Case Seatbelt law Q3

*Unverified author*

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 07:07:44 -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/t1227795053smd8igim4w3sq3e.htm/, Retrieved Thu, 27 Nov 2008 14:10:53 +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/t1227795053smd8igim4w3sq3e.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 «

93.7 105.7 109.5 105.3 102.8 100.6 97.6 110.3 107.2 107.2 108.1 97.1 92.2 112.2 111.6 115.7 111.3 104.2 103.2 112.7 106.4 102.6 110.6 95.2 89 112.5 116.8 107.2 113.6 101.8 102.6 122.7 110.3 110.5 121.6 100.3 100.7 123.4 127.1 124.1 131.2 111.6 114.2 130.1 125.9 119 133.8 107.5 113.5 134.4 126.8 135.6 139.9 129.8 131 153.1 134.1 144.1 155.9 123.3 128.1 144.3 153 149.9 150.9 141 138.9 157.4 142.9 151.7 161 138.5 135.9 151.5 164 159.1 157 142.1 144.8 152.1 154.6 148.7 157.7 146.4 136.5

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 omzet[t] = + 80.276 -0.605238095238104M1[t] + 18.1466666666667M2[t] + 20.9562857142857M3[t] + 18.5230476190476M4[t] + 19.1898095238095M5[t] + 7.65657142857143M6[t] + 7.0947619047619M7[t] + 21.5186666666667M8[t] + 12.6425714285714M9[t] + 12.2521904761905M10[t] + 20.7903809523809M11[t] + 0.733238095238095t + 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) 80.276 2.632945 30.489 0 0 M1 -0.605238095238104 3.140923 -0.1927 0.847741 0.42387 M2 18.1466666666667 3.252165 5.5799 0 0 M3 20.9562857142857 3.250034 6.448 0 0 M4 18.5230476190476 3.248126 5.7027 0 0 M5 19.1898095238095 3.246441 5.911 0 0 M6 7.65657142857143 3.24498 2.3595 0.021014 0.010507 M7 7.0947619047619 3.243744 2.1872 0.031976 0.015988 M8 21.5186666666667 3.242732 6.636 0 0 M9 12.6425714285714 3.241944 3.8997 0.000214 0.000107 M10 12.2521904761905 3.241382 3.7799 0.000321 0.00016 M11 20.7903809523809 3.241044 6.4147 0 0 t 0.733238095238095 0.027008 27.1492 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.960511433070904 R-squared 0.922582213059921 Adjusted R-squared 0.909679248569908 F-TEST (value) 71.5015695636452 F-TEST (DF numerator) 12 F-TEST (DF denominator) 72 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.0632278322715 Sum Squared Residuals 2646.91668571429 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 93.7 80.404 13.2960000000000 2 105.7 99.8891428571428 5.81085714285715 3 109.5 103.432 6.06799999999999 4 105.3 101.732 3.56799999999999 5 102.8 103.132 -0.331999999999998 6 100.6 92.332 8.268 7 97.6 92.5034285714286 5.09657142857142 8 110.3 107.660571428571 2.63942857142856 9 107.2 99.5177142857143 7.68228571428573 10 107.2 99.8605714285714 7.33942857142857 11 108.1 109.132 -1.03199999999999 12 97.1 89.0748571428571 8.02514285714285 13 92.2 89.2028571428571 2.9971428571429 14 112.2 108.688 3.51199999999999 15 111.6 112.230857142857 -0.630857142857143 16 115.7 110.530857142857 5.16914285714286 17 111.3 111.930857142857 -0.630857142857147 18 104.2 101.130857142857 3.06914285714286 19 103.2 101.302285714286 1.89771428571428 20 112.7 116.459428571429 -3.75942857142857 21 106.4 108.316571428571 -1.91657142857142 22 102.6 108.659428571429 -6.05942857142858 23 110.6 117.930857142857 -7.33085714285715 24 95.2 97.8737142857143 -2.67371428571428 25 89 98.0017142857143 -9.00171428571429 26 112.5 117.486857142857 -4.98685714285715 27 116.8 121.029714285714 -4.22971428571428 28 107.2 119.329714285714 -12.1297142857143 29 113.6 120.729714285714 -7.12971428571429 30 101.8 109.929714285714 -8.12971428571429 31 102.6 110.101142857143 -7.50114285714286 32 122.7 125.258285714286 -2.55828571428571 33 110.3 117.115428571429 -6.81542857142858 34 110.5 117.458285714286 -6.95828571428571 35 121.6 126.729714285714 -5.12971428571429 36 100.3 106.672571428571 -6.37257142857144 37 100.7 106.800571428571 -6.10057142857143 38 123.4 126.285714285714 -2.88571428571428 39 127.1 129.828571428571 -2.72857142857143 40 124.1 128.128571428571 -4.02857142857143 41 131.2 129.528571428571 1.67142857142857 42 111.6 118.728571428571 -7.12857142857143 43 114.2 118.9 -4.7 44 130.1 134.057142857143 -3.95714285714286 45 125.9 125.914285714286 -0.0142857142857130 46 119 126.257142857143 -7.25714285714286 47 133.8 135.528571428571 -1.72857142857141 48 107.5 115.471428571429 -7.97142857142857 49 113.5 115.599428571429 -2.09942857142857 50 134.4 135.084571428571 -0.684571428571427 51 126.8 138.627428571429 -11.8274285714286 52 135.6 136.927428571429 -1.32742857142857 53 139.9 138.327428571429 1.57257142857144 54 129.8 127.527428571429 2.27257142857144 55 131 127.698857142857 3.30114285714286 56 153.1 142.856 10.244 57 134.1 134.713142857143 -0.613142857142862 58 144.1 135.056 9.044 59 155.9 144.327428571429 11.5725714285714 60 123.3 124.270285714286 -0.970285714285722 61 128.1 124.398285714286 3.70171428571428 62 144.3 143.883428571429 0.416571428571444 63 153 147.426285714286 5.57371428571429 64 149.9 145.726285714286 4.1737142857143 65 150.9 147.126285714286 3.77371428571429 66 141 136.326285714286 4.67371428571428 67 138.9 136.497714285714 2.40228571428572 68 157.4 151.654857142857 5.74514285714286 69 142.9 143.512 -0.612000000000001 70 151.7 143.854857142857 7.84514285714285 71 161 153.126285714286 7.87371428571428 72 138.5 133.069142857143 5.43085714285715 73 135.9 