Bouwvergunningen (BouwV)

*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, 19 Nov 2009 08:42:06 -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/19/t1258645437q6ee1y5rr8vhn2m.htm/, Retrieved Thu, 19 Nov 2009 16:44:10 +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/19/t1258645437q6ee1y5rr8vhn2m.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:

Bouwvergunningen (BouwV): Bouwvergunningen volgens effectieve datum van toekenning - woongebouwen - koninkrijk, Ruimte

Dataseries X:

» Textbox « » Textfile « » CSV «

100 0 108.1560276 0 114.0150276 0 102.1880309 0 110.3672031 0 96.8602511 0 94.1944583 0 99.51621961 0 94.06333487 0 97.5541476 0 78.15062422 0 81.2434643 0 92.36262465 0 96.06324371 0 114.0523777 0 110.6616666 0 104.9171949 0 90.00187193 0 95.7008067 0 86.02741157 0 84.85287668 0 100.04328 0 80.91713823 0 74.06539709 0 77.30281369 0 97.23043249 0 90.75515676 0 100.5614455 0 92.01293267 0 99.24012138 0 105.8672755 0 90.9920463 0 93.30624423 0 91.17419413 0 77.33295039 0 91.1277721 0 85.01249943 0 83.90390242 0 104.8626302 0 110.9039108 0 95.43714373 0 111.6238727 0 108.8925403 0 96.17511682 0 101.9740205 0 99.11953031 0 86.78158147 0 118.4195003 0 118.7441447 0 106.5296192 0 134.7772694 0 104.6778714 0 105.2954304 0 139.4139849 0 103.6060491 0 99.78182974 0 103.4610301 0 120.0594945 0 96.71377168 0 107.1308929 0 105.3608372 0 111.6942359 0 132.0519998 0 126.8037879 0 154.4824253 0 141.5570984 0 109.9506882 0 127.904198 0 133.0888617 0 120.079 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 BouwV[t] = + 103.885297340278 + 18.0026589708333X[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) 103.885297340278 1.912641 54.3151 0 0 X 18.0026589708333 3.312792 5.4343 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.4667909175743 R-squared 0.217893760729857 Adjusted R-squared 0.210515399982025 F-TEST (value) 29.5314593819359 F-TEST (DF numerator) 1 F-TEST (DF denominator) 106 p-value 3.53844414435756e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.2293012130874 Sum Squared Residuals 27919.3630937027 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 103.885297340278 -3.88529734027768 2 108.1560276 103.885297340278 4.27073025972225 3 114.0150276 103.885297340278 10.1297302597222 4 102.1880309 103.885297340278 -1.69726644027778 5 110.3672031 103.885297340278 6.48190575972222 6 96.8602511 103.885297340278 -7.02504624027778 7 94.1944583 103.885297340278 -9.69083904027779 8 99.51621961 103.885297340278 -4.36907773027779 9 94.06333487 103.885297340278 -9.82196247027777 10 97.5541476 103.885297340278 -6.33114974027779 11 78.15062422 103.885297340278 -25.7346731202778 12 81.2434643 103.885297340278 -22.6418330402778 13 92.36262465 103.885297340278 -11.5226726902778 14 96.06324371 103.885297340278 -7.82205363027778 15 114.0523777 103.885297340278 10.1670803597222 16 110.6616666 103.885297340278 6.77636925972222 17 104.9171949 103.885297340278 1.03189755972222 18 90.00187193 103.885297340278 -13.8834254102778 19 95.7008067 103.885297340278 -8.18449064027778 20 86.02741157 103.885297340278 -17.8578857702778 21 84.85287668 103.885297340278 -19.0324206602778 22 100.04328 103.885297340278 -3.84201734027778 23 80.91713823 103.885297340278 -22.9681591102778 24 74.06539709 103.885297340278 -29.8199002502778 25 77.30281369 103.885297340278 -26.5824836502778 26 97.23043249 103.885297340278 -6.65486485027778 27 