11.1 the 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: Mon, 24 Nov 2008 00:34:59 -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/24/t12275123110cdo8s4ns3fuq1k.htm/, Retrieved Mon, 24 Nov 2008 07:38:31 +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/24/t12275123110cdo8s4ns3fuq1k.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?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

90,7 0 94,3 0 104,6 0 111,1 0 110,8 0 107,2 0 99 0 99 0 91 0 96,2 0 96,9 0 96,2 0 100,1 0 99 0 115,4 0 106,9 0 107,1 0 99,3 0 99,2 0 108,3 0 105,6 0 99,5 0 107,4 0 93,1 0 88,1 0 110,7 0 113,1 0 99,6 0 93,6 0 98,6 0 99,6 0 114,3 0 107,8 0 101,2 0 112,5 0 100,5 0 93,9 0 116,2 0 112 0 106,4 0 95,7 0 96 0 95,8 0 103 0 102,2 0 98,4 0 111,4 1 86,6 1 91,3 1 107,9 1 101,8 1 104,4 1 93,4 1 100,1 1 98,5 1 112,9 1 101,4 1 107,1 1 110,8 1 90,3 1 95,5 1 111,4 1 113 1 107,5 1 95,9 1 106,3 1 105,2 1 117,2 1 106,9 1 108,2 1 113 1 97,2 1 99,9 1 108,1 1 118,1 1 109,1 1 93,3 1 112,1 1 111,8 1 112,5 1 116,3 1 110,3 1 117,1 1 103,4 1 96,2 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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Prodintergoed[t] = + 102.110869565217 + 3.10451505016723invest[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) 102.110869565217 1.135202 89.9495 0 0 invest 3.10451505016723 1.675909 1.8524 0.067518 0.033759 Multiple Linear Regression - Regression Statistics Multiple R 0.199254124190547 R-squared 0.0397022060069419 Adjusted R-squared 0.0281323530672665 F-TEST (value) 3.43152209573856 F-TEST (DF numerator) 1 F-TEST (DF denominator) 83 p-value 0.0675178440910564 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.69931567483898 Sum Squared Residuals 4920.19533444816 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 90.7 102.110869565218 -11.4108695652175 2 94.3 102.110869565217 -7.81086956521739 3 104.6 102.110869565217 2.48913043478261 4 111.1 102.110869565217 8.9891304347826 5 110.8 102.110869565217 8.68913043478261 6 107.2 102.110869565217 5.08913043478262 7 99 102.110869565217 -3.11086956521739 8 99 102.110869565217 -3.11086956521739 9 91 102.110869565217 -11.1108695652174 10 96.2 102.110869565217 -5.91086956521738 11 96.9 102.110869565217 -5.21086956521738 12 96.2 102.110869565217 -5.91086956521738 13 100.1 102.110869565217 -2.01086956521739 14 99 102.110869565217 -3.11086956521739 15 115.4 102.110869565217 13.2891304347826 16 106.9 102.110869565217 4.78913043478262 17 107.1 102.110869565217 4.98913043478261 18 99.3 102.110869565217 -2.81086956521739 19 99.2 102.110869565217 -2.91086956521738 20 108.3 102.110869565217 6.18913043478261 21 105.6 102.110869565217 3.48913043478261 22 99.5 102.110869565217 -2.61086956521739 23 107.4 102.110869565217 5.28913043478262 24 93.1 102.110869565217 -9.0108695652174 25 88.1 102.110869565217 -14.0108695652174 26 110.7 102.110869565217 8.58913043478262 27 113.1 102.110869565217 10.9891304347826 28 99.6 102.110869565217 -2.51086956521739 29 93.6 102.110869565217 -8.5108695652174 30 98.6 102.110869565217 -3.51086956521739 31 99.6 102.110869565217 -2.51086956521739 32 114.3 102.110869565217 12.1891304347826 33 107.8 102.110869565217 5.68913043478261 34 101.2 102.110869565217 -0.910869565217384 35 112.5 102.110869565217 10.3891304347826 36 100.5 102.110869565217 -1.61086956521739 37 93.9 102.110869565217 -8.21086956521738 38 116.2 102.110869565217 14.0891304347826 39 112 102.110869565217 9.88913043478261 40 106.4 102.110869565217 4.28913043478262 41 95.7 102.110869565217 -6.41086956521738 42 96 102.110869565217 -6.11086956521739 43 95.8 102.110869565217 -6.31086956521739 44 103 102.110869565217 0.889130434782613 45 102.2 102.110869565217 0.0891304347826157 46 98.4 102.110869565217 -3.71086956521738 