MODUL 3_CLUSTERING
Alifiyanti, Astrid, Gresya, Zikri
2026-04-13
Analisis Clustering Dataset WineQT
1. Deskripsi Dataset
Dataset yang digunakan dalam praktikum ini adalah WineQT, yaitu dataset yang berisi data karakteristik kimia dari wine. Setiap baris data merepresentasikan satu sampel wine dengan berbagai atribut numerik seperti tingkat keasaman (acidity), kandungan alkohol, kadar gula, dan lain-lain.
Dataset ini cocok digunakan untuk clustering karena tidak memiliki label kelas utama, sehingga dapat digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik antar wine. Jumlah data cukup banyak dan seluruh variabel bersifat numerik, sehingga perlu dilakukan preprocessing seperti scaling agar hasil clustering lebih optimal.
2. Import Library
library(tidyr)
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(corrplot)## corrplot 0.95 loadedlibrary(caret) #Scaling (standardisasi)## Loading required package: latticelibrary(outliers)##
## Attaching package: 'outliers'## The following object is masked from 'package:psych':
##
## outlierlibrary(factoextra) #evaluasi & plot K-means## Welcome to factoextra!## Want to learn more? See two factoextra-related books at https://www.datanovia.com/en/product/practical-guide-to-principal-component-methods-in-r/library(cluster) #K-Means
library(flexclust) #K-Median
library(dbscan) #DBSCAN##
## Attaching package: 'dbscan'## The following object is masked from 'package:stats':
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## as.dendrogramlibrary(meanShiftR) #Mean Shift
library(e1071) #Fuzzy C-Means##
## Attaching package: 'e1071'## The following object is masked from 'package:flexclust':
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## bclust## The following object is masked from 'package:ggplot2':
##
## element3. Load Dataset
data <- read.csv("WineQT.csv")
data## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.700 0.00 1.90 0.076
## 2 7.8 0.880 0.00 2.60 0.098
## 3 7.8 0.760 0.04 2.30 0.092
## 4 11.2 0.280 0.56 1.90 0.075
## 5 7.4 0.700 0.00 1.90 0.076
## 6 7.4 0.660 0.00 1.80 0.075
## 7 7.9 0.600 0.06 1.60 0.069
## 8 7.3 0.650 0.00 1.20 0.065
## 9 7.8 0.580 0.02 2.00 0.073
## 10 6.7 0.580 0.08 1.80 0.097
## 11 5.6 0.615 0.00 1.60 0.089
## 12 7.8 0.610 0.29 1.60 0.114
## 13 8.5 0.280 0.56 1.80 0.092
## 14 7.9 0.320 0.51 1.80 0.341
## 15 7.6 0.390 0.31 2.30 0.082
## 16 7.9 0.430 0.21 1.60 0.106
## 17 8.5 0.490 0.11 2.30 0.084
## 18 6.9 0.400 0.14 2.40 0.085
## 19 6.3 0.390 0.16 1.40 0.080
## 20 7.6 0.410 0.24 1.80 0.080
## 21 7.1 0.710 0.00 1.90 0.080
## 22 7.8 0.645 0.00 2.00 0.082
## 23 6.7 0.675 0.07 2.40 0.089
## 24 8.3 0.655 0.12 2.30 0.083
## 25 5.2 0.320 0.25 1.80 0.103
## 26 7.8 0.645 0.00 5.50 0.086
## 27 7.8 0.600 0.14 2.40 0.086
## 28 8.1 0.380 0.28 2.10 0.066
## 29 7.3 0.450 0.36 5.90 0.074
## 30 8.8 0.610 0.30 2.80 0.088
## 31 7.5 0.490 0.20 2.60 0.332
## 32 8.1 0.660 0.22 2.20 0.069
## 33 4.6 0.520 0.15 2.10 0.054
## 34 7.7 0.935 0.43 2.20 0.114
## 35 8.8 0.660 0.26 1.70 0.074
## 36 6.6 0.520 0.04 2.20 0.069
## 37 6.6 0.500 0.04 2.10 0.068
## 38 8.6 0.380 0.36 3.00 0.081
## 39 7.6 0.510 0.15 2.80 0.110
## 40 10.2 0.420 0.57 3.40 0.070
## 41 7.8 0.590 0.18 2.30 0.076
## 42 7.3 0.390 0.31 2.40 0.074
## 43 8.8 0.400 0.40 2.20 0.079
## 44 7.7 0.690 0.49 1.80 0.115
## 45 7.0 0.735 0.05 2.00 0.081
## 46 7.2 0.725 0.05 4.65 0.086
## 47 7.2 0.725 0.05 4.65 0.086
## 48 6.6 0.705 0.07 1.60 0.076
## 49 8.0 0.705 0.05 1.90 0.074
## 50 7.7 0.690 0.22 1.90 0.084
## 51 8.3 0.675 0.26 2.10 0.084
## 52 8.8 0.410 0.64 2.20 0.093
## 53 6.8 0.785 0.00 2.40 0.104
## 54 6.7 0.750 0.12 2.00 0.086
## 55 8.3 0.625 0.20 1.50 0.080
## 56 6.2 0.450 0.20 1.60 0.069
## 57 7.4 0.500 0.47 2.00 0.086
## 58 6.3 0.300 0.48 1.80 0.069
## 59 6.9 0.550 0.15 2.20 0.076
## 60 8.6 0.490 0.28 1.90 0.110
## 61 7.7 0.490 0.26 1.90 0.062
## 62 9.3 0.390 0.44 2.10 0.107
## 63 7.0 0.620 0.08 1.80 0.076
## 64 7.9 0.520 0.26 1.90 0.079
## 65 8.6 0.490 0.28 1.90 0.110
## 66 7.7 0.490 0.26 1.90 0.062
## 67 5.0 1.020 0.04 1.40 0.045
## 68 6.8 0.775 0.00 3.00 0.102
## 69 7.6 0.900 0.06 2.50 0.079
## 70 8.1 0.545 0.18 1.90 0.080
## 71 8.3 0.610 0.30 2.10 0.084
## 72 8.1 0.545 0.18 1.90 0.080
## 73 8.1 0.575 0.22 2.10 0.077
## 74 7.2 0.490 0.24 2.20 0.070
## 75 8.1 0.575 0.22 2.10 0.077
## 76 7.8 0.410 0.68 1.70 0.467
## 77 6.2 0.630 0.31 1.70 0.088
## 78 7.8 0.560 0.19 1.80 0.104
## 79 8.4 0.620 0.09 2.20 0.084
## 80 10.1 0.310 0.44 2.30 0.080
## 81 7.8 0.560 0.19 1.80 0.104
## 82 9.4 0.400 0.31 2.20 0.090
## 83 8.3 0.540 0.28 1.90 0.077
## 84 7.3 1.070 0.09 1.70 0.178
## 85 8.8 0.550 0.04 2.20 0.119
## 86 7.3 0.695 0.00 2.50 0.075
## 87 7.8 0.500 0.17 1.60 0.082
## 88 8.2 1.330 0.00 1.70 0.081
## 89 8.1 1.330 0.00 1.80 0.082
## 90 8.0 0.590 0.16 1.80 0.065
## 91 8.0 0.745 0.56 2.00 0.118
## 92 5.6 0.500 0.09 2.30 0.049
## 93 7.9 1.040 0.05 2.20 0.084
## 94 8.4 0.745 0.11 1.90 0.090
## 95 7.2 0.415 0.36 2.00 0.081
## 96 8.4 0.745 0.11 1.90 0.090
## 97 5.2 0.340 0.00 1.80 0.050
## 98 6.3 0.390 0.08 1.70 0.066
## 99 5.2 0.340 0.00 1.80 0.050
## 100 8.1 0.670 0.55 1.80 0.117
## 101 5.8 0.680 0.02 1.80 0.087
## 102 6.9 0.490 0.10 2.30 0.074
## 103 7.3 0.330 0.47 2.10 0.077
## 104 9.2 0.520 1.00 3.40 0.610
## 105 7.5 0.600 0.03 1.80 0.095
## 106 7.5 0.600 0.03 1.80 0.095
## 107 7.1 0.430 0.42 5.50 0.071
## 108 7.1 0.430 0.42 5.50 0.070
## 109 7.1 0.430 0.42 5.50 0.071
## 110 7.1 0.680 0.00 2.20 0.073
## 111 6.8 0.600 0.18 1.90 0.079
## 112 7.6 0.950 0.03 2.00 0.090
## 113 7.6 0.680 0.02 1.30 0.072
## 114 7.8 0.530 0.04 1.70 0.076
## 115 7.4 0.600 0.26 7.30 0.070
## 116 7.3 0.590 0.26 7.20 0.070
## 117 7.8 0.630 0.48 1.70 0.100
## 118 6.8 0.640 0.10 2.10 0.085
## 119 7.3 0.550 0.03 1.60 0.072
## 120 6.8 0.630 0.07 2.10 0.089
## 121 7.9 0.885 0.03 1.80 0.058
## 122 8.0 0.420 0.17 2.00 0.073
## 123 7.4 0.620 0.05 1.90 0.068
## 124 6.9 0.500 0.04 1.50 0.085
## 125 7.3 0.380 0.21 2.00 0.080
## 126 7.5 0.520 0.42 2.30 0.087
## 127 7.0 0.805 0.00 2.50 0.068
## 128 8.8 0.610 0.14 2.40 0.067
## 129 8.8 0.610 0.14 2.40 0.067
## 130 8.9 0.610 0.49 2.00 0.270
## 131 7.2 0.730 0.02 2.50 0.076
## 132 6.8 0.610 0.20 1.80 0.077
## 133 6.7 0.620 0.21 1.90 0.079
## 134 8.9 0.310 0.57 2.00 0.111
## 135 7.4 0.390 0.48 2.00 0.082
## 136 7.9 0.500 0.33 2.00 0.084
## 137 8.2 0.500 0.35 2.90 0.077
## 138 6.4 0.370 0.25 1.90 0.074
## 139 7.6 0.550 0.21 2.20 0.071
## 140 7.6 0.550 0.21 2.20 0.071
## 141 7.3 0.580 0.30 2.40 0.074
## 142 11.5 0.300 0.60 2.00 0.067
## 143 6.9 1.090 0.06 2.10 0.061
## 144 9.6 0.320 0.47 1.40 0.056
## 145 7.0 0.430 0.36 1.60 0.089
## 146 12.8 0.300 0.74 2.60 0.095
## 147 12.8 0.300 0.74 2.60 0.095
## 148 7.8 0.440 0.28 2.70 0.100
## 149 9.7 0.530 0.60 2.00 0.039
## 150 8.0 0.725 0.24 2.80 0.083
## 151 8.2 0.570 0.26 2.20 0.060
## 152 7.8 0.735 0.08 2.40 0.092
## 153 7.0 0.490 0.49 5.60 0.060
## 154 8.7 0.625 0.16 2.00 0.101
## 155 8.1 0.725 0.22 2.20 0.072
## 156 7.5 0.490 0.19 1.90 0.076
## 157 7.8 0.340 0.37 2.00 0.082
## 158 7.4 0.530 0.26 2.00 0.101
## 159 6.8 0.610 0.04 1.50 0.057
## 160 8.6 0.645 0.25 2.00 0.083
## 161 7.7 0.430 0.25 2.60 0.073
## 162 8.9 0.590 0.50 2.00 0.337
## 163 5.2 0.480 0.04 1.60 0.054
## 164 8.0 0.380 0.06 1.80 0.078
## 165 8.5 0.370 0.20 2.80 0.090
## 166 8.2 1.000 0.09 2.30 0.065
## 167 7.2 0.630 0.00 1.90 0.097
## 168 8.9 0.635 0.37 1.70 0.263
## 169 12.0 0.380 0.56 2.10 0.093
## 170 7.7 0.580 0.10 1.80 0.102
## 171 15.0 0.210 0.44 2.20 0.075
## 172 15.0 0.210 0.44 2.20 0.075
## 173 7.3 0.660 0.00 2.00 0.084
## 174 7.1 0.680 0.07 1.90 0.075
## 175 7.3 0.660 0.00 2.00 0.084
## 176 10.8 0.320 0.44 1.60 0.063
## 177 7.1 0.600 0.00 1.80 0.074
## 178 11.1 0.350 0.48 3.10 0.090
## 179 7.7 0.775 0.42 1.90 0.092
## 180 8.0 0.570 0.23 3.20 0.073
## 181 9.4 0.340 0.37 2.20 0.075
## 182 6.6 0.695 0.00 2.10 0.075
## 183 7.7 0.410 0.76 1.80 0.611
## 184 10.0 0.310 0.47 2.60 0.085
## 185 7.9 0.330 0.23 1.70 0.077
## 186 7.0 0.975 0.04 2.00 0.087
## 187 8.0 0.520 0.03 1.70 0.070
## 188 7.9 0.370 0.23 1.80 0.077
## 189 12.5 0.560 0.49 2.40 0.064
## 190 8.1 0.870 0.00 3.30 0.096
## 191 7.9 0.350 0.46 3.60 0.078
## 192 6.9 0.540 0.04 3.00 0.077
## 193 11.5 0.180 0.51 4.00 0.104
## 194 7.9 0.545 0.06 4.00 0.087
## 195 7.9 0.545 0.06 4.00 0.087
## 196 6.9 0.540 0.04 3.00 0.077
## 197 11.5 0.180 0.51 4.00 0.104
## 198 10.3 0.320 0.45 6.40 0.073
## 199 8.9 0.400 0.32 5.60 0.087
## 200 11.4 0.260 0.44 3.60 0.071
## 201 7.7 0.270 0.68 3.50 0.358
## 202 8.9 0.400 0.32 5.60 0.087
## 203 9.9 0.590 0.07 3.40 0.102
## 204 9.9 0.590 0.07 3.40 0.102
## 205 12.0 0.450 0.55 2.00 0.073
## 206 7.5 0.400 0.12 3.00 0.092
## 207 8.7 0.520 0.09 2.50 0.091
## 208 11.6 0.420 0.53 3.30 0.105
## 209 8.7 0.520 0.09 2.50 0.091
## 210 10.4 0.550 0.23 2.70 0.091
## 211 6.9 0.360 0.25 2.40 0.098
## 212 13.3 0.340 0.52 3.20 0.094
## 213 10.8 0.500 0.46 2.50 0.073
## 214 10.6 0.830 0.37 2.60 0.086
## 215 7.1 0.630 0.06 2.00 0.083
## 216 7.2 0.650 0.02 2.30 0.094
## 217 7.5 0.530 0.06 2.60 0.086
## 218 11.1 0.180 0.48 1.50 0.068
## 219 8.3 0.705 0.12 2.60 0.092
## 220 8.4 0.650 0.60 2.10 0.112
## 221 7.6 0.620 0.32 2.20 0.082
## 222 10.3 0.410 0.42 2.40 0.213
## 223 10.3 0.430 0.44 2.40 0.214
## 224 7.9 0.530 0.24 2.00 0.072
## 225 9.0 0.460 0.31 2.80 0.093
## 226 8.6 0.470 0.30 3.00 0.076
## 227 7.4 0.360 0.29 2.60 0.087
## 228 9.8 0.660 0.39 3.20 0.083
## 229 9.6 0.770 0.12 2.90 0.082
## 230 9.3 0.610 0.26 3.40 0.090
## 231 10.0 0.490 0.20 11.00 0.071
## 232 10.0 0.490 0.20 11.00 0.071
## 233 11.6 0.530 0.66 3.65 0.121
## 234 10.3 0.440 0.50 4.50 0.107
## 235 13.4 0.270 0.62 2.60 0.082
## 236 8.4 0.560 0.08 2.10 0.105
## 237 7.9 0.650 0.01 2.50 0.078
## 238 11.9 0.695 0.53 3.40 0.128
## 239 8.9 0.430 0.45 1.90 0.052
## 240 7.8 0.430 0.32 2.80 0.080
## 241 12.5 0.280 0.54 2.30 0.082
## 242 10.9 0.390 0.47 1.80 0.118
## 243 10.9 0.390 0.47 1.80 0.118
## 244 11.9 0.570 0.50 2.60 0.082
## 245 13.8 0.490 0.67 3.00 0.093
## 246 9.6 0.560 0.31 2.80 0.089
## 247 9.1 0.795 0.00 2.60 0.096
## 248 7.7 0.665 0.00 2.40 0.090
## 249 13.5 0.530 0.79 4.80 0.120
## 250 6.1 0.210 0.40 1.40 0.066
## 251 6.7 0.750 0.01 2.40 0.078
## 252 11.5 0.410 0.52 3.00 0.080
## 253 10.5 0.420 0.66 2.95 0.116
## 254 11.9 0.430 0.66 3.10 0.109
## 255 12.6 0.380 0.66 2.60 0.088
## 256 8.2 0.700 0.23 2.00 0.099
## 257 8.6 0.450 0.31 2.60 0.086
## 258 11.9 0.580 0.66 2.50 0.072
## 259 12.5 0.460 0.63 2.00 0.071
## 260 12.8 0.615 0.66 5.80 0.083
## 261 12.8 0.615 0.66 5.80 0.083
## 262 10.4 0.575 0.61 2.60 0.076
## 263 9.4 0.270 0.53 2.40 0.074
## 264 7.9 0.240 0.40 1.60 0.056
## 265 9.1 0.280 0.48 1.80 0.067
## 266 11.5 0.450 0.50 3.00 0.078
## 267 9.4 0.270 0.53 2.40 0.074
## 268 11.4 0.625 0.66 6.20 0.088
## 269 8.3 0.260 0.42 2.00 0.080
## 270 7.7 0.510 0.28 2.10 0.087
## 271 7.8 0.460 0.26 1.90 0.088
## 272 5.6 0.850 0.05 1.40 0.045
## 273 13.7 0.415 0.68 2.90 0.085
## 274 9.5 0.370 0.52 2.00 0.082
## 275 12.0 0.370 0.76 4.20 0.066
## 276 6.6 0.735 0.02 7.90 0.122
## 277 11.5 0.590 0.59 2.60 0.087
## 278 8.7 0.765 0.22 2.30 0.064
## 279 6.6 0.735 0.02 7.90 0.122
## 280 12.2 0.480 0.54 2.60 0.085
## 281 11.4 0.600 0.49 2.70 0.085
## 282 7.7 0.690 0.05 2.70 0.075
## 283 9.8 0.440 0.47 2.50 0.063
## 284 12.0 0.390 0.66 3.00 0.093
## 285 12.5 0.460 0.49 4.50 0.070
## 286 9.0 0.430 0.34 2.50 0.080
## 287 7.1 0.735 0.16 1.90 0.100
## 288 9.9 0.400 0.53 6.70 0.097
## 289 8.8 0.520 0.34 2.70 0.087
## 290 8.6 0.725 0.24 6.60 0.117
## 291 10.6 0.480 0.64 2.20 0.111
## 292 7.0 0.580 0.12 1.90 0.091
## 293 11.9 0.380 0.51 2.00 0.121
## 294 6.8 0.770 0.00 1.80 0.066
## 295 6.6 0.840 0.03 2.30 0.059
## 296 7.7 0.960 0.20 2.00 0.047
## 297 10.5 0.240 0.47 2.10 0.066
## 298 6.6 0.840 0.03 2.30 0.059
## 299 6.4 0.670 0.08 2.10 0.045
## 300 9.5 0.780 0.22 1.90 0.077
## 301 9.1 0.520 0.33 1.30 0.070
## 302 12.8 0.840 0.63 2.40 0.088
## 303 10.5 0.240 0.47 2.10 0.066
## 304 7.8 0.550 0.35 2.20 0.074
## 305 12.3 0.390 0.63 2.30 0.091
## 306 10.4 0.410 0.55 3.20 0.076
## 307 12.3 0.390 0.63 2.30 0.091
## 308 8.0 0.670 0.30 2.00 0.060
## 309 11.1 0.450 0.73 3.20 0.066
## 310 7.0 0.620 0.18 1.50 0.062
## 311 12.6 0.310 0.72 2.20 0.072
## 312 15.6 0.685 0.76 3.70 0.100
## 313 5.3 0.570 0.01 1.70 0.054
## 314 12.5 0.380 0.60 2.60 0.081
## 315 9.3 0.480 0.29 2.10 0.127
## 316 8.6 0.530 0.22 2.00 0.100
## 317 11.9 0.390 0.69 2.80 0.095
## 318 11.9 0.390 0.69 2.80 0.095
## 319 6.8 0.560 0.03 1.70 0.084
## 320 10.4 0.330 0.63 2.80 0.084
## 321 7.0 0.230 0.40 1.60 0.063
## 322 11.3 0.620 0.67 5.20 0.086
## 323 8.9 0.590 0.39 2.30 0.095
## 324 9.2 0.630 0.21 2.70 0.097
## 325 11.6 0.580 0.66 2.20 0.074
## 326 9.2 0.430 0.52 2.30 0.083
## 327 8.3 0.615 0.22 2.60 0.087
## 328 11.5 0.315 0.54 2.10 0.084
## 329 10.3 0.500 0.42 2.00 0.069
## 330 8.8 0.460 0.45 2.60 0.065
## 331 11.4 0.360 0.69 2.10 0.090
## 332 8.7 0.820 0.02 1.20 0.070
## 333 13.0 0.320 0.65 2.60 0.093
## 334 9.6 0.540 0.42 2.40 0.081
## 335 12.5 0.370 0.55 2.60 0.083
## 336 9.6 0.680 0.24 2.20 0.087
## 337 9.3 0.270 0.41 2.00 0.091
## 338 10.4 