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Data X:
1 149 0 0.5 0.67 0.67 2011 1 0.5 0 0.89 139 0.5 0.5 0.83 0.33 2011 1 1 1 0.89 148 0 0.4 1 0.67 2011 1 1 0 0.89 158 0 0.5 0.83 0 2011 1 0 1 0.89 128 1 0.7 0.67 0 2011 1 1 1 0.78 224 0.5 0.3 0 0 2011 1 0.5 1 0.89 159 0.5 0.4 0.83 0.67 2011 1 0 0 1 105 1 0.4 0.5 0.67 2011 1 1 1 0.89 159 0.5 0.7 0.83 0 2011 1 0 1 0.78 167 0.5 0.6 0.33 0.67 2011 1 0.5 1 1 165 0 0.6 0.5 1 2011 1 0.5 1 0.78 159 0.5 0.2 0.67 0 2011 1 0.5 1 0.89 119 0.5 0.4 1 0 2011 1 0.5 1 0.89 176 0 0.4 0.5 0.67 2011 1 1 0 0.89 54 0 0.5 0.67 0.33 2011 1 0 0 0.89 91 0 0.3 0.17 0.67 2011 0 0.5 0 0.89 163 0.5 0.4 0.83 0.33 2011 1 0.5 1 0.67 124 0.5 0.7 0.67 0.33 2011 1 1 0 1 137 0 0.5 0.67 0.33 2011 0 1 1 0.78 121 0 0.2 0.67 0 2011 1 1 0 0.78 153 0 0.3 0.5 0.67 2011 1 0.5 1 0.89 148 0 0.6 1 0.33 2011 1 1 1 0.78 221 0 0.6 0.83 0.33 2011 1 1 0 0.89 188 0 0.2 0.83 0.33 2011 1 1 1 0.89 149 1 0.7 1 0.67 2011 1 0 1 0.33 244 0 0.2 0.67 0 2011 1 0 1 1 148 1 1 1 0.33 2011 0 1 1 0.89 92 0 0.4 0.83 0.67 2011 0 0.5 0 0.89 150 0 0.4 1 1 2011 1 1 1 0.67 153 0 0.2 0.83 0.67 2011 1 0.5 0 0.56 94 0 0.4 0.67 0.33 2011 1 1 0 0.89 156 0.5 0.4 0.67 0 2011 1 1 0 0.89 132 0.5 0.7 1 0.67 2011 1 0.5 1 1 161 0 0.2 0.67 0.67 2011 1 0.5 1 0.78 105 0 0.6 1 1 2011 1 0.5 1 0.78 97 0.5 0.3 1 1 2011 1 0.5 1 0.33 151 0 0.3 0.5 0.33 2011 1 0 0 0.78 131 0.5 0.2 0.67 0 2011 0 0 1 0.89 166 0.5 0.5 0.83 0.67 2011 1 0.5 1 0.89 157 0.5 0.7 1 0.67 2011 1 1 0 0.78 111 0.5 0.6 1 0.67 2011 1 0.5 1 0.89 145 0.5 0.4 1 0.67 2011 1 1 1 0.89 162 0.5 0.6 1 0.33 2011 1 1 1 1 163 0 0.4 1 1 2011 1 1 1 0.67 59 0 0.3 0.83 0.67 2011 0 1 1 1 187 0.5 0.5 0.83 0.67 2011 1 0.5 0 0.89 109 0 0.2 0.5 0 2011 1 1 1 0.89 90 0.5 0.3 0.83 0 2011 0 1 1 0.89 105 0 0.5 0.17 0 2011 1 1 0 0.78 83 0.5 0.7 0.83 1 2011 0 1 1 0.89 116 1 0.4 1 0.67 2011 0 0.5 1 0.78 42 0 0.3 1 0 2011 0 0.5 1 0.78 148 1 0.2 0.67 0.67 2011 1 1 1 1 155 0 0.5 1 0 2011 0 0.5 1 0.78 125 0.5 0.4 1 0 2011 1 0 1 1 116 1 0.6 1 0.67 2011 1 1 1 