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Data X:
2011 0 0 26 50 13 12 21 149 96 86 12.9 2011 0 1 57 62 8 8 22 139 70 70 12.2 2011 0 0 37 54 14 11 22 148 88 71 12.8 2011 0 1 67 71 16 13 18 158 114 108 7.4 2011 0 1 43 54 14 11 23 128 69 64 6.7 2011 0 1 52 65 13 10 12 224 176 119 12.6 2011 0 0 52 73 15 7 20 159 114 97 14.8 2011 0 1 43 52 13 10 22 105 121 129 13.3 2011 0 1 84 84 20 15 21 159 110 153 11.1 2011 0 1 67 42 17 12 19 167 158 78 8.2 2011 0 1 49 66 15 12 22 165 116 80 11.4 2011 0 1 70 65 16 10 15 159 181 99 6.4 2011 0 1 52 78 12 10 20 119 77 68 10.6 2011 0 0 58 73 17 14 19 176 141 147 12.0 2011 0 0 68 75 11 6 18 54 35 40 6.3 2011 1 0 62 72 16 12 15 91 80 57 11.3 2011 0 1 43 66 16 14 20 163 152 120 11.9 2011 0 0 56 70 15 11 21 124 97 71 9.3 2011 1 1 56 61 13 8 21 137 99 84 9.6 2011 0 0 74 81 14 12 15 121 84 68 10.0 2011 0 1 65 71 19 15 16 153 68 55 6.4 2011 0 1 63 69 16 13 23 148 101 137 13.8 2011 0 0 58 71 17 11 21 221 107 79 10.8 2011 0 1 57 72 10 12 18 188 88 116 13.8 2011 0 1 63 68 15 7 25 149 112 101 11.7 2011 0 1 53 70 14 11 9 244 171 111 10.9 2011 1 1 57 68 14 7 30 148 137 189 16.1 2011 1 0 51 61 16 12 20 92 77 66 13.4 2011 0 1 64 67 15 12 23 150 66 81 9.9 2011 0 0 53 76 17 13 16 153 93 63 11.5 2011 0 0 29 70 14 9 16 94 105 69 8.3 2011 0 0 54 60 16 11 19 156 131 71 11.7 2011 0 1 58 72 15 12 25 132 102 64 9.0 2011 0 1 43 69 16 15 18 161 161 143 9.7 2011 0 1 51 71 16 12 23 105 120 85 10.8 2011 0 1 53 62 10 6 21 97 127 86 10.3 2011 0 0 54 70 8 5 10 151 77 55 10.4 2011 1 1 56 64 17 13 14 131 108 69 12.7 2011 0 1 61 58 14 11 22 166 85 120 9.3 2011 0 0 47 76 10 6 26 157 168 96 11.8 2011 0 1 39 52 14 12 23 111 48 60 5.9 2011 0 1 48 59 12 10 23 145 152 95 11.4 2011 0 1 50 68 16 6 24 162 75 100 13.0 2011 0 1 35 76 16 12 24 163 107 68 10.8 2011 1 1 30 65 16 11 18 59 62 57 12.3 2011 0 0 68 67 8 6 23 187 121 105 11.3 2011 0 1 49 59 16 12 15 109 124 85 11.8 2011 1 1 61 69 15 12 19 90 72 103 7.9 2011 0 0 67 76 8 8 16 105 40 57 12.7 2011 1 1 47 63 13 10 25 83 58 51 12.3 2011 1 1 56 75 14 11 23 116 97 69 11.6 2011 1 1 50 63 13 7 17 42 88 41 6.7 2011 0 1 43 60 16 12 19 148 126 49 10.9 2011 1 1 67 73 19 13 21 155 104 50 12.1 2011 0 1 62 63 19 14 18 125 148 93 13.3 2011 0 1 57 70 14 12 27 116 146 58 10.1 2011 1 0 41 75 15 6 21 128 80 54 5.7 2011 0 1 54 66 13 14 13 138 97 74 14.3 2011 1 0 45 63 10 10 8 49 25 15 8.0 2011 1 1 48 63 16 12 29 96 99 69 13.3 2011 0 1 61 64 15 11 28 164 118 107 9.3 2011 0 0 56 70 11 10 23 162 58 65 12.5 2011 0 0 41 75 9 7 21 99 63 58 7.6 2011 0 1 43 61 16 12 19 202 139 107 15.9 2011 0 0 53 60 12 7 19 186 50 70 9.2 2011 1 1 44 62 12 12 20 66 60 53 9.1 2011 0 0 66 73 14 12 18 183 152 136 11.1 2011 0 1 58 61 14 10 19 214 142 126 13.0 2011 0 1 46 66 13 10 17 188 94 95 14.5 2011 1 0 37 64 15 12 19 104 66 69 12.2 2011 0 0 51 59 17 12 25 177 127 