0.240 0.49 1.80 0.075
## 339 9.4 0.685 0.11 2.70 0.077
## 340 10.6 0.280 0.39 15.50 0.069
## 341 9.4 0.300 0.56 2.80 0.080
## 342 10.6 0.360 0.60 2.20 0.152
## 343 10.2 0.670 0.39 1.90 0.054
## 344 10.2 0.645 0.36 1.80 0.053
## 345 9.3 0.390 0.40 2.60 0.073
## 346 9.2 0.410 0.50 2.50 0.055
## 347 8.9 0.400 0.51 2.60 0.052
## 348 8.7 0.690 0.31 3.00 0.086
## 349 6.5 0.390 0.23 8.30 0.051
## 350 10.7 0.350 0.53 2.60 0.070
## 351 7.8 0.520 0.25 1.90 0.081
## 352 7.2 0.340 0.32 2.50 0.090
## 353 10.7 0.350 0.53 2.60 0.070
## 354 8.7 0.690 0.31 3.00 0.086
## 355 7.8 0.520 0.25 1.90 0.081
## 356 10.4 0.440 0.73 6.55 0.074
## 357 10.4 0.440 0.73 6.55 0.074
## 358 10.5 0.240 0.42 1.80 0.077
## 359 10.2 0.490 0.63 2.90 0.072
## 360 10.4 0.240 0.46 1.80 0.075
## 361 11.2 0.670 0.55 2.30 0.084
## 362 13.3 0.290 0.75 2.80 0.084
## 363 10.0 0.590 0.31 2.20 0.090
## 364 10.7 0.400 0.48 2.10 0.125
## 365 10.5 0.510 0.64 2.40 0.107
## 366 10.5 0.510 0.64 2.40 0.107
## 367 8.5 0.655 0.49 6.10 0.122
## 368 12.5 0.600 0.49 4.30 0.100
## 369 10.4 0.610 0.49 2.10 0.200
## 370 9.8 0.250 0.49 2.70 0.088
## 371 9.3 0.400 0.49 2.50 0.085
## 372 9.2 0.430 0.49 2.40 0.086
## 373 7.0 0.380 0.49 2.50 0.097
## 374 9.9 0.630 0.24 2.40 0.077
## 375 9.1 0.220 0.24 2.10 0.078
## 376 11.9 0.380 0.49 2.70 0.098
## 377 11.9 0.380 0.49 2.70 0.098
## 378 10.3 0.270 0.24 2.10 0.072
## 379 10.0 0.480 0.24 2.70 0.102
## 380 9.1 0.220 0.24 2.10 0.078
## 381 9.9 0.630 0.24 2.40 0.077
## 382 8.1 0.825 0.24 2.10 0.084
## 383 12.9 0.350 0.49 5.80 0.066
## 384 11.2 0.500 0.74 5.15 0.100
## 385 9.2 0.590 0.24 3.30 0.101
## 386 9.5 0.460 0.49 6.30 0.064
## 387 9.3 0.715 0.24 2.10 0.070
## 388 11.2 0.660 0.24 2.50 0.085
## 389 14.3 0.310 0.74 1.80 0.075
## 390 9.1 0.470 0.49 2.60 0.094
## 391 7.5 0.550 0.24 2.00 0.078
## 392 10.6 0.310 0.49 2.50 0.067
## 393 12.4 0.350 0.49 2.60 0.079
## 394 6.8 0.510 0.01 2.10 0.074
## 395 9.4 0.430 0.24 2.80 0.092
## 396 9.5 0.460 0.24 2.70 0.092
## 397 5.0 1.040 0.24 1.60 0.050
## 398 15.5 0.645 0.49 4.20 0.095
## 399 10.9 0.530 0.49 4.60 0.118
## 400 15.6 0.645 0.49 4.20 0.095
## 401 13.0 0.470 0.49 4.30 0.085
## 402 12.7 0.600 0.49 2.80 0.075
## 403 9.0 0.540 0.49 2.90 0.094
## 404 7.6 0.290 0.49 2.70 0.092
## 405 13.0 0.470 0.49 4.30 0.085
## 406 12.7 0.600 0.49 2.80 0.075
## 407 8.7 0.700 0.24 2.50 0.226
## 408 9.8 0.500 0.49 2.60 0.250
## 409 6.2 0.360 0.24 2.20 0.095
## 410 11.5 0.350 0.49 3.30 0.070
## 411 10.2 0.240 0.49 2.40 0.075
## 412 9.9 0.500 0.24 2.30 0.103
## 413 8.8 0.440 0.49 2.80 0.083
## 414 8.8 0.470 0.49 2.90 0.085
## 415 10.6 0.310 0.49 2.20 0.063
## 416 12.3 0.500 0.49 2.20 0.089
## 417 12.3 0.500 0.49 2.20 0.089
## 418 12.0 0.280 0.49 1.90 0.074
## 419 7.3 0.730 0.24 1.90 0.108
## 420 5.0 0.420 0.24 2.00 0.060
## 421 9.0 0.450 0.49 2.60 0.084
## 422 6.6 0.390 0.49 1.70 0.070
## 423 9.0 0.450 0.49 2.60 0.084
## 424 9.9 0.490 0.58 3.50 0.094
## 425 8.9 0.595 0.41 7.90 0.086
## 426 12.4 0.400 0.51 2.00 0.059
## 427 8.5 0.585 0.18 2.10 0.078
## 428 7.7 0.835 0.00 2.60 0.081
## 429 8.3 0.580 0.13 2.90 0.096
## 430 8.8 0.480 0.41 3.30 0.092
## 431 10.1 0.650 0.37 5.10 0.110
## 432 6.3 0.360 0.19 3.20 0.075
## 433 8.8 0.240 0.54 2.50 0.083
## 434 13.2 0.380 0.55 2.70 0.081
## 435 7.5 0.640 0.00 2.40 0.077
## 436 9.6 0.600 0.50 2.30 0.079
## 437 11.5 0.310 0.51 2.20 0.079
## 438 11.3 0.370 0.41 2.30 0.088
## 439 8.3 0.540 0.24 3.40 0.076
## 440 8.2 0.560 0.23 3.40 0.078
## 441 10.0 0.580 0.22 1.90 0.080
## 442 6.8 0.690 0.00 5.60 0.124
## 443 8.8 0.600 0.29 2.20 0.098
## 444 8.8 0.600 0.29 2.20 0.098
## 445 8.7 0.540 0.26 2.50 0.097
## 446 7.6 0.685 0.23 2.30 0.111
## 447 8.7 0.540 0.26 2.50 0.097
## 448 10.4 0.280 0.54 2.70 0.105
## 449 7.6 0.410 0.14 3.00 0.087
## 450 10.1 0.935 0.22 3.40 0.105
## 451 7.9 0.350 0.21 1.90 0.073
## 452 8.7 0.840 0.00 1.40 0.065
## 453 9.6 0.880 0.28 2.40 0.086
## 454 9.5 0.885 0.27 2.30 0.084
## 455 8.9 0.290 0.35 1.90 0.067
## 456 9.9 0.540 0.45 2.30 0.071
## 457 9.9 0.540 0.45 2.30 0.071
## 458 9.9 0.540 0.45 2.30 0.071
## 459 8.3 0.845 0.01 2.20 0.070
## 460 8.7 0.480 0.30 2.80 0.066
## 461 6.7 0.420 0.27 8.60 0.068
## 462 10.7 0.430 0.39 2.20 0.106
## 463 15.9 0.360 0.65 7.50 0.096
## 464 9.4 0.330 0.59 2.80 0.079
## 465 8.6 0.470 0.47 2.40 0.074
## 466 9.7 0.550 0.17 2.90 0.087
## 467 10.7 0.430 0.39 2.20 0.106
## 468 12.0 0.500 0.59 1.40 0.073
## 469 7.2 0.520 0.07 1.40 0.074
## 470 7.2 0.520 0.07 1.40 0.074
## 471 7.5 0.420 0.31 1.60 0.080
## 472 7.2 0.570 0.06 1.60 0.076
## 473 9.4 0.590 0.14 2.00 0.084
## 474 8.3 0.490 0.36 1.80 0.222
## 475 11.3 0.340 0.45 2.00 0.082
## 476 11.3 0.340 0.45 2.00 0.082
## 477 8.2 0.730 0.21 1.70 0.074
## 478 8.2 0.730 0.21 1.70 0.074
## 479 10.8 0.400 0.41 2.20 0.084
## 480 8.6 0.800 0.11 2.30 0.084
## 481 8.3 0.780 0.10 2.60 0.081
## 482 10.8 0.260 0.45 3.30 0.060
## 483 8.0 0.450 0.23 2.20 0.094
## 484 8.5 0.460 0.31 2.25 0.078
## 485 8.1 0.780 0.23 2.60 0.059
## 486 9.8 0.980 0.32 2.30 0.078
## 487 8.1 0.780 0.23 2.60 0.059
## 488 7.7 0.660 0.04 1.60 0.039
## 489 8.1 0.380 0.48 1.80 0.157
## 490 9.2 0.920 0.24 2.60 0.087
## 491 8.6 0.490 0.51 2.00 0.422
## 492 9.0 0.480 0.32 2.80 0.084
## 493 9.0 0.470 0.31 2.70 0.084
## 494 5.1 0.470 0.02 1.30 0.034
## 495 7.0 0.650 0.02 2.10 0.066
## 496 9.4 0.615 0.28 3.20 0.087
## 497 11.8 0.380 0.55 2.10 0.071
## 498 10.6 1.020 0.43 2.90 0.076
## 499 7.0 0.650 0.02 2.10 0.066
## 500 7.0 0.640 0.02 2.10 0.067
## 501 7.5 0.380 0.48 2.60 0.073
## 502 9.1 0.765 0.04 1.60 0.078
## 503 8.4 1.035 0.15 6.00 0.073
## 504 7.0 0.780 0.08 2.00 0.093
## 505 7.4 0.490 0.19 3.00 0.077
## 506 7.8 0.545 0.12 2.50 0.068
## 507 10.6 1.025 0.43 2.80 0.080
## 508 8.9 0.565 0.34 3.00 0.093
## 509 9.9 0.740 0.28 2.60 0.078
## 510 7.6 0.460 0.11 2.60 0.079
## 511 8.4 0.560 0.04 2.00 0.082
## 512 7.1 0.660 0.00 3.90 0.086
## 513 8.4 0.560 0.04 2.00 0.082
## 514 8.9 0.480 0.24 2.85 0.094
## 515 7.1 0.310 0.30 2.20 0.053
## 516 9.0 0.660 0.17 3.00 0.077
## 517 8.1 0.720 0.09 2.80 0.084
## 518 6.4 0.570 0.02 1.80 0.067
## 519 6.4 0.570 0.02 1.80 0.067
## 520 6.4 0.865 0.03 3.20 0.071
## 521 9.5 0.550 0.66 2.30 0.387
## 522 8.9 0.875 0.13 3.45 0.088
## 523 7.3 0.835 0.03 2.10 0.092
## 524 7.0 0.450 0.34 2.70 0.082
## 525 7.7 0.560 0.20 2.00 0.075
## 526 7.7 0.965 0.10 2.10 0.112
## 527 8.2 0.590 0.00 2.50 0.093
## 528 9.0 0.690 0.00 2.40 0.088
## 529 8.3 0.760 0.29 4.20 0.075
## 530 9.2 0.530 0.24 2.60 0.078
## 531 11.1 0.390 0.54 2.70 0.095
## 532 7.3 0.510 0.18 2.10 0.070
## 533 8.2 0.340 0.38 2.50 0.080
## 534 7.2 0.500 0.18 2.10 0.071
## 535 7.3 0.510 0.18 2.10 0.070
## 536 8.3 0.650 0.10 2.90 0.089
## 537 7.6 0.540 0.13 2.50 0.097
## 538 8.3 0.650 0.10 2.90 0.089
## 539 7.8 0.480 0.68 1.70 0.415
## 540 7.8 0.910 0.07 1.90 0.058
## 541 6.3 0.980 0.01 2.00 0.057
## 542 8.1 0.870 0.00 2.20 0.084
## 543 8.1 0.870 0.00 2.20 0.084
## 544 8.8 0.420 0.21 2.50 0.092
## 545 9.0 0.580 0.25 2.80 0.075
## 546 9.3 0.655 0.26 2.00 0.096
## 547 8.8 0.700 0.00 1.70 0.069
## 548 9.1 0.680 0.11 2.80 0.093
## 549 9.2 0.670 0.10 3.00 0.091
## 550 8.8 0.590 0.18 2.90 0.089
## 551 7.1 0.590 0.02 2.30 0.082
## 552 7.9 0.720 0.01 1.90 0.076
## 553 7.1 0.590 0.02 2.30 0.082
## 554 9.4 0.685 0.26 2.40 0.082
## 555 9.5 0.570 0.27 2.30 0.082
## 556 7.9 0.400 0.29 1.80 0.157
## 557 7.2 1.000 0.00 3.00 0.102
## 558 6.9 0.635 0.17 2.40 0.241
## 559 8.3 0.430 0.30 3.40 0.079
## 560 7.1 0.520 0.03 2.60 0.076
## 561 7.0 0.570 0.00 2.00 0.190
## 562 6.5 0.460 0.14 2.40 0.114
## 563 7.1 0.590 0.01 2.50 0.077
## 564 9.9 0.350 0.41 2.30 0.083
## 565 10.0 0.560 0.24 2.20 0.079
## 566 10.0 0.560 0.24 2.20 0.079
## 567 8.6 0.630 0.17 2.90 0.099
## 568 7.4 0.370 0.43 2.60 0.082
## 569 8.8 0.640 0.17 2.90 0.084
## 570 7.1 0.610 0.02 2.50 0.081
## 571 7.7 0.600 0.00 2.60 0.055
## 572 10.1 0.270 0.54 2.30 0.065
## 573 10.8 0.890 0.30 2.60 0.132
## 574 8.7 0.460 0.31 2.50 0.126
## 575 9.3 0.370 0.44 1.60 0.038
## 576 9.4 0.500 0.34 3.60 0.082
## 577 8.6 0.550 0.09 3.30 0.068
## 578 5.1 0.585 0.00 1.70 0.044
## 579 7.7 0.560 0.08 2.50 0.114
## 580 8.4 0.520 0.22 2.70 0.084
## 581 8.4 0.250 0.39 2.00 0.041
## 582 7.4 0.530 0.12 1.90 0.165
## 583 7.6 0.480 0.31 2.80 0.070
## 584 7.3 0.490 0.10 2.60 0.068
## 585 12.9 0.500 0.55 2.80 0.072
## 586 6.9 0.390 0.24 2.10 0.102
## 587 12.6 0.410 0.54 2.80 0.103
## 588 9.1 0.660 0.15 3.20 0.097
## 589 7.0 0.685 0.00 1.90 0.099
## 590 4.9 0.420 0.00 2.10 0.048
## 591 6.7 0.540 0.13 2.00 0.076
## 592 7.1 0.480 0.28 2.80 0.068
## 593 7.5 0.270 0.34 2.30 0.050
## 594 7.1 0.460 0.14 2.80 0.076
## 595 7.5 0.685 0.07 2.50 0.058
## 596 5.9 0.610 0.08 2.10 0.071
## 597 11.6 0.470 0.44 1.60 0.147
## 598 6.7 0.280 0.28 2.40 0.012
## 599 6.7 0.280 0.28 2.40 0.012
## 600 10.1 0.310 0.35 1.60 0.075
## 601 6.6 0.660 0.00 3.00 0.115
## 602 10.6 0.500 0.45 2.60 0.119
## 603 7.1 0.685 0.35 2.00 0.088
## 604 6.4 0.640 0.21 1.80 0.081
## 605 7.4 0.680 0.16 1.80 0.078
## 606 6.4 0.640 0.21 1.80 0.081
## 607 9.3 0.430 0.44 1.90 0.085
## 608 9.3 0.430 0.44 1.90 0.085
## 609 9.3 0.360 0.39 1.50 0.080
## 610 9.3 0.360 0.39 1.50 0.080
## 611 9.3 0.360 0.39 1.50 0.080
## 612 8.2 0.260 0.34 2.50 0.073
## 613 11.7 0.280 0.47 1.70 0.054
## 614 7.2 0.620 0.06 2.70 0.077
## 615 7.5 0.420 0.32 2.70 0.067
## 616 7.2 0.620 0.06 2.50 0.078
## 617 7.2 0.635 0.07 2.60 0.077
## 618 6.9 0.560 0.03 1.50 0.086
## 619 7.3 0.350 0.24 2.00 0.067
## 620 8.8 0.310 0.40 2.80 0.109
## 621 7.7 0.715 0.01 2.10 0.064
## 622 7.6 0.715 0.00 2.10 0.068
## 623 8.4 0.310 0.29 3.10 0.194
## 624 8.8 0.610 0.19 4.00 0.094
## 625 8.9 0.750 0.14 2.50 0.086
## 626 9.0 0.800 0.12 2.40 0.083
## 627 6.8 0.570 0.00 2.50 0.072
## 628 10.7 0.900 0.34 6.60 0.112
## 629 7.2 0.660 0.03 2.30 0.078
## 630 10.1 0.450 0.23 1.90 0.082
## 631 7.2 0.660 0.03 2.30 0.078
## 632 7.2 0.630 0.03 2.20 0.080
## 633 7.1 0.590 0.01 2.30 0.080
## 634 8.3 0.310 0.39 2.40 0.078
## 635 7.1 0.590 0.01 2.30 0.080
## 636 8.3 0.310 0.39 2.40 0.078
## 637 8.9 0.310 0.36 2.60 0.056
## 638 7.4 0.635 0.10 2.40 0.080
## 639 7.4 0.635 0.10 2.40 0.080
## 640 6.8 0.590 0.06 6.00 0.060
## 641 7.2 0.540 0.27 2.60 0.084
## 642 6.1 0.560 0.00 2.20 0.079
## 643 7.4 0.520 0.13 2.40 0.078
## 644 9.3 0.380 0.48 3.80 0.132
## 645 9.1 0.280 0.46 9.00 0.114
## 646 10.0 0.460 0.44 2.90 0.065
## 647 8.6 0.315 0.40 2.20 0.079
## 648 5.3 0.715 0.19 1.50 0.161
## 649 6.8 0.410 0.31 8.80 0.084
## 650 8.4 0.360 0.32 2.20 0.081
## 651 8.4 0.620 0.12 1.80 0.072
## 652 9.6 0.410 0.37 2.30 0.091
## 653 8.4 0.620 0.12 1.80 0.072
## 654 8.6 0.470 0.27 2.30 0.055
## 655 8.6 0.220 0.36 1.90 0.064
## 656 9.4 0.240 0.33 2.30 0.061
## 657 8.4 0.670 0.19 2.20 0.093
## 658 8.6 0.470 0.27 2.30 0.055
## 659 8.7 0.330 0.38 3.30 0.063
## 660 7.4 0.610 0.01 2.00 0.074
## 661 7.6 0.400 0.29 1.90 0.078
## 662 6.6 0.610 0.01 1.90 0.080
## 663 8.8 0.300 0.38 2.30 0.060
## 664 6.2 0.460 0.17 1.60 0.073
## 665 9.9 0.270 0.49 5.00 0.082
## 666 10.1 0.430 0.40 2.60 0.092
## 667 8.3 0.300 0.49 3.80 0.090
## 668 10.2 0.440 0.58 4.10 0.092
## 669 8.3 0.280 0.48 2.10 0.093
## 670 8.9 0.120 0.45 1.80 0.075
## 671 8.9 0.120 0.45 1.80 0.075
## 672 8.3 0.280 0.48 2.10 0.093
## 673 8.2 0.310 0.40 2.20 0.058
## 674 10.2 0.340 0.48 2.10 0.052
## 675 8.5 0.210 0.52 1.90 0.090
## 676 9.0 0.360 0.52 2.10 0.111
## 677 6.4 0.570 0.12 2.30 0.120
## 678 8.5 0.470 0.27 1.90 0.058
## 679 7.1 0.560 0.14 1.60 0.078
## 680 6.6 0.570 0.02 2.10 0.115
## 681 8.5 0.470 0.27 1.90 0.058
## 682 8.5 0.660 0.20 2.10 0.097
## 683 9.0 0.400 0.43 2.40 0.068
## 684 10.4 0.260 0.48 1.90 0.066
## 685 8.8 0.330 0.41 5.90 0.073
## 686 7.2 0.410 0.30 2.10 0.083
## 687 8.4 0.590 0.29 2.60 0.109
## 688 7.0 0.400 0.32 3.60 0.061
## 689 9.1 0.500 0.30 1.90 0.065
## 690 9.5 0.860 0.26 1.90 0.079
## 691 7.3 0.520 0.32 2.10 0.070
## 692 9.1 0.500 0.30 1.90 0.065
## 693 7.4 0.580 0.00 2.00 0.064
## 694 7.1 0.360 0.30 1.60 0.080
## 695 7.7 0.390 0.12 1.70 0.097
## 696 9.7 0.295 0.40 1.50 0.073
## 697 7.7 0.390 0.12 1.70 0.097
## 698 7.1 0.340 0.28 2.00 0.082
## 699 7.1 0.340 0.28 2.00 0.082
## 700 7.7 0.600 0.06 2.00 0.079
## 701 8.9 0.840 0.34 1.40 0.050
## 702 6.4 0.690 0.00 1.65 0.055
## 703 7.5 0.430 0.30 2.20 0.062
## 704 8.2 0.430 0.29 1.60 0.081
## 705 6.8 0.360 0.32 1.80 0.067
## 706 9.1 0.290 0.33 2.05 0.063
## 707 9.1 0.300 0.34 2.00 0.064
## 708 8.9 0.350 0.40 3.60 0.110
## 709 8.9 0.280 0.45 1.70 0.067
## 710 8.9 0.320 0.31 2.00 0.088
## 711 7.7 1.005 0.15 2.10 0.102
## 712 7.5 0.710 0.00 1.60 0.092
## 713 8.0 0.580 0.16 2.00 0.120
## 714 8.9 0.380 0.40 2.20 0.068
## 715 8.0 0.180 0.37 0.90 0.049
## 716 7.0 0.500 0.14 1.80 0.078
## 717 11.3 0.360 0.66 2.40 0.123
## 718 11.3 0.360 0.66 2.40 0.123
## 719 7.0 0.510 0.09 2.10 0.062
## 720 8.2 0.320 0.42 2.30 0.098
## 721 7.7 0.580 0.01 1.80 0.088
## 722 8.6 0.830 0.00 2.80 0.095
## 723 7.9 0.310 0.32 1.90 0.066
## 724 6.4 0.795 0.00 2.20 0.065
## 725 7.7 0.580 0.01 1.80 0.088
## 726 8.1 0.820 0.00 4.10 0.095
## 727 8.9 0.745 0.18 2.50 0.077
## 728 10.1 0.370 0.34 2.40 0.085
## 729 7.6 0.310 