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Data X:
2011 1 11 8 7 18 12 20 4 12.9 149 21 2011 1 19 18 20 23 20 19 4 12.2 139 22 2011 1 16 12 9 22 14 18 5 12.8 148 22 2011 1 24 24 19 22 25 24 4 7.4 158 18 2011 1 15 16 12 19 15 20 4 6.7 128 23 2011 1 17 19 16 25 20 20 9 12.6 224 12 2011 1 19 16 17 28 21 24 8 14.8 159 20 2011 1 19 15 9 16 15 21 11 13.3 105 22 2011 1 28 28 28 28 28 28 4 11.1 159 21 2011 1 26 21 20 21 11 10 4 8.2 167 19 2011 1 15 18 16 22 22 22 6 11.4 165 22 2011 1 26 22 22 24 22 19 4 6.4 159 15 2011 1 16 19 17 24 27 27 8 10.6 119 20 2011 1 24 22 12 26 24 23 4 12.0 176 19 2011 1 25 25 18 28 23 24 4 6.3 54 18 2011 0 22 20 20 24 24 24 11 11.3 91 15 2011 1 15 16 12 20 21 25 4 11.9 163 20 2011 1 21 19 16 26 20 24 4 9.3 124 21 2011 0 22 18 16 21 19 21 6 9.6 137 21 2011 1 27 26 21 28 25 28 6 10.0 121 15 2011 1 26 24 15 27 16 28 4 6.4 153 16 2011 1 26 20 17 23 24 22 8 13.8 148 23 2011 1 22 19 17 24 21 26 5 10.8 221 21 2011 1 21 19 17 24 22 26 4 13.8 188 18 2011 1 22 23 18 22 25 21 9 11.7 149 25 2011 1 20 18 15 21 23 26 4 10.9 244 9 2011 0 21 16 20 25 20 23 7 16.1 148 30 2011 0 20 18 13 20 21 20 10 13.4 92 20 2011 1 22 21 21 21 22 24 4 9.9 150 23 2011 1 21 20 12 26 25 25 4 11.5 153 16 2011 1 8 15 6 23 23 24 7 8.3 94 16 2011 1 22 19 13 21 19 20 12 11.7 156 19 2011 1 20 19 19 27 21 24 7 9.0 132 25 2011 1 24 7 12 25 19 25 5 9.7 161 18 2011 1 17 20 14 23 25 23 8 10.8 105 23 2011 1 20 20 13 25 16 21 5 10.3 97 21 2011 1 23 19 12 23 24 23 4 10.4 151 10 2011 0 20 19 17 19 24 21 9 12.7 131 14 2011 1 22 20 19 22 18 18 7 9.3 166 22 2011 1 19 18 10 24 28 24 4 11.8 157 26 2011 1 15 14 10 19 15 18 4 5.9 111 23 2011 1 20 17 11 21 17 21 4 11.4 145 23 2011 1 22 17 11 27 18 23 4 13.0 162 24 2011 1 17 8 10 25 26 25 4 10.8 163 24 2011 0 14 9 7 25 18 22 7 12.3 59 18 2011 1 24 22 22 23 22 22 4 11.3 187 23 2011 1 17 20 12 17 19 23 7 11.8 109 15 2011 0 23 20 18 28 17 24 4 7.9 90 19 2011 1 25 22 20 25 26 25 4 12.7 105 16 2011 0 16 22 9 20 21 22 4 12.3 83 25 2011 0 18 22 16 25 26 24 4 11.6 116 23 2011 0 20 16 14 21 21 21 8 6.7 42 17 2011 1 18 14 11 24 12 24 4 10.9 148 19 2011 0 23 24 20 28 20 25 4 12.1 155 21 2011 1 24 21 17 20 20 23 4 13.3 125 18 2011 1 23 20 14 19 24 27 4 10.1 116 27 2011 0 13 20 8 24 24 27 7 5.7 128 21 2011 1 20 18 16 21 22 23 12 14.3 138 13 2011 0 20 14 11 24 21 18 4 8.0 49 8 2011 0 19 19 10 23 20 20 4 13.3 96 29 2011 1 22 24 15 18 23 23 4 9.3 164 28 2011 1 22 19 15 27 19 24 5 12.5 162 23 2011 1 15 16 10 25 24 26 15 7.6 99 21 2011 1 17 16 10 20 21 20 5 15.9 202 19 2011 1 19 16 18 21 16 23 10 9.2 186 19 2011 0 20 14 10 23 17 22 9 9.1 66 20 2011 1 22 22 22 27 23 23 8 11.1 183 18 2011 1 21 21 16 24 20 17 4 13.0 214 19 2011 1 21 15 10 27 19 20 5 14.5 188 17 2011 0 16 14 7 24 18 22 4 12.2 104 19 2011 1 20 15 16 23 18 18 9 12.3 177 25 