voeding

*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, 20 Dec 2008 01:12:25 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88.htm/, Retrieved Sat, 20 Dec 2008 09:17:14 +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/2008/Dec/20/t12297610243n1iiovmo948e88.htm/}, year = {2008}, } @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 = {2008}, 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 «

99,2 11554,5 93,6 13182,1 104,2 14800,1 95,3 12150,7 102,7 14478,2 103,1 13253,9 100 12036,8 107,2 12653,2 107 14035,4 119 14571,4 110,4 15400,9 101,7 14283,2 102,4 14485,3 98,8 14196,3 105,6 15559,1 104,4 13767,4 106,3 14634 107,2 14381,1 108,5 12509,9 106,9 12122,3 114,2 13122,3 125,9 13908,7 110,6 13456,5 110,5 12441,6 106,7 12953 104,7 13057,2 107,4 14350,1 109,8 13830,2 103,4 13755,5 114,8 13574,4 114,3 12802,6 109,6 11737,3 118,3 13850,2 127,3 15081,8 112,3 13653,3 114,9 14019,1 108,2 13962 105,4 13768,7 122,1 14747,1 113,5 13858,1 110 13188 125,3 13693,1 114,3 12970 115,6 11392,8 127,1 13985,2 123 14994,7 122,2 13584,7 126,4 14257,8 112,7 13553,4 105,8 14007,3 120,9 16535,8 116,3 14721,4 115,7 13664,6 127,9 16405,9 108,3 13829,4 121,1 13735,6 128,6 15870,5 123,1 15962,4 127,7 15744,1 126,6 16083,7 118,4 14863,9 110 15533,1 129,6 17473,1 115,8 15925,5 125,9 15573,7 128,4 17495 114 14155,8 125,6 14913,9 128,5 17250,4 136,6 15879,8 133,1 17647,8 124,6 17749,9

