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mark: adding the R codez

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0 parents commit 2c78a566acabec836e41cd3b5aaef4f9c7a5f124 @mneedham committed Oct 7, 2012
1 DAA.01.txt
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+cond pre.wm.s post.wm.s pre.wm.v post.wm.v des 18 23 27 24 des 15 17 15 16 des 20 21 25 41 des 15 15 13 17 des 19 24 17 25 des 18 20 21 26 des 16 24 7 3 des 21 21 17 4 des 19 22 17 28 des 20 26 18 18 des 13 26 16 13 des 14 23 12 0 des 15 19 10 2 des 15 16 20 15 des 17 26 7 17 des 20 24 37 47 des 11 26 26 44 des 18 23 6 18 des 23 31 8 19 des 16 24 16 0 des 14 20 16 35 des 21 25 6 17 des 19 21 12 14 des 20 20 29 19 des 19 25 26 42 des 22 24 22 0 des 19 21 37 40 des 18 30 22 19 des 17 21 12 21 des 19 25 0 9 des 26 29 19 33 des 19 23 1 0 des 20 17 0 0 des 16 21 15 14 des 18 21 4 18 des 25 31 13 23 des 19 24 25 30 des 23 28 30 32 des 14 20 15 15 des 19 25 18 20 des 19 22 26 31 des 11 16 29 30 des 22 25 22 14 des 14 20 5 4 des 14 29 9 15 des 17 19 20 23 des 17 22 9 16 des 19 28 10 0 des 22 27 16 22 des 18 26 23 7 des 20 26 14 15 des 15 27 17 34 des 19 29 32 35 des 18 22 21 28 des 14 14 0 0 des 20 21 19 10 des 13 24 12 3 des 13 15 16 29 des 17 25 9 23 des 18 19 8 9 des 21 20 7 8 des 19 22 16 8 des 19 23 22 1 des 16 25 32 16 des 19 25 7 16 des 20 19 15 26 des 15 23 13 12 des 18 16 18 23 des 21 27 15 12 des 20 24 15 13 des 17 19 27 38 des 24 33 37 37 des 18 24 16 3 des 18 21 11 13 des 21 27 17 2 des 21 25 18 8 des 19 24 16 27 des 14 20 15 9 des 18 18 23 17 des 12 29 22 0 des 23 33 25 18 des 16 22 24 24 des 19 26 14 29 des 18 24 10 15 des 24 21 16 15 des 19 29 8 14 des 19 22 9 10 des 15 20 9 14 des 16 21 4 0 des 17 21 8 16 des 16 20 25 27 des 24 29 18 23 des 23 21 6 20 des 19 22 12 5 des 17 28 12 18 des 18 26 11 0 des 18 21 23 35 des 10 11 12 16 des 20 27 45 52 des 16 20 17 36 aer 15 12 0 0 aer 21 12 22 23 aer 18 15 13 22 aer 15 12 23 25 aer 18 21 31 32 aer 17 12 20 17 aer 20 12 34 25 aer 18 21 10 16 aer 19 23 13 8 aer 18 11 22 34 aer 18 23 0 0 aer 12 18 18 31 aer 14 11 8 6 aer 15 19 9 18 aer 20 11 21 33 aer 16 23 19 24 aer 17 11 7 10 aer 25 37 29 0 aer 2 24 13 0 aer 19 24 0 4 aer 18 25 13 13 aer 3 18 28 0 aer 16 22 25 0 aer 17 22 6 0 aer 16 20 13 13 aer 4 29 18 2 aer 19 26 6 0 aer 19 25 12 9 aer 3 29 2 20 aer 22 31 21 16 aer 19 28 22 10 aer 21 26 13 11 aer 2 27 28 30 aer 13 17 16 17 aer 18 25 16 31 aer 17 26 10 20 aer 3 22 13 3 aer 19 20 6 16 aer 18 21 28 29 aer 2 21 4 12 aer 23 32 20 28 aer 16 24 18 16 aer 3 20 19 24 aer 16 22 9 10 aer 22 30 12 33 aer 2 30 8 33 aer 18 25 6 4 aer 20 29 2 19 aer 1 18 19 20 aer 19 27 8 5 aer 21 20 11 0 aer 2 21 0 0 aer 17 20 1 11 aer 17 27 10 10 aer 3 32 14 11 aer 16 28 15 9 aer 13 16 10 11 aer 2 23 8 14 aer 20 25 19 10 aer 24 28 14 33 aer 3 27 15 19 aer 19 22 10 18 aer 14 21 7 16 aer 14 22 13 41 aer 15 22 0 11 aer 18 24 14 37 aer 17 29 35 33 aer 15 18 7 0 aer 16 23 18 30 aer 20 26 5 0 aer 20 30 10 15 aer 21 30 32 37 aer 13 23 15 38 aer 22 28 28 43 aer 16 21 20 26 aer 15 23 0 6 aer 15 17 36 35 aer 13 18 19 35 aer 23 29 14 11 aer 20 27 12 8 aer 14 23 0 0 aer 17 24 7 19 aer 22 27 24 27 aer 13 22 6 15 aer 21 23 23 21 aer 20 32 35 39 aer 14 22 15 20 aer 19 29 13 19 aer 20 26 16 32 aer 21 29 7 14 aer 15 15 0 0 aer 21 31 27 32 aer 17 24 9 14 aer 17 23 2 12 aer 21 30 7 5 aer 17 24 0 0 aer 19 27 16 30 aer 22 31 14 15 aer 23 32 24 25 aer 18 27 11 13
1 DAA.02.txt
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+pid cond pre.wm.s1 pre.wm.s2 post.wm.s1 post.wm.s2 pre.wm.v1 pre.wm.v2 post.wm.v1 post.wm.v2 1 des 21 31 25 35 7 1 19 4 2 des 15 26 21 29 0 0 0 0 3 des 18 27 23 31 8 2 3 0 4 des 17 31 18 39 0 0 0 0 5 des 15 26 16 29 26 15 32 20 6 des 17 28 16 28 2 4 0 7 7 des 15 27 16 34 9 11 10 11 8 des 17 27 28 30 16 10 20 13 9 des 20 27 22 39 12 11 4 12 10 des 17 27 14 30 6 6 22 7 11 des 15 24 17 25 11 12 0 13 12 des 22 29 30 29 9 3 19 13 13 des 14 26 17 29 15 14 9 14 14 des 21 31 29 30 23 12 35 16 15 des 18 26 24 32 3 8 20 8 16 des 18 30 19 41 20 12 27 16 17 des 20 32 18 33 26 16 26 16 18 des 16 28 23 25 3 3 11 7 19 des 20 28 24 29 16 8 31 11 20 des 24 33 29 29 53 37 63 38 21 des 17 26 22 26 25 21 7 27 22 des 19 28 27 31 20 17 15 19 23 des 16 21 18 30 5 3 13 12 24 des 16 27 26 29 9 10 19 10 25 des 16 25 23 31 19 14 21 21 26 des 16 25 22 26 10 12 17 14 27 des 19 28 21 33 22 21 21 26 28 des 19 28 17 34 17 13 21 18 29 des 12 24 10 25 2 2 0 14 30 des 19 27 23 28 12 3 22 10 31 des 20 29 19 34 10 7 0 9 32 des 18 24 21 24 0 4 22 8 33 des 19 32 23 32 16 15 24 16 34 des 20 24 20 22 9 6 10 5 35 des 17 23 20 22 8 13 6 14 36 des 19 31 29 34 5 6 10 10 37 des 17 27 18 28 0 0 1 0 38 des 23 30 24 29 22 23 25 32 39 des 17 34 19 40 20 21 36 27 40 des 26 29 33 34 11 11 22 15 41 des 22 25 22 29 9 9 7 8 42 des 21 26 31 30 13 5 15 9 43 des 16 25 18 26 20 15 11 13 44 des 19 32 24 37 22 18 43 16 45 des 20 28 26 33 0 0 20 0 46 des 22 33 24 37 29 24 30 27 47 des 20 27 19 32 2 3 0 3 48 des 17 28 21 28 19 14 18 14 49 des 17 19 22 24 5 3 2 6 50 des 18 22 20 23 0 3 6 2 51 des 23 28 26 30 24 15 26 18 52 des 18 26 20 24 25 17 20 20 53 des 16 27 24 29 0 5 6 11 54 des 19 25 25 