multiple linear regression zonder seiz 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: Sun, 28 Nov 2010 10:02:14 +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/Nov/28/t129093853571pgprob1300n9k.htm/, Retrieved Sun, 28 Nov 2010 11:02:25 +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/Nov/28/t129093853571pgprob1300n9k.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 «

1.579 9.769 2.146 9.321 2.462 9.939 3.695 9.336 4.831 10.195 5.134 9.464 6.250 10.010 5.760 10.213 6.249 9.563 2.917 9.890 1.741 9.305 2.359 9.391 1.511 9.928 2.059 8.686 2.635 9.843 2.867 9.627 4.403 10.074 5.720 9.503 4.502 10.119 5.749 10.000 5.627 9.313 2.846 9.866 1.762 9.172 2.429 9.241 1.169 9.659 2.154 8.904 2.249 9.755 2.687 9.080 4.359 9.435 5.382 8.971 4.459 10.063 6.398 9.793 4.596 9.454 3.024 9.759 1.887 8.820 2.070 9.403 1.351 9.676 2.218 8.642 2.461 9.402 3.028 9.610 4.784 9.294 4.975 9.448 4.607 10.319 6.249 9.548 4.809 9.801 3.157 9.596 1.910 8.923 2.228 9.746 1.594 9.829 2.467 9.125 2.222 9.782 3.607 9.441 4.685 9.162 4.962 9.915 5.770 10.444 5.480 10.209 5.000 9.985 3.228 9.842 1.993 9.429 2.288 10.132 1.580 9.849 2.111 9.172 2.192 10.313 3.601 9.819 4.665 9.955 4.876 10.048 5.813 10.082 5.589 10.541 5.331 10.208 3.075 10.233 2.002 9.439 2.306 9.963 1.507 10.158 1.992 9.225 2.487 10.474 3.490 9.757 4.647 10.490 5.5 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation huwelijken[t] = -8.06040832663057 + 1.18849990912292geboortes[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) -8.06040832663057 2.919378 -2.761 0.00693 0.003465 geboortes 1.18849990912292 0.296986 4.0019 0.000125 6.3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.381537512253697 R-squared 0.14557087325674 Adjusted R-squared 0.136481201695641 F-TEST (value) 16.0149761493855 F-TEST (DF numerator) 1 F-TEST (DF denominator) 94 p-value 0.00012532493577333 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.48456585486723 Sum Squared Residuals 207.169963079142 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.579 3.55004728559125 -1.97104728559125 2 2.146 3.01759932630420 -0.871599326304203 3 2.462 3.75209227014217 -1.29009227014217 4 3.695 3.03542682494105 0.659573175058952 5 4.831 4.05634824687764 0.774651753122361 6 5.134 3.18755481330878 1.94644518669122 7 6.25 3.8364757636899 2.4135242363101 8 5.76 4.07774124524185 1.68225875475815 9 6.249 3.30521630431195 2.94378369568805 10 2.917 3.69385577459515 -0.776855774595149 11 1.741 2.99858332775824 -1.25758332775824 12 2.359 3.10079431994281 -0.741794319942809 13 1.511 3.73901877114182 -2.22801877114182 14 2.059 2.26290188401115 -0.203901884011147 15 2.635 3.63799627886637 -1.00299627886637 16 2.867 3.38128029849582 -0.514280298495819 17 4.403 3.91253975787377 0.490460242126234 18 5.72 3.23390630976458 2.48609369023542 19 4.502 3.9660222537843 0.535977746215703 20 5.749 3.82459076459867 1.92440923540133 21 5.627 3.00809132703122 2.61890867296878 22 2.846 3.6653317767762 -0.819331776776197 23 1.762 2.84051283984489 -1.07851283984489 24 2.429 2.92251933357437 -0.49351933357437 25 1.169 3.41931229558775 -2.25031229558775 26 2.154 2.52199486419994 -0.367994864199944 27 2.249 3.53340828686355 -1.28440828686355 28 2.687 2.73117084820558 -0.0441708482055793 29 4.359 3.15308831594422 1.20591168405578 30 5.382 2.60162435811118 2.78037564188882 31 4.459 3.89946625887341 0.559533741126585 32 6.398 3.57857128341022 2.81942871658978 33 4.596 3.17566981421755 1.42033018578245 34 3.024 3.53816228650004 -0.514162286500045 35 1.887 2.42216087183362 -0.535160871833619 36 2.07 3.11505631885228 -1.04505631885228 37 1.351 3.43951679404284 -2.08851679404284 38 2.218 2.21060788800974 0.00739211199026221 39 2.461 3.11386781894316 -0.65286781894316 40 3.028 3.36107580004073 -0.333075800040728 41 4.784 2.98550982875788 1.79849017124211 42 4.975 3.16853881476282 1.80646118523718 43 4.607 4.20372223560888 0.403277764391117 44 6.249 3.28738880567511 2.96161119432489 45 4.809 3.58807928268321 1.22092071731679 46 3.157 3.34443680131301 -0.187436801313008 47 1.91 2.54457636247328 -0.63457636247328 48 2.228 3.52271178768145 -1.29471178768145 49 1.594 3.62135728013865 -2.02735728013865 50 2.467 2.78465334411611 -0.317653344116111 51 2.222 3.56549778440987 -1.34349778440987 52 3.607 3.16021931539896 0.446780684601045 53 4.685 2.82862784075366 1.85637215924634 54 4.962 3.72356827232322 1.23843172767678 55 5.77 4.35228472424925 1.41771527575075 56 5.48 4.07298724560536 1.40701275439464 57 5 3.80676326596182 1.19323673403818 58 3.228 3.63680777895725 -0.408807778957248 59 1.993 3.14595731648948 -1.15295731648948 60 2.288 3.98147275260289 -1.69347275260289 61 1.58 3.64512727832111 -2.06512727832111 62 2.111 2.84051283984489 -0.729512839844889 63 2.192 4.19659123615414 -2.00459123615415 64 3.601 3.60947228104742 -0.00847228104742098 65 4.665 3.77110826868814 0.893891731311862 66 4.876 3.88163876023657 0.99436123976343 67 5.813 3.92204775714675 1.89095224285325 68 5.589 4.46756921543417 1.12143078456583 69 5.331 4.07179874569624 1.25920125430376 70 3.075 4.10151124342431 -1.02651124342431 71 2.002 3.15784231558071 -1.15584231558071 72 2.306 3.78061626796112 -1.47461626796112 73 1.507 4.01237375024009 -2.50537375024009 74 1.992 2.9035033350284 -0.911503335028403 75 2.487 4.38793972152293 -1.90093972152294 76 3.49 3.5357852866818 -0.0457852866817981 77 4.647 4.4069557200689 0.240044279931098 78 5.594 4.15855923906221 1.43544076093779 79 5.611 4.35228472424925 1.25871527575075 80 5.788 4.58523070643734 1.20276929356266 81 6.204 4.6505982014391 1.55340179856090 82 3.013 4.75875169316929 -1.74575169316929 83 1.931 3.62492277986602 -1.69392277986602 84 2.549 3.52390028759057 -0.97490028759057 85 1.504 4.31306422724819 -2.80906422724819 86 2.09 3.24341430903756 -1.15341430903756 87 2.702 4.30236772806608 -1.60036772806608 88 2.939 3.46922929177092 -0.530229291770915 89 4.5 4.46638071552505 0.0336192844749527 90 6.208 3.95770275442044 2.25029724557956 91 6.415 4.91206818144614 1.50293181855386 92 5.657 5.23058615709109 0.426413842908912 93 5.964 4.28097472970187 1.68302527029813 94 3.163 4.81579968880719 -1.65279968880719 95 1.997 3.68910177495865 -1.69210177495865 96 2.422 4.08130674496922 -1.65930674496922 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.542004549354247 0.915990901291506 0.457995450645753 6 0.684574676083913 0.630850647832174 0.315425323916087 7 0.803498257240918 0.393003485518164 0.196501742759082 8 0.751927203520609 0.496145592958782 0.248072796479391 9 0.861151979262762 0.277696041474475 0.138848020737238 10 0.842025600350672 0.315948799298655 0.157974399649328 11 0.828132091467974 0.343735817064051 0.171867908532026 12 0.776935732085422 0.446128535829157 0.223064267914578 13 0.863095757708222 0.273808484583555 0.136904242291778 14 0.810831459433524 0.378337081132952 0.189168540566476 15 0.779305610889601 0.441388778220798 0.220694389110399 16 0.720177916956564 0.559644166086872 0.279822083043436 17 0.652448344425302 0.695103311149396 0.347551655574698 18 0.755483957061639 0.489032085876722 0.244516042938361 19 0.694887548184815 0.61022490363037 0.305112451815185 20 0.707014937691082 0.585970124617835 0.292985062308918 21 0.798803636587615 0.40239272682477 0.201196363412385 22 0.7717734567374 0.456453086525199 0.228226543262600 23 0.749101084358892 0.501797831282217 0.250898915641108 24 0.698239183306247 0.603521633387506 0.301760816693753 25 0.772606869294474 0.454786261411051 0.227393130705526 26 0.720224035872285 0.55955192825543 0.279775964127715 27 0.708218597679171 0.583562804641657 0.291781402320829 28 0.649432980450203 0.701134039099593 0.350567019549797 29 0.626172961678066 0.747654076643868 0.373827038321934 30 0.755397207486384 0.489205585027232 0.244602792513616 31 0.70836605163929 0.58326789672142 0.29163394836071 32 0.814250798877428 0.371498402245143 0.185749201122572 33 0.805658940555675 0.388682118888649 0.194341059444325 34 0.76926604454586 0.461467910908279 0.230733955454139 35 0.727720990647414 0.544558018705172 0.272279009352586 36 0.703408341050205 0.59318331789959 0.296591658949795 37 0.754653798540467 0.490692402919067 0.245346201459533 38 0.705080140465174 0.589839719069653 0.294919859534826 39 0.66171645640686 0.676567087186279 0.338283543593140 40 0.608573507239828 0.782852985520345 0.391426492760172 41 0.635732479627768 0.728535040744463 0.364267520372232 42 0.664464574154461 0.671070851691078 