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Lineair Regressie Iran

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

Title produced by software: Multiple Regression

Date of computation: Mon, 17 Dec 2007 06:13:40 -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/Dec/17/t1197898015o8jisp79titomc5.htm/, Retrieved Mon, 17 Dec 2007 14:27:06 +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 0 24.61 0 25.68 0 25.62 0 20.54 0 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 0 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 0 44.36 0 45.22 0 53.1 0 52.1 0 48.52 0 54.84 1 57.57 1 64.14 1 62.85 1 58.75 1 55.33 1 57.03 1 63.18 1 60.19 1 62.12 1 70.12 1 69.75 1 68.56 1 73.77 1 73.23 1 61.96 1 57.81 1 58.76 1 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Brent[t] = + 15.3991514616441 + 14.3949587992937Iran[t] + 2.07831497519547M1[t] + 2.37793730205442M2[t] + 4.07470248605622M3[t] + 5.0171819557723M4[t] + 5.11108999691695M5[t] + 3.50000392387678M6[t] + 5.33962625073572M7[t] + 6.0321057204518M8[t] + 3.560656828947M9[t] + 2.26432677485355M10[t] -0.675336612573228M11[t] + 0.454663387426778t + 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) 15.3991514616441 2.494298 6.1737 0 0 Iran 14.3949587992937 2.154636 6.6809 0 0 M1 2.07831497519547 2.885458 0.7203 0.473901 0.23695 M2 2.37793730205442 2.883726 0.8246 0.412566 0.206283 M3 4.07470248605622 2.882663 1.4135 0.162202 0.081101 M4 5.0171819557723 2.88227 1.7407 0.086396 0.043198 M5 5.11108999691695 2.882547 1.7731 0.080823 0.040412 M6 3.50000392387678 2.888965 1.2115 0.230021 0.11501 M7 5.33962625073572 2.886739 1.8497 0.068833 0.034417 M8 6.0321057204518 2.885181 2.0907 0.040408 0.020204 M9 3.560656828947 2.993354 1.1895 0.238497 0.119248 M10 2.26432677485355 2.99174 0.7569 0.451828 0.225914 M11 -0.675336612573228 2.990771 -0.2258 0.822049 0.411025 t 0.454663387426778 0.043955 10.3438 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.962629332889965 R-squared 0.92665523254018 Adjusted R-squared 0.912208535919305 F-TEST (value) 64.1430533815784 F-TEST (DF numerator) 13 F-TEST (DF denominator) 66 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.1796083934521 Sum Squared Residuals 1770.67064522828 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.62 17.9321298242665 7.68787017573355 2 27.5 18.6864155385521 8.81358446144793 3 24.5 20.8378441099807 3.66215589001934 4 25.66 22.2349869671235 3.4250130328765 5 28.31 22.7835583956949 5.52644160430505 6 27.85 21.6271357100816 6.22286428991844 7 24.61 23.9214214243673 0.68857857563273 8 25.68 25.0685642815101 0.611435718489871 9 25.62 23.0517787774321 2.5682212225679 10 20.54 22.2101121107654 -1.67011211076543 11 18.8 19.7251121107654 -0.925112110765436 12 18.71 20.8551121107654 -2.14511211076543 13 19.46 23.3880904733877 -3.92809047338770 14 20.12 24.1423761876734 -4.02237618767342 15 23.54 26.293804759102 -2.75380475910199 16 25.6 27.6909476162449 -2.09094761624485 17 25.39 28.2395190448163 -2.84951904481627 18 24.09 27.0830963592029 -2.99309635920289 19 25.69 29.3773820734886 -3.68738207348860 20 26.56 30.5245249306315 -3.96452493063146 21 28.33 28.5077394265534 -0.177739426553438 22 27.5 27.6660727598868 -0.166072759886770 23 24.23 25.1810727598868 -0.951072759886767 24 28.23 26.3110727598868 1.91892724011323 25 31.29 28.844051122509 2.44594887749098 26 32.72 29.5983368367948 3.12166316320524 27 30.46 31.7497654082233 -1.28976540822332 28 24.89 33.1469082653662 -8.25690826536619 29 25.68 33.6954796939376 -8.0154796939376 30 27.52 32.5390570083242 -5.01905700832422 31 28.4 34.8333427226099 -6.43334272260994 32 29.71 35.9804855797528 -6.27048557975279 33 26.85 33.9637000756748 -7.11370007567476 34 29.62 33.1220334090081 -3.5020334090081 35 28.69 30.6370334090081 -1.9470334090081 36 29.76 31.7670334090081 -2.0070334090081 37 31.3 34.3000117716304 -3.00001177163035 38 30.86 35.0542974859161 -4.19429748591609 39 33.46 37.2057260573447 -3.74572605734466 40 33.15 38.6028689144875 -5.45286891448752 41 37.99 39.1514403430589 -1.16144034305894 42 35.24 37.9950176574456 -2.75501765744556 43 38.24 40.2893033717313 -2.04930337173127 44 43.16 41.4364462288741 1.72355377112587 45 43.33 39.4196607247961 3.91033927520389 46 49.67 38.5779940581294 11.0920059418706 47 43.17 36.0929940581294 7.07700594187056 48 39.56 37.2229940581294 2.33700594187057 49 44.36 39.7559724207517 4.60402757924831 50 45.22 40.5102581350374 4.70974186496258 51 53.1 42.661686706466 10.4383132935340 52 52.1 44.0588295636088 8.04117043639115 53 48.52 44.6074009921803 3.91259900781973 54 54.84 57.8459371058606 -3.00593710586058 55 57.57 60.1402228201463 -2.5702228201463 56 64.14 61.2873656772892 2.85263432271084 57 62.85 59.2705801732111 3.57941982678887 58 58.75 58.4289135065445 0.321086493455532 59 55.33 55.9439135065445 -0.613913506544466 60 57.03 57.0739135065445 -0.0439135065444644 61 63.18 59.6068918691667 3.57310813083328 62 60.19 60.3611775834525 -0.171177583452458 63 62.12 62.512606154881 -0.392606154881023 64 70.12 63.9097490120239 6.21025098797612 65 69.75 64.4583204405953 5.29167955940469 66 68.56 63.3018977549819 5.25810224501808 67 73.77 65.5961834692676 8.17381653073236 68 73.23 66.7433263264105 6.48667367358951 69 61.96 64.7265408223325 -2.76654082233247 70 57.81 63.8848741556658 -6.0748741556658 71 58.76 61.3998741556658 -2.6398741556658 72 62.47 62.5298741556658 -0.0598741556658028 73 53.68 65.062852518288 -11.3828525182881 74 57.56 65.8171382325738 -8.25713823257379 75 62.05 67.9685668040024 -5.91856680400236 76 67.49 69.3657096611452 -1.87570966114522 77 67.21 69.9142810897166 -2.70428108971665 78 71.05 68.7578584041033 2.29214159589674 79 76.93 71.052144118389 5.87785588161104 80 70.76 72.1992869755318 -1.43928697553182

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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