25 26 21 34 21 55 des 22 35 22 35 3 1 4 4 56 des 16 26 14 34 0 0 0 0 57 des 18 23 21 26 24 17 26 19 58 des 20 31 23 34 18 9 27 9 59 des 20 28 23 30 17 17 28 19 60 des 14 25 23 26 14 18 13 18 61 des 17 25 20 34 4 8 0 10 62 des 21 32 21 34 8 3 11 10 63 des 17 22 18 32 4 2 0 6 64 des 19 26 30 29 24 19 43 27 65 des 15 22 21 25 10 7 6 7 66 des 14 23 19 24 16 13 3 22 67 des 20 23 22 27 13 8 27 10 68 des 21 31 26 38 17 10 19 10 69 des 17 23 18 30 14 9 11 9 70 des 20 24 26 31 3 4 0 0 71 des 16 29 21 28 28 20 25 21 72 des 18 26 27 34 10 8 18 13 73 des 15 24 19 26 4 7 0 6 74 des 20 23 23 23 6 7 22 5 75 des 21 31 24 36 9 11 9 22 76 des 24 32 26 42 36 26 39 29 77 des 20 25 24 22 17 14 20 15 78 des 21 30 24 34 29 25 24 33 79 des 19 26 26 35 0 0 0 3 80 des 17 29 18 27 2 4 0 2 81 des 17 24 20 30 23 20 0 17 82 des 17 28 18 30 14 12 20 11 83 des 12 22 13 26 2 0 7 5 84 des 20 26 17 28 14 11 26 17 85 des 20 27 21 32 36 24 22 25 86 des 15 24 23 32 2 0 13 7 87 des 15 27 14 35 0 0 0 0 88 des 21 28 26 39 2 0 11 4 89 des 20 31 21 35 0 0 0 0 90 des 15 24 16 31 24 17 39 21 91 des 19 25 21 34 1 1 13 1 92 des 23 33 28 40 2 4 0 9 93 des 19 24 29 31 2 5 0 10 94 des 19 31 24 35 23 16 15 23 95 des 21 31 25 30 11 6 6 6 96 des 16 27 21 29 29 23 44 29 97 des 18 22 29 31 0 0 5 0 98 des 16 24 24 23 15 13 17 13 99 des 18 28 23 26 17 16 39 20 100 des 20 27 24 27 13 8 0 14 101 aer 24 33 27 39 14 10 13 14 102 aer 19 25 29 29 7 5 7 5 103 aer 16 28 20 33 19 15 9 13 104 aer 19 24 28 23 18 11 27 11 105 aer 20 24 27 34 20 13 43 18 106 aer 17 21 25 30 20 11 21 13 107 aer 20 22 21 30 26 14 38 16 108 aer 16 24 26 30 20 12 3 11 109 aer 21 29 33 40 35 26 30 30 110 aer 19 30 26 33 27 19 34 21 111 aer 20 27 25 35 16 13 5 16 112 aer 13 26 22 32 8 3 15 2 113 aer 15 28 20 38 19 15 35 15 114 aer 15 26 23 26 0 0 0 6 115 aer 15 29 25 37 0 2 2 6 116 aer 25 31 29 43 10 2 32 5 117 aer 18 26 31 33 0 0 1 0 118 aer 17 24 26 25 4 5 2 7 119 aer 17 30 20 39 13 12 20 15 120 aer 17 28 22 32 20 15 16 19 121 aer 18 23 22 29 25 16 34 23 122 aer 15 30 21 39 6 3 24 3 123 aer 22 27 31 42 0 0 11 0 124 aer 18 24 24 30 27 19 30 21 125 aer 21 25 28 38 3 1 11 6 126 aer 17 28 21 36 36 26 35 29 127 aer 19 31 24 35 10 9 10 12 128 aer 18 27 20 38 23 17 21 16 129 aer 16 26 25 39 26 20 22 26 130 aer 18 25 28 34 35 24 43 28 131 aer 18 25 26 30 25 23 38 23 132 aer 14 33 22 38 10 5 26 9 133 aer 11 23 20 24 25 21 20 25 134 aer 20 28 26 33 3 5 15 5 135 aer 16 23 28 32 22 19 39 24 136 aer 21 26 21 35 23 16 33 22 137 aer 19 31 29 38 6 5 22 6 138 aer 15 21 22 27 32 20 42 24 139 aer 17 24 23 32 6 7 12 6 140 aer 17 26 23 34 30 23 32 22 141 aer 12 24 23 34 3 1 3 1 142 aer 14 25 14 29 0 0 0 0 143 aer 20 26 27 30 21 14 12 18 144 aer 18 27 23 33 6 2 6 4 145 aer 21 26 28 36 8 8 15 9 146 aer 20 30 31 37 4 0 3 1 147 aer 17 28 25 36 8 4 18 1 148 aer 17 25 27 33 15 20 6 18 149 aer 23 24 30 33 14 11 0 20 150 aer 21 30 30 32 9 12 0 20 151 aer 14 26 20 34 0 0 1 0 152 aer 16 26 24 36 9 6 7 10 153 aer 16 30 22 36 17 12 10 14 154 aer 15 25 30 29 11 8 21 8 155 aer 15 21 22 24 22 20 26 25 156 aer 18 26 24 32 12 7 3 9 157 aer 17 29 24 33 19 13 36 14 158 aer 22 34 27 41 21 14 15 19 159 aer 18 24 27 32 6 10 12 12 160 aer 21 27 30 36 5 1 0 13 161 aer 21 26 29 32 18 16 26 15 162 aer 17 26 21 33 1 2 2 3 163 aer 15 23 22 25 10 6 10 7 164 aer 21 34 30 37 8 8 4 13 165 aer 19 26 29 33 5 4 10 6 166 aer 21 29 33 44 14 11 25 12 167 aer 25 34 34 38 0 0 0 0 168 aer 21 23 27 31 23 10 34 17 169 aer 17 26 23 37 10 7 28 7 170 aer 22 35 28 42 14 6 26 13 171 aer 21 28 28 35 13 4 0 3 172 aer 22 29 27 30 19 5 36 1 173 aer 16 24 27 32 9 1 7 6 174 aer 19 22 24 29 0 1 0 4 175 aer 13 24 25 27 9 6 13 8 176 aer 13 25 21 31 18 17 24 23 177 aer 23 33 23 35 24 17 21 24 178 aer 15 27 20 37 19 15 37 20 179 aer 20 32 28 38 8 7 10 15 180 aer 18 28 21 38 6 6 0 8 181 aer 19 28 25 34 1 0 5 0 182 aer 15 22 20 29 26 16 22 12 183 aer 23 33 31 42 32 23 32 26 184 aer 20 29 28 35 12 9 28 9 185 aer 22 27 30 34 20 19 13 16 186 aer 19 27 27 34 1 0 0 0 187 aer 20 28 26 32 2 3 0 9 188 aer 13 28 23 31 14 14 14 17 189 aer 15 25 20 36 27 21 28 23 190 aer 15 24 23 33 16 12 25 11 191 aer 17 21 22 27 14 11 20 16 192 aer 17 25 25 37 0 4 2 3 193 aer 14 26 21 26 9 8 30 9 194 aer 18 28 23 40 10 4 3 6 195 aer 17 29 20 35 0 0 38 0 196 aer 20 25 30 33 19 13 20 19 197 aer 13 23 23 35 22 16 17 21 198 aer 19 24 26 33 15 9 16 14 199 aer 18 30 31 43 20 9 23 9 200 aer 16 29 20 34 22 16 45 19
1 DAA.03.txt
