Multiple Linear Regression met seizonaliteit en met 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: Sat, 19 Dec 2009 05:49:05 -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/Dec/19/t1261228826p42asyduiwafiou.htm/, Retrieved Sat, 19 Dec 2009 14:20:38 +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/Dec/19/t1261228826p42asyduiwafiou.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:

kvn paper

Dataseries X:

» Textbox « » Textfile « » CSV «

9487 1169 8700 2154 9627 2249 8947 2687 9283 4359 8829 5382 9947 4459 9628 6398 9318 4596 9605 3024 8640 1887 9214 2070 9567 1351 8547 2218 9185 2461 9470 3028 9123 4784 9278 4975 10170 4607 9434 6249 9655 4809 9429 3157 8739 1910 9552 2228 9784 1594 9089 2467 9763 2222 9330 3607 9144 4685 9895 4962 10404 5770 10195 5480 9987 5000 9789 3228 9437 1993 10096 2288 9776 1580 9106 2111 10258 2192 9766 3601 9826 4665 9957 4876 10036 5813 10508 5589 10146 5331 10166 3075 9365 2002 9968 2306 10123 1507 9144 1992 10447 2487 9699 3490 10451 4647 10192 5594 10404 5611 10597 5788 10633 6204 10727 3013 9784 1931 9667 2549 10297 1504 9426 2090 10274 2702 9598 2939 10400 4500 9985 6208 10761 6415 11081 5657 10297 5964 10751 3163 9760 1997 10133 2422 10806 1376 9734 2202 10083 2683 10691 3303 10446 5202 10517 5231 11353 4880 10436 7998 10721 4977 10701 3531 9793 2025 10142 2205

