Multiple regression 2

*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: Mon, 20 Dec 2010 20:40:20 +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/Dec/20/t129287776175ssae7bx8i9fyc.htm/, Retrieved Mon, 20 Dec 2010 21:42:44 +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/Dec/20/t129287776175ssae7bx8i9fyc.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 «

21454 -11,5 0,012095933 8,02 8,3 20780 23899 -11 0,017384968 8,03 8,2 19815 24939 -14,9 0,017547503 8,45 8 19761 23580 -16,2 0,014844804 7,74 7,9 21454 24562 -14,4 0,010364842 7,26 7,6 23899 24696 -17,3 0,016214531 7,9 7,6 24939 23785 -15,7 0,014814047 7,34 8,3 23580 23812 -12,6 0,017823834 6,91 8,4 24562 21917 -9,4 0,017980779 7,22 8,4 24696 19713 -8,1 0,015828678 7,47 8,4 23785 19282 -5,4 0,018533858 7,16 8,4 23812 18788 -4,6 0,017385905 8,09 8,6 21917 21453 -4,9 0,015866474 7,91 8,9 19713 24482 -4 0,012585695 7,74 8,8 19282 27474 -3,1 0,011326531 8,01 8,3 18788 27264 -1,3 0,019230769 7,56 7,5 21453 27349 0 0,026056627 7,56 7,2 24482 30632 -0,4 0,022604071 8,06 7,4 27474 29429 3 0,024091466 8,06 8,8 27264 30084 0,4 0,022602321 7,87 9,3 27349 26290 1,2 0,020302507 7,97 9,3 30632 24379 0,6 0,028617986 7,89 8,7 29429 23335 -1,3 0,025515909 7,83 8,2 30084 21346 -3,2 0,022785068 8,17 8,3 26290 21106 -1,8 0,022515213 8,84 8,5 24379 24514 -3,6 0,025666936 8,44 8,6 23335 28353 -4,2 0,03067 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Vacatures[t] = + 20606.8812794916 + 168.158236460417Ondernemersvertrouwen[t] + 56401.8572264319Inflatie[t] -621.10278051104Rente[t] -1393.10882957294Werkloosheidsgraad[t] + 0.587840385394207Vacatures_3m[t] + 3649.21467940801M1[t] + 6828.56710343529M2[t] + 9676.46988365844M3[t] + 8533.49626620066M4[t] + 7432.16340329468M5[t] + 7701.319746343M6[t] + 7237.49835577374M7[t] + 6708.04816702846M8[t] + 3891.65143784099M9[t] + 2602.21309852869M10[t] + 460.457422895243M11[t] + 86.2936753033444t + 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) 20606.8812794916 8471.00306 2.4326 0.017341 0.008671 Ondernemersvertrouwen 168.158236460417 26.987593 6.2309 0 0 Inflatie 56401.8572264319 18671.87958 3.0207 0.003434 0.001717 Rente -621.10278051104 450.146998 -1.3798 0.171701 0.085851 Werkloosheidsgraad -1393.10882957294 635.180267 -2.1932 0.031348 0.015674 Vacatures_3m 0.587840385394207 0.070933 8.2873 0 0 M1 3649.21467940801 1122.442969 3.2511 0.001715 0.000857 M2 6828.56710343529 1124.827643 6.0708 0 0 M3 9676.46988365844 1166.489723 8.2954 0 0 M4 8533.49626620066 1170.905296 7.2879 0 0 M5 7432.16340329468 1218.114012 6.1014 0 0 M6 7701.319746343 1174.005375 6.5599 0 0 M7 7237.49835577374 1127.986845 6.4163 0 0 M8 6708.04816702846 1154.567546 5.81 0 0 M9 3891.65143784099 1154.140266 3.3719 0.001176 0.000588 M10 2602.21309852869 1118.234556 2.3271 0.022627 0.011313 M11 460.457422895243 1157.480704 0.3978 0.691885 0.345943 t 86.2936753033444 18.020151 4.7887 8e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.972195984429727 R-squared 0.945165032141285 Adjusted R-squared 0.932899315646572 F-TEST (value) 77.0574660313339 F-TEST (DF numerator) 17 F-TEST (DF denominator) 76 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2143.55200542283 Sum Squared Residuals 349205955.19637 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 21454 18762.0786243323 2691.92137566772 2 23899 21975.9491220755 1923.05087792448 3 24939 24249.5109485593 689.489051440655 4 23580 24397.2956854749 -817.295685474873 5 24562 25585.5948722032 -1023.59487220317 6 24696 25997.1675498996 -1301.16754989956 7 23785 24383.4694069888 -598.469406988808 8 23812 25336.3835743497 -1524.3835743497 9 21917 23039.4676163057 -1122.46761630572 10 19713 21242.7478801346 -1529.74788013456 11 19282 20002.3038467434 -720.303846743446 12 18788 17727.7151249994 1060.28487500065 13 21453 19725.3429206564 1727.65707934356 14 24482 22948.8285535912 1533.17144640877 15 27474 26201.8117474432 1272.18825255682 16 27264 28854.2082760411 -1590.20827604112 17 27349 