0.34 2.50 0.082
## 730 8.7 0.410 0.41 6.20 0.078
## 731 8.9 0.500 0.21 2.20 0.088
## 732 6.9 0.490 0.19 1.70 0.079
## 733 6.4 0.390 0.33 3.30 0.046
## 734 6.9 0.440 0.00 1.40 0.070
## 735 7.6 0.780 0.00 1.70 0.076
## 736 7.1 0.430 0.17 1.80 0.082
## 737 9.3 0.490 0.36 1.70 0.081
## 738 9.3 0.500 0.36 1.80 0.084
## 739 8.5 0.460 0.59 1.40 0.414
## 740 5.6 0.605 0.05 2.40 0.073
## 741 8.3 0.330 0.42 2.30 0.070
## 742 8.2 0.640 0.27 2.00 0.095
## 743 8.9 0.480 0.53 4.00 0.101
## 744 7.6 0.420 0.25 3.90 0.104
## 745 9.9 0.530 0.57 2.40 0.093
## 746 8.9 0.480 0.53 4.00 0.101
## 747 11.6 0.230 0.57 1.80 0.074
## 748 9.1 0.400 0.50 1.80 0.071
## 749 8.0 0.380 0.44 1.90 0.098
## 750 10.2 0.290 0.65 2.40 0.075
## 751 8.2 0.740 0.09 2.00 0.067
## 752 7.7 0.610 0.18 2.40 0.083
## 753 6.6 0.520 0.08 2.40 0.070
## 754 11.1 0.310 0.53 2.20 0.060
## 755 8.0 0.620 0.35 2.80 0.086
## 756 9.3 0.330 0.45 1.50 0.057
## 757 7.5 0.770 0.20 8.10 0.098
## 758 8.0 0.620 0.33 2.70 0.088
## 759 9.9 0.320 0.56 2.00 0.073
## 760 8.6 0.370 0.65 6.40 0.080
## 761 7.9 0.300 0.68 8.30 0.050
## 762 7.9 0.300 0.68 8.30 0.050
## 763 7.2 0.380 0.30 1.80 0.073
## 764 8.7 0.420 0.45 2.40 0.072
## 765 6.8 0.480 0.08 1.80 0.074
## 766 8.5 0.340 0.40 4.70 0.055
## 767 7.9 0.190 0.42 1.60 0.057
## 768 11.6 0.410 0.54 1.50 0.095
## 769 11.6 0.410 0.54 1.50 0.095
## 770 10.0 0.260 0.54 1.90 0.083
## 771 7.9 0.340 0.42 2.00 0.086
## 772 7.0 0.540 0.09 2.00 0.081
## 773 9.2 0.310 0.36 2.20 0.079
## 774 6.6 0.725 0.09 5.50 0.117
## 775 6.6 0.725 0.09 5.50 0.117
## 776 8.6 0.520 0.38 1.50 0.096
## 777 8.4 0.340 0.42 2.10 0.072
## 778 7.4 0.490 0.27 2.10 0.071
## 779 7.4 0.490 0.27 2.10 0.071
## 780 8.0 0.480 0.34 2.20 0.073
## 781 6.3 0.570 0.28 2.10 0.048
## 782 9.1 0.300 0.41 2.00 0.068
## 783 8.1 0.780 0.10 3.30 0.090
## 784 10.8 0.470 0.43 2.10 0.171
## 785 8.3 0.530 0.00 1.40 0.070
## 786 5.4 0.420 0.27 2.00 0.092
## 787 7.9 0.330 0.41 1.50 0.056
## 788 5.0 0.400 0.50 4.30 0.046
## 789 7.0 0.690 0.07 2.50 0.091
## 790 7.0 0.690 0.07 2.50 0.091
## 791 7.0 0.690 0.07 2.50 0.091
## 792 7.1 0.390 0.12 2.10 0.065
## 793 5.6 0.660 0.00 2.50 0.066
## 794 7.9 0.540 0.34 2.50 0.076
## 795 6.3 0.470 0.00 1.40 0.055
## 796 8.8 0.240 0.35 1.70 0.055
## 797 6.3 0.760 0.00 2.90 0.072
## 798 10.0 0.430 0.33 2.70 0.095
## 799 9.1 0.600 0.00 1.90 0.058
## 800 7.4 0.360 0.34 1.80 0.075
## 801 7.2 0.480 0.07 5.50 0.089
## 802 8.5 0.280 0.35 1.70 0.061
## 803 8.0 0.250 0.43 1.70 0.067
## 804 10.4 0.520 0.45 2.00 0.080
## 805 7.5 0.410 0.15 3.70 0.104
## 806 8.2 0.510 0.24 2.00 0.079
## 807 7.3 0.400 0.30 1.70 0.080
## 808 8.2 0.380 0.32 2.50 0.080
## 809 6.9 0.450 0.11 2.40 0.043
## 810 7.0 0.220 0.30 1.80 0.065
## 811 7.3 0.320 0.23 2.30 0.066
## 812 8.2 0.200 0.43 2.50 0.076
## 813 7.8 0.500 0.12 1.80 0.178
## 814 10.0 0.410 0.45 6.20 0.071
## 815 7.8 0.390 0.42 2.00 0.086
## 816 10.0 0.350 0.47 2.00 0.061
## 817 6.1 0.580 0.23 2.50 0.044
## 818 8.3 0.600 0.25 2.20 0.118
## 819 9.6 0.420 0.35 2.10 0.083
## 820 8.3 0.600 0.25 2.20 0.118
## 821 8.5 0.180 0.51 1.75 0.071
## 822 5.1 0.510 0.18 2.10 0.042
## 823 8.5 0.320 0.42 2.30 0.075
## 824 9.0 0.785 0.24 1.70 0.078
## 825 8.2 0.330 0.39 2.50 0.074
## 826 6.5 0.340 0.27 2.80 0.067
## 827 7.6 0.500 0.29 2.30 0.086
## 828 9.2 0.360 0.34 1.60 0.062
## 829 9.7 0.420 0.46 2.10 0.074
## 830 7.6 0.360 0.31 1.70 0.079
## 831 7.6 0.360 0.31 1.70 0.079
## 832 6.5 0.610 0.00 2.20 0.095
## 833 6.5 0.880 0.03 5.60 0.079
## 834 5.6 0.915 0.00 2.10 0.041
## 835 8.2 0.350 0.33 2.40 0.076
## 836 9.8 0.390 0.43 1.65 0.068
## 837 6.8 0.660 0.07 1.60 0.070
## 838 6.7 0.640 0.23 2.10 0.080
## 839 6.7 0.640 0.23 2.10 0.080
## 840 8.8 0.955 0.05 1.80 0.075
## 841 9.1 0.400 0.57 4.60 0.080
## 842 6.5 0.885 0.00 2.30 0.166
## 843 7.2 0.250 0.37 2.50 0.063
## 844 7.0 0.745 0.12 1.80 0.114
## 845 6.2 0.430 0.22 1.80 0.078
## 846 7.7 0.570 0.21 1.50 0.069
## 847 7.7 0.260 0.26 2.00 0.052
## 848 7.9 0.580 0.23 2.30 0.076
## 849 7.7 0.570 0.21 1.50 0.069
## 850 7.9 0.340 0.36 1.90 0.065
## 851 8.6 0.420 0.39 1.80 0.068
## 852 9.9 0.740 0.19 5.80 0.111
## 853 7.2 0.360 0.46 2.10 0.074
## 854 7.2 0.360 0.46 2.10 0.074
## 855 9.9 0.720 0.55 1.70 0.136
## 856 7.2 0.360 0.46 2.10 0.074
## 857 6.2 0.390 0.43 2.00 0.071
## 858 6.8 0.650 0.02 2.10 0.078
## 859 6.8 0.650 0.02 2.10 0.078
## 860 10.2 0.330 0.46 1.90 0.081
## 861 7.9 0.570 0.31 2.00 0.079
## 862 9.0 0.390 0.40 1.30 0.044
## 863 10.9 0.320 0.52 1.80 0.132
## 864 10.9 0.320 0.52 1.80 0.132
## 865 12.6 0.390 0.49 2.50 0.080
## 866 9.2 0.460 0.23 2.60 0.091
## 867 7.5 0.580 0.03 4.10 0.080
## 868 9.0 0.580 0.25 2.00 0.104
## 869 5.1 0.420 0.00 1.80 0.044
## 870 7.6 0.430 0.29 2.10 0.075
## 871 7.7 0.180 0.34 2.70 0.066
## 872 7.8 0.815 0.01 2.60 0.074
## 873 7.6 0.430 0.29 2.10 0.075
## 874 7.1 0.750 0.01 2.20 0.059
## 875 7.8 0.550 0.00 1.70 0.070
## 876 7.1 0.750 0.01 2.20 0.059
## 877 8.1 0.730 0.00 2.50 0.081
## 878 6.5 0.670 0.00 4.30 0.057
## 879 7.5 0.610 0.20 1.70 0.076
## 880 8.3 0.560 0.22 2.40 0.082
## 881 7.4 0.550 0.19 1.80 0.082
## 882 7.4 0.740 0.07 1.70 0.086
## 883 7.1 0.600 0.01 2.30 0.079
## 884 7.5 0.580 0.14 2.20 0.077
## 885 7.1 0.720 0.00 1.80 0.123
## 886 7.9 0.660 0.00 1.40 0.096
## 887 7.8 0.700 0.06 1.90 0.079
## 888 7.5 0.590 0.22 1.80 0.082
## 889 7.0 0.580 0.28 4.80 0.085
## 890 6.8 0.640 0.00 2.70 0.123
## 891 8.6 0.635 0.68 1.80 0.403
## 892 6.3 1.020 0.00 2.00 0.083
## 893 9.8 0.450 0.38 2.50 0.081
## 894 8.5 0.370 0.32 1.80 0.066
## 895 7.2 0.570 0.05 2.30 0.081
## 896 7.2 0.570 0.05 2.30 0.081
## 897 10.4 0.430 0.50 2.30 0.068
## 898 6.9 0.410 0.31 2.00 0.079
## 899 5.0 0.380 0.01 1.60 0.048
## 900 7.3 0.440 0.20 1.60 0.049
## 901 5.9 0.460 0.00 1.90 0.077
## 902 7.5 0.580 0.20 2.00 0.073
## 903 7.8 0.580 0.13 2.10 0.102
## 904 8.0 0.715 0.22 2.30 0.075
## 905 8.5 0.400 0.40 6.30 0.050
## 906 7.0 0.690 0.00 1.90 0.114
## 907 8.0 0.715 0.22 2.30 0.075
## 908 9.8 0.300 0.39 1.70 0.062
## 909 7.1 0.460 0.20 1.90 0.077
## 910 7.1 0.460 0.20 1.90 0.077
## 911 7.9 0.765 0.00 2.00 0.084
## 912 8.7 0.630 0.28 2.70 0.096
## 913 7.0 0.420 0.19 2.30 0.071
## 914 11.3 0.370 0.50 1.80 0.090
## 915 7.0 0.600 0.30 4.50 0.068
## 916 7.0 0.600 0.30 4.50 0.068
## 917 7.6 0.740 0.00 1.90 0.100
## 918 8.2 0.635 0.10 2.10 0.073
## 919 5.9 0.395 0.13 2.40 0.056
## 920 6.6 0.630 0.00 4.30 0.093
## 921 7.2 0.530 0.14 2.10 0.064
## 922 5.7 0.600 0.00 1.40 0.063
## 923 7.6 1.580 0.00 2.10 0.137
## 924 5.2 0.645 0.00 2.15 0.080
## 925 6.7 0.860 0.07 2.00 0.100
## 926 9.1 0.370 0.32 2.10 0.064
## 927 7.6 0.790 0.21 2.30 0.087
## 928 7.5 0.610 0.26 1.90 0.073
## 929 9.7 0.690 0.32 2.50 0.088
## 930 7.0 0.620 0.10 1.40 0.071
## 931 6.5 0.510 0.15 3.00 0.064
## 932 8.0 1.180 0.21 1.90 0.083
## 933 7.5 0.630 0.27 2.00 0.083
## 934 5.4 0.740 0.00 1.20 0.041
## 935 9.1 0.760 0.68 1.70 0.414
## 936 5.0 0.740 0.00 1.20 0.041
## 937 9.1 0.340 0.42 1.80 0.058
## 938 6.7 0.460 0.24 1.70 0.077
## 939 6.7 0.460 0.24 1.70 0.077
## 940 6.7 0.460 0.24 1.70 0.077
## 941 6.7 0.460 0.24 1.70 0.077
## 942 6.5 0.520 0.11 1.80 0.073
## 943 7.4 0.600 0.26 2.10 0.083
## 944 7.4 0.600 0.26 2.10 0.083
## 945 7.8 0.870 0.26 3.80 0.107
## 946 8.4 0.390 0.10 1.70 0.075
## 947 9.1 0.775 0.22 2.20 0.079
## 948 7.2 0.835 0.00 2.00 0.166
## 949 6.6 0.580 0.02 2.40 0.069
## 950 6.0 0.500 0.00 1.40 0.057
## 951 6.0 0.500 0.00 1.40 0.057
## 952 6.0 0.500 0.00 1.40 0.057
## 953 7.5 0.510 0.02 1.70 0.084
## 954 7.5 0.510 0.02 1.70 0.084
## 955 7.5 0.510 0.02 1.70 0.084
## 956 7.5 0.510 0.02 1.70 0.084
## 957 11.5 0.420 0.48 2.60 0.077
## 958 8.2 0.440 0.24 2.30 0.063
## 959 6.1 0.590 0.01 2.10 0.056
## 960 7.2 0.655 0.03 1.80 0.078
## 961 7.2 0.620 0.01 2.30 0.065
## 962 7.6 0.645 0.03 1.90 0.086
## 963 6.1 0.320 0.25 1.80 0.086
## 964 6.1 0.340 0.25 1.80 0.084
## 965 7.3 0.430 0.24 2.50 0.078
## 966 7.4 0.640 0.17 5.40 0.168
## 967 11.6 0.475 0.40 1.40 0.091
## 968 8.3 0.850 0.14 2.50 0.093
## 969 11.6 0.475 0.40 1.40 0.091
## 970 7.2 0.605 0.02 1.90 0.096
## 971 7.3 0.740 0.08 1.70 0.094
## 972 6.9 0.540 0.30 2.20 0.088
## 973 8.0 0.770 0.32 2.10 0.079
## 974 8.7 0.780 0.51 1.70 0.415
## 975 7.5 0.580 0.56 3.10 0.153
## 976 8.7 0.780 0.51 1.70 0.415
## 977 8.2 0.885 0.20 1.40 0.086
## 978 7.2 0.450 0.15 2.00 0.078
## 979 7.5 0.570 0.02 2.60 0.077
## 980 7.5 0.570 0.02 2.60 0.077
## 981 8.0 0.600 0.22 2.10 0.080
## 982 8.0 0.600 0.22 2.10 0.080
## 983 7.1 0.755 0.15 1.80 0.107
## 984 8.0 0.810 0.25 3.40 0.076
## 985 7.4 0.640 0.07 1.80 0.100
## 986 6.6 0.640 0.31 6.10 0.083
## 987 6.0 0.490 0.00 2.30 0.068
## 988 8.0 0.640 0.22 2.40 0.094
## 989 7.1 0.620 0.06 1.30 0.070
## 990 8.0 0.520 0.25 2.00 0.078
## 991 8.6 0.685 0.10 1.60 0.092
## 992 8.7 0.675 0.10 1.60 0.090
## 993 7.3 0.590 0.26 2.00 0.080
## 994 7.2 0.670 0.00 2.20 0.068
## 995 7.9 0.690 0.21 2.10 0.080
## 996 7.6 0.300 0.42 2.00 0.052
## 997 7.2 0.330 0.33 1.70 0.061
## 998 8.0 0.500 0.39 2.60 0.082
## 999 8.2 0.240 0.34 5.10 0.062
## 1000 6.0 0.510 0.00 2.10 0.064
## 1001 8.1 0.290 0.36 2.20 0.048
## 1002 6.0 0.510 0.00 2.10 0.064
## 1003 6.6 0.960 0.00 1.80 0.082
## 1004 6.4 0.470 0.40 2.40 0.071
## 1005 8.2 0.240 0.34 5.10 0.062
## 1006 9.9 0.570 0.25 2.00 0.104
## 1007 10.0 0.320 0.59 2.20 0.077
## 1008 6.2 0.580 0.00 1.60 0.065
## 1009 10.0 0.320 0.59 2.20 0.077
## 1010 7.3 0.340 0.33 2.50 0.064
## 1011 7.8 0.530 0.01 1.60 0.077
## 1012 7.7 0.640 0.21 2.20 0.077
## 1013 7.8 0.530 0.01 1.60 0.077
## 1014 7.5 0.400 0.18 1.60 0.079
## 1015 7.0 0.540 0.00 2.10 0.079
## 1016 6.4 0.530 0.09 3.90 0.123
## 1017 7.6 0.410 0.33 2.50 0.078
## 1018 7.8 0.640 0.00 1.90 0.072
## 1019 7.9 0.180 0.40 2.20 0.049
## 1020 7.4 0.410 0.24 1.80 0.066
## 1021 7.6 0.430 0.31 2.10 0.069
## 1022 6.1 0.400 0.16 1.80 0.069
## 1023 10.2 0.540 0.37 15.40 0.214
## 1024 10.0 0.380 0.38 1.60 0.169
## 1025 6.8 0.915 0.29 4.80 0.070
## 1026 7.0 0.590 0.00 1.70 0.052
## 1027 7.3 0.670 0.02 2.20 0.072
## 1028 6.9 0.580 0.20 1.75 0.058
## 1029 7.4 0.785 0.19 5.20 0.094
## 1030 6.8 0.670 0.00 1.90 0.080
## 1031 7.2 0.380 0.31 2.00 0.056
## 1032 7.2 0.370 0.32 2.00 0.062
## 1033 7.8 0.320 0.44 2.70 0.104
## 1034 6.6 0.580 0.02 2.00 0.062
## 1035 11.7 0.450 0.63 2.20 0.073
## 1036 6.5 0.900 0.00 1.60 0.052
## 1037 7.6 0.490 0.33 1.90 0.074
## 1038 8.4 0.290 0.40 1.70 0.067
## 1039 7.9 0.200 0.35 1.70 0.054
## 1040 6.4 0.420 0.09 2.30 0.054
## 1041 6.2 0.785 0.00 2.10 0.060
## 1042 6.9 0.630 0.01 2.40 0.076
## 1043 6.8 0.590 0.10 1.70 0.063
## 1044 7.3 0.480 0.32 2.10 0.062
## 1045 6.7 1.040 0.08 2.30 0.067
## 1046 7.3 0.480 0.32 2.10 0.062
## 1047 7.3 0.980 0.05 2.10 0.061
## 1048 10.0 0.690 0.11 1.40 0.084
## 1049 6.7 0.700 0.08 3.75 0.067
## 1050 7.6 0.350 0.60 2.60 0.073
## 1051 6.1 0.600 0.08 1.80 0.071
## 1052 9.9 0.500 0.50 13.80 0.205
## 1053 5.3 0.470 0.11 2.20 0.048
## 1054 9.9 0.500 0.50 13.80 0.205
## 1055 5.3 0.470 0.11 2.20 0.048
## 1056 7.1 0.875 0.05 5.70 0.082
## 1057 8.2 0.280 0.60 3.00 0.104
## 1058 5.6 0.620 0.03 1.50 0.080
## 1059 8.2 0.280 0.60 3.00 0.104
## 1060 8.1 0.330 0.44 1.50 0.042
## 1061 7.0 0.655 0.16 2.10 0.074
## 1062 6.8 0.680 0.21 2.10 0.070
## 1063 6.0 0.640 0.05 1.90 0.066
## 1064 5.6 0.540 0.04 1.70 0.049
## 1065 7.1 0.220 0.49 1.80 0.039
## 1066 6.2 0.650 0.06 1.60 0.050
## 1067 7.7 0.540 0.26 1.90 0.089
## 1068 6.4 0.310 0.09 1.40 0.066
## 1069 7.0 0.430 0.02 1.90 0.080
## 1070 6.9 0.740 0.03 2.30 0.054
## 1071 6.9 0.740 0.03 2.30 0.054
## 1072 7.8 0.820 0.29 4.30 0.083
## 1073 6.2 0.440 0.39 2.50 0.077
## 1074 7.5 0.380 0.57 2.30 0.106
## 1075 6.7 0.760 0.02 1.80 0.078
## 1076 6.8 0.810 0.05 2.00 0.070
## 1077 7.5 0.380 0.57 2.30 0.106
## 1078 7.1 0.270 0.60 2.10 0.074
## 1079 7.9 0.180 0.40 1.80 0.062
## 1080 6.4 0.360 0.21 2.20 0.047
## 1081 7.1 0.690 0.04 2.10 0.068
## 1082 6.9 0.840 0.21 4.10 0.074
## 1083 6.9 0.840 0.21 4.10 0.074
## 1084 6.5 0.530 0.06 2.00 0.063
## 1085 7.4 0.470 0.46 2.20 0.114
## 1086 6.6 0.700 0.08 2.60 0.106
## 1087 6.1 0.320 0.25 2.30 0.071
## 1088 6.8 0.480 0.25 2.00 0.076
## 1089 6.7 0.480 0.08 2.10 0.064
## 1090 6.8 0.470 0.08 2.20 0.064
## 1091 7.9 0.290 0.49 2.20 0.096
## 1092 7.1 0.690 0.08 2.10 0.063
## 1093 6.6 0.440 0.09 2.20 0.063
## 1094 6.1 0.705 0.10 2.80 0.081
## 1095 7.2 0.530 0.13 2.00 0.058
## 1096 8.0 0.390 0.30 1.90 0.074
## 1097 6.6 0.560 0.14 2.40 0.064
## 1098 7.0 0.550 0.13 2.20 0.075
## 1099 6.2 0.640 0.09 2.50 0.081
## 1100 6.2 0.520 0.08 4.40 0.071
## 1101 8.4 0.370 0.43 2.30 0.063
## 1102 6.5 0.630 0.33 1.80 0.059
## 1103 7.0 0.570 0.02 2.00 0.072
## 1104 11.2 0.400 0.50 2.00 0.099
## 1105 7.4 0.360 0.30 1.80 0.074
## 1106 7.1 0.670 0.00 2.30 0.083
## 1107 6.3 0.680 0.01 3.70 0.103
## 1108 7.3 0.735 0.00 2.20 0.080
## 1109 7.0 0.560 0.17 1.70 0.065
## 1110 6.6 0.880 0.04 2.20 0.066
## 1111 6.6 0.855 0.02 2.40 0.062
## 1112 6.9 0.630 0.33 6.70 0.235
## 1113 7.8 0.600 0.26 2.00 0.080
## 1114 7.8 0.600 0.26 2.00 0.080
## 1115 7.8 0.600 0.26 2.00 0.080
## 1116 7.2 0.695 0.13 2.00 0.076
## 1117 7.2 0.695 0.13 2.00 0.076
## 1118 6.7 0.670 0.02 