0.78 128 0 0.4 0.83 1 2011 0 1 0 0.67 138 0 0.4 0.33 0 2011 1 0.5 1 0.33 49 0 0.2 0.33 0.33 2011 0 0 0 1 96 0.5 0.9 1 0.67 2011 0 1 1 1 164 1 0.8 1 0.67 2011 1 0.5 1 0.78 162 0.5 0.8 0.83 0 2011 1 1 0 0.67 99 0.5 0.3 1 1 2011 1 1 0 1 202 0 0.2 0.83 0.67 2011 1 0.5 1 0.89 186 0.5 0.4 0.67 0 2011 1 1 0 0.89 66 0 0.2 0.83 1 2011 0 1 1 0.78 183 0.5 0.2 0.67 0.67 2011 1 1 0 1 214 0 0.1 0.83 0.67 2011 1 1 1 0.56 188 0.5 0.4 0.67 1 2011 1 0 1 0.67 104 0.5 0.5 1 0 2011 0 0.5 0 0.89 177 0.5 0.8 0.83 0.33 2011 1 1 0 0.89 126 0 0.4 0.67 0.67 2011 1 0.5 0 0.89 76 0.5 0.6 0.83 0.33 2011 0 0.5 0 0.89 99 0.5 0.5 0.83 0.67 2011 0 1 1 0.78 139 0 0.3 0.67 0 2011 1 0 0 1 162 0.5 0.4 0.33 0 2011 1 0 0 1 108 0.5 0.6 0.83 0.67 2011 0 0.5 1 0.89 159 0 0.4 1 0.33 2011 1 0.5 0 0.44 74 0 0.3 0.83 0 2011 0 0 0 0.78 110 1 0.8 0.83 0 2011 1 1 1 0.89 96 1 0.6 0.5 0.33 2011 0 1 0 0.67 116 0 0.3 0.5 0 2011 0 0 0 0.78 87 0.5 0.5 0.83 0.67 2011 0 1 0 0.78 97 0 0.4 1 0.33 2011 0 1 1 0.33 127 0 0.3 0.33 0.67 2011 0 0 0 0.89 106 0 0.7 1 0.33 2011 0 0.5 1 0.89 80 0.5 0.2 0.67 0.33 2011 0 0.5 1 0.89 74 0 0.4 0.83 1 2011 0 1 0 0.89 91 0.5 0.6 1 0.67 2011 0 0.5 0 0.56 133 0 0.6 0.83 0 2011 0 1 0 0.67 74 0.5 0.6 0.83 0.67 2011 0 0.5 1 0.67 114 0.5 0.4 1 0.33 2011 0 1 1 0.78 140 0 0.6 0.83 0 2011 0 1 1 0.78 95 0.5 0.5 1 0.33 2011 0 1 0 0.78 98 0 0.5 0.83 0 2011 0 1 1 0.89 121 0 0.6 0.67 0 2011 0 1 0 1 126 0.5 0.8 0.83 0.33 2011 0 1 1 0.89 98 1 0.5 0.83 0.67 2011 0 0.5 1 0.89 95 0.5 0.6 0.83 0.67 2011 0 1 1 0.78 110 0.5 0.4 0.83 0.67 2011 0 1 1 1 70 0.5 0.3 0.67 0.67 2011 0 1 1 0.78 102 0 0.3 0.83 1 2011 0 0.5 0 0.67 86 0 0.2 0 0 2011 0 0 1 0.78 130 0 0.4 0.83 0 2011 0 0.5 1 0.89 96 0 0.5 1 0 2011 0 0.5 1 0.67 102 0.5 0.3 0.17 0 2011 0 0 0 0.22 100 0.5 0.4 0.17 0 2011 0 0 0 0.44 94 0 0.5 0.5 1 2011 0 0 0 0.89 52 0 0.3 0.5 0.67 2011 0 1 0 0.67 98 0 0.5 1 0 2011 0 0.5 0 0.89 118 0 0.4 0.67 0.67 2011 0 0.5 0 0.67 99 0 0.4 0.83 0.67 2011 0 1 1 0.78 48 1 0.6 1 0 2012 1 1 1 0.78 50 1 0.3 1 0.67 2012 1 1 