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Adding ipynb shell, lecture doc, and CSVs

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Noah L. Schrick 2023-02-27 13:36:02 -06:00
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0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,14.1,18.3,0
0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,12.92,21.2,0
0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,15.1,17.5,0
0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,14.33,16.8,0
0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,9.67,22.4,0
0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,9.08,20.6,0
0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,5.64,23.9,0
0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,6.48,22,0
0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,7.88,11.9,0
1 CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO LSTAT MEDV CAT. MEDV
2 0.00632 18 2.31 0 0.538 6.575 65.2 4.09 1 296 15.3 4.98 24 0
3 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 9.14 21.6 0
4 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 4.03 34.7 1
5 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 2.94 33.4 1
6 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 5.33 36.2 1
7 0.02985 0 2.18 0 0.458 6.43 58.7 6.0622 3 222 18.7 5.21 28.7 0
8 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 15.2 12.43 22.9 0
9 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 15.2 19.15 27.1 0
10 0.21124 12.5 7.87 0 0.524 5.631 100 6.0821 5 311 15.2 29.93 16.5 0
11 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 15.2 17.1 18.9 0
12 0.22489 12.5 7.87 0 0.524 6.377 94.3 6.3467 5 311 15.2 20.45 15 0
13 0.11747 12.5 7.87 0 0.524 6.009 82.9 6.2267 5 311 15.2 13.27 18.9 0
14 0.09378 12.5 7.87 0 0.524 5.889 39 5.4509 5 311 15.2 15.71 21.7 0
15 0.62976 0 8.14 0 0.538 5.949 61.8 4.7075 4 307 21 8.26 20.4 0
16 0.63796 0 8.14 0 0.538 6.096 84.5 4.4619 4 307 21 10.26 18.2 0
17 0.62739 0 8.14 0 0.538 5.834 56.5 4.4986 4 307 21 8.47 19.9 0
18 1.05393 0 8.14 0 0.538 5.935 29.3 4.4986 4 307 21 6.58 23.1 0
19 0.7842 0 8.14 0 0.538 5.99 81.7 4.2579 4 307 21 14.67 17.5 0
20 0.80271 0 8.14 0 0.538 5.456 36.6 3.7965 4 307 21 11.69 20.2 0
21 0.7258 0 8.14 0 0.538 5.727 69.5 3.7965 4 307 21 11.28 18.2 0
22 1.25179 0 8.14 0 0.538 5.57 98.1 3.7979 4 307 21 21.02 13.6 0
23 0.85204 0 8.14 0 0.538 5.965 89.2 4.0123 4 307 21 13.83 19.6 0
24 1.23247 0 8.14 0 0.538 6.142 91.7 3.9769 4 307 21 18.72 15.2 0
25 0.98843 0 8.14 0 0.538 5.813 100 4.0952 4 307 21 19.88 14.5 0
26 0.75026 0 8.14 0 0.538 5.924 94.1 4.3996 4 307 21 16.3 15.6 0
27 0.84054 0 8.14 0 0.538 5.599 85.7 4.4546 4 307 21 16.51 13.9 0
28 0.67191 0 8.14 0 0.538 5.813 90.3 4.682 4 307 21 14.81 16.6 0
29 0.95577 0 8.14 0 0.538 6.047 88.8 4.4534 4 307 21 17.28 14.8 0
30 0.77299 0 8.14 0 0.538 6.495 94.4 4.4547 4 307 21 12.8 18.4 0
31 1.00245 0 8.14 0 0.538 6.674 87.3 4.239 4 307 21 11.98 21 0
32 1.13081 0 8.14 0 0.538 5.713 94.1 4.233 4 307 21 22.6 12.7 0
33 1.35472 0 8.14 0 0.538 6.072 100 4.175 4 307 21 13.04 14.5 0
34 1.38799 0 8.14 0 0.538 5.95 82 3.99 4 307 21 27.71 13.2 0
35 1.15172 0 8.14 0 0.538 5.701 95 3.7872 4 307 21 18.35 13.1 0
36 1.61282 0 8.14 0 0.538 6.096 96.9 3.7598 4 307 21 20.34 13.5 0
37 0.06417 0 5.96 0 0.499 5.933 68.2 3.3603 5 279 19.2 9.68 18.9 0
38 0.09744 0 5.96 0 0.499 5.841 61.4 3.3779 5 279 19.2 11.41 20 0
39 0.08014 0 5.96 0 0.499 5.85 41.5 3.9342 5 279 19.2 8.77 21 0
40 0.17505 0 5.96 0 0.499 5.966 30.2 3.8473 5 279 19.2 10.13 24.7 0
41 0.02763 75 2.95 0 0.428 6.595 21.8 5.4011 3 252 18.3 4.32 30.8 1
42 0.03359 75 2.95 0 0.428 7.024 15.8 5.4011 3 252 18.3 1.98 34.9 1
43 0.12744 0 6.91 0 0.448 6.77 2.9 5.7209 3 233 17.9 4.84 26.6 0
44 0.1415 0 6.91 0 0.448 6.169 6.6 5.7209 3 233 17.9 5.81 25.3 0
45 0.15936 0 6.91 0 0.448 6.211 6.5 5.7209 3 233 17.9 7.44 24.7 0
46 0.12269 0 6.91 0 0.448 6.069 40 5.7209 3 233 17.9 9.55 21.2 0
47 0.17142 0 6.91 0 0.448 5.682 33.8 5.1004 3 233 17.9 10.21 19.3 0
48 0.18836 0 6.91 0 0.448 5.786 33.3 5.1004 3 233 17.9 14.15 20 0
49 0.22927 0 6.91 0 0.448 6.03 85.5 5.6894 3 233 17.9 18.8 16.6 0
50 0.25387 0 6.91 0 0.448 5.399 95.3 5.87 3 233 17.9 30.81 14.4 0
51 0.21977 0 6.91 0 0.448 5.602 62 6.0877 3 233 17.9 16.2 19.4 0
52 0.08873 21 5.64 0 0.439 5.963 45.7 6.8147 4 243 16.8 13.45 19.7 0
53 0.04337 21 5.64 0 0.439 6.115 63 6.8147 4 243 16.8 9.43 20.5 0
54 0.0536 21 5.64 0 0.439 6.511 21.1 6.8147 4 243 16.8 5.28 25 0