1.90 0.061
## 1119 6.7 0.160 0.64 2.10 0.059
## 1120 7.2 0.695 0.13 2.00 0.076
## 1121 7.0 0.560 0.13 1.60 0.077
## 1122 6.2 0.510 0.14 1.90 0.056
## 1123 6.4 0.360 0.53 2.20 0.230
## 1124 6.4 0.380 0.14 2.20 0.038
## 1125 7.3 0.690 0.32 2.20 0.069
## 1126 6.0 0.580 0.20 2.40 0.075
## 1127 7.5 0.520 0.40 2.20 0.060
## 1128 8.0 0.300 0.63 1.60 0.081
## 1129 6.2 0.700 0.15 5.10 0.076
## 1130 6.8 0.670 0.15 1.80 0.118
## 1131 7.4 0.350 0.33 2.40 0.068
## 1132 6.1 0.715 0.10 2.60 0.053
## 1133 6.2 0.460 0.29 2.10 0.074
## 1134 6.7 0.320 0.44 2.40 0.061
## 1135 7.5 0.310 0.41 2.40 0.065
## 1136 5.8 0.610 0.11 1.80 0.066
## 1137 6.3 0.550 0.15 1.80 0.077
## 1138 5.4 0.740 0.09 1.70 0.089
## 1139 6.3 0.510 0.13 2.30 0.076
## 1140 6.8 0.620 0.08 1.90 0.068
## 1141 6.2 0.600 0.08 2.00 0.090
## 1142 5.9 0.550 0.10 2.20 0.062
## 1143 5.9 0.645 0.12 2.00 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11.0 34.0 0.99780 3.51 0.56 9.400000
## 2 25.0 67.0 0.99680 3.20 0.68 9.800000
## 3 15.0 54.0 0.99700 3.26 0.65 9.800000
## 4 17.0 60.0 0.99800 3.16 0.58 9.800000
## 5 11.0 34.0 0.99780 3.51 0.56 9.400000
## 6 13.0 40.0 0.99780 3.51 0.56 9.400000
## 7 15.0 59.0 0.99640 3.30 0.46 9.400000
## 8 15.0 21.0 0.99460 3.39 0.47 10.000000
## 9 9.0 18.0 0.99680 3.36 0.57 9.500000
## 10 15.0 65.0 0.99590 3.28 0.54 9.200000
## 11 16.0 59.0 0.99430 3.58 0.52 9.900000
## 12 9.0 29.0 0.99740 3.26 1.56 9.100000
## 13 35.0 103.0 0.99690 3.30 0.75 10.500000
## 14 17.0 56.0 0.99690 3.04 1.08 9.200000
## 15 23.0 71.0 0.99820 3.52 0.65 9.700000
## 16 10.0 37.0 0.99660 3.17 0.91 9.500000
## 17 9.0 67.0 0.99680 3.17 0.53 9.400000
## 18 21.0 40.0 0.99680 3.43 0.63 9.700000
## 19 11.0 23.0 0.99550 3.34 0.56 9.300000
## 20 4.0 11.0 0.99620 3.28 0.59 9.500000
## 21 14.0 35.0 0.99720 3.47 0.55 9.400000
## 22 8.0 16.0 0.99640 3.38 0.59 9.800000
## 23 17.0 82.0 0.99580 3.35 0.54 10.100000
## 24 15.0 113.0 0.99660 3.17 0.66 9.800000
## 25 13.0 50.0 0.99570 3.38 0.55 9.200000
## 26 5.0 18.0 0.99860 3.40 0.55 9.600000
## 27 3.0 15.0 0.99750 3.42 0.60 10.800000
## 28 13.0 30.0 0.99680 3.23 0.73 9.700000
## 29 12.0 87.0 0.99780 3.33 0.83 10.500000
## 30 17.0 46.0 0.99760 3.26 0.51 9.300000
## 31 8.0 14.0 0.99680 3.21 0.90 10.500000
## 32 9.0 23.0 0.99680 3.30 1.20 10.300000
## 33 8.0 65.0 0.99340 3.90 0.56 13.100000
## 34 22.0 114.0 0.99700 3.25 0.73 9.200000
## 35 4.0 23.0 0.99710 3.15 0.74 9.200000
## 36 8.0 15.0 0.99560 3.40 0.63 9.400000
## 37 6.0 14.0 0.99550 3.39 0.64 9.400000
## 38 30.0 119.0 0.99700 3.20 0.56 9.400000
## 39 33.0 73.0 0.99550 3.17 0.63 10.200000
## 40 4.0 10.0 0.99710 3.04 0.63 9.600000
## 41 17.0 54.0 0.99750 3.43 0.59 10.000000
## 42 9.0 46.0 0.99620 3.41 0.54 9.400000
## 43 19.0 52.0 0.99800 3.44 0.64 9.200000
## 44 20.0 112.0 0.99680 3.21 0.71 9.300000
## 45 13.0 54.0 0.99660 3.39 0.57 9.800000
## 46 4.0 11.0 0.99620 3.41 0.39 10.900000
## 47 4.0 11.0 0.99620 3.41 0.39 10.900000
## 48 6.0 15.0 0.99620 3.44 0.58 10.700000
## 49 8.0 19.0 0.99620 3.34 0.95 10.500000
## 50 18.0 94.0 0.99610 3.31 0.48 9.500000
## 51 11.0 43.0 0.99760 3.31 0.53 9.200000
## 52 9.0 42.0 0.99860 3.54 0.66 10.500000
## 53 14.0 30.0 0.99660 3.52 0.55 10.700000
## 54 12.0 80.0 0.99580 3.38 0.52 10.100000
## 55 27.0 119.0 0.99720 3.16 1.12 9.100000
## 56 3.0 15.0 0.99580 3.41 0.56 9.200000
## 57 21.0 73.0 0.99700 3.36 0.57 9.100000
## 58 18.0 61.0 0.99590 3.44 0.78 10.300000
## 59 19.0 40.0 0.99610 3.41 0.59 10.100000
## 60 20.0 136.0 0.99720 2.93 1.95 9.900000
## 61 9.0 31.0 0.99660 3.39 0.64 9.600000
## 62 34.0 125.0 0.99780 3.14 1.22 9.500000
## 63 8.0 24.0 0.99780 3.48 0.53 9.000000
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## 65 20.0 136.0 0.99720 2.93 1.95 9.900000
## 66 9.0 31.0 0.99660 3.39 0.64 9.600000
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## 68 8.0 23.0 0.99650 3.45 0.56 10.700000
## 69 5.0 10.0 0.99670 3.39 0.56 9.800000
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## 72 13.0 35.0 0.99720 3.30 0.59 9.000000
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## 75 12.0 65.0 0.99670 3.29 0.51 9.200000
## 76 18.0 69.0 0.99730 3.08 1.31 9.300000
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## 78 12.0 47.0 0.99640 3.19 0.93 9.500000
## 79 11.0 108.0 0.99640 3.15 0.66 9.800000
## 80 22.0 46.0 0.99880 3.32 0.67 9.700000
## 81 12.0 47.0 0.99640 3.19 0.93 9.500000
## 82 13.0 62.0 0.99660 3.07 0.63 10.500000
## 83 11.0 40.0 0.99780 3.39 0.61 10.000000
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## 87 21.0 102.0 0.99600 3.39 0.48 9.500000
## 88 3.0 12.0 0.99640 3.53 0.49 10.900000
## 89 3.0 12.0 0.99640 3.54 0.48 10.900000
## 90 3.0 16.0 0.99620 3.42 0.92 10.500000
## 91 30.0 134.0 0.99680 3.24 0.66 9.400000
## 92 17.0 99.0 0.99370 3.63 0.63 13.000000
## 93 13.0 29.0 0.99590 3.22 0.55 9.900000
## 94 16.0 63.0 0.99650 3.19 0.82 9.600000
## 95 13.0 45.0 0.99720 3.48 0.64 9.200000
## 96 16.0 63.0 0.99650 3.19 0.82 9.600000
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## 98 3.0 20.0 0.99540 3.34 0.58 9.400000
## 99 27.0 63.0 0.99160 3.68 0.79 14.000000
## 100 32.0 141.0 0.99680 3.17 0.62 9.400000
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## 102 12.0 30.0 0.99590 3.42 0.58 10.200000
## 103 5.0 11.0 0.99580 3.33 0.53 10.300000
## 104 32.0 69.0 0.99960 2.74 2.00 9.400000
## 105 25.0 99.0 0.99500 3.35 0.54 10.100000
## 106 25.0 99.0 0.99500 3.35 0.54 10.100000
## 107 28.0 128.0 0.99730 3.42 0.71 10.500000
## 108 29.0 129.0 0.99730 3.42 0.72 10.500000
## 109 28.0 128.0 0.99730 3.42 0.71 10.500000
## 110 12.0 22.0 0.99690 3.48 0.50 9.300000
## 111 18.0 86.0 0.99680 3.59 0.57 9.300000
## 112 7.0 20.0 0.99590 3.20 0.56 9.600000
## 113 9.0 20.0 0.99650 3.17 1.08 9.200000
## 114 17.0 31.0 0.99640 3.33 0.56 10.000000
## 115 36.0 121.0 0.99820 3.37 0.49 9.400000
## 116 35.0 121.0 0.99810 3.37 0.49 9.400000
## 117 14.0 96.0 0.99610 3.19 0.62 9.500000
## 118 18.0 101.0 0.99560 3.34 0.52 10.200000
## 119 17.0 42.0 0.99560 3.37 0.48 9.000000
## 120 11.0 44.0 0.99530 3.47 0.55 10.400000
## 121 4.0 8.0 0.99720 3.36 0.33 9.100000
## 122 6.0 18.0 0.99720 3.29 0.61 9.200000
## 123 24.0 42.0 0.99610 3.42 0.57 11.500000
## 124 19.0 49.0 0.99580 3.35 0.78 9.500000
## 125 7.0 35.0 0.99610 3.33 0.47 9.500000
## 126 8.0 38.0 0.99720 3.58 0.61 10.500000
## 127 7.0 20.0 0.99690 3.48 0.56 9.600000
## 128 10.0 42.0 0.99690 3.19 0.59 9.500000
## 129 10.0 42.0 0.99690 3.19 0.59 9.500000
## 130 23.0 110.0 0.99720 3.12 1.02 9.300000
## 131 16.0 42.0 0.99720 3.44 0.52 9.300000
## 132 11.0 65.0 0.99710 3.54 0.58 9.300000
## 133 8.0 62.0 0.99700 3.52 0.58 9.300000
## 134 26.0 85.0 0.99710 3.26 0.53 9.700000
## 135 14.0 67.0 0.99720 3.34 0.55 9.200000
## 136 15.0 143.0 0.99680 3.20 0.55 9.500000
## 137 21.0 127.0 0.99760 3.23 0.62 9.400000
## 138 21.0 49.0 0.99740 3.57 0.62 9.800000
## 139 7.0 28.0 0.99640 3.28 0.55 9.700000
## 140 7.0 28.0 0.99640 3.28 0.55 9.700000
## 141 15.0 55.0 0.99680 3.46 0.59 10.200000
## 142 12.0 27.0 0.99810 3.11 0.97 10.100000
## 143 12.0 31.0 0.99480 3.51 0.43 11.400000
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## 145 14.0 37.0 0.99615 3.34 0.56 9.200000
## 146 9.0 28.0 0.99940 3.20 0.77 10.800000
## 147 9.0 28.0 0.99940 3.20 0.77 10.800000
## 148 18.0 95.0 0.99660 3.22 0.67 9.400000
## 149 5.0 19.0 0.99585 3.30 0.86 12.400000
## 150 10.0 62.0 0.99685 3.35 0.56 10.000000
## 151 28.0 65.0 0.99590 3.30 0.43 10.100000
## 152 10.0 41.0 0.99740 3.24 0.71 9.800000
## 153 26.0 121.0 0.99740 3.34 0.76 10.500000
## 154 13.0 49.0 0.99620 3.14 0.57 11.000000
## 155 11.0 41.0 0.99670 3.36 0.55 9.100000
## 156 10.0 44.0 0.99570 3.39 0.54 9.700000
## 157 24.0 58.0 0.99640 3.34 0.59 9.400000
## 158 16.0 72.0 0.99570 3.15 0.57 9.400000
## 159 5.0 10.0 0.99525 3.42 0.60 9.500000
## 160 8.0 28.0 0.99815 3.28 0.60 10.000000
## 161 29.0 63.0 0.99615 3.37 0.58 10.500000
## 162 27.0 81.0 0.99640 3.04 1.61 9.500000
## 163 19.0 106.0 0.99270 3.54 0.62 12.200000
## 164 12.0 49.0 0.99625 3.37 0.52 9.900000
## 165 18.0 58.0 0.99800 3.34 0.70 9.600000
## 166 7.0 37.0 0.99685 3.32 0.55 9.000000
## 167 14.0 38.0 0.99675 3.37 0.58 9.000000
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## 170 28.0 109.0 0.99565 3.08 0.49 9.800000
## 171 10.0 24.0 1.00005 3.07 0.84 9.200000
## 172 10.0 24.0 1.00005 3.07 0.84 9.200000
## 173 6.0 23.0 0.99830 3.61 0.96 9.900000
## 174 16.0 51.0 0.99685 3.38 0.52 9.500000
## 175 6.0 23.0 0.99830 3.61 0.96 9.900000
## 176 16.0 37.0 0.99850 3.22 0.78 10.000000
## 177 16.0 34.0 0.99720 3.47 0.70 9.900000
## 178 5.0 21.0 0.99860 3.17 0.53 10.500000
## 179 8.0 86.0 0.99590 3.23 0.59 9.500000
## 180 17.0 119.0 0.99675 3.26 0.57 9.300000
## 181 5.0 13.0 0.99800 3.22 0.62 9.200000
## 182 12.0 56.0 0.99680 3.49 0.67 9.200000
## 183 8.0 45.0 0.99680 3.06 1.26 9.400000
## 184 14.0 33.0 0.99965 3.36 0.80 10.500000
## 185 18.0 45.0 0.99625 3.29 0.65 9.300000
## 186 12.0 67.0 0.99565 3.35 0.60 9.400000
## 187 10.0 35.0 0.99575 3.34 0.57 10.000000
## 188 23.0 49.0 0.99630 3.28 0.67 9.300000
## 189 5.0 27.0 0.99990 3.08 0.87 10.900000
## 190 26.0 61.0 1.00025 3.60 0.72 9.800000
## 191 15.0 37.0 0.99730 3.35 0.86 12.800000
## 192 7.0 27.0 0.99870 3.69 0.91 9.400000
## 193 4.0 23.0 0.99960 3.28 0.97 10.100000
## 194 27.0 61.0 0.99650 3.36 0.67 10.700000
## 195 27.0 61.0 0.99650 3.36 0.67 10.700000
## 196 7.0 27.0 0.99870 3.69 0.91 9.400000
## 197 4.0 23.0 0.99960 3.28 0.97 10.100000
## 198 5.0 13.0 0.99760 3.23 0.82 12.600000
## 199 10.0 47.0 0.99910 3.38 0.77 10.500000
## 200 6.0 19.0 0.99860 3.12 0.82 9.300000
## 201 5.0 10.0 0.99720 3.25 1.08 9.900000
## 202 10.0 47.0 0.99910 3.38 0.77 10.500000
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## 204 32.0 71.0 1.00015 3.31 0.71 9.800000
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## 207 20.0 49.0 0.99760 3.34 0.86 10.600000
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## 209 20.0 49.0 0.99760 3.34 0.86 10.600000
## 210 18.0 48.0 0.99940 3.22 0.64 10.300000
## 211 5.0 16.0 0.99640 3.41 0.60 10.100000
## 212 17.0 53.0 1.00140 3.05 0.81 9.500000
## 213 5.0 27.0 1.00010 3.05 0.64 9.500000
## 214 26.0 70.0 0.99810 3.16 0.52 9.900000
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## 216 5.0 31.0 0.99930 3.67 0.80 9.700000
## 217 20.0 44.0 0.99650 3.38 0.59 10.700000
## 218 7.0 15.0 0.99730 3.22 0.64 10.100000
## 219 12.0 28.0 0.99940 3.51 0.72 10.000000
## 220 12.0 90.0 0.99730 3.20 0.52 9.200000
## 221 7.0 54.0 0.99660 3.36 0.52 9.400000
## 222 6.0 14.0 0.99940 3.19 0.62 9.500000
## 223 5.0 12.0 0.99940 3.19 0.63 9.500000
## 224 15.0 105.0 0.99600 3.27 0.54 9.400000
## 225 19.0 98.0 0.99815 3.32 0.63 9.500000
## 226 30.0 135.0 0.99760 3.30 0.53 9.400000
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## 228 21.0 59.0 0.99890 3.37 0.71 11.500000
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## 230 25.0 87.0 0.99975 3.24 0.62 9.700000
## 231 13.0 50.0 1.00150 3.16 0.69 9.200000
## 232 13.0 50.0 1.00150 3.16 0.69 9.200000
## 233 6.0 14.0 0.99780 3.05 0.74 11.500000
## 234 5.0 13.0 0.99800 3.28 0.83 11.500000
## 235 6.0 21.0 1.00020 3.16 0.67 9.700000
## 236 16.0 44.0 0.99580 3.13 0.52 11.000000
## 237 17.0 38.0 0.99630 3.34 0.74 11.700000
## 238 7.0 21.0 0.99920 3.17 0.84 12.200000
## 239 6.0 16.0 0.99480 3.35 0.70 12.500000
## 240 29.0 58.0 0.99740 3.31 0.64 10.300000
## 241 12.0 29.0 0.99970 3.11 1.36 9.800000
## 242 6.0 14.0 0.99820 3.30 0.75 9.800000
## 243 6.0 14.0 0.99820 3.30 0.75 9.800000
## 244 6.0 32.0 1.00060 3.12 0.78 10.700000
## 245 6.0 15.0 0.99860 3.02 0.93 12.000000
## 246 15.0 46.0 0.99790 3.11 0.92 10.000000
## 247 11.0 26.0 0.99940 3.35 0.83 9.400000
## 248 8.0 19.0 0.99740 3.27 0.73 9.300000
## 249 23.0 77.0 1.00180 3.18 0.77 13.000000
## 250 40.5 165.0 0.99120 3.25 0.59 11.900000
## 251 17.0 32.0 0.99550 3.55 0.61 12.800000
## 252 29.0 55.0 1.00010 3.26 0.88 11.000000
## 253 12.0 29.0 0.99700 3.24 0.75 11.700000
## 254 10.0 23.0 1.00000 3.15 0.85 10.400000
## 255 10.0 41.0 1.00100 3.17 0.68 9.800000
## 256 14.0 81.0 0.99730 3.19 0.70 9.400000
## 257 21.0 50.0 0.99820 3.37 0.91 9.900000
## 258 6.0 37.0 0.99920 3.05 0.56 10.000000
## 259 6.0 15.0 0.99880 2.99 0.87 10.200000
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## 261 7.0 42.0 1.00220 3.07 0.73 10.000000
## 262 11.0 24.0 1.00000 3.16 0.69 9.000000
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## 266 19.0 47.0 1.00030 3.26 1.11 11.000000
## 267 6.0 18.0 0.99620 3.20 1.13 12.000000
## 268 6.0 24.0 0.99880 3.11 0.99 13.300000
## 269 11.0 27.0 0.99740 3.21 0.80 9.400000
## 270 23.0 54.0 0.99800 3.42 0.74 9.200000
## 271 23.0 53.0 0.99810 3.43 0.74 9.200000
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## 273 17.0 43.0 1.00140 3.06 0.80 10.000000