47 111.4 105.215384615385 6.18461538461539 48 86.6 105.215384615385 -18.6153846153846 49 91.3 105.215384615385 -13.9153846153846 50 107.9 105.215384615385 2.68461538461539 51 101.8 105.215384615385 -3.41538461538462 52 104.4 105.215384615385 -0.81538461538461 53 93.4 105.215384615385 -11.8153846153846 54 100.1 105.215384615385 -5.11538461538462 55 98.5 105.215384615385 -6.71538461538462 56 112.9 105.215384615385 7.68461538461539 57 101.4 105.215384615385 -3.81538461538461 58 107.1 105.215384615385 1.88461538461538 59 110.8 105.215384615385 5.58461538461538 60 90.3 105.215384615385 -14.9153846153846 61 95.5 105.215384615385 -9.71538461538462 62 111.4 105.215384615385 6.18461538461539 63 113 105.215384615385 7.78461538461538 64 107.5 105.215384615385 2.28461538461538 65 95.9 105.215384615385 -9.31538461538461 66 106.3 105.215384615385 1.08461538461538 67 105.2 105.215384615385 -0.0153846153846133 68 117.2 105.215384615385 11.9846153846154 69 106.9 105.215384615385 1.68461538461539 70 108.2 105.215384615385 2.98461538461539 71 113 105.215384615385 7.78461538461538 72 97.2 105.215384615385 -8.01538461538461 73 99.9 105.215384615385 -5.31538461538461 74 108.1 105.215384615385 2.88461538461538 75 118.1 105.215384615385 12.8846153846154 76 109.1 105.215384615385 3.88461538461538 77 93.3 105.215384615385 -11.9153846153846 78 112.1 105.215384615385 6.88461538461538 79 111.8 105.215384615385 6.58461538461538 80 112.5 105.215384615385 7.28461538461538 81 116.3 105.215384615385 11.0846153846154 82 110.3 105.215384615385 5.08461538461538 83 117.1 105.215384615385 11.8846153846154 84 103.4 105.215384615385 -1.81538461538461 85 96.2 105.215384615385 -9.01538461538461 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.881032356450958 0.237935287098084 0.118967643549042 6 0.812968108972897 0.374063782054206 0.187031891027103 7 0.72791037305537 0.54417925388926 0.27208962694463 8 0.631338570753946 0.737322858492108 0.368661429246054 9 0.698684001713036 0.602631996573928 0.301315998286964 10 0.630604530548858 0.738790938902283 0.369395469451142 11 0.549959803218741 0.900080393562517 0.450040196781258 12 0.477477180156762 0.954954360313524 0.522522819843238 13 0.383346319052437 0.766692638104873 0.616653680947563 14 0.300110254797414 0.600220509594828 0.699889745202586 15 0.538053850126644 0.923892299746712 0.461946149873356 16 0.497170011587742 0.994340023175483 0.502829988412258 17 0.456182682718626 0.912365365437251 0.543817317281375 18 0.383278389627838 0.766556779255675 0.616721610372162 19 0.315889177986008 0.631778355972017 0.684110822013992 20 0.299715169074943 0.599430338149885 0.700284830925057 21 0.250808309078211 0.501616618156421 0.74919169092179 22 0.199209921717408 0.398419843434816 0.800790078282592 23 0.175611811122191 0.351223622244383 0.824388188877809 24 0.194659441850927 0.389318883701854 0.805340558149073 25 0.323702318411859 0.647404636823717 0.676297681588141 26 0.347892919817969 0.695785839635939 0.65210708018203 27 0.419339133406383 0.838678266812767 0.580660866593616 28 0.359815900804011 0.719631801608023 0.640184099195989 29 0.371495717657045 0.74299143531409 0.628504282342955 30 0.322045607765488 0.644091215530977 0.677954392234512 31 0.270927792628512 0.541855585257024 0.729072207371488 32 0.359029090389597 0.718058180779195 0.640970909610403 33 0.330472473537213 0.660944947074427 0.669527526462787 34 0.274307053075056 0.548614106150112 0.725692946924944 35 0.318343920318582 0.636687840637165 0.681656079681418 36 0.265038623971081 0.530077247942163 0.734961376028919 37 0.272179359155597 0.544358718311194 0.727820640844403 38 0.404340360061894 0.808680720123787 0.595659639938106 39 0.454264258458554 0.908528516917108 0.545735741541446 