Seatbelt Law part 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: Fri, 20 Nov 2009 11:54:44 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8.htm/, Retrieved Fri, 20 Nov 2009 19:55:24 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

519164 0,9 517009 1,3 509933 1,4 509127 1,5 500857 1,1 506971 1,6 569323 1,5 579714 1,6 577992 1,7 565464 1,6 547344 1,7 554788 1,6 562325 1,6 560854 1,3 555332 1,1 543599 1,6 536662 1,9 542722 1,6 593530 1,7 610763 1,6 612613 1,4 611324 2,1 594167 1,9 595454 1,7 590865 1,8 589379 2 584428 2,5 573100 2,1 567456 2,1 569028 2,3 620735 2,4 628884 2,4 628232 2,3 612117 1,7 595404 2 597141 2,3 593408 2 590072 2 579799 1,3 574205 1,7 572775 1,9 572942 1,7 619567 1,6 625809 1,7 619916 1,8 587625 1,9 565742 1,9 557274 1,9 560576 2 548854 2,1 531673 1,9 525919 1,9 511038 1,3 498662 1,3 555362 1,4 564591 1,2 541657 1,3 527070 1,8 509846 2,2 514258 2,6 516922 2,8 507561 3,1 492622 3,9 490243 3,7 469357 4,6 477580 5,1 528379 5,2 533590 4,9 517945 5,1 506174 4,8 501866 3,9 516141 3,5

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation TWIB[t] = + 590737.37772413 -15986.3450682317`GI `[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) 590737.37772413 10340.353582 57.1293 0 0 `GI ` -15986.3450682317 4312.694333 -3.7068 0.000416 0.000208 Multiple Linear Regression - Regression Statistics Multiple R 0.405072588600541 R-squared 0.164083802035543 Adjusted R-squared 0.152142142064622 F-TEST (value) 13.7404516989350 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0.000416220845781301 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 37681.9910683079 Sum Squared Residuals 99395271561.0424 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 519164 576349.667162721 -57185.6671627208 2 517009 569955.129135428 -52946.1291354283 3 509933 568356.494628605 -58423.4946286051 4 509127 566757.860121782 -57630.8601217819 5 500857 573152.398149075 -72295.3981490746 6 506971 565159.225614959 -58188.2256149588 7 569323 566757.860121782 2565.13987821805 8 579714 565159.225614959 14554.7743850412 9 577992 563560.591108136 14431.4088918644 10 565464 565159.225614959 304.774385041221 11 547344 563560.591108136 -16216.5911081356 12 554788 565159.225614959 -10371.2256149588 13 562325 565159.225614959 -2834.22561495878 14 560854 569955.129135428 -9101.1291354283 15 555332 573152.398149075 -17820.3981490746 16 543599 565159.225614959 -21560.2256149588 17 536662 560363.322094489 -23701.3220944893 18 542722 565159.225614959 -22437.2256149588 19 593530 563560.591108136 29969.4088918644 20 610763 565159.225614959 45603.7743850412 21 612613 568356.494628605 44256.5053713949 22 611324 557166.053080843 54157.9469191571 23 594167 560363.322094489 33803.6779055107 24 595454 563560.591108136 31893.4088918644 25 590865 561961.956601312 28903.0433986876 26 589379 558764.687587666 30614.3124123339 27 584428 550771.51505355 33656.4849464498 28 573100 557166.053080843 15933.9469191571 29 567456 557166.053080843 10289.9469191571 30 569028 553968.784067197 15059.2159328034 31 620735 552370.149560373 68364.8504396266 32 628884 552370.149560373 76513.8504396266 33 628232 553968.784067197 74263.2159328034 34 612117 563560.591108136 48556.4088918644 35 595404 558764.687587666 36639.3124123339 36 597141 553968.784067197 43172.2159328034 37 593408 558764.687587666 34643.3124123339 38 590072 558764.687587666 31307.3124123339 39 579799 569955.129135428 9843.8708645717 40 574205 563560.591108136 10644.4088918644 41 572775 560363.322094489 12411.6779055107 42 572942 563560.591108136 9381.4088918644 43 619567 565159.225614959 54407.7743850412 44 625809 563560.591108136 62248.4088918644 45 619916 561961.956601312 57954.0433986876 46 587625 560363.322094489 27261.6779055107 47 565742 560363.322094489 5378.67790551074 48 557274 560363.322094489 -3089.32209448926 49 560576 558764.687587666 1811.31241233392 50 548854 557166.053080843 -8312.05308084291 51 531673 560363.322094489 -28690.3220944893 52 525919 560363.322094489 -34444.3220944893 53 511038 569955.129135428 -58917.1291354283 54 498662 569955.129135428 -71293.1291354283 55 555362 568356.494628605 -12994.4946286051 56 564591 571553.763642251 -6962.76364225148 57 541657 569955.129135428 -28298.1291354283 58 527070 561961.956601312 -34891.9566013124 59 509846 555567.41857402 -45721.4185740197 60 514258 549172.880546727 -34914.8805467270 61 516922 545975.611533081 -29053.6115330807 62 507561 541179.708012611 -33618.7080126112 63 492622 528390.631958026 -35768.6319580258 64 490243 531587.900971672 -41344.9009716721 65 469357 517200.190410264 -47843.1904102636 66 477580 509207.017876148 -31627.0178761477 67 528379 507608.383369324 20770.6166306755 68 533590 512404.286889794 21185.7131102060 69 517945 509207.017876148 8737.9821238523 70 506174 514002.921396617 -7828.92139661722 71 501866 528390.631958026 -26524.6319580258 72 516141 534785.169985318 -18644.1699853185 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.016913510593275 0.03382702118655 0.983086489406725 6 0.00358411995793588 0.00716823991587176 0.996415880042064 7 