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Data X:
2011 1 26 50 4 0 13 12 21 149 12.9 2011 1 57 62 4 1 8 8 22 139 12.2 2011 1 37 54 5 0 14 11 22 148 12.8 2011 1 67 71 4 1 16 13 18 158 7.4 2011 1 43 54 4 1 14 11 23 128 6.7 2011 1 52 65 9 1 13 10 12 224 12.6 2011 1 52 73 8 0 15 7 20 159 14.8 2011 1 43 52 11 1 13 10 22 105 13.3 2011 1 84 84 4 1 20 15 21 159 11.1 2011 1 67 42 4 1 17 12 19 167 8.2 2011 1 49 66 6 1 15 12 22 165 11.4 2011 1 70 65 4 1 16 10 15 159 6.4 2011 1 52 78 8 1 12 10 20 119 10.6 2011 1 58 73 4 0 17 14 19 176 12 2011 1 68 75 4 0 11 6 18 54 6.3 2011 0 62 72 11 0 16 12 15 91 11.3 2011 1 43 66 4 1 16 14 20 163 11.9 2011 1 56 70 4 0 15 11 21 124 9.3 2011 0 56 61 6 1 13 8 21 137 9.6 2011 1 74 81 6 0 14 12 15 121 10 2011 1 65 71 4 1 19 15 16 153 6.4 2011 1 63 69 8 1 16 13 23 148 13.8 2011 1 58 71 5 0 17 11 21 221 10.8 2011 1 57 72 4 1 10 12 18 188 13.8 2011 1 63 68 9 1 15 7 25 149 11.7 2011 1 53 70 4 1 14 11 9 244 10.9 2011 0 57 68 7 1 14 7 30 148 16.1 2011 0 51 61 10 0 16 12 20 92 13.4 2011 1 64 67 4 1 15 12 23 150 9.9 2011 1 53 76 4 0 17 13 16 153 11.5 2011 1 29 70 7 0 14 9 16 94 8.3 2011 1 54 60 12 0 16 11 19 156 11.7 2011 1 58 72 7 1 15 12 25 132 9 2011 1 43 69 5 1 16 15 18 161 9.7 2011 1 51 71 8 1 16 12 23 105 10.8 2011 1 53 62 5 1 10 6 21 97 10.3 2011 1 54 70 4 0 8 5 10 151 10.4 2011 0 56 64 9 1 17 13 14 131 12.7 2011 1 61 58 7 1 14 11 22 166 9.3 2011 1 47 76 4 0 10 6 26 157 11.8 2011 1 39 52 4 1 14 12 23 111 5.9 2011 1 48 59 4 1 12 10 23 145 11.4 2011 1 50 68 4 1 16 6 24 162 13 2011 1 35 76 4 1 16 12 24 163 10.8 2011 0 30 65 7 1 16 11 18 59 12.3 2011 1 68 67 4 0 8 6 23 187 11.3 2011 1 49 59 7 1 16 12 15 109 11.8 2011 0 61 69 4 1 15 12 19 90 7.9 2011 1 67 76 4 0 8 8 16 105 12.7 2011 0 47 63 4 1 13 10 25 83 12.3 2011 0 56 75 4 1 14 11 23 116 11.6 2011 0 50 63 8 1 13 7 17 42 6.7 2011 1 43 60 4 1 16 12 19 148 10.9 2011 0 67 73 4 1 19 13 21 155 12.1 2011 1 62 63 4 1 19 14 18 125 13.3 2011 1 57 70 4 1 14 12 27 116 10.1 2011 0 41 75 7 0 15 6 21 128 5.7 2011 1 54 66 12 1 13 14 13 138 14.3 2011 0 45 63 4 0 10 10 8 49 8 2011 0 48 63 4 1 16 12 29 96 13.3 2011 1 61 64 4 1 15 11 28 164 9.3 2011 1 56 70 5 0 11 10 23 162 12.5 2011 1 41 75 15 0 9 7 21 99 7.6 2011 1 43 61 5 1 16 12 19 202 15.9 2011 1 53 60 10 0 12 7 19 186 9.2 2011 0 44 62 9 1 12 12 20 66 9.1 2011 1 66 73 8 0 14 12 18 183 11.1 2011 1 58 61 4 1 14 10 19 214 13 2011 1 46 66 5 1 13 10 17 188 14.5 2011 0 37 64 4 0 15 12 19 104 12.2 2011 1 51 59 9 0 17 12 25 177 12.3 2011 1 51 64 4 0 14 12 19 126 11.4 2011 0 56 60 10 0 11 8 22 76 8.8 2011 0 66 56 4 1 9 10 23 99 14.6 2011 1 37 78 