1 0.78 150 1 0.4 1 0.33 2012 1 0.5 1 1 154 1 0.3 1 1 2012 1 1 1 0.78 109 1 1 1 1 2012 0 1 0 0.67 68 0 0.4 1 0 2012 0 0.5 1 0.89 194 0.5 0.8 0.83 1 2012 1 1 1 0.89 158 1 0.3 1 0.67 2012 1 1 0 1 159 0 0.5 0.83 0.67 2012 1 1 1 0.78 67 0 0.4 1 0 2012 1 0.5 0 0.67 147 0 0.3 0.83 0.67 2012 1 1 0 0.89 39 0 0.5 0.83 1 2012 1 1 1 0.67 100 0 0.3 1 0.67 2012 1 1 1 0.67 111 0 0.3 0.67 0 2012 1 1 1 1 138 0 0.4 0.83 0 2012 1 1 1 0.67 101 0 0.3 1 0 2012 1 0.5 1 1 131 0.5 0.6 1 0.33 2012 0 0.5 1 0.89 101 1 0.6 0.83 0.67 2012 1 1 1 0.89 114 1 0.4 1 1 2012 1 1 1 1 165 0 0.4 1 0 2012 1 0 0 0.67 114 0 0.4 1 0.67 2012 1 0.5 1 0.44 111 0.5 0.3 0.67 0.67 2012 1 1 1 0.89 75 1 0.2 1 0.33 2012 1 0 1 0.56 82 0 0.5 0.83 0.67 2012 1 1 1 0.78 121 1 0.4 1 0.67 2012 1 1 1 1 32 0 0.4 1 0.67 2012 1 0 1 1 150 0 0.4 0.83 0.67 2012 1 1 0 0.89 117 0.5 0.3 0.67 0.67 2012 1 0.5 1 0.67 71 1 0.4 0.83 0.67 2012 0 0.5 1 0.89 165 0.5 0.2 1 0.33 2012 1 1 1 0.33 154 0 0 0 0 2012 1 0 1 0.89 126 0.5 0.4 1 0.67 2012 1 1 1 0.78 149 1 0.6 1 0 2012 1 1 0 1 145 0 0.4 0.67 0.67 2012 1 0.5 0 0.44 120 0 0.4 1 0 2012 1 0.5 1 0.67 109 0.5 0.4 0.83 0 2012 1 0 0 0.33 132 0.5 0.2 0.17 0 2012 1 0 0 0.89 172 1 0.4 0.83 1 2012 1 1 1 0.89 169 0 0.3 0.83 0 2012 1 0.5 0 1 114 1 0.6 0.83 0.67 2012 1 0 1 0.89 156 0 0.6 0.83 1 2012 1 1 1 0.89 172 0 0.4 0.83 0 2012 1 1 0 1 68 1 0.5 1 0.67 2012 0 0.5 1 0.89 89 0.5 0.4 0.83 0 2012 0 1 1 1 167 1 0.6 1 1 2012 1 1 1 0.78 113 0.5 0.6 0.83 0.67 2012 1 1 0 0.78 115 0.5 0.9 1 0.67 2012 0 1 0 0.67 78 0.5 0.4 0.83 0.67 2012 0 0 0 0.89 118 0.5 0.8 1 1 2012 0 1 0 0.67 87 0 0.5 0.83 1 2012 0 1 1 0.78 173 0 0.4 0.83 1 2012 1 0 0 0.89 2 1 0.4 1 0.67 2012 1 0.5 1 0.89 162 1 0.7 1 1 2012 0 0.5 0 0.78 49 1 0.4 1 0.33 2012 0 1 1 1 122 0.5 0.8 1 0.67 2012 0 1 0 1 96 1 0.4 1 1 2012 0 0.5 1 1 100 0 0.3 1 0.67 2012 0 0.5 0 0.67 82 0.5 0.5 1 0.67 2012 0 1 0 0.89 100 1 0.8 1 0.67 2012 0 1 1 1 115 0 0.4 0.83 0.33 2012 0 0.5 0 1 141 0.5 1 1 1 2012 0 0 1 0.89 165 1 0.5 1 0.67 2012 1 1 1 0.89 165 1 0.5 1 