2011 1 21 14 16 24 21 19 4 11.4 126 19 2011 0 20 20 16 21 20 19 10 8.8 76 22 2011 0 23 21 22 23 17 16 4 14.6 99 23 2011 1 18 14 5 27 25 26 4 12.6 139 14 2011 1 22 19 18 24 15 14 6 NA 78 28 2011 1 16 16 10 25 17 25 7 13.0 162 16 2011 0 17 13 8 19 17 23 5 12.6 108 24 2011 1 24 26 16 24 24 18 4 13.2 159 20 2011 0 13 13 8 25 21 22 4 9.9 74 12 2011 1 19 18 16 23 22 26 4 7.7 110 24 2011 0 20 15 14 23 18 25 4 10.5 96 22 2011 0 22 18 15 25 22 26 4 13.4 116 12 2011 0 19 21 9 26 20 26 4 10.9 87 22 2011 0 21 17 21 26 21 24 6 4.3 97 20 2011 0 15 18 7 16 21 22 10 10.3 127 10 2011 0 21 20 17 23 20 21 7 11.8 106 23 2011 0 24 18 18 26 18 22 4 11.2 80 17 2011 0 22 25 16 25 25 28 4 11.4 74 22 2011 0 20 20 16 23 23 22 7 8.6 91 24 2011 0 21 19 14 26 21 26 4 13.2 133 18 2011 0 19 18 15 22 20 20 8 12.6 74 21 2011 0 14 12 8 20 21 24 11 5.6 114 20 2011 0 25 22 22 27 20 21 6 9.9 140 20 2011 0 11 16 5 20 22 23 14 8.8 95 22 2011 0 17 18 13 22 15 23 5 7.7 98 19 2011 0 22 23 22 24 24 23 4 9.0 121 20 2011 0 20 20 18 21 22 22 8 7.3 126 26 2011 0 22 20 15 24 21 23 9 11.4 98 23 2011 0 15 16 11 26 17 21 4 13.6 95 24 2011 0 23 22 19 24 23 27 4 7.9 110 21 2011 0 20 19 19 24 22 23 5 10.7 70 21 2011 0 22 23 21 27 23 26 4 10.3 102 19 2011 0 16 6 4 25 16 27 5 8.3 86 8 2011 0 25 19 17 27 18 27 4 9.6 130 17 2011 0 18 24 10 19 25 23 4 14.2 96 20 2011 0 19 19 13 22 18 23 7 8.5 102 11 2011 0 25 15 15 22 14 23 10 13.5 100 8 2011 0 21 18 11 25 20 28 4 4.9 94 15 2011 0 22 18 20 23 19 24 5 6.4 52 18 2011 0 21 22 13 24 18 20 4 9.6 98 18 2011 0 22 23 18 24 22 23 4 11.6 118 19 2011 0 23 18 20 23 21 22 4 11.1 99 19 2012 1 20 17 15 22 14 15 6 4.35 48 23 2012 1 6 6 4 24 5 27 4 12.7 50 22 2012 1 15 22 9 19 25 23 8 18.1 150 21 2012 1 18 20 18 25 21 23 5 17.85 154 25 2012 0 24 16 12 26 11 20 4 16.6 109 30 2012 0 22 16 17 18 20 18 17 12.6 68 17 2012 1 21 17 12 24 9 22 4 17.1 194 27 2012 1 23 20 16 28 15 20 4 19.1 158 23 2012 1 20 23 17 23 23 21 8 16.1 159 23 2012 1 20 18 14 19 21 25 4 13.35 67 18 2012 1 18 13 13 19 9 19 7 18.4 147 18 2012 1 25 22 20 27 24 25 4 14.7 39 23 2012 1 16 20 16 24 16 24 4 10.6 100 19 2012 1 20 20 15 26 20 22 5 12.6 111 15 2012 1 14 13 10 21 15 28 7 16.2 138 20 2012 1 22 16 16 25 18 22 4 13.6 101 16 2012 0 26 25 21 28 22 21 4 18.9 131 24 2012 1 20 16 15 19 21 23 7 14.1 101 25 2012 1 17 15 16 20 21 19 11 14.5 114 25 2012 1 22 19 19 26 21 21 7 16.15 165 19 2012 1 22 19 9 27 20 25 4 14.75 114 19 2012 1 20 24 19 23 24 23 4 14.8 111 16 2012 1 17 9 7 18 15 28 4 12.45 75 19 2012 1 22 22 23 23 24 14 4 12.65 82 19 2012 1 17 15 14 21 18 23 4 17.35 121 23 2012 1 22 22 10 23 24 24 4 8.6 32 21 2012 1 21 22 16 22 24 25 6 18.4 150 22 2012 1 25 24 12 21 15 15 8 16.1 117 19 2012 0 11 12 10 14 19 23 23 11.6 71 20 2012 1 19 21 7 24 20 26 4 17.75 165 20 2012 1 24 25 20 26 26 21 8 15.25 154 