90.75515676 103.885297340278 -13.1301405802778 28 100.5614455 103.885297340278 -3.32385184027777 29 92.01293267 103.885297340278 -11.8723646702778 30 99.24012138 103.885297340278 -4.64517596027777 31 105.8672755 103.885297340278 1.98197815972223 32 90.9920463 103.885297340278 -12.8932510402778 33 93.30624423 103.885297340278 -10.5790531102778 34 91.17419413 103.885297340278 -12.7111032102778 35 77.33295039 103.885297340278 -26.5523469502778 36 91.1277721 103.885297340278 -12.7575252402778 37 85.01249943 103.885297340278 -18.8727979102778 38 83.90390242 103.885297340278 -19.9813949202778 39 104.8626302 103.885297340278 0.97733285972222 40 110.9039108 103.885297340278 7.01861345972223 41 95.43714373 103.885297340278 -8.44815361027778 42 111.6238727 103.885297340278 7.73857535972223 43 108.8925403 103.885297340278 5.00724295972221 44 96.17511682 103.885297340278 -7.71018052027778 45 101.9740205 103.885297340278 -1.91127684027778 46 99.11953031 103.885297340278 -4.76576703027778 47 86.78158147 103.885297340278 -17.1037158702778 48 118.4195003 103.885297340278 14.5342029597222 49 118.7441447 103.885297340278 14.8588473597222 50 106.5296192 103.885297340278 2.64432185972222 51 134.7772694 103.885297340278 30.8919720597222 52 104.6778714 103.885297340278 0.792574059722222 53 105.2954304 103.885297340278 1.41013305972222 54 139.4139849 103.885297340278 35.5286875597222 55 103.6060491 103.885297340278 -0.279248240277772 56 99.78182974 103.885297340278 -4.10346760027777 57 103.4610301 103.885297340278 -0.424267240277777 58 120.0594945 103.885297340278 16.1741971597222 59 96.71377168 103.885297340278 -7.17152566027779 60 107.1308929 103.885297340278 3.24559555972223 61 105.3608372 103.885297340278 1.47553985972223 62 111.6942359 103.885297340278 7.80893855972222 63 132.0519998 103.885297340278 28.1667024597222 64 126.8037879 103.885297340278 22.9184905597222 65 154.4824253 103.885297340278 50.5971279597222 66 141.5570984 103.885297340278 37.6718010597222 67 109.9506882 103.885297340278 6.06539085972222 68 127.904198 103.885297340278 24.0189006597222 69 133.0888617 103.885297340278 29.2035643597222 70 120.0796299 103.885297340278 16.1943325597222 71 117.5557142 103.885297340278 13.6704168597222 72 143.0362309 103.885297340278 39.1509335597222 73 159.982927 121.887956311111 38.0949706888889 74 128.5991124 121.887956311111 6.71115608888889 75 149.7373327 121.887956311111 27.8493763888889 76 126.8169313 121.887956311111 4.92897498888888 77 140.9639674 121.887956311111 19.0760110888889 78 137.6691981 121.887956311111 15.7812417888889 79 117.9402337 121.887956311111 -3.94772261111112 80 122.3095247 121.887956311111 0.421568388888889 81 127.7804207 121.887956311111 5.89246438888889 82 136.1677176 121.887956311111 14.2797612888889 83 116.2405856 121.887956311111 -5.6473707111111 84 123.1576893 121.887956311111 1.26973298888889 85 116.3400234 121.887956311111 -5.5479329111111 86 108.6119282 121.887956311111 -13.2760281111111 87 125.8982264 121.887956311111 4.01027008888889 88 112.8003105 121.887956311111 -9.08764581111111 89 107.5182447 121.887956311111 -14.3697116111111 90 135.0955413 121.887956311111 13.2075849888889 91 115.5096488 121.887956311111 -6.37830751111111 92 115.8640759 121.887956311111 -6.0238804111111 93 104.5883906 121.887956311111 -17.2995657111111 94 163.7213386 121.887956311111 41.8333822888889 95 113.4482275 121.887956311111 -8.4397288111111 96 