seasonal dummies en trend

*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: Thu, 18 Dec 2008 07:02:11 -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/Dec/18/t12296089830dg2pei2tgt16os.htm/, Retrieved Thu, 18 Dec 2008 15:03:03 +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/2008/Dec/18/t12296089830dg2pei2tgt16os.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

19 0 23 0 22 0 23 0 25 0 25 0 23 0 22 0 21 0 16 0 21 0 21 0 26 0 23 0 22 0 22 0 22 0 12 0 20 0 18 0 23 0 25 0 28 0 28 0 29 0 31 0 33 0 32 0 33 0 35 0 33 0 36 0 30 0 34 0 34 0 35 0 33 0 28 0 27 0 23 0 23 0 24 0 24 0 20 0 16 1 6 1 2 1 12 1 19 1 21 1 22 1 20 1 21 1 20 1 19 1 17 1 17 1 17 1 16 1 12 1 11 1 7 1 2 1 9 1 11 1 10 1 7 1 9 1 15 1 5 1 14 1 14 1 17 1 19 1 17 1 16 1 14 1 20 1 16 1 18 1 18 1 14 1 13 1 14 1 14 1 17 1 18 1 15 1 9 1 9 1 9 1 10 1 6 1 12 1 11 1 15 1 19 1 18 1 15 1 16 1 14 1 18 1 18 1 18 1 18 1 22 1 21 1 12 1 19 1 21 1 19 1 22 1 22 1 21 1 19 1 18 1 18 1 19 1 12 1 16 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Vertrouwen[t] = + 23.0000724637681 -15.9155797101449Aanslag[t] + 2.11524758454106M1[t] + 2.22371980676329M2[t] + 1.03219202898551M3[t] + 1.04066425120773M4[t] + 0.549136473429953M5[t] + 0.457608695652175M6[t] -0.233919082125603M7[t] -0.525446859903381M8[t] + 0.574583333333334M9[t] -0.716944444444444M10[t] -0.608472222222221M11[t] + 0.0915277777777778t + 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) 23.0000724637681 1.829912 12.5689 0 0 Aanslag -15.9155797101449 1.747644 -9.1069 0 0 M1 2.11524758454106 2.265608 0.9336 0.352615 0.176307 M2 2.22371980676329 2.264433 0.982 0.328326 0.164163 M3 1.03219202898551 2.263519 0.456 0.649314 0.324657 M4 1.04066425120773 2.262866 0.4599 0.646539 0.323269 M5 0.549136473429953 2.262474 0.2427 0.808695 0.404348 M6 0.457608695652175 2.262343 0.2023 0.840092 0.420046 M7 -0.233919082125603 2.262474 -0.1034 0.917848 0.458924 M8 -0.525446859903381 2.262866 -0.2322 0.816827 0.408414 M9 0.574583333333334 2.261474 0.2541 0.79993 0.399965 M10 -0.716944444444444 2.26082 -0.3171 0.751779 0.375889 M11 -0.608472222222221 2.260428 -0.2692 0.788311 0.394155 t 0.0915277777777778 0.024317 3.764 0.000275 0.000137 Multiple Linear Regression - Regression Statistics Multiple R 0.752083013113572 R-squared 0.56562885861399 Adjusted R-squared 0.512356926179857 F-TEST (value) 10.6177649799610 F-TEST (DF numerator) 13 F-TEST (DF denominator) 106 p-value 4.42978986825437e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.05417759584665 Sum Squared Residuals 2707.73938405797 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 19 25.206847826087 -6.20684782608699 2 23 25.4068478260870 -2.40684782608696 3 22 24.3068478260870 -2.30684782608696 4 23 24.4068478260870 -1.40684782608696 5 25 24.0068478260870 0.993152173913045 6 25 24.0068478260870 0.993152173913044 7 23 23.4068478260870 -0.406847826086955 8 22 23.2068478260870 -1.20684782608695 9 21 24.3984057971014 -3.39840579710145 10 16 23.1984057971014 -7.19840579710145 11 21 23.3984057971014 -2.39840579710145 12 21 24.0984057971014 -3.09840579710144 13 26 26.3051811594203 -0.305181159420285 14 23 26.5051811594203 -3.50518115942029 15 22 25.4051811594203 -3.40518115942029 16 22 