3 2012 1 17 26 9 24 26 26 6 17.65 126 23 2012 1 22 21 12 22 23 23 4 16.35 149 23 2012 1 17 14 10 20 13 15 7 17.65 145 20 2012 1 26 28 19 20 16 16 4 13.6 120 15 2012 1 20 21 11 18 22 20 4 14.35 109 16 2012 1 19 16 15 18 21 20 4 14.75 132 7 2012 1 21 16 14 25 11 21 10 18.25 172 24 2012 1 24 25 11 28 23 28 6 9.9 169 17 2012 1 21 21 14 23 18 19 5 16 114 24 2012 1 19 22 15 20 19 21 5 18.25 156 24 2012 1 13 9 7 22 15 22 4 16.85 172 19 2012 0 24 20 22 27 8 27 4 14.6 68 25 2012 0 28 19 19 24 15 20 5 13.85 89 20 2012 1 27 24 22 23 21 17 5 18.95 167 28 2012 1 22 22 11 20 25 26 5 15.6 113 23 2012 0 23 22 19 22 14 21 5 14.85 115 27 2012 0 19 12 9 21 21 24 4 11.75 78 18 2012 0 18 17 11 24 18 21 6 18.45 118 28 2012 0 23 18 17 26 18 25 4 15.9 87 21 2012 1 21 10 12 24 12 22 4 17.1 173 19 2012 1 22 22 17 18 24 17 4 16.1 2 23 2012 0 17 24 10 17 17 14 9 19.9 162 27 2012 0 15 18 17 23 20 23 18 10.95 49 22 2012 0 21 18 13 21 24 28 6 18.45 122 28 2012 0 20 23 11 21 22 24 5 15.1 96 25 2012 0 26 21 19 24 15 22 4 15 100 21 2012 0 19 21 21 22 22 24 11 11.35 82 22 2012 0 28 28 24 24 26 25 4 15.95 100 28 2012 0 21 17 13 24 17 21 10 18.1 115 20 2012 0 19 21 16 24 23 22 6 14.6 141 29 2012 1 22 21 13 23 19 16 8 15.4 165 25 2012 1 21 20 15 21 21 18 8 15.4 165 25 2012 0 20 18 15 24 23 27 6 17.6 110 20 2012 1 19 17 11 19 19 17 8 13.35 118 20 2012 1 11 7 7 19 18 25 4 19.1 158 16 2012 0 17 17 13 23 16 24 4 15.35 146 20 2012 1 19 14 13 25 23 21 9 7.6 49 20 2012 0 20 18 12 24 13 21 9 13.4 90 23 2012 0 17 14 8 21 18 19 5 13.9 121 18 2012 1 21 23 7 18 23 27 4 19.1 155 25 2012 0 21 20 17 23 21 28 4 15.25 104 18 2012 0 12 14 9 20 23 19 15 12.9 147 19 2012 0 23 17 18 23 16 23 10 16.1 110 25 2012 0 22 21 17 23 17 25 9 17.35 108 25 2012 0 22 23 17 23 20 26 7 13.15 113 25 2012 0 21 24 18 23 18 25 9 12.15 115 24 2012 0 20 21 12 27 20 25 6 12.6 61 19 2012 0 18 14 14 19 19 24 4 10.35 60 26 2012 0 21 24 22 25 26 24 7 15.4 109 10 2012 0 24 16 19 25 9 24 4 9.6 68 17 2012 0 22 21 21 21 23 22 7 18.2 111 13 2012 0 20 8 10 25 9 21 4 13.6 77 17 2012 0 17 17 16 17 13 17 15 14.85 73 30 2012 1 19 18 11 22 27 23 4 14.75 151 25 2012 0 16 17 15 23 22 17 9 14.1 89 4 2012 0 19 16 12 27 12 25 4 14.9 78 16 2012 0 23 22 21 27 18 19 4 16.25 110 21 2012 1 8 17 22 5 6 8 28 19.25 220 23 2012 0 22 21 20 19 17 14 4 13.6 65 22 2012 1 23 20 15 24 22 22 4 13.6 141 17 2012 0 15 20 9 23 22 25 4 15.65 117 20 2012 1 17 19 15 28 23 28 5 12.75 122 20 2012 0 21 8 14 25 19 25 4 14.6 63 22 2012 1 25 19 11 27 20 24 4 9.85 44 16 2012 0 18 11 9 16 17 15 12 12.65 52 23 2012 0 20 13 12 25 24 24 4 19.2 131 0 2012 0 21 18 11 26 20 28 6 16.6 101 18 2012 0 21 19 14 24 18 24 6 11.2 42 25 2012 1 24 23 10 23 23 25 5 15.25 152 23 2012 1 22 20 18 24 27 23 4 11.9 107 12 2012 0 22 22 11 27 25 26 4 13.2 77 18 2012 1 23 19 14 25 24 26 4 