4 0 7 5 14 139 12.6 2011 1 42 67 7 0 15 10 16 162 13 2011 0 38 59 5 1 12 12 24 108 12.6 2011 1 66 66 4 0 15 11 20 159 13.2 2011 0 34 68 4 0 14 9 12 74 9.9 2011 1 53 71 4 1 16 12 24 110 7.7 2011 0 49 66 4 0 14 11 22 96 10.5 2011 0 55 73 4 0 13 10 12 116 13.4 2011 0 49 72 4 0 16 12 22 87 10.9 2011 0 59 71 6 1 13 10 20 97 4.3 2011 0 40 59 10 0 16 9 10 127 10.3 2011 0 58 64 7 1 16 11 23 106 11.8 2011 0 60 66 4 1 16 12 17 80 11.2 2011 0 63 78 4 0 10 7 22 74 11.4 2011 0 56 68 7 0 12 11 24 91 8.6 2011 0 54 73 4 0 12 12 18 133 13.2 2011 0 52 62 8 1 12 6 21 74 12.6 2011 0 34 65 11 1 12 9 20 114 5.6 2011 0 69 68 6 1 19 15 20 140 9.9 2011 0 32 65 14 0 14 10 22 95 8.8 2011 0 48 60 5 1 13 11 19 98 7.7 2011 0 67 71 4 0 16 12 20 121 9 2011 0 58 65 8 1 15 12 26 126 7.3 2011 0 57 68 9 1 12 12 23 98 11.4 2011 0 42 64 4 1 8 11 24 95 13.6 2011 0 64 74 4 1 10 9 21 110 7.9 2011 0 58 69 5 1 16 11 21 70 10.7 2011 0 66 76 4 0 16 12 19 102 10.3 2011 0 26 68 5 1 10 12 8 86 8.3 2011 0 61 72 4 1 18 14 17 130 9.6 2011 0 52 67 4 1 12 8 20 96 14.2 2011 0 51 63 7 0 16 10 11 102 8.5 2011 0 55 59 10 0 10 9 8 100 13.5 2011 0 50 73 4 0 14 10 15 94 4.9 2011 0 60 66 5 0 12 9 18 52 6.4 2011 0 56 62 4 0 11 10 18 98 9.6 2011 0 63 69 4 0 15 12 19 118 11.6 2011 0 61 66 4 1 7 11 19 99 11.1 2012 1 52 51 6 1 16 9 23 48 4.35 2012 1 16 56 4 1 16 11 22 50 12.7 2012 1 46 67 8 1 16 12 21 150 18.1 2012 1 56 69 5 1 16 12 25 154 17.85 2012 0 52 57 4 0 12 7 30 109 16.6 2012 0 55 56 17 1 15 12 17 68 12.6 2012 1 50 55 4 1 14 12 27 194 17.1 2012 1 59 63 4 0 15 12 23 158 19.1 2012 1 60 67 8 1 16 10 23 159 16.1 2012 1 52 65 4 0 13 15 18 67 13.35 2012 1 44 47 7 0 10 10 18 147 18.4 2012 1 67 76 4 1 17 15 23 39 14.7 2012 1 52 64 4 1 15 10 19 100 10.6 2012 1 55 68 5 1 18 15 15 111 12.6 2012 1 37 64 7 1 16 9 20 138 16.2 2012 1 54 65 4 1 20 15 16 101 13.6 2012 0 72 71 4 1 16 12 24 131 18.9 2012 1 51 63 7 1 17 13 25 101 14.1 2012 1 48 60 11 1 16 12 25 114 14.5 2012 1 60 68 7 0 15 12 19 165 16.15 2012 1 50 72 4 1 13 8 19 114 14.75 2012 1 63 70 4 1 16 9 16 111 14.8 2012 1 33 61 4 1 16 15 19 75 12.45 2012 1 67 61 4 1 16 12 19 82 12.65 2012 1 46 62 4 1 17 12 23 121 17.35 2012 1 54 71 4 1 20 15 21 32 8.6 2012 1 59 71 6 0 14 11 22 150 18.4 2012 1 61 51 8 1 17 12 19 117 16.1 2012 0 33 56 23 1 6 6 20 71 11.6 2012 1 47 70 4 1 16 14 20 165 17.75 2012 1 69 73 8 1 15 12 3 154 15.25 2012 1 52 76 6 1 16 12 23 126 17.65 2012 1 55 68 4 0 16 12 23 149 16.35 2012 1 41 48 7 0 14 11 20 145 17.65 2012 1 73 52 4 1 16 12 15 120 13.6 2012 1 52 60 4 0 16 12 16 109 14.35 2012 1 50 59 4 0 16 12 7 132 14.75 2012 1 51 57 10 1 14 12 24 172 18.25 