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Voeding[t] = + 53.3282006913322 + 0.00426477832917221Invoer[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) 53.3282006913322 9.142947 5.8327 0 0 Invoer 0.00426477832917221 0.000637 6.6981 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.624968656363812 R-squared 0.390585821437189 Adjusted R-squared 0.381879904600578 F-TEST (value) 44.8644098912859 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 4.41529390826645e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.81540198444406 Sum Squared Residuals 4275.63557249165 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.2 102.605581895752 -3.40558189575215 2 93.6 109.546935104313 -15.9469351043131 3 104.2 116.447346440914 -12.2473464409138 4 95.3 105.148242735605 -9.84824273560491 5 102.7 115.074514296753 -12.3745142967532 6 103.1 109.853146188348 -6.7531461883477 7 100 104.662484483912 -4.66248448391219 8 107.2 107.291293846014 -0.091293846013945 9 107 113.186070452596 -6.18607045259577 10 119 115.471991637032 3.52800836296792 11 110.4 119.009625261080 -8.60962526108042 12 101.7 114.242882522565 -12.5428825225646 13 102.4 115.104794222890 -12.7047942228903 14 98.8 113.872273285760 -15.0722732857596 15 105.6 119.684313192755 -14.0843131927555 16 104.4 112.043109860378 -7.64310986037762 17 106.3 115.738966760438 -9.43896676043826 18 107.2 114.660404320991 -7.46040432099061 19 108.5 106.680151111444 1.81984888855643 20 106.9 105.027123031056 1.87287696894359 21 114.2 109.291901360229 4.90809863977138 22 125.9 112.645723038290 13.2542769617103 23 110.6 110.717190277838 -0.117190277837988 24 110.5 106.388866751561 4.11113324843889 25 106.7 108.569874389100 -1.86987438909977 26 104.7 109.014264291000 -4.31426429099952 27 107.4 114.528196192786 -7.12819619278627 28 109.8 112.310937939450 -2.51093793944964 29 103.4 111.992358998260 -8.59235899826047 30 114.8 111.220007642847 3.57999235715261 31 114.3 107.928451728392 6.37154827160772 32 109.6 103.385183374325 6.21481662567488 33 118.3 112.396233506033 5.90376649396691 34 127.3 117.648734496242 9.65126550375842 35 112.3 111.556498653019 0.743501346980927 36 114.9 113.116554565830 1.78344543416974 37 108.2 112.873035723235 -4.67303572323453 38 105.4 112.048654072206 -6.64865407220554 39 122.1 116.221313189468 5.87868681053235 40 113.5 112.429925254834 1.07007474516646 41 110 109.572097296455 0.427902703544757 42 125.3 111.72623683052 13.5737631694799 43 114.3 108.642375620696 5.6576243793043 44 115.6 101.915967239925 13.6840327600747 45 127.1 112.971978580471 14.1280214195287 46 123 117.277272303771 5.72272769622932 47 122.2 111.263934859638 10.9360651403621 48 126.4 114.134557153004 12.2654428469963 49 112.7 111.130447297935 1.56955270206524 50 105.8 113.066230181546 -7.26623018154604 51 120.9 123.849722186858 -2.94972218685796 52 116.3 116.111708386408 0.188291613592086 53 115.7 111.604690648139 4.09530935186128 54 127.9 123.295727481899 4.6042725181015 55 108.3 112.307526116786 -4.0075261167863 56 121.1 111.90748990951 9.19251009049005 57 128.6 121.012365164460 7.5876348355403 58 123.1 121.404298292911 1.69570170708937 59 127.7 120.473297183652 7.22670281634767 60 126.6 121.921615904239 4.67838409576077 61 118.4 116.719439298315 1.68056070168505 62 110 119.573428956197 -9.573428956197 63 129.6 127.847098914791 1.75290108520892 64 115.8 121.246927972564 -5.44692797256417 65 125.9 119.746578956361 6.15342104363861 66 128.4 127.9404975602 0.459502439800046 67 114 113.699549763428 0.300450236571893 68 125.6 116.932678214774 8.66732178522643 69 128.5 126.897332780884 1.60266721911556 70 136.6 121.052027602921 15.547972397079 71 133.1 128.592155688897 4.50784431110252 72 124.6 129.027589556306 -4.42758955630597 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.172800070773851 0.345600141547703 0.827199929226149 6 0.114290090627352 0.228580181254704 0.885709909372648 7 0.0618631911477584 0.123726382295517 0.938136808852242 8 0.110752316275419 0.221504632550838 0.889247683724581 9 0.086154735887269 0.172309471774538 0.913845264112731 10 0.308208574680742 0.616417149361483 0.691791425319258 11 0.229956015130057 0.459912030260114 0.770043984869943 12 0.217178068651324 0.434356137302649 0.782821931348676 13 0.205243563033036 0.410487126066072 0.794756436966964 14 0.256661508045541 0.513323016091082 0.743338491954459 15 0.259666435315176 0.519332870630352 0.740333564684824 16 0.221309768190063 0.442619536380127 0.778690231809937 17 0.199240909964133 0.398481819928266 0.800759090035867 18 0.178406387155506 0.356812774311013 0.821593612844494 19 0.210290006577058 0.420580013154116 0.789709993422942 20 0.205045197641412 0.410090395282824 0.794954802358588 21 0.304233813059656 0.608467626119311 0.695766186940344 22 0.783570422611728 0.432859154776543 0.216429577388272 23 0.753665271153005 0.492669457693991 0.246334728846995 24 0.728773692152062 0.542452615695877 0.271226307847938 25 0.683290903506289 0.633418192987422 0.316709096493711 26 0.655039523571532 0.689920952856937 0.344960476428468 27 0.654120515246267 0.691758969507467 0.345879484753733 28 0.624221983246642 0.751556033506715 0.375778016753357 29 0.679027155673667 0.641945688652666 0.320972844326333 30 0.682353232884836 0.635293534230329 0.317646767115164 31 0.685405938934622 0.629188122130755 0.314594061065378 32 0.640884829929057 0.718230340141886 0.359115170070943 33 0.674422750212602 0.651154499574797 0.325577249787398 34 0.82691369084053 0.34617261831894 0.17308630915947 35 0.796238394935852 0.407523210128296 0.203761605064148 36 0.767014129926062 0.465971740147876 0.232985870073938 37 0.768384978995324 0.463230042009352 0.231615021004676 38 0.814364858351654 0.371270283296693 0.185635141648346 39 0.824767398471084 0.350465203057832 0.175232601528916 40 0.798105251136483 0.403789497727033 0.201894748863517 41 0.777457842843187 0.445084314313626 0.222542157156813 42 0.863709923331605 0.272580153336791 0.136290076668395 43 0.835539714427142 0.328920571145717 0.164460285572858 44 0.843010095731223 0.313979808537554 0.156989904268777 45 0.918928559619677 0.162142880760645 0.0810714403803225 46 0.909795043029208 0.180409913941585 0.0902049569707923 47 0.925560168998885 0.148879662002230 0.0744398310011151 48 0.957630350201897 0.084739299596206 0.042369649798103 49 0.93737575170223 0.125248496595541 0.0626242482977705 50 0.954509856808848 0.0909802863823048 0.0454901431911524 51 0.944237749991439 0.111524500017123 0.0557622500085615 52 0.9229910346901 0.154017930619799 0.0770089653098997 53 0.891313572454749 0.217372855090503 0.108686427545252 54 0.86465573530535 0.270688529389299 0.135344264694649 55 0.87921670379967 0.24156659240066 0.12078329620033 56 0.857001229394796 0.285997541210408 0.142998770605204 57 0.840778659639457 0.318442680721086 0.159221340360543 58 0.780113628668077 0.439772742663845 0.219886371331923 59 0.746081778243696 0.507836443512609 0.253918221756304 60 0.6768049143378 0.6463901713244 0.3231950856622 61 0.580715199836502 0.838569600326995 0.419284800163498 62 0.754709714070312 0.490580571859376 0.245290285929688 63 0.654502172566976 0.690995654866048 0.345497827433024 64 0.727140856997712 0.545718286004576 0.272859143002288 65 0.609070831173956 0.781858337652088 0.390929168826044 66 0.467845447055418 0.935690894110836 0.532154552944582 67 0.633793516309285 0.73241296738143 0.366206483690715 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.0317460317460317 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/10nldy1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/10nldy1229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/1axb21229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/1axb21229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/2xrdd1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/2xrdd1229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/35vzv1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/35vzv1229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/4kkxn1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/4kkxn1229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/5ynnz1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/5ynnz1229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/6kbf21229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/6kbf21229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/7zmvh1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/7zmvh1229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/8njnk1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/8njnk1229760739.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/9ajvj1229760739.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/20/t12297610243n1iiovmo948e88/9ajvj1229760739.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

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

Privacy Policy

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

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

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

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

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

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

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

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

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

FreeStatistics.org is powered by