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Data X:
'7.5' 2011 1 11 8 7 18 12 20 4 0 21 149 86 '6.5' 2011 1 16 12 9 22 14 18 5 0 22 148 71 '1.0' 2011 1 24 24 19 22 25 24 4 1 18 158 108 '1.0' 2011 1 15 16 12 19 15 20 4 1 23 128 64 '5.5' 2011 1 17 19 16 25 20 20 9 1 12 224 119 '8.5' 2011 1 19 16 17 28 21 24 8 0 20 159 97 '6.5' 2011 1 19 15 9 16 15 21 11 1 22 105 129 '4.5' 2011 1 28 28 28 28 28 28 4 1 21 159 153 '2.0' 2011 1 26 21 20 21 11 10 4 1 19 167 78 '5.0' 2011 1 15 18 16 22 22 22 6 1 22 165 80 '0.5' 2011 1 26 22 22 24 22 19 4 1 15 159 99 '5.0' 2011 1 24 22 12 26 24 23 4 0 19 176 147 '2.5' 2011 1 25 25 18 28 23 24 4 0 18 54 40 '5.0' 2011 0 22 20 20 24 24 24 11 0 15 91 57 '5.5' 2011 1 15 16 12 20 21 25 4 1 20 163 120 '3.5' 2011 1 21 19 16 26 20 24 4 0 21 124 71 '4.0' 2011 1 27 26 21 28 25 28 6 0 15 121 68 '6.5' 2011 1 26 20 17 23 24 22 8 1 23 148 137 '4.5' 2011 1 22 19 17 24 21 26 5 0 21 221 79 '5.5' 2011 1 22 23 18 22 25 21 9 1 25 149 101 '4.0' 2011 1 20 18 15 21 23 26 4 1 9 244 111 '7.5' 2011 0 21 16 20 25 20 23 7 1 30 148 189 '4.0' 2011 1 22 21 21 21 22 24 4 1 23 150 81 '5.5' 2011 1 21 20 12 26 25 25 4 0 16 153 63 '2.5' 2011 1 8 15 6 23 23 24 7 0 16 94 69 '5.5' 2011 1 22 19 13 21 19 20 12 0 19 156 71 '3.5' 2011 1 20 19 19 27 21 24 7 1 25 132 64 '4.5' 2011 1 17 20 14 23 25 23 8 1 23 105 85 '4.5' 2011 1 23 19 12 23 24 23 4 0 10 151 55 '6.0' 2011 0 20 19 17 19 24 21 9 1 14 131 69 '5.0' 2011 1 19 18 10 24 28 24 4 0 26 157 96 '6.5' 2011 1 22 17 11 27 18 23 4 1 24 162 100 '5.0' 2011 1 17 8 10 25 26 25 4 1 24 163 68 '6.0' 2011 0 14 9 7 25 18 22 7 1 18 59 57 '4.5' 2011 1 24 22 22 23 22 22 4 0 23 187 105 '5.0' 2011 0 18 22 16 25 26 24 4 1 23 116 69 '5.0' 2011 1 18 14 11 24 12 24 4 1 19 148 49 '6.5' 2011 0 23 24 20 28 20 25 4 1 21 155 50 '7.0' 2011 1 24 21 17 20 20 23 4 1 18 125 93 '4.5' 2011 1 23 20 14 19 24 27 4 1 27 116 58 '8.5' 2011 1 20 18 16 21 22 23 12 1 13 138 74 '3.5' 2011 1 22 24 15 18 23 23 4 1 28 164 107 '6.0' 2011 1 22 19 15 27 19 24 5 0 23 162 65 '1.5' 2011 1 15 16 10 25 24 26 15 0 21 99 58 '3.5' 2011 1 19 16 18 21 16 23 10 0 19 186 70 '7.5' 2011 1 21 15 10 27 19 20 5 1 17 188 95 '5.0' 2011 1 20 15 16 23 18 18 9 0 25 177 136 '6.5' 2011 1 18 14 5 27 25 26 4 0 14 139 82 NA 2011 1 22 19 18 24 15 14 6 1 28 78 50 '6.5' 2011 1 16 16 10 25 17 25 7 0 16 162 102 '6.5' 2011 0 17 13 8 19 17 23 5 1 24 108 65 '7.0' 2011 1 24 26 16 24 24 18 4 0 20 159 90 '3.5' 2011 0 13 13 8 25 21 22 4 0 12 74 64 '1.5' 2011 1 19 18 16 23 22 26 4 1 24 110 83 '4.0' 2011 0 20 15 14 23 18 25 4 0 22 96 70 '4.5' 2011 0 19 21 9 26 20 26 4 0 22 87 77 '0.0' 2011 0 21 17 21 26 21 24 6 1 20 97 