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Mukltiple regression aardolie-irakoorlog

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 02:50:26 -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/t1196329234lnj2wucdxput9t8.htm/, Retrieved Thu, 29 Nov 2007 10:40:45 +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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Aardolie[t] = + 89.103846153846 + 75.4591973244149Irakoorlog[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) 89.103846153846 8.249138 10.8016 0 0 Irakoorlog 75.4591973244149 10.320385 7.3117 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.65804011806457 R-squared 0.433016796982433 Adjusted R-squared 0.424917036939325 F-TEST (value) 53.4604475537367 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 3.36486394303392e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 42.0625141544103 Sum Squared Residuals 123847.856789298 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 90.8 89.103846153846 1.69615384615394 2 96.4 89.1038461538467 7.29615384615331 3 90 89.1038461538461 0.896153846153871 4 92.1 89.1038461538461 2.99615384615387 5 97.2 89.1038461538461 8.09615384615387 6 95.1 89.1038461538461 5.99615384615386 7 88.5 89.1038461538461 -0.603846153846129 8 91 89.1038461538461 1.89615384615387 9 90.5 89.1038461538461 1.39615384615387 10 75 89.1038461538461 -14.1038461538461 11 66.3 89.1038461538461 -22.8038461538461 12 66 89.1038461538461 -23.1038461538461 13 68.4 89.1038461538461 -20.7038461538461 14 70.6 89.1038461538461 -18.5038461538461 15 83.9 89.1038461538461 -5.20384615384612 16 90.1 89.1038461538461 0.996153846153865 17 90.6 89.1038461538461 1.49615384615387 18 87.1 89.1038461538461 -2.00384615384613 19 90.8 89.1038461538461 1.69615384615387 20 94.1 89.1038461538461 4.99615384615386 21 99.8 89.1038461538461 10.6961538461539 22 96.8 89.1038461538461 7.69615384615387 23 87 89.1038461538461 -2.10384615384613 24 96.3 89.1038461538461 7.19615384615387 25 107.1 89.1038461538461 17.9961538461539 26 115.2 89.1038461538461 26.0961538461539 27 106.1 164.563043478261 -58.4630434782609 28 89.5 164.563043478261 -75.0630434782609 29 91.3 164.563043478261 -73.2630434782609 30 97.6 164.563043478261 -66.9630434782609 31 100.7 164.563043478261 -63.8630434782609 32 104.6 164.563043478261 -59.9630434782609 33 94.7 164.563043478261 -69.8630434782609 34 101.8 164.563043478261 -62.7630434782609 35 102.5 164.563043478261 -62.0630434782609 36 105.3 164.563043478261 -59.2630434782609 37 110.3 164.563043478261 -54.2630434782609 38 109.8 164.563043478261 -54.7630434782609 39 117.3 164.563043478261 -47.2630434782609 40 118.8 164.563043478261 -45.7630434782609 41 131.3 164.563043478261 -33.2630434782609 42 125.9 164.563043478261 -38.6630434782609 43 133.1 164.563043478261 -31.4630434782609 44 147 164.563043478261 -17.5630434782609 45 145.8 164.563043478261 -18.7630434782609 46 164.4 164.563043478261 -0.163043478260863 47 149.8 164.563043478261 -14.7630434782609 48 137.7 164.563043478261 -26.8630434782609 49 151.7 164.563043478261 -12.8630434782609 50 156.8 164.563043478261 -7.76304347826086 51 180 164.563043478261 15.4369565217391 52 180.4 164.563043478261 15.8369565217391 53 170.4 164.563043478261 5.83695652173914 54 191.6 164.563043478261 27.0369565217391 55 199.5 164.563043478261 34.9369565217391 56 218.2 164.563043478261 53.6369565217391 57 217.5 164.563043478261 52.9369565217391 58 205 164.563043478261 40.4369565217391 59 194 164.563043478261 29.4369565217391 60 199.3 164.563043478261 34.7369565217391 61 219.3 164.563043478261 54.7369565217391 62 211.1 164.563043478261 46.5369565217391 63 215.2 164.563043478261 50.6369565217391 64 240.2 164.563043478261 75.6369565217391 65 242.2 164.563043478261 77.6369565217391 66 240.7 164.563043478261 76.1369565217391 67 255.4 164.563043478261 90.8369565217391 68 253 164.563043478261 88.4369565217391 69 218.2 164.563043478261 53.6369565217391 70 203.7 164.563043478261 39.1369565217391 71 205.6 164.563043478261 41.0369565217391 72 215.6 164.563043478261 51.0369565217391

Charts produced by software:

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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')

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Software written by Ed van Stee & Patrick Wessa

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