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+pid age activeyears endurance 1 60 10 18 2 40 9 36 3 29 2 51 4 47 10 18 5 48 9 23 6 42 6 30 7 55 8 8 8 43 19 40 9 39 9 28 10 51 14 15 11 54 15 49 12 52 4 27 13 53 3 12 14 68 17 43 15 57 24 47 16 30 4 21 17 35 4 32 18 56 16 33 19 62 14 25 20 39 13 30 21 32 5 41 22 67 8 25 23 56 13 45 24 47 14 33 25 47 10 29 26 61 11 44 27 40 15 28 28 49 4 20 29 28 13 45 30 40 6 28 31 44 5 18 32 41 18 29 33 53 13 24 34 67 19 55 35 52 6 26 36 51 10 46 37 46 11 19 38 44 4 25 39 64 16 29 40 58 14 32 41 29 2 32 42 44 11 12 43 51 12 27 44 51 15 33 46 53 10 28 47 44 9 34 48 46 0 28 49 49 14 24 50 34 6 28 51 64 13 25 52 44 9 9 53 37 5 35 54 52 16 36 55 59 12 40 56 60 6 30 57 44 7 14 58 48 4 22 59 56 9 31 60 48 13 18 61 51 16 15 62 47 5 31 63 62 14 11 64 52 14 30 65 45 9 9 66 44 0 7 67 46 14 48 68 42 2 21 69 46 13 43 70 42 13 28 71 52 11 28 72 76 13 13 73 61 11 25 74 34 13 35 75 62 10 13 76 48 9 15 77 47 15 14 78 45 7 29 79 40 5 24 80 64 7 5 81 41 3 11 82 33 2 24 83 39 10 28 84 67 10 22 85 50 16 35 86 44 13 32 87 60 10 8 88 55 11 31 89 39 12 18 90 60 10 34 91 70 14 27 92 56 10 7 93 40 25 32 94 54 12 22 95 44 15 42 96 42 7 10 97 56 12 16 98 53 22 40 99 55 12 28 100 40 12 29 101 69 17 41 102 59 9 16 103 47 10 28 104 45 10 24 105 62 14 17 106 45 5 10 107 45 12 33 108 70 11 14 109 44 11 32 110 64 13 16 111 57 12 19 112 53 14 30 113 48 8 13 114 38 6 7 115 53 12 28 116 34 7 42 117 47 9 39 118 43 14 48 119 62 6 22 120 47 14 34 121 41 20 40 122 43 4 26 123 34 12 10 124 28 14 26 125 55 12 7 126 39 3 17 127 48 11 23 128 47 7 18 129 49 6 3 130 41 5 20 131 46 1 22 133 51 10 31 134 62 13 31 135 67 13 40 136 41 3 22 137 47 11 27 138 48 9 31 139 60 4 26 140 58 26 40 141 36 6 20 142 55 11 13 143 51 16 35 144 60 11 14 145 47 11 34 146 63 10 35 147 40 13 17 148 53 11 31 149 37 13 23 150 48 13 36 151 33 6 21 152 44 14 31 153 32 4 35 154 62 12 17 155 57 11 33 156 62 11 32 157 82 15 18 158 52 23 44 159 45 15 10 160 55 10 22 161 65 18 18 162 47 7 42 163 56 16 52 164 58 11 33 165 43 7 26 166 59 4 15 167 42 7 16 168 27 6 42 169 62 18 29 170 67 9 20 171 28 10 29 172 57 10 17 173 39 7 28 174 51 7 8 175 35 14 38 177 61 9 37 178 36 10 50 179 55 15 42 180 50 10 23 181 60 15 24 182 64 12 21 183 59 8 19 184 47 10 19 185 68 16 30 186 59 18 50 187 44 8 34 188 30 9 21 189 41 11 27 190 20 6 24 191 63 11 16 192 41 11 13 193 60 6 21 194 52 14 15 196 45 10 21 197 42 14 29 198 48 5 40 199 49 13 21 200 43 12 30 201 53 21 26 202 52 13 20 203 46 9 14 204 44 17 33 206 41 6 38 207 57 10 16 208 49 12 35 209 48 15 9 210 39 9 29 211 45 18 21 212 39 11 24 213 65 5 2 214 42 2 21 215 47 14 25 216 67 12 24 217 37 20 39 218 34 5 22 219 45 11 21 220 35 17 38 221 46 19 50 222 46 13 35 223 43 14 22 224 39 17 37 225 50 16 29 226 49 3 31 227 69 15 24 228 58 22 34 229 40 8 29 230 44 11 31 231 48 12 44 232 53 20 31 233 58 11 28 234 48 6 25 235 44 9 35 236 43 11 14 237 56 15 46 238 46 7 25 239 50 8 25 240 43 3 29 241 48 6 24 242 43 7 20 243 41 8 26 244 55 10 20 245 43 3 40 246 56 0 3 247 45 9 37 248 60 7 0 249 57 11 18 250 56 12 24
257 lecture3.R
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+library(psych)
+
+ratings <- read.table('~/Documents/Statistics/supplemental_stats1_ex01.txt')
+
+layout(matrix(c(1,2,3,4), 2, 2, byrow = TRUE))
+
+hist(ratings$FourPlay, xlab = "Rating")
+hist(ratings$HobNob, xlab = "Rating")
+hist(ratings$RedTruck, xlab = "Rating")
+hist(ratings$WoopWoop, xlab = "Rating")
+
+class(ratings)
+names(ratings)
+describe(ratings)
+
+memory <- read.table('~/Documents/Statistics/DAA.01.txt', header = T)
+
+des <- subset(memory, cond=='des')
+aer <- subset(memory, cond=='aer')
+
+hist(des$pre.wm.s, xlab = "Rating")
+hist(aer$pre.wm.s, xlab = "Rating")
+
+hist(des$post.wm.s, xlab = "Rating")
+hist(aer$post.wm.s, xlab = "Rating")
+
+describe(des$pre.wm.s)
+describe(des$post.wm.s)
+
+describe(aer$pre.wm.s)
+describe(aer$post.wm.s)
+
+describe(des$pre.wm.v)
+describe(des$post.wm.v)
+
+describe(aer$pre.wm.v)
+describe(aer$post.wm.v)
+
+
+hist(des$pre.wm.v, xlab = "Rating")
+hist(aer$pre.wm.v, xlab = "Rating")
+
+hist(des$post.wm.v, xlab = "Rating")
+hist(aer$post.wm.v, xlab = "Rating")
+
+hist(memory$post.wm.v, xlab = "Rating")
+hist(memory$pre.wm.v, xlab = "Rating")
+hist(memory$post.wm.v, xlab = "Rating")
+
+impact <- read.table('~/Documents/Statistics/supplemental_STATS1.EX.02.txt', header = T)
+
+hist(impact$memory.visual, xlab = "Visual Memory", main = "Histogram", col = "red")
+
+describe(impact)
+
+plot(impact$memory.verbal ~ impact$memory.visual, main = "Scatterplot", ylab = "Verbal memory", xlab = "Visual memory")
+
+abline(lm(impact$memory.verbal ~ impact$memory.visual), col = "blue")
+
+cor(impact$memory.verbal, impact$memory.visual)
+
+cor.test(impact$memory.verbal, impact$memory.visual)
+
+cor(impact)
+
+names(impact)
+
+library(gclus)
+impact.r = abs(cor(impact))
+impact.col = dmat.color(impact.r)
+impact.o <- order.single(impact.r)
+
+cpairs(impact, impact.o, panel.colors=impact.col, gap=.5, main = "Variables Ordered and Coloured by Correlation")
+
+# test/re-test reliability analysis
+
+setwd("~/Documents/Statistics")
+
+impact.col <- read.table("supplemental_STATS1.EX.03.COL.txt", header = T)
+
+names(impact.col)
+describe(impact.col)
+
+cor.test(impact.col$memory.verbal.A, impact.col$memory.verbal.B)
+
+cor(impact.col$memory.verbal.A, impact.col$memory.verbal.B)
+cor(impact.col$memory.visual.A, impact.col$memory.visual.B)
+cor(impact.col$speed.vismotor.A, impact.col$speed.vismotor.B)
+cor(impact.col$speed.general.A, impact.col$speed.general.B)
+cor(impact.col$impulse.control.A, impact.col$impulse.control.B)
+
+impact.row <- read.table("supplemental_STATS1.EX.03.ROW.txt", header = T)
+describeBy(impact.row, impact.row$test)
+
+cor(impact.row$memory.verbal[impact.row$test=="A"], impact.row$memory.verbal[impact.row$test=="B"])