133.197142857143 2.70285714285715 74 151.5 152.682285714286 -1.18228571428572 75 164 156.225142857143 7.77485714285715 76 159.1 154.525142857143 4.57485714285714 77 157 155.925142857143 1.07485714285714 78 142.1 145.125142857143 -3.02514285714286 79 144.8 145.296571428571 -0.496571428571425 80 152.1 160.453714285714 -8.35371428571429 81 154.6 152.310857142857 2.28914285714284 82 148.7 152.653714285714 -3.95371428571429 83 157.7 161.925142857143 -4.22514285714288 84 146.4 141.868 4.53200000000001 85 136.5 141.996 -5.496 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.319968650611613 0.639937301223226 0.680031349388387 17 0.220163063662968 0.440326127325936 0.779836936337032 18 0.136527711061506 0.273055422123012 0.863472288938494 19 0.0778475043359461 0.155695008671892 0.922152495664054 20 0.0446169022919558 0.0892338045839117 0.955383097708044 21 0.044394892094921 0.088789784189842 0.955605107905079 22 0.0830884018488371 0.166176803697674 0.916911598151163 23 0.0486939845373919 0.0973879690747839 0.951306015462608 24 0.0418317276571135 0.083663455314227 0.958168272342886 25 0.0651871609755554 0.130374321951111 0.934812839024445 26 0.0396168646691861 0.0792337293383722 0.960383135330814 27 0.0282496075391929 0.0564992150783859 0.971750392460807 28 0.0381259418923839 0.0762518837847677 0.961874058107616 29 0.0314702979729401 0.0629405959458803 0.96852970202706 30 0.0229703459077655 0.0459406918155309 0.977029654092235 31 0.0139222223712454 0.0278444447424907 0.986077777628755 32 0.0254473822209817 0.0508947644419634 0.974552617779018 33 0.0158173092305735 0.031634618461147 0.984182690769427 34 0.0109367454526542 0.0218734909053083 0.989063254547346 35 0.0207713064361344 0.0415426128722689 0.979228693563866 36 0.013235203059774 0.026470406119548 0.986764796940226 37 0.00990145092800461 0.0198029018560092 0.990098549071995 38 0.0125328139306275 0.0250656278612550 0.987467186069372 39 0.0162351160280115 0.0324702320560231 0.983764883971988 40 0.0198021765061794 0.0396043530123587 0.98019782349382 41 0.0644890694726382 0.128978138945276 0.935510930527362 42 0.0519852655106217 0.103970531021243 0.948014734489378 43 0.0434639785441304 0.0869279570882608 0.95653602145587 44 0.0408423380370563 0.0816846760741126 0.959157661962944 45 0.0429577500545119 0.0859155001090238 0.957042249945488 46 0.0511543814072896 0.102308762814579 0.94884561859271 47 0.0783673766629162 0.156734753325832 0.921632623337084 48 0.109730995372247 0.219461990744493 0.890269004627753 49 0.105495889154848 0.210991778309696 0.894504110845152 50 0.0974410253520347 0.194882050704069 0.902558974647965 51 0.466006252840892 0.932012505681785 0.533993747159108 52 0.605491876460702 0.789016247078597 0.394508123539298 53 0.666756608296317 0.666486783407366 0.333243391703683 54 0.6812167807978 0.637566438404399 0.318783219202199 55 0.691546897525614 0.616906204948772 0.308453102474386 56 0.822502115528112 0.354995768943776 0.177497884471888 57 0.82420956024377 0.351580879512460 0.175790439756230 58 0.848544033007907 0.302911933984185 0.151455966992093 59 0.891298552432886 0.217402895134228 0.108701447567114 60 0.95636739535789 0.08726520928422 0.04363260464211 61 0.933851996986239 0.132296006027523 0.0661480030137615 62 0.901538682434886 0.196922635130228 0.0984613175651142 63 0.913326267966937 0.173347464066126 0.086673732033063 64 0.906396406329893 0.187207187340214 0.0936035936701068 65 0.866132979053923 0.267734041892155 0.133867020946077 66 0.787322524671918 0.425354950656165 0.212677475328082 67 0.704434197107399 0.591131605785203 0.295565802892601 68 0.683074071143363 0.633851857713273 0.316925928856637 69 0.788965891212908 0.422068217574183 0.211034108787092 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 10 0.185185185185185 NOK 10% type I error level 23 0.425925925925926 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/10v0jd1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/10v0jd1227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/1u55k1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/1u55k1227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/26dez1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/26dez1227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/32cxn1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/32cxn1227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/4b1v21227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/4b1v21227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/5mgzu1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/5mgzu1227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/61xdi1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/61xdi1227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/76lx11227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/76lx11227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/8vhqg1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/8vhqg1227794852.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/9gjmc1227794852.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227795053smd8igim4w3sq3e/9gjmc1227794852.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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