98.0428844 121.887956311111 -23.8450719111111 97 116.7868521 121.887956311111 -5.10110421111110 98 126.5330444 121.887956311111 4.64508808888889 99 113.0336597 121.887956311111 -8.85429661111111 100 124.3392163 121.887956311111 2.45125998888890 101 109.8298759 121.887956311111 -12.0580804111111 102 124.4434777 121.887956311111 2.55552138888889 103 111.5039454 121.887956311111 -10.3840109111111 104 102.0350019 121.887956311111 -19.8529544111111 105 116.8726598 121.887956311111 -5.01529651111111 106 112.2073122 121.887956311111 -9.6806441111111 107 101.1513902 121.887956311111 -20.7365661111111 108 124.4255108 121.887956311111 2.53755448888889 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0787621459489388 0.157524291897878 0.921237854051061 6 0.0608673300550259 0.121734660110052 0.939132669944974 7 0.0524970373291991 0.104994074658398 0.94750296267080 8 0.0242007698072505 0.048401539614501 0.97579923019275 9 0.0170988855224703 0.0341977710449407 0.98290111447753 10 0.00806685693399365 0.0161337138679873 0.991933143066006 11 0.0529571894476661 0.105914378895332 0.947042810552334 12 0.080376905281759 0.160753810563518 0.919623094718241 13 0.0531355764199753 0.106271152839951 0.946864423580025 14 0.0313333000292319 0.0626666000584639 0.968666699970768 15 0.0388281831857341 0.0776563663714681 0.961171816814266 16 0.0334811211090817 0.0669622422181635 0.966518878890918 17 0.0214190042818892 0.0428380085637783 0.97858099571811 18 0.0167603065561136 0.0335206131122272 0.983239693443886 19 0.0101287447552609 0.0202574895105219 0.98987125524474 20 0.0101689434662982 0.0203378869325964 0.989831056533702 21 0.0107489191315675 0.0214978382631350 0.989251080868432 22 0.0063843752719433 0.0127687505438866 0.993615624728057 23 0.00950466856588625 0.0190093371317725 0.990495331434114 24 0.0253257833238490 0.0506515666476979 0.974674216676151 25 0.0403065992860307 0.0806131985720614 0.95969340071397 26 0.0281869950852667 0.0563739901705335 0.971813004914733 27 0.0212865245061066 0.0425730490122132 0.978713475493893 28 0.0149645781628233 0.0299291563256465 0.985035421837177 29 0.0108460157570762 0.0216920315141525 0.989153984242924 30 0.00734898567943645 0.0146979713588729 0.992651014320564 31 0.00585599677205853 0.0117119935441171 0.994144003227941 32 0.00439666802741335 0.0087933360548267 0.995603331972587 33 0.00307856369259617 0.00615712738519234 0.996921436307404 34 0.00231943102872675 0.0046388620574535 0.997680568971273 35 0.00523983629130157 0.0104796725826031 0.994760163708698 36 0.0042283107680864 0.0084566215361728 0.995771689231914 37 0.0048982100307368 0.0097964200614736 0.995101789969263 38 0.00641345089555615 0.0128269017911123 0.993586549104444 39 0.00552971063102585 0.0110594212620517 0.994470289368974 40 0.00623641653045005 0.0124728330609001 0.99376358346955 41 0.00505677097399696 0.0101135419479939 0.994943229026003 42 0.00574088931181939 0.0114817786236388 0.99425911068818 43 0.00546022080244678 0.0109204416048936 0.994539779197553 44 0.00454938218488873 0.00909876436977746 0.995450617815111 45 0.00366736859211772 0.00733473718423545 0.996332631407882 46 0.00301960571050053 0.00603921142100107 0.9969803942895 47 0.00462140526685546 0.00924281053371092 0.995378594733145 48 0.00771545813885953 0.0154309162777191 0.99228454186114 49 0.0116318344730961 0.0232636689461923 0.988368165526904 50 0.0103157418822994 0.0206314837645988 0.9896842581177 