+cor(impact.row$memory.visual[impact.row$test=="A"], impact.row$memory.visual[impact.row$test=="B"])
+
+assignment2 <- read.table("DAA.02.txt", header = T)
+
+describe(assignment2)
+
+cor(assignment2$pre.wm.s1[assignment2$cond=="des"], assignment2$pre.wm.s2[assignment2$cond=="des"])
+cor(assignment2$pre.wm.s1[assignment2$cond=="aer"], assignment2$pre.wm.s2[assignment2$cond=="aer"])
+cor(assignment2$pre.wm.v1[assignment2$cond=="des"], assignment2$pre.wm.v2[assignment2$cond=="des"])
+cor(assignment2$pre.wm.v1[assignment2$cond=="aer"], assignment2$pre.wm.v2[assignment2$cond=="aer"])
+
+
+cor(assignment2$pre.wm.s1[assignment2$cond=="aer"], assignment2$post.wm.s1[assignment2$cond=="aer"])
+cor(assignment2$pre.wm.s2[assignment2$cond=="aer"], assignment2$post.wm.s2[assignment2$cond=="aer"])
+cor(assignment2$pre.wm.v1[assignment2$cond=="aer"], assignment2$post.wm.v1[assignment2$cond=="aer"])
+cor(assignment2$pre.wm.v2[assignment2$cond=="aer"], assignment2$post.wm.v2[assignment2$cond=="aer"])
+
+
+cor(assignment2$pre.wm.s1[assignment2$cond=="des"], assignment2$post.wm.s1[assignment2$cond=="des"])
+cor(assignment2$pre.wm.s2[assignment2$cond=="des"], assignment2$post.wm.s2[assignment2$cond=="des"])
+cor(assignment2$pre.wm.v1[assignment2$cond=="des"], assignment2$post.wm.v1[assignment2$cond=="des"])
+cor(assignment2$pre.wm.v2[assignment2$cond=="des"], assignment2$post.wm.v2[assignment2$cond=="des"])
+
+
+# Multiple regression analysis
+
+endur <- read.table("supplemental_STATS1.EX.04.txt", header = T)
+plot(endur$endurance ~ endur$age, main = "Scatterplot", ylab = "Endurance", xlab = "Age")
+abline(lm(endur$endurance ~ endur$age), col = "blue")
+
+plot(endur$endurance ~ endur$activeyears, main = "Scatterplot", ylab = "Endurance", xlab = "Active Years")
+abline(lm(endur$endurance ~ endur$activeyears), col = "blue")
+
+describe(endur)
+
+model1 = lm(endur$endurance ~ endur$age)
+summary(model1)
+
+model2 = lm(endur$endurance ~ endur$activeyears)
+summary(model2)
+
+model3 = lm(endur$endurance ~ endur$age + endur$activeyears)
+summary(model3)
+
+# standardized regression coefficients - what's the difference?
+# anova = analysis of variance
+
+model1.z= lm(scale(endur$endurance) ~ scale(endur$age))
+summary(model1.z)
+
+model2.z= lm(scale(endur$endurance) ~ scale(endur$activeyears))
+summary(model2.z)
+
+model3.z= lm(scale(endur$endurance) ~ scale(endur$activeyears) + scale(endur$age))
+summary(model3.z)
+
+comp1 = anova(model1.z, model3.z)
+comp1
+
+comp2 = anova(model2.z, model3.z)
+comp2
+
+lm(formula = scale(endur$endurance) ~ scale(endur$activeyears))
+
+# week 3 assignment
+
+week3 <- read.table("DAA.03.txt", header = T)
+
+cor(week3$age, week3$endurance)
+
+ageEndurance = lm(week3$endurance ~ week3$age)
+summary(ageEndurance)
+
+standardizedAgeEndurance = lm(scale(week3$endurance) ~ scale(week3$age))
+summary(standardizedAgeEndurance)
+
+ageActiveYearsEndurance = lm(week3$endurance ~ week3$age + week3$activeyears)
+summary(ageActiveYearsEndurance)
+
+standardizedAgeActiveYearsEndurance = lm(scale(week3$endurance) ~ scale(week3$age) + scale(week3$activeyears))
+summary(standardizedAgeActiveYearsEndurance)
+
+cor(week3$activeyears, week3$endurance)
+
+activeYearsEndurance = lm(week3$endurance ~ week3$activeyears)
+summary(activeYearsEndurance)
+
+standardizedActiveYearsEndurance = lm(scale(week3$endurance) ~ scale(week3$activeyears))
+summary(standardizedActiveYearsEndurance)
+
+plot(week3$endurance ~ week3$activeyears, main = "Scatterplot", ylab = "Endurance", xlab = "Active Years")
+abline(lm(week3$endurance ~ week3$activeyears), col = "blue")
+
+plot(scale(week3$endurance) ~ scale(week3$activeyears), main = "Scatterplot", ylab = "Endurance", xlab = "Active Years")
+abline(lm(scale(week3$endurance) ~ scale(week3$activeyears)), col = "blue")
+
+# Mediation analysis
+
+# X is extraversion
+# Y is happiness
+# M is diversity of life experience
+
+med <- read.table("supplemental_STATS1.EX.05.txt", header=T)
+
+describe(med)
+# how do you work out kurtosis?
+
+hist(med$happy)
+hist(med$extra)
+hist(med$diverse)
+
+plot(med$happy ~ med$extra)
+abline(lm(med$happy ~ med$extra))
+
+plot(med$diverse ~ med$extra)
+abline(lm(med$diverse ~ med$extra))
+
+plot(med$happy ~ med$diverse)
+abline(lm(med$happy ~ med$diverse))
+
+# residuals shouldn't be a function of any of the variables - i.e. shouldn't be predictable from X, Y, M
+
+model1= lm(med$happy ~ med$extra)
+summary(model1)
+# happy = 2.19 + 0.275(extra)
+
+model2 = lm(med$diverse ~ med$extra)
+summary(model2)
+# diverse = 1.63 + 0.284(extra)
+
+model3 = lm(med$happy ~ med$extra + med$diverse)
+summary(model3)
+# happy = 1.886 + 0.222(extra) + 0.1868(diverse)
+# partial mediation - because there is still a correlation between extraversion and happiness even though
+# we've now included diversity in the equation
+
+# Sobel test
+# tests the null hypothesis that the indirect effect through the mediator is 0
+
+# need to read up on what exactly z value is - to do with standard deviation
+# we have a z value associated with an error rate? e.g. z value for non directional p of 0.05 is 1.96
+# checking if it's statistically significant...