55 0.04981 21 5.64 0 0.439 5.998 21.4 6.8147 4 243 16.8 8.43 23.4 0
56 0.0136 75 4 0 0.41 5.888 47.6 7.3197 3 469 21.1 14.8 18.9 0
57 0.01311 90 1.22 0 0.403 7.249 21.9 8.6966 5 226 17.9 4.81 35.4 1
58 0.02055 85 0.74 0 0.41 6.383 35.7 9.1876 2 313 17.3 5.77 24.7 0
59 0.01432 100 1.32 0 0.411 6.816 40.5 8.3248 5 256 15.1 3.95 31.6 1
60 0.15445 25 5.13 0 0.453 6.145 29.2 7.8148 8 284 19.7 6.86 23.3 0
61 0.10328 25 5.13 0 0.453 5.927 47.2 6.932 8 284 19.7 9.22 19.6 0
62 0.14932 25 5.13 0 0.453 5.741 66.2 7.2254 8 284 19.7 13.15 18.7 0
63 0.17171 25 5.13 0 0.453 5.966 93.4 6.8185 8 284 19.7 14.44 16 0
64 0.11027 25 5.13 0 0.453 6.456 67.8 7.2255 8 284 19.7 6.73 22.2 0
65 0.1265 25 5.13 0 0.453 6.762 43.4 7.9809 8 284 19.7 9.5 25 0
66 0.01951 17.5 1.38 0 0.4161 7.104 59.5 9.2229 3 216 18.6 8.05 33 1
67 0.03584 80 3.37 0 0.398 6.29 17.8 6.6115 4 337 16.1 4.67 23.5 0
68 0.04379 80 3.37 0 0.398 5.787 31.1 6.6115 4 337 16.1 10.24 19.4 0
69 0.05789 12.5 6.07 0 0.409 5.878 21.4 6.498 4 345 18.9 8.1 22 0
70 0.13554 12.5 6.07 0 0.409 5.594 36.8 6.498 4 345 18.9 13.09 17.4 0
71 0.12816 12.5 6.07 0 0.409 5.885 33 6.498 4 345 18.9 8.79 20.9 0
72 0.08826 0 10.81 0 0.413 6.417 6.6 5.2873 4 305 19.2 6.72 24.2 0
73 0.15876 0 10.81 0 0.413 5.961 17.5 5.2873 4 305 19.2 9.88 21.7 0
74 0.09164 0 10.81 0 0.413 6.065 7.8 5.2873 4 305 19.2 5.52 22.8 0
75 0.19539 0 10.81 0 0.413 6.245 6.2 5.2873 4 305 19.2 7.54 23.4 0
76 0.07896 0 12.83 0 0.437 6.273 6 4.2515 5 398 18.7 6.78 24.1 0
77 0.09512 0 12.83 0 0.437 6.286 45 4.5026 5 398 18.7 8.94 21.4 0
78 0.10153 0 12.83 0 0.437 6.279 74.5 4.0522 5 398 18.7 11.97 20 0
79 0.08707 0 12.83 0 0.437 6.14 45.8 4.0905 5 398 18.7 10.27 20.8 0
80 0.05646 0 12.83 0 0.437 6.232 53.7 5.0141 5 398 18.7 12.34 21.2 0
81 0.08387 0 12.83 0 0.437 5.874 36.6 4.5026 5 398 18.7 9.1 20.3 0
82 0.04113 25 4.86 0 0.426 6.727 33.5 5.4007 4 281 19 5.29 28 0
83 0.04462 25 4.86 0 0.426 6.619 70.4 5.4007 4 281 19 7.22 23.9 0
84 0.03659 25 4.86 0 0.426 6.302 32.2 5.4007 4 281 19 6.72 24.8 0
85 0.03551 25 4.86 0 0.426 6.167 46.7 5.4007 4 281 19 7.51 22.9 0
86 0.05059 0 4.49 0 0.449 6.389 48 4.7794 3 247 18.5 9.62 23.9 0
87 0.05735 0 4.49 0 0.449 6.63 56.1 4.4377 3 247 18.5 6.53 26.6 0
88 0.05188 0 4.49 0 0.449 6.015 45.1 4.4272 3 247 18.5 12.86 22.5 0
89 0.07151 0 4.49 0 0.449 6.121 56.8 3.7476 3 247 18.5 8.44 22.2 0
90 0.0566 0 3.41 0 0.489 7.007 86.3 3.4217 2 270 17.8 5.5 23.6 0
91 0.05302 0 3.41 0 0.489 7.079 63.1 3.4145 2 270 17.8 5.7 28.7 0
92 0.04684 0 3.41 0 0.489 6.417 66.1 3.0923 2 270 17.8 8.81 22.6 0
93 0.03932 0 3.41 0 0.489 6.405 73.9 3.0921 2 270 17.8 8.2 22 0
94 0.04203 28 15.04 0 0.464 6.442 53.6 3.6659 4 270 18.2 8.16 22.9 0
95 0.02875 28 15.04 0 0.464 6.211 28.9 3.6659 4 270 18.2 6.21 25 0
96 0.04294 28 15.04 0 0.464 6.249 77.3 3.615 4 270 18.2 10.59 20.6 0
97 0.12204 0 2.89 0 0.445 6.625 57.8 3.4952 2 276 18 6.65 28.4 0
98 0.11504 0 2.89 0 0.445 6.163 69.6 3.4952 2 276 18 11.34 21.4 0
99 0.12083 0 2.89 0 0.445 8.069 76 3.4952 2 276 18 4.21 38.7 1
100 0.08187 0 2.89 0 0.445 7.82 36.9 3.4952 2 276 18 3.57 43.8 1
101 0.0686 0 2.89 0 0.445 7.416 62.5 3.4952 2 276 18 6.19 33.2 1
102 0.14866 0 8.56 0 0.52 6.727 79.9 2.7778 5 384 20.9 9.42 27.5 0
103 0.11432 0 8.56 0 0.52 6.781 71.3 2.8561 5 384 20.9 7.67 26.5 0
104 0.22876 0 8.56 0 0.52 6.405 85.4 2.7147 5 384 20.9 10.63 18.6 0
105 0.21161 0 8.56 0 0.52 6.137 87.4 2.7147 5 384 20.9 13.44 19.3 0
106 0.1396 0 8.56 0 0.52 6.167 90 2.421 5 384 20.9 12.33 20.1 0
107 0.13262 0 8.56 0 0.52 5.851 96.7 2.1069 5 384 20.9 16.47 19.5 0
108 0.1712 0 8.56 0 0.52 5.836 91.9 2.211 5 384 20.9 18.66 19.5 0
109 0.13117 0 8.56 0 0.52 6.127 85.2 2.1224 5 384 20.9 14.09 20.4 0
110 0.12802 0 8.56 0 0.52 6.474 97.1 2.4329 5 384 20.9 12.27 19.8 0
111 0.26363 0 8.56 0 0.52 6.229 91.2 2.5451 5 384 20.9 15.55 19.4 0
112 0.10793 0 8.56 0 0.52 6.195 54.4 2.7778 5 384 20.9 13 21.7 0
113 0.10084 0 10.01 0 0.547 6.715 81.6 2.6775 6 432 17.8 10.16 22.8 0
114 0.12329 0 10.01 0 0.547 5.913 92.9 2.3534 6 432 17.8 16.21 18.8 0
115 0.22212 0 10.01 0 0.547 6.092 95.4 2.548 6 432 17.8 17.09 18.7 0
116 0.14231 0 10.01 0 0.547 6.254 84.2 2.2565 6 432 17.8 10.45 18.5 0
117 0.17134 0 10.01 0 0.547 5.928 88.2 2.4631 6 432 17.8 15.76 18.3 0
118 0.13158 0 10.01 0 0.547 6.176 72.5 2.7301 6 432 17.8 12.04 21.2 0
119 0.15098 0 10.01 0 0.547 6.021 82.6 2.7474 6 432 17.8 10.3 19.2 0
120 0.13058 0 10.01 0 0.547 5.872 73.1 2.4775 6 432 17.8 15.37 20.4 0
121 0.14476 0 10.01 0 0.547 5.731 65.2 2.7592 6 432 17.8 13.61 19.3 0
