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Data X:
13 13 14 182 13 169 3 39 2 26 12 12 8 96 13 156 5 60 1 12 15 10 12 120 16 160 6 60 0 0 12 9 7 63 12 108 6 54 3 27 10 10 10 100 11 110 5 50 3 30 12 12 7 84 12 144 3 36 1 12 15 13 16 208 18 234 8 104 3 39 9 12 11 132 11 132 4 48 1 12 12 12 14 168 14 168 4 48 4 48 11 6 6 36 9 54 4 24 0 0 11 5 16 80 14 70 6 30 3 15 11 12 11 132 12 144 6 72 2 24 15 11 16 176 11 121 5 55 4 44 7 14 12 168 12 168 4 56 3 42 11 14 7 98 13 182 6 84 1 14 11 12 13 156 11 132 4 48 1 12 10 12 11 132 12 144 6 72 2 24 14 11 15 165 16 176 6 66 3 33 10 11 7 77 9 99 4 44 1 11 6 7 9 63 11 77 4 28 1 7 11 9 7 63 13 117 2 18 2 18 15 11 14 154 15 165 7 77 3 33 11 11 15 165 10 110 5 55 4 44 12 12 7 84 11 132 4 48 2 24 14 12 15 180 13 156 6 72 1 12 15 11 17 187 16 176 6 66 2 22 9 11 15 165 15 165 7 77 2 22 13 8 14 112 14 112 5 40 4 32 13 9 14 126 14 126 6 54 2 18 16 12 8 96 14 168 4 48 3 36 13 10 8 80 8 80 4 40 3 30 12 10 14 140 13 130 7 70 3 30 14 12 14 168 15 180 7 84 4 48 11 8 8 64 13 104 4 32 2 16 9 12 11 132 11 132 4 48 2 24 16 11 16 176 15 165 6 66 4 44 12 12 10 120 15 180 6 72 3 36 10 7 8 56 9 63 5 35 4 28 13 11 14 154 13 143 6 66 2 22 16 11 16 176 16 176 7 77 5 55 14 12 13 156 13 156 6 72 3 36 15 9 5 45 11 99 3 27 1 9 5 15 8 120 12 180 3 45 1 15 8 11 10 110 12 132 4 44 1 11 11 11 8 88 12 132 6 66 2 22 16 11 13 143 14 154 7 77 3 33 17 11 15 165 14 154 5 55 9 99 9 15 6 90 8 120 4 60 0 0 9 11 12 132 13 143 5 55 0 0 13 12 16 192 16 192 6 72 2 24 10 12 5 60 13 156 6 72 2 24 6 9 15 135 11 99 6 54 3 27 12 12 12 144 14 168 5 60 1 12 8 12 8 96 13 156 4 48 2 24 14 13 13 169 13 169 5 65 0 0 12 11 14 154 13 143 5 55 5 55 11 9 12 108 12 108 4 36 2 18 16 9 16 144 16 144 6 54 4 36 8 11 10 110 15 165 2 22 3 33 15 11 15 165 15 165 8 88 0 0 7 12 8 96 12 144 3 36 0 0 16 12 16 192 14 168 6 72 4 48 14 9 19 171 12 108 6 54 1 9 16 11 14 154 15 165 6 66 1 11 9 9 6 54 12 108 5 45 4 36 14 12 13 156 13 156 5 60 2 24 11 12 15 180 12 144 6 72 4 48 13 12 7 84 12 144 5 60 1 12 15 12 13 156 13 156 6 72 4 48 5 14 4 56 5 70 2 28 2 28 15 11 14 154 13 143 5 55 5 55 13 12 13 156 13 156 5 60 4 48 11 11 11 121 14 154 5 55 4 44 11 6 14 84 17 102 6 36 4 24 12 10 12 120 13 130 6 60 4 40 12 12 15 180 13 156 6 72 3 36 12 13 14 182 12 156 5 65 3 39 12 8 13 104 13 104 5 40 3 24 14 12 8 96 14 168 4 48 2 24 6 12 6 72 11 132 2 24 1 12 7 12 7 84 12 144 4 48 1 12 14 6 13 78 12 72 6 36 5 30 14 11 13 143 16 176 6 66 4 44 10 10 11 110 12 120 5 50 2 20 13 12 5 60 12 144 3 36 3 36 12 13 12 156 12 156 6 78 2 26 9 11 8 88 10 110 4 44 2 22 12 7 11 77 15 105 5 35 2 14 16 11 14 