*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: Sun, 22 Nov 2009 08:45:38 -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/22/t1258904900xwwkecv7lyl091e.htm/, Retrieved Sun, 22 Nov 2009 16:48:32 +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/22/t1258904900xwwkecv7lyl091e.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:

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264768 236089 268586 268361 272433 274412 269974 236997 264768 268586 268361 272433 304744 264579 269974 264768 268586 268361 309365 270349 304744 269974 264768 268586 308347 269645 309365 304744 269974 264768 298427 267037 308347 309365 304744 269974 289231 258113 298427 308347 309365 304744 291975 262813 289231 298427 308347 309365 294912 267413 291975 289231 298427 308347 293488 267366 294912 291975 289231 298427 290555 264777 293488 294912 291975 289231 284736 258863 290555 293488 294912 291975 281818 254844 284736 290555 293488 294912 287854 254868 281818 284736 290555 293488 316263 277267 287854 281818 284736 290555 325412 285351 316263 287854 281818 284736 326011 286602 325412 316263 287854 281818 328282 283042 326011 325412 316263 287854 317480 276687 328282 326011 325412 316263 317539 277915 317480 328282 326011 325412 313737 277128 317539 317480 328282 326011 312276 277103 313737 317539 317480 328282 309391 275037 312276 313737 317539 317480 302950 270150 309391 312276 313737 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 x[t] = + 29406.7468837864 + 1.28058662621852y[t] + 0.0213665756704032y1[t] -0.109951546684521y2[t] + 0.189129502860375y3[t] -0.583466893077811y4[t] + 645.041944741420M1[t] -7862.50086321254M2[t] -27771.3308989242M3[t] -36054.2742253516M4[t] -28597.9078937059M5[t] -24167.9784519473M6[t] -1856.21403375339M7[t] + 3394.51888208249M8[t] + 1692.51508426616M9[t] + 874.021498903283M10[t] -2481.97169085193M11[t] + 347.710441098343t + 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) 29406.7468837864 15680.061815 1.8754 0.06586 0.03293 y 1.28058662621852 0.240176 5.3319 2e-06 1e-06 y1 0.0213665756704032 0.354919 0.0602 0.952206 0.476103 y2 -0.109951546684521 0.357412 -0.3076 0.759483 0.379742 y3 0.189129502860375 0.360595 0.5245 0.60197 0.300985 y4 -0.583466893077811 0.236417 -2.468 0.016614 0.008307 M1 645.041944741420 3894.791103 0.1656 0.869045 0.434522 M2 -7862.50086321254 4442.824227 -1.7697 0.082125 0.041062 M3 -27771.3308989242 9119.007139 -3.0454 0.003515 0.001757 M4 -36054.2742253516 9351.733966 -3.8554 0.000296 0.000148 M5 -28597.9078937059 8984.909714 -3.1829 0.002362 0.001181 M6 -24167.9784519473 8377.212555 -2.885 0.005517 0.002759 M7 -1856.21403375339 4557.770143 -0.4073 0.68534 0.34267 M8 3394.51888208249 4770.991306 0.7115 0.479682 0.239841 M9 1692.51508426616 5147.300768 0.3288 0.7435 0.37175 M10 874.021498903283 4915.03157 0.1778 0.85949 0.429745 M11 -2481.97169085193 3988.729095 -0.6222 0.536261 0.268131 t 347.710441098343 49.249558 7.0602 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.93604685848075 R-squared 0.87618372127168 Adjusted R-squared 0.839256059194813 F-TEST (value) 23.727029332316 F-TEST (DF numerator) 17 F-TEST (DF denominator) 57 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6717.02322907089 Sum Squared Residuals 2571748860.41304 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 236089 237104.715982948 -1015.71598294806 2 236997 235889.846554026 1107.15344597378 3 264579 263804.184677969 774.81532203129 4 270349 261103.674183552 9245.32581644765 5 269645 267092.11822842 2552.88177157981 6 267037 262175.00567702 4861.9943229800 7 258113 253545.003725884 4567.99627411648 8 262813 260662.874751582 2150.12524841765 9 267413 262857.213251797 4555.78674820312 10 267366 264372.678011467 2993.32198853329 11 264777 263139.613896336 1637.38610366372 12 258863 257565.905481654 1297.09451834614 13 254844 253037.099197745 1806.90080225508 14 254868 253460.488112949 1407.51188705142 15 277267 271340.125066802 5926.87493319829 16 285351 277907.628698652 7443.37130134816 17 286602 286394.788244815 207.211755185062 18 283042 294938.666114167 -11896.6661141668 19 276687 288902.541610152 -12215.5416101521 20 277915 288871.187832565 -10956.1878325655 21 277128 283917.077790258 -6789.07779025772 22 277103 278119.604519057 -1016.60451905721 23 275037 278117.416586895 -3080.41658689528 24 270150 272044.341981702 -1894.34198170234 25 267140 271785.640216177 -4645.64021617714 26 264993 269347.034379142 -4354.03437914184 27 287259 288321.859174519 -1062.85917451887 28 291186 289273.349725057 1912.65027494299 29 292300 293909.261129945 -1609.26112994523 30 288186 286214.880427174 1971.11957282623 31 281477 279621.776796573 1855.22320342702 32 282656 284243.164855065 -1587.16485506543 33 280190 281138.905434533 -948.905434532673 34 280408 281138.627643475 -730.627643474796 35 276836 275555.042773191 1280.95722680916 36 275216 272978.062419437 2237.93758056337 37 274352 273964.963617819 387.036382180673 38 271311 271144.391651899 166.608348101282 39 289802 290900.283755638 -1098.28375563846 40 290726 292235.263361159 -1509.26336115896 