## 274 6.0 26.0 0.99800 3.18 0.51 9.500000
## 275 7.0 38.0 1.00040 3.22 0.60 13.000000
## 276 68.0 124.0 0.99940 3.47 0.53 9.900000
## 277 13.0 49.0 0.99880 3.18 0.65 11.000000
## 278 9.0 42.0 0.99630 3.10 0.55 9.400000
## 279 68.0 124.0 0.99940 3.47 0.53 9.900000
## 280 19.0 64.0 1.00000 3.10 0.61 10.500000
## 281 10.0 41.0 0.99940 3.15 0.63 10.500000
## 282 15.0 27.0 0.99740 3.26 0.61 9.100000
## 283 9.0 28.0 0.99810 3.24 0.65 10.800000
## 284 12.0 30.0 0.99960 3.18 0.63 10.800000
## 285 26.0 49.0 0.99810 3.05 0.57 9.600000
## 286 26.0 86.0 0.99870 3.38 0.62 9.500000
## 287 15.0 77.0 0.99660 3.27 0.64 9.300000
## 288 6.0 19.0 0.99860 3.27 0.82 11.700000
## 289 24.0 122.0 0.99820 3.26 0.61 9.500000
## 290 31.0 134.0 1.00140 3.32 1.07 9.300000
## 291 6.0 20.0 0.99700 3.26 0.66 11.700000
## 292 34.0 124.0 0.99560 3.44 0.48 10.500000
## 293 7.0 20.0 0.99960 3.24 0.76 10.400000
## 294 34.0 52.0 0.99760 3.62 0.68 9.900000
## 295 32.0 48.0 0.99520 3.52 0.56 12.300000
## 296 15.0 60.0 0.99550 3.36 0.44 10.900000
## 297 6.0 24.0 0.99780 3.15 0.90 11.000000
## 298 32.0 48.0 0.99520 3.52 0.56 12.300000
## 299 19.0 48.0 0.99490 3.49 0.49 11.400000
## 300 6.0 32.0 0.99880 3.26 0.56 10.600000
## 301 9.0 30.0 0.99780 3.24 0.60 9.300000
## 302 13.0 35.0 0.99970 3.10 0.60 10.400000
## 303 6.0 24.0 0.99780 3.15 0.90 11.000000
## 304 21.0 66.0 0.99740 3.25 0.56 9.200000
## 305 6.0 18.0 1.00040 3.16 0.49 9.500000
## 306 22.0 54.0 0.99960 3.15 0.89 9.900000
## 307 6.0 18.0 1.00040 3.16 0.49 9.500000
## 308 38.0 62.0 0.99580 3.26 0.56 10.200000
## 309 6.0 22.0 0.99860 3.17 0.66 11.200000
## 310 7.0 50.0 0.99510 3.08 0.60 9.300000
## 311 6.0 29.0 0.99870 2.88 0.82 9.800000
## 312 6.0 43.0 1.00320 2.95 0.68 11.200000
## 313 5.0 27.0 0.99340 3.57 0.84 12.500000
## 314 31.0 72.0 0.99960 3.10 0.73 10.500000
## 315 6.0 16.0 0.99680 3.22 0.72 11.200000
## 316 7.0 27.0 0.99670 3.20 0.56 10.200000
## 317 17.0 35.0 0.99940 3.10 0.61 10.800000
## 318 17.0 35.0 0.99940 3.10 0.61 10.800000
## 319 18.0 35.0 0.99680 3.44 0.63 10.000000
## 320 5.0 22.0 0.99980 3.26 0.74 11.200000
## 321 21.0 67.0 0.99520 3.50 0.63 11.100000
## 322 6.0 19.0 0.99880 3.22 0.69 13.400000
## 323 5.0 22.0 0.99860 3.37 0.58 10.300000
## 324 29.0 65.0 0.99880 3.28 0.58 9.600000
## 325 10.0 47.0 1.00080 3.25 0.57 9.000000
## 326 14.0 23.0 0.99760 3.35 0.61 11.300000
## 327 6.0 19.0 0.99820 3.26 0.61 9.300000
## 328 5.0 15.0 0.99870 2.98 0.70 9.200000
## 329 21.0 51.0 0.99820 3.16 0.72 11.500000
## 330 7.0 18.0 0.99470 3.32 0.79 14.000000
## 331 6.0 21.0 1.00000 3.17 0.62 9.200000
## 332 36.0 48.0 0.99520 3.20 0.58 9.800000
## 333 15.0 47.0 0.99960 3.05 0.61 10.600000
## 334 25.0 52.0 0.99700 3.20 0.71 11.400000
## 335 25.0 68.0 0.99950 3.15 0.82 10.400000
## 336 5.0 28.0 0.99880 3.14 0.60 10.200000
## 337 6.0 16.0 0.99800 3.28 0.70 9.700000
## 338 6.0 20.0 0.99770 3.18 1.06 11.000000
## 339 6.0 31.0 0.99840 3.19 0.70 10.100000
## 340 6.0 23.0 1.00260 3.12 0.66 9.200000
## 341 6.0 17.0 0.99640 3.15 0.92 11.700000
## 342 7.0 18.0 0.99860 3.04 1.06 9.400000
## 343 6.0 17.0 0.99760 3.17 0.47 10.000000
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## 347 13.0 27.0 0.99500 3.32 0.90 13.400000
## 348 23.0 81.0 1.00020 3.48 0.74 11.600000
## 349 28.0 91.0 0.99520 3.44 0.55 12.100000
## 350 5.0 16.0 0.99720 3.15 0.65 11.000000
## 351 14.0 38.0 0.99840 3.43 0.65 9.000000
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## 353 5.0 16.0 0.99720 3.15 0.65 11.000000
## 354 23.0 81.0 1.00020 3.48 0.74 11.600000
## 355 14.0 38.0 0.99840 3.43 0.65 9.000000
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## 360 6.0 21.0 0.99760 3.25 1.02 10.800000
## 361 6.0 13.0 1.00000 3.17 0.71 9.500000
## 362 23.0 43.0 0.99860 3.04 0.68 11.400000
## 363 26.0 62.0 0.99940 3.18 0.63 10.200000
## 364 15.0 49.0 0.99800 3.03 0.81 9.700000
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## 366 6.0 15.0 0.99730 3.09 0.66 11.800000
## 367 34.0 151.0 1.00100 3.31 1.14 9.300000
## 368 5.0 14.0 1.00100 3.25 0.74 11.900000
## 369 5.0 16.0 0.99940 3.16 0.63 8.400000
## 370 15.0 33.0 0.99820 3.42 0.90 10.000000
## 371 38.0 142.0 0.99780 3.22 0.55 9.400000
## 372 23.0 116.0 0.99760 3.23 0.64 9.500000
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## 374 6.0 33.0 0.99740 3.09 0.57 9.400000
## 375 1.0 28.0 0.99900 3.41 0.87 10.300000
## 376 12.0 42.0 1.00040 3.16 0.61 10.300000
## 377 12.0 42.0 1.00040 3.16 0.61 10.300000
## 378 15.0 33.0 0.99560 3.22 0.66 12.800000
## 379 13.0 32.0 1.00000 3.28 0.56 10.000000
## 380 1.0 28.0 0.99900 3.41 0.87 10.300000
## 381 6.0 33.0 0.99740 3.09 0.57 9.400000
## 382 5.0 13.0 0.99720 3.37 0.77 10.700000
## 383 5.0 35.0 1.00140 3.20 0.66 12.000000
## 384 5.0 17.0 0.99960 3.22 0.62 11.200000
## 385 20.0 47.0 0.99880 3.26 0.67 9.600000
## 386 5.0 17.0 0.99880 3.21 0.73 11.000000
## 387 5.0 20.0 0.99660 3.12 0.59 9.900000
## 388 16.0 53.0 0.99930 3.06 0.72 11.000000
## 389 6.0 15.0 1.00080 2.86 0.79 8.400000
## 390 38.0 106.0 0.99820 3.08 0.59 9.100000
## 391 10.0 28.0 0.99830 3.45 0.78 9.500000
## 392 6.0 21.0 0.99870 3.26 0.86 10.700000
## 393 27.0 69.0 0.99940 3.12 0.75 10.400000
## 394 9.0 25.0 0.99580 3.33 0.56 9.500000
## 395 14.0 45.0 0.99800 3.19 0.73 10.000000
## 396 14.0 44.0 0.99800 3.12 0.74 10.000000
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## 398 10.0 23.0 1.00315 2.92 0.74 11.100000
## 399 10.0 17.0 1.00020 3.07 0.56 11.700000
## 400 10.0 23.0 1.00315 2.92 0.74 11.100000
## 401 6.0 47.0 1.00210 3.30 0.68 12.700000
## 402 5.0 19.0 0.99940 3.14 0.57 11.400000
## 403 41.0 110.0 0.99820 3.08 0.61 9.200000
## 404 25.0 60.0 0.99710 3.31 0.61 10.100000
## 405 6.0 47.0 1.00210 3.30 0.68 12.700000
## 406 5.0 19.0 0.99940 3.14 0.57 11.400000
## 407 5.0 15.0 0.99910 3.32 0.60 9.000000
## 408 5.0 20.0 0.99900 3.31 0.79 10.700000
## 409 19.0 42.0 0.99460 3.57 0.57 11.700000
## 410 10.0 37.0 1.00030 3.32 0.91 11.000000
## 411 10.0 28.0 0.99780 3.14 0.61 10.400000
## 412 6.0 14.0 0.99780 3.34 0.52 10.000000
## 413 18.0 111.0 0.99820 3.30 0.60 9.500000
## 414 17.0 110.0 0.99820 3.29 0.60 9.800000
## 415 18.0 40.0 0.99760 3.14 0.51 9.800000
## 416 5.0 14.0 1.00020 3.19 0.44 9.600000
## 417 5.0 14.0 1.00020 3.19 0.44 9.600000
## 418 10.0 21.0 0.99760 2.98 0.66 9.900000
## 419 18.0 102.0 0.99670 3.26 0.59 9.300000
## 420 19.0 50.0 0.99170 3.72 0.74 14.000000
## 421 21.0 75.0 0.99870 3.35 0.57 9.700000
## 422 23.0 149.0 0.99220 3.12 0.50 11.500000
## 423 21.0 75.0 0.99870 3.35 0.57 9.700000
## 424 9.0 43.0 1.00040 3.29 0.58 9.000000
## 425 30.0 109.0 0.99980 3.27 0.57 9.300000
## 426 6.0 24.0 0.99940 3.04 0.60 9.300000
## 427 5.0 30.0 0.99670 3.20 0.48 9.800000
## 428 6.0 14.0 0.99750 3.30 0.52 9.300000
## 429 14.0 63.0 0.99840 3.17 0.62 9.100000
## 430 26.0 52.0 0.99820 3.31 0.53 10.500000
## 431 11.0 65.0 1.00260 3.32 0.64 10.400000
## 432 15.0 39.0 0.99560 3.56 0.52 12.700000
## 433 25.0 57.0 0.99830 3.39 0.54 9.200000
## 434 5.0 16.0 1.00060 2.98 0.54 9.400000
## 435 18.0 29.0 0.99650 3.32 0.60 10.000000
## 436 28.0 71.0 0.99970 3.50 0.57 9.700000
## 437 14.0 28.0 0.99820 3.03 0.93 9.800000
## 438 6.0 16.0 0.99880 3.09 0.80 9.300000
## 439 16.0 112.0 0.99760 3.27 0.61 9.400000
## 440 14.0 104.0 0.99760 3.28 0.62 9.400000
## 441 9.0 32.0 0.99740 3.13 0.55 9.500000
## 442 21.0 58.0 0.99970 3.46 0.72 10.200000
## 443 5.0 15.0 0.99880 3.36 0.49 9.100000
## 444 5.0 15.0 0.99880 3.36 0.49 9.100000
## 445 7.0 31.0 0.99760 3.27 0.60 9.300000
## 446 20.0 84.0 0.99640 3.21 0.61 9.300000
## 447 7.0 31.0 0.99760 3.27 0.60 9.300000
## 448 5.0 19.0 0.99880 3.25 0.63 9.500000
## 449 21.0 43.0 0.99640 3.32 0.57 10.500000
## 450 11.0 86.0 1.00100 3.43 0.64 11.300000
## 451 46.0 102.0 0.99640 3.27 0.58 9.500000
## 452 24.0 33.0 0.99540 3.27 0.55 9.700000
## 453 30.0 147.0 0.99790 3.24 0.53 9.400000
## 454 31.0 145.0 0.99780 3.24 0.53 9.400000
## 455 25.0 57.0 0.99700 3.18 1.36 10.300000
## 456 16.0 40.0 0.99910 3.39 0.62 9.400000
## 457 16.0 40.0 0.99910 3.39 0.62 9.400000
## 458 16.0 40.0 0.99910 3.39 0.62 9.400000
## 459 5.0 14.0 0.99670 3.32 0.58 11.000000
## 460 10.0 28.0 0.99640 3.33 0.67 11.200000
## 461 24.0 148.0 0.99480 3.16 0.57 11.300000
## 462 8.0 32.0 0.99860 2.89 0.50 9.600000
## 463 22.0 71.0 0.99760 2.98 0.84 14.900000
## 464 9.0 30.0 0.99760 3.12 0.54 12.000000
## 465 7.0 29.0 0.99790 3.08 0.46 9.500000
## 466 20.0 53.0 1.00040 3.14 0.61 9.400000
## 467 8.0 32.0 0.99860 2.89 0.50 9.600000
## 468 23.0 42.0 0.99800 2.92 0.68 10.500000
## 469 5.0 20.0 0.99730 3.32 0.81 9.600000
## 470 5.0 20.0 0.99730 3.32 0.81 9.600000
## 471 15.0 42.0 0.99780 3.31 0.64 9.000000
## 472 9.0 27.0 0.99720 3.36 0.70 9.600000
## 473 25.0 48.0 0.99810 3.14 0.56 9.700000
## 474 6.0 16.0 0.99800 3.18 0.60 9.500000
## 475 6.0 15.0 0.99880 2.94 0.66 9.200000
## 476 6.0 15.0 0.99880 2.94 0.66 9.200000
## 477 5.0 13.0 0.99680 3.20 0.52 9.500000
## 478 5.0 13.0 0.99680 3.20 0.52 9.500000
## 479 7.0 17.0 0.99840 3.08 0.67 9.300000
## 480 12.0 31.0 0.99790 3.40 0.48 9.900000
## 481 45.0 87.0 0.99830 3.48 0.53 10.000000
## 482 20.0 49.0 0.99720 3.13 0.54 9.600000
## 483 16.0 29.0 0.99620 3.21 0.49 10.200000
## 484 32.0 58.0 0.99800 3.33 0.54 9.800000
## 485 5.0 15.0 0.99700 3.37 0.56 11.300000
## 486 35.0 152.0 0.99800 3.25 0.48 9.400000
## 487 5.0 15.0 0.99700 3.37 0.56 11.300000
## 488 4.0 9.0 0.99620 3.40 0.47 9.400000
## 489 5.0 17.0 0.99760 3.30 1.05 9.400000
## 490 12.0 93.0 0.99980 3.48 0.54 9.800000
## 491 16.0 62.0 0.99790 3.03 1.17 9.000000
## 492 21.0 122.0 0.99840 3.32 0.62 9.400000
## 493 24.0 125.0 0.99840 3.31 0.61 9.400000
## 494 18.0 44.0 0.99210 3.90 0.62 12.800000
## 495 8.0 25.0 0.99720 3.47 0.67 9.500000
## 496 18.0 72.0 1.00010 3.31 0.53 9.700000
## 497 5.0 19.0 0.99860 3.11 0.62 10.800000
## 498 26.0 88.0 0.99840 3.08 0.57 10.100000
## 499 8.0 25.0 0.99720 3.47 0.67 9.500000
## 500 9.0 23.0 0.99700 3.47 0.67 9.400000
## 501 22.0 84.0 0.99720 3.32 0.70 9.600000
## 502 4.0 14.0 0.99800 3.29 0.54 9.700000
## 503 11.0 54.0 0.99900 3.37 0.49 9.900000
## 504 10.0 19.0 0.99560 3.40 0.47 10.000000
## 505 16.0 37.0 0.99660 3.37 0.51 10.500000
## 506 11.0 35.0 0.99600 3.34 0.61 11.600000
## 507 21.0 84.0 0.99850 3.06 0.57 10.100000
## 508 16.0 112.0 0.99980 3.38 0.61 9.500000
## 509 21.0 77.0 0.99800 3.28 0.51 9.800000
## 510 12.0 49.0 0.99680 3.21 0.57 10.000000
## 511 10.0 22.0 0.99760 3.22 0.44 9.600000
## 512 17.0 45.0 0.99760 3.46 0.54 9.500000
## 513 10.0 22.0 0.99760 3.22 0.44 9.600000
## 514 35.0 106.0 0.99820 3.10 0.53 9.200000
## 515 36.0 127.0 0.99650 2.94 1.62 9.500000
## 516 5.0 13.0 0.99760 3.29 0.55 10.400000
## 517 18.0 49.0 0.99940 3.43 0.72 11.100000
## 518 4.0 11.0 0.99700 3.46 0.68 9.500000
## 519 4.0 11.0 0.99700 3.46 0.68 9.500000
## 520 27.0 58.0 0.99500 3.61 0.49 12.700000
## 521 12.0 37.0 0.99820 3.17 0.67 9.600000
## 522 4.0 14.0 0.99940 3.44 0.52 11.500000
## 523 10.0 19.0 0.99660 3.39 0.47 9.600000
## 524 16.0 72.0 0.99800 3.55 0.60 9.500000
## 525 9.0 39.0 0.99870 3.48 0.62 9.300000
## 526 11.0 22.0 0.99630 3.26 0.50 9.500000
## 527 19.0 58.0 1.00020 3.50 0.65 9.300000
## 528 19.0 38.0 0.99900 3.35 0.60 9.300000
## 529 12.0 16.0 0.99650 3.45 0.68 11.500000
## 530 28.0 139.0 0.99788 3.21 0.57 9.500000
## 531 21.0 101.0 1.00010 3.13 0.51 9.500000
## 532 12.0 28.0 0.99768 3.52 0.73 9.500000
## 533 12.0 57.0 0.99780 3.30 0.47 9.000000
## 534 12.0 31.0 0.99761 3.52 0.72 9.600000
## 535 12.0 28.0 0.99768 3.52 0.73 9.500000
## 536 17.0 40.0 0.99803 3.29 0.55 9.500000
## 537 24.0 66.0 0.99785 3.39 0.61 9.400000
## 538 17.0 40.0 0.99803 3.29 0.55 9.500000
## 539 14.0 32.0 0.99656 3.09 1.06 9.100000
## 540 22.0 47.0 0.99525 3.51 0.43 10.700000
## 541 15.0 33.0 0.99488 3.60 0.46 11.200000
## 542 10.0 31.0 0.99656 3.25 0.50 9.800000
## 543 10.0 31.0 0.99656 3.25 0.50 9.800000
## 544 33.0 88.0 0.99823 3.19 0.52 9.200000
## 545 9.0 104.0 0.99779 3.23 0.57 9.700000
## 546 5.0 35.0 0.99738 3.25 0.42 9.600000
## 547 8.0 19.0 0.99701 3.31 0.53 10.000000
## 548 11.0 44.0 0.99888 3.31 0.55 9.500000
## 549 12.0 48.0 0.99888 3.31 0.54 9.500000
## 550 12.0 74.0 0.99738 3.14 0.54 9.400000
## 551 24.0 94.0 0.99744 3.55 0.53 9.700000
## 552 7.0 32.0 0.99668 3.39 0.54 9.600000
## 553 24.0 94.0 0.99744 3.55 0.53 9.700000
## 554 23.0 143.0 0.99780 3.28 0.55 9.400000
## 555 23.0 144.0 0.99782 3.27 0.55 9.400000
## 556 1.0 44.0 0.99730 3.30 0.92 9.500000
## 557 7.0 16.0 0.99586 3.43 0.46 10.000000
## 558 6.0 18.0 0.99610 3.40 0.59 10.300000
## 559 7.0 34.0 0.99788 3.36 0.61 10.500000
## 560 21.0 92.0 0.99745 3.50 0.60 9.800000
## 561 12.0 45.0 0.99676 3.31 0.60 9.400000
## 562 9.0 37.0 0.99732 3.66 0.65 9.800000
## 563 20.0 85.0 0.99746 3.55 0.59 9.800000
## 564 11.0 61.0 0.99820 3.21 0.50 9.500000
## 565 19.0 58.0 0.99910 3.18 0.56 10.100000
## 566 19.0 58.0 0.99910 3.18 0.56 10.100000
## 567 21.0 119.0 0.99800 3.09 0.52 9.300000
## 568 18.0 82.0 0.99708 3.33 0.68 9.700000
## 569 25.0 130.0 0.99818 3.23 0.54 9.600000
## 570 17.0 87.0 0.99745 3.48 0.60 9.700000
## 571 7.0 13.0 0.99639 3.38 0.56 10.800000
## 572 7.0 26.0 0.99531 3.17 0.53 12.500000
## 573 7.0 60.0 0.99786 2.99 1.18 10.200000
## 574 24.0 64.0 0.99746 3.10 0.74 9.600000
## 575 21.0 42.0 0.99526 3.24 0.81 10.800000
## 576 5.0 14.0 0.99870 3.29 0.52 10.700000
## 577 8.0 17.0 0.99735 3.23 0.44 10.000000
## 578 14.0 86.0 0.99264 3.56 0.94 12.900000
