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Data X:
106 87 1 65.3 170 2.2 70 1 65.73 165 62.3 75 1 69.44 168 14.7 79 1 73.74 170 5 64.5 1 74.31 157 74.4 75 0 70.53 146 66.1 70 0 69.42 149 22 67 1 69.77 159 3.4 52 0 65.47 151 0.3 67.2 1 66.2 174 53.2 47 0 70.46 156 0 46.4 0 74.44 151.5 57.2 76 0 69.28 146 9.2 71.6 1 67.67 157 15.9 63.8 1 67.22 171.5 17.6 48.2 1 64.85 150 21 64.5 1 71.35 170 7.6 75.9 1 72.28 164.5 71.6 80 1 71.87 163 12.9 56 1 67.34 162.5 10.5 75.5 0 73.5 161 25.7 77 1 64.91 166.5 26.8 88 0 68.13 160 7.3 48 0 72.5 147 17.1 73 1 72.36 162.5 27.3 72 1 70.59 161 16.5 64 1 74.76 163.5 5.4 76 0 65.63 161 5.6 67.4 1 67.04 172.5 36.5 73.7 1 66.72 169.5 1.1 59.2 0 65.8 158 3.9 53 0 72.44 153.5 34.2 41.9 1 71.83 165.5 40.3 65.5 1 72.67 153.5 15.6 63 1 69.56 157.5 15.5 54 0 67 145.5 52.9 77.7 0 68.86 156 1.6 47.6 0 71.25 163 14.2 53.1 1 69.88 159 7.5 55.5 1 67.18 167 2 64 1 67.47 157.5 71.4 75.6 1 73.2 156 3.2 57 0 69.6 156.5 20 63 0 71.24 148.5 2.8 59.5 1 73.83 162.5 15.3 84.5 1 66.07 164 8 59.9 0 70.68 152 36.6 60 1 74.01 157.5 3.8 64 0 68.53 148 25.5 54 0 66.72 145.5 3.2 53.8 0 72.69 154.5 33.1 84 1 67.46 166.5 42 63.2 0 73.81 157 16.2 54.3 1 72.96 150 0 60 0 71.65 152 22.7 68 1 72.79 171 36.4 74 1 73.83 165.5 69 74 1 66.74 165 11.2 68.5 1 65.62 168.5 12.5 76 0 66.18 154 51.7 83 0 67.78 156.5 3.6 62.5 0 68.84 152 22.2 57 1 65.27 164.5 39.2 85 1 72.84 161 27.9 50 1 75.36 162 58.8 53 1 76.88 169 1 57 0 76.51 150 4.7 46 1 80.63 146 25.6 65.4 1 75.27 165 5.3 71.4 1 81.19 165.5 38.7 41 1 81.3 164 31.6 66 1 77.77 163 19.3 69.5 1 75.51 167.5 26.5 59 1 78.64 166 12.8 80 1 80.68 167.5 18.3 72 1 77.4 162 13.2 73 0 80.71 165 36 66.4 0 83.16 145 34.1 37 0 87.99 139 71.5 70 1 72.21 164 43.3 75 1 70.24 167 47.7 54 1 66.06 163 74.9 76.2 1 68.67 162.5 0.9 74.9 1 68.77 159.5 35.9 98 1 68.07 169 45.8 86.5 0 67.33 152.5 54.2 72.8 1 69.47 165 34 65 1 70.81 166 7.9 50 1 73.17 163 54.5 81 1 71.28 167.5 8.2 52 0 69.47 157.5 49.3 68 1 65.31 160 46.9 58.5 1 70.23 162 16.8 65.5 1 73.23 164.5 2.8 62.5 0 68.67 150 60.9 64 1 72.66 167 5.6 55.7 0 74.79 155 6.6 84 1 73.04 173.5 22.9 63.7 1 69.95 173 51.1 65 0 67.51 156 23.3 87.5 0 67.5 149.5 11.5 79 1 71.32 167 79.1 58.5 0 71.23 146 53.6 75 1 67.49 166 1.5 52.5 0 68.62 151.5 40.4 57.5 1 72.53 164 25.4 70 1 66.67 160 6.7 72 1 66.19 152.5 76 88 1 78.4 160 0.6 58 1 75.67 163 43.4 73 1 76.07 168 13 56 1 82.88 165.5 27.8 49 0 77.14 147 6.5 54.7 0 77.31 158 7.1 67 1 76.58 168 6 47 0 82.86 154.5 6.5 47 0 76.64 147
Names of X columns:
y weight sex age height
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
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$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) 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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Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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