41 292300 288434.420137580 3865.57986241972 42 278506 272229.990289954 6276.0097100456 43 269826 262998.203698562 6827.79630143823 44 265861 260066.729402835 5794.27059716494 45 269034 261091.527177735 7942.47282226516 46 264176 260623.975829083 3552.02417091696 47 255198 253802.060298558 1395.93970144219 48 253353 254857.115830237 -1504.11583023651 49 246057 245581.577112526 475.422887474289 50 235372 237977.495322387 -2605.49532238681 51 258556 266181.240391493 -7625.24039149274 52 260993 268692.026892898 -7699.02689289788 53 254663 255799.007461843 -1136.0074618429 54 250643 253268.633871999 -2625.63387199911 55 243422 246447.3467803 -3025.34678029991 56 247105 246824.252521854 280.747478146419 57 248541 255847.293933219 -7306.29393321932 58 245039 252093.269465372 -7054.26946537164 59 237080 245860.579538571 -8780.5795385706 60 237085 245939.31423192 -8854.31423191995 61 225554 233844.095506987 -8290.09550698674 62 226839 236729.621921312 -9890.62192131193 63 247934 260005.599671992 -12071.5996719923 64 248333 257726.057138682 -9393.05713868196 65 246969 250849.404797396 -3880.40479739647 66 245098 243684.823619686 1413.17638031405 67 246263 244273.12738853 1989.87261147028 68 255765 251446.790636098 4318.20936390187 69 264319 261772.982412459 2546.01758754143 70 268347 266090.844531547 2256.15546845338 71 273046 265499.286906449 7546.71309355081 72 273963 265245.260055051 8717.7399449493 73 267430 256147.908365798 11282.0916342019 74 271993 257824.122058286 14168.8779417141 75 292710 277553.707261587 15156.2927384128 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.349923334180428 0.699846668360856 0.650076665819572 22 0.236068188816044 0.472136377632088 0.763931811183956 23 0.146066008069133 0.292132016138267 0.853933991930867 24 0.0888411485817269 0.177682297163454 0.911158851418273 25 0.0468393897260121 0.0936787794520242 0.953160610273988 26 0.040349227105183 0.080698454210366 0.959650772894817 27 0.076931814653297 0.153863629306594 0.923068185346703 28 0.62229991790806 0.75540016418388 0.37770008209194 29 0.614616521829831 0.770766956340338 0.385383478170169 30 0.557365701738416 0.885268596523167 0.442634298261584 31 0.481415239664417 0.962830479328834 0.518584760335583 32 0.428173257176285 0.85634651435257 0.571826742823715 33 0.468055863311585 0.93611172662317 0.531944136688415 34 0.422758885386186 0.84551777077237 0.577241114613815 35 0.342899566926469 0.685799133852938 0.657100433073531 36 0.282885479409400 0.565770958818801 0.7171145205906 37 0.280160707268189 0.560321414536378 0.719839292731811 38 0.230935510149074 0.461871020298147 0.769064489850926 39 0.181631878896699 0.363263757793398 0.818368121103301 40 0.288822124894768 0.577644249789536 0.711177875105232 41 0.507700867280505 0.98459826543899 0.492299132719495 42 0.883400919792576 0.233198160414848 0.116599080207424 43 0.919966908746612 0.160066182506775 0.0800330912533877 44 0.941497416143007 0.117005167713986 0.0585025838569928 45 0.923465694703016 0.153068610593968 0.0765343052969842 46 0.903591044891752 0.192817910216497 0.0964089551082483 47 0.896735715935605 0.20652856812879 0.103264284064395 48 0.860256711977625 0.279486576044749 0.139743288022375 49 0.837617535098684 0.324764929802631 0.162382464901316 50 0.997539101643485 0.00492179671303087 0.00246089835651544 51 0.997322415555544 0.005355168888912 0.002677584444456 52 0.993880374768 0.0122392504639997 0.00611962523199985 53 0.979513271779469 0.0409734564410623 0.0204867282205311 54 0.942043055915325 0.115913888169350 0.0579569440846752 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0588235294117647 NOK 5% type I error level 4 0.117647058823529 NOK 10% type I error level 6 0.176470588235294 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/10j73g1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/10j73g1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/1dson1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/1dson1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/2d3sm1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/2d3sm1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/3ymb91258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/3ymb91258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/4wc8n1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/4wc8n1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/59q9s1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/59q9s1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/68hnx1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/68hnx1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/70k6n1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/70k6n1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/8edko1258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/8edko1258904733.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/9oju81258904733.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258904900xwwkecv7lyl091e/9oju81258904733.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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