*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, 03 Dec 2010 21:20:55 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm.htm/, Retrieved Fri, 03 Dec 2010 22:21:32 +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/2010/Dec/03/t129141128274qcjm5ta7zjlpm.htm/}, year = {2010}, } @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 = {2010}, 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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1221.53 2617.2 10168.52 6957.61 23448.78 1180.55 2506.13 9937.04 6688.49 23007.99 1183.26 2679.07 9202.45 6601.37 23096.32 1141.2 2589.73 9369.35 6229.02 22358.17 1049.33 2457.46 8824.06 5925.22 20536.49 1101.6 2517.3 9537.3 6147.97 21029.81 1030.71 2386.53 9382.64 5965.52 20128.99 1089.41 2453.37 9768.7 5964.33 19765.19 1186.69 2529.66 11057.4 6135.7 21108.59 1169.43 2475.14 11089.94 6153.55 21239.35 1104.49 2525.93 10126.03 5598.46 20608.7 1073.87 2480.93 10198.04 5608.79 20121.99 1115.1 2229.85 10546.44 5957.43 21872.5 1095.63 2169.14 9345.55 5625.95 21821.5 1036.19 2030.98 10034.74 5414.96 21752.87 1057.08 2071.37 10133.23 5675.16 20955.25 1020.62 1953.35 10492.53 5458.04 19724.19 987.48 1748.74 10356.83 5332.14 20573.33 919.32 1696.58 9958.44 4808.64 18378.73 919.14 1900.09 9522.5 4940.82 18171 872.81 1908.64 8828.26 4769.45 15520.99 797.87 1881.46 8109.53 4084.76 13576.02 735.09 2100.18 7568.42 3843.74 12811.57 825.88 2672.2 7994.05 4338.35 13278.21 903.25 3136 8859.56 4810.2 143 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation S&P[t] = + 3901.66133003445 + 0.679202303098418Bel20[t] + 0.0093168895553967Nikkei225[t] -0.815900090317011DAX[t] + 0.0111161931322036HangSeng[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) 3901.66133003445 1240.443424 3.1454 0.002474 0.001237 Bel20 0.679202303098418 0.262972 2.5828 0.01199 0.005995 Nikkei225 0.0093168895553967 0.067189 0.1387 0.890129 0.445065 DAX -0.815900090317011 0.26881 -3.0352 0.00342 0.00171 HangSeng 0.0111161931322036 0.063968 0.1738 0.862563 0.431282 Multiple Linear Regression - Regression Statistics Multiple R 0.419785427527184 R-squared 0.176219805164180 Adjusted R-squared 0.127038898009803 F-TEST (value) 3.58309383377236 F-TEST (DF numerator) 4 F-TEST (DF denominator) 67 p-value 0.0104409700206471 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1812.90836227926 Sum Squared Residuals 220204660.911477 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1221.53 357.955115289487 863.574884710513 2 1180.55 495.034567425437 685.515432574563 3 1183.26 677.714829032561 505.545170967439 4 1141.2 914.184864809546 227.015135190454 5 1049.33 1046.88667020629 2.44332979370736 6 1101.6 917.917409608057 183.682590391942 7 1030.71 966.504456674227 64.2055433257735 8 1089.41 1012.42606704106 76.9839329589371 9 1186.69 951.361781690657 235.328218309343 10 1169.43 901.524580513672 267.905419486328 11 1104.49 1372.92817641194 -268.438176411944 12 1073.87 1329.19637169705 -255.32637169705 13 1115.1 896.91186150793 218.18813849207 14 1095.63 1114.37656627719 -18.7465662771854 15 1036.19 1198.34293891511 -162.152938915115 16 1057.08 1005.52983892298 51.5501610770234 17 1020.62 1092.18146842085 -71.561468420854 18 987.48 1064.10660887841 -76.6266088784103 19 919.32 1427.69576095184 -508.375760951844 20 919.14 1451.70377608517 -532.563776085168 21 872.81 1561.40557388708 -688.595573887076 22 797.87 2073.26649794152 -1275.39649794152 23 735.09 2404.93062949618 -1669.84062949618 24 825.88 2399.04839530752 -1573.16839530752 25 903.25 2349.47468650333 -1446.22468650333 26 896.24 2359.34684221854 -1463.10684221854 27 968.75 2219.02768615725 -1250.27768615725 28 1166.36 1997.27643916016 -830.916439160158 29 1282.83 1686.01703820086 -403.187038200863 30 1267.38 1518.60785032155 -251.227850321548 31 1280 1588.09275965233 -308.092759652332 32 1400.38 1045.84241035678 354.537589643219 33 1385.59 1450.84586636828 -65.2558663682768 34 1322.7 1767.65349638071 -444.95349638071 35 1330.63 1809.96920542103 -479.339205421033 36 1378.55 1636.44289344723 -257.892893447225 37 1468.36 726.419654324607 741.940345675393 38 1481.14 922.548444366672 558.591555633328 39 1549.38 1014.33415735923 535.04584264077 40 1526.75 1136.36330108647 390.38669891353 41 1473.99 1225.01807133746 248.971928662538 42 1455.27 1169.35007158938 285.919928410624 43 1503.35 703.633318736995 799.716681263005 44 1530.62 877.102379180439 653.517620819561 45 1482.37 1225.45071522196 256.919284778038 46 1420.86 1451.33531162696 -30.4753116269564 47 1406.82 1619.87129069039 -213.051290690387 48 1438.24 1512.77527142158 -74.5352714215836 49 1418.3 1554.62753310622 -136.327533106215 50 1400.63 1694.57794631895 -293.947946318949 51 1377.94 1658.64072798274 -280.700727982741 52 1335.85 1844.61058005421 -508.760580054208 53 1303.82 2120.9561079593 -817.136107959299 54 1276.66 2255.63716502283 -978.977165022828 55 1270.2 2197.56968522333 -927.369685223326 56 1270.09 2120.60443535990 -850.514435359897 57 1310.61 1751.57401685293 -440.96401685293 58 1294.87 