Aanslagen Nigeria Brent-olie

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

Title produced by software: Multiple Regression

Date of computation: Thu, 03 Jan 2008 08:18:54 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Jan/03/t1199373582vuj5mducnxkym69.htm/, Retrieved Thu, 03 Jan 2008 16:19:43 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

25.62 0 27.5 0 24.5 0 25.66 0 28.31 0 27.85 1 24.61 0 25.68 0 25.62 1 20.54 1 18.8 0 18.71 0 19.46 0 20.12 0 23.54 0 25.6 0 25.39 0 24.09 0 25.69 0 26.56 0 28.33 0 27.5 0 24.23 0 28.23 1 31.29 0 32.72 0 30.46 0 24.89 0 25.68 0 27.52 0 28.4 0 29.71 0 26.85 0 29.62 0 28.69 0 29.76 0 31.3 0 30.86 0 33.46 0 33.15 0 37.99 0 35.24 0 38.24 0 43.16 0 43.33 0 49.67 0 43.17 0 39.56 1 44.36 0 45.22 0 53.1 0 52.1 0 48.52 0 54.84 0 57.57 0 64.14 0 62.85 0 58.75 0 55.33 0 57.03 0 63.18 0 60.19 0 62.12 0 70.12 1 69.75 1 68.56 1 73.77 1 73.23 1 61.96 0 57.81 0 58.76 0 62.47 1 53.68 1 57.56 1 62.05 1 67.49 1 67.21 1 71.05 1 76.93 1 70.76 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 6 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Prijs_Brentolie[t] = + 9.3461699708377 + 4.29011878962999Aanslagen_Nigeria[t] + 3.96148337340126M1[t] + 4.05381423131363M2[t] + 5.54328794636885M3[t] + 5.66560183433408M4[t] + 5.55221840653215M5[t] + 5.17738943735452M6[t] + 7.42259440807117M7[t] + 7.90778240884068M8[t] + 5.61257083233004M9[t] + 4.10894930929002M10[t] + 1.67701425118834M11[t] + 0.661954856373349t + 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) 9.3461699708377 2.978561 3.1378 0.002544 0.001272 Aanslagen_Nigeria 4.29011878962999 1.991486 2.1542 0.034877 0.017439 M1 3.96148337340126 3.664931 1.0809 0.283668 0.141834 M2 4.05381423131363 3.666429 1.1057 0.272889 0.136444 M3 5.54328794636885 3.668262 1.5111 0.135524 0.067762 M4 5.66560183433408 3.628494 1.5614 0.123207 0.061604 M5 5.55221840653215 3.62981 1.5296 0.130891 0.065445 M6 5.17738943735452 3.609107 1.4345 0.156141 0.078071 M7 7.42259440807117 3.633458 2.0428 0.045065 0.022532 M8 7.90778240884068 3.63579 2.175 0.033219 0.01661 M9 5.61257083233004 3.794177 1.4793 0.143828 0.071914 M10 4.10894930929002 3.79608 1.0824 0.283005 0.141503 M11 1.67701425118834 3.868806 0.4335 0.666086 0.333043 t 0.661954856373349 0.03512 18.8481 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.94081361649493 R-squared 0.885130260982268 Adjusted R-squared 0.862504403296957 F-TEST (value) 39.1202964896624 F-TEST (DF numerator) 13 F-TEST (DF denominator) 66 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.48209176855291 Sum Squared Residuals 2773.15590393214 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.62 13.9696082006123 11.6503917993877 2 27.5 14.723893914898 12.776106085102 3 24.5 16.8753224863266 7.62467751367342 4 25.66 17.6595912306652 8.00040876933485 5 28.31 18.2081626592366 10.1018373407634 6 27.85 22.7854073360623 5.06459266393771 7 24.61 21.4024483735223 3.2075516264777 8 25.68 22.5495912306652 3.13040876933484 9 25.62 25.2064533001579 0.413546699842143 10 20.54 24.3647866334912 -3.82478663349118 11 18.8 18.3046876421329 0.495312357867139 12 18.71 17.2896282473179 1.42037175268212 13 19.46 21.9130664770925 -2.45306647709248 14 20.12 