30641.2670404319 -3292.26704043186 18 30632 31904.3684705505 -1272.36847055046 19 29429 30108.6717573444 -679.671757344364 20 30084 28615.734831695 1468.26516830497 21 26290 27758.214293302 -1468.21429330197 22 24379 27101.5646832996 -2722.56468329957 23 23335 25670.4951636856 -2335.49516368561 24 21346 22242.0540121122 -896.054012112164 25 21106 24379.639969341 -3273.63996934095 26 24514 27015.7891406017 -2501.78914060166 27 28353 29138.8039204666 -785.803920466583 28 30805 28147.7280059581 2657.2719940419 29 31348 29656.6193602976 1691.38063970242 30 34556 32786.02682 1769.97317999997 31 33855 32946.1972016893 908.8027983107 32 34787 32895.2912995413 1891.70870045869 33 32529 32490.8054412962 38.1945587037565 34 29998 31295.2987834677 -1297.29878346769 35 29257 30059.5716293569 -802.571629356902 36 28155 28641.9030664793 -486.903066479334 37 30466 30465.0767410274 0.923258972624129 38 35704 33488.5199755972 2215.48002440278 39 39327 35591.6177399131 3735.38226008688 40 39351 36496.9095877755 2854.09041222446 41 42234 39078.155816935 3155.84418306503 42 43630 41999.433272018 1630.56672798197 43 43722 40960.3173417357 2761.68265826426 44 43121 42176.6354362517 944.364563748254 45 37985 40031.6391197585 -2046.63911975849 46 37135 39340.1476077797 -2205.14760777969 47 34646 36655.2256175396 -2009.22561753959 48 33026 33600.7874497622 -574.787449762219 49 35087 36716.2799583862 -1629.27995838617 50 38846 38330.194244782 515.805755218004 51 42013 40076.5695901448 1936.43040985522 52 43908 40697.3154177835 3210.68458221648 53 42868 42331.0786798513 536.921320148684 54 44423 45411.9431244136 -988.943124413582 55 44167 45203.6019938997 -1036.60199389968 56 43636 43892.6501028236 -256.65010282358 57 44382 42946.5846577509 1435.41534224911 58 42142 41493.8774848983 648.122515101674 59 43452 39419.4065362876 4032.59346371242 60 36912 39063.4860574174 -2151.4860574174 61 42413 41220.3468308677 1192.65316913232 62 45344 46192.5671262766 -848.567126276623 63 44873 46153.0681577107 -1280.0681577107 64 47510 47849.8256814648 -339.825681464755 65 49554 50331.3691234235 -777.369123423511 66 47369 49487.6286121382 -2118.62861213822 67 45998 48314.9530982183 -2316.95309821828 68 48140 49120.2800233683 -980.280023368263 69 48441 44306.3369045728 4134.66309542723 70 44928 41525.8943733688 3402.10562663123 71 40454 39036.7011030828 1417.29889691716 72 38661 37230.0412384022 1430.95876159779 73 37246 38363.5468651114 -1117.54686511141 74 36843 37652.6281901733 -809.62819017326 75 36424 38784.2278528224 -2360.2278528224 76 37594 37582.0681885397 11.9318114602829 77 38144 36596.9072784663 1547.09272153374 78 38737 37096.8673390517 1640.13266094827 79 34560 36463.5101129533 -1903.51011295327 80 36080 37294.6961879822 -1214.69618798219 81 33508 34756.7660742434 -1248.76607424343 82 35462 32088.0883831836 3373.91161681644 83 33374 32956.296103304 417.703896695969 84 32110 30492.0130508273 1617.98694917268 85 35533 35125.6880902777 407.311909722305 86 35532 37559.5236469025 -2027.52364690248 87 37903 41110.3900429399 -3207.39004293989 88 36763 42749.6491569624 -5986.64915696238 89 40399 42237.0078283913 -1838.00782839133 90 44164 43523.5648119284 640.435188071616 91 44496 41631.2790871706 2864.72091282943 92 43110 43438.3285439882 -328.328543988178 93 43880 43602.1858927705 277.814107229519 94 43930 43599.3808038678 330.619196132168 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0163124598122142 0.0326249196244284 0.983687540187786 22 0.0271050025149212 0.0542100050298423 0.972894997485079 23 0.00974308103664749 0.019486162073295 0.990256918963353 24 0.00318052238341078 0.00636104476682157 0.99681947761659 25 0.0176434308110823 0.0352868616221647 0.982356569188918 26 0.00936356632909705 0.0187271326581941 0.990636433670903 27 0.0087409210085693 0.0174818420171386 0.99125907899143 28 0.193042618657772 0.386085237315543 0.806957381342228 29 0.156944881923493 0.313889763846985 0.843055118076507 30 0.104117056478095 0.20823411295619 0.895882943521905 