1670.61506904796 -375.745069047958 59 1280.66 1717.70178002432 -437.041780024319 60 1280.08 1863.28993985208 -583.209939852076 61 1248.29 1994.27383160702 -745.983831607025 62 1249.48 2161.44393949087 -911.963939490869 63 1207.01 2280.96590213628 -1073.95590213628 64 1228.81 2156.84132071685 -928.031320716848 65 1220.33 2307.02173592291 -1086.69173592291 66 1234.18 2314.01490805587 -1079.83490805587 67 1191.33 2535.47285390333 -1344.14285390333 68 1191.5 301.022528781206 890.477471218794 69 11008.9 6040.07344767483 4968.82655232517 70 4348.77 3562.77545169209 785.99454830791 71 14195.35 1491.08000980255 12704.2699901975 72 1221.53 357.955115289489 863.574884710511 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 5.36273322959642e-07 1.07254664591928e-06 0.999999463726677 9 3.15527804493776e-09 6.31055608987552e-09 0.999999996844722 10 1.79494028428203e-11 3.58988056856406e-11 0.99999999998205 11 1.04130978981112e-13 2.08261957962223e-13 0.999999999999896 12 1.69155465453844e-15 3.38310930907689e-15 0.999999999999998 13 2.70526922025508e-17 5.41053844051015e-17 1 14 5.56578304165071e-18 1.11315660833014e-17 1 15 1.84468942640024e-19 3.68937885280048e-19 1 16 1.63058770792088e-21 3.26117541584175e-21 1 17 1.38687527344559e-23 2.77375054689118e-23 1 18 1.35855314024320e-25 2.71710628048639e-25 1 19 1.32544241649339e-27 2.65088483298678e-27 1 20 9.82195934084636e-30 1.96439186816927e-29 1 21 4.44804048810969e-31 8.89608097621937e-31 1 22 1.04873714870125e-32 2.09747429740251e-32 1 23 2.62750697505035e-34 5.25501395010071e-34 1 24 9.17111452502764e-36 1.83422290500553e-35 1 25 1.05802366763262e-36 2.11604733526525e-36 1 26 1.41365010644719e-38 2.82730021289437e-38 1 27 6.27802954579363e-40 1.25560590915873e-39 1 28 9.19973329948479e-42 1.83994665989696e-41 1 29 2.79269319753747e-43 5.58538639507494e-43 1 30 2.26869897788983e-44 4.53739795577965e-44 1 31 2.79079508823505e-46 5.58159017647010e-46 1 32 2.77467256855098e-48 5.54934513710197e-48 1 33 4.01943873964431e-50 8.03887747928863e-50 1 34 5.43999990118575e-52 1.08799998023715e-51 1 35 3.81008557387632e-53 7.62017114775264e-53 1 36 4.54084950442022e-55 9.08169900884043e-55 1 37 1.96134261175591e-54 3.92268522351183e-54 1 38 1.88462165447589e-55 3.76924330895179e-55 1 39 8.51355491308592e-57 1.70271098261718e-56 1 40 1.21138050960024e-58 2.42276101920049e-58 1 41 1.54276271900177e-60 3.08552543800354e-60 1 42 2.26365496577007e-62 4.52730993154015e-62 1 43 4.59705866633819e-64 9.19411733267637e-64 1 44 3.05901795840702e-65 6.11803591681404e-65 1 45 1.63920497744831e-66 3.27840995489663e-66 1 46 6.34194580966901e-68 1.26838916193380e-67 1 47 2.13139586892324e-69 4.26279173784648e-69 1 48 2.83217813915127e-70 5.66435627830255e-70 1 49 4.60690595704502e-71 9.21381191409003e-71 1 50 4.26953699749887e-71 8.53907399499775e-71 1 51 5.97032935514885e-72 1.19406587102977e-71 1 52 3.09015698871613e-73 6.18031397743225e-73 1 53 6.43141343624823e-75 1.28628268724965e-74 1 54 1.15044798584418e-76 2.30089597168837e-76 1 55 1.66635811039386e-78 3.33271622078772e-78 1 56 2.37617786841222e-80 4.75235573682445e-80 1 57 6.66009072526662e-82 1.33201814505332e-81 1 58 2.29455760689867e-83 4.58911521379733e-83 1 59 7.23210479023434e-85 1.44642095804687e-84 1 60 1.54057096113466e-85 3.08114192226932e-85 1 61 8.46689505415489e-86 1.69337901083098e-85 1 62 3.36748534633244e-85 6.73497069266489e-85 1 63 1.16534381872894e-85 2.33068763745788e-85 1 64 7.02047072427143e-86 1.40409414485429e-85 1 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 57 1 NOK 5% type I error level 57 1 NOK 10% type I error level 57 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/10txhc1291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/10txhc1291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/1me211291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/1me211291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/2f5141291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/2f5141291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/3f5141291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/3f5141291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/4f5141291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/4f5141291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/58w1p1291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/58w1p1291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/68w1p1291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/68w1p1291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/70n0a1291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/70n0a1291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/80n0a1291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/80n0a1291411245.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/9txhc1291411245.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t129141128274qcjm5ta7zjlpm/9txhc1291411245.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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