+
+library(multilevel)
+
+indirect = sobel(med$extra, med$diverse, med$happy)
+indirect
+
+# Moderation analysis
+# X extraversion
+# Y happiness
+# Z SES
+
+mod <- read.table("supplemental_STATS1.EX.06.txt", header = T)
+
+no.mod.model = lm(mod$happy ~ mod$extra + mod$ses)
+summary(no.mod.model)
+
+mod.model = lm(mod$happy ~ mod$extra + mod$ses + mod$mod)
+summary(mod.model)
+
+anova(no.mod.model, mod.model)
1 supplemental_STATS1.EX.02.txt
@@ -0,0 +1 @@
+memory.verbal memory.visual speed.vismotor speed.general impulse.control 80 74 49 0.51 8 75 84 49.4 0.53 5 100 94 49.7 0.57 2 88 86 45.08 0.6 5 80 81 26.45 0.58 7 100 92 51.97 0.51 1 96 95 49.53 0.44 7 99 92 49.4 0.63 3 74 83 40.4 0.57 13 85 92 49.6 0.51 2 76 64 43.35 0.58 1 99 98 52 0.52 2 100 78 50.1 0.49 4 96 99 52.05 0.51 4 82 65 43.65 0.53 0 100 98 39.03 0.56 1 97 72 44.3 0.5 4 100 89 51.97 0.55 3 92 94 51.4 0.43 6 86 59 43.58 0.52 6 94 73 45.78 0.46 11 96 93 53.95 0.46 2 100 85 44.75 0.57 1 96 94 49.43 0.49 2 81 60 30.63 0.6 3 93 95 52.5 0.45 8 99 73 42.45 0.59 4 82 93 48.22 0.56 3 90 91 46.93 0.54 5 91 80 43.58 0.53 5 86 81 44.53 0.61 4 100 94 44.28 0.51 3 90 92 49.58 0.53 2 93 73 37.17 0.58 4 94 78 39.15 0.55 1 96 77 49.08 0.56 6 99 85 53.63 0.48 0 87 79 50.58 0.53 5 100 95 46.7 0.54 7 99 83 52.83 0.51 1 80 78 50.75 0.53 6 83 80 44.18 0.64 7 100 75 50.45 0.51 1 85 81 48.9 0.61 8 86 91 48.33 0.53 7 100 93 51.4 0.46 11 71 86 47.13 0.58 10 100 59 48.2 0.51 5 99 80 44.88 0.59 11 94 74 50.45 0.51 1 99 88 46.9 0.57 2 85 74 40.15 0.5 4 97 68 47.53 0.54 4 91 92 43.1 0.53 6 96 82 33.5 0.68 3 99 95 52.38 0.51 1 92 82 49.72 0.44 13 87 90 47.03 0.53 4 91 76 47.45 0.49 5 99 72 35.75 0.78 3 94 88 48.75 0.56 5 100 92 45.97 0.53 7 100 91 52.43 0.5 3 77 85 36.98 0.58 8 68 80 45.28 0.5 7 79 80 44.78 0.64 2 90 79 48.95 0.48 2 96 88 45.75 0.57 1 100 85 51.88 0.5 0 78 83 42.93 0.54 1 91 72 47.58 0.68 13 100 86 50.97 0.49 2 100 95 47.7 0.78 0 86 75 50.47 0.52 7 90 91 47.25 0.77 20 100 93 37.67 0.56 3 92 84 42.13 0.51 9 87 83 47.8 0.57 5 100 83 51.7 0.43 24 88 93 34.85 0.59 7 93 72 31.67 0.51 2 81 72 42.05 0.49 3 98 97 35.28 0.62 9 84 79 53.7 0.48 3 85 71 41.8 0.55 6 68 58 46.05 0.55 7 89 82 45.28 0.45 5 93 64 49.83 0.53 4 89 94 48.53 0.53 4 99 81 48 0.64 3 90 72 45.4 0.53 2 99 73 50.85 0.52 0 99 92 53.95 0.42 3 100 92 52.43 0.47 1 80 88 51.15 0.44 5 93 82 53.25 0.52 6 98 96 44.08 0.57 3 94 92 31.05 0.62 1 93 77 47.53 0.45 4 99 78 54.08 0.45 4 78 83 45.95 0.55 3 85 69 41.9 0.63 9 94 77 31.13 0.65 0 98 72 31.28 0.57 6 99 92 49.55 0.52 3 83 89 32.83 0.59 2 91 98 49.97 0.46 5 90 53 31.33 0.63 4 99 92 52.13 0.58 1 98 91 40.95 0.58 2 96 88 41.58 0.48 5 96 69 38.38 0.65 6 72 48 31.73 0.74 2 95 65 51.9 0.57 6 58 76 33.9 0.6 14 100 88 50.18 0.47 2 100 83 40.13 0.54 3 70 84 43.8 0.51 8 89 67 42.33 0.53 10 97 92 39.17 0.52 0 98 75 44.9 0.53 5 100 77 52.6 0.45 3 90 92 43.28 0.45 7 84 84 44.9 0.54 5 99 92 53.13 0.51 2 100 88 36.13 0.59 8 68 50 40.53 0.59 2 77 61 33.15 0.62 7 85 75 49.93 0.46 11 99 78 48.9 0.75 1 95 82 51.25 0.56 1 84 83 45.5 0.47 9 96 76 47.65 0.55 4 90 88 43.28 0.57 1 93 83 49.43 0.55 3 60 41 19.1 0.98 12 100 86 52.7 0.5 2 95 76 48.38 0.55 4 100 81 41.88 0.62 5 100 85 50.03 0.48 12 97 85 54 0.45 2 98 85 51.22 0.41 7 93 83 47.05 0.53 5 96 97 50.45 0.59 2 96 83 38.2 0.57 3 96 76 48 0.49 7 93 88 53.28 0.5 4 98 98 45.33 0.84 2 80 57 40.42 0.54 1 89 80 48.58 0.55 2 89 86 43.7 0.52 4 100 68 37.75 0.55 1 80 67 43.55 0.53 10 100 80 40.35 0.56 4 76 75 40.8 0.65 8 100 100 54.5 0.45 1 85 98 51.33 0.51 3 100 95 40 0.54 1 96 53 40.3 0.56 2 94 94 45.63 0.47 4 100 89 39.55 0.62 7 94 90 35 0.61 9 75 69 42.78 0.68 3 100 92 48.8 0.51 0 93 96 49.9 0.46 3 99 83 49.28 0.51 1 85 81 51.13 0.45 4 95 77 49.47 0.44 7 85 88 40.7 0.54 6 99 96 54.13 0.43 3 96 94 51.68 0.43 12 88 57 45.33 0.56 4 96 88 48.5 0.48 2 98 71 40.25 0.52 3 93 91 51.47 0.52 3 63 67 42.35 0.47 13 81 61 50.78 0.46 5 100 61 39.55 0.6 6 81 75 50.68 0.53 8 78 51 46 0.59 2 74 72 39.28 0.53 5 92 63 43.63 0.59 1 100 86 52.38 0.56 4 99 92 48.95 0.56 3 83 75 52.53 0.5 10 92 90 35.83 0.52 10 94 70 44.33 0.53 1 100 80 47.93 0.55 3 93 83 39 0.5 4 100 94 45.58 0.58 5 100 99 53.5 0.48 2 63 56 43.97 0.52 4 100 84 49.97 0.47 6 95 70 51.5 0.51 5 98 86 50.53 0.52 6 99 92 51.25 0.52 3 100 86 42.58 0.57 5 92 84 42.55 0.52 3 91 56 36.03 0.61 2 88 72 49.08 0.51 4
1 supplemental_STATS1.EX.03.COL.txt
@@ -0,0 +1 @@
