paper - 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: Tue, 21 Dec 2010 20:42:43 +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/21/t1292964046n9orm27qrtmokyx.htm/, Retrieved Tue, 21 Dec 2010 21:40:49 +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/21/t1292964046n9orm27qrtmokyx.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 «

631923 -12 -10.8 654294 -13 -12.2 671833 -16 -14.1 586840 -10 -15.2 600969 -4 -15.8 625568 -9 -15.8 558110 -8 -14.9 630577 -9 -12.6 628654 -3 -9.9 603184 -13 -7.8 656255 -3 -6 600730 -1 -5 670326 -2 -4.5 678423 0 -3.9 641502 0 -2.9 625311 -3 -1.5 628177 0 -0.5 589767 5 0 582471 3 0.5 636248 4 0.9 599885 3 0.8 621694 1 0.1 637406 -1 -1 596994 0 -2 696308 -2 -3 674201 -1 -3.7 648861 2 -4.7 649605 0 -6.4 672392 -6 -7.5 598396 -7 -7.8 613177 -6 -7.7 638104 -4 -6.6 615632 -9 -4.2 634465 -2 -2 638686 -3 -0.7 604243 2 0.1 706669 3 0.9 677185 1 2.1 644328 0 3.5 644825 1 4.9 605707 1 5.7 600136 3 6.2 612166 5 6.5 599659 5 6.5 634210 4 6.3 618234 11 6.2 613576 8 6.4 627200 -1 6.3 668973 4 5.8 651479 4 5.1 619661 4 5.1 644260 6 5.8 579936 6 6.7 601752 6 7.1 595376 6 6.7 588902 4 5.5 634341 1 4.2 594305 6 3 606200 0 2.2 610926 2 2 633685 -2 1.8 639696 0 1.8 659451 1 1.5 593248 -3 0.4 606677 -3 -0.9 599434 -5 -1.7 569578 -7 -2.6 629873 -7 -4.4 613438 -5 - 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 Werkloosheid[t] = + 625621.704483033 + 2021.02707171529Consumentenvertrouwen[t] -3338.43957822753Ondernemersvertrouwen[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) 625621.704483033 4314.125304 145.017 0 0 Consumentenvertrouwen 2021.02707171529 1180.811964 1.7116 0.090805 0.045403 Ondernemersvertrouwen -3338.43957822753 820.468089 -4.0689 0.000109 5.5e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.539281172544876 R-squared 0.290824183061377 Adjusted R-squared 0.273313669062892 F-TEST (value) 16.6085463331657 F-TEST (DF numerator) 2 F-TEST (DF denominator) 81 p-value 9.0267967600699e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 35366.6665765281 Sum Squared Residuals 101314889483.56 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 631923 637424.527067307 -5501.52706730695 2 654294 640077.31540511 14216.6845948902 3 671833 640357.269388596 31475.7306114038 4 586840 656155.715354938 -69315.7153549382 5 600969 670284.941532166 -69315.9415321664 6 625568 660179.80617359 -34611.80617359 7 558110 659196.2376249 -101086.237624901 8 630577 649496.799523262 -18919.7995232619 9 628654 652609.17509234 -23955.1750923393 10 603184 625388.181260909 -22204.1812609087 11 656255 639589.260737252 16665.7392627481 12 600730 640292.875302455 -39562.875302455 13 670326 636602.628441626 33723.3715583741 14 678423 638641.61883812 39781.38116188 15 641502 635303.179259892 6198.82074010754 16 625311 624566.282635228 744.71736477193 17 628177 627290.924272146 886.075727853605 18 589767 635726.839841609 -45959.839841609 19 582471 630015.565909065 -47544.5659090647 20 636248 630701.217149489 5546.78285051101 21 599885 629014.034035596 -29129.0340355965 22 621694 627308.887596925 -5614.88759692516 23 637406 626939.116989545 10466.8830104551 24 596994 632298.583639488 -35304.5836394877 25 696308 631594.969074285 64713.0309257153 26 674201 635952.903850759 38248.0961492408 27 648861 645354.424644133 3506.57535586742 28 649605 646987.717783689 2617.2822163112 29 672392 638533.838889447 33858.1611105526 30 598396 637514.3436912 -39118.3436912004 31 613177 639201.526805093 -26024.5268050929 32 638104 639571.297412473 -1467.29741247318 33 615632 621453.907066151 -5821.90706615069 34 634465 628256.529496057 6208.47050394288 35 638686 621895.530972646 16790.469027354 36 604243 629329.91466864 -25086.9146686404 37 706669 628680.190077774 77988.8099222263 38 677185 620632.00844047 56552.9915595299 39 644328 613937.165959236 