Home » date » 2007 » Dec » 24 » attachments

verklaring verloop werkloosheid

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 24 Dec 2007 15:04:38 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf.htm/, Retrieved Mon, 24 Dec 2007 22:46:08 +0100

User-defined keywords:

s0650062

Dataseries X:

» Textbox « » Textfile « » CSV «

8,0 0 8,1 0 8,3 0 8,2 0 8,1 0 7,7 0 7,6 0 7,7 0 8,2 0 8,4 0 8,4 0 8,6 0 8,4 0 8,5 0 8,7 0 8,7 0 8,6 0 7,4 0 7,3 0 7,4 0 9,0 0 9,2 0 9,2 0 8,5 0 8,3 0 8,3 0 8,6 0 8,6 0 8,5 0 8,1 0 8,1 0 8,0 0 8,6 0 8,7 0 8,7 0 8,6 0 8,4 0 8,4 0 8,7 0 8,7 0 8,5 0 8,3 1 8,3 1 8,3 1 8,1 1 8,2 1 8,1 1 8,1 1 7,9 1 7,7 1 8,1 1 8,0 1 7,7 1 7,8 1 7,6 1 7,4 1 7,7 1 7,8 1 7,5 1 7,2 1

Text written by user:

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 compuational 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 werkloosheidsgraad[t] = + 8.24455407969638 -0.587855787476284generatiepact[t] -0.0593358633776172M1[t] -0.0646299810246668M2[t] + 0.210075901328274M3[t] + 0.164781783681214M4[t] -0.000512333965844012M5[t] -0.308235294117646M6[t] -0.393529411764705M7[t] -0.418823529411764M8[t] + 0.135882352941177M9[t] + 0.270588235294118M10[t] + 0.185294117647059M11[t] + 0.00529411764705896t + 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) 8.24455407969638 0.205935 40.0348 0 0 generatiepact -0.587855787476284 0.17735 -3.3147 0.001795 0.000898 M1 -0.0593358633776172 0.237197 -0.2502 0.803581 0.401791 M2 -0.0646299810246668 0.236768 -0.273 0.7861 0.39305 M3 0.210075901328274 0.236434 0.8885 0.378885 0.189442 M4 0.164781783681214 0.236195 0.6977 0.488907 0.244454 M5 -0.000512333965844012 0.236052 -0.0022 0.998278 0.499139 M6 -0.308235294117646 0.23678 -1.3018 0.199474 0.099737 M7 -0.393529411764705 0.236255 -1.6657 0.102571 0.051286 M8 -0.418823529411764 0.235825 -1.776 0.082349 0.041175 M9 0.135882352941177 0.23549 0.577 0.566739 0.28337 M10 0.270588235294118 0.23525 1.1502 0.256 0.128 M11 0.185294117647059 0.235106 0.7881 0.434664 0.217332 t 0.00529411764705896 0.004751 1.1144 0.270915 0.135458 Multiple Linear Regression - Regression Statistics Multiple R 0.701757416636056 R-squared 0.492463471803710 Adjusted R-squared 0.349029235574324 F-TEST (value) 3.43337465830781 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.000966121413879861 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.371659585225281 Sum Squared Residuals 6.35401897533209 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 8.19051233396588 -0.190512333965881 2 8.1 8.19051233396584 -0.0905123339658426 3 8.3 8.47051233396584 -0.170512333965841 4 8.2 8.43051233396584 -0.230512333965842 5 8.1 8.27051233396584 -0.170512333965842 6 7.7 7.9680834914611 -0.268083491461098 7 7.6 7.8880834914611 -0.288083491461098 8 7.7 7.8680834914611 -0.168083491461099 9 8.2 8.4280834914611 -0.228083491461098 10 8.4 8.5680834914611 -0.168083491461098 11 8.4 8.4880834914611 -0.0880834914610977 12 8.6 8.3080834914611 0.291916508538901 13 8.4 8.25404174573054 0.145958254269460 14 8.5 8.25404174573055 0.245958254269451 15 8.7 8.53404174573055 0.165958254269450 16 8.7 8.49404174573055 0.205958254269450 17 8.6 8.33404174573055 0.265958254269450 18 7.4 8.0316129032258 -0.631612903225806 19 7.3 7.9516129032258 -0.651612903225806 20 7.4 7.9316129032258 -0.531612903225806 21 9 8.4916129032258 0.508387096774194 22 9.2 8.6316129032258 0.568387096774194 23 9.2 8.5516129032258 0.648387096774194 24 8.5 8.3716129032258 0.128387096774194 25 8.3 8.31757115749525 -0.0175711574952469 26 8.3 8.31757115749526 -0.0175711574952562 27 8.6 8.59757115749526 0.00242884250474315 28 8.6 8.55757115749526 0.0424288425047433 29 8.5 8.39757115749526 0.102428842504743 30 8.1 8.09514231499051 0.00485768500948562 31 8.1 8.01514231499051 0.0848576850094862 32 8 7.99514231499051 0.00485768500948614 33 8.6 8.55514231499051 0.0448576850094861 34 8.7 8.69514231499051 0.00485768500948601 35 8.7 8.61514231499051 0.0848576850094858 36 8.6 8.43514231499051 0.164857685009486 37 8.4 8.38110056925995 0.0188994307400452 38 8.4 8.38110056925996 0.0188994307400359 39 8.7 8.66110056925996 0.0388994307400352 40 8.7 8.62110056925996 0.0788994307400353 41 8.5 8.46110056925996 0.0388994307400358 42 8.3 7.57081593927894 0.729184060721063 43 8.3 7.49081593927894 0.809184060721064 44 8.3 7.47081593927894 0.829184060721063 45 8.1 8.03081593927894 0.0691840607210626 46 8.2 8.17081593927894 0.0291840607210625 47 8.1 8.09081593927894 0.00918406072106273 48 8.1 7.91081593927894 0.189184060721063 49 7.9 7.85677419354838 0.0432258064516217 50 7.7 7.85677419354839 -0.156774193548388 51 8.1 8.13677419354839 -0.036774193548388 52 8 8.09677419354839 -0.0967741935483874 53 7.7 7.93677419354839 -0.236774193548388 54 7.8 7.63434535104365 0.165654648956355 55 7.6 7.55434535104364 0.045654648956355 56 7.4 7.53434535104364 -0.134345351043645 57 7.7 8.09434535104364 -0.394345351043644 58 7.8 8.23434535104365 -0.434345351043644 59 7.5 8.15434535104364 -0.654345351043644 60 7.2 7.97434535104364 -0.774345351043644

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/1ptyq1198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/1ptyq1198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/2fsjx1198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/2fsjx1198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/3snsk1198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/3snsk1198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/4l6vu1198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/4l6vu1198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/5z99d1198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/5z99d1198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/631fc1198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/631fc1198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/7syz71198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/7syz71198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/814mw1198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/814mw1198533873.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/9qxu31198533873.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/24/t1198532756w3ir8yrvr0fzbcf/9qxu31198533873.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) 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)) 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$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$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() 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')

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