136 12.3 2011 0 0 51 64 14 12 19 126 67 58 11.4 2011 1 0 56 60 11 8 22 76 90 59 8.8 2011 1 1 66 56 9 10 23 99 75 118 14.6 2011 0 0 37 78 7 5 14 139 128 82 12.6 2011 0 1 59 53 13 10 28 78 41 50 NA 2011 0 0 42 67 15 10 16 162 146 102 13.0 2011 1 1 38 59 12 12 24 108 69 65 12.6 2011 0 0 66 66 15 11 20 159 186 90 13.2 2011 1 0 34 68 14 9 12 74 81 64 9.9 2011 0 1 53 71 16 12 24 110 85 83 7.7 2011 1 0 49 66 14 11 22 96 54 70 10.5 2011 1 0 55 73 13 10 12 116 46 50 13.4 2011 1 0 49 72 16 12 22 87 106 77 10.9 2011 1 1 59 71 13 10 20 97 34 37 4.3 2011 1 0 40 59 16 9 10 127 60 81 10.3 2011 1 1 58 64 16 11 23 106 95 101 11.8 2011 1 1 60 66 16 12 17 80 57 79 11.2 2011 1 0 63 78 10 7 22 74 62 71 11.4 2011 1 0 56 68 12 11 24 91 36 60 8.6 2011 1 0 54 73 12 12 18 133 56 55 13.2 2011 1 1 52 62 12 6 21 74 54 44 12.6 2011 1 1 34 65 12 9 20 114 64 40 5.6 2011 1 1 69 68 19 15 20 140 76 56 9.9 2011 1 0 32 65 14 10 22 95 98 43 8.8 2011 1 1 48 60 13 11 19 98 88 45 7.7 2011 1 0 67 71 16 12 20 121 35 32 9.0 2011 1 1 58 65 15 12 26 126 102 56 7.3 2011 1 1 57 68 12 12 23 98 61 40 11.4 2011 1 1 42 64 8 11 24 95 80 34 13.6 2011 1 1 64 74 10 9 21 110 49 89 7.9 2011 1 1 58 69 16 11 21 70 78 50 10.7 2011 1 0 66 76 16 12 19 102 90 56 10.3 2011 1 1 26 68 10 12 8 86 45 46 8.3 2011 1 1 61 72 18 14 17 130 55 76 9.6 2011 1 1 52 67 12 8 20 96 96 64 14.2 2011 1 0 51 63 16 10 11 102 43 74 8.5 2011 1 0 55 59 10 9 8 100 52 57 13.5 2011 1 0 50 73 14 10 15 94 60 45 4.9 2011 1 0 60 66 12 9 18 52 54 30 6.4 2011 1 0 56 62 11 10 18 98 51 62 9.6 2011 1 0 63 69 15 12 19 118 51 51 11.6 2011 1 1 61 66 7 11 19 99 38 36 11.1 2012 0 1 52 51 16 9 23 48 41 34 4.35 2012 0 1 16 56 16 11 22 50 146 61 12.7 2012 0 1 46 67 16 12 21 150 182 70 18.1 2012 0 1 56 69 16 12 25 154 192 69 17.85 2012 1 0 52 57 12 7 30 109 263 145 16.6 2012 1 1 55 56 15 12 17 68 35 23 12.6 2012 0 1 50 55 14 12 27 194 439 120 17.1 2012 0 0 59 63 15 12 23 158 214 147 19.1 2012 0 1 60 67 16 10 23 159 341 215 16.1 2012 0 0 52 65 13 15 18 67 58 24 13.35 2012 0 0 44 47 10 10 18 147 292 84 18.4 2012 0 1 67 76 17 15 23 39 85 30 14.7 2012 0 1 52 64 15 10 19 100 200 77 10.6 2012 0 1 55 68 18 15 15 111 158 46 12.6 2012 0 1 37 64 16 9 20 138 199 61 16.2 2012 0 1 54 65 20 15 16 101 297 178 13.6 2012 1 1 72 71 16 12 24 131 227 160 18.9 2012 0 1 51 63 17 13 25 101 108 57 14.1 2012 0 1 48 60 16 12 25 114 86 42 14.5 2012 0 0 60 68 15 12 19 165 302 163 16.15 2012 0 1 50 72 13 8 19 114 148 75 14.75 2012 0 1 63 70 16 9 16 111 178 94 14.8 2012 0 1 33 61 16 15 19 75 120 45 12.45 2012 0 1 67 61 16 12 19 82 207 78 12.65 2012 0 1 46 62 17 12 23 121 157 47 17.35 2012 0 1 54 71 20 15 21 32 128 29 8.6 2012 0 0 59 71 14 11 22 150 296 97 18.4 2012 0 1 61 51 17 12 19 117 323 116 16.1 2012 1 1 33 56 6 6 20 71 79 32 11.6 2012 0 1 47 70 16 14 20 165 70 50 17.75 2012 0 1 69 73 15 12 3 154 146 