## 579 14.0 46.0 0.99710 3.24 0.66 9.600000
## 580 4.0 18.0 0.99682 3.26 0.57 9.900000
## 581 4.0 10.0 0.99386 3.27 0.71 12.500000
## 582 4.0 12.0 0.99702 3.26 0.86 9.200000
## 583 4.0 15.0 0.99693 3.22 0.55 10.300000
## 584 4.0 14.0 0.99562 3.30 0.47 10.500000
## 585 7.0 24.0 1.00012 3.09 0.68 10.900000
## 586 4.0 7.0 0.99462 3.44 0.58 11.400000
## 587 19.0 41.0 0.99939 3.21 0.76 11.300000
## 588 9.0 59.0 0.99976 3.28 0.54 9.600000
## 589 9.0 22.0 0.99606 3.34 0.60 9.700000
## 590 16.0 42.0 0.99154 3.71 0.74 14.000000
## 591 15.0 36.0 0.99730 3.61 0.64 9.800000
## 592 6.0 16.0 0.99682 3.24 0.53 10.300000
## 593 4.0 8.0 0.99510 3.40 0.64 11.000000
## 594 15.0 37.0 0.99624 3.36 0.49 10.700000
## 595 5.0 9.0 0.99632 3.38 0.55 10.900000
## 596 16.0 24.0 0.99376 3.56 0.77 11.100000
## 597 36.0 51.0 0.99836 3.38 0.86 9.900000
## 598 36.0 100.0 0.99064 3.26 0.39 11.700000
## 599 36.0 100.0 0.99064 3.26 0.39 11.700000
## 600 9.0 28.0 0.99672 3.24 0.83 11.200000
## 601 21.0 31.0 0.99629 3.45 0.63 10.300000
## 602 34.0 68.0 0.99708 3.23 0.72 10.900000
## 603 9.0 92.0 0.99630 3.28 0.62 9.400000
## 604 14.0 31.0 0.99689 3.59 0.66 9.800000
## 605 12.0 39.0 0.99770 3.50 0.70 9.900000
## 606 14.0 31.0 0.99689 3.59 0.66 9.800000
## 607 9.0 22.0 0.99708 3.28 0.55 9.500000
## 608 9.0 22.0 0.99708 3.28 0.55 9.500000
## 609 41.0 55.0 0.99652 3.47 0.73 10.900000
## 610 41.0 55.0 0.99652 3.47 0.73 10.900000
## 611 41.0 55.0 0.99652 3.47 0.73 10.900000
## 612 16.0 47.0 0.99594 3.40 0.78 11.300000
## 613 17.0 32.0 0.99686 3.15 0.67 10.600000
## 614 15.0 85.0 0.99746 3.51 0.54 9.500000
## 615 7.0 25.0 0.99628 3.24 0.44 10.400000
## 616 17.0 84.0 0.99746 3.51 0.53 9.700000
## 617 16.0 86.0 0.99748 3.51 0.54 9.700000
## 618 36.0 46.0 0.99522 3.53 0.57 10.600000
## 619 28.0 48.0 0.99576 3.43 0.54 10.000000
## 620 7.0 16.0 0.99614 3.31 0.79 11.800000
## 621 31.0 43.0 0.99371 3.41 0.57 11.800000
## 622 30.0 35.0 0.99533 3.48 0.65 11.400000
## 623 14.0 26.0 0.99536 3.22 0.78 12.000000
## 624 30.0 69.0 0.99787 3.22 0.50 10.000000
## 625 9.0 30.0 0.99824 3.34 0.64 10.500000
## 626 8.0 28.0 0.99836 3.33 0.65 10.400000
## 627 32.0 64.0 0.99491 3.43 0.56 11.200000
## 628 23.0 99.0 1.00289 3.22 0.68 9.300000
## 629 16.0 86.0 0.99743 3.53 0.57 9.700000
## 630 10.0 18.0 0.99774 3.22 0.65 9.300000
## 631 16.0 86.0 0.99743 3.53 0.57 9.700000
## 632 17.0 88.0 0.99745 3.53 0.58 9.800000
## 633 27.0 43.0 0.99550 3.42 0.58 10.700000
## 634 17.0 43.0 0.99444 3.31 0.77 12.500000
## 635 27.0 43.0 0.99550 3.42 0.58 10.700000
## 636 17.0 43.0 0.99444 3.31 0.77 12.500000
## 637 10.0 39.0 0.99562 3.40 0.69 11.800000
## 638 16.0 33.0 0.99736 3.58 0.69 10.800000
## 639 16.0 33.0 0.99736 3.58 0.69 10.800000
## 640 11.0 18.0 0.99620 3.41 0.59 10.800000
## 641 12.0 78.0 0.99640 3.39 0.71 11.000000
## 642 6.0 9.0 0.99480 3.59 0.54 11.500000
## 643 34.0 61.0 0.99528 3.43 0.59 10.800000
## 644 3.0 11.0 0.99577 3.23 0.57 13.200000
## 645 3.0 9.0 0.99901 3.18 0.60 10.900000
## 646 4.0 8.0 0.99674 3.33 0.62 12.200000
## 647 3.0 6.0 0.99512 3.27 0.67 11.900000
## 648 7.0 62.0 0.99395 3.62 0.61 11.000000
## 649 26.0 45.0 0.99824 3.38 0.64 10.100000
## 650 32.0 79.0 0.99640 3.30 0.72 11.000000
## 651 38.0 46.0 0.99504 3.38 0.89 11.800000
## 652 10.0 23.0 0.99786 3.24 0.56 10.500000
## 653 38.0 46.0 0.99504 3.38 0.89 11.800000
## 654 14.0 28.0 0.99516 3.18 0.80 11.200000
## 655 53.0 77.0 0.99604 3.47 0.87 11.000000
## 656 52.0 73.0 0.99786 3.47 0.90 10.200000
## 657 11.0 75.0 0.99736 3.20 0.59 9.200000
## 658 14.0 28.0 0.99516 3.18 0.80 11.200000
## 659 10.0 19.0 0.99468 3.30 0.73 12.000000
## 660 13.0 38.0 0.99748 3.48 0.65 9.800000
## 661 29.0 66.0 0.99710 3.45 0.59 9.500000
## 662 8.0 25.0 0.99746 3.69 0.73 10.500000
## 663 19.0 72.0 0.99543 3.39 0.72 11.800000
## 664 7.0 11.0 0.99425 3.61 0.54 11.400000
## 665 9.0 17.0 0.99484 3.19 0.52 12.500000
## 666 13.0 52.0 0.99834 3.22 0.64 10.000000
## 667 11.0 24.0 0.99498 3.27 0.64 12.100000
## 668 11.0 24.0 0.99745 3.29 0.99 12.000000
## 669 6.0 12.0 0.99408 3.26 0.62 12.400000
## 670 10.0 21.0 0.99552 3.41 0.76 11.900000
## 671 10.0 21.0 0.99552 3.41 0.76 11.900000
## 672 6.0 12.0 0.99408 3.26 0.62 12.400000
## 673 6.0 10.0 0.99536 3.31 0.68 11.200000
## 674 5.0 9.0 0.99458 3.20 0.69 12.100000
## 675 9.0 23.0 0.99648 3.36 0.67 10.400000
## 676 5.0 10.0 0.99568 3.31 0.62 11.300000
## 677 25.0 36.0 0.99519 3.47 0.71 11.300000
## 678 18.0 38.0 0.99518 3.16 0.85 11.100000
## 679 7.0 18.0 0.99592 3.27 0.62 9.300000
## 680 6.0 16.0 0.99654 3.38 0.69 9.500000
## 681 18.0 38.0 0.99518 3.16 0.85 11.100000
## 682 23.0 113.0 0.99733 3.13 0.48 9.200000
## 683 29.0 46.0 0.99430 3.20 0.60 12.200000
## 684 6.0 10.0 0.99724 3.33 0.87 10.900000
## 685 7.0 13.0 0.99658 3.30 0.62 12.100000
## 686 35.0 72.0 0.99700 3.44 0.52 9.400000
## 687 31.0 119.0 0.99801 3.15 0.50 9.100000
## 688 9.0 29.0 0.99416 3.28 0.49 11.300000
## 689 8.0 17.0 0.99774 3.32 0.71 10.500000
## 690 13.0 28.0 0.99712 3.25 0.62 10.000000
## 691 51.0 70.0 0.99418 3.34 0.82 12.900000
## 692 8.0 17.0 0.99774 3.32 0.71 10.500000
## 693 7.0 11.0 0.99562 3.45 0.58 11.300000
## 694 35.0 70.0 0.99693 3.44 0.50 9.400000
## 695 19.0 27.0 0.99596 3.16 0.49 9.400000
## 696 14.0 21.0 0.99556 3.14 0.51 10.900000
## 697 19.0 27.0 0.99596 3.16 0.49 9.400000
## 698 31.0 68.0 0.99694 3.45 0.48 9.400000
## 699 31.0 68.0 0.99694 3.45 0.48 9.400000
## 700 19.0 41.0 0.99697 3.39 0.62 10.100000
## 701 4.0 10.0 0.99554 3.12 0.48 9.100000
## 702 7.0 12.0 0.99162 3.47 0.53 12.900000
## 703 6.0 12.0 0.99495 3.44 0.72 11.500000
## 704 27.0 45.0 0.99603 3.25 0.54 10.300000
## 705 4.0 8.0 0.99280 3.36 0.55 12.800000
## 706 13.0 27.0 0.99516 3.26 0.84 11.700000
## 707 12.0 25.0 0.99516 3.26 0.84 11.700000
## 708 12.0 24.0 0.99549 3.23 0.70 12.000000
## 709 7.0 12.0 0.99354 3.25 0.55 12.300000
## 710 12.0 19.0 0.99570 3.17 0.55 10.400000
## 711 11.0 32.0 0.99604 3.23 0.48 10.000000
## 712 22.0 31.0 0.99635 3.38 0.58 10.000000
## 713 3.0 7.0 0.99454 3.22 0.58 11.200000
## 714 12.0 28.0 0.99486 3.27 0.75 12.600000
## 715 36.0 109.0 0.99007 2.89 0.44 12.700000
## 716 10.0 23.0 0.99636 3.53 0.61 10.400000
## 717 3.0 8.0 0.99642 3.20 0.53 11.900000
## 718 3.0 8.0 0.99642 3.20 0.53 11.900000
## 719 4.0 9.0 0.99584 3.35 0.54 10.500000
## 720 3.0 9.0 0.99506 3.27 0.55 12.300000
## 721 12.0 18.0 0.99568 3.32 0.56 10.500000
## 722 17.0 43.0 0.99822 3.33 0.60 10.400000
## 723 14.0 36.0 0.99364 3.41 0.56 12.600000
## 724 28.0 52.0 0.99378 3.49 0.52 11.600000
## 725 12.0 18.0 0.99568 3.32 0.56 10.500000
## 726 5.0 14.0 0.99854 3.36 0.53 9.600000
## 727 15.0 48.0 0.99739 3.20 0.47 9.700000
## 728 5.0 17.0 0.99683 3.17 0.65 10.600000
## 729 26.0 35.0 0.99356 3.22 0.59 12.500000
## 730 25.0 42.0 0.99530 3.24 0.77 12.600000
## 731 21.0 39.0 0.99692 3.33 0.83 11.100000
## 732 13.0 26.0 0.99547 3.38 0.64 9.800000
## 733 12.0 53.0 0.99294 3.36 0.62 12.200000
## 734 32.0 38.0 0.99438 3.32 0.58 11.400000
## 735 33.0 45.0 0.99612 3.31 0.62 10.700000
## 736 27.0 51.0 0.99634 3.49 0.64 10.400000
## 737 3.0 14.0 0.99702 3.27 0.78 10.900000
## 738 6.0 17.0 0.99704 3.27 0.77 10.800000
## 739 16.0 45.0 0.99702 3.03 1.34 9.200000
## 740 19.0 25.0 0.99258 3.56 0.55 12.900000
## 741 9.0 20.0 0.99426 3.38 0.77 12.700000
## 742 5.0 77.0 0.99747 3.13 0.62 9.100000
## 743 3.0 10.0 0.99586 3.21 0.59 12.100000
## 744 28.0 90.0 0.99784 3.15 0.57 9.100000
## 745 30.0 52.0 0.99710 3.19 0.76 11.600000
## 746 3.0 10.0 0.99586 3.21 0.59 12.100000
## 747 3.0 8.0 0.99810 3.14 0.70 9.900000
## 748 7.0 16.0 0.99462 3.21 0.69 12.500000
## 749 6.0 15.0 0.99560 3.30 0.64 11.400000
## 750 6.0 17.0 0.99565 3.22 0.63 11.800000
## 751 5.0 10.0 0.99418 3.28 0.57 11.800000
## 752 6.0 20.0 0.99630 3.29 0.60 10.200000
## 753 13.0 26.0 0.99358 3.40 0.72 12.500000
## 754 3.0 10.0 0.99572 3.02 0.83 10.900000
## 755 28.0 52.0 0.99700 3.31 0.62 10.800000
## 756 19.0 37.0 0.99498 3.18 0.89 11.100000
## 757 30.0 92.0 0.99892 3.20 0.58 9.200000
## 758 16.0 37.0 0.99720 3.31 0.58 10.700000
## 759 3.0 8.0 0.99534 3.15 0.73 11.400000
## 760 3.0 8.0 0.99817 3.27 0.58 11.000000
## 761 37.5 278.0 0.99316 3.01 0.51 12.300000
## 762 37.5 289.0 0.99316 3.01 0.51 12.300000
## 763 31.0 70.0 0.99685 3.42 0.59 9.500000
## 764 32.0 59.0 0.99617 3.33 0.77 12.000000
## 765 40.0 64.0 0.99529 3.12 0.49 9.600000
## 766 3.0 9.0 0.99738 3.38 0.66 11.600000
## 767 18.0 30.0 0.99400 3.29 0.69 11.200000
## 768 22.0 41.0 0.99735 3.02 0.76 9.900000
## 769 22.0 41.0 0.99735 3.02 0.76 9.900000
## 770 42.0 74.0 0.99451 2.98 0.63 11.800000
## 771 8.0 19.0 0.99546 3.35 0.60 11.400000
## 772 10.0 16.0 0.99479 3.43 0.59 11.500000
## 773 11.0 31.0 0.99615 3.33 0.86 12.000000
## 774 9.0 17.0 0.99655 3.35 0.49 10.800000
## 775 9.0 17.0 0.99655 3.35 0.49 10.800000
## 776 5.0 18.0 0.99666 3.20 0.52 9.400000
## 777 23.0 36.0 0.99392 3.11 0.78 12.400000
## 778 14.0 25.0 0.99388 3.35 0.63 12.000000
## 779 14.0 25.0 0.99388 3.35 0.63 12.000000
## 780 16.0 25.0 0.99360 3.28 0.66 12.400000
## 781 13.0 49.0 0.99374 3.41 0.60 12.800000
## 782 10.0 24.0 0.99523 3.27 0.85 11.700000
## 783 4.0 13.0 0.99855 3.36 0.49 9.500000
## 784 27.0 66.0 0.99820 3.17 0.76 10.800000
## 785 6.0 14.0 0.99593 3.25 0.64 10.000000
## 786 23.0 55.0 0.99471 3.78 0.64 12.300000
## 787 6.0 35.0 0.99396 3.29 0.71 11.000000
## 788 29.0 80.0 0.99020 3.49 0.66 13.600000
## 789 15.0 21.0 0.99572 3.38 0.60 11.300000
## 790 15.0 21.0 0.99572 3.38 0.60 11.300000
## 791 15.0 21.0 0.99572 3.38 0.60 11.300000
## 792 14.0 24.0 0.99252 3.30 0.53 13.300000
## 793 7.0 15.0 0.99256 3.52 0.58 12.900000
## 794 8.0 17.0 0.99235 3.20 0.72 13.100000
## 795 27.0 33.0 0.99220 3.45 0.48 12.300000
## 796 13.0 27.0 0.99394 3.14 0.59 11.300000
## 797 26.0 52.0 0.99379 3.51 0.60 11.500000
## 798 28.0 89.0 0.99840 3.22 0.68 10.000000
## 799 5.0 10.0 0.99770 3.18 0.63 10.400000
## 800 18.0 38.0 0.99330 3.38 0.88 13.600000
## 801 10.0 18.0 0.99684 3.37 0.68 11.200000
## 802 6.0 15.0 0.99524 3.30 0.74 11.800000
## 803 22.0 50.0 0.99460 3.38 0.60 11.900000
## 804 6.0 13.0 0.99774 3.22 0.76 11.400000
## 805 29.0 94.0 0.99786 3.14 0.58 9.100000
## 806 16.0 86.0 0.99764 3.34 0.64 9.500000
## 807 33.0 79.0 0.99690 3.41 0.65 9.500000
## 808 24.0 71.0 0.99624 3.27 0.85 11.000000
## 809 6.0 12.0 0.99354 3.30 0.65 11.400000
## 810 16.0 20.0 0.99672 3.61 0.82 10.000000
## 811 35.0 70.0 0.99588 3.43 0.62 10.100000
## 812 31.0 51.0 0.99672 3.53 0.81 10.400000
## 813 6.0 21.0 0.99600 3.28 0.87 9.800000
## 814 6.0 14.0 0.99702 3.21 0.49 11.800000
## 815 9.0 21.0 0.99526 3.39 0.66 11.600000
## 816 6.0 11.0 0.99585 3.23 0.52 12.000000
## 817 16.0 70.0 0.99352 3.46 0.65 12.500000
## 818 9.0 38.0 0.99616 3.15 0.53 9.800000
## 819 17.0 38.0 0.99622 3.23 0.66 11.100000
## 820 9.0 38.0 0.99616 3.15 0.53 9.800000
## 821 45.0 88.0 0.99524 3.33 0.76 11.800000
## 822 16.0 101.0 0.99240 3.46 0.87 12.900000
## 823 12.0 19.0 0.99434 3.14 0.71 11.800000
## 824 10.0 21.0 0.99692 3.29 0.67 10.000000
## 825 29.0 48.0 0.99528 3.32 0.88 12.400000
## 826 8.0 44.0 0.99384 3.21 0.56 12.000000
## 827 5.0 14.0 0.99502 3.32 0.62 11.500000
## 828 5.0 12.0 0.99667 3.20 0.67 10.500000
## 829 5.0 16.0 0.99649 3.27 0.74 12.300000
## 830 26.0 65.0 0.99716 3.46 0.62 9.500000
## 831 26.0 65.0 0.99716 3.46 0.62 9.500000
## 832 48.0 59.0 0.99541 3.61 0.70 11.500000
## 833 23.0 47.0 0.99572 3.58 0.50 11.200000
## 834 17.0 78.0 0.99346 3.68 0.73 11.400000
## 835 11.0 47.0 0.99599 3.27 0.81 11.000000
## 836 5.0 11.0 0.99478 3.19 0.46 11.400000
## 837 16.0 61.0 0.99572 3.29 0.60 9.300000
## 838 11.0 119.0 0.99538 3.36 0.70 10.900000
## 839 11.0 119.0 0.99538 3.36 0.70 10.900000
## 840 5.0 19.0 0.99616 3.30 0.44 9.600000
## 841 6.0 20.0 0.99652 3.28 0.57 12.500000
## 842 6.0 12.0 0.99551 3.56 0.51 10.800000
## 843 11.0 41.0 0.99439 3.52 0.80 12.400000
## 844 15.0 64.0 0.99588 3.22 0.59 9.500000
## 845 21.0 56.0 0.99633 3.52 0.60 9.500000
## 846 4.0 9.0 0.99458 3.16 0.54 9.800000
## 847 19.0 77.0 0.99510 3.15 0.79 10.900000
## 848 23.0 94.0 0.99686 3.21 0.58 9.500000
## 849 4.0 9.0 0.99458 3.16 0.54 9.800000
## 850 5.0 10.0 0.99419 3.27 0.54 11.200000
## 851 6.0 12.0 0.99516 3.35 0.69 11.700000
## 852 33.0 76.0 0.99878 3.14 0.55 9.400000
## 853 24.0 44.0 0.99534 3.40 0.85 11.000000
## 854 24.0 44.0 0.99534 3.40 0.85 11.000000
## 855 24.0 52.0 0.99752 3.35 0.94 10.000000
## 856 24.0 44.0 0.99534 3.40 0.85 11.000000
## 857 14.0 24.0 0.99428 3.45 0.87 11.200000
## 858 8.0 15.0 0.99498 3.35 0.62 10.400000
## 859 8.0 15.0 0.99498 3.35 0.62 10.400000
## 860 6.0 9.0 0.99628 3.10 0.48 10.400000
## 861 10.0 79.0 0.99677 3.29 0.69 9.500000
## 862 25.0 50.0 0.99478 3.20 0.83 10.900000
## 863 17.0 44.0 0.99734 3.28 0.77 11.500000
## 864 17.0 44.0 0.99734 3.28 0.77 11.500000
## 865 8.0 20.0 0.99920 3.07 0.82 10.300000
## 866 18.0 77.0 0.99922 3.15 0.51 9.400000
## 867 27.0 46.0 0.99592 3.02 0.47 9.200000
## 868 8.0 21.0 0.99769 3.27 0.72 9.600000
## 869 18.0 88.0 0.99157 3.68 0.73 13.600000
## 870 19.0 66.0 0.99718 3.40 0.64 9.500000
## 871 15.0 58.0 0.99470 3.37 0.78 