37 '3.5' 2011 0 15 18 7 16 21 22 10 0 10 127 81 '4.5' 2011 0 22 25 16 25 25 28 4 0 22 74 71 '0.0' 2011 0 14 12 8 20 21 24 11 1 20 114 40 '3.0' 2011 0 11 16 5 20 22 23 14 0 22 95 43 '3.5' 2011 0 22 23 22 24 24 23 4 0 20 121 32 '3.0' 2011 0 25 19 17 27 18 27 4 1 17 130 76 '1.0' 2011 0 22 18 20 23 19 24 5 0 18 52 30 '5.5' 2011 0 22 23 18 24 22 23 4 0 19 118 51 '0.5' 2012 1 20 17 15 22 14 15 6 1 23 48 34 '7.5' 2012 1 6 6 4 24 5 27 4 1 22 50 61 9 2012 1 15 22 9 19 25 23 8 1 21 150 70 '9.5' 2012 1 18 20 18 25 21 23 5 1 25 154 69 '8.5' 2012 0 24 16 12 26 11 20 4 0 30 109 145 7 2012 0 22 16 17 18 20 18 17 1 17 68 23 8 2012 1 21 17 12 24 9 22 4 1 27 194 120 10 2012 1 23 20 16 28 15 20 4 0 23 158 147 7 2012 1 20 23 17 23 23 21 8 1 23 159 215 '8.5' 2012 1 20 18 14 19 21 25 4 0 18 67 24 9 2012 1 18 13 13 19 9 19 7 0 18 147 84 '9.5' 2012 1 25 22 20 27 24 25 4 1 23 39 30 4 2012 1 16 20 16 24 16 24 4 1 19 100 77 6 2012 1 20 20 15 26 20 22 5 1 15 111 46 8 2012 1 14 13 10 21 15 28 7 1 20 138 61 '5.5' 2012 1 22 16 16 25 18 22 4 1 16 101 178 '9.5' 2012 0 26 25 21 28 22 21 4 1 24 131 160 '7.5' 2012 1 20 16 15 19 21 23 7 1 25 101 57 7 2012 1 17 15 16 20 21 19 11 1 25 114 42 '7.5' 2012 1 22 19 19 26 21 21 7 0 19 165 163 8 2012 1 22 19 9 27 20 25 4 1 19 114 75 7 2012 1 20 24 19 23 24 23 4 1 16 111 94 7 2012 1 17 9 7 18 15 28 4 1 19 75 45 6 2012 1 22 22 23 23 24 14 4 1 19 82 78 10 2012 1 17 15 14 21 18 23 4 1 23 121 47 '2.5' 2012 1 22 22 10 23 24 24 4 1 21 32 29 9 2012 1 21 22 16 22 24 25 6 0 22 150 97 8 2012 1 25 24 12 21 15 15 8 1 19 117 116 6 2012 0 11 12 10 14 19 23 23 1 20 71 32 '8.5' 2012 1 19 21 7 24 20 26 4 1 20 165 50 6 2012 1 24 25 20 26 26 21 8 1 3 154 118 9 2012 1 17 26 9 24 26 26 6 1 23 126 66 8 2012 1 22 21 12 22 23 23 4 0 23 149 86 9 2012 1 17 14 10 20 13 15 7 0 20 145 89 '5.5' 2012 1 26 28 19 20 16 16 4 1 15 120 76 7 2012 1 20 21 11 18 22 20 4 0 16 109 75 '5.5' 2012 1 19 16 15 18 21 20 4 0 7 132 57 9 2012 1 21 16 14 25 11 21 10 1 24 172 72 2 2012 1 24 25 11 28 23 28 6 0 17 169 60 '8.5' 2012 1 21 21 14 23 18 19 5 1 24 114 109 9 2012 1 19 22 15 20 19 21 5 1 24 156 76 '8.5' 2012 1 13 9 7 22 15 22 4 0 19 172 65 9 2012 0 24 20 22 27 8 27 4 1 25 68 40 '7.5' 2012 0 28 19 19 24 15 20 5 1 20 89 58 10 2012 1 27 24 22 23 21 17 5 1 28 167 123 9 2012 1 22 22 11 20 25 26 5 0 23 113 71 '7.5' 2012 0 23 22 19 22 14 21 5 0 27 115 102 6 2012 0 19 12 9 21 21 24 4 0 18 78 80 '10.5' 2012 0 18 17 11 24 18 21 6 0 28 118 97 '8.5' 2012 0 23 18 17 26 18 25 4 1 21 87 46 8 2012 1 21 10 12 24 12 22 4 0 19 173 93 10 2012 1 22 22 17 18 24 17 4 1 23 2 19 '10.5' 2012 0 17 24 10 17 17 14 9 0 27 162 140 '6.5' 