118 15.25 2012 0 1 52 76 16 12 23 126 246 66 17.65 2012 0 0 55 68 16 12 23 149 196 86 16.35 2012 0 0 41 48 14 11 20 145 199 89 17.65 2012 0 1 73 52 16 12 15 120 127 76 13.6 2012 0 0 52 60 16 12 16 109 153 75 14.35 2012 0 0 50 59 16 12 7 132 299 57 14.75 2012 0 1 51 57 14 12 24 172 228 72 18.25 2012 0 0 60 79 14 8 17 169 190 60 9.9 2012 0 1 56 60 16 8 24 114 180 109 16 2012 0 1 56 60 16 12 24 156 212 76 18.25 2012 0 0 29 59 15 12 19 172 269 65 16.85 2012 1 1 66 62 16 11 25 68 130 40 14.6 2012 1 1 66 59 16 10 20 89 179 58 13.85 2012 0 1 73 61 18 11 28 167 243 123 18.95 2012 0 0 55 71 15 12 23 113 190 71 15.6 2012 1 0 64 57 16 13 27 115 299 102 14.85 2012 1 0 40 66 16 12 18 78 121 80 11.75 2012 1 0 46 63 16 12 28 118 137 97 18.45 2012 1 1 58 69 17 10 21 87 305 46 15.9 2012 0 0 43 58 14 10 19 173 157 93 17.1 2012 0 1 61 59 18 11 23 2 96 19 16.1 2012 1 0 51 48 9 8 27 162 183 140 19.9 2012 1 1 50 66 15 12 22 49 52 78 10.95 2012 1 0 52 73 14 9 28 122 238 98 18.45 2012 1 1 54 67 15 12 25 96 40 40 15.1 2012 1 0 66 61 13 9 21 100 226 80 15 2012 1 0 61 68 16 11 22 82 190 76 11.35 2012 1 1 80 75 20 15 28 100 214 79 15.95 2012 1 0 51 62 14 8 20 115 145 87 18.1 2012 1 1 56 69 12 8 29 141 119 95 14.6 2012 0 1 56 58 15 11 25 165 222 49 15.4 2012 0 1 56 60 15 11 25 165 222 49 15.4 2012 1 1 53 74 15 11 20 110 159 80 17.6 2012 0 1 47 55 16 13 20 118 165 86 13.35 2012 0 0 25 62 11 7 16 158 249 69 19.1 2012 1 1 47 63 16 12 20 146 125 79 15.35 2012 0 0 46 69 7 8 20 49 122 52 7.6 2012 1 0 50 58 11 8 23 90 186 120 13.4 2012 1 0 39 58 9 4 18 121 148 69 13.9 2012 0 1 51 68 15 11 25 155 274 94 19.1 2012 1 0 58 72 16 10 18 104 172 72 15.25 2012 1 1 35 62 14 7 19 147 84 43 12.9 2012 1 0 58 62 15 12 25 110 168 87 16.1 2012 1 0 60 65 13 11 25 108 102 52 17.35 2012 1 0 62 69 13 9 25 113 106 71 13.15 2012 1 0 63 66 12 10 24 115 2 61 12.15 2012 1 1 53 72 16 8 19 61 139 51 12.6 2012 1 1 46 62 14 8 26 60 95 50 10.35 2012 1 1 67 75 16 11 10 109 130 67 15.4 2012 1 1 59 58 14 12 17 68 72 30 9.6 2012 1 0 64 66 15 10 13 111 141 70 18.2 2012 1 0 38 55 10 10 17 77 113 52 13.6 2012 1 1 50 47 16 12 30 73 206 75 14.85 2012 0 0 48 72 14 8 25 151 268 87 14.75 2012 1 0 48 62 16 11 4 89 175 69 14.1 2012 1 0 47 64 12 8 16 78 77 72 14.9 2012 1 0 66 64 16 10 21 110 125 79 16.25 2012 0 1 47 19 16 14 23 220 255 121 19.25 2012 1 1 63 50 15 9 22 65 111 43 13.6 2012 0 0 58 68 14 9 17 141 132 58 13.6 2012 1 0 44 70 16 10 20 117 211 57 15.65 2012 0 1 51 79 11 13 20 122 92 50 12.75 2012 1 0 43 69 15 12 22 63 76 69 14.6 2012 0 1 55 71 18 13 16 44 171 64 9.85 2012 1 1 38 48 13 8 23 52 83 38 12.65 2012 1 0 45 73 7 3 0 131 266 90 19.2 2012 1 1 50 74 7 8 18 101 186 96 16.6 2012 1 1 54 66 17 12 25 42 50 49 11.2 2012 0 1 57 71 18 11 23 152 117 56 15.25 2012 0 0 60 74 15 9 12 107 219 102 11.9 2012 1 0 55 78 8 12 18 77 246 40 13.2 2012 