11.800000
## 872 48.0 90.0 0.99621 3.38 0.62 10.800000
## 873 19.0 66.0 0.99718 3.40 0.64 9.500000
## 874 11.0 18.0 0.99242 3.39 0.40 12.800000
## 875 7.0 17.0 0.99659 3.26 0.64 9.400000
## 876 11.0 18.0 0.99242 3.39 0.40 12.800000
## 877 12.0 24.0 0.99798 3.38 0.46 9.600000
## 878 11.0 20.0 0.99488 3.45 0.56 11.800000
## 879 36.0 60.0 0.99494 3.10 0.40 9.300000
## 880 10.0 86.0 0.99830 3.37 0.62 9.500000
## 881 15.0 34.0 0.99655 3.49 0.68 10.500000
## 882 15.0 48.0 0.99502 3.12 0.48 10.000000
## 883 24.0 37.0 0.99514 3.40 0.61 10.900000
## 884 27.0 60.0 0.99630 3.28 0.59 9.800000
## 885 6.0 14.0 0.99627 3.45 0.58 9.800000
## 886 6.0 13.0 0.99569 3.43 0.58 9.500000
## 887 20.0 35.0 0.99628 3.40 0.69 10.900000
## 888 43.0 60.0 0.99499 3.10 0.42 9.200000
## 889 12.0 69.0 0.99633 3.32 0.70 11.000000
## 890 15.0 33.0 0.99538 3.44 0.63 11.300000
## 891 19.0 56.0 0.99632 3.02 1.15 9.300000
## 892 17.0 24.0 0.99437 3.59 0.55 11.200000
## 893 34.0 66.0 0.99726 3.15 0.58 9.800000
## 894 26.0 51.0 0.99456 3.38 0.72 11.800000
## 895 16.0 36.0 0.99564 3.38 0.60 10.300000
## 896 16.0 36.0 0.99564 3.38 0.60 10.300000
## 897 13.0 19.0 0.99600 3.10 0.87 11.400000
## 898 21.0 51.0 0.99668 3.47 0.55 9.500000
## 899 26.0 60.0 0.99084 3.70 0.75 14.000000
## 900 24.0 64.0 0.99350 3.38 0.57 11.700000
## 901 25.0 44.0 0.99385 3.50 0.53 11.200000
## 902 34.0 44.0 0.99494 3.10 0.43 9.300000
## 903 17.0 36.0 0.99440 3.24 0.53 11.200000
## 904 13.0 81.0 0.99688 3.24 0.54 9.500000
## 905 3.0 10.0 0.99566 3.28 0.56 12.000000
## 906 3.0 10.0 0.99636 3.35 0.60 9.700000
## 907 13.0 81.0 0.99688 3.24 0.54 9.500000
## 908 3.0 9.0 0.99480 3.14 0.57 11.500000
## 909 28.0 54.0 0.99560 3.37 0.64 10.400000
## 910 28.0 54.0 0.99560 3.37 0.64 10.400000
## 911 9.0 22.0 0.99619 3.33 0.68 10.900000
## 912 17.0 69.0 0.99734 3.26 0.63 10.200000
## 913 18.0 36.0 0.99476 3.39 0.56 10.900000
## 914 20.0 47.0 0.99734 3.15 0.57 10.500000
## 915 20.0 110.0 0.99914 3.30 1.17 10.200000
## 916 20.0 110.0 0.99914 3.30 1.17 10.200000
## 917 6.0 12.0 0.99521 3.36 0.59 11.000000
## 918 25.0 60.0 0.99638 3.29 0.75 10.900000
## 919 14.0 28.0 0.99362 3.62 0.67 12.400000
## 920 51.0 77.5 0.99558 3.20 0.45 9.500000
## 921 15.0 29.0 0.99323 3.35 0.61 12.100000
## 922 11.0 18.0 0.99191 3.45 0.56 12.200000
## 923 5.0 9.0 0.99476 3.50 0.40 10.900000
## 924 15.0 28.0 0.99444 3.78 0.61 12.500000
## 925 20.0 57.0 0.99598 3.60 0.74 11.700000
## 926 4.0 15.0 0.99576 3.30 0.80 11.200000
## 927 21.0 68.0 0.99550 3.12 0.44 9.200000
## 928 24.0 88.0 0.99612 3.30 0.53 9.800000
## 929 22.0 91.0 0.99790 3.29 0.62 10.100000
## 930 27.0 63.0 0.99600 3.28 0.61 9.200000
## 931 12.0 27.0 0.99290 3.33 0.59 12.800000
## 932 14.0 41.0 0.99532 3.34 0.47 10.500000
## 933 17.0 91.0 0.99616 3.26 0.58 9.800000
## 934 16.0 46.0 0.99258 4.01 0.59 12.500000
## 935 18.0 64.0 0.99652 2.90 1.33 9.100000
## 936 16.0 46.0 0.99258 4.01 0.59 12.500000
## 937 9.0 18.0 0.99392 3.18 0.55 11.400000
## 938 18.0 34.0 0.99480 3.39 0.60 10.600000
## 939 18.0 34.0 0.99480 3.39 0.60 10.600000
## 940 18.0 34.0 0.99480 3.39 0.60 10.600000
## 941 18.0 34.0 0.99480 3.39 0.60 10.600000
## 942 13.0 38.0 0.99550 3.34 0.52 9.300000
## 943 17.0 91.0 0.99616 3.29 0.56 9.800000
## 944 17.0 91.0 0.99616 3.29 0.56 9.800000
## 945 31.0 67.0 0.99668 3.26 0.46 9.200000
## 946 6.0 25.0 0.99581 3.09 0.43 9.700000
## 947 12.0 48.0 0.99760 3.18 0.51 9.600000
## 948 4.0 11.0 0.99608 3.39 0.52 10.000000
## 949 19.0 40.0 0.99387 3.38 0.66 12.600000
## 950 15.0 26.0 0.99448 3.36 0.45 9.500000
## 951 15.0 26.0 0.99448 3.36 0.45 9.500000
## 952 15.0 26.0 0.99448 3.36 0.45 9.500000
## 953 13.0 31.0 0.99538 3.36 0.54 10.500000
## 954 13.0 31.0 0.99538 3.36 0.54 10.500000
## 955 13.0 31.0 0.99538 3.36 0.54 10.500000
## 956 13.0 31.0 0.99538 3.36 0.54 10.500000
## 957 8.0 20.0 0.99852 3.09 0.53 11.000000
## 958 10.0 28.0 0.99613 3.25 0.53 10.200000
## 959 5.0 13.0 0.99472 3.52 0.56 11.400000
## 960 7.0 12.0 0.99587 3.34 0.39 9.500000
## 961 8.0 46.0 0.99332 3.32 0.51 11.800000
## 962 14.0 57.0 0.99690 3.37 0.46 10.300000
## 963 5.0 32.0 0.99464 3.36 0.44 10.100000
## 964 4.0 28.0 0.99464 3.36 0.44 10.100000
## 965 27.0 67.0 0.99648 3.60 0.59 11.100000
## 966 52.0 98.0 0.99736 3.28 0.50 9.500000
## 967 6.0 28.0 0.99704 3.07 0.65 10.033333
## 968 13.0 54.0 0.99724 3.36 0.54 10.100000
## 969 6.0 28.0 0.99704 3.07 0.65 10.033333
## 970 10.0 31.0 0.99500 3.46 0.53 11.800000
## 971 10.0 45.0 0.99576 3.24 0.50 9.800000
## 972 9.0 105.0 0.99725 3.25 1.18 10.500000
## 973 16.0 74.0 0.99656 3.27 0.50 9.800000
## 974 12.0 66.0 0.99623 3.00 1.17 9.200000
## 975 5.0 14.0 0.99476 3.21 1.03 11.600000
## 976 12.0 66.0 0.99623 3.00 1.17 9.200000
## 977 7.0 31.0 0.99460 3.11 0.46 10.000000
## 978 10.0 28.0 0.99609 3.29 0.51 9.900000
## 979 11.0 35.0 0.99557 3.36 0.62 10.800000
## 980 11.0 35.0 0.99557 3.36 0.62 10.800000
## 981 25.0 105.0 0.99613 3.30 0.49 9.900000
## 982 25.0 105.0 0.99613 3.30 0.49 9.900000
## 983 20.0 84.0 0.99593 3.19 0.50 9.500000
## 984 34.0 85.0 0.99668 3.19 0.42 9.200000
## 985 8.0 23.0 0.99610 3.30 0.58 9.600000
## 986 7.0 49.0 0.99718 3.35 0.68 10.300000
## 987 15.0 33.0 0.99292 3.58 0.59 12.500000
## 988 5.0 33.0 0.99612 3.37 0.58 11.000000
## 989 5.0 12.0 0.99420 3.17 0.48 9.800000
## 990 19.0 59.0 0.99612 3.30 0.48 10.200000
## 991 3.0 12.0 0.99745 3.31 0.65 9.550000
## 992 4.0 11.0 0.99745 3.31 0.65 9.550000
## 993 17.0 104.0 0.99584 3.28 0.52 9.900000
## 994 10.0 24.0 0.99560 3.42 0.72 11.100000
## 995 33.0 141.0 0.99620 3.25 0.51 9.900000
## 996 6.0 24.0 0.99630 3.44 0.82 11.900000
## 997 3.0 13.0 0.99600 3.23 1.10 10.000000
## 998 12.0 46.0 0.99850 3.43 0.62 10.700000
## 999 8.0 22.0 0.99740 3.22 0.94 10.900000
## 1000 40.0 54.0 0.99500 3.54 0.93 10.700000
## 1001 35.0 53.0 0.99500 3.27 1.01 12.400000
## 1002 40.0 54.0 0.99500 3.54 0.93 10.700000
## 1003 5.0 16.0 0.99360 3.50 0.44 11.900000
## 1004 8.0 19.0 0.99630 3.56 0.73 10.600000
## 1005 8.0 22.0 0.99740 3.22 0.94 10.900000
## 1006 12.0 89.0 0.99630 3.04 0.90 10.100000
## 1007 3.0 15.0 0.99940 3.20 0.78 9.600000
## 1008 8.0 18.0 0.99660 3.56 0.84 9.400000
## 1009 3.0 15.0 0.99940 3.20 0.78 9.600000
## 1010 21.0 37.0 0.99520 3.35 0.77 12.100000
## 1011 3.0 19.0 0.99500 3.16 0.46 9.800000
## 1012 32.0 133.0 0.99560 3.27 0.45 9.900000
## 1013 3.0 19.0 0.99500 3.16 0.46 9.800000
## 1014 24.0 58.0 0.99650 3.34 0.58 9.400000
## 1015 39.0 55.0 0.99560 3.39 0.84 11.400000
## 1016 14.0 31.0 0.99680 3.50 0.67 11.000000
## 1017 6.0 23.0 0.99570 3.30 0.58 11.200000
## 1018 27.0 55.0 0.99620 3.31 0.63 11.000000
## 1019 38.0 67.0 0.99600 3.33 0.93 11.300000
## 1020 18.0 47.0 0.99560 3.37 0.62 10.400000
## 1021 13.0 74.0 0.99580 3.26 0.54 9.900000
## 1022 11.0 25.0 0.99550 3.42 0.74 10.100000
## 1023 55.0 95.0 1.00369 3.18 0.77 9.000000
## 1024 27.0 90.0 0.99914 3.15 0.65 8.500000
## 1025 15.0 39.0 0.99577 3.53 0.54 11.100000
## 1026 3.0 8.0 0.99600 3.41 0.47 10.300000
## 1027 31.0 92.0 0.99566 3.32 0.68 11.066667
## 1028 8.0 22.0 0.99322 3.38 0.49 11.700000
## 1029 19.0 98.0 0.99713 3.16 0.52 9.600000
## 1030 22.0 39.0 0.99701 3.40 0.74 9.700000
## 1031 15.0 29.0 0.99472 3.23 0.76 11.300000
## 1032 15.0 28.0 0.99470 3.23 0.73 11.300000
## 1033 8.0 17.0 0.99732 3.33 0.78 11.000000
## 1034 37.0 53.0 0.99374 3.35 0.76 11.600000
## 1035 7.0 23.0 0.99974 3.21 0.69 10.900000
## 1036 9.0 17.0 0.99467 3.50 0.63 10.900000
## 1037 27.0 85.0 0.99706 3.41 0.58 9.000000
## 1038 8.0 20.0 0.99603 3.39 0.60 10.500000
## 1039 7.0 15.0 0.99458 3.32 0.80 11.900000
## 1040 34.0 64.0 0.99724 3.41 0.68 10.400000
## 1041 6.0 13.0 0.99664 3.59 0.61 10.000000
## 1042 14.0 39.0 0.99522 3.34 0.53 10.800000
## 1043 34.0 53.0 0.99580 3.41 0.67 9.700000
## 1044 31.0 54.0 0.99728 3.30 0.65 10.000000
## 1045 19.0 32.0 0.99648 3.52 0.57 11.000000
## 1046 31.0 54.0 0.99728 3.30 0.65 10.000000
## 1047 20.0 49.0 0.99705 3.31 0.55 9.700000
## 1048 8.0 24.0 0.99578 2.88 0.47 9.700000
## 1049 8.0 16.0 0.99334 3.43 0.52 12.600000
## 1050 23.0 44.0 0.99656 3.38 0.79 11.100000
## 1051 14.0 45.0 0.99336 3.38 0.54 11.000000
## 1052 48.0 82.0 1.00242 3.16 0.75 8.800000
## 1053 16.0 89.0 0.99182 3.54 0.88 13.566667
## 1054 48.0 82.0 1.00242 3.16 0.75 8.800000
## 1055 16.0 89.0 0.99182 3.54 0.88 13.600000
## 1056 3.0 14.0 0.99808 3.40 0.52 10.200000
## 1057 10.0 22.0 0.99828 3.39 0.68 10.600000
## 1058 6.0 13.0 0.99498 3.66 0.62 10.100000
## 1059 10.0 22.0 0.99828 3.39 0.68 10.600000
## 1060 6.0 12.0 0.99542 3.35 0.61 10.700000
## 1061 8.0 25.0 0.99606 3.37 0.55 9.700000
## 1062 9.0 23.0 0.99546 3.38 0.60 10.300000
## 1063 9.0 17.0 0.99496 3.52 0.78 10.600000
## 1064 5.0 13.0 0.99420 3.72 0.58 11.400000
## 1065 8.0 18.0 0.99344 3.39 0.56 12.400000
## 1066 6.0 18.0 0.99348 3.57 0.54 11.950000
## 1067 23.0 147.0 0.99636 3.26 0.59 9.700000
## 1068 15.0 28.0 0.99459 3.42 0.70 10.000000
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## 1143 32.0 44.0 0.99547 3.57 0.71 10.200000
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## 921 6 1297
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## 923 3 1299
## 924 6 1300
## 925 6 1301
## 926 6 1302
## 927 5 1304
## 928 5 1305
## 929 5 1306
## 930 5 1309
## 931 6 1311
## 932 5 1312
## 933 6 1315
## 934 6 1316
## 935 6 1319
## 936 6 1321
## 937 5 1322
## 938 6 1324
## 939 6 1325
## 940 6 1326
## 941 6 1327
## 942 5 1328
## 943 6 1329
## 944 6 1330
## 945 5 1331
## 946 6 1332
## 947 5 1333
## 948 5 1334
## 949 6 1335
## 950 5 1336
## 951 5 1337
## 952 5 1338
## 953 6 1339
## 954 6 1340
## 955 6 1341
## 956 6 1343
## 957 5 1344
## 958 6 1345
## 959 5 1346
## 960 5 1347
## 961 6 1351
## 962 5 1353
## 963 5 1355
## 964 5 1356
## 965 6 1357
## 966 5 1358
## 967 6 1359
## 968 5 1361
## 969 6 1362
## 970 6 1364
## 971 5 1366
## 972 6 1367
## 973 6 1368
## 974 5 1370
## 975 6 1371
## 976 5 1372
## 977 5 1376
## 978 6 1378
## 979 6 1379
## 980 6 1380
## 981 5 1382
## 982 5 1383
## 983 5 1384
## 984 5 1385
## 985 5 1386
## 986 5 1388
## 987 6 1390
## 988 5 1391
## 989 5 1392
## 990 5 1393
## 991 6 1395
## 992 5 1396
## 993 5 1397
## 994 6 1399
## 995 5 1400
## 996 6 1402
## 997 8 1403
## 998 6 1404
## 999 6 1406
## 1000 6 1407
## 1001 7 1408
## 1002 6 1409
## 1003 6 1410
## 1004 6 1411
## 1005 6 1412
## 1006 5 1413
## 1007 5 1414
## 1008 5 1415
## 1009 5 1416
## 1010 7 1417
## 1011 5 1418
## 1012 5 1419
## 1013 5 1420
## 1014 5 1421
## 1015 6 1422
## 1016 4 1423
## 1017 5 1427
## 1018 5 1428
## 1019 5 1429
## 1020 5 1430
## 1021 6 1431
## 1022 7 1433
## 1023 6 1434
## 1024 5 1436
## 1025 5 1437
## 1026 5 1438
## 1027 6 1439
## 1028 5 1443
## 1029 6 1445
## 1030 5 1447
## 1031 8 1449
## 1032 7 1450
## 1033 7 1451
## 1034 7 1452
## 1035 6 1454
## 1036 6 1455
## 1037 5 1457
## 1038 5 1458
## 1039 7 1459
## 1040 6 1460
## 1041 4 1461
## 1042 6 1463
## 1043 5 1464
## 1044 7 1466
## 1045 4 1467
## 1046 7 1468
## 1047 3 1469
## 1048 5 1470
## 1049 5 1471
## 1050 6 1472
## 1051 5 1473
## 1052 5 1474
## 1053 7 1475
## 1054 5 1476
## 1055 7 1477
## 1056 3 1478
## 1057 5 1479
## 1058 4 1480
## 1059 5 1481
## 1060 5 1483
## 1061 5 1485
## 1062 5 1486
## 1063 5 1487
## 1064 5 1488
## 1065 6 1490
## 1066 5 1492
## 1067 5 1493
## 1068 7 1494
## 1069 6 1495
## 1070 6 1497
## 1071 6 1499
## 1072 5 1501
## 1073 6 1503
## 1074 6 1504
## 1075 3 1505
## 1076 6 1506
## 1077 6 1507
## 1078 6 1508
## 1079 5 1509
## 1080 6 1510
## 1081 5 1511
## 1082 6 1514
## 1083 6 1515
## 1084 6 1517
## 1085 5 1518
## 1086 5 1519
## 1087 5 1522
## 1088 5 1523
## 1089 5 1525
## 1090 6 1526
## 1091 6 1528
## 1092 6 1529
## 1093 6 1530
## 1094 5 1531
## 1095 6 1532
## 1096 5 1533
## 1097 7 1534
## 1098 6 1535
## 1099 5 1538
## 1100 6 1540
## 1101 7 1544
## 1102 6 1545
## 1103 5 1546
## 1104 5 1548
## 1105 8 1549
## 1106 5 1551
## 1107 6 1552
## 1108 5 1553
## 1109 7 1555
## 1110 5 1556
## 1111 6 1557
## 1112 5 1558
## 1113 5 1559
## 1114 5 1560
## 1115 5 1561
## 1116 5 1562
## 1117 5 1563
## 1118 6 1565
## 1119 6 1566
## 1120 5 1567
## 1121 5 1568
## 1122 6 1569
## 1123 6 1570
## 1124 6 1571
## 1125 5 1572
## 1126 6 1573
## 1127 6 1575
## 1128 6 1576
## 1129 6 1577
## 1130 6 1578
## 1131 6 1580
## 1132 5 1582
## 1133 5 1583
## 1134 7 1584
## 1135 6 1586
## 1136 6 1587
## 1137 6 1590
## 1138 6 1591
## 1139 6 1592
## 1140 6 1593
## 1141 5 1594
## 1142 6 1595
## 1143 5 15974. Preprocessing Data
4.1 Pembersihan Data
str(data)## 'data.frame': 1143 obs. of 13 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 6.7 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.58 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.08 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 1.8 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.097 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 15 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 65 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.28 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.54 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 9.2 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...