0.237672048766163 0.475344097532327 0.762327951233837 8 0.384538040768116 0.769076081536232 0.615461959231884 9 0.354224872645195 0.708449745290391 0.645775127354805 10 0.278194853072496 0.556389706144992 0.721805146927504 11 0.199997735136453 0.399995470272905 0.800002264863547 12 0.136930357558613 0.273860715117227 0.863069642441387 13 0.0952596214103148 0.190519242820630 0.904740378589685 14 0.0969543674891625 0.193908734978325 0.903045632510837 15 0.105780792006617 0.211561584013233 0.894219207993383 16 0.0746677856259587 0.149335571251917 0.925332214374041 17 0.0675754175387887 0.135150835077577 0.932424582461211 18 0.0470591332045387 0.0941182664090773 0.952940866795461 19 0.0612403563719815 0.122480712743963 0.938759643628019 20 0.132379852643227 0.264759705286453 0.867620147356773 21 0.262268071191072 0.524536142382144 0.737731928808928 22 0.254723682582553 0.509447365165107 0.745276317417447 23 0.213619458183434 0.427238916366868 0.786380541816566 24 0.193846903508987 0.387693807017973 0.806153096491014 25 0.157702034085317 0.315404068170635 0.842297965914683 26 0.121832088532867 0.243664177065734 0.878167911467133 27 0.117541858343752 0.235083716687505 0.882458141656248 28 0.0899837376832852 0.179967475366570 0.910016262316715 29 0.0690646080268569 0.138129216053714 0.930935391973143 30 0.0557032393204555 0.111406478640911 0.944296760679544 31 0.0666317555899576 0.133263511179915 0.933368244410042 32 0.0991057900091866 0.198211580018373 0.900894209990813 33 0.152472157093061 0.304944314186121 0.84752784290694 34 0.206412209122353 0.412824418244706 0.793587790877647 35 0.192719671156356 0.385439342312712 0.807280328843644 36 0.195067265580262 0.390134531160524 0.804932734419738 37 0.186005648149231 0.372011296298462 0.813994351850769 38 0.174340112418894 0.348680224837788 0.825659887581106 39 0.166742121373003 0.333484242746005 0.833257878626997 40 0.134813088727866 0.269626177455733 0.865186911272134 41 0.110013583822211 0.220027167644422 0.88998641617779 42 0.0867604456649165 0.173520891329833 0.913239554335084 43 0.211140508393730 0.422281016787459 0.78885949160627 44 0.510457264349699 0.979085471300602 0.489542735650301 45 0.836585974399956 0.326828051200088 0.163414025600044 46 0.91321926181324 0.173561476373521 0.0867807381867603 47 0.927422972461033 0.145154055077933 0.0725770275389666 48 0.932290419110847 0.135419161778305 0.0677095808891525 49 0.94958109651814 0.100837806963719 0.0504189034818597 50 0.957413705728958 0.0851725885420837 0.0425862942710418 51 0.951823327196537 0.0963533456069268 0.0481766728034634 52 0.945237864170957 0.109524271658086 0.0547621358290432 53 0.939908600422248 0.120182799155504 0.0600913995777521 54 0.964711917351541 0.0705761652969175 0.0352880826484588 55 0.957799025333656 0.0844019493326885 0.0422009746663442 56 0.974375434053107 0.0512491318937856 0.0256245659468928 57 0.97049070544024 0.0590185891195197 0.0295092945597599 58 0.964190621924762 0.071618756150475 0.0358093780752375 59 0.959398879485293 0.081202241029414 0.040601120514707 60 0.955349686728394 0.0893006265432116 0.0446503132716058 61 0.950395200691818 0.099209598616364 0.049604799308182 62 0.936318247767871 0.127363504464258 0.0636817522321288 63 0.91155854186784 0.176882916264320 0.0884414581321601 64 0.869254621834917 0.261490756330165 0.130745378165083 65 0.924968201344993 0.150063597310013 0.0750317986550066 66 0.98471615300073 0.0305676939985379 0.0152838469992690 67 0.951552258192939 0.0968954836141228 0.0484477418070614 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0158730158730159 NOK 5% type I error level 3 0.0476190476190476 OK 10% type I error level 15 0.238095238095238 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/10nqkz1258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/10nqkz1258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/10sgb1258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/10sgb1258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/2i1l71258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/2i1l71258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/336ck1258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/336ck1258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/4bzr11258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/4bzr11258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/55dpe1258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/55dpe1258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/6ci1m1258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/6ci1m1258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/7ethf1258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/7ethf1258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/8rszu1258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/8rszu1258743279.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/9t3041258743279.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743312i2tyuyfl9el01u8/9t3041258743279.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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