25.5051811594203 -3.50518115942029 17 22 25.1051811594203 -3.10518115942029 18 12 25.1051811594203 -13.1051811594203 19 20 24.5051811594203 -4.50518115942029 20 18 24.3051811594203 -6.30518115942029 21 23 25.4967391304348 -2.49673913043478 22 25 24.2967391304348 0.703260869565216 23 28 24.4967391304348 3.50326086956522 24 28 25.1967391304348 2.80326086956522 25 29 27.4035144927536 1.59648550724638 26 31 27.6035144927536 3.39648550724638 27 33 26.5035144927536 6.49648550724638 28 32 26.6035144927536 5.39648550724638 29 33 26.2035144927536 6.79648550724638 30 35 26.2035144927536 8.79648550724638 31 33 25.6035144927536 7.39648550724638 32 36 25.4035144927536 10.5964855072464 33 30 26.5950724637681 3.40492753623188 34 34 25.3950724637681 8.60492753623188 35 34 25.5950724637681 8.40492753623188 36 35 26.2950724637681 8.70492753623188 37 33 28.5018478260870 4.49815217391305 38 28 28.7018478260870 -0.701847826086957 39 27 27.6018478260870 -0.601847826086956 40 23 27.7018478260870 -4.70184782608696 41 23 27.3018478260870 -4.30184782608696 42 24 27.3018478260870 -3.30184782608696 43 24 26.7018478260870 -2.70184782608696 44 20 26.5018478260870 -6.50184782608696 45 16 11.7778260869565 4.22217391304348 46 6 10.5778260869565 -4.57782608695652 47 2 10.7778260869565 -8.77782608695652 48 12 11.4778260869565 0.52217391304348 49 19 13.6846014492754 5.31539855072464 50 21 13.8846014492754 7.11539855072464 51 22 12.7846014492754 9.21539855072464 52 20 12.8846014492754 7.11539855072464 53 21 12.4846014492754 8.51539855072464 54 20 12.4846014492754 7.51539855072464 55 19 11.8846014492754 7.11539855072464 56 17 11.6846014492754 5.31539855072464 57 17 12.8761594202899 4.12384057971015 58 17 11.6761594202899 5.32384057971015 59 16 11.8761594202899 4.12384057971015 60 12 12.5761594202899 -0.576159420289854 61 11 14.7829347826087 -3.78293478260869 62 7 14.9829347826087 -7.9829347826087 63 2 13.8829347826087 -11.8829347826087 64 9 13.9829347826087 -4.98293478260869 65 11 13.5829347826087 -2.58293478260870 66 10 13.5829347826087 -3.58293478260869 67 7 12.9829347826087 -5.9829347826087 68 9 12.7829347826087 -3.78293478260869 69 15 13.9744927536232 1.02550724637681 70 5 12.7744927536232 -7.77449275362319 71 14 12.9744927536232 1.02550724637681 72 14 13.6744927536232 0.325507246376813 73 17 15.8812681159420 1.11873188405798 74 19 16.0812681159420 2.91873188405797 75 17 14.9812681159420 2.01873188405797 76 16 15.0812681159420 0.918731884057971 77 14 14.6812681159420 -0.681268115942029 78 20 14.6812681159420 5.31873188405797 79 16 14.0812681159420 1.91873188405797 80 18 13.8812681159420 4.11873188405797 81 18 15.0728260869565 2.92717391304348 82 14 13.8728260869565 0.127173913043478 83 13 14.0728260869565 -1.07282608695652 84 14 14.7728260869565 -0.772826086956521 85 14 16.9796014492754 -2.97960144927536 86 17 17.1796014492754 -0.179601449275363 87 18 16.0796014492754 1.92039855072464 88 15 16.1796014492754 -1.17960144927536 89 9 15.7796014492754 -6.77960144927536 90 9 15.7796014492754 -6.77960144927536 91 9 15.1796014492754 -6.17960144927536 92 10 14.9796014492754 -4.97960144927536 93 6 16.1711594202899 -10.1711594202899 94 12 14.9711594202899 -2.97115942028986 95 11 15.1711594202899 -4.17115942028986 96 15 15.8711594202899 -0.871159420289855 97 19 18.0779347826087 0.922065217391308 98 18 18.2779347826087 -0.277934782608697 99 15 17.1779347826087 -2.17793478260870 100 16 17.2779347826087 -1.27793478260870 101 14 16.8779347826087 -2.8779347826087 102 18 16.8779347826087 1.12206521739130 103 18 16.2779347826087 1.72206521739130 104 18 16.0779347826087 1.92206521739130 105 18 17.2694927536232 0.730507246376809 106 22 16.0694927536232 5.93050724637681 107 21 16.2694927536232 4.73050724637681 108 12 16.9694927536232 -4.96949275362319 109 19 19.1762681159420 -0.176268115942026 110 21 19.3762681159420 1.62373188405797 111 19 18.2762681159420 0.72373188405797 112 22 18.3762681159420 3.62373188405797 113 22 17.9762681159420 4.02373188405797 114 21 17.9762681159420 3.02373188405797 115 19 17.3762681159420 1.62373188405797 116 18 17.1762681159420 0.823731884057968 117 18 18.3678260869565 -0.367826086956524 118 19 17.1678260869565 1.83217391304348 119 12 17.3678260869565 -5.36782608695652 120 16 18.0678260869565 -2.06782608695652 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.210049531820582 0.420099063641164 0.789950468179418 18 0.619217855565213 0.761564288869574 0.380782144434787 19 0.491115157774751 0.982230315549502 0.508884842225249 20 0.392364766820406 0.784729533640811 0.607635233179594 21 0.32384096776376 0.64768193552752 0.67615903223624 22 0.454794181602090 0.909588363204179 0.54520581839791 23 0.457052804802383 0.914105609604767 0.542947195197617 24 0.440357367436846 0.880714734873692 0.559642632563154 25 0.404846645874802 0.809693291749604 0.595153354125198 26 0.380636250523846 0.761272501047692 0.619363749476154 27 0.413212386588837 0.826424773177673 0.586787613411163 28 0.381525192980382 0.763050385960764 0.618474807019618 29 0.348084425235581 0.696168850471161 0.651915574764419 30 0.485568921675909 0.971137843351819 0.514431078324091 31 0.461909067024229 0.923818134048458 0.538090932975771 32 0.553114326054948 0.893771347890104 0.446885673945052 33 0.481046494433861 0.962092988867722 0.518953505566139 34 0.494096297351624 0.988192594703249 0.505903702648376 35 0.484713581863146 0.969427163726292 0.515286418136854 36 0.505532500831924 0.988934998336153 0.494467499168076 37 0.485857152908149 0.971714305816297 0.514142847091851 38 0.534629233944165 0.93074153211167 0.465370766055835 39 0.586272743413549 0.827454513172901 0.413727256586451 40 0.699548637171962 0.600902725656075 0.300451362828038 41 0.782494116889705 0.435011766220589 0.217505883110295 42 0.778854710645805 0.44229057870839 0.221145289354195 43 0.78891892197034 0.422162156059320 0.211081078029660 44 0.836605323480368 0.326789353039264 0.163394676519632 45 0.801643193258352 0.396713613483297 0.198356806741648 46 0.815493048067541 0.369013903864917 0.184506951932459 47 0.880478484068701 0.239043031862598 0.119521515931299 48 0.84857568335424 0.302848633291518 0.151424316645759 49 0.855051436188656 0.289897127622688 0.144948563811344 50 0.876933705787793 0.246132588424414 0.123066294212207 51 0.91779183283557 0.164416334328861 0.0822081671644307 52 0.925293994703366 0.149412010593269 0.0747060052966343 53 0.947142813632887 0.105714372734227 0.0528571863671133 54 