Home » date » 2007 » Nov » 15 » attachments

Workshop6-q3a

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

Title produced by software: Multiple Regression

Date of computation: Thu, 15 Nov 2007 15:08:49 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0.htm/, Retrieved Thu, 15 Nov 2007 23:05:32 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

36.409 0 33.163 0 34.122 0 35.225 0 28.249 0 30.374 0 26.311 0 22.069 0 23.651 0 28.628 0 23.187 0 14.727 0 43.080 0 32.519 0 39.657 0 33.614 0 28.671 0 34.243 0 27.336 0 22.916 0 24.537 0 26.128 0 22.602 0 15.744 0 41.086 0 39.690 0 43.129 0 37.863 0 35.953 0 29.133 0 24.693 0 22.205 0 21.725 0 27.192 0 21.790 0 13.253 0 37.702 0 30.364 0 32.609 0 30.212 0 29.965 0 28.352 0 25.814 0 22.414 0 20.506 0 28.806 0 22.228 0 13.971 0 36.845 0 35.338 0 35.022 0 34.777 0 26.887 0 23.970 0 22.780 0 17.351 0 21.382 0 24.561 0 17.409 0 11.514 0 31.514 0 27.071 0 29.462 0 26.105 0 22.397 0 23.843 0 21.705 0 18.089 0 20.764 0 25.316 0 17.704 0 15.548 0 28.029 0 29.383 0 36.438 0 32.034 0 22.679 0 24.319 0 18.004 0 17.537 0 20.366 0 22.782 0 19.169 0 13.807 0 29.743 0 25.591 0 29.096 1 26.482 1 22.405 1 27.044 1 17.970 1 18.730 1 19.684 1 19.785 1 18.479 1 10.698 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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Inschr_pw[t] = + 26.6866511627907 -5.6493511627907Olieprijzen[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) 26.6866511627907 0.770439 34.6383 0 0 Olieprijzen -5.6493511627907 2.387117 -2.3666 0.020004 0.010002 Multiple Linear Regression - Regression Statistics Multiple R 0.237133822191584 R-squared 0.0562324496271896 Adjusted R-squared 0.0461923693040746 F-TEST (value) 5.60079678822162 F-TEST (DF numerator) 1 F-TEST (DF denominator) 94 p-value 0.0200037728180864 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.14475387336817 Sum Squared Residuals 4798.46574363488 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 36.409 26.6866511627906 9.72234883720936 2 33.163 26.6866511627907 6.4763488372093 3 34.122 26.6866511627907 7.4353488372093 4 35.225 26.6866511627907 8.5383488372093 5 28.249 26.6866511627907 1.5623488372093 6 30.374 26.6866511627907 3.6873488372093 7 26.311 26.6866511627907 -0.375651162790698 8 22.069 26.6866511627907 -4.6176511627907 9 23.651 26.6866511627907 -3.0356511627907 10 28.628 26.6866511627907 1.94134883720930 11 23.187 26.6866511627907 -3.4996511627907 12 14.727 26.6866511627907 -11.9596511627907 13 43.08 26.6866511627907 16.3933488372093 14 32.519 26.6866511627907 5.8323488372093 15 39.657 26.6866511627907 12.9703488372093 16 33.614 26.6866511627907 6.9273488372093 17 28.671 26.6866511627907 1.9843488372093 18 34.243 26.6866511627907 7.5563488372093 19 27.336 26.6866511627907 0.649348837209301 20 22.916 26.6866511627907 -3.7706511627907 21 24.537 26.6866511627907 -2.1496511627907 22 26.128 26.6866511627907 -0.558651162790697 23 22.602 26.6866511627907 -4.0846511627907 24 15.744 26.6866511627907 -10.9426511627907 25 41.086 26.6866511627907 14.3993488372093 26 39.69 26.6866511627907 13.0033488372093 27 43.129 26.6866511627907 16.4423488372093 28 37.863 26.6866511627907 11.1763488372093 29 35.953 26.6866511627907 9.2663488372093 30 29.133 26.6866511627907 2.4463488372093 31 24.693 26.6866511627907 -1.99365116279070 32 22.205 26.6866511627907 -4.4816511627907 33 21.725 26.6866511627907 -4.9616511627907 34 27.192 26.6866511627907 0.505348837209302 35 21.79 26.6866511627907 -4.8966511627907 36 13.253 26.6866511627907 -13.4336511627907 37 37.702 26.6866511627907 11.0153488372093 38 30.364 26.6866511627907 3.6773488372093 39 32.609 