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Data X:
1 13 12 14 12 53 32 41 38 2 16 11 18 11 86 51 39 32 3 19 15 11 14 66 42 30 35 4 15 6 12 12 67 41 31 33 5 14 13 16 21 76 46 34 37 6 13 10 18 12 78 47 35 29 7 19 12 14 22 53 37 39 31 8 15 14 14 11 80 49 34 36 9 14 12 15 10 74 45 36 35 10 15 6 15 13 76 47 37 38 11 16 10 17 10 79 49 38 31 12 16 12 19 8 54 33 36 34 13 16 12 10 15 67 42 38 35 14 16 11 16 14 54 33 39 38 15 17 15 18 10 87 53 33 37 16 15 12 14 14 58 36 32 33 17 15 10 14 14 75 45 36 32 18 20 12 17 11 88 54 38 38 19 18 11 14 10 64 41 39 38 20 16 12 16 13 57 36 32 32 21 16 11 18 7 66 41 32 33 22 16 12 11 14 68 44 31 31 23 19 13 14 12 54 33 39 38 24 16 11 12 14 56 37 37 39 25 17 9 17 11 86 52 39 32 26 17 13 9 9 80 47 41 32 27 16 10 16 11 76 43 36 35 28 15 14 14 15 69 44 33 37 29 16 12 15 14 78 45 33 33 30 14 10 11 13 67 44 34 33 31 15 12 16 9 80 49 31 28 32 12 8 13 15 54 33 27 32 33 14 10 17 10 71 43 37 31 34 16 12 15 11 84 54 34 37 35 14 12 14 13 74 42 34 30 36 7 7 16 8 71 44 32 33 37 10 6 9 20 63 37 29 31 38 14 12 15 12 71 43 36 33 39 16 10 17 10 76 46 29 31 40 16 10 13 10 69 42 35 33 41 16 10 15 9 74 45 37 32 42 14 12 16 14 75 44 34 33 43 20 15 16 8 54 33 38 32 44 14 10 12 14 52 31 35 33 45 14 10 12 11 69 42 38 28 46 11 12 11 13 68 40 37 35 47 14 13 15 9 65 43 38 39 48 15 11 15 11 75 46 33 34 49 16 11 17 15 74 42 36 38 50 14 12 13 11 75 45 38 32 51 16 14 16 10 72 44 32 38 52 14 10 14 14 67 40 32 30 53 12 12 11 18 63 37 32 33 54 16 13 12 14 62 46 34 38 55 9 5 12 11 63 36 32 32 56 14 6 15 12 76 47 37 32 57 16 12 16 13 74 45 39 34 58 16 12 15 9 67 42 29 34 59 15 11 12 10 73 43 37 36 60 16 10 12 15 70 43 35 34 61 12 7 8 20 53 32 30 28 62 16 12 13 12 77 45 38 34 63 16 14 11 12 77 45 34 35 64 14 11 14 14 52 31 31 35 65 16 12 15 13 54 33 34 31 66 17 13 10 11 80 49 35 37 67 18 14 11 17 66 42 36 35 68 18 11 12 12 73 41 30 27 69 12 12 15 13 63 38 39 40 70 16 12 15 14 69 42 35 37 71 10 8 14 13 67 44 38 36 72 14 11 16 15 54 33 31 38 73 18 14 15 13 81 48 34 39 74 18 14 15 10 69 40 38 41 75 16 12 13 11 84 50 34 27 76 17 9 12 19 80 49 39 30 77 16 13 17 13 70 43 37 37 78 16 11 13 17 69 44 34 31 79 13 12 15 13 77 47 28 31 80 16 12 13 9 54 33 37 27 81 16 12 15 11 79 46 33 36 82 20 12 16 10 30 0 37 38 83 16 12 15 9 71 45 35 37 84 15 12 16 12 73 43 37 33 85 15 11 15 12 72 44 32 34 86 16 10 14 13 77 47 33 31 87 14 9 15 13 75 45 38 39 88 16 12 14 12 69 42 33 34 89 16 12 13 15 54 33 29 32 90 15 12 7 22 70 43 33 33 91 12 9 17 13 73 46 31 36 92 17 15 13 15 54 33 36 32 93 16 12 15 13 77 46 35 41 94 15 12 14 15 82 48 32 28 95 13 12 13 10 80 47 29 30 96 16 10 16 11 80 47 39 36 97 16 13 12 16 69 43 37 35 98 16 9 14 11 78 46 35 31 99 16 12 17 11 81 48 37 34 100 14 10 15 10 76 46 32 36 101 16 14 17 10 76 45 38 36 102 16 11 12 16 73 45 37 35 103 20 15 16 12 85 52 36 37 104 15 11 11 11 66 42 32 28 105 16 11 15 16 79 47 33 39 106 13 12 9 19 68 41 40 32 107 17 12 16 11 76 47 38 35 108 16 12 15 16 71 43 41 39 109 16 11 10 15 54 33 36 35 110 12 7 10 24 46 30 43 42 111 16 12 15 14 82 49 30 34 112 16 14 11 15 74 44 31 33 113 17 11 13 11 88 55 32 41 114 13 11 14 15 38 11 32 33 115 12 10 18 12 76 47 37 34 116 18 13 16 10 86 53 37 32 117 14 13 14 14 54 33 33 40 118 14 8 14 13 70 44 34 40 119 13 11 14 9 69 42 33 35 120 16 12 14 15 90 55 38 36 121 13 11 12 15 54 33 33 37 122 16 13 14 14 76 46 31 27 123 13 12 15 11 89 54 38 39 124 16 14 15 8 76 47 37 38 125 15 13 15 11 73 45 33 31 126 16 15 13 11 79 47 31 33 127 15 10 17 8 90 55 39 32 128 17 11 17 10 74 44 44 39 129 15 9 19 11 81 53 33 36 130 12 11 15 13 72 44 35 33 131 16 10 13 11 71 42 32 33 132 10 11 9 20 66 40 28 32 133 16 8 15 10 77 46 40 37 134 12 11 15 15 65 40 27 30 135 14 12 15 12 74 46 37 38 136 15 12 16 14 82 53 32 29 137 13 9 11 23 54 33 28 22 138 15 11 14 14 63 42 34 35 139 11 10 11 16 54 35 30 35 140 12 8 15 11 64 40 35 34 141 8 9 13 12 69 41 31 35 142 16 8 15 10 54 33 32 34 143 15 9 16 14 84 51 30 34 144 17 15 14 12 86 53 30 35 145 16 11 15 12 77 46 31 23 146 10 8 16 11 89 55 40 31 147 18 13 16 12 76 47 32 27 148 13 12 11 13 60 38 36 36 149 16 12 12 11 75 46 32 31 150 13 9 9 19 73 46 35 32 151 10 7 16 12 85 53 38 39 152 15 13 13 17 79 47 42 37 153 16 9 16 9 71 41 34 38 154 16 6 12 12 72 44 35 39 155 14 8 9 19 69 43 35 34 156 10 8 13 18 78 51 33 31 157 17 15 13 15 54 33 36 32 158 13 6 14 14 69 43 32 37 159 15 9 19 11 81 53 33 36 160 16 11 13 9 84 51 34 32 161 12 8 12 18 84 50 32 35 162 13 8 13 16 69 46 34 36
Names of X columns:
number Learning Software Happiness Depression Belonging Belonging_Final Connected Separate
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
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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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