Home » date » 2007 » Nov » 16 » attachments

test WS8

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

Title produced by software: Multiple Regression

Date of computation: Fri, 16 Nov 2007 08:29:15 -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/16/t1195226651i4zflwn20msj26d.htm/, Retrieved Fri, 16 Nov 2007 16:24:21 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

97,5 0 92,4 0 106,5 0 91,3 0 85,2 0 77,7 0 102,5 0 104,8 0 107,1 0 94 0 44,7 0 105,9 0 99 0 88,5 0 103,3 1 84 1 76,7 1 76,5 1 93,6 1 96,5 1 107,4 1 93,6 1 44,1 1 108,8 1 90,7 1 100,7 1 90,1 1 82,9 1 71,9 1 79,8 1 91,1 0 103,5 0 107,7 0 92,9 0 49,1 0 109,1 0 89,2 0 96 0 109,4 0 90,1 0 82,7 0 74,5 0 89,6 0 112,5 0 113,1 0 87,6 0 58,5 0 105 1 94 0 100,8 0 105,9 0 88,9 0 82,7 0 72,5 0 97,4 0 113,8 0 109,1 0 93,4 0 56,3 0 106,9 0 103 0 104,6 0

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 y[t] = + 92.9555555555555 -4.39084967320261x[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) 92.9555555555555 2.450538 37.9327 0 0 x -4.39084967320261 4.67986 -0.9382 0.351881 0.17594 Multiple Linear Regression - Regression Statistics Multiple R 0.120247851916258 R-squared 0.0144595458904743 Adjusted R-squared -0.00196612834468435 F-TEST (value) 0.88030151356125 F-TEST (DF numerator) 1 F-TEST (DF denominator) 60 p-value 0.351880759002941 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.4387093647081 Sum Squared Residuals 16213.8699346405 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.5 92.9555555555559 4.54444444444408 2 92.4 92.9555555555555 -0.555555555555517 3 106.5 92.9555555555555 13.5444444444445 4 91.3 92.9555555555555 -1.65555555555555 5 85.2 92.9555555555555 -7.75555555555554 6 77.7 92.9555555555555 -15.2555555555555 7 102.5 92.9555555555555 9.54444444444445 8 104.8 92.9555555555555 11.8444444444444 9 107.1 92.9555555555555 14.1444444444444 10 94 92.9555555555555 1.04444444444445 11 44.7 92.9555555555555 -48.2555555555555 12 105.9 92.9555555555555 12.9444444444445 13 99 92.9555555555555 6.04444444444445 14 88.5 92.9555555555555 -4.45555555555555 15 103.3 88.564705882353 14.7352941176471 16 84 88.564705882353 -4.56470588235294 17 76.7 88.564705882353 -11.8647058823529 18 76.5 88.564705882353 -12.0647058823529 19 93.6 88.564705882353 5.03529411764705 20 96.5 88.564705882353 7.93529411764706 21 107.4 88.564705882353 18.8352941176471 22 93.6 88.564705882353 5.03529411764705 23 44.1 88.564705882353 -44.4647058823529 24 108.8 88.564705882353 20.2352941176471 25 90.7 88.564705882353 2.13529411764706 26 100.7 88.564705882353 12.1352941176471 27 90.1 88.564705882353 1.53529411764705 28 82.9 88.564705882353 -5.66470588235294 29 71.9 88.564705882353 -16.6647058823529 30 79.8 88.564705882353 -8.76470588235295 31 91.1 92.9555555555555 -1.85555555555555 32 103.5 92.9555555555555 10.5444444444445 33 107.7 92.9555555555555 14.7444444444445 34 92.9 92.9555555555555 -0.0555555555555426 35 49.1 92.9555555555555 -43.8555555555555 36 109.1 92.9555555555555 16.1444444444444 37 89.2 92.9555555555555 -3.75555555555554 38 96 92.9555555555555 3.04444444444445 39 109.4 92.9555555555555 16.4444444444445 40 90.1 92.9555555555555 -2.85555555555555 41 82.7 92.9555555555555 -10.2555555555555 42 74.5 92.9555555555555 -18.4555555555555 43 89.6 92.9555555555555 -3.35555555555555 44 112.5 92.9555555555555 19.5444444444445 45 113.1 92.9555555555555 20.1444444444444 46 87.6 92.9555555555555 -5.35555555555555 47 58.5 92.9555555555555 -34.4555555555555 48 105 88.564705882353 16.4352941176471 49 94 92.9555555555555 1.04444444444445 50 100.8 92.9555555555555 7.84444444444445 51 105.9 92.9555555555555 12.9444444444445 52 88.9 92.9555555555555 -4.05555555555554 53 82.7 92.9555555555555 -10.2555555555555 54 72.5 92.9555555555555 -20.4555555555555 55 97.4 92.9555555555555 4.44444444444446 56 113.8 92.9555555555555 20.8444444444444 57 109.1 92.9555555555555 16.1444444444444 58 93.4 92.9555555555555 0.444444444444457 59 56.3 92.9555555555555 -36.6555555555556 60 106.9 92.9555555555555 13.9444444444445 61 103 92.9555555555555 10.0444444444445 62 104.6 92.9555555555555 11.6444444444444

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/17u1y1195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/17u1y1195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/2ihs21195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/2ihs21195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/3pho71195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/3pho71195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/4onyy1195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/4onyy1195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/5s4vb1195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/5s4vb1195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/6mqz51195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/6mqz51195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/7a1ff1195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/7a1ff1195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/827lc1195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/827lc1195226951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/9soj01195226951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/16/t1195226651i4zflwn20msj26d/9soj01195226951.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