51 0.0472571861082816 0.0945143722165632 0.952742813891718 52 0.0416647248854043 0.0833294497708086 0.958335275114596 53 0.0370736014932048 0.0741472029864096 0.962926398506795 54 0.133790707391586 0.267581414783172 0.866209292608414 55 0.122781688376571 0.245563376753143 0.877218311623429 56 0.122062047883039 0.244124095766078 0.877937952116961 57 0.117982545739986 0.235965091479971 0.882017454260014 58 0.123970415073652 0.247940830147305 0.876029584926348 59 0.147593333261397 0.295186666522794 0.852406666738603 60 0.148935580984675 0.297871161969349 0.851064419015325 61 0.161496699523163 0.322993399046325 0.838503300476837 62 0.169790134247940 0.339580268495879 0.83020986575206 63 0.228722367136195 0.457444734272389 0.771277632863805 64 0.254764908355812 0.509529816711625 0.745235091644188 65 0.582584176854118 0.834831646291763 0.417415823145882 66 0.695816514858851 0.608366970282298 0.304183485141149 67 0.689278457524662 0.621443084950677 0.310721542475338 68 0.685551509441902 0.628896981116196 0.314448490558098 69 0.701387759397603 0.597224481204795 0.298612240602397 70 0.678362178601241 0.643275642797517 0.321637821398759 71 0.68617681831603 0.62764636336794 0.31382318168397 72 0.726914674971448 0.546170650057104 0.273085325028552 73 0.868582869815772 0.262834260368455 0.131417130184227 74 0.858357231250921 0.283285537498158 0.141642768749079 75 0.915328865301453 0.169342269397094 0.0846711346985468 76 0.900722355879453 0.198555288241095 0.0992776441205473 77 0.918872672913806 0.162254654172387 0.0811273270861937 78 0.927924440852928 0.144151118294143 0.0720755591470715 79 0.912493649175098 0.175012701649804 0.0875063508249022 80 0.890100064910757 0.219799870178487 0.109899935089243 81 0.870059934539163 0.259880130921674 0.129940065460837 82 0.882007780552894 0.235984438894212 0.117992219447106 83 0.853369913580667 0.293260172838665 0.146630086419333 84 0.819114939704816 0.361770120590368 0.180885060295184 85 0.776925065059802 0.446149869880396 0.223074934940198 86 0.752012419240148 0.495975161519704 0.247987580759852 87 0.709017808118574 0.581964383762852 0.290982191881426 88 0.65599546729325 0.6880090654135 0.34400453270675 89 0.623548262080461 0.752903475839079 0.376451737919539 90 0.636939273258257 0.726121453483485 0.363060726741743 91 0.564906251543936 0.870187496912128 0.435093748456064 92 0.487886389176181 0.975772778352363 0.512113610823819 93 0.462806317325371 0.925612634650742 0.537193682674629 94 0.982103362894111 0.0357932742117773 0.0178966371058886 95 0.967971722006086 0.0640565559878277 0.0320282779939139 96 0.9817479482404 0.036504103519199 0.0182520517595995 97 0.96489012471753 0.0702197505649413 0.0351098752824707 98 0.962759830354305 0.0744803392913907 0.0372401696456954 99 0.929030183115254 0.141939633769492 0.0709698168847458 100 0.914925680949316 0.170148638101369 0.0850743190506845 101 0.847977570706551 0.304044858586898 0.152022429293449 102 0.831214362401126 0.337571275197748 0.168785637598874 103 0.688484830059195 0.623030339881609 0.311515169940805 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.090909090909091 NOK 5% type I error level 36 0.363636363636364 NOK 10% type I error level 48 0.484848484848485 NOK

Charts produced by software:

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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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