22.6673521913782 -2.54735219137819 15 23.54 24.8187807628068 -1.27878076280677 16 25.6 25.6030495071453 -0.00304950714534466 17 25.39 26.1516209357168 -0.761620935716772 18 24.09 26.4387468229125 -2.34874682291249 19 25.69 29.3459066500025 -3.65590665000248 20 26.56 30.4930495071453 -3.93304950714535 21 28.33 28.8597927870081 -0.529792787008056 22 27.5 28.0181261203414 -0.518126120341386 23 24.23 26.2481459186131 -2.01814591861305 24 28.23 29.5232053134280 -1.29320531342805 25 31.29 29.8565247535727 1.43347524642733 26 32.72 30.6108104678584 2.10918953214161 27 30.46 32.7622390392870 -2.30223903928695 28 24.89 33.5465077836255 -8.65650778362553 29 25.68 34.0950792121969 -8.41507921219695 30 27.52 34.3822050993927 -6.86220509939267 31 28.4 37.2893649264827 -8.88936492648267 32 29.71 38.4365077836255 -8.72650778362553 33 26.85 36.8032510634882 -9.95325106348824 34 29.62 35.9615843968216 -6.34158439682157 35 28.69 34.1916041950932 -5.50160419509324 36 29.76 33.1765448002783 -3.41654480027825 37 31.3 37.7999830300529 -6.49998303005286 38 30.86 38.5542687443386 -7.69426874433857 39 33.46 40.7056973157671 -7.24569731576714 40 33.15 41.4899660601057 -8.33996606010571 41 37.99 42.0385374886772 -4.04853748867715 42 35.24 42.3256633758729 -7.08566337587286 43 38.24 45.2328232029629 -6.99282320296286 44 43.16 46.3799660601057 -3.21996606010572 45 43.33 44.7467093399684 -1.41670933996843 46 49.67 43.9050426733018 5.76495732669824 47 43.17 42.1350624715734 1.03493752842658 48 39.56 45.4101218663884 -5.85012186638842 49 44.36 45.7434413065331 -1.38344130653305 50 45.22 46.4977270208188 -1.27772702081876 51 53.1 48.6491555922473 4.45084440775267 52 52.1 49.4334243365859 2.66657566341409 53 48.52 49.9819957651573 -1.46199576515733 54 54.84 50.2691216523531 4.57087834764695 55 57.57 53.176281479443 4.39371852055695 56 64.14 54.3234243365859 9.8165756634141 57 62.85 52.6901676164486 10.1598323835514 58 58.75 51.848500949782 6.90149905021805 59 55.33 50.0785207480536 5.25147925194638 60 57.03 49.0634613532386 7.96653864676138 61 63.18 53.6868995830132 9.49310041698677 62 60.19 54.441185297299 5.74881470270104 63 62.12 56.5926138687275 5.52738613127247 64 70.12 61.6670014026961 8.45299859730392 65 69.75 62.2155728312675 7.53442716873249 66 68.56 62.5026987184632 6.05730128153678 67 73.77 65.4098585455532 8.36014145444677 68 73.23 66.5570014026961 6.67299859730392 69 61.96 60.6336258929288 1.32637410707120 70 57.81 59.7919592262621 -1.98195922626214 71 58.76 58.0219790245338 0.738020975466195 72 62.47 61.2970384193488 1.17296158065120 73 53.68 65.9204766491234 -12.2404766491234 74 57.56 66.6747623634091 -9.11476236340912 75 62.05 68.8261909348377 -6.7761909348377 76 67.49 69.6104596791763 -2.12045967917627 77 67.21 70.1590311077477 -2.9490311077477 78 71.05 70.4461569949434 0.603843005056587 79 76.93 73.3533168220334 3.5766831779666 80 70.76 74.5004596791763 -3.74045967917626

Charts produced by software:

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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')

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Software written by Ed van Stee & Patrick Wessa

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