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 9427.27878966371 -0.210497510801521X[t] + 174.311352889719M1[t] -559.647058313326M2[t] + 316.56637917964M3[t] + 163.252502328825M4[t] + 618.671872465051M5[t] + 729.363234007044M6[t] + 1352.56753892856M7[t] + 1331.88546735001M8[t] + 964.470335293804M9[t] + 563.097337400108M10[t] -516.369023715773M11[t] + 18.3432216545027t + 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) 9427.27878966371 174.494873 54.0261 0 0 X -0.210497510801521 0.070009 -3.0067 0.003665 0.001833 M1 174.311352889719 139.223936 1.252 0.214729 0.107365 M2 -559.647058313326 127.792651 -4.3793 4.1e-05 2e-05 M3 316.56637917964 128.314206 2.4671 0.01607 0.008035 M4 163.252502328825 145.170713 1.1246 0.264619 0.132309 M5 618.671872465051 213.099945 2.9032 0.004937 0.002468 M6 729.363234007044 249.234142 2.9264 0.004621 0.00231 M7 1352.56753892856 251.65592 5.3747 1e-06 0 M8 1331.88546735001 300.926047 4.426 3.5e-05 1.7e-05 M9 964.470335293804 245.096928 3.9351 0.000194 9.7e-05 M10 563.097337400108 141.62439 3.976 0.000168 8.4e-05 M11 -516.369023715773 129.300125 -3.9936 0.000159 7.9e-05 t 18.3432216545027 1.163808 15.7614 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.930079238786886 R-squared 0.865047390422394 Adjusted R-squared 0.83998476292941 F-TEST (value) 34.5154310203331 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 238.097500772848 Sum Squared Residuals 3968329.39119933 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9487 9373.86177408092 113.138225919084 2 8700 8450.90653639291 249.093463607091 3 9627 9325.46593201423 301.534067985768 4 8947 9098.29736708685 -151.297367086852 5 9283 9220.10812081744 62.891879182564 6 8829 9133.80375046398 -304.803750463977 7 9947 9969.6404795098 -22.6404795097946 8 9628 9559.1469561416 68.8530438583945 9 9318 9589.39156020424 -271.391560204239 10 9605 9537.26387094504 67.7361290549608 11 8640 8715.47640126499 -75.4764012649886 12 9214 9211.66760215859 2.33239784141178 13 9567 9555.6698869691 11.3301130308970 14 8547 8657.55335555564 -110.553355555641 15 9185 9500.95911957834 -315.959119578342 16 9470 9246.63637575757 223.363624242434 17 9123 9350.76533858082 -227.765338580823 18 9278 9439.59489721423 -161.594897214228 19 10170 10158.6055077652 11.3944922347973 20 9434 9810.62974510507 -376.629745105065 21 9655 9764.67425025755 -109.674250257548 22 9429 9729.38636186247 -300.386361862469 23 8739 8930.75361837059 -191.753618370588 24 9552 9398.52765530598 153.47234469402 25 9784 9724.63765169837 59.3623483016336 26 9089 8825.2581352201 263.741864779903 27 9763 9771.38668451394 -8.3866845139377 28 9330 9344.87697685752 -14.8769768575179 29 9144 9591.7232520042 -447.723252004206 30 9895 9662.45002470868 232.549975291319 31 10404 10133.9155625571 270.084437442935 32 10195 10192.6209907655 2.37900923453264 33 9987 9944.5878855485 42.4121144515092 34 9789 9934.5596984496 -145.559698449594 35 9437 9133.4009848281 303.599015171906 36 10096 9606.01646451192 489.983535488079 37 9776 9947.70327670362 -171.70327670362 38 9106 9120.31390891947 -14.3139089194707 39 10258 9997.82026969202 260.179730307984 40 9766 9566.25862177636 199.741378223640 41 9826 9816.05186207427 9.9481379257307 42 9957 9900.67147049164 56.3285295083557 43 10036 10344.9828294466 -308.982829446633 44 10508 10389.7954219421 118.204578057865 45 10146 10095.0318693272 50.9681306727801 46 10166 10186.8844774563 -20.884477456259 47 9365 9351.62516708491 13.3748329150867 48 9968 9822.34616917153 145.653830828473 49 10123 10183.1882548462 -60.1882548461639 50 9144 9365.48177255888 -221.481772558884 51 10447 10155.8421638596 291.1578361404 52 9699 9809.74250532936 -110.742505329361 53 10451 10039.9594771227 411.040522877270 54 10192 9969.65291759019 222.347082409815 55 10404 10607.6219864826 -203.621986482573 56 10597 10568.0250771467 28.9749228533357 57 10633 10131.3862022515 501.613797748476 58 10727 10420.0539829800 306.946017020014 59 9784 9586.68915020585 197.310849794147 60 9667 9991.3139339008 -324.313933900789 61 10297 10403.9384072326 -106.938407232601 62 9426 9564.97167635437 -138.971676354368 63 10274 10330.7038588913 -56.7038588913053 64 9598 10145.8452936350 -547.845293635032 65 10400 10291.0212710646 108.978728935414 66 9985 10060.5261058121 -75.5261058120828 67 10761 10658.5006476522 102.499352347818 68 11081 10815.7189109157 265.281089084304 69 10297 10402.0242646979 -105.024264697922 70 10751 10608.5980162138 142.401983786210 71 9760 9792.91497434699 -32.9149743469859 72 10133 10238.1657776266 -105.165777626615 73 10806 10651.0007484692 154.999251530771 74 9734 9761.51461499863 -27.5146149986302 75 10083 10554.8219714506 -471.821971450567 76 10691 10289.3428595573 401.657140442689 77 10446 10363.3706783360 82.6293216640496 78 10517 10486.3008337192 30.6991662807976 79 11353 