WS 7 Multiple Regression - Include monthly dummies

*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: Fri, 20 Nov 2009 06:21:40 -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/20/t1258723356dvucnl3ylra0ddx.htm/, Retrieved Fri, 20 Nov 2009 14:22:48 +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/20/t1258723356dvucnl3ylra0ddx.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:

SHW WS 7 Multiple Regression - Include monthly dummies

Dataseries X:

» Textbox « » Textfile « » CSV «

14.2 -0.8 13.5 -0.2 11.9 0.2 14.6 1 15.6 0 14.1 -0.2 14.9 1 14.2 0.4 14.6 1 17.2 1.7 15.4 3.1 14.3 3.3 17.5 3.1 14.5 3.5 14.4 6 16.6 5.7 16.7 4.7 16.6 4.2 16.9 3.6 15.7 4.4 16.4 2.5 18.4 -0.6 16.9 -1.9 16.5 -1.9 18.3 0.7 15.1 -0.9 15.7 -1.7 18.1 -3.1 16.8 -2.1 18.9 0.2 19 1.2 18.1 3.8 17.8 4 21.5 6.6 17.1 5.3 18.7 7.6 19 4.7 16.4 6.6 16.9 4.4 18.6 4.6 19.3 6 19.4 4.8 17.6 4 18.6 2.7 18.1 3 20.4 4.1 18.1 4 19.6 2.7 19.9 2.6 19.2 3.1 17.8 4.4 19.2 3 22 2 21.1 1.3 19.5 1.5 22.2 1.3 20.9 3.2 22.2 1.8 23.5 3.3 21.5 1 24.3 2.4 22.8 0.4 20.3 -0.1 23.7 1.3 23.3 -1.1 19.6 -4.4 18 -7.5 17.3 -12.2 16.8 -14.5 18.2 -16 16.5 -16.7 16 -16.3 18.4 -16.9