+memory.verbal.A memory.visual.A speed.vismotor.A speed.general.A impulse.control.A memory.verbal.B memory.visual.B speed.vismotor.B speed.general.B impulse.control.B 80 74 49 0.51 8 84 60 45.5 0.59 8 75 84 49.4 0.53 5 45 52 36.8 0.71 11 99 98 52 0.52 2 98 76 45.97 0.53 4 100 98 39.03 0.56 1 79 79 45.1 0.55 2 86 59 43.58 0.52 6 82 64 41.65 0.69 6 71 86 47.13 0.58 10 83 80 46.18 0.57 4 92 82 49.72 0.44 13 93 79 49.05 0.48 7 91 76 47.45 0.49 5 96 91 45.28 0.56 4 94 88 48.75 0.56 5 98 89 40.6 0.58 6 100 95 47.7 0.78 0 100 92 46.2 0.7 2 100 93 37.67 0.56 3 99 89 33.15 0.6 2 100 92 52.43 0.47 1 99 72 42.7 0.56 4 98 96 44.08 0.57 3 99 80 39.4 0.53 1 91 98 49.97 0.46 5 75 92 48.55 0.47 17 98 91 40.95 0.58 2 92 80 35.17 0.67 5 72 48 31.73 0.74 2 81 64 35.33 0.79 2 58 76 33.9 0.6 14 60 51 40.17 0.62 3 70 84 43.8 0.51 8 64 74 40.3 0.65 5 100 77 52.6 0.45 3 96 78 45.45 0.57 7 99 92 53.13 0.51 2 96 79 48.85 0.55 1 68 50 40.53 0.59 2 74 59 42.6 0.59 2 77 61 33.15 0.62 7 71 78 28.3 0.63 5 98 85 51.22 0.41 7 98 91 51.35 0.52 5 80 67 43.55 0.53 10 93 66 48.38 0.51 17 99 96 54.13 0.43 3 98 91 52.33 0.5 2 93 91 51.47 0.52 3 99 90 46.88 0.5 1 100 86 52.38 0.56 4 100 82 51.95 0.66 2 95 70 51.5 0.51 5 84 70 51.33 0.44 2 91 56 36.03 0.61 2 55 59 34.85 0.68 3 67 74 33.55 0.53 8 80 59 41.25 0.59 4 84 80 47.83 0.58 7 89 77 46.5 0.71 6 89 99 51.13 0.49 2 87 94 51.83 0.5 1 94 82 43 0.48 13 99 76 44.6 0.56 3 100 95 48.1 0.47 5 93 86 43.33 0.52 2 100 99 44.08 0.48 2 100 93 47.28 0.53 1 87 78 44.85 0.49 7 85 84 44.2 0.71 3 100 86 52.43 0.5 2 100 80 47.55 0.61 1 96 85 52.18 0.48 3 93 88 51.78 0.5 7 93 67 47.35 0.57 4 99 85 48.2 0.62 5 89 89 35.48 0.48 6 90 83 43.38 0.48 8
1 supplemental_STATS1.EX.03.ROW.txt
@@ -0,0 +1 @@
+test memory.verbal memory.visual speed.vismotor speed.general impulse.control A 80 74 49 0.51 8 B 84 60 45.5 0.59 8 A 75 84 49.4 0.53 5 B 45 52 36.8 0.71 11 A 99 98 52 0.52 2 B 98 76 45.97 0.53 4 A 100 98 39.03 0.56 1 B 79 79 45.1 0.55 2 A 86 59 43.58 0.52 6 B 82 64 41.65 0.69 6 A 71 86 47.13 0.58 10 B 83 80 46.18 0.57 4 A 92 82 49.72 0.44 13 B 93 79 49.05 0.48 7 A 91 76 47.45 0.49 5 B 96 91 45.28 0.56 4 A 94 88 48.75 0.56 5 B 98 89 40.6 0.58 6 A 100 95 47.7 0.78 0 B 100 92 46.2 0.7 2 A 100 93 37.67 0.56 3 B 99 89 33.15 0.6 2 A 100 92 52.43 0.47 1 B 99 72 42.7 0.56 4 A 98 96 44.08 0.57 3 B 99 80 39.4 0.53 1 A 91 98 49.97 0.46 5 B 75 92 48.55 0.47 17 A 98 91 40.95 0.58 2 B 92 80 35.17 0.67 5 A 72 48 31.73 0.74 2 B 81 64 35.33 0.79 2 A 58 76 33.9 0.6 14 B 60 51 40.17 0.62 3 A 70 84 43.8 0.51 8 B 64 74 40.3 0.65 5 A 100 77 52.6 0.45 3 B 96 78 45.45 0.57 7 A 99 92 53.13 0.51 2 B 96 79 48.85 0.55 1 A 68 50 40.53 0.59 2 B 74 59 42.6 0.59 2 A 77 61 33.15 0.62 7 B 71 78 28.3 0.63 5 A 98 85 51.22 0.41 7 B 98 91 51.35 0.52 5 A 80 67 43.55 0.53 10 B 93 66 48.38 0.51 17 A 99 96 54.13 0.43 3 B 98 91 52.33 0.5 2 A 93 91 51.47 0.52 3 B 99 90 46.88 0.5 1 A 100 86 52.38 0.56 4 B 100 82 51.95 0.66 2 A 95 70 51.5 0.51 5 B 84 70 51.33 0.44 2 A 91 56 36.03 0.61 2 B 55 59 34.85 0.68 3 A 67 74 33.55 0.53 8 B 80 59 41.25 0.59 4 A 84 80 47.83 0.58 7 B 89 77 46.5 0.71 6 A 89 99 51.13 0.49 2 B 87 94 51.83 0.5 1 A 94 82 43 0.48 13 B 99 76 44.6 0.56 3 A 100 95 48.1 0.47 5 B 93 86 43.33 0.52 2 A 100 99 44.08 0.48 2 B 100 93 47.28 0.53 1 A 87 78 44.85 0.49 7 B 85 84 44.2 0.71 3 A 100 86 52.43 0.5 2 B 100 80 47.55 0.61 1 A 96 85 52.18 0.48 3 B 93 88 51.78 0.5 7 A 93 67 47.35 0.57 4 B 99 85 48.2 0.62 5 A 89 89 35.48 0.48 6 B 90 83 43.38 0.48 8
1 supplemental_STATS1.EX.04.txt
@@ -0,0 +1 @@
+pid age activeyears endurance 1 60 10 18 2 40 9 36 3 29 2 51 4 47 10 18 5 48 9 23 6 42 6 30 7 55 8 8 8 43 19 40 9 39 9 28 10 51 14 15 11 54 15 49 12 52 4 27 13 53 3 12 14 68 17 43 15 57 24 47 16 30 4 21 17 35 4 32 18 56 16 33 19 62 14 25 20 39 13 30 21 32 5 41 22 67 8 25 23 56 13 45 24 47 14 33 25 47 10 29 26 61 11 44 27 40 15 28 28 49 4 20 29 28 13 45 30 40 6 28 31 44 5 18 32 41 18 29 33 53 13 24 34 67 19 55 35 52 6 26 36 51 10 46 37 46 11 19 38 44 4 25 39 64 16 29 40 58 14 32 41 29 2 32 42 44 11 12 43 51 12 27 44 51 15 33 46 53 10 28 47 44 9 34 48 46 0 28 49 49 14 24 50 34 6 28 51 64 13 25 52 44 9 9 53 37 5 35 54 52 16 36 55 59 12 40 56 60 6 30 57 44 7 14 58 48 4 22 59 56 9 31 60 48 13 18 61 51 16 15 62 47 5 31 63 62 14 11 64 52 14 30 65 45 9 9 66 44 0 7 67 46 14 48 68 42 2 21 69 46 13 43 70 42 13 28 71 52 11 28 72 76 13 13 73 61 11 25 74 34 13 35 75 62 10 13 76 48 9 15 77 47 15 14 78 45 7 29 79 40 5 24 80 64 7 5 81 41 3 11 82 33 2 24 83 39 10 28 84 67 10 22 85 50 16 35 86 44 13 32 87 60 