11201.7329865866 151.267013413450 80 10436 10543.0628979834 -107.062897983367 81 10721 10829.9039677131 -108.903967713056 82 10701 10751.2535920929 -50.2535920928631 83 9793 10007.1397038986 -214.139703898577 84 10142 10503.9623973246 -361.962397324578 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.703566477323916 0.592867045352168 0.296433522676084 18 0.689544998117861 0.620910003764278 0.310455001882139 19 0.576416218059154 0.847167563881692 0.423583781940846 20 0.586368189901025 0.82726362019795 0.413631810098975 21 0.550857843306231 0.898284313387538 0.449142156693769 22 0.513932521242759 0.972134957514483 0.486067478757241 23 0.429851404878132 0.859702809756264 0.570148595121868 24 0.389761103673414 0.779522207346828 0.610238896326586 25 0.316638201942855 0.633276403885709 0.683361798057145 26 0.32637647091124 0.65275294182248 0.67362352908876 27 0.255929899530221 0.511859799060443 0.744070100469779 28 0.187552457219445 0.375104914438890 0.812447542780555 29 0.270611950648403 0.541223901296806 0.729388049351597 30 0.403916880121556 0.807833760243113 0.596083119878444 31 0.414150350906213 0.828300701812427 0.585849649093787 32 0.372258365630846 0.744516731261691 0.627741634369154 33 0.337791462249424 0.675582924498848 0.662208537750576 34 0.309075102037354 0.618150204074709 0.690924897962646 35 0.348192351698918 0.696384703397837 0.651807648301082 36 0.466286069054781 0.932572138109562 0.533713930945219 37 0.469856812552691 0.939713625105381 0.530143187447309 38 0.415298677217641 0.830597354435282 0.584701322782359 39 0.400216448519176 0.800432897038352 0.599783551480824 40 0.360261027399003 0.720522054798005 0.639738972600997 41 0.327669404565106 0.655338809130213 0.672330595434894 42 0.266373985122834 0.532747970245667 0.733626014877166 43 0.359341640831259 0.718683281662518 0.640658359168741 44 0.297557973431409 0.595115946862817 0.702442026568591 45 0.254207916257450 0.508415832514899 0.74579208374255 46 0.229289097085102 0.458578194170205 0.770710902914898 47 0.182091602172847 0.364183204345695 0.817908397827153 48 0.163087790348305 0.326175580696609 0.836912209651695 49 0.134038204622976 0.268076409245952 0.865961795377024 50 0.156076143096808 0.312152286193616 0.843923856903192 51 0.184900969848444 0.369801939696888 0.815099030151556 52 0.153699002337901 0.307398004675801 0.8463009976621 53 0.192876718622119 0.385753437244239 0.80712328137788 54 0.162726578016684 0.325453156033367 0.837273421983316 55 0.194795699060973 0.389591398121946 0.805204300939027 56 0.156592780572812 0.313185561145624 0.843407219427188 57 0.323387717022945 0.64677543404589 0.676612282977055 58 0.295491693346099 0.590983386692198 0.704508306653901 59 0.280801647705646 0.561603295411291 0.719198352294354 60 0.291204053043661 0.582408106087321 0.70879594695634 61 0.249558898866078 0.499117797732156 0.750441101133922 62 0.192671015656471 0.385342031312942 0.80732898434353 63 0.197201953624159 0.394403907248319 0.80279804637584 64 0.995822686187874 0.00835462762425287 0.00417731381212643 65 0.996385270170696 0.00722945965860845 0.00361472982930422 66 0.996116228178137 0.00776754364372545 0.00388377182186273 67 0.984774109584157 0.0304517808316852 0.0152258904158426 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0588235294117647 NOK 5% type I error level 4 0.0784313725490196 NOK 10% type I error level 4 0.0784313725490196 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/10sgrw1261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/10sgrw1261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/11yld1261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/11yld1261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/2ng9p1261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/2ng9p1261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/32qbx1261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/32qbx1261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/4w6ge1261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/4w6ge1261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/5s2981261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/5s2981261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/65yip1261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/65yip1261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/7b9o81261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/7b9o81261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/8j1oh1261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/8j1oh1261226940.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/91nu61261226940.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261228826p42asyduiwafiou/91nu61261226940.ps (open in new window)

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') }

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