Multiple Regression

*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, 14 Dec 2009 16:44:49 -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/15/t1260834383xmcnqtgusbit4kr.htm/, Retrieved Tue, 15 Dec 2009 00:46:35 +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/15/t1260834383xmcnqtgusbit4kr.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

107.1 0 96.3 87.0 96.8 115.2 0 107.1 96.3 87.0 106.1 0 115.2 107.1 96.3 89.5 0 106.1 115.2 107.1 91.3 0 89.5 106.1 115.2 97.6 0 91.3 89.5 106.1 100.7 0 97.6 91.3 89.5 104.6 0 100.7 97.6 91.3 94.7 0 104.6 100.7 97.6 101.8 0 94.7 104.6 100.7 102.5 0 101.8 94.7 104.6 105.3 0 102.5 101.8 94.7 110.3 0 105.3 102.5 101.8 109.8 0 110.3 105.3 102.5 117.3 0 109.8 110.3 105.3 118.8 0 117.3 109.8 110.3 131.3 0 118.8 117.3 109.8 125.9 0 131.3 118.8 117.3 133.1 0 125.9 131.3 118.8 147.0 0 133.1 125.9 131.3 145.8 0 147.0 133.1 125.9 164.4 0 145.8 147.0 133.1 149.8 0 164.4 145.8 147.0 137.7 0 149.8 164.4 145.8 151.7 0 137.7 149.8 164.4 156.8 0 151.7 137.7 149.8 180.0 0 156.8 151.7 137.7 180.4 0 180.0 156.8 151.7 170.4 0 180.4 180.0 156.8 191.6 0 170.4 180.4 180.0 199.5 0 191.6 170.4 180.4 218.2 0 199.5 191.6 170.4 217.5 0 218.2 199.5 191.6 205.0 0 217.5 218.2 199.5 194.0 0 205.0 217.5 218.2 199.3 0 194.0 205.0 217.5 219.3 0 199.3 194.0 205.0 211.1 0 219.3 199.3 194.0 215.2 0 211.1 219.3 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 Y[t] = + 12.0629978135911 + 4.10249057534315D[t] + 1.26715012400874Y1[t] -0.0818377397940402Y2[t] -0.306104094987486Y3[t] + 8.12828354888152M1[t] + 1.82765873193793M2[t] + 8.39071715010505M3[t] + 3.13299489510839M4[t] + 6.63553113116698M5[t] + 8.96123770976727M6[t] -0.419420596737340M7[t] -5.56120073377028M8[t] -10.1703238068274M9[t] -3.70759284651021M10[t] + 1.00728573412857M11[t] + 0.248197689412285t + 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) 12.0629978135911 9.998003 1.2065 0.23205 0.116025 D 4.10249057534315 7.905934 0.5189 0.605611 0.302806 Y1 1.26715012400874 0.120146 10.5468 0 0 Y2 -0.0818377397940402 0.201534 -0.4061 0.686043 0.343021 Y3 -0.306104094987486 0.124114 -2.4663 0.01634 0.00817 M1 8.12828354888152 10.637038 0.7641 0.447587 0.223793 M2 1.82765873193793 10.719273 0.1705 0.865153 0.432577 M3 8.39071715010505 10.823729 0.7752 0.441068 0.220534 M4 3.13299489510839 10.86559 0.2883 0.774018 0.387009 M5 6.63553113116698 10.972058 0.6048 0.547473 0.273736 M6 8.96123770976727 10.933532 0.8196 0.41548 0.20774 M7 -0.419420596737340 11.029918 -0.038 0.969786 0.484893 M8 -5.56120073377028 11.000799 -0.5055 0.614925 0.307463 M9 -10.1703238068274 10.810949 -0.9407 0.350374 0.175187 M10 -3.70759284651021 11.080287 -0.3346 0.739012 0.369506 M11 1.00728573412857 11.027544 0.0913 0.927506 0.463753 t 0.248197689412285 0.182391 1.3608 0.178351 0.089175 Multiple Linear Regression - Regression Statistics Multiple R 0.980859105002279 R-squared 0.962084583865872 Adjusted R-squared 0.95260572983234 F-TEST (value) 101.497984931769 F-TEST (DF numerator) 16 F-TEST (DF denominator) 64 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19.0574718264531 Sum Squared Residuals 23243.9828746274 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 107.1 105.715276237056 1.38472376294368 2 115.2 115.586799599612 -0.38679959961207 3 106.1 128.931356038502 -22.8313560385025 4 89.5 108.421955426242 -18.9219554262423 5 91.3 89.4032775558953 1.89672244410471 6 97.6 98.4021057920908 -0.802105792090774 7 100.7 102.186711001416 -1.48671100141647 8 104.6 100.154728806543 4.44527119345701 9 94.7 98.5535361147495 -3.85353611474951 10 101.8 91.4515886571345 10.3484113428655 11 102.5 105.027818461157 -2.52781846115744 