10 8 88 55 11 31 89 39 12 18 90 60 10 34 91 70 14 27 92 56 10 7 93 40 25 32 94 54 12 22 95 44 15 42 96 42 7 10 97 56 12 16 98 53 22 40 99 55 12 28 100 40 12 29 101 69 17 41 102 59 9 16 103 47 10 28 104 45 10 24 105 62 14 17 106 45 5 10 107 45 12 33 108 70 11 14 109 44 11 32 110 64 13 16 111 57 12 19 112 53 14 30 113 48 8 13 114 38 6 7 115 53 12 28 116 34 7 42 117 47 9 39 118 43 14 48 119 62 6 22 120 47 14 34 121 41 20 40 122 43 4 26 123 34 12 10 124 28 14 26 125 55 12 7 126 39 3 17 127 48 11 23 128 47 7 18 129 49 6 3 130 41 5 20 131 46 1 22 133 51 10 31 134 62 13 31 135 67 13 40 136 41 3 22 137 47 11 27 138 48 9 31 139 60 4 26 140 58 26 40 141 36 6 20 142 55 11 13 143 51 16 35 144 60 11 14 145 47 11 34 146 63 10 35 147 40 13 17 148 53 11 31 149 37 13 23 150 48 13 36 151 33 6 21 152 44 14 31 153 32 4 35 154 62 12 17 155 57 11 33 156 62 11 32 157 82 15 18 158 52 23 44 159 45 15 10 160 55 10 22 161 65 18 18 162 47 7 42 163 56 16 52 164 58 11 33 165 43 7 26 166 59 4 15 167 42 7 16 168 27 6 42 169 62 18 29 170 67 9 20 171 28 10 29 172 57 10 17 173 39 7 28 174 51 7 8 175 35 14 38 177 61 9 37 178 36 10 50 179 55 15 42 180 50 10 23 181 60 15 24 182 64 12 21 183 59 8 19 184 47 10 19 185 68 16 30 186 59 18 50 187 44 8 34 188 30 9 21 189 41 11 27 190 20 6 24 191 63 11 16 192 41 11 13 193 60 6 21 194 52 14 15 196 45 10 21 197 42 14 29 198 48 5 40 199 49 13 21 200 43 12 30 201 53 21 26 202 52 13 20 203 46 9 14 204 44 17 33 206 41 6 38 207 57 10 16 208 49 12 35 209 48 15 9 210 39 9 29 211 45 18 21 212 39 11 24 213 65 5 2 214 42 2 21 215 47 14 25 216 67 12 24 217 37 20 39 218 34 5 22 219 45 11 21 220 35 17 38 221 46 19 50 222 46 13 35 223 43 14 22 224 39 17 37 225 50 16 29 226 49 3 31 227 69 15 24 228 58 22 34 229 40 8 29 230 44 11 31 231 48 12 44 232 53 20 31 233 58 11 28 234 48 6 25 235 44 9 35 236 43 11 14 237 56 15 46 238 46 7 25 239 50 8 25 240 43 3 29 241 48 6 24 242 43 7 20 243 41 8 26 244 55 10 20 245 43 3 40 246 56 0 3 247 45 9 37 248 60 7 0 249 57 11 18 250 56 12 24
1 supplemental_STATS1.EX.05.txt
@@ -0,0 +1 @@
+diverse happy extra 2 1 3 2 1 3 2 1 1 2 1 2.5 1 1 3.25 2 1 3.25 2 1 2 1 1 3.5 3 1 3.5 3 2 3.5 2 2 4 3 2 3.5 3 2 3.75 1 2 3.25 2 2 3 1 2 3.75 1 2 2.5 2 2 3.5 2 2 2 2 2 4.25 4 2 3.25 3 2 3.25 2 2 3.5 4 2 4.75 3 2 3.25 4 2 4 2 2 3.25 2 2 2.5 4 2 2.5 4 2 3.25 2 2 3 3 2 5 2 2 4 1 2 4.5 3 2 3 2 2 3.5 2 2 3.25 3 2 3.75 4 2 3.75 4 2 4.75 2 2 4.25 2 2 1.5 3 2 3.75 1 3 4.75 2 3 3.5 4 3 4 2 3 4 2 3 2.75 2 3 2.5 3 3 4 3 3 3.25 2 3 4 3 3 4.5 2 3 3.25 4 3 4 4 3 4.25 3 3 5 3 3 2.75 3 3 3.25 3 3 3.25 2 3 3.25 2 3 4 3 3 3.25 2 3 4 1 3 3.25 3 3 3.5 3 3 3.75 4 3 4 2 3 4 1 3 3.5 5 3 4 4 3 3.75 2 3 3.25 2 3 2 1 3 3.25 2 3 4.25 3 3 2.75 2 3 3.75 3 3 3.25 1 3 4.25 4 3 3.75 3 3 3.5 2 3 3.75 2 3 3 2 3 4.5 4 3 4.75 1 3 4.5 3 3 3.75 3 3 3 4 3 4.5 4 3 3.75 4 3 3.75 2 3 3.75 3 3 2.75 3 3 4.5 2 3 4 1 3 2.25 1 3 3.5 2 3 4 2 3 3.75 4 3 3 2 3 4.25 4 3 2.5 3 3 3 3 3 4.75 2 3 4.25 2 3 3.25 2 3 3.5 3 3 4 2 3 3.75 2 3 3.5 3 3 4 2 3 3.25 2 3 4.75 4 3 3.5 4 4 3.75 2 4 3 3 4 4 3 4 2.75 4 4 5 4 4 4.25 5 4 4.25 2 4 3.25 2 4 3.5 3 4 4 3 4 3.75 4 4 3.25 3 4 3.5 2 4 3.5 4 4 4.25 3 4 4.5 3 4 2.75 2 4 4.25 4 4 4.5 3 4 2.75 2 4 2.75 2 4 3.75 4 4 3.75 4 4 3.25 2 4 4.25 2 4 4 3 4 3.5 2 4 3.75 3 4 3.5 3 4 3 3 4 3.75 2 4 4.25 3 4 4 3 4 4.25 2 4 3 2 4 3.5 2 4 2.75 3 4 3.5 2 4 3.75 1 4 3 2 4 3 3 4 3.25 4 4 3.25 4 4 3.75 2 4 3 2 4 3 4 4 4.25 3 4 4.25 3 4 4.25 3 4 3 2 4 3 3 4 3.75 2 4 3.75 2 4 3 4 4 3.75 3 4 3.75 2 4 4.75 3 4 3.5 3 4 3.5 2 4 4 2 4 2.5 4 5 3.25 4 5 5 3 5 3 4 5 5 3 5 3 4 5 3.25 2 5 3.75 2 5 3.75 3 5 2.75 2 5 3.75 2 5 4.75 3 5 3.75
1 supplemental_STATS1.EX.06.txt
@@ -0,0 +1 @@
+diverse happy extra ses mod 2 1 3 1 3 2 1 3 1 3 2 1 1 1 1 2 1 2.5 1 2.5 1 1 3.25 1 3.25 2 1 3.25 1 3.25 2 1 2 1 2 1 1 3.5 1 3.5 3 1 3.5 1 3.5 3 2 3.5 1 3.5 2 2 4 1 4 3 2 3.5 1 3.5 3 2 3.75 1 3.75 1 2 3.25 1 3.25 2 2 3 1 3 1 2 3.75 1 3.75 1 2 2.5 1 2.5 2 2 3.5 1 3.5 2 2 2 1 2 2 2 4.25 1 4.25 4 2 3.25 1 3.25 3 2 3.25 1 3.25 2 2 3.5 1 3.5 4 2 4.75 1 4.75 3 2 3.25 1 3.25 4 2 4 1 4 2 2 3.25 1 3.25 2 2 2.5 1 2.5 4 2 2.5 1 2.5 4 2 3.25 1 3.25 2 2 3 1 3 3 2 5 1 5 2 2 4 1 4 1 2 4.5 1 4.5 3 2 3 1 3 2 2 3.5 1 3.5 2 2 3.25 1 3.25 3 2 3.75 1 3.75 4 2 3.75 1 3.75 4 2 4.75 1 4.75 2 2 4.25 1 4.25 2 2 1.5 1 1.5 3 2 3.75 1 3.75 1 3 4.75 1 4.75 2 3 3.5 1 3.5 4 3 4 1 4 2 3 4 1 4 2 3 2.75 1 2.75 2 3 2.5 1 2.5 3 3 4 1 4 3 3 3.25 1 3.25 2 3 4 1 4 3 3 4.5 1 4.5 2 3 3.25 1 3.25 4 3 4 1 4 4 3 4.25 1 4.25 3 3 5 1 5 3 3 2.75 1 2.75 3 3 3.25 1 