16.35 154 24 2012 1 17 16 16 19 12 22 10 12.4 103 11 2012 0 15 11 11 19 16 25 7 15.85 96 18 2012 1 22 21 16 24 24 22 4 18.15 175 23 2012 0 19 14 13 20 23 26 7 11.15 57 24 2012 0 18 21 12 21 24 20 4 15.65 112 29 2012 1 21 20 17 28 24 26 4 17.75 143 18 2012 0 20 21 23 26 26 26 12 7.65 49 15 2012 1 19 20 14 19 19 21 5 12.35 110 29 2012 1 19 19 10 23 28 21 8 15.6 131 16 2012 1 16 19 16 23 23 24 6 19.3 167 19 2012 0 18 18 11 21 21 21 17 15.2 56 22 2012 1 23 20 16 26 19 18 4 17.1 137 16 2012 0 22 21 19 25 23 23 5 15.6 86 23 2012 1 23 22 17 25 23 26 4 18.4 121 23 2012 1 20 19 12 24 20 23 5 19.05 149 19 2012 1 24 23 17 23 18 25 5 18.55 168 4 2012 1 25 16 11 22 20 20 6 19.1 140 20 2012 0 25 23 19 27 28 25 4 13.1 88 24 2012 1 20 18 12 26 21 26 4 12.85 168 20 2012 1 23 23 8 23 25 19 4 9.5 94 4 2012 1 21 20 17 22 18 21 6 4.5 51 24 2012 0 23 20 13 26 24 23 8 11.85 48 22 2012 1 23 23 17 22 28 24 10 13.6 145 16 2012 1 11 13 7 17 9 6 4 11.7 66 3 2012 0 21 21 23 25 22 22 5 12.4 85 15 2012 1 27 26 18 22 26 21 4 13.35 109 24 2012 0 19 18 13 28 28 28 4 11.4 63 17 2012 0 21 19 17 22 18 24 4 14.9 102 20 2012 0 16 18 13 21 23 14 16 19.9 162 27 2012 0 21 18 8 24 15 20 7 11.2 86 26 2012 0 22 19 16 26 24 28 4 14.6 114 23 2012 1 16 13 14 26 12 19 4 17.6 164 17 2012 1 18 10 13 24 12 24 14 14.05 119 20 2012 1 23 21 19 27 20 21 5 16.1 126 22 2012 1 24 24 15 22 25 21 5 13.35 132 19 2012 1 20 21 15 23 24 26 5 11.85 142 24 2012 1 20 23 8 22 23 24 5 11.95 83 19 2012 0 18 18 14 23 18 26 7 14.75 94 23 2012 0 4 11 7 15 20 25 19 15.15 81 15 2012 1 14 16 11 20 22 23 16 13.2 166 27 2012 0 22 20 17 22 20 24 4 16.85 110 26 2012 0 17 20 19 25 25 24 4 7.85 64 22 2012 1 23 26 17 27 28 26 7 7.7 93 22 2012 0 20 21 12 24 25 23 9 12.6 104 18 2012 0 18 12 12 21 14 20 5 7.85 105 15 2012 0 19 15 18 17 16 16 14 10.95 49 22 2012 0 20 18 16 26 24 24 4 12.35 88 27 2012 0 15 14 15 20 13 20 16 9.95 95 10 2012 0 24 18 20 22 19 23 10 14.9 102 20 2012 0 21 16 16 24 18 23 5 16.65 99 17 2012 0 19 19 12 23 16 18 6 13.4 63 23 2012 0 19 7 10 22 8 21 4 13.95 76 19 2012 0 27 21 28 28 27 25 4 15.7 109 13 2012 0 23 24 19 21 23 23 4 16.85 117 27 2012 0 23 21 18 24 20 26 5 10.95 57 23 2012 0 20 20 19 28 20 26 4 15.35 120 16 2012 0 17 22 8 25 26 24 4 12.2 73 25 2012 0 21 17 17 24 23 23 5 15.1 91 2 2012 0 23 19 16 24 24 21 4 17.75 108 26 2012 0 22 20 18 21 21 23 4 15.2 105 20 2012 1 16 16 12 20 15 20 5 14.6 117 23 2012 0 20 20 17 26 22 23 8 16.65 119 22 2012 0 16 16 13 16 25 24 15 8.1 31 24
Names of X columns:
year group_num. AMS.I1 AMS.I2 AMS.I3 AMS.E1 AMS.E2 AMS.E3 AMS.A TOT LFM NUMERACYTOT
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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Raw Input
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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