12 105.3 107.605118091086 -2.30511809108570 13 110.3 117.298994184337 -6.99899418433698 14 109.8 117.138899138935 -7.3388991389348 15 117.3 122.050300019575 -4.75030001957469 16 118.8 125.054799779015 -6.25479977901542 17 131.3 130.245527889538 1.05447211046218 18 125.9 146.240271385562 -20.3402713855624 19 133.1 128.783072208916 4.31692779108381 20 147 129.628593261703 17.3714067382973 21 145.8 143.944784988195 1.85521501180541 22 164.4 145.793639422067 18.6063605779335 23 149.8 170.169066366107 -20.3690663661069 24 137.7 149.754729464679 -12.0547294646789 25 151.7 138.299989036693 13.4000109633073 26 156.8 155.447020083609 1.35297991639111 27 180 171.278873015865 8.72112698413508 28 180.4 190.964401524509 -10.5644015245088 29 170.4 191.762229051925 -21.3622290519252 30 191.6 174.530281980223 17.0697180197768 31 199.5 192.957339752061 6.54266024793854 32 218.2 199.400324150351 18.7996758496490 33 217.5 211.599181127562 5.90081887243807 34 205 213.474516605936 -8.47451660593562 35 194 196.931356167467 -2.93135616746732 36 199.3 183.470861372572 15.8291386274283 37 219.3 203.28975459319 16.0102454068102 38 211.1 225.513734969787 -14.4137349697872 39 215.2 218.675253561180 -3.47525356118045 40 240.2 213.410032070593 26.7899679294067 41 242.2 251.014037942024 -8.81403794202438 42 240.7 252.821272173755 -12.1212721737547 43 255.4 233.971808516374 21.4281914836260 44 253 247.215881311398 5.78411868860205 45 218.2 239.069936997641 -20.869936997641 46 203.7 197.380721711056 6.31927828894391 47 205.6 187.552724355783 18.0472756442170 48 215.6 201.040291279261 14.5597087207386 49 188.5 226.371291429352 -37.8712914293525 50 202.9 184.579120762768 18.3208792372322 51 214 208.794100454617 5.20589954538335 52 230.3 224.966899786656 5.33310021334409 53 230 244.055882853936 -14.0558828539356 54 241 241.517931471742 -0.51793147174156 55 259.6 245.461667367731 14.1383326322694 56 247.8 263.328693317434 -15.5286933174343 57 270.3 239.126069465455 31.1739305345452 58 289.7 269.620025068183 20.0799749318167 59 322.7 300.93649291949 21.7635070805097 60 315 333.518364677839 -18.5183646778394 61 320.2 323.498725105305 -3.29872510530546 62 329.5 314.564194084447 14.9358059155534 63 360.6 335.091391629782 25.5086083702181 64 382.2 367.13740364685 15.0625963531502 65 435.4 392.866658459931 42.5333415400689 66 464 451.565416791546 12.4345832084537 67 468.8 467.707833512331 1.09216648766877 68 403 450.271274448509 -47.2712744485087 69 351.6 353.384472637436 -1.78447263743551 70 252 298.879508535624 -46.8795085356239 71 188 201.982541729995 -13.9825417299951 72 146.5 144.010635114563 2.48936488543716 73 152.9 135.525969414066 17.3740305859338 74 148.1 160.570231360843 -12.4702313608426 75 165.1 173.478725280479 -8.37872528047889 76 177 188.444507766135 -11.4445077661345 77 206.1 207.352386246751 -1.25238624675061 78 244.9 240.622720405081 4.27727959491894 79 228.6 274.63156764117 -46.03156764117 80 253.4 237.000504704062 16.3994952959376 81 241.1 253.522018668963 -12.4220186689626 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.125549422788242 0.251098845576484 0.874450577211758 21 0.04962978960154 0.09925957920308 0.95037021039846 22 0.0216690565589811 0.0433381131179621 0.97833094344102 23 0.0205302516342858 0.0410605032685715 0.979469748365714 24 0.00852163370866007 0.0170432674173201 0.99147836629134 25 0.00323456605366182 0.00646913210732364 0.996765433946338 26 0.00183559444985915 0.00367118889971831 0.99816440555014 27 0.00320158024471357 0.00640316048942714 0.996798419755286 28 0.00153553220882011 0.00307106441764023 0.99846446779118 29 0.00154424389719505 0.00308848779439009 