122 0.06899 0 25.65 0 0.581 5.87 69.7 2.2577 2 188 19.1 14.37 22 0
123 0.07165 0 25.65 0 0.581 6.004 84.1 2.1974 2 188 19.1 14.27 20.3 0
124 0.09299 0 25.65 0 0.581 5.961 92.9 2.0869 2 188 19.1 17.93 20.5 0
125 0.15038 0 25.65 0 0.581 5.856 97 1.9444 2 188 19.1 25.41 17.3 0
126 0.09849 0 25.65 0 0.581 5.879 95.8 2.0063 2 188 19.1 17.58 18.8 0
127 0.16902 0 25.65 0 0.581 5.986 88.4 1.9929 2 188 19.1 14.81 21.4 0
128 0.38735 0 25.65 0 0.581 5.613 95.6 1.7572 2 188 19.1 27.26 15.7 0
129 0.25915 0 21.89 0 0.624 5.693 96 1.7883 4 437 21.2 17.19 16.2 0
130 0.32543 0 21.89 0 0.624 6.431 98.8 1.8125 4 437 21.2 15.39 18 0
131 0.88125 0 21.89 0 0.624 5.637 94.7 1.9799 4 437 21.2 18.34 14.3 0
132 0.34006 0 21.89 0 0.624 6.458 98.9 2.1185 4 437 21.2 12.6 19.2 0
133 1.19294 0 21.89 0 0.624 6.326 97.7 2.271 4 437 21.2 12.26 19.6 0
134 0.59005 0 21.89 0 0.624 6.372 97.9 2.3274 4 437 21.2 11.12 23 0
135 0.32982 0 21.89 0 0.624 5.822 95.4 2.4699 4 437 21.2 15.03 18.4 0
136 0.97617 0 21.89 0 0.624 5.757 98.4 2.346 4 437 21.2 17.31 15.6 0
137 0.55778 0 21.89 0 0.624 6.335 98.2 2.1107 4 437 21.2 16.96 18.1 0
138 0.32264 0 21.89 0 0.624 5.942 93.5 1.9669 4 437 21.2 16.9 17.4 0
139 0.35233 0 21.89 0 0.624 6.454 98.4 1.8498 4 437 21.2 14.59 17.1 0
140 0.2498 0 21.89 0 0.624 5.857 98.2 1.6686 4 437 21.2 21.32 13.3 0
141 0.54452 0 21.89 0 0.624 6.151 97.9 1.6687 4 437 21.2 18.46 17.8 0
142 0.2909 0 21.89 0 0.624 6.174 93.6 1.6119 4 437 21.2 24.16 14 0
143 1.62864 0 21.89 0 0.624 5.019 100 1.4394 4 437 21.2 34.41 14.4 0
144 3.32105 0 19.58 1 0.871 5.403 100 1.3216 5 403 14.7 26.82 13.4 0
145 4.0974 0 19.58 0 0.871 5.468 100 1.4118 5 403 14.7 26.42 15.6 0
146 2.77974 0 19.58 0 0.871 4.903 97.8 1.3459 5 403 14.7 29.29 11.8 0
147 2.37934 0 19.58 0 0.871 6.13 100 1.4191 5 403 14.7 27.8 13.8 0
148 2.15505 0 19.58 0 0.871 5.628 100 1.5166 5 403 14.7 16.65 15.6 0
149 2.36862 0 19.58 0 0.871 4.926 95.7 1.4608 5 403 14.7 29.53 14.6 0
150 2.33099 0 19.58 0 0.871 5.186 93.8 1.5296 5 403 14.7 28.32 17.8 0
151 2.73397 0 19.58 0 0.871 5.597 94.9 1.5257 5 403 14.7 21.45 15.4 0
152 1.6566 0 19.58 0 0.871 6.122 97.3 1.618 5 403 14.7 14.1 21.5 0
153 1.49632 0 19.58 0 0.871 5.404 100 1.5916 5 403 14.7 13.28 19.6 0
154 1.12658 0 19.58 1 0.871 5.012 88 1.6102 5 403 14.7 12.12 15.3 0
155 2.14918 0 19.58 0 0.871 5.709 98.5 1.6232 5 403 14.7 15.79 19.4 0
156 1.41385 0 19.58 1 0.871 6.129 96 1.7494 5 403 14.7 15.12 17 0
157 3.53501 0 19.58 1 0.871 6.152 82.6 1.7455 5 403 14.7 15.02 15.6 0
158 2.44668 0 19.58 0 0.871 5.272 94 1.7364 5 403 14.7 16.14 13.1 0
159 1.22358 0 19.58 0 0.605 6.943 97.4 1.8773 5 403 14.7 4.59 41.3 1
160 1.34284 0 19.58 0 0.605 6.066 100 1.7573 5 403 14.7 6.43 24.3 0
161 1.42502 0 19.58 0 0.871 6.51 100 1.7659 5 403 14.7 7.39 23.3 0
162 1.27346 0 19.58 1 0.605 6.25 92.6 1.7984 5 403 14.7 5.5 27 0
163 1.46336 0 19.58 0 0.605 7.489 90.8 1.9709 5 403 14.7 1.73 50 1
164 1.83377 0 19.58 1 0.605 7.802 98.2 2.0407 5 403 14.7 1.92 50 1
165 1.51902 0 19.58 1 0.605 8.375 93.9 2.162 5 403 14.7 3.32 50 1
166 2.24236 0 19.58 0 0.605 5.854 91.8 2.422 5 403 14.7 11.64 22.7 0
167 2.924 0 19.58 0 0.605 6.101 93 2.2834 5 403 14.7 9.81 25 0
168 2.01019 0 19.58 0 0.605 7.929 96.2 2.0459 5 403 14.7 3.7 50 1
169 1.80028 0 19.58 0 0.605 5.877 79.2 2.4259 5 403 14.7 12.14 23.8 0
170 2.3004 0 19.58 0 0.605 6.319 96.1 2.1 5 403 14.7 11.1 23.8 0
171 2.44953 0 19.58 0 0.605 6.402 95.2 2.2625 5 403 14.7 11.32 22.3 0
172 1.20742 0 19.58 0 0.605 5.875 94.6 2.4259 5 403 14.7 14.43 17.4 0
173 2.3139 0 19.58 0 0.605 5.88 97.3 2.3887 5 403 14.7 12.03 19.1 0
174 0.13914 0 4.05 0 0.51 5.572 88.5 2.5961 5 296 16.6 14.69 23.1 0
175 0.09178 0 4.05 0 0.51 6.416 84.1 2.6463 5 296 16.6 9.04 23.6 0
176 0.08447 0 4.05 0 0.51 5.859 68.7 2.7019 5 296 16.6 9.64 22.6 0
177 0.06664 0 4.05 0 0.51 6.546 33.1 3.1323 5 296 16.6 5.33 29.4 0
178 0.07022 0 4.05 0 0.51 6.02 47.2 3.5549 5 296 16.6 10.11 23.2 0
179 0.05425 0 4.05 0 0.51 6.315 73.4 3.3175 5 296 16.6 6.29 24.6 0
180 0.06642 0 4.05 0 0.51 6.86 74.4 2.9153 5 296 16.6 6.92 29.9 0
181 0.0578 0 2.46 0 0.488 6.98 58.4 2.829 3 193 17.8 5.04 37.2 1
182 0.06588 0 2.46 0 0.488 7.765 83.3 2.741 3 193 17.8 7.56 39.8 1
183 0.06888 0 2.46 0 0.488 6.144 62.2 2.5979 3 193 17.8 9.45 36.2 1
184 0.09103 0 2.46 0 0.488 7.155 92.2 2.7006 3 193 17.8 4.82 37.9 1
185 0.10008 0 2.46 0 0.488 6.563 95.6 2.847 3 193 17.8 5.68 32.5 1
186 0.08308 0 2.46 0 0.488 5.604 89.8 2.9879 3 193 17.8 13.98 26.4 0
187 0.06047 0 2.46 0 0.488 6.153 68.8 3.2797 3 193 17.8 13.15 29.6 0
