Home » date » 2007 » Nov » 29 » attachments

multiple regression aardolie-seizoenaliteit

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

Title produced by software: Multiple Regression

Date of computation: Thu, 29 Nov 2007 02:53:56 -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/Nov/29/t1196329444bvmwcj5hwzo5jps.htm/, Retrieved Thu, 29 Nov 2007 10:44:14 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

90.8 0 96.4 0 90 0 92.1 0 97.2 0 95.1 0 88.5 0 91 0 90.5 0 75 0 66.3 0 66 0 68.4 0 70.6 0 83.9 0 90.1 0 90.6 0 87.1 0 90.8 0 94.1 0 99.8 0 96.8 0 87 0 96.3 0 107.1 0 115.2 0 106.1 1 89.5 1 91.3 1 97.6 1 100.7 1 104.6 1 94.7 1 101.8 1 102.5 1 105.3 1 110.3 1 109.8 1 117.3 1 118.8 1 131.3 1 125.9 1 133.1 1 147 1 145.8 1 164.4 1 149.8 1 137.7 1 151.7 1 156.8 1 180 1 180.4 1 170.4 1 191.6 1 199.5 1 218.2 1 217.5 1 205 1 194 1 199.3 1 219.3 1 211.1 1 215.2 1 240.2 1 242.2 1 240.7 1 255.4 1 253 1 218.2 1 203.7 1 205.6 1 215.6 1

Text written by user:

paper

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Aardolie[t] = + 86.4925170068026 + 75.3112244897961Irakoorlog[t] + 0.451870748299337M1[t] + 2.50187074829923M2[t] -4.61666666666666M3[t] -1.51666666666667M4[t] + 0.466666666666678M5[t] + 2.96666666666668M6[t] + 7.96666666666666M7[t] + 14.6166666666667M8[t] + 7.71666666666667M9[t] + 4.41666666666667M10[t] -2.5M11[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) 86.4925170068026 20.019705 4.3204 6.1e-05 3e-05 Irakoorlog 75.3112244897961 11.250103 6.6943 0 0 M1 0.451870748299337 26.31712 0.0172 0.986359 0.493179 M2 2.50187074829923 26.31712 0.0951 0.924584 0.462292 M3 -4.61666666666666 26.25024 -0.1759 0.860997 0.430499 M4 -1.51666666666667 26.25024 -0.0578 0.954121 0.477061 M5 0.466666666666678 26.25024 0.0178 0.985876 0.492938 M6 2.96666666666668 26.25024 0.113 0.910402 0.455201 M7 7.96666666666666 26.25024 0.3035 0.762585 0.381292 M8 14.6166666666667 26.25024 0.5568 0.579756 0.289878 M9 7.71666666666667 26.25024 0.294 0.769816 0.384908 M10 4.41666666666667 26.25024 0.1683 0.86696 0.43348 M11 -2.5 26.25024 -0.0952 0.924449 0.462225 Multiple Linear Regression - Regression Statistics Multiple R 0.664553012068554 R-squared 0.441630705849387 Adjusted R-squared 0.328064069750957 F-TEST (value) 3.88873634917406 F-TEST (DF numerator) 12 F-TEST (DF denominator) 59 p-value 0.000215146292460888 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 45.4667497447671 Sum Squared Residuals 121966.294608844 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 90.8 86.944387755102 3.85561224489801 2 96.4 88.9943877551025 7.4056122448975 3 90 81.875850340136 8.12414965986397 4 92.1 84.975850340136 7.12414965986395 5 97.2 86.9591836734694 10.2408163265306 6 95.1 89.4591836734694 5.64081632653064 7 88.5 94.4591836734694 -5.95918367346938 8 91 101.109183673469 -10.1091836734694 9 90.5 94.2091836734694 -3.70918367346938 10 75 90.9091836734694 -15.9091836734694 11 66.3 83.9925170068027 -17.6925170068027 12 66 86.4925170068027 -20.4925170068027 13 68.4 86.944387755102 -18.5443877551021 14 70.6 88.994387755102 -18.3943877551019 15 83.9 81.875850340136 2.02414965986396 16 90.1 84.975850340136 5.12414965986395 17 90.6 86.9591836734694 3.64081632653062 18 87.1 89.4591836734694 -2.35918367346937 19 90.8 94.4591836734694 -3.65918367346937 20 94.1 101.109183673469 -7.00918367346936 21 99.8 94.2091836734694 5.59081632653062 22 96.8 90.9091836734694 5.89081632653062 23 87 83.9925170068027 3.00748299319730 24 96.3 86.4925170068027 9.8074829931973 25 107.1 86.944387755102 20.1556122448979 26 115.2 88.994387755102 26.2056122448981 27 106.1 157.187074829932 -51.087074829932 28 89.5 160.287074829932 -70.787074829932 29 91.3 162.270408163265 -70.9704081632653 30 97.6 164.770408163265 -67.1704081632653 31 100.7 169.770408163265 -69.0704081632653 32 104.6 176.420408163265 -71.8204081632653 33 94.7 169.520408163265 -74.8204081632653 34 101.8 166.220408163265 -64.4204081632653 35 102.5 159.303741496599 -56.8037414965987 36 105.3 161.803741496599 -56.5037414965986 37 110.3 162.255612244898 -51.955612244898 38 109.8 164.305612244898 -54.5056122448979 39 117.3 157.187074829932 -39.887074829932 40 118.8 160.287074829932 -41.487074829932 41 131.3 162.270408163265 -30.9704081632653 42 125.9 164.770408163265 -38.8704081632653 43 133.1 169.770408163265 -36.6704081632653 44 147 176.420408163265 -29.4204081632653 45 145.8 169.520408163265 -23.7204081632653 46 164.4 166.220408163265 -1.82040816326531 47 149.8 159.303741496599 -9.50374149659863 48 137.7 161.803741496599 -24.1037414965986 49 151.7 162.255612244898 -10.555612244898 50 156.8 164.305612244898 -7.50561224489786 51 180 157.187074829932 22.812925170068 52 180.4 160.287074829932 20.1129251700680 53 170.4 162.270408163265 8.1295918367347 54 191.6 164.770408163265 26.8295918367347 55 199.5 169.770408163265 29.7295918367347 56 218.2 176.420408163265 41.7795918367347 57 217.5 169.520408163265 47.9795918367347 58 205 166.220408163265 38.7795918367347 59 194 159.303741496599 34.6962585034014 60 199.3 161.803741496599 37.4962585034014 61 219.3 162.255612244898 57.044387755102 62 211.1 164.305612244898 46.7943877551021 63 215.2 157.187074829932 58.012925170068 64 240.2 160.287074829932 79.912925170068 65 242.2 162.270408163265 79.9295918367347 66 240.7 164.770408163265 75.9295918367346 67 255.4 169.770408163265 85.6295918367347 68 253 176.420408163265 76.5795918367347 69 218.2 169.520408163265 48.6795918367347 70 203.7 166.220408163265 37.4795918367347 71 205.6 159.303741496599 46.2962585034013 72 215.6 161.803741496599 53.7962585034014

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/136pp1196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/136pp1196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/204gs1196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/204gs1196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/3tfp71196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/3tfp71196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/4biqt1196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/4biqt1196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/5c8mi1196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/5c8mi1196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/61fb11196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/61fb11196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/7d17u1196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/7d17u1196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/8do7d1196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/8do7d1196330031.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/96ai31196330031.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/29/t1196329444bvmwcj5hwzo5jps/96ai31196330031.ps (open in new window)

Parameters:

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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