154 15 165 8 88 2 22 10 11 9 99 12 132 4 44 3 33 14 11 10 110 16 176 6 66 2 22 10 11 13 143 15 165 6 66 3 33 16 12 16 192 16 192 7 84 4 48 15 10 16 160 13 130 6 60 3 30 12 11 11 121 12 132 5 55 3 33 10 12 8 96 11 132 4 48 0 0 8 7 4 28 13 91 6 42 1 7 8 13 7 91 10 130 3 39 2 26 11 8 14 112 15 120 5 40 2 16 13 12 11 132 13 156 6 72 3 36 16 11 17 187 16 176 7 77 4 44 16 12 15 180 15 180 7 84 4 48 14 14 17 238 18 252 6 84 1 14 11 10 5 50 13 130 3 30 2 20 4 10 4 40 10 100 2 20 2 20 14 13 10 130 16 208 8 104 3 39 9 10 11 110 13 130 3 30 3 30 14 11 15 165 15 165 8 88 3 33 8 10 10 100 14 140 3 30 1 10 8 7 9 63 15 105 4 28 1 7 11 10 12 120 14 140 5 50 1 10 12 8 15 120 13 104 7 56 1 8 11 12 7 84 13 156 6 72 0 0 14 12 13 156 15 180 6 72 1 12 15 12 12 144 16 192 7 84 3 36 16 11 14 154 14 154 6 66 3 33 16 12 14 168 14 168 6 72 0 0 11 12 8 96 16 192 6 72 2 24 14 12 15 180 14 168 6 72 5 60 14 11 12 132 12 132 4 44 2 22 12 12 12 144 13 156 4 48 3 36 14 11 16 176 12 132 5 55 3 33 8 11 9 99 12 132 4 44 5 55 13 13 15 195 14 182 6 78 4 52 16 12 15 180 14 168 6 72 4 48 12 12 6 72 14 168 5 60 0 0 16 12 14 168 16 192 8 96 3 36 12 12 15 180 13 156 6 72 0 0 11 8 10 80 14 112 5 40 2 16 4 8 6 48 4 32 4 32 0 0 16 12 14 168 16 192 8 96 6 72 15 11 12 132 13 143 6 66 3 33 10 12 8 96 16 192 4 48 1 12 13 13 11 143 15 195 6 78 6 78 15 12 13 156 14 168 6 72 2 24 12 12 9 108 13 156 4 48 1 12 14 11 15 165 14 154 6 66 3 33 7 12 13 156 12 144 3 36 1 12 19 12 15 180 15 180 6 72 2 24 12 10 14 140 14 140 5 50 4 40 12 11 16 176 13 143 4 44 1 11 13 12 14 168 14 168 6 72 2 24 15 12 14 168 16 192 4 48 0 0 8 10 10 100 6 60 4 40 5 50 12 12 10 120 13 156 4 48 2 24 10 13 4 52 13 169 6 78 1 13 8 12 8 96 14 168 5 60 1 12 10 15 15 225 15 225 6 90 4 60 15 11 16 176 14 154 6 66 3 33 16 12 12 144 15 180 8 96 0 0 13 11 12 132 13 143 7 77 3 33 16 12 15 180 16 192 7 84 3 36 9 11 9 99 12 132 4 44 0 0 14 10 12 120 15 150 6 60 2 20 14 11 14 154 12 132 6 66 5 55 12 11 11 121 14 154 2 22 2 22
Names of X columns:
Popularity FindingFriends KnowingPeople friends_knowning Liked friends_liked Celebrity friends_celeb Sum friends_sum
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) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test 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)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$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() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() 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') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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