188 0.05602 0 2.46 0 0.488 7.831 53.6 3.1992 3 193 17.8 4.45 50 1
189 0.07875 45 3.44 0 0.437 6.782 41.1 3.7886 5 398 15.2 6.68 32 1
190 0.12579 45 3.44 0 0.437 6.556 29.1 4.5667 5 398 15.2 4.56 29.8 0
191 0.0837 45 3.44 0 0.437 7.185 38.9 4.5667 5 398 15.2 5.39 34.9 1
192 0.09068 45 3.44 0 0.437 6.951 21.5 6.4798 5 398 15.2 5.1 37 1
193 0.06911 45 3.44 0 0.437 6.739 30.8 6.4798 5 398 15.2 4.69 30.5 1
194 0.08664 45 3.44 0 0.437 7.178 26.3 6.4798 5 398 15.2 2.87 36.4 1
195 0.02187 60 2.93 0 0.401 6.8 9.9 6.2196 1 265 15.6 5.03 31.1 1
196 0.01439 60 2.93 0 0.401 6.604 18.8 6.2196 1 265 15.6 4.38 29.1 0
197 0.01381 80 0.46 0 0.422 7.875 32 5.6484 4 255 14.4 2.97 50 1
198 0.04011 80 1.52 0 0.404 7.287 34.1 7.309 2 329 12.6 4.08 33.3 1
199 0.04666 80 1.52 0 0.404 7.107 36.6 7.309 2 329 12.6 8.61 30.3 1
200 0.03768 80 1.52 0 0.404 7.274 38.3 7.309 2 329 12.6 6.62 34.6 1
201 0.0315 95 1.47 0 0.403 6.975 15.3 7.6534 3 402 17 4.56 34.9 1
202 0.01778 95 1.47 0 0.403 7.135 13.9 7.6534 3 402 17 4.45 32.9 1
203 0.03445 82.5 2.03 0 0.415 6.162 38.4 6.27 2 348 14.7 7.43 24.1 0
204 0.02177 82.5 2.03 0 0.415 7.61 15.7 6.27 2 348 14.7 3.11 42.3 1
205 0.0351 95 2.68 0 0.4161 7.853 33.2 5.118 4 224 14.7 3.81 48.5 1
206 0.02009 95 2.68 0 0.4161 8.034 31.9 5.118 4 224 14.7 2.88 50 1
207 0.13642 0 10.59 0 0.489 5.891 22.3 3.9454 4 277 18.6 10.87 22.6 0
208 0.22969 0 10.59 0 0.489 6.326 52.5 4.3549 4 277 18.6 10.97 24.4 0
209 0.25199 0 10.59 0 0.489 5.783 72.7 4.3549 4 277 18.6 18.06 22.5 0
210 0.13587 0 10.59 1 0.489 6.064 59.1 4.2392 4 277 18.6 14.66 24.4 0
211 0.43571 0 10.59 1 0.489 5.344 100 3.875 4 277 18.6 23.09 20 0
212 0.17446 0 10.59 1 0.489 5.96 92.1 3.8771 4 277 18.6 17.27 21.7 0
213 0.37578 0 10.59 1 0.489 5.404 88.6 3.665 4 277 18.6 23.98 19.3 0
214 0.21719 0 10.59 1 0.489 5.807 53.8 3.6526 4 277 18.6 16.03 22.4 0
215 0.14052 0 10.59 0 0.489 6.375 32.3 3.9454 4 277 18.6 9.38 28.1 0
216 0.28955 0 10.59 0 0.489 5.412 9.8 3.5875 4 277 18.6 29.55 23.7 0
217 0.19802 0 10.59 0 0.489 6.182 42.4 3.9454 4 277 18.6 9.47 25 0
218 0.0456 0 13.89 1 0.55 5.888 56 3.1121 5 276 16.4 13.51 23.3 0
219 0.07013 0 13.89 0 0.55 6.642 85.1 3.4211 5 276 16.4 9.69 28.7 0
220 0.11069 0 13.89 1 0.55 5.951 93.8 2.8893 5 276 16.4 17.92 21.5 0
221 0.11425 0 13.89 1 0.55 6.373 92.4 3.3633 5 276 16.4 10.5 23 0
222 0.35809 0 6.2 1 0.507 6.951 88.5 2.8617 8 307 17.4 9.71 26.7 0
223 0.40771 0 6.2 1 0.507 6.164 91.3 3.048 8 307 17.4 21.46 21.7 0
224 0.62356 0 6.2 1 0.507 6.879 77.7 3.2721 8 307 17.4 9.93 27.5 0
225 0.6147 0 6.2 0 0.507 6.618 80.8 3.2721 8 307 17.4 7.6 30.1 1
226 0.31533 0 6.2 0 0.504 8.266 78.3 2.8944 8 307 17.4 4.14 44.8 1
227 0.52693 0 6.2 0 0.504 8.725 83 2.8944 8 307 17.4 4.63 50 1
228 0.38214 0 6.2 0 0.504 8.04 86.5 3.2157 8 307 17.4 3.13 37.6 1
229 0.41238 0 6.2 0 0.504 7.163 79.9 3.2157 8 307 17.4 6.36 31.6 1
230 0.29819 0 6.2 0 0.504 7.686 17 3.3751 8 307 17.4 3.92 46.7 1
231 0.44178 0 6.2 0 0.504 6.552 21.4 3.3751 8 307 17.4 3.76 31.5 1
232 0.537 0 6.2 0 0.504 5.981 68.1 3.6715 8 307 17.4 11.65 24.3 0
233 0.46296 0 6.2 0 0.504 7.412 76.9 3.6715 8 307 17.4 5.25 31.7 1
234 0.57529 0 6.2 0 0.507 8.337 73.3 3.8384 8 307 17.4 2.47 41.7 1
235 0.33147 0 6.2 0 0.507 8.247 70.4 3.6519 8 307 17.4 3.95 48.3 1
236 0.44791 0 6.2 1 0.507 6.726 66.5 3.6519 8 307 17.4 8.05 29 0
237 0.33045 0 6.2 0 0.507 6.086 61.5 3.6519 8 307 17.4 10.88 24 0
238 0.52058 0 6.2 1 0.507 6.631 76.5 4.148 8 307 17.4 9.54 25.1 0
239 0.51183 0 6.2 0 0.507 7.358 71.6 4.148 8 307 17.4 4.73 31.5 1
240 0.08244 30 4.93 0 0.428 6.481 18.5 6.1899 6 300 16.6 6.36 23.7 0
241 0.09252 30 4.93 0 0.428 6.606 42.2 6.1899 6 300 16.6 7.37 23.3 0
242 0.11329 30 4.93 0 0.428 6.897 54.3 6.3361 6 300 16.6 11.38 22 0
243 0.10612 30 4.93 0 0.428 6.095 65.1 6.3361 6 300 16.6 12.4 20.1 0
244 0.1029 30 4.93 0 0.428 6.358 52.9 7.0355 6 300 16.6 11.22 22.2 0
245 0.12757 30 4.93 0 0.428 6.393 7.8 7.0355 6 300 16.6 5.19 23.7 0
246 0.20608 22 5.86 0 0.431 5.593 76.5 7.9549 7 330 19.1 12.5 17.6 0
247 0.19133 22 5.86 0 0.431 5.605 70.2 7.9549 7 330 19.1 18.46 18.5 0
248 0.33983 22 5.86 0 0.431 6.108 34.9 8.0555 7 330 19.1 9.16 24.3 0
249 0.19657 22 5.86 0 0.431 6.226 79.2 8.0555 7 330 19.1 10.15 20.5 0
250 0.16439 22 5.86 0 0.431 6.433 49.1 7.8265 7 330 19.1 9.52 24.5 0
251 0.19073 22 5.86 0 0.431 6.718 17.5 7.8265 7 330 19.1 6.56 26.2 0
252 0.1403 22 5.86 0 0.431 6.487 13 7.3967 7 330 19.1 5.9 24.4 0
253 0.21409 22 5.86 0 0.431 6.438 8.9 7.3967 7 330 19.1 3.59 24.8 0
