18.9844028281166 0.215597171883414 52 17.8 18.6324104739471 -0.832410473947091 53 19.2 19.5185725022936 -0.318572502293647 54 22 21.9828639563099 0.0171360436900842 55 21.1 20.7753430328265 0.324656967173468 56 19.5 19.1938218324244 0.306178167575571 57 22.2 21.7100507049858 0.489949295014173 58 20.9 21.2657712584743 -0.365771258474305 59 22.2 22.0595628788108 0.140437121189171 60 23.5 23.2179452729639 0.282054727036063 61 21.5 21.3576915894849 0.142308410515105 62 24.3 24.2870433070368 0.0129566929632053 63 22.8 22.4045501782425 0.395449821757501 64 20.3 20.0776106067048 0.222389393295187 65 23.7 23.251919456483 0.448080543517001 66 23.3 22.8961732191293 0.403826780870677 67 19.6 19.1935518566409 0.406448143359093 68 18 18.6122661912514 -0.612266191251356 69 17.3 17.3577816884607 -0.0577816884607114 70 16.8 16.6966004918305 0.103399508169533 71 18.2 17.9771830260723 0.222816973927707 72 16.5 16.4828622922384 0.0171377077615822 73 16 15.8783338919981 0.121666108001909 74 18.4 17.9427062061629 0.457293793837084 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.140892591958867 0.281785183917735 0.859107408041133 20 0.0816927564588042 0.163385512917608 0.918307243541196 21 0.0321589608781497 0.0643179217562995 0.96784103912185 22 0.0270294425065019 0.0540588850130037 0.972970557493498 23 0.0156514428776132 0.0313028857552263 0.984348557122387 24 0.00671161504975673 0.0134232300995135 0.993288384950243 25 0.00266421960112299 0.00532843920224599 0.997335780398877 26 0.00131780914872147 0.00263561829744294 0.998682190851279 27 0.000514479266098116 0.00102895853219623 0.999485520733902 28 0.000313850786462428 0.000627701572924856 0.999686149213538 29 0.000182748398298825 0.000365496796597651 0.999817251601701 30 6.48234554235487e-05 0.000129646910847097 0.999935176544577 31 2.89211500530379e-05 5.78423001060759e-05 0.999971078849947 32 8.78169117727839e-05 0.000175633823545568 0.999912183088227 33 0.000684759796810431 0.00136951959362086 0.99931524020319 34 0.000564659430888075 0.00112931886177615 0.999435340569112 35 0.00839222909960589 0.0167844581992118 0.991607770900394 36 0.0267674660354495 0.0535349320708989 0.97323253396455 37 0.023755207853885 0.04751041570777 0.976244792146115 38 0.0939826031931715 0.187965206386343 0.906017396806828 39 0.082094245206579 0.164188490413158 0.91790575479342 40 0.146831725442035 0.29366345088407 0.853168274557965 41 0.162607300282250 0.325214600564501 0.83739269971775 42 0.117601541514601 0.235203083029202 0.8823984584854 43 0.12420545625703 0.24841091251406 0.87579454374297 44 0.112554235349954 0.225108470699909 0.887445764650046 45 0.265465792915371 0.530931585830742 0.734534207084629 46 0.287746617391625 0.57549323478325 0.712253382608375 47 0.489586768215312 0.979173536430624 0.510413231784688 48 0.414466547197268 0.828933094394535 0.585533452802733 49 0.595281375725423 0.809437248549154 0.404718624274577 50 0.644907623920363 0.710184752159273 0.355092376079637 51 0.601565374380767 0.796869251238466 0.398434625619233 52 0.574503131603196 0.850993736793609 0.425496868396804 53 0.443440378240194 0.886880756480388 0.556559621759806 54 0.359094734906536 0.718189469813073 0.640905265093464 55 0.392801283381074 0.785602566762148 0.607198716618926 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.270270270270270 NOK 5% type I error level 14 0.378378378378378 NOK 10% type I error level 17 0.459459459459459 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/10oh4c1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/10oh4c1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/1dg6e1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/1dg6e1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/2wtul1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/2wtul1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/36wom1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/36wom1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/4wtjp1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/4wtjp1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/5iqlz1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/5iqlz1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/6asmr1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/6asmr1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/743gk1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/743gk1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/8jolq1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/8jolq1259104493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/9mqan1259104493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259104578nzi0ai2ucvf64lx/9mqan1259104493.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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