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rente lening (zonder dummy variabele)

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

Title produced by software: Multiple Regression

Date of computation: Thu, 22 Nov 2007 11:44:39 -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/22/t1195756630i39ngpzdf9lzixq.htm/, Retrieved Thu, 22 Nov 2007 19:37:11 +0100

User-defined keywords:

Florence & Inge

Dataseries X:

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5,22 0 5,09 0 4,77 0 4,54 0 4,56 0 4,39 0 4,73 0 4,44 0 4,3 0 4,24 0 4,01 0 3,5 0 3,23 0 3,28 1 3,49 1 3,7 1 3,63 1 3,95 1 3,73 1 3,87 1 3,66 1 3,49 1 3,4 1 3,32 1 3,11 1 3,06 1 2,68 1 2,55 1 2,34 1 2,34 1 2,39 1 2,21 1 2,09 1 2,14 1 2,31 1 2,14 1 2,45 1 2,52 1 2,3 1 2,25 1 2,06 1 1,99 1 2,25 1 2,26 1 2,36 1 2,3 1 2,19 1 2,31 1 2,21 1 2,21 1 2,26 1 2,18 1 2,21 1 2,33 1 2,12 1 2,08 1 1,97 1 2,09 1 2,11 1 2,24 1 2,45 1 2,68 1 2,73 1 2,76 1 2,83 1 3,16 1 3,22 1 3,22 1 3,34 1 3,35 1 3,42 1 3,58 1 3,71 1 3,68 1 3,83 1 3,94 1 3,88 1 4,03 1 4,15 1 4,32 1 4,4 1 4,37 1 4,14 1 4,11 1 4,16 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 Rj1[t] = + 4.38615384615385 -1.44740384615385Ter[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) 4.38615384615385 0.203047 21.6017 0 0 Ter -1.44740384615385 0.220617 -6.5607 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.584374216130123 R-squared 0.341493224477695 Adjusted R-squared 0.333559407905137 F-TEST (value) 43.0427425885896 F-TEST (DF numerator) 1 F-TEST (DF denominator) 83 p-value 4.31809976664255e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.73209630672266 Sum Squared Residuals 44.4850951923077 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5.22 4.38615384615385 0.833846153846149 2 5.09 4.38615384615385 0.703846153846153 3 4.77 4.38615384615385 0.383846153846154 4 4.54 4.38615384615385 0.153846153846154 5 4.56 4.38615384615385 0.173846153846154 6 4.39 4.38615384615385 0.00384615384615394 7 4.73 4.38615384615385 0.343846153846155 8 4.44 4.38615384615385 0.0538461538461546 9 4.3 4.38615384615385 -0.0861538461538458 10 4.24 4.38615384615385 -0.146153846153845 11 4.01 4.38615384615385 -0.376153846153846 12 3.5 4.38615384615385 -0.886153846153846 13 3.23 4.38615384615385 -1.15615384615385 14 3.28 2.93875 0.34125 15 3.49 2.93875 0.55125 16 3.7 2.93875 0.76125 17 3.63 2.93875 0.69125 18 3.95 2.93875 1.01125 19 3.73 2.93875 0.79125 20 3.87 2.93875 0.93125 21 3.66 2.93875 0.72125 22 3.49 2.93875 0.55125 23 3.4 2.93875 0.46125 24 3.32 2.93875 0.38125 25 3.11 2.93875 0.17125 26 3.06 2.93875 0.12125 27 2.68 2.93875 -0.25875 28 2.55 2.93875 -0.38875 29 2.34 2.93875 -0.59875 30 2.34 2.93875 -0.59875 31 2.39 2.93875 -0.54875 32 2.21 2.93875 -0.72875 33 2.09 2.93875 -0.84875 34 2.14 2.93875 -0.79875 35 2.31 2.93875 -0.62875 36 2.14 2.93875 -0.79875 37 2.45 2.93875 -0.48875 38 2.52 2.93875 -0.41875 39 2.3 2.93875 -0.63875 40 2.25 2.93875 -0.68875 41 2.06 2.93875 -0.87875 42 1.99 2.93875 -0.94875 43 2.25 2.93875 -0.68875 44 2.26 2.93875 -0.67875 45 2.36 2.93875 -0.57875 46 2.3 2.93875 -0.63875 47 2.19 2.93875 -0.74875 48 2.31 2.93875 -0.62875 49 2.21 2.93875 -0.72875 50 2.21 2.93875 -0.72875 51 2.26 2.93875 -0.67875 52 2.18 2.93875 -0.75875 53 2.21 2.93875 -0.72875 54 2.33 2.93875 -0.60875 55 2.12 2.93875 -0.81875 56 2.08 2.93875 -0.85875 57 1.97 2.93875 -0.96875 58 2.09 2.93875 -0.84875 59 2.11 2.93875 -0.82875 60 2.24 2.93875 -0.69875 61 2.45 2.93875 -0.48875 62 2.68 2.93875 -0.25875 63 2.73 2.93875 -0.20875 64 2.76 2.93875 -0.178750000000000 65 2.83 2.93875 -0.108750000000000 66 3.16 2.93875 0.22125 67 3.22 2.93875 0.28125 68 3.22 2.93875 0.28125 69 3.34 2.93875 0.40125 70 3.35 2.93875 0.41125 71 3.42 2.93875 0.48125 72 3.58 2.93875 0.64125 73 3.71 2.93875 0.77125 74 3.68 2.93875 0.74125 75 3.83 2.93875 0.89125 76 3.94 2.93875 1.00125 77 3.88 2.93875 0.94125 78 4.03 2.93875 1.09125 79 4.15 2.93875 1.21125 80 4.32 2.93875 1.38125 81 4.4 2.93875 1.46125 82 4.37 2.93875 1.43125 83 4.14 2.93875 1.20125 84 4.11 2.93875 1.17125 85 4.16 2.93875 1.22125

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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