30390.8340407637 40 644825 611284.377621433 33540.622378567 41 605707 608613.625958851 -2906.62595885101 42 600136 610986.460313168 -10850.4603131678 43 612166 614026.98258313 -1860.98258313012 44 599659 614026.98258313 -14367.9825831301 45 634210 612673.64342706 21536.3565729397 46 618234 627154.67688689 -8920.67688689008 47 613576 620423.907756099 -6847.90775609872 48 627200 602568.508068484 24631.4919315161 49 668973 614342.863216174 54630.1367838259 50 651479 616679.770920933 34799.2290790666 51 619661 616679.770920933 2981.22907906663 52 644260 618384.917359605 25875.0826403953 53 579936 615380.3217392 -35444.3217391999 54 601752 614044.945907909 -12292.9459079089 55 595376 615380.3217392 -20004.3217391999 56 588902 615344.395089642 -26442.3950896424 57 634341 613621.285326192 20719.7146738077 58 594305 627732.548178642 -33427.5481786418 59 606200 618277.137410932 -12077.1374109321 60 610926 622986.879470008 -12060.8794700081 61 633685 615570.459098793 18114.5409012075 62 639696 619612.513242223 20083.4867577769 63 659451 622635.072187407 36815.9278125934 64 593248 618223.247436596 -24975.2474365958 65 606677 622563.218888292 -15886.2188882916 66 599434 621191.916407443 -21757.916407443 67 569578 620154.457884417 -50576.4578844172 68 629873 626163.649125227 3709.35087477324 69 613438 643225.617623745 -29787.6176237447 70 604172 647421.88247721 -43249.8824772103 71 658328 664394.034351834 -6066.03435183442 72 612633 673669.811871756 -61036.8118717564 73 707372 686725.652876401 20646.3471235987 74 739770 686004.07498642 53765.9250135805 75 777535 688358.946015958 89176.0539840424 76 685030 691769.2388933 -6739.23889330016 77 730234 687799.038048985 42434.9619510153 78 714154 682827.305331201 31326.6946687991 79 630872 678523.260529063 -47651.2605290626 80 719492 683990.507127678 35501.4928723219 81 677023 674291.069026039 2731.93097396054 82 679272 663608.062375711 15663.9376242886 83 718317 656651.22923577 61665.7707642301 84 645672 635827.160577308 9844.83942269217 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.34243703959728 0.68487407919456 0.65756296040272 7 0.595338610690744 0.809322778618512 0.404661389309256 8 0.486930114493681 0.973860228987362 0.513069885506319 9 0.421884389837713 0.843768779675426 0.578115610162287 10 0.582453014266232 0.835093971467537 0.417546985733768 11 0.566878398170677 0.866243203658645 0.433121601829323 12 0.536765206973707 0.926469586052586 0.463234793026293 13 0.564982380693952 0.870035238612096 0.435017619306048 14 0.579603201602375 0.840793596795249 0.420396798397625 15 0.494784927096942 0.989569854193884 0.505215072903058 16 0.479758688634304 0.959517377268607 0.520241311365696 17 0.423847790043185 0.84769558008637 0.576152209956815 18 0.497632514962691 0.995265029925382 0.502367485037309 19 0.586531363173551 0.826937273652899 0.413468636826449 20 0.516388883815276 0.967222232369449 0.483611116184724 21 0.496613251560439 0.993226503120879 0.503386748439561 22 0.424161515918865 0.84832303183773 0.575838484081135 23 0.354914286991708 0.709828573983417 0.645085713008292 24 0.352104732588068 0.704209465176136 0.647895267411932 25 0.573725297913613 0.852549404172773 0.426274702086387 26 0.623491593806794 0.753016812386412 0.376508406193206 27 0.598690055318886 0.802619889362229 0.401309944681114 28 0.566663259027126 0.866673481945748 0.433336740972874 29 0.579642532036119 0.840714935927763 0.420357467963881 30 0.598482946381679 0.803034107236643 0.401517053618322 31 0.565030115745748 0.869939768508503 0.434969884254252 32 0.502453102938698 0.995093794122603 0.497546897061302 33 0.463509195808357 0.927018391616715 0.536490804191643 34 0.398323684293577 0.796647368587154 0.601676315706423 35 0.341436900595783 0.682873801191567 0.658563099404217 36 0.328232068203882 0.656464136407764 0.671767931796118 