2012 1 60 79 6 0 14 8 17 169 9.9 2012 1 56 60 5 1 16 8 24 114 16 2012 1 56 60 5 1 16 12 24 156 18.25 2012 1 29 59 4 0 15 12 19 172 16.85 2012 0 66 62 4 1 16 11 25 68 14.6 2012 0 66 59 5 1 16 10 20 89 13.85 2012 1 73 61 5 1 18 11 28 167 18.95 2012 1 55 71 5 0 15 12 23 113 15.6 2012 0 64 57 5 0 16 13 27 115 14.85 2012 0 40 66 4 0 16 12 18 78 11.75 2012 0 46 63 6 0 16 12 28 118 18.45 2012 0 58 69 4 1 17 10 21 87 15.9 2012 1 43 58 4 0 14 10 19 173 17.1 2012 1 61 59 4 1 18 11 23 2 16.1 2012 0 51 48 9 0 9 8 27 162 19.9 2012 0 50 66 18 1 15 12 22 49 10.95 2012 0 52 73 6 0 14 9 28 122 18.45 2012 0 54 67 5 1 15 12 25 96 15.1 2012 0 66 61 4 0 13 9 21 100 15 2012 0 61 68 11 0 16 11 22 82 11.35 2012 0 80 75 4 1 20 15 28 100 15.95 2012 0 51 62 10 0 14 8 20 115 18.1 2012 0 56 69 6 1 12 8 29 141 14.6 2012 1 56 58 8 1 15 11 25 165 15.4 2012 1 56 60 8 1 15 11 25 165 15.4 2012 0 53 74 6 1 15 11 20 110 17.6 2012 1 47 55 8 1 16 13 20 118 13.35 2012 1 25 62 4 0 11 7 16 158 19.1 2012 0 47 63 4 1 16 12 20 146 15.35 2012 1 46 69 9 0 7 8 20 49 7.6 2012 0 50 58 9 0 11 8 23 90 13.4 2012 0 39 58 5 0 9 4 18 121 13.9 2012 1 51 68 4 1 15 11 25 155 19.1 2012 0 58 72 4 0 16 10 18 104 15.25 2012 0 35 62 15 1 14 7 19 147 12.9 2012 0 58 62 10 0 15 12 25 110 16.1 2012 0 60 65 9 0 13 11 25 108 17.35 2012 0 62 69 7 0 13 9 25 113 13.15 2012 0 63 66 9 0 12 10 24 115 12.15 2012 0 53 72 6 1 16 8 19 61 12.6 2012 0 46 62 4 1 14 8 26 60 10.35 2012 0 67 75 7 1 16 11 10 109 15.4 2012 0 59 58 4 1 14 12 17 68 9.6 2012 0 64 66 7 0 15 10 13 111 18.2 2012 0 38 55 4 0 10 10 17 77 13.6 2012 0 50 47 15 1 16 12 30 73 14.85 2012 1 48 72 4 0 14 8 25 151 14.75 2012 0 48 62 9 0 16 11 4 89 14.1 2012 0 47 64 4 0 12 8 16 78 14.9 2012 0 66 64 4 0 16 10 21 110 16.25 2012 1 47 19 28 1 16 14 23 220 19.25 2012 0 63 50 4 1 15 9 22 65 13.6 2012 1 58 68 4 0 14 9 17 141 13.6 2012 0 44 70 4 0 16 10 20 117 15.65 2012 1 51 79 5 1 11 13 20 122 12.75 2012 0 43 69 4 0 15 12 22 63 14.6 2012 1 55 71 4 1 18 13 16 44 9.85 2012 0 38 48 12 1 13 8 23 52 12.65 2012 0 45 73 4 0 7 3 0 131 19.2 2012 0 50 74 6 1 7 8 18 101 16.6 2012 0 54 66 6 1 17 12 25 42 11.2 2012 1 57 71 5 1 18 11 23 152 15.25 2012 1 60 74 4 0 15 9 12 107 11.9 2012 0 55 78 4 0 8 12 18 77 13.2 2012 1 56 75 4 0 13 12 24 154 16.35 2012 1 49 53 10 1 13 12 11 103 12.4 2012 0 37 60 7 1 15 10 18 96 15.85 2012 1 59 70 4 1 18 13 23 175 18.15 2012 0 46 69 7 1 16 9 24 57 11.15 2012 0 51 65 4 0 14 12 29 112 15.65 2012 1 58 78 4 0 15 11 18 143 17.75 2012 0 64 78 12 0 19 14 15 49 7.65 2012 1 53 59 5 1 16 11 29 110 12.35 2012 1 48 72 8 1 12 9 16 131 15.6 2012 1 51 70 6 0 16 12 19 