0.955918325121473 0.0881633497570534 0.0440816748785267 55 0.965394680519992 0.0692106389600167 0.0346053194800083 56 0.965101616413215 0.0697967671735698 0.0348983835867849 57 0.964569203543835 0.0708615929123297 0.0354307964561649 58 0.968265458821356 0.0634690823572882 0.0317345411786441 59 0.97075631384025 0.0584873723194984 0.0292436861597492 60 0.970658308446181 0.0586833831076373 0.0293416915538187 61 0.972926708537566 0.0541465829248679 0.0270732914624339 62 0.987749410309576 0.0245011793808481 0.0122505896904241 63 0.99868511219688 0.00262977560623972 0.00131488780311986 64 0.998745803628186 0.00250839274362742 0.00125419637181371 65 0.998288382096614 0.00342323580677282 0.00171161790338641 66 0.997862757796262 0.00427448440747671 0.00213724220373835 67 0.998169821079602 0.0036603578407964 0.0018301789203982 68 0.997768279908467 0.00446344018306518 0.00223172009153259 69 0.996870560245256 0.0062588795094881 0.00312943975474405 70 0.998719953260712 0.00256009347857517 0.00128004673928759 71 0.998062778931704 0.00387444213659145 0.00193722106829572 72 0.997193864958704 0.00561227008259171 0.00280613504129585 73 0.995726065757423 0.00854786848515468 0.00427393424257734 74 0.994055994242073 0.0118880115158535 0.00594400575792674 75 0.99138363268287 0.0172327346342590 0.00861636731712948 76 0.986903695928791 0.0261926081424172 0.0130963040712086 77 0.981478935242727 0.0370421295145450 0.0185210647572725 78 0.98597682404836 0.0280463519032813 0.0140231759516407 79 0.983217843936345 0.0335643121273091 0.0167821560636545 80 0.986443825823323 0.0271123483533539 0.0135561741766769 81 0.993558503126577 0.0128829937468469 0.00644149687342345 82 0.989973770149527 0.0200524597009465 0.0100262298504733 83 0.988019523459433 0.0239609530811343 0.0119804765405672 84 0.989680679703093 0.0206386405938143 0.0103193202969071 85 0.983971871698718 0.0320562566025635 0.0160281283012818 86 0.976766592444418 0.046466815111165 0.0232334075555825 87 0.979650955933213 0.0406980881335735 0.0203490440667868 88 0.969100176945506 0.0617996461089877 0.0308998230544939 89 0.963341924404623 0.0733161511907539 0.0366580755953769 90 0.963915724284473 0.0721685514310539 0.0360842757155269 91 0.960755036156256 0.0784899276874888 0.0392449638437444 92 0.949862375093735 0.100275249812531 0.0501376249062655 93 0.978511408153144 0.0429771836937115 0.0214885918468558 94 0.981196384261246 0.0376072314775089 0.0188036157387545 95 0.974672353508408 0.050655292983185 0.0253276464915925 96 0.960738497921875 0.0785230041562492 0.0392615020781246 97 0.932493912972552 0.135012174054895 0.0675060870274477 98 0.888150781742327 0.223698436515345 0.111849218257673 99 0.831946113874496 0.336107772251008 0.168053886125504 100 0.797033885929116 0.405932228141768 0.202966114070884 101 0.849402208164958 0.301195583670084 0.150597791835042 102 0.782559238655 0.434881522689999 0.217440761344999 103 0.648046916468174 0.703906167063652 0.351953083531826 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.126436781609195 NOK 5% type I error level 28 0.32183908045977 NOK 10% type I error level 42 0.482758620689655 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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