0.67 2012 1 1 1 0.89 110 0 0.3 1 0.33 2012 0 1 1 0.89 118 0.5 0.3 0.83 0.33 2012 1 1 1 0.89 158 0 0.3 0.5 0 2012 1 1 0 1 146 0.5 0.4 0.67 0.33 2012 0 0.5 1 0.67 49 0 0.5 1 0.33 2012 1 1 0 1 90 0.5 0.5 0.67 0.67 2012 0 1 0 0.89 121 0 0.4 1 0 2012 0 0 0 0.89 155 0.5 0.7 1 1 2012 1 0 1 0.89 104 0 0.5 0.5 0.33 2012 0 0.5 0 0.89 147 1 0.4 0.67 0.33 2012 0 0 1 1 110 0 0.7 0.67 1 2012 0 1 0 1 108 0 0.7 0.67 1 2012 0 1 0 1 113 0 0.7 0.67 1 2012 0 1 0 0.89 115 0 0.7 0.67 1 2012 0 1 0 0.89 61 0 0.7 0.67 0 2012 0 0 1 0.89 60 0.5 0.7 1 0.67 2012 0 1 1 0.33 109 0.5 0.1 0.67 0.33 2012 0 0 1 0.67 68 0.5 0.2 0.67 0.67 2012 0 1 1 0.56 111 0 0.3 0.33 0.33 2012 0 1 0 0.44 77 0 0.6 0.83 0.33 2012 0 0.5 0 1 73 1 0.8 1 1 2012 0 1 1 0.89 151 0.5 0.8 1 0.33 2012 1 0.5 0 0.33 89 0 0 0.17 0 2012 0 0 0 0.67 78 0 0.3 0.67 0.33 2012 0 1 0 0.67 110 0.5 0.6 0.83 0.33 2012 0 1 0 1 220 0 0.5 0.83 0.67 2012 1 1 1 0.78 65 0 0.7 1 0.33 2012 0 0.5 1 0.67 141 0.5 0.3 0.83 0 2012 1 1 0 1 117 0 0.3 1 0.67 2012 0 0 0 0.78 122 0 0.4 1 0.67 2012 1 0.5 1 0.89 63 0 0.4 0.83 1 2012 0 1 0 0.89 44 0 0.1 0.83 0 2012 1 1 1 0.89 52 0 0.5 1 0.67 2012 0 1 1 0 131 0 0 0 0 2012 0 0 0 0.67 101 0.5 0.4 1 0.33 2012 0 0 1 1 42 1 0.6 0.83 0.67 2012 0 0.5 1 1 152 0.5 0.4 1 0.33 2012 1 1 1 0.67 107 0.5 0.1 0.33 0 2012 1 1 0 0.89 77 0 0.3 0.83 0 2012 0 1 0 0.89 154 0 0.7 0.83 0.67 2012 1 1 0 0.56 103 0 0.3 0.17 0 2012 1 1 1 0.67 96 0.5 0.5 0.83 0.33 2012 0 0 1 1 175 1 0.3 0.83 0.67 2012 1 1 1 1 57 0.5 0.6 0.67 0.67 2012 0 1 1 1 112 0 0.9 1 1 2012 0 1 0 0.67 143 0.5 0.4 0.83 0 2012 1 1 0 0.44 49 0.5 0.3 1 0 2012 0 0.5 0 0.89 110 1 0.9 1 0.67 2012 1 1 1 0.44 131 0.5 0.5 1 0 2012 1 0 1 0.56 167 0.5 0.3 1 1 2012 1 0.5 0 0.89 56 0 0.6 0.83 0.67 2012 0 0.5 0 0.67 137 0 0.2 1 0.33 2012 1 0.5 0 0.89 86 0.5 0.4 0.83 1 2012 0 1 1 1 121 0.5 0.5 0.83 0.67 2012 1 0.5 1 0.78 149 0 0.4 0.83 0.67 2012 1 0.5 0 0.44 168 0 0 0 0 2012 1 0 0 0.89 140 0.5 0.2 1 0.33 2012 1 1 0 0.89 88 0.5 0.5 1 0.67 2012 0 1 1 0.89 168 0 0.3 1 0.67 2012 1 