254 0.08221 22 5.86 0 0.431 6.957 6.8 8.9067 7 330 19.1 3.53 29.6 0
255 0.36894 22 5.86 0 0.431 8.259 8.4 8.9067 7 330 19.1 3.54 42.8 1
256 0.04819 80 3.64 0 0.392 6.108 32 9.2203 1 315 16.4 6.57 21.9 0
257 0.03548 80 3.64 0 0.392 5.876 19.1 9.2203 1 315 16.4 9.25 20.9 0
258 0.01538 90 3.75 0 0.394 7.454 34.2 6.3361 3 244 15.9 3.11 44 1
259 0.61154 20 3.97 0 0.647 8.704 86.9 1.801 5 264 13 5.12 50 1
260 0.66351 20 3.97 0 0.647 7.333 100 1.8946 5 264 13 7.79 36 1
261 0.65665 20 3.97 0 0.647 6.842 100 2.0107 5 264 13 6.9 30.1 1
262 0.54011 20 3.97 0 0.647 7.203 81.8 2.1121 5 264 13 9.59 33.8 1
263 0.53412 20 3.97 0 0.647 7.52 89.4 2.1398 5 264 13 7.26 43.1 1
264 0.52014 20 3.97 0 0.647 8.398 91.5 2.2885 5 264 13 5.91 48.8 1
265 0.82526 20 3.97 0 0.647 7.327 94.5 2.0788 5 264 13 11.25 31 1
266 0.55007 20 3.97 0 0.647 7.206 91.6 1.9301 5 264 13 8.1 36.5 1
267 0.76162 20 3.97 0 0.647 5.56 62.8 1.9865 5 264 13 10.45 22.8 0
268 0.7857 20 3.97 0 0.647 7.014 84.6 2.1329 5 264 13 14.79 30.7 1
269 0.57834 20 3.97 0 0.575 8.297 67 2.4216 5 264 13 7.44 50 1
270 0.5405 20 3.97 0 0.575 7.47 52.6 2.872 5 264 13 3.16 43.5 1
271 0.09065 20 6.96 1 0.464 5.92 61.5 3.9175 3 223 18.6 13.65 20.7 0
272 0.29916 20 6.96 0 0.464 5.856 42.1 4.429 3 223 18.6 13 21.1 0
273 0.16211 20 6.96 0 0.464 6.24 16.3 4.429 3 223 18.6 6.59 25.2 0
274 0.1146 20 6.96 0 0.464 6.538 58.7 3.9175 3 223 18.6 7.73 24.4 0
275 0.22188 20 6.96 1 0.464 7.691 51.8 4.3665 3 223 18.6 6.58 35.2 1
276 0.05644 40 6.41 1 0.447 6.758 32.9 4.0776 4 254 17.6 3.53 32.4 1
277 0.09604 40 6.41 0 0.447 6.854 42.8 4.2673 4 254 17.6 2.98 32 1
278 0.10469 40 6.41 1 0.447 7.267 49 4.7872 4 254 17.6 6.05 33.2 1
279 0.06127 40 6.41 1 0.447 6.826 27.6 4.8628 4 254 17.6 4.16 33.1 1
280 0.07978 40 6.41 0 0.447 6.482 32.1 4.1403 4 254 17.6 7.19 29.1 0
281 0.21038 20 3.33 0 0.4429 6.812 32.2 4.1007 5 216 14.9 4.85 35.1 1
282 0.03578 20 3.33 0 0.4429 7.82 64.5 4.6947 5 216 14.9 3.76 45.4 1
283 0.03705 20 3.33 0 0.4429 6.968 37.2 5.2447 5 216 14.9 4.59 35.4 1
284 0.06129 20 3.33 1 0.4429 7.645 49.7 5.2119 5 216 14.9 3.01 46 1
285 0.01501 90 1.21 1 0.401 7.923 24.8 5.885 1 198 13.6 3.16 50 1
286 0.00906 90 2.97 0 0.4 7.088 20.8 7.3073 1 285 15.3 7.85 32.2 1
287 0.01096 55 2.25 0 0.389 6.453 31.9 7.3073 1 300 15.3 8.23 22 0
288 0.01965 80 1.76 0 0.385 6.23 31.5 9.0892 1 241 18.2 12.93 20.1 0
289 0.03871 52.5 5.32 0 0.405 6.209 31.3 7.3172 6 293 16.6 7.14 23.2 0
290 0.0459 52.5 5.32 0 0.405 6.315 45.6 7.3172 6 293 16.6 7.6 22.3 0
291 0.04297 52.5 5.32 0 0.405 6.565 22.9 7.3172 6 293 16.6 9.51 24.8 0
292 0.03502 80 4.95 0 0.411 6.861 27.9 5.1167 4 245 19.2 3.33 28.5 0
293 0.07886 80 4.95 0 0.411 7.148 27.7 5.1167 4 245 19.2 3.56 37.3 1
294 0.03615 80 4.95 0 0.411 6.63 23.4 5.1167 4 245 19.2 4.7 27.9 0
295 0.08265 0 13.92 0 0.437 6.127 18.4 5.5027 4 289 16 8.58 23.9 0
296 0.08199 0 13.92 0 0.437 6.009 42.3 5.5027 4 289 16 10.4 21.7 0
297 0.12932 0 13.92 0 0.437 6.678 31.1 5.9604 4 289 16 6.27 28.6 0
298 0.05372 0 13.92 0 0.437 6.549 51 5.9604 4 289 16 7.39 27.1 0
299 0.14103 0 13.92 0 0.437 5.79 58 6.32 4 289 16 15.84 20.3 0
300 0.06466 70 2.24 0 0.4 6.345 20.1 7.8278 5 358 14.8 4.97 22.5 0
301 0.05561 70 2.24 0 0.4 7.041 10 7.8278 5 358 14.8 4.74 29 0
302 0.04417 70 2.24 0 0.4 6.871 47.4 7.8278 5 358 14.8 6.07 24.8 0
303 0.03537 34 6.09 0 0.433 6.59 40.4 5.4917 7 329 16.1 9.5 22 0
304 0.09266 34 6.09 0 0.433 6.495 18.4 5.4917 7 329 16.1 8.67 26.4 0
305 0.1 34 6.09 0 0.433 6.982 17.7 5.4917 7 329 16.1 4.86 33.1 1
306 0.05515 33 2.18 0 0.472 7.236 41.1 4.022 7 222 18.4 6.93 36.1 1
307 0.05479 33 2.18 0 0.472 6.616 58.1 3.37 7 222 18.4 8.93 28.4 0
308 0.07503 33 2.18 0 0.472 7.42 71.9 3.0992 7 222 18.4 6.47 33.4 1
309 0.04932 33 2.18 0 0.472 6.849 70.3 3.1827 7 222 18.4 7.53 28.2 0
310 0.49298 0 9.9 0 0.544 6.635 82.5 3.3175 4 304 18.4 4.54 22.8 0
311 0.3494 0 9.9 0 0.544 5.972 76.7 3.1025 4 304 18.4 9.97 20.3 0
312 2.63548 0 9.9 0 0.544 4.973 37.8 2.5194 4 304 18.4 12.64 16.1 0
313 0.79041 0 9.9 0 0.544 6.122 52.8 2.6403 4 304 18.4 5.98 22.1 0
314 0.26169 0 9.9 0 0.544 6.023 90.4 2.834 4 304 18.4 11.72 19.4 0
315 0.26938 0 9.9 0 0.544 6.266 82.8 3.2628 4 304 18.4 7.9 21.6 0
316 0.3692 0 9.9 0 0.544 6.567 87.3 3.6023 4 304 18.4 9.28 23.8 0
317 0.25356 0 9.9 0 0.544 5.705 77.7 3.945 4 304 18.4 11.5 16.2 0
318 0.31827 0 9.9 0 0.544 5.914 83.2 3.9986 4 304 18.4 18.33 17.8 0
319 0.24522 0 9.9 0 0.544 5.782 71.7 4.0317 4 304 18.4 15.94 19.8 0