167 19.3 2012 0 47 63 17 0 11 8 22 56 15.2 2012 1 59 63 4 0 16 15 16 137 17.1 2012 0 62 71 5 1 15 12 23 86 15.6 2012 1 62 74 4 1 19 14 23 121 18.4 2012 1 51 67 5 0 15 12 19 149 19.05 2012 1 64 66 5 0 14 9 4 168 18.55 2012 1 52 62 6 0 14 9 20 140 19.1 2012 0 67 80 4 1 17 13 24 88 13.1 2012 1 50 73 4 1 16 13 20 168 12.85 2012 1 54 67 4 1 20 15 4 94 9.5 2012 1 58 61 6 1 16 11 24 51 4.5 2012 0 56 73 8 0 9 7 22 48 11.85 2012 1 63 74 10 1 13 10 16 145 13.6 2012 1 31 32 4 1 15 11 3 66 11.7 2012 0 65 69 5 1 19 14 15 85 12.4 2012 1 71 69 4 0 16 14 24 109 13.35 2012 0 50 84 4 0 17 13 17 63 11.4 2012 0 57 64 4 1 16 12 20 102 14.9 2012 0 47 58 16 0 9 8 27 162 19.9 2012 0 47 59 7 1 11 13 26 86 11.2 2012 0 57 78 4 1 14 9 23 114 14.6 2012 1 43 57 4 0 19 12 17 164 17.6 2012 1 41 60 14 1 13 13 20 119 14.05 2012 1 63 68 5 0 14 11 22 126 16.1 2012 1 63 68 5 1 15 11 19 132 13.35 2012 1 56 73 5 1 15 13 24 142 11.85 2012 1 51 69 5 0 14 12 19 83 11.95 2012 0 50 67 7 1 16 12 23 94 14.75 2012 0 22 60 19 0 17 10 15 81 15.15 2012 1 41 65 16 1 12 9 27 166 13.2 2012 0 59 66 4 0 15 10 26 110 16.85 2012 0 56 74 4 1 17 13 22 64 7.85 2012 1 66 81 7 0 15 13 22 93 7.7 2012 0 53 72 9 0 10 9 18 104 12.6 2012 0 42 55 5 1 16 11 15 105 7.85 2012 0 52 49 14 1 15 12 22 49 10.95 2012 0 54 74 4 0 11 8 27 88 12.35 2012 0 44 53 16 1 16 12 10 95 9.95 2012 0 62 64 10 1 16 12 20 102 14.9 2012 0 53 65 5 0 16 12 17 99 16.65 2012 0 50 57 6 1 14 9 23 63 13.4 2012 0 36 51 4 0 14 12 19 76 13.95 2012 0 76 80 4 0 16 12 13 109 15.7 2012 0 66 67 4 1 16 11 27 117 16.85 2012 0 62 70 5 1 18 12 23 57 10.95 2012 0 59 74 4 0 14 6 16 120 15.35 2012 0 47 75 4 1 20 7 25 73 12.2 2012 0 55 70 5 0 15 10 2 91 15.1 2012 0 58 69 4 0 16 12 26 108 17.75 2012 0 60 65 4 1 16 10 20 105 15.2 2012 1 44 55 5 0 16 12 23 117 14.6 2012 0 57 71 8 0 12 9 22 119 16.65 2012 0 45 65 15 1 8 3 24 31 8.1
Names of X columns:
Course_id_year Course_id_letter AMS.I AMS.E AMS.A gender CONFSTATTOT CONFSOFTTOT NUMERACYTOT LFM TOT
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) 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, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) 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,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) 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,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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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Summary of computational transaction
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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