0.5 1 0.44 94 0 0 0 0 2012 1 0 1 1 51 0 0.5 0.83 1 2012 1 1 1 0.89 48 0 0.6 0.83 0.33 2012 0 1 0 0.67 145 0.5 0.3 0.83 0 2012 1 0.5 1 0.33 66 0 0 0 0 2012 1 0 1 0.78 85 0.5 0.3 0.67 0 2012 0 0 1 0.89 109 0.5 0.5 1 0.67 2012 1 1 0 0.78 63 0 0.4 0.67 0 2012 0 1 0 0.78 102 0 0.5 0.83 0.67 2012 0 0.5 1 0.89 162 1 0.7 1 1 2012 0 0.5 0 0.78 86 0.5 0.8 1 0.67 2012 0 1 1 0.78 114 0.5 0.6 1 0.33 2012 0 1 1 0.67 164 0 0.4 0.83 0.33 2012 1 0.5 0 0.89 119 0.5 0.5 0.83 0.33 2012 1 0 1 0.89 126 0.5 0.5 1 0 2012 1 1 0 0.78 132 0 0.3 1 0.33 2012 1 1 1 1 142 0.5 0.6 1 0 2012 1 1 1 1 83 0 0.3 0.67 0.67 2012 1 0.5 0 0.78 94 0.5 0.6 0.83 1 2012 0 0.5 1 0.78 81 0 0.3 0.33 0.33 2012 0 1 0 0.89 166 1 0.7 1 0.67 2012 1 1 1 0.89 110 0 0.7 1 1 2012 0 1 0 0.67 64 0.5 0.6 0.67 1 2012 0 1 1 1 93 0.5 0.5 1 0.33 2012 1 0 0 0.67 104 0 0.5 0.83 0.33 2012 0 0.5 0 0.56 105 0 0.4 0.67 0 2012 0 1 1 0.78 49 1 0.4 1 0.33 2012 0 1 1 1 88 0 0.7 1 1 2012 0 1 0 0.67 95 0.5 0.2 0.17 0 2012 0 0 1 0.78 102 0 0.5 0.83 0.67 2012 0 0.5 1 0.56 99 0.5 0.4 0.83 0.67 2012 0 0 0 1 63 1 0.2 1 0.67 2012 0 1 1 0.89 76 0 0.5 0.67 0.67 2012 0 0 0 0.44 109 0 0.4 0.5 0 2012 0 1 0 1 117 1 0.7 0.67 1 2012 0 1 1 0.89 57 1 0.6 0.83 0.67 2012 0 0 1 0.78 120 0 0.4 0.83 0 2012 0 0 0 0.89 73 1 0.5 1 0.67 2012 0 1 1 0.11 91 0 0 0.17 0 2012 0 0 0 0.89 108 0.5 0.7 1 0.67 2012 0 1 0 0.89 105 0 0.4 0.67 0.67 2012 0 1 1 1 117 0 0.5 0.67 1 2012 1 1 0 0.89 119 0 0.6 0.83 0.67 2012 0 0.5 0 1 31 0.5 0.8 0.5 0.67 2012 0 0.5 1
Names of X columns:
Calculation LFM Probability_and_Sampling Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio year group Estimation gender
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
1
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
0
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
White Noise
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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Big Analytics Cloud Computing Center
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