320 0.40202 0 9.9 0 0.544 6.382 67.2 3.5325 4 304 18.4 10.36 23.1 0
321 0.47547 0 9.9 0 0.544 6.113 58.8 4.0019 4 304 18.4 12.73 21 0
322 0.1676 0 7.38 0 0.493 6.426 52.3 4.5404 5 287 19.6 7.2 23.8 0
323 0.18159 0 7.38 0 0.493 6.376 54.3 4.5404 5 287 19.6 6.87 23.1 0
324 0.35114 0 7.38 0 0.493 6.041 49.9 4.7211 5 287 19.6 7.7 20.4 0
325 0.28392 0 7.38 0 0.493 5.708 74.3 4.7211 5 287 19.6 11.74 18.5 0
326 0.34109 0 7.38 0 0.493 6.415 40.1 4.7211 5 287 19.6 6.12 25 0
327 0.19186 0 7.38 0 0.493 6.431 14.7 5.4159 5 287 19.6 5.08 24.6 0
328 0.30347 0 7.38 0 0.493 6.312 28.9 5.4159 5 287 19.6 6.15 23 0
329 0.24103 0 7.38 0 0.493 6.083 43.7 5.4159 5 287 19.6 12.79 22.2 0
330 0.06617 0 3.24 0 0.46 5.868 25.8 5.2146 4 430 16.9 9.97 19.3 0
331 0.06724 0 3.24 0 0.46 6.333 17.2 5.2146 4 430 16.9 7.34 22.6 0
332 0.04544 0 3.24 0 0.46 6.144 32.2 5.8736 4 430 16.9 9.09 19.8 0
333 0.05023 35 6.06 0 0.4379 5.706 28.4 6.6407 1 304 16.9 12.43 17.1 0
334 0.03466 35 6.06 0 0.4379 6.031 23.3 6.6407 1 304 16.9 7.83 19.4 0
335 0.05083 0 5.19 0 0.515 6.316 38.1 6.4584 5 224 20.2 5.68 22.2 0
336 0.03738 0 5.19 0 0.515 6.31 38.5 6.4584 5 224 20.2 6.75 20.7 0
337 0.03961 0 5.19 0 0.515 6.037 34.5 5.9853 5 224 20.2 8.01 21.1 0
338 0.03427 0 5.19 0 0.515 5.869 46.3 5.2311 5 224 20.2 9.8 19.5 0
339 0.03041 0 5.19 0 0.515 5.895 59.6 5.615 5 224 20.2 10.56 18.5 0
340 0.03306 0 5.19 0 0.515 6.059 37.3 4.8122 5 224 20.2 8.51 20.6 0
341 0.05497 0 5.19 0 0.515 5.985 45.4 4.8122 5 224 20.2 9.74 19 0
342 0.06151 0 5.19 0 0.515 5.968 58.5 4.8122 5 224 20.2 9.29 18.7 0
343 0.01301 35 1.52 0 0.442 7.241 49.3 7.0379 1 284 15.5 5.49 32.7 1
344 0.02498 0 1.89 0 0.518 6.54 59.7 6.2669 1 422 15.9 8.65 16.5 0
345 0.02543 55 3.78 0 0.484 6.696 56.4 5.7321 5 370 17.6 7.18 23.9 0
346 0.03049 55 3.78 0 0.484 6.874 28.1 6.4654 5 370 17.6 4.61 31.2 1
347 0.03113 0 4.39 0 0.442 6.014 48.5 8.0136 3 352 18.8 10.53 17.5 0
348 0.06162 0 4.39 0 0.442 5.898 52.3 8.0136 3 352 18.8 12.67 17.2 0
349 0.0187 85 4.15 0 0.429 6.516 27.7 8.5353 4 351 17.9 6.36 23.1 0
350 0.01501 80 2.01 0 0.435 6.635 29.7 8.344 4 280 17 5.99 24.5 0
351 0.02899 40 1.25 0 0.429 6.939 34.5 8.7921 1 335 19.7 5.89 26.6 0
352 0.06211 40 1.25 0 0.429 6.49 44.4 8.7921 1 335 19.7 5.98 22.9 0
353 0.0795 60 1.69 0 0.411 6.579 35.9 10.7103 4 411 18.3 5.49 24.1 0
354 0.07244 60 1.69 0 0.411 5.884 18.5 10.7103 4 411 18.3 7.79 18.6 0
355 0.01709 90 2.02 0 0.41 6.728 36.1 12.1265 5 187 17 4.5 30.1 1
356 0.04301 80 1.91 0 0.413 5.663 21.9 10.5857 4 334 22 8.05 18.2 0
357 0.10659 80 1.91 0 0.413 5.936 19.5 10.5857 4 334 22 5.57 20.6 0
358 8.98296 0 18.1 1 0.77 6.212 97.4 2.1222 24 666 20.2 17.6 17.8 0
359 3.8497 0 18.1 1 0.77 6.395 91 2.5052 24 666 20.2 13.27 21.7 0
360 5.20177 0 18.1 1 0.77 6.127 83.4 2.7227 24 666 20.2 11.48 22.7 0
361 4.26131 0 18.1 0 0.77 6.112 81.3 2.5091 24 666 20.2 12.67 22.6 0
362 4.54192 0 18.1 0 0.77 6.398 88 2.5182 24 666 20.2 7.79 25 0
363 3.83684 0 18.1 0 0.77 6.251 91.1 2.2955 24 666 20.2 14.19 19.9 0
364 3.67822 0 18.1 0 0.77 5.362 96.2 2.1036 24 666 20.2 10.19 20.8 0
365 4.22239 0 18.1 1 0.77 5.803 89 1.9047 24 666 20.2 14.64 16.8 0
366 3.47428 0 18.1 1 0.718 8.78 82.9 1.9047 24 666 20.2 5.29 21.9 0
367 4.55587 0 18.1 0 0.718 3.561 87.9 1.6132 24 666 20.2 7.12 27.5 0
368 3.69695 0 18.1 0 0.718 4.963 91.4 1.7523 24 666 20.2 14 21.9 0
369 13.5222 0 18.1 0 0.631 3.863 100 1.5106 24 666 20.2 13.33 23.1 0
370 4.89822 0 18.1 0 0.631 4.97 100 1.3325 24 666 20.2 3.26 50 1
371 5.66998 0 18.1 1 0.631 6.683 96.8 1.3567 24 666 20.2 3.73 50 1
372 6.53876 0 18.1 1 0.631 7.016 97.5 1.2024 24 666 20.2 2.96 50 1
373 9.2323 0 18.1 0 0.631 6.216 100 1.1691 24 666 20.2 9.53 50 1
374 8.26725 0 18.1 1 0.668 5.875 89.6 1.1296 24 666 20.2 8.88 50 1
375 11.1081 0 18.1 0 0.668 4.906 100 1.1742 24 666 20.2 34.77 13.8 0
376 18.4982 0 18.1 0 0.668 4.138 100 1.137 24 666 20.2 37.97 13.8 0
377 19.6091 0 18.1 0 0.671 7.313 97.9 1.3163 24 666 20.2 13.44 15 0
378 15.288 0 18.1 0 0.671 6.649 93.3 1.3449 24 666 20.2 23.24 13.9 0
379 9.82349 0 18.1 0 0.671 6.794 98.8 1.358 24 666 20.2 21.24 13.3 0
380 23.6482 0 18.1 0 0.671 6.38 96.2 1.3861 24 666 20.2 23.69 13.1 0
381 17.8667 0 18.1 0 0.671 6.223 100 1.3861 24 666 20.2 21.78 10.2 0
382 88.9762 0 18.1 0 0.671 6.968 91.9 1.4165 24 666 20.2 17.21 10.4 0
383 15.8744 0 18.1 0 0.671 6.545 99.1 1.5192 24 666 20.2 21.08 10.9 0
384 9.18702 0 18.1 0 0.7 5.536 100 1.5804 24 666 20.2 23.6 11.3 0
385 7.99248 0 18.1 0 0.7 5.52 100 1.5331 24 666 20.2 24.56 12.3 0