26.6866511627907 5.9223488372093 40 30.212 26.6866511627907 3.5253488372093 41 29.965 26.6866511627907 3.2783488372093 42 28.352 26.6866511627907 1.66534883720930 43 25.814 26.6866511627907 -0.872651162790698 44 22.414 26.6866511627907 -4.2726511627907 45 20.506 26.6866511627907 -6.1806511627907 46 28.806 26.6866511627907 2.11934883720930 47 22.228 26.6866511627907 -4.4586511627907 48 13.971 26.6866511627907 -12.7156511627907 49 36.845 26.6866511627907 10.1583488372093 50 35.338 26.6866511627907 8.6513488372093 51 35.022 26.6866511627907 8.3353488372093 52 34.777 26.6866511627907 8.0903488372093 53 26.887 26.6866511627907 0.200348837209303 54 23.97 26.6866511627907 -2.7166511627907 55 22.78 26.6866511627907 -3.9066511627907 56 17.351 26.6866511627907 -9.3356511627907 57 21.382 26.6866511627907 -5.3046511627907 58 24.561 26.6866511627907 -2.12565116279070 59 17.409 26.6866511627907 -9.2776511627907 60 11.514 26.6866511627907 -15.1726511627907 61 31.514 26.6866511627907 4.8273488372093 62 27.071 26.6866511627907 0.384348837209304 63 29.462 26.6866511627907 2.7753488372093 64 26.105 26.6866511627907 -0.581651162790697 65 22.397 26.6866511627907 -4.2896511627907 66 23.843 26.6866511627907 -2.8436511627907 67 21.705 26.6866511627907 -4.9816511627907 68 18.089 26.6866511627907 -8.5976511627907 69 20.764 26.6866511627907 -5.9226511627907 70 25.316 26.6866511627907 -1.3706511627907 71 17.704 26.6866511627907 -8.9826511627907 72 15.548 26.6866511627907 -11.1386511627907 73 28.029 26.6866511627907 1.34234883720930 74 29.383 26.6866511627907 2.6963488372093 75 36.438 26.6866511627907 9.7513488372093 76 32.034 26.6866511627907 5.3473488372093 77 22.679 26.6866511627907 -4.0076511627907 78 24.319 26.6866511627907 -2.3676511627907 79 18.004 26.6866511627907 -8.6826511627907 80 17.537 26.6866511627907 -9.1496511627907 81 20.366 26.6866511627907 -6.3206511627907 82 22.782 26.6866511627907 -3.9046511627907 83 19.169 26.6866511627907 -7.5176511627907 84 13.807 26.6866511627907 -12.8796511627907 85 29.743 26.6866511627907 3.0563488372093 86 25.591 26.6866511627907 -1.09565116279070 87 29.096 21.0373 8.0587 88 26.482 21.0373 5.4447 89 22.405 21.0373 1.3677 90 27.044 21.0373 6.0067 91 17.97 21.0373 -3.0673 92 18.73 21.0373 -2.3073 93 19.684 21.0373 -1.3533 94 19.785 21.0373 -1.2523 95 18.479 21.0373 -2.5583 96 10.698 21.0373 -10.3393

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/10e7x1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/10e7x1195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/24wej1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/24wej1195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/3o3x81195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/3o3x81195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/4xnud1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/4xnud1195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/5dwyq1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/5dwyq1195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/60avc1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/60avc1195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/71bhk1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/71bhk1195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/8vlcb1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/8vlcb1195164516.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/9ddpd1195164516.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t1195164328v4oyw0ha245lju0/9ddpd1195164516.ps (open in new window)

Parameters:

par1 = 1 ; par2 = Do not include Seasonal 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