3.25 3 3 3.25 1 3.25 2 3 3.25 1 3.25 2 3 4 1 4 3 3 3.25 1 3.25 2 3 4 1 4 1 3 3.25 1 3.25 3 3 3.5 1 3.5 3 3 3.75 1 3.75 4 3 4 1 4 2 3 4 1 4 1 3 3.5 1 3.5 5 3 4 1 4 4 3 3.75 1 3.75 2 3 3.25 1 3.25 2 3 2 1 2 1 3 3.25 1 3.25 2 3 4.25 1 4.25 3 3 2.75 1 2.75 2 3 3.75 1 3.75 3 3 3.25 1 3.25 1 3 4.25 1 4.25 4 3 3.75 1 3.75 3 3 3.5 1 3.5 2 3 3.75 1 3.75 2 3 3 1 3 2 3 4.5 1 4.5 4 3 4.75 1 4.75 1 3 4.5 1 4.5 3 3 3.75 1 3.75 3 3 3 1 3 4 3 4.5 1 4.5 4 3 3.75 1 3.75 4 3 3.75 1 3.75 2 3 3.75 1 3.75 3 3 2.75 1 2.75 3 3 4.5 1 4.5 2 3 4 1 4 1 3 2.25 1 2.25 1 3 3.5 1 3.5 2 3 4 1 4 2 3 3.75 1 3.75 4 3 3 1 3 2 3 4.25 1 4.25 4 3 2.5 1 2.5 3 3 3 1 3 3 3 4.75 1 4.75 2 3 4.25 1 4.25 2 3 3.25 1 3.25 2 3 3.5 1 3.5 3 3 4 1 4 2 3 3.75 1 3.75 2 3 3.5 1 3.5 3 3 4 1 4 2 3 3.25 1 3.25 2 3 4.75 1 4.75 4 3 3.5 1 3.5 4 4 3.75 1 3.75 2 4 3 1 3 3 4 4 1 4 3 4 2.75 1 2.75 4 4 5 1 5 4 4 4.25 1 4.25 5 4 4.25 1 4.25 2 4 3.25 1 3.25 2 4 3.5 1 3.5 3 4 4 1 4 3 4 3.75 1 3.75 4 4 3.25 1 3.25 3 4 3.5 1 3.5 2 4 3.5 1 3.5 4 4 4.25 1 4.25 3 4 4.5 1 4.5 3 4 2.75 1 2.75 2 4 4.25 1 4.25 4 4 4.5 1 4.5 3 4 2.75 1 2.75 2 4 2.75 1 2.75 2 4 3.75 1 3.75 4 4 3.75 1 3.75 4 4 3.25 1 3.25 2 4 4.25 1 4.25 2 4 4 1 4 3 4 3.5 1 3.5 2 4 3.75 1 3.75 3 4 3.5 1 3.5 3 4 3 1 3 3 4 3.75 1 3.75 2 4 4.25 1 4.25 3 4 4 1 4 3 4 4.25 1 4.25 2 4 3 1 3 2 4 3.5 1 3.5 2 4 2.75 1 2.75 3 4 3.5 1 3.5 2 4 3.75 1 3.75 1 4 3 1 3 2 4 3 1 3 3 4 3.25 1 3.25 4 4 3.25 1 3.25 4 4 3.75 1 3.75 2 4 3 1 3 2 4 3 1 3 4 4 4.25 1 4.25 3 4 4.25 1 4.25 3 4 4.25 1 4.25 3 4 3 1 3 2 4 3 1 3 3 4 3.75 1 3.75 2 4 3.75 1 3.75 2 4 3 1 3 4 4 3.75 1 3.75 3 4 3.75 1 3.75 2 4 4.75 1 4.75 3 4 3.5 1 3.5 3 4 3.5 1 3.5 2 4 4 1 4 2 4 2.5 1 2.5 4 5 3.25 1 3.25 4 5 5 1 5 3 5 3 1 3 4 5 5 1 5 3 5 3 1 3 4 5 3.25 1 3.25 2 5 3.75 1 3.75 2 5 3.75 1 3.75 3 5 2.75 1 2.75 2 5 3.75 1 3.75 2 5 4.75 1 4.75 3 5 3.75 1 3.75 2 5 3 0 0 2 5 3 0 0 2 5 1 0 0 2 5 2.5 0 0 1 5 3.25 0 0 2 5 3.25 0 0 2 5 2 0 0 1 5 3.5 0 0 3 5 3.5 0 0 3 5 3.5 0 0 2 5 4 0 0 3 5 3.5 0 0 3 4 3.75 0 0 1 4 3.25 0 0 2 4 3 0 0 1 4 3.75 0 0 1 4 2.5 0 0 2 4 3.5 0 0 2 4 2 0 0 2 4 4.25 0 0 4 4 3.25 0 0 3 4 3.25 0 0 2 4 3.5 0 0 4 4 4.75 0 0 3 4 3.25 0 0 4 4 4 0 0 2 4 3.25 0 0 2 4 2.5 0 0 4 4 2.5 0 0 4 4 3.25 0 0 2 4 3 0 0 3 4 5 0 0 2 4 4 0 0 1 4 4.5 0 0 3 4 3 0 0 2 4 3.5 0 0 2 4 3.25 0 0 3 4 3.75 0 0 4 4 3.75 0 0 4 4 4.75 0 0 2 4 4.25 0 0 2 4 1.5 0 0 3 4 3.75 0 0 1 4 4.75 0 0 2 4 3.5 0 0 4 4 4 0 0 2 4 4 0 0 2 4 2.75 0 0 2 4 2.5 0 0 3 4 4 0 0 3 4 3.25 0 0 2 4 4 0 0 3 4 4.5 0 0 2 4 3.25 0 0 4 4 4 0 0 4 4 4.25 0 0 3 4 5 0 0 3 4 2.75 0 0 3 4 3.25 0 0 3 4 3.25 0 0 2 4 3.25 0 0 2 4 4 0 0 3 4 3.25 0 0 2 4 4 0 0 1 4 3.25 0 0 3 4 3.5 0 0 3 4 3.75 0 0 4 4 4 0 0 2 4 4 0 0 1 4 3.5 0 0 5 4 4 0 0 4 4 3.75 0 0 2 4 3.25 0 0 2 3 2 0 0 1 3 3.25 0 0 2 3 4.25 0 0 3 3 2.75 0 0 2 3 3.75 0 0 3 3 3.25 0 0 1 3 4.25 0 0 4 3 3.75 0 0 3 3 3.5 0 0 2 3 3.75 0 0 2 3 3 0 0 2 3 4.5 0 0 4 3 4.75 0 0 1 3 4.5 0 0 3 3 3.75 0 0 3 3 3 0 0 4 3 4.5 0 0 4 3 3.75 0 0 4 3 3.75 0 0 2 3 3.75 0 0 3 3 2.75 0 0 3 3 4.5 0 0 2 3 4 0 0 1 3 2.25 0 0 1 3 3.5 0 0 2 3 4 0 0 2 3 3.75 0 0 4 3 3 0 0 2 3 4.25 0 0 4 3 2.5 0 0 3 3 3 0 0 3 3 4.75 0 0 2 3 4.25 0 0 2 3 3.25 0 0 2 3 3.5 0 0 3 3 4 0 0 2 3 3.75 0 0 2 3 3.5 0 0 3 3 4 0 0 2 3 3.25 0 0 2 3 4.75 0 0 4 3 3.5 0 0 4 3 3.75 0 0 2 3 3 0 0 3 3 4 0 0 3 3 2.75 0 0 4 3 5 0 0 4 3 4.25 0 0 5 3 4.25 0 0 2 3 3.25 0 0 2 3 3.5 0 0 3 3 4 0 0 3 3 3.75 0 0 4 3 3.25 0 0 3 3 3.5 0 0 2 3 3.5 0 0 4 3 4.25 0 0 3 3 4.5 0 0 3 3 2.75 0 0 2 3 4.25 0 0 4 3 4.5 0 0 3 3 2.75 0 0 2 3 2.75 0 0 2 3 3.75 0 0 4 3 3.75 0 0 4 3 3.25 0 0 2 3 4.25 0 0 2 3 4 0 0 3 3 3.5 0 0 2 3 3.75 0 0 3 3 3.5 0 0 3 3 3 0 0 3 2 3.75 0 0 2 2 4.25 0 0 3 2 4 0 0 3 2 4.25 0 0 2 2 3 0 0 2 2 3.5 0 0 2 2 2.75 0 0 3 2 3.5 0 0 2 2 3.75 0 0 1 2 3 0 0 2 2 3 0 0 3 2 3.25 0 0 4 2 3.25 0 0 4 2 3.75 0 0 2 2 3 0 0 2 2 3 0 0 4 2 4.25 0 0 3 2 4.25 0 0 3 2 4.25 0 0 3 2 3 0 0 2 2 3 0 0 3 2 3.75 0 0 2 2 3.75 0 0 2 2 3 0 0 4 2 3.75 0 0 3 2 3.75 0 0 2 2 4.75 0 0 3 2 3.5 0 0 3 2 3.5 0 0 2 2 4 0 0 2 2 2.5 0 0 4 2 3.25 0 0 4 2 5 0 0 3 2 3 0 0 4 1 5 0 0 3 1 3 0 0 4 1 3.25 0 0 2 1 3.75 0 0 2 1 3.75 0 0 3 1 2.75 0 0 2 1 3.75 0 0 2 1 4.75 0 0 3 1 3.75 0 0
1 supplemental_stats1_ex01.txt
@@ -0,0 +1 @@
+RedTruck WoopWoop HobNob FourPlay 1 1 1 1 2 1 2 2 2 1 2 5 3 2 3 3 3 2 3 5 3 2 3 7 4 3 4 4 4 3 4 4 4 3 4 7 4 4 4 6 5 4 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 7 6 6 6 7 6 6 7 7 7 7 7 8 7 7 7 8 5 7 7 8 4 7 8 9 4 8 8 9 6 8 8 9 8 8 9 10 6 9 9 10 9 9 10 10 10 10

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