386 20.0849 0 18.1 0 0.7 4.368 91.2 1.4395 24 666 20.2 30.63 8.8 0
387 16.8118 0 18.1 0 0.7 5.277 98.1 1.4261 24 666 20.2 30.81 7.2 0
388 24.3938 0 18.1 0 0.7 4.652 100 1.4672 24 666 20.2 28.28 10.5 0
389 22.5971 0 18.1 0 0.7 5 89.5 1.5184 24 666 20.2 31.99 7.4 0
390 14.3337 0 18.1 0 0.7 4.88 100 1.5895 24 666 20.2 30.62 10.2 0
391 8.15174 0 18.1 0 0.7 5.39 98.9 1.7281 24 666 20.2 20.85 11.5 0
392 6.96215 0 18.1 0 0.7 5.713 97 1.9265 24 666 20.2 17.11 15.1 0
393 5.29305 0 18.1 0 0.7 6.051 82.5 2.1678 24 666 20.2 18.76 23.2 0
394 11.5779 0 18.1 0 0.7 5.036 97 1.77 24 666 20.2 25.68 9.7 0
395 8.64476 0 18.1 0 0.693 6.193 92.6 1.7912 24 666 20.2 15.17 13.8 0
396 13.3598 0 18.1 0 0.693 5.887 94.7 1.7821 24 666 20.2 16.35 12.7 0
397 8.71675 0 18.1 0 0.693 6.471 98.8 1.7257 24 666 20.2 17.12 13.1 0
398 5.87205 0 18.1 0 0.693 6.405 96 1.6768 24 666 20.2 19.37 12.5 0
399 7.67202 0 18.1 0 0.693 5.747 98.9 1.6334 24 666 20.2 19.92 8.5 0
400 38.3518 0 18.1 0 0.693 5.453 100 1.4896 24 666 20.2 30.59 5 0
401 9.91655 0 18.1 0 0.693 5.852 77.8 1.5004 24 666 20.2 29.97 6.3 0
402 25.0461 0 18.1 0 0.693 5.987 100 1.5888 24 666 20.2 26.77 5.6 0
403 14.2362 0 18.1 0 0.693 6.343 100 1.5741 24 666 20.2 20.32 7.2 0
404 9.59571 0 18.1 0 0.693 6.404 100 1.639 24 666 20.2 20.31 12.1 0
405 24.8017 0 18.1 0 0.693 5.349 96 1.7028 24 666 20.2 19.77 8.3 0
406 41.5292 0 18.1 0 0.693 5.531 85.4 1.6074 24 666 20.2 27.38 8.5 0
407 67.9208 0 18.1 0 0.693 5.683 100 1.4254 24 666 20.2 22.98 5 0
408 20.7162 0 18.1 0 0.659 4.138 100 1.1781 24 666 20.2 23.34 11.9 0
409 11.9511 0 18.1 0 0.659 5.608 100 1.2852 24 666 20.2 12.13 27.9 0
410 7.40389 0 18.1 0 0.597 5.617 97.9 1.4547 24 666 20.2 26.4 17.2 0
411 14.4383 0 18.1 0 0.597 6.852 100 1.4655 24 666 20.2 19.78 27.5 0
412 51.1358 0 18.1 0 0.597 5.757 100 1.413 24 666 20.2 10.11 15 0
413 14.0507 0 18.1 0 0.597 6.657 100 1.5275 24 666 20.2 21.22 17.2 0
414 18.811 0 18.1 0 0.597 4.628 100 1.5539 24 666 20.2 34.37 17.9 0
415 28.6558 0 18.1 0 0.597 5.155 100 1.5894 24 666 20.2 20.08 16.3 0
416 45.7461 0 18.1 0 0.693 4.519 100 1.6582 24 666 20.2 36.98 7 0
417 18.0846 0 18.1 0 0.679 6.434 100 1.8347 24 666 20.2 29.05 7.2 0
418 10.8342 0 18.1 0 0.679 6.782 90.8 1.8195 24 666 20.2 25.79 7.5 0
419 25.9406 0 18.1 0 0.679 5.304 89.1 1.6475 24 666 20.2 26.64 10.4 0
420 73.5341 0 18.1 0 0.679 5.957 100 1.8026 24 666 20.2 20.62 8.8 0
421 11.8123 0 18.1 0 0.718 6.824 76.5 1.794 24 666 20.2 22.74 8.4 0
422 11.0874 0 18.1 0 0.718 6.411 100 1.8589 24 666 20.2 15.02 16.7 0
423 7.02259 0 18.1 0 0.718 6.006 95.3 1.8746 24 666 20.2 15.7 14.2 0
424 12.0482 0 18.1 0 0.614 5.648 87.6 1.9512 24 666 20.2 14.1 20.8 0
425 7.05042 0 18.1 0 0.614 6.103 85.1 2.0218 24 666 20.2 23.29 13.4 0
426 8.79212 0 18.1 0 0.584 5.565 70.6 2.0635 24 666 20.2 17.16 11.7 0
427 15.8603 0 18.1 0 0.679 5.896 95.4 1.9096 24 666 20.2 24.39 8.3 0
428 12.2472 0 18.1 0 0.584 5.837 59.7 1.9976 24 666 20.2 15.69 10.2 0
429 37.6619 0 18.1 0 0.679 6.202 78.7 1.8629 24 666 20.2 14.52 10.9 0
430 7.36711 0 18.1 0 0.679 6.193 78.1 1.9356 24 666 20.2 21.52 11 0
431 9.33889 0 18.1 0 0.679 6.38 95.6 1.9682 24 666 20.2 24.08 9.5 0
432 8.49213 0 18.1 0 0.584 6.348 86.1 2.0527 24 666 20.2 17.64 14.5 0
433 10.0623 0 18.1 0 0.584 6.833 94.3 2.0882 24 666 20.2 19.69 14.1 0
434 6.44405 0 18.1 0 0.584 6.425 74.8 2.2004 24 666 20.2 12.03 16.1 0
435 5.58107 0 18.1 0 0.713 6.436 87.9 2.3158 24 666 20.2 16.22 14.3 0
436 13.9134 0 18.1 0 0.713 6.208 95 2.2222 24 666 20.2 15.17 11.7 0
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"# Learning Practice 5 for the University of Tulsa's QM-7063 Data Mining Course\n",
"# Prediction and Classification Methods\n",
"# # Professor: Dr. Abdulrashid, Spring 2023\n",
"# Noah L. Schrick - 1492657"
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"# Problem 6.1\n",
"The file BostonHousing.csv contains information collected by the US Bureau of the Census concerning housing in the area of Boston, Massachusetts. The dataset includes information on 506 census housing tracts in the Boston area. The goal is to predict the median house price in new tracts based on information such as crime rate, pollution, and number of rooms. The dataset contains 13 predictors, and the outcome variable is the median house price (MEDV). Table 6.11 describes each of the predictors and the outcome variable.\n",
"\n",
"TABLE 6.11 DESCRIPTION OF VARIABLES FOR BOSTON HOUSING EXAMPLE\n",
"CRIM Per capita crime rate by town\n",
"ZN Proportion of residential land zoned for lots over 25,000 ft2\n",