37 0.568035243867679 0.863929512264642 0.431964756132321 38 0.623360621044172 0.753278757911655 0.376639378955828 39 0.592379183269785 0.815241633460431 0.407620816730215 40 0.57281827042107 0.85436345915786 0.42718172957893 41 0.562686550154138 0.874626899691723 0.437313449845862 42 0.548925993931482 0.902148012137036 0.451074006068518 43 0.501252507388309 0.997494985223382 0.498747492611691 44 0.474147701451301 0.948295402902602 0.525852298548699 45 0.428105948904265 0.85621189780853 0.571894051095735 46 0.377917707844636 0.755835415689273 0.622082292155363 47 0.3276407266353 0.6552814532706 0.6723592733647 48 0.315570987465865 0.631141974931731 0.684429012534134 49 0.399148436493036 0.798296872986072 0.600851563506964 50 0.402712410988624 0.805424821977247 0.597287589011376 51 0.348183205531386 0.696366411062773 0.651816794468614 52 0.326685065036798 0.653370130073596 0.673314934963202 53 0.350353668181441 0.700707336362882 0.649646331818559 54 0.30659213213483 0.613184264269659 0.69340786786517 55 0.275771977887957 0.551543955775915 0.724228022112043 56 0.260298145544528 0.520596291089057 0.739701854455472 57 0.238243019807574 0.476486039615148 0.761756980192426 58 0.248498505124266 0.496997010248533 0.751501494875734 59 0.204635247820511 0.409270495641022 0.79536475217949 60 0.165422412504262 0.330844825008525 0.834577587495738 61 0.14665906178424 0.29331812356848 0.85334093821576 62 0.129389717760642 0.258779435521285 0.870610282239358 63 0.160666810269915 0.32133362053983 0.839333189730085 64 0.133507355472961 0.267014710945923 0.866492644527039 65 0.101887485080826 0.203774970161653 0.898112514919174 66 0.0783029332529422 0.156605866505884 0.921697066747058 67 0.0793437480134308 0.158687496026862 0.920656251986569 68 0.0653450824904175 0.130690164980835 0.934654917509583 69 0.0483387419940446 0.096677483988089 0.951661258005955 70 0.0447622770577817 0.0895245541155634 0.955237722942218 71 0.0312256164376757 0.0624512328753515 0.968774383562324 72 0.14272483742188 0.28544967484376 0.85727516257812 73 0.130741948491944 0.261483896983889 0.869258051508056 74 0.129410396090771 0.258820792181543 0.870589603909229 75 0.444267626801267 0.888535253602535 0.555732373198732 76 0.328468772838729 0.656937545677458 0.671531227161271 77 0.407288010082729 0.814576020165457 0.592711989917271 78 0.929924475516064 0.140151048967871 0.0700755244839357 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 3 0.0410958904109589 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/109dbr1292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/109dbr1292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/1kueg1292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/1kueg1292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/2kueg1292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/2kueg1292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/3v4v01292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/3v4v01292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/4v4v01292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/4v4v01292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/5v4v01292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/5v4v01292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/65vc31292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/65vc31292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/7gmup1292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/7gmup1292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/8gmup1292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/8gmup1292964149.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/9gmup1292964149.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292964046n9orm27qrtmokyx/9gmup1292964149.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