0.335535425845539 43 0.611442825414784 0.777114349170432 0.388557174585216 44 0.773600107139245 0.452799785721511 0.226399892860755 45 0.762806056322486 0.474387887355027 0.237193943677514 46 0.71783133822626 0.564337323547479 0.282168661773740 47 0.673790074224371 0.652419851551257 0.326209925775629 48 0.659134067869185 0.68173186426163 0.340865932130815 49 0.700594906875654 0.598810186248693 0.299405093124346 50 0.650343784593892 0.699312430812216 0.349656215406108 51 0.635160099663997 0.729679800672005 0.364839900336002 52 0.59048842512172 0.819023149756559 0.409511574878279 53 0.666511087744798 0.666977824510405 0.333488912255203 54 0.664403836194963 0.671192327610075 0.335596163805037 55 0.656614834030613 0.686770331938775 0.343385165969387 56 0.657942966023526 0.684114067952947 0.342057033976474 57 0.655535028733843 0.688929942532315 0.344464971266157 58 0.603795510363307 0.792408979273385 0.396204489636693 59 0.564129926800545 0.871740146398911 0.435870073199455 60 0.57551978826554 0.84896042346892 0.42448021173446 61 0.607724697962039 0.784550604075921 0.392275302037961 62 0.554321822126266 0.891356355747467 0.445678177873734 63 0.59915066605051 0.80169866789898 0.40084933394949 64 0.541562378679733 0.916875242640534 0.458437621320267 65 0.518892862281402 0.962214275437196 0.481107137718598 66 0.502103308549699 0.995793382900602 0.497896691450301 67 0.581024555561686 0.837950888876628 0.418975444438314 68 0.556421242397935 0.887157515204129 0.443578757602065 69 0.566767416992004 0.866465166015992 0.433232583007996 70 0.520474458042764 0.959051083914473 0.479525541957236 71 0.465375788677442 0.930751577354884 0.534624211322558 72 0.433377707752861 0.866755415505722 0.566622292247139 73 0.518818618793366 0.962362762413267 0.481181381206634 74 0.455106251332323 0.910212502664646 0.544893748667677 75 0.490766445337974 0.981532890675948 0.509233554662026 76 0.428777569754422 0.857555139508845 0.571222430245578 77 0.358747423905755 0.71749484781151 0.641252576094245 78 0.376265097339809 0.752530194679617 0.623734902660191 79 0.370492684493663 0.740985368987326 0.629507315506337 80 0.352352397528443 0.704704795056886 0.647647602471557 81 0.380625711059025 0.761251422118051 0.619374288940975 82 0.389889528618519 0.779779057237038 0.610110471381481 83 0.341706764603457 0.683413529206914 0.658293235396543 84 0.265725713463115 0.531451426926229 0.734274286536885 85 0.42036081001837 0.84072162003674 0.57963918998163 86 0.332148220972012 0.664296441944025 0.667851779027988 87 0.327922175015247 0.655844350030493 0.672077824984753 88 0.232234426931447 0.464468853862893 0.767765573068553 89 0.149599373547875 0.299198747095749 0.850400626452125 90 0.31166148570507 0.62332297141014 0.68833851429493 91 0.276972414497289 0.553944828994577 0.723027585502711 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/102mg71290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/102mg71290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/1vljv1290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/1vljv1290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/2odiy1290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/2odiy1290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/3odiy1290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/3odiy1290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/4h4z11290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/4h4z11290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/5h4z11290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/5h4z11290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/6h4z11290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/6h4z11290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/79vgm1290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/79vgm1290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/89vgm1290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/89vgm1290938526.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/92mg71290938526.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t129093853571pgprob1300n9k/92mg71290938526.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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by