"INDUS Proportion of nonretail business acres per town\n",
"CHAS Charles River dummy variable (=1 if tract bounds river; =0 otherwise)\n",
"NOX Nitric oxide concentration (parts per 10 million)\n",
"RM Average number of rooms per dwelling\n",
"AGE Proportion of owner-occupied units built prior to 1940\n",
"DIS Weighted distances to five Boston employment centers\n",
"RAD Index of accessibility to radial highways\n",
"TAX Full-value property-tax rate per $10,000\n",
"PTRATIO Pupil/teacher ratio by town\n",
"LSTAT Percentage lower status of the population\n",
"MEDV Median value of owner-occupied homes in $100\n",
"\n",
"\n",
"a.\n",
" Why should the data be partitioned into training and validation sets? What will the training set be used for? What will the validation set be used for?\n",
"b.\n",
" Fit a multiple linear regression model to the median house price (MEDV) as a function of CRIM, CHAS, and RM. Write the equation for predicting the median house price from the predictors in the model.\n",
"c. \n",
" Using the estimated regression model, what median house price is predicted for a tract in the Boston area that does not bound the Charles River, has a crime rate of 0.1, and where the average number of rooms per house is 6?\n",
"d. \n",
" Reduce the number of predictors:\n",
" i. Which predictors are likely to be measuring the same thing among the 13 predictors? Discuss the relationships among INDUS, NOX, and TAX.\n",
" ii. Compute the correlation table for the 12 numerical predictors and search for highly correlated pairs. These have potential redundancy and can cause multi-collinearity. Choose which ones to remove based on this table.\n",
" iii. Use three subset selection algorithms: backward, forward, and stepwise) to reduce the remaining predictors. Compute the validation performance for each of the three selected models. Compare RMSE, MAPE, and mean error, as well as histograms of the errors. Finally, describe the best model."
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"# Problem 6.2\n",
"Tayko Software is a software catalog firm that sells games and educational software. It started out as a software manufacturer and then added third-party titles to its offerings. It recently revised its collection of\n",
"items in a new catalog, which it mailed out to its customers. This mailing yielded 2000 purchases. Based on these data, Tayko wants to devise a model for predicting the spending amount that a purchasing customer will yield. The file Tayko.csv contains information on 2000 purchases. Table 6.12 describes the variables to be used in the problem (the Excel file contains additional variables).\n",
"\n",
"TABLE 6.12 DESCRIPTION OF VARIABLES FOR TAYKO SOFTWARE EXAMPLE\n",
"FREQ Number of transactions in the preceding year\n",
"LAST_UPDATE Number of days since last update to customer record\n",
"WEB Whether customer purchased by Web order at least once\n",
"GENDER Male or female\n",
"ADDRESS_RES Whether it is a residential address\n",
"ADDRESS_US Whether it is a US address\n",
"SPENDING (outcome) Amount spent by customer in test mailing ($)\n",
"\n",
"a. \n",
" Explore the spending amount by creating a pivot table for the categorical variables and computing the average and standard deviation of spending in each category.\n",
"b. \n",
" Explore the relationship between spending and each of the two continuous predictors by creating two scatterplots (Spending vs. Freq, and Spending vs. last_update_days_ago). Does there seem to be a linear relationship?\n",
"c. \n",
" To fit a predictive model for Spending:\n",
" i. Partition the 2000 records into training and validation sets.\n",
" ii. Run a multiple linear regression model for Spending vs. all six predictors. Give the estimated predictive equation.\n",
" iii. Based on this model, what type of purchaser is most likely to spend a large amount of money?\n",
" iv. If we used backward elimination to reduce the number of predictors, which predictor would be dropped first from the model?\n",
" v. Show how the prediction and the prediction error are computed for the first purchase in the validation set.\n",
" vi. Evaluate the predictive accuracy of the model by examining its performance on the validation set.\n",
" vii. Create a histogram of the model residuals. Do they appear to follow a normal distribution? How does this affect the predictive performance of the model?"
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