MR

*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: Mon, 13 Dec 2010 21:17:36 +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/13/t1292274933jcnlgfp12pdo9ls.htm/, Retrieved Mon, 13 Dec 2010 22:15:43 +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/13/t1292274933jcnlgfp12pdo9ls.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:

» Textbox « » Textfile « » CSV «

2649.2 31077 2579.4 31293 2504.6 30236 2462.3 30160 2467.4 32436 2446.7 30695 2656.3 27525 2626.2 26434 2482.6 25739 2539.9 25204 2502.7 24977 2466.9 24320 2513.2 22680 2443.3 22052 2293.4 21467 2070.8 21383 2029.6 21777 2052 21928 1864.4 21814 1670.1 22937 1811 23595 1905.4 20830 1862.8 19650 2014.5 19195 2197.8 19644 2962.3 18483 3047 18079 3032.6 19178 3504.4 18391 3801.1 18441 3857.6 18584 3674.4 20108 3721 20148 3844.5 19394 4116.7 17745 4105.2 17696 4435.2 17032 4296.5 16438 4202.5 15683 4562.8 15594 4621.4 15713 4697 15937 4591.3 16171 4357 15928 4502.6 16348 4443.9 15579 4290.9 15305 4199.8 15648 4138.5 14954 3970.1 15137 3862.3 15839 3701.6 16050 3570.12 15168 3801.06 17064 3895.51 16005 3917.96 14886 3813.06 14931 3667.03 14544 3494.17 13812 3364 13031 3295.3 12574 3277.0 11964 3257.2 11451 3161.7 11346 3097.3 11353 3061.3 10702 3119.3 10646 310 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 Multiple Linear Regression - Estimated Regression Equation Bel20[t] = + 4765.30342448302 -0.081675043345911Goudprijs[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) 4765.30342448302 301.416177 15.8097 0 0 Goudprijs -0.081675043345911 0.015442 -5.2892 1e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.539896177404536 R-squared 0.29148788237603 Adjusted R-squared 0.281068586528619 F-TEST (value) 27.9757755845325 F-TEST (DF numerator) 1 F-TEST (DF denominator) 68 p-value 1.41125002617315e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 719.60904727157 Sum Squared Residuals 35212928.3022266 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2649.2 2227.08810242213 422.111897577871 2 2579.4 2209.44629305942 369.953706940576 3 2504.6 2295.77681387605 208.823186123949 4 2462.3 2301.98411717034 160.315882829660 5 2467.4 2116.09171851505 351.308281484953 6 2446.7 2258.28796898028 188.412031019722 7 2656.3 2517.19785638682 139.102143613184 8 2626.2 2606.30532867721 19.8946713227949 9 2482.6 2663.06948380261 -180.469483802613 10 2539.9 2706.76563199268 -166.865631992675 11 2502.7 2725.30586683220 -222.605866832197 12 2466.9 2778.96637031046 -312.066370310461 13 2513.2 2912.91344139775 -399.713441397755 14 2443.3 2964.20536861899 -520.905368618987 15 2293.4 3011.98526897634 -718.585268976345 16 2070.8 3018.8459726174 -948.045972617401 17 2029.6 2986.66600553911 -957.066005539113 18 2052 2974.33307399388 -922.33307399388 19 1864.4 2983.64402893531 -1119.24402893531 20 1670.1 2891.92295525786 -1221.82295525786 21 1811 2838.18077673625 -1027.18077673625 22 1905.4 3064.01227158769 -1158.61227158769 23 1862.8 3160.38882273587 -1297.58882273587 24 2014.5 3197.55096745825 -1183.05096745825 25 2197.8 3160.87887299594 -963.07887299594 26 2962.3 3255.70359832054 -293.403598320543 27 3047 3288.70031583229 -241.700315832291 28 3032.6 3198.93944319514 -166.339443195135 29 3504.4 3263.21770230837 241.182297691633 30 3801.1 3259.13395014107 541.966049858928 31 3857.6 3247.45441894261 610.145581057394 32 3674.4 3122.98165288344 551.418347116562 33 3721 3119.7146511496 601.285348850399 34 3844.5 3181.29763383242 663.202366167582 35 4116.7 3315.97978030983 800.720219690174 36 4105.2 3319.98185743378 785.218142566225 37 4435.2 3374.21408621546 1060.98591378454 38 4296.5 3422.72906196293 873.770938037069 39 4202.5 3484.39371968909 718.106280310906 40 4562.8 3491.66279854688 1071.13720145312 41 4621.4 3481.94346838872 1139.45653161128 42 4697 3463.64825867923 1233.35174132077 43 4591.3 3444.53629853629 1146.76370146371 44 4357 3464.38333406935 892.616665930654 45 4502.6 3430.07981586406 1072.52018413594 46 4443.9 3492.88792419707 951.01207580293 47 4290.9 3515.26688607385 775.633113926151 48 4199.8 3487.2523462062 712.5476537938 49 4138.5 3543.93482628826 594.565173711737 50 3970.1 3528.98829335596 441.111706644038 51 3862.3 3471.65241292713 390.647587072868 52 3701.6 3454.41897878114 247.181021218855 53 3570.12 3526.45636701224 43.6636329877617 54 3801.06 3371.60048482839 429.459515171609 55 3895.51 3458.09435573171 437.415644268290 56 3917.96 3549.48872923579 368.471270764215 57 3813.06 3545.81335228522 267.246647714781 58 3667.03 3577.42159406009 89.6084059399136 59 3494.17 3637.20772578929 -143.037725789293 60 3364 3700.99593464245 -336.99593464245 61 3295.3 3738.32142945153 -443.021429451531 62 3277 3788.14320589254 -511.143205892537 63 3257.2 3830.04250312899 -572.84250312899 64 3161.7 3838.61838268031 -676.91838268031 65 3097.3 3838.04665737689 -740.746657376889 66 3061.3 3891.21711059508 -829.917110595077 67 3119.3 3895.79091302245 -776.490913022448 68 3106.22 3903.14166692358 -796.92166692358 69 3080.58 3910.73744595475 -830.15744595475 70 2981.85 3915.31124838212 -933.46124838212 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00271782252297893 0.00543564504595785 0.997282177477021 6 0.000415089198144224 0.000830178396288449 0.999584910801856 7 6.68518771421995e-05 0.000133703754284399 