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] = + 17.8062459764093 + 0.0659655162376716X[t] + 1.03333333333334M1[t] -1.02700746857109M2[t] -1.78470344546548M3[t] + 0.522992531428915M4[t] + 1.03930862288108M5[t] + 0.412221265957019M6[t] -0.198024136693129M7[t] -0.127310344158448M8[t] -0.364117240910914M9[t] + 1.8701402300858M10[t] + 0.142304023105606M11[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) 17.8062459764093 1.112638 16.0036 0 0 X 0.0659655162376716 0.059181 1.1146 0.269453 0.134727 M1 1.03333333333334 1.515497 0.6818 0.497961 0.248981 M2 -1.02700746857109 1.580703 -0.6497 0.518356 0.259178 M3 -1.78470344546548 1.581412 -1.1286 0.26358 0.13179 M4 0.522992531428915 1.580703 0.3309 0.741902 0.370951 M5 1.03930862288108 1.578005 0.6586 0.512658 0.256329 M6 0.412221265957019 1.575495 0.2616 0.794491 0.397245 M7 -0.198024136693129 1.574399 -0.1258 0.900328 0.450164 M8 -0.127310344158448 1.573201 -0.0809 0.935771 0.467886 M9 -0.364117240910914 1.572949 -0.2315 0.817724 0.408862 M10 1.8701402300858 1.572751 1.1891 0.239089 0.119545 M11 0.142304023105606 1.572721 0.0905 0.928205 0.464103 Multiple Linear Regression - Regression Statistics Multiple R 0.364144441329375 R-squared 0.132601174151083 Adjusted R-squared -0.0408785910187008 F-TEST (value) 0.7643610424611 F-TEST (DF numerator) 12 F-TEST (DF denominator) 60 p-value 0.683563158515654 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.72400674830111 Sum Squared Residuals 445.212765887398 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14.2 18.7868068967525 -4.58680689675246 2 13.5 16.7660454045907 -3.26604540459065 3 11.9 16.0347356341913 -4.13473563419133 4 14.6 18.3952040240759 -3.79520402407586 5 15.6 18.8455545992904 -3.24555459929035 6 14.1 18.2052741391188 -4.10527413911876 7 14.9 17.6741873559538 -2.77418735595381 8 14.2 17.7053218387459 -3.50532183874589 9 14.6 17.5080942517360 -2.90809425173603 10 17.2 19.7885275840991 -2.58852758409911 11 15.4 18.1530430998517 -2.75304309985166 12 14.3 18.0239321799936 -3.72393217999359 13 17.5 19.0440724100794 -1.54407241007939 14 14.5 17.0101178146700 -2.51011781467003 15 14.4 16.4173356283698 -2.01733562836982 16 16.6 18.7052419503929 -2.10524195039291 17 16.7 19.1555925256074 -2.45559252560741 18 16.6 18.4955224105645 -1.89552241056451 19 16.9 17.8456976981718 -0.94569769817176 20 15.7 17.9691839036966 -2.26918390369658 21 16.4 17.6070425260925 -1.20704252609254 22 18.4 19.6368068967525 -1.23680689675247 23 16.9 17.8232155186633 -0.923215518663301 24 16.5 17.6809114955577 -1.18091149555769 25 18.3 18.8857551711090 -0.585755171108976 26 15.1 16.7198695432243 -1.61986954322428 27 15.7 15.9094011533397 -0.209401153339747 28 18.1 18.1247454075014 -0.0247454075014015 29 16.8 18.7070270151912 -1.90702701519124 30 18.9 18.2316603456138 0.668339654386175 31 19 17.6873804592013 1.31261954079865 32 18.1 17.9296045939540 0.170395406046027 33 17.8 17.7059908004490 0.0940091995509578 34 21.5 20.1117586136637 1.3882413863363 35 17.1 18.2981672355745 -1.19816723557453 36 18.7 18.3075838998156 0.392416100184425 37 19 19.1496172360597 -0.149617236059662 38 16.4 17.2146109150068 -0.814610915006818 39 16.9 16.3117908023895 0.588209197610455 40 18.6 18.6326798825315 -0.0326798825314726 41 19.3 19.2413476967164 0.0586523032836177 42 19.4 18.5351017203071 0.864898279692886 43 17.6 17.8720839046668 -0.272083904666826 44 18.6 17.8570425260925 0.742957473907466 45 18.1 17.6400252842114 0.45997471578863 46 20.4 19.9468448230695 0.453155176930476 47 18.1 18.2124120644656 -0.112412064465561 48 19.6 17.9843528702510 1.61564712974902 49 19.9 19.0110896519606 0.888910348039446 50 19.2 16.9837316081750 2.21626839182503 51 17.8 16.3117908023895 1.48820919761046 52 19.2 18.5271350565512 0.6728649434488 53 22 18.9774856317657 3.0225143682343 54 21.1 18.3042224134753 2.79577758652474 55 19.5 17.7071701140726 1.79282988592735 56 22.2 17.7646908033598 4.4353091966402 57 20.9 17.6532183874589 3.24678161254109 58 22.2 19.7951241357229 2.40487586427712 59 23.5 18.1662362030992 5.33376379690081 60 21.5 17.8722114926469 3.62778850735306 61 24.3 18.997896548713 5.30210345128698 62 22.8 16.8056247143333 5.99437528566675 63 20.3 16.0149459793200 4.28505402067998 64 23.7 18.4149936789472 5.28500632105284 65 23.3 18.7729925314289 4.52700746857109 66 19.6 17.9282189709205 1.67178102907947 67 18 17.1134804679336 0.886519532066396 68 17.3 16.8741563341512 0.425843665848771 69 16.8 16.4856287500521 0.314371249947881 70 18.2 18.6209379466923 -0.420937946692322 71 16.5 16.8469258783458 -0.346925878345760 72 16 16.7310080617352 -0.731008061735223 73 18.4 17.7247620853260 0.675237914674042 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.034408847286182 0.068817694572364 0.965591152713818 17 0.0165160347273925 0.0330320694547850 0.983483965272608 18 0.00568714487622783 0.0113742897524557 0.994312855123772 19 0.00222517700515862 0.00445035401031723 0.997774822994841 20 0.00073152702209578 0.00146305404419156 0.999268472977904 21 0.000440695015921012 0.000881390031842024 0.999559304984079 22 0.00137059440022768 0.00274118880045536 0.998629405599772 23 0.00919990173344255 0.0183998034668851 0.990800098266557 24 0.0288774476543625 0.057754895308725 0.971122552345637 25 0.0390655243693445 0.078131048738689 0.960934475630655 26 0.0390602628314886 0.0781205256629771 0.960939737168511 27 0.0693268188162819 0.138653637632564 0.930673181183718 28 0.0860921807517459 0.172184361503492 0.913907819248254 29 0.0827856466816494 0.165571293363299 0.91721435331835 30 0.120085877416420 0.240171754832839 0.87991412258358 31 0.132632838399620 0.265265676799241 0.86736716160038 32 0.154552374725552 0.309104749451105 0.845447625274448 33 0.141687525085788 0.283375050171575 0.858312474914212 34 0.155586350424021 0.311172700848042 0.844413649575979 35 0.138989182781183 0.277978365562365 0.861010817218817 36 0.131445088575046 0.262890177150092 0.868554911424954 37 0.129933025230844 0.259866050461688 0.870066974769156 38 0.183272214290956 0.366544428581911 0.816727785709044 39 0.187517462101556 0.375034924203111 0.812482537898444 40 0.192092941216756 0.384185882433513 0.807907058783244 41 0.251973407050812 0.503946814101625 0.748026592949188 42 0.238366252700967 0.476732505401934 0.761633747299033 43 0.210449122591403 0.420898245182805 0.789550877408597 44 0.232712776326307 0.465425552652614 0.767287223673693 45 0.224573156749101 0.449146313498201 0.775426843250899 46 0.196250343606907 0.392500687213813 0.803749656393093 47 0.293876728822875 0.58775345764575 0.706123271177125 48 0.297357922907768 0.594715845815536 0.702642077092232 49 0.451506581877229 0.903013163754458 0.548493418122771 50 0.677119431413356 0.645761137173287 0.322880568586644 51 0.797704234284973 0.404591531430054 0.202295765715027 52 0.98792783755546 0.0241443248890812 0.0120721624445406 53 0.99646646393302 0.00706707213395907 0.00353353606697954 54 0.99150889504209 0.0169822099158212 0.00849110495791059 55 0.98613339586209 0.0277332082758209 0.0138666041379104 56 0.977369920385756 0.0452601592284885 0.0226300796142443 57 0.959617899573769 0.0807642008524625 0.0403821004262313 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.119047619047619 NOK 5% type I error level 12 0.285714285714286 NOK 10% type I error level 17 0.404761904761905 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/10v3ru1258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/10v3ru1258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/1o25j1258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/1o25j1258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/2vt6n1258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/2vt6n1258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/39trc1258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/39trc1258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/4lnhe1258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/4lnhe1258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/5vfgc1258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/5vfgc1258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/6j3uw1258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/6j3uw1258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/7til51258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/7til51258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/85w291258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/85w291258723296.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/9fv471258723296.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258723356dvucnl3ylra0ddx/9fv471258723296.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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