40 0.424456185815474 0.848912371630948 0.575543814184526 41 0.39315677926771 0.78631355853542 0.60684322073229 42 0.359999270046756 0.719998540093513 0.640000729953244 43 0.332273621909555 0.664547243819111 0.667726378090445 44 0.279068792827541 0.558137585655081 0.72093120717246 45 0.230979335941168 0.461958671882335 0.769020664058833 46 0.1906654893067 0.3813309786134 0.8093345106933 47 0.162030222089125 0.324060444178249 0.837969777910875 48 0.386211943987472 0.772423887974943 0.613788056012528 49 0.464211233279719 0.928422466559438 0.535788766720281 50 0.447419363218545 0.89483872643709 0.552580636781455 51 0.396969381754953 0.793938763509906 0.603030618245047 52 0.344660275236603 0.689320550473206 0.655339724763397 53 0.403788333389234 0.807576666778467 0.596211666610766 54 0.368495315559686 0.736990631119371 0.631504684440314 55 0.352596629904803 0.705193259809607 0.647403370095197 56 0.377744009149982 0.755488018299965 0.622255990850018 57 0.335192981273927 0.670385962547854 0.664807018726073 58 0.287621149769965 0.57524229953993 0.712378850230035 59 0.266246237756690 0.532492475513379 0.73375376224331 60 0.453045168150394 0.906090336300788 0.546954831849606 61 0.526420239953317 0.947159520093366 0.473579760046683 62 0.500558426206428 0.998883147587145 0.499441573793572 63 0.49356123631582 0.98712247263164 0.50643876368418 64 0.42763917224412 0.85527834448824 0.57236082775588 65 0.505399250723888 0.989201498552225 0.494600749276112 66 0.436501578674533 0.873003157349066 0.563498421325467 67 0.371918294359715 0.74383658871943 0.628081705640285 68 0.433441495323582 0.866882990647163 0.566558504676418 69 0.357956061762498 0.715912123524997 0.642043938237502 70 0.287365504656017 0.574731009312035 0.712634495343983 71 0.257978912536875 0.515957825073751 0.742021087463125 72 0.301247798782606 0.602495597565213 0.698752201217394 73 0.305130364615001 0.610260729230001 0.694869635385 74 0.229670096657596 0.459340193315192 0.770329903342404 75 0.273310980078805 0.546621960157609 0.726689019921196 76 0.195197128563425 0.39039425712685 0.804802871436575 77 0.451277968868353 0.902555937736706 0.548722031131647 78 0.344252203565424 0.688504407130849 0.655747796434575 79 0.238938003542211 0.477876007084422 0.761061996457789 80 0.152899966781576 0.305799933563151 0.847100033218424 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/103s9d1227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/103s9d1227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/1bnas1227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/1bnas1227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/2j7ni1227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/2j7ni1227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/349811227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/349811227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/4ryh31227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/4ryh31227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/51lrf1227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/51lrf1227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/6yto11227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/6yto11227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/7t18c1227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/7t18c1227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/8mxdr1227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/8mxdr1227512086.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/90pw31227512086.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275123110cdo8s4ns3fuq1k/90pw31227512086.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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