0.999933148122858 8 6.89900309341026e-06 1.37980061868205e-05 0.999993100996907 9 2.22126770705098e-06 4.44253541410196e-06 0.999997778732293 10 2.53784292155581e-07 5.07568584311163e-07 0.999999746215708 11 3.24732413134827e-08 6.49464826269653e-08 0.999999967526759 12 4.9143344232158e-09 9.8286688464316e-09 0.999999995085666 13 4.81655077028751e-10 9.63310154057503e-10 0.999999999518345 14 6.98408639029019e-11 1.39681727805804e-10 0.99999999993016 15 8.25195391093207e-11 1.65039078218641e-10 0.99999999991748 16 1.87646094775231e-09 3.75292189550463e-09 0.99999999812354 17 6.24599451275262e-09 1.24919890255052e-08 0.999999993754006 18 6.3583802258866e-09 1.27167604517732e-08 0.99999999364162 19 3.11472950128520e-08 6.22945900257039e-08 0.999999968852705 20 7.9197403583946e-07 1.58394807167892e-06 0.999999208025964 21 2.76752853902068e-06 5.53505707804136e-06 0.999997232471461 22 3.89737589362887e-06 7.79475178725774e-06 0.999996102624106 23 8.86779339765293e-06 1.77355867953059e-05 0.999991132206602 24 2.5134035825225e-05 5.026807165045e-05 0.999974865964175 25 0.000154690071026657 0.000309380142053315 0.999845309928973 26 0.010872838392084 0.021745676784168 0.989127161607916 27 0.0932399037851689 0.186479807570338 0.906760096214831 28 0.329169012007137 0.658338024014275 0.670830987992863 29 0.70350127626752 0.59299744746496 0.29649872373248 30 0.914434304614233 0.171131390771533 0.0855656953857666 31 0.973694889238028 0.0526102215239438 0.0263051107619719 32 0.995222638774283 0.00955472245143393 0.00477736122571697 33 0.999688296482678 0.000623407034644084 0.000311703517322042 34 0.999988174220698 2.36515586048543e-05 1.18257793024272e-05 35 0.999996632441126 6.7351177476962e-06 3.3675588738481e-06 36 0.99999890515189 2.18969622156121e-06 1.09484811078060e-06 37 0.999999272321587 1.45535682619666e-06 7.27678413098331e-07 38 0.999999125301375 1.74939725070256e-06 8.74698625351279e-07 39 0.999998570914354 2.85817129294742e-06 1.42908564647371e-06 40 0.999999349304193 1.30139161501156e-06 6.50695807505778e-07 41 0.999999786133657 4.27732686701193e-07 2.13866343350597e-07 42 0.999999962682362 7.4635276161619e-08 3.73176380808095e-08 43 0.99999998594877 2.81024586127777e-08 1.40512293063889e-08 44 0.999999982891823 3.42163538686300e-08 1.71081769343150e-08 45 0.999999990677896 1.86442086833993e-08 9.32210434169965e-09 46 0.999999998748654 2.50269238949582e-09 1.25134619474791e-09 47 0.999999999800752 3.98495372887314e-10 1.99247686443657e-10 48 0.999999999917193 1.65613181862986e-10 8.2806590931493e-11 49 0.999999999997135 5.73107512732147e-12 2.86553756366074e-12 50 0.99999999999879 2.4199652667224e-12 1.2099826333612e-12 51 0.99999999999293 1.41415934540421e-11 7.07079672702105e-12 52 0.999999999975129 4.97425056713071e-11 2.48712528356536e-11 53 0.999999999945206 1.09587845250076e-10 5.47939226250381e-11 54 0.99999999999377 1.24601356693217e-11 6.23006783466084e-12 55 0.99999999994904 1.01919167268810e-10 5.09595836344052e-11 56 0.999999999984359 3.12830561633148e-11 1.56415280816574e-11 57 0.999999999980774 3.84528040195124e-11 1.92264020097562e-11 58 0.999999999906154 1.87692099076313e-10 9.38460495381565e-11 59 0.999999998782952 2.43409558434155e-09 1.21704779217078e-09 60 0.99999998461159 3.07768201524078e-08 1.53884100762039e-08 61 0.999999855797429 2.88405142925528e-07 1.44202571462764e-07 62 0.999998304116465 3.39176707099134e-06 1.69588353549567e-06 63 0.999994542429673 1.09151406548946e-05 5.45757032744732e-06 64 0.999939549651601 0.000120900696797492 6.0450348398746e-05 65 0.999457515169032 0.00108496966193609 0.000542484830968046 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 55 0.901639344262295 NOK 5% type I error level 56 0.918032786885246 NOK 10% type I error level 57 0.934426229508197 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/105yzo1292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/105yzo1292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/1962x1292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/1962x1292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/2962x1292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/2962x1292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/3ky101292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/3ky101292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/4ky101292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/4ky101292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/5ky101292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/5ky101292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/6ky101292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/6ky101292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/7c7031292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/7c7031292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/85yzo1292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/85yzo1292275047.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/95yzo1292275047.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292274933jcnlgfp12pdo9ls/95yzo1292275047.ps (open in new window)

Parameters (Session):

par1 = pearson ;

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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