2012 0 15 18 17 23 20 23 18 1 22 49 78 '9.5' 2012 0 21 18 13 21 24 28 6 0 28 122 98 '8.5' 2012 0 20 23 11 21 22 24 5 1 25 96 40 '7.5' 2012 0 26 21 19 24 15 22 4 0 21 100 80 5 2012 0 19 21 21 22 22 24 11 0 22 82 76 8 2012 0 28 28 24 24 26 25 4 1 28 100 79 10 2012 0 21 17 13 24 17 21 10 0 20 115 87 7 2012 0 19 21 16 24 23 22 6 1 29 141 95 '7.5' 2012 1 22 21 13 23 19 16 8 1 25 165 49 '7.5' 2012 1 21 20 15 21 21 18 8 1 25 165 49 '9.5' 2012 0 20 18 15 24 23 27 6 1 20 110 80 6 2012 1 19 17 11 19 19 17 8 1 20 118 86 10 2012 1 11 7 7 19 18 25 4 0 16 158 69 7 2012 0 17 17 13 23 16 24 4 1 20 146 79 3 2012 1 19 14 13 25 23 21 9 0 20 49 52 6 2012 0 20 18 12 24 13 21 9 0 23 90 120 7 2012 0 17 14 8 21 18 19 5 0 18 121 69 10 2012 1 21 23 7 18 23 27 4 1 25 155 94 7 2012 0 21 20 17 23 21 28 4 0 18 104 72 '3.5' 2012 0 12 14 9 20 23 19 15 1 19 147 43 8 2012 0 23 17 18 23 16 23 10 0 25 110 87 10 2012 0 22 21 17 23 17 25 9 0 25 108 52 '5.5' 2012 0 22 23 17 23 20 26 7 0 25 113 71 6 2012 0 21 24 18 23 18 25 9 0 24 115 61 '6.5' 2012 0 20 21 12 27 20 25 6 1 19 61 51 '6.5' 2012 0 18 14 14 19 19 24 4 1 26 60 50 '8.5' 2012 0 21 24 22 25 26 24 7 1 10 109 67 4 2012 0 24 16 19 25 9 24 4 1 17 68 30 '9.5' 2012 0 22 21 21 21 23 22 7 0 13 111 70 8 2012 0 20 8 10 25 9 21 4 0 17 77 52 '8.5' 2012 0 17 17 16 17 13 17 15 1 30 73 75 '5.5' 2012 1 19 18 11 22 27 23 4 0 25 151 87 7 2012 0 16 17 15 23 22 17 9 0 4 89 69 9 2012 0 19 16 12 27 12 25 4 0 16 78 72 8 2012 0 23 22 21 27 18 19 4 0 21 110 79 10 2012 1 8 17 22 5 6 8 28 1 23 220 121 8 2012 0 22 21 20 19 17 14 4 1 22 65 43 6 2012 1 23 20 15 24 22 22 4 0 17 141 58 8 2012 0 15 20 9 23 22 25 4 0 20 117 57 5 2012 1 17 19 15 28 23 28 5 1 20 122 50 9 2012 0 21 8 14 25 19 25 4 0 22 63 69 '4.5' 2012 1 25 19 11 27 20 24 4 1 16 44 64 '8.5' 2012 0 18 11 9 16 17 15 12 1 23 52 38 '9.5' 2012 0 20 13 12 25 24 24 4 0 0 131 90 '8.5' 2012 0 21 18 11 26 20 28 6 1 18 101 96 '7.5' 2012 0 21 19 14 24 18 24 6 1 25 42 49 '7.5' 2012 1 24 23 10 23 23 25 5 1 23 152 56 5 2012 1 22 20 18 24 27 23 4 0 12 107 102 7 2012 0 22 22 11 27 25 26 4 0 18 77 40 8 2012 1 23 19 14 25 24 26 4 0 24 154 100 '5.5' 2012 1 17 16 16 19 12 22 10 1 11 103 67 '8.5' 2012 0 15 11 11 19 16 25 7 1 18 96 78 '9.5' 2012 1 22 21 16 24 24 22 4 1 23 175 55 7 2012 0 19 14 13 20 23 26 7 1 24 57 59 8 2012 0 18 21 12 21 24 20 4 0 29 112 96 '8.5' 2012 1 21 20 17 28 24 26 4 0 18 143 86 '3.5' 2012 0 20 21 23 26 26 26 12 0 15 49 38 '6.5' 2012 1 19 20 14 19 19 21 5 1 29 110 43 '6.5' 2012 1 19 19 10 23 28 21 8 1 16 131 23 '10.5' 2012 1 16 19 16 23 23 24 6 0 19 167 77 '8.5' 2012 0 18 18 11 21 21 21 17 0 22 56 48 8 2012 1 23 20 16 26 