0.998455756102805 30 0.00334858971486137 0.00669717942972274 0.996651410285139 31 0.00151281855507527 0.00302563711015054 0.998487181444925 32 0.00101816535138939 0.00203633070277879 0.99898183464861 33 0.000444231121941818 0.000888462243883636 0.999555768878058 34 0.00067215266522871 0.00134430533045742 0.999327847334771 35 0.000334809623714204 0.000669619247428408 0.999665190376286 36 0.000145017022799282 0.000290034045598564 0.9998549829772 37 6.73814050148104e-05 0.000134762810029621 0.999932618594985 38 6.8115814751985e-05 0.00013623162950397 0.999931884185248 39 3.69029308455588e-05 7.38058616911175e-05 0.999963097069154 40 6.17858702644598e-05 0.000123571740528920 0.999938214129735 41 3.79029761361041e-05 7.58059522722082e-05 0.999962097023864 42 3.50038478885096e-05 7.00076957770192e-05 0.999964996152112 43 2.19425160535088e-05 4.38850321070175e-05 0.999978057483947 44 2.46594667591877e-05 4.93189335183755e-05 0.99997534053324 45 0.000126884000135388 0.000253768000270776 0.999873115999865 46 0.000175071419906436 0.000350142839812871 0.999824928580094 47 0.000114131914920165 0.00022826382984033 0.99988586808508 48 9.97179702781939e-05 0.000199435940556388 0.999900282029722 49 0.00160317849514561 0.00320635699029122 0.998396821504854 50 0.00124982545750492 0.00249965091500983 0.998750174542495 51 0.000835585411376288 0.00167117082275258 0.999164414588624 52 0.000604563475448113 0.00120912695089623 0.999395436524552 53 0.000322187722337781 0.000644375444675561 0.999677812277662 54 0.000139624901501338 0.000279249803002675 0.999860375098499 55 5.5328327126079e-05 0.000110656654252158 0.999944671672874 56 0.000259317199087798 0.000518634398175596 0.999740682800912 57 0.00194326982731245 0.00388653965462491 0.998056730172687 58 0.00095912637135529 0.00191825274271058 0.999040873628645 59 0.000973435488021913 0.00194687097604383 0.999026564511978 60 0.000498352772885365 0.00099670554577073 0.999501647227115 61 0.0108630437244747 0.0217260874489493 0.989136956275525 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 36 0.857142857142857 NOK 5% type I error level 40 0.952380952380952 NOK 10% type I error level 41 0.976190476190476 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/102kwe1260834281.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/102kwe1260834281.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/1ij9i1260834280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/1ij9i1260834280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/2vm5f1260834280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/2vm5f1260834280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/3wfjo1260834280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/3wfjo1260834280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/4vg9b1260834280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/4vg9b1260834280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/5nvyf1260834280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/5nvyf1260834280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/6rpwm1260834280.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/6rpwm1260834280.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/7363e1260834281.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/7363e1260834281.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/8mnh01260834281.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/8mnh01260834281.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/9egev1260834281.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t1260834383xmcnqtgusbit4kr/9egev1260834281.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