0 0 56 75 13 12 24 154 279 100 16.35 2012 0 1 49 53 13 12 11 103 148 67 12.4 2012 1 1 37 60 15 10 18 96 137 78 15.85 2012 0 1 59 70 18 13 23 175 181 55 18.15 2012 1 1 46 69 16 9 24 57 98 59 11.15 2012 1 0 51 65 14 12 29 112 226 96 15.65 2012 0 0 58 78 15 11 18 143 234 86 17.75 2012 1 0 64 78 19 14 15 49 138 38 7.65 2012 0 1 53 59 16 11 29 110 85 43 12.35 2012 0 1 48 72 12 9 16 131 66 23 15.6 2012 0 0 51 70 16 12 19 167 236 77 19.3 2012 1 0 47 63 11 8 22 56 106 48 15.2 2012 0 0 59 63 16 15 16 137 135 26 17.1 2012 1 1 62 71 15 12 23 86 122 91 15.6 2012 0 1 62 74 19 14 23 121 218 94 18.4 2012 0 0 51 67 15 12 19 149 199 62 19.05 2012 0 0 64 66 14 9 4 168 112 74 18.55 2012 0 0 52 62 14 9 20 140 278 114 19.1 2012 1 1 67 80 17 13 24 88 94 52 13.1 2012 0 1 50 73 16 13 20 168 113 64 12.85 2012 0 1 54 67 20 15 4 94 84 31 9.5 2012 0 1 58 61 16 11 24 51 86 38 4.5 2012 1 0 56 73 9 7 22 48 62 27 11.85 2012 0 1 63 74 13 10 16 145 222 105 13.6 2012 0 1 31 32 15 11 3 66 167 64 11.7 2012 1 1 65 69 19 14 15 85 82 62 12.4 2012 0 0 71 69 16 14 24 109 207 65 13.35 2012 1 0 50 84 17 13 17 63 184 58 11.4 2012 1 1 57 64 16 12 20 102 83 76 14.9 2012 1 0 47 58 9 8 27 162 183 140 19.9 2012 1 1 47 59 11 13 26 86 89 68 11.2 2012 1 1 57 78 14 9 23 114 225 80 14.6 2012 0 0 43 57 19 12 17 164 237 71 17.6 2012 0 1 41 60 13 13 20 119 102 76 14.05 2012 0 0 63 68 14 11 22 126 221 63 16.1 2012 0 1 63 68 15 11 19 132 128 46 13.35 2012 0 1 56 73 15 13 24 142 91 53 11.85 2012 0 0 51 69 14 12 19 83 198 74 11.95 2012 1 1 50 67 16 12 23 94 204 70 14.75 2012 1 0 22 60 17 10 15 81 158 78 15.15 2012 0 1 41 65 12 9 27 166 138 56 13.2 2012 1 0 59 66 15 10 26 110 226 100 16.85 2012 1 1 56 74 17 13 22 64 44 51 7.85 2012 0 0 66 81 15 13 22 93 196 52 7.7 2012 1 0 53 72 10 9 18 104 83 102 12.6 2012 1 1 42 55 16 11 15 105 79 78 7.85 2012 1 1 52 49 15 12 22 49 52 78 10.95 2012 1 0 54 74 11 8 27 88 105 55 12.35 2012 1 1 44 53 16 12 10 95 116 98 9.95 2012 1 1 62 64 16 12 20 102 83 76 14.9 2012 1 0 53 65 16 12 17 99 196 73 16.65 2012 1 1 50 57 14 9 23 63 153 47 13.4 2012 1 0 36 51 14 12 19 76 157 45 13.95 2012 1 0 76 80 16 12 13 109 75 83 15.7 2012 1 1 66 67 16 11 27 117 106 60 16.85 2012 1 1 62 70 18 12 23 57 58 48 10.95 2012 1 0 59 74 14 6 16 120 75 50 15.35 2012 1 1 47 75 20 7 25 73 74 56 12.2 2012 1 0 55 70 15 10 2 91 185 77 15.1 2012 1 0 58 69 16 12 26 108 265 91 17.75 2012 1 1 60 65 16 10 20 105 131 76 15.2 2012 0 0 44 55 16 12 23 117 139 68 14.6 2012 1 0 57 71 12 9 22 119 196 74 16.65 2012 1 1 45 65 8 3 24 31 78 29 8.1
Names of X columns:
year group gender AMS.I AMS.II CONFSTATTOT CONFSOFTTOT NUMERACYTOT LFM B H 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') }
Compute
Summary of computational transaction
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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