31 0.0672208526701152 0.13444170534023 0.932779147329885 32 0.0499459753940983 0.0998919507881965 0.950054024605902 33 0.0348858053681071 0.0697716107362143 0.965114194631893 34 0.03023838309795 0.0604767661959001 0.96976161690205 35 0.0254500150829681 0.0509000301659363 0.974549984917032 36 0.0586591075052944 0.117318215010589 0.941340892494706 37 0.0861035041410764 0.172207008282153 0.913896495858924 38 0.0698070483442254 0.139614096688451 0.930192951655775 39 0.0621395375637105 0.124279075127421 0.93786046243629 40 0.0446777134448778 0.0893554268897556 0.955322286555122 41 0.0525648042619671 0.105129608523934 0.947435195738033 42 0.0372805857844497 0.0745611715688994 0.96271941421555 43 0.0880726040708824 0.176145208141765 0.911927395929118 44 0.0678653704664815 0.135730740932963 0.932134629533519 45 0.0670246555442564 0.134049311088513 0.932975344455744 46 0.0651074747835758 0.130214949567152 0.934892525216424 47 0.0764355155202021 0.152871031040404 0.923564484479798 48 0.0724644790531708 0.144928958106342 0.92753552094683 49 0.122018860757635 0.244037721515269 0.877981139242365 50 0.101070782990386 0.202141565980772 0.898929217009614 51 0.0849399009534594 0.169879801906919 0.91506009904654 52 0.10106924360766 0.202138487215321 0.89893075639234 53 0.0777985946679639 0.155597189335928 0.922201405332036 54 0.059315828346748 0.118631656693496 0.940684171653252 55 0.0460883064599356 0.0921766129198712 0.953911693540064 56 0.0396617185055944 0.0793234370111888 0.960338281494406 57 0.0904566679948583 0.180913335989717 0.909543332005142 58 0.123961385053845 0.247922770107689 0.876038614946155 59 0.394432155398168 0.788864310796337 0.605567844601832 60 0.370318046021493 0.740636092042987 0.629681953978507 61 0.354509805084667 0.709019610169333 0.645490194915333 62 0.411180372912691 0.822360745825383 0.588819627087309 63 0.346324866164991 0.692649732329982 0.65367513383501 64 0.47619500539015 0.9523900107803 0.52380499460985 65 0.728646968779448 0.542706062441104 0.271353031220552 66 0.643193620349104 0.713612759301793 0.356806379650896 67 0.824423762452137 0.351152475095726 0.175576237547863 68 0.74123822081623 0.517523558367541 0.25876177918377 69 0.93001257132621 0.139974857347579 0.0699874286737897 70 0.943480703449882 0.113038593100236 0.056519296550118 71 0.894897782169327 0.210204435661345 0.105102217830673 72 0.803994720630633 0.392010558738734 0.196005279369367 73 0.787663935508594 0.424672128982812 0.212336064491406 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0188679245283019 NOK 5% type I error level 6 0.113207547169811 NOK 10% type I error level 15 0.283018867924528 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/10kanw1292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/10kanw1292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/1d88k1292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/1d88k1292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/2d88k1292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/2d88k1292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/3o0751292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/3o0751292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/4o0751292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/4o0751292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/5o0751292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/5o0751292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/6g96q1292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/6g96q1292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/7r06t1292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/7r06t1292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/8r06t1292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/8r06t1292877609.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/9r06t1292877609.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t129287776175ssae7bx8i9fyc/9r06t1292877609.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