19 18 4 0 16 137 26 10 2012 0 22 21 19 25 23 23 5 1 23 86 91 10 2012 1 23 22 17 25 23 26 4 1 23 121 94 '9.5' 2012 1 20 19 12 24 20 23 5 0 19 149 62 9 2012 1 24 23 17 23 18 25 5 0 4 168 74 10 2012 1 25 16 11 22 20 20 6 0 20 140 114 '7.5' 2012 0 25 23 19 27 28 25 4 1 24 88 52 '4.5' 2012 1 20 18 12 26 21 26 4 1 20 168 64 '4.5' 2012 1 23 23 8 23 25 19 4 1 4 94 31 '0.5' 2012 1 21 20 17 22 18 21 6 1 24 51 38 '6.5' 2012 0 23 20 13 26 24 23 8 0 22 48 27 '4.5' 2012 1 23 23 17 22 28 24 10 1 16 145 105 '5.5' 2012 1 11 13 7 17 9 6 4 1 3 66 64 5 2012 0 21 21 23 25 22 22 5 1 15 85 62 6 2012 1 27 26 18 22 26 21 4 0 24 109 65 4 2012 0 19 18 13 28 28 28 4 0 17 63 58 8 2012 0 21 19 17 22 18 24 4 1 20 102 76 '10.5' 2012 0 16 18 13 21 23 14 16 0 27 162 140 '6.5' 2012 0 21 18 8 24 15 20 7 1 26 86 68 8 2012 0 22 19 16 26 24 28 4 1 23 114 80 '8.5' 2012 1 16 13 14 26 12 19 4 0 17 164 71 '5.5' 2012 1 18 10 13 24 12 24 14 1 20 119 76 7 2012 1 23 21 19 27 20 21 5 0 22 126 63 5 2012 1 24 24 15 22 25 21 5 1 19 132 46 '3.5' 2012 1 20 21 15 23 24 26 5 1 24 142 53 5 2012 1 20 23 8 22 23 24 5 0 19 83 74 9 2012 0 18 18 14 23 18 26 7 1 23 94 70 '8.5' 2012 0 4 11 7 15 20 25 19 0 15 81 78 5 2012 1 14 16 11 20 22 23 16 1 27 166 56 '9.5' 2012 0 22 20 17 22 20 24 4 0 26 110 100 3 2012 0 17 20 19 25 25 24 4 1 22 64 51 '1.5' 2012 1 23 26 17 27 28 26 7 0 22 93 52 6 2012 0 20 21 12 24 25 23 9 0 18 104 102 '0.5' 2012 0 18 12 12 21 14 20 5 1 15 105 78 '6.5' 2012 0 19 15 18 17 16 16 14 1 22 49 78 '7.5' 2012 0 20 18 16 26 24 24 4 0 27 88 55 '4.5' 2012 0 15 14 15 20 13 20 16 1 10 95 98 8 2012 0 24 18 20 22 19 23 10 1 20 102 76 9 2012 0 21 16 16 24 18 23 5 0 17 99 73 '7.5' 2012 0 19 19 12 23 16 18 6 1 23 63 47 '8.5' 2012 0 19 7 10 22 8 21 4 0 19 76 45 7 2012 0 27 21 28 28 27 25 4 0 13 109 83 '9.5' 2012 0 23 24 19 21 23 23 4 1 27 117 60 '6.5' 2012 0 23 21 18 24 20 26 5 1 23 57 48 '9.5' 2012 0 20 20 19 28 20 26 4 0 16 120 50 6 2012 0 17 22 8 25 26 24 4 1 25 73 56 8 2012 0 21 17 17 24 23 23 5 0 2 91 77 '9.5' 2012 0 23 19 16 24 24 21 4 0 26 108 91 8 2012 0 22 20 18 21 21 23 4 1 20 105 76 8 2012 1 16 16 12 20 15 20 5 0 23 117 68 9 2012 0 20 20 17 26 22 23 8 0 22 119 74 5 2012 0 16 16 13 16 25 24 15 1 24 31 29
Names of X columns:
Examenresultaten Academiejaar Groep I1 I2 I3 E1 E2 E3 A Geslacht Numeracytotaal RFC_LFM RFC_Uren
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
par3 <- 'No Linear Trend' par2 <- 'Do not include Seasonal Dummies' par1 <- '1' 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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Raw Output
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