## $ Id : int 0 1 2 3 4 5 6 7 8 10 ...sum(is.na(data))## [1] 0summary(data)## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 4.600 Min. :0.1200 Min. :0.0000 Min. : 0.900
## 1st Qu.: 7.100 1st Qu.:0.3925 1st Qu.:0.0900 1st Qu.: 1.900
## Median : 7.900 Median :0.5200 Median :0.2500 Median : 2.200
## Mean : 8.311 Mean :0.5313 Mean :0.2684 Mean : 2.532
## 3rd Qu.: 9.100 3rd Qu.:0.6400 3rd Qu.:0.4200 3rd Qu.: 2.600
## Max. :15.900 Max. :1.5800 Max. :1.0000 Max. :15.500
## chlorides free.sulfur.dioxide total.sulfur.dioxide density
## Min. :0.01200 Min. : 1.00 Min. : 6.00 Min. :0.9901
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 21.00 1st Qu.:0.9956
## Median :0.07900 Median :13.00 Median : 37.00 Median :0.9967
## Mean :0.08693 Mean :15.62 Mean : 45.91 Mean :0.9967
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 61.00 3rd Qu.:0.9978
## Max. :0.61100 Max. :68.00 Max. :289.00 Max. :1.0037
## pH sulphates alcohol quality
## Min. :2.740 Min. :0.3300 Min. : 8.40 Min. :3.000
## 1st Qu.:3.205 1st Qu.:0.5500 1st Qu.: 9.50 1st Qu.:5.000
## Median :3.310 Median :0.6200 Median :10.20 Median :6.000
## Mean :3.311 Mean :0.6577 Mean :10.44 Mean :5.657
## 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :4.010 Max. :2.0000 Max. :14.90 Max. :8.000
## Id
## Min. : 0
## 1st Qu.: 411
## Median : 794
## Mean : 805
## 3rd Qu.:1210
## Max. :1597colSums(is.na(data))## fixed.acidity volatile.acidity citric.acid
## 0 0 0
## residual.sugar chlorides free.sulfur.dioxide
## 0 0 0
## total.sulfur.dioxide density pH
## 0 0 0
## sulphates alcohol quality
## 0 0 0
## Id
## 04.2 Handling Outlier
detect_outliers <- function(x) {
Q1 <- quantile(x, 0.25)
Q3 <- quantile(x, 0.75)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
outliers <- x[x < lower_bound | x > upper_bound]
return(length(outliers))
}
# hitung jumlah outlier tiap kolom
outlier_count <- sapply(data, detect_outliers)
outlier_count## fixed.acidity volatile.acidity citric.acid
## 44 14 1
## residual.sugar chlorides free.sulfur.dioxide
## 110 77 18
## total.sulfur.dioxide density pH
## 40 36 20
## sulphates alcohol quality
## 43 12 22
## Id
## 0boxplot(data, main="Boxplot Deteksi Outlier", las=2)
data %>%
pivot_longer(cols = everything()) %>%
ggplot(aes(value)) +
geom_histogram(bins = 30, fill = "skyblue", color = "black") +
facet_wrap(~name, scales = "free")
data <- data %>%
select(-Id, -quality)str(data)## 'data.frame': 1143 obs. of 11 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 6.7 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.58 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.08 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 1.8 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.097 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 15 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 65 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.28 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.54 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 9.2 ...handle_outliers <- function(x) {
Q1 <- quantile(x, 0.25)
Q3 <- quantile(x, 0.75)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
x[x < lower_bound] <- lower_bound
x[x > upper_bound] <- upper_bound
return(x)
}
handle_outlier <- as.data.frame(lapply(data, handle_outliers))4.3 Scaling Data
Scaling dilakukan untuk menyamakan skala antar variabel agar tidak ada variabel yang mendominasi dalam proses clustering.
outlier_after <- sapply(handle_outlier, detect_outliers)
outlier_after## fixed.acidity volatile.acidity citric.acid
## 0 0 0
## residual.sugar chlorides free.sulfur.dioxide
## 0 0 0
## total.sulfur.dioxide density pH
## 0 0 0
## sulphates alcohol
## 0 0par(mfrow=c(3,4), mar=c(2,2,2,1))
for(i in 1:ncol(handle_outlier)){
hist(handle_outlier[[i]],
main=names(handle_outlier)[i],
col="pink")
}
cor_matrix <- cor(handle_outlier)
corrplot(cor_matrix, method="color", tl.cex=0.7)
scaled_data <- scale(handle_outlier)
scaled_data <- as.data.frame(scaled_data)summary(scaled_data)## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. :-2.2512 Min. :-2.35197 Min. :-1.36586 Min. :-2.3346
## 1st Qu.:-0.7177 1st Qu.:-0.78833 1st Qu.:-0.90767 1st Qu.:-0.6941
## Median :-0.2270 Median :-0.05672 Median :-0.09311 Median :-0.2020
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.5091 3rd Qu.: 0.63186 3rd Qu.: 0.77236 3rd Qu.: 0.4542
## Max. : 2.3494 Max. : 2.76214 Max. : 3.29241 Max. : 2.1766
## chlorides free.sulfur.dioxide total.sulfur.dioxide density
## Min. :-2.3053 Min. :-1.4871 Min. :-1.3053 Min. :-2.47812
## 1st Qu.:-0.6154 1st Qu.:-0.8702 1st Qu.:-0.8049 1st Qu.:-0.62660
## Median :-0.1085 Median :-0.2533 Median :-0.2711 Median :-0.02435
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.5111 3rd Qu.: 0.5693 3rd Qu.: 0.5295 3rd Qu.: 0.60774
## Max. : 2.2010 Max. : 2.7285 Max. : 2.5312 Max. : 2.45926
## pH sulphates alcohol
## Min. :-2.6195159 Min. :-2.3374 Min. :-1.9070
## 1st Qu.:-0.6924566 1st Qu.:-0.7254 1st Qu.:-0.8776
## Median :-0.0006917 Median :-0.2125 Median :-0.2226
## Mean : 0.0000000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.5922497 3rd Qu.: 0.5936 3rd Qu.: 0.6196
## Max. : 2.5193090 Max. : 2.5720 Max. : 2.8655par(mfrow=c(3,4), mar=c(2,2,2,1))
for(i in 1:ncol(scaled_data)){
hist(scaled_data[[i]],
main = names(scaled_data)[i],
col = "lightblue",
border = "white")
}
# Menggunakan Silhouette Method
fviz_nbclust(scaled_data, kmeans, method = "silhouette") +
labs(subtitle = "Silhouette Method")
k_optimal <- 25. Metode Clustering
5.1 K-Means
Metode K-Means digunakan untuk mengelompokkan data dengan membagi data ke dalam beberapa cluster berdasarkan titik pusat (centroid).
set.seed(123)
kmeans_result <- kmeans(scaled_data, centers = k_optimal, nstart = 25)
# lihat hasil cluster
kmeans_result$cluster## [1] 1 1 1 2 1 1 1 1 1 1 1 2 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 2 2 1 2 2 1 1 2 1 1
## [38] 2 1 2 1 1 2 2 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 1
## [75] 1 2 1 2 1 2 2 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 2 2 2 1 1
## [112] 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 2 1 1 1 1 2 1 2 1 2 2 2
## [149] 2 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 2 1 1 2 2 1 2 2 1 1 1 2 1 2 1 1 2 1 2 2 1
## [186] 1 1 1 2 1 2 1 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2 1 2 1 2
## [223] 2 1 2 2 1 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 2 2 2 1 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 1 2 1 1 1
## [297] 2 1 1 1 2 2 2 1 2 2 2 1 2 1 2 2 1 2 2 1 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2
## [408] 2 1 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 1 1 2 2 2 1 2 2 1 2 2 2 2 2 2 1 1 1
## [445] 2 1 2 2 1 2 1 1 1 1 2 2 2 2 1 2 1 2 2 2 2 2 2 2 1 1 1 1 1 2 2 2 1 1 2 1 1
## [482] 2 1 1 1 1 1 1 2 1 2 2 2 1 1 2 2 2 1 1 2 1 1 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1
## [519] 1 1 2 1 1 1 1 1 1 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 1 2 2 1 1 1 1 2 1 1 1 1 2
## [556] 2 1 1 2 1 1 1 1 2 2 2 2 2 1 1 1 2 2 2 2 2 1 1 1 1 2 1 1 1 2 1 2 2 1 1 1 1
## [593] 1 1 1 1 2 1 1 2 1 2 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 2 1 1 2 2 1 1 1 2 1
## [630] 2 1 1 1 2 1 2 2 1 1 1 1 1 1 2 2 2 2 1 2 2 1 2 1 2 2 2 1 2 2 1 1 1 2 1 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 1 2 2 2 1 2 1 2 1 1 2 1 1 1 2 1 1 1 1 1 1 1
## [704] 1 1 2 2 2 2 2 1 1 1 2 1 1 2 2 1 2 1 1 1 1 1 1 1 2 1 2 2 1 1 1 1 1 2 2 2 1
## [741] 2 2 2 2 2 2 2 2 2 2 1 1 1 2 1 2 1 1 2 2 2 2 1 2 1 2 1 2 2 2 2 1 2 1 1 2 2
## [778] 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 2 1 2 2 1 1 2 1 1 1 2 1 2
## [815] 1 2 1 1 2 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1
## [852] 2 2 2 2 2 1 1 1 2 1 2 2 2 2 2 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 2 1 2 1 1 1 2 1 1 1 1 1 1 1 2 1 1 2 1 1 1 2 1 2 2 2 1 1 1 1 1 1 1 1 1
## [926] 2 1 1 2 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## [963] 1 1 1 1 2 1 2 1 1 2 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2
## [1000] 1 2 1 1 1 2 2 2 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 2 1 2 1
## [1037] 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1074] 2 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1
## [1111] 1 2 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1data_clustered <- scaled_data
data_clustered <- as.data.frame(data_clustered)
data_clustered$cluster_kmeans <- as.factor(kmeans_result$cluster)
head(data_clustered)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 -0.5336665 0.9761464 -1.365864 -0.69412924 -0.2774561
## 2 -0.2883001 2.0090089 -1.365864 0.45416561 0.9617820
## 3 -0.2883001 1.3204339 -1.162223 -0.03796075 0.6238080
## 4 1.7973142 -1.4338660 1.485099 -0.69412924 -0.3337851
## 5 -0.5336665 0.9761464 -1.365864 -0.69412924 -0.2774561
## 6 -0.5336665 0.7466214 -1.365864 -0.85817137 -0.3337851
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates
## 1 -0.45890593 -0.3712223 0.58332538 1.3169558 -0.652099841
## 2 0.98055553 0.7297042 0.04075673 -0.7253978 0.227196405
## 3 -0.04763122 0.2960059 0.14927046 -0.3301035 0.007372344
## 4 0.15800613 0.4961744 0.69183911 -0.9889273 -0.505550466
## 5 -0.45890593 -0.3712223 0.58332538 1.3169558 -0.652099841
## 6 -0.25326858 -0.1710538 0.58332538 1.3169558 -0.652099841
## alcohol cluster_kmeans
## 1 -0.9711883 1
## 2 -0.5968818 1
## 3 -0.5968818 1
## 4 -0.5968818 2
## 5 -0.9711883 1
## 6 -0.9711883 1fviz_cluster(kmeans_result, data = scaled_data,
palette = c("lightblue", "pink"),
geom = "point",
ellipse.type = "convex",
main = "Visualisasi K-Means")
sil_kmeans <- silhouette(kmeans_result$cluster, dist(scaled_data))
fviz_silhouette(sil_kmeans,
palette = c("lightblue", "pink"),
ggtheme = theme_minimal(),
main = "Silhouette Plot K-Means")## cluster size ave.sil.width
## 1 1 662 0.24
## 2 2 481 0.12
mean(sil_kmeans[, 3])## [1] 0.18700315.2 K-Median
Metode K-Median mirip dengan K-Means, namun menggunakan median sebagai pusat cluster sehingga lebih tahan terhadap outlier.
kmedian_result <- kcca(scaled_data, k = k_optimal, family = kccaFamily("kmedians"))
cluster_kmedian <- clusters(kmedian_result)
# tampilkan hasil
cluster_kmedian## [1] 2 2 2 1 2 2 2 2 2 2 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 1 2 2
## [38] 1 2 1 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2
## [75] 2 1 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 1 2 2
## [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 1 2 2 2 2 1 2 1 2 1 1 1
## [149] 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 1 2 1 1 2 2 2 1 2 1 2 2 1 2 1 1 2
## [186] 2 2 2 1 2 1 2 1 2 2 2 1 1 1 1 1 1 2 2 1 2 2 1 2 1 2 1 1 1 2 2 2 1 2 2 2 1
## [223] 1 2 1 2 2 1 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 1 2 2 1 1 1 1 2 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 2 1 2 2 1 1 2 1 1 1 1 2 1 1 2 1 2 1 2 2 2
## [297] 1 2 2 1 1 1 1 2 1 1 1 2 1 2 1 1 2 1 1 2 1 1 2 1 2 1 1 2 1 1 2 1 1 1 1 2 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 2
## [408] 1 2 1 1 1 1 1 1 1 1 1 2 2 1 2 1 1 1 1 2 2 2 1 1 2 1 1 2 2 1 1 2 2 1 2 2 2
## [445] 1 2 1 1 2 1 2 2 2 2 1 1 1 1 2 1 2 1 1 1 1 2 1 1 2 2 2 2 2 1 1 1 2 2 1 2 2
## [482] 1 2 2 2 2 2 2 1 2 1 1 1 2 2 2 1 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 1 2 1 2 2
## [519] 2 2 1 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2
## [556] 1 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 1 1 2 2 2 2 1 2 1 2 1 2 1 2 2 2 2 1
## [593] 1 2 2 2 1 2 2 1 2 1 2 2 2 2 1 1 1 1 1 1 1 2 1 2 2 2 2 1 2 2 1 2 1 1 2 1 2
## [630] 1 2 2 2 1 2 1 1 2 2 2 2 2 2 1 1 1 1 2 2 1 2 1 2 1 1 1 2 1 1 2 2 2 1 2 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 2 1 1 1 2 2 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 1
## [704] 2 2 1 1 1 1 1 2 2 2 1 2 2 1 1 2 1 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 2
## [741] 1 2 1 2 1 1 1 1 1 1 2 2 2 1 2 1 2 2 1 1 1 1 2 1 2 1 1 1 1 1 1 2 1 2 2 1 1
## [778] 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 1 2 1 1 2 2 1 2 1 2 2 2 1 2 2 2 1 2 1
## [815] 1 1 2 2 1 2 1 2 1 2 1 1 2 1 1 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2 2 1 1
## [852] 2 2 2 1 2 1 2 2 1 2 1 1 1 1 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [889] 2 2 1 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2
## [926] 1 2 2 1 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2
## [963] 2 2 2 2 1 2 1 2 2 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
## [1000] 2 1 2 2 1 1 1 1 2 1 1 2 2 2 2 2 2 1 2 1 2 2 2 1 1 2 2 2 2 2 2 1 1 1 2 1 2
## [1037] 2 1 1 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1
## [1074] 1 2 2 1 1 1 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 1 2 2 2 2 2
## [1111] 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 2 1 2 2 1 1 2 2 2 2 2 2 2 2fviz_cluster(list(data = scaled_data, cluster = cluster_kmedian),
palette = c("lightblue", "pink"),
geom = "point",
ellipse.type = "convex",
main = "Visualisasi K-Median")
sil_kmedian <- silhouette(cluster_kmedian, dist(scaled_data))
fviz_silhouette(sil_kmedian,
palette = c("lightblue", "pink"),
ggtheme = theme_minimal(),
main = "Silhouette Plot K-Median")## cluster size ave.sil.width
## 1 1 472 0.13
## 2 2 671 0.22
mean(sil_kmedian[, 3])## [1] 0.1803081comparison <- data.frame(
Method = c("K-Means", "K-Median"),
Avg_Silhouette = c(mean(sil_kmeans[, 3]),
mean(sil_kmedian[, 3]))
)
comparison## Method Avg_Silhouette
## 1 K-Means 0.1870031
## 2 K-Median 0.18030815.3 DBSCAN
DBSCAN merupakan metode clustering berbasis densitas yang mampu mendeteksi noise atau data outlier secara otomatis.
dbscan::kNNdistplot(scaled_data, k = 5)
abline(h = 1.5, col = "red", lty = 2)
set.seed(123)
db_20 <- dbscan(scaled_data, eps = 2.0, minPts = 5)
db_21 <- dbscan(scaled_data, eps = 2.1, minPts = 5)
db_22 <- dbscan(scaled_data, eps = 2.2, minPts = 5)
print(db_20)## DBSCAN clustering for 1143 objects.
## Parameters: eps = 2, minPts = 5
## Using euclidean distances and borderpoints = TRUE
## The clustering contains 3 cluster(s) and 127 noise points.
##
## 0 1 2 3
## 127 998 13 5
##
## Available fields: cluster, eps, minPts, metric, borderPointsprint(db_21)## DBSCAN clustering for 1143 objects.
## Parameters: eps = 2.1, minPts = 5
## Using euclidean distances and borderpoints = TRUE
## The clustering contains 3 cluster(s) and 84 noise points.
##
## 0 1 2 3
## 84 1037 16 6
##
## Available fields: cluster, eps, minPts, metric, borderPointsprint(db_22)## DBSCAN clustering for 1143 objects.
## Parameters: eps = 2.2, minPts = 5
## Using euclidean distances and borderpoints = TRUE
## The clustering contains 1 cluster(s) and 64 noise points.
##
## 0 1
## 64 1079
##
## Available fields: cluster, eps, minPts, metric, borderPointsdb_final <- db_20
# Melihat hasil
print(db_final)## DBSCAN clustering for 1143 objects.
## Parameters: eps = 2, minPts = 5
## Using euclidean distances and borderpoints = TRUE
## The clustering contains 3 cluster(s) and 127 noise points.
##
## 0 1 2 3
## 127 998 13 5
##
## Available fields: cluster, eps, minPts, metric, borderPoints# Visualisasi DBSCAN
fviz_cluster(db_final, data = scaled_data, stand = FALSE,
ellipse = FALSE, show.clust.cent = FALSE,
geom = "point", main = "DBSCAN Clustering") +
theme_minimal()
cluster_db <- db_final$cluster
# Buang noise (cluster 0)
non_noise_idx <- which(cluster_db != 0)
# Data tanpa noise
data_non_noise <- scaled_data[non_noise_idx, ]
cluster_non_noise <- cluster_db[non_noise_idx]
# Hitung silhouette
sil_db <- silhouette(cluster_non_noise, dist(data_non_noise))
# Visualisasi
fviz_silhouette(sil_db,
palette = c("lightblue", "pink", "purple"),
ggtheme = theme_minimal(),
main = "Silhouette Plot DBSCAN")## cluster size ave.sil.width
## 1 1 998 0.20
## 2 2 13 0.64
## 3 3 5 0.74
# Average silhouette
mean(sil_db[, 3])## [1] 0.2069663get_sil <- function(db, data){
library(cluster)
cl <- db$cluster
# ambil yang bukan noise
idx <- which(cl != 0)
# kalau cluster cuma 1, silhouette tidak bisa dihitung
if(length(unique(cl[idx])) < 2){
return(NA)
}
sil <- silhouette(cl[idx], dist(data[idx, ]))
return(mean(sil[, 3]))
}
get_sil(db_20, scaled_data)## [1] 0.2069663get_sil(db_21, scaled_data)## [1] 0.188681get_sil(db_22, scaled_data)## [1] NAbandwidth_values <- seq(0.8, 2.0, by = 0.1)
n_clusters <- c()
for (bw in bandwidth_values) {
ms <- meanShift(
as.matrix(scaled_data),
bandwidth = rep(bw, ncol(scaled_data))
)
n_clusters <- c(n_clusters, length(unique(ms$assignment)))
}
plot(bandwidth_values, n_clusters, type = "b",
xlab = "Bandwidth",
ylab = "Jumlah Cluster",
main = "Bandwidth vs Jumlah Cluster")
5.4 Mean Shift
bandwidth <- rep(1.6, ncol(scaled_data))
ms_final <- meanShift(
as.matrix(scaled_data),
bandwidth = bandwidth
)
# hasil cluster
ms_cluster <- ms_final$assignment
table(ms_cluster)## ms_cluster
## 1 2
## 1045 98fviz_cluster(
list(data = scaled_data, cluster = ms_final$assignment),
geom = "point",
palette = c("lightblue", "pink"),
main = "Mean Shift (Bandwidth = 1.6)"
)
# ambil cluster hasil Mean Shift
cluster_ms <- ms_final$assignment
# cek jumlah cluster unik
length(unique(cluster_ms))## [1] 2# hitung silhouette
sil_ms <- silhouette(cluster_ms, dist(scaled_data))
# visualisasi
fviz_silhouette(
sil_ms,
palette = c("#ADD8E6", "#FFB6C1"),
ggtheme = theme_minimal(),
main = "Silhouette Plot Mean Shift"
)## cluster size ave.sil.width
## 1 1 1045 0.21
## 2 2 98 0.33
# rata-rata silhouette
mean(sil_ms[, 3])## [1] 0.2215629Fuzzy C-Means
6.1 Konsep Fuzzy C-Means
Fuzzy C-Means merupakan metode clustering yang bersifat soft clustering, dimana setiap data dapat memiliki derajat keanggotaan pada lebih dari satu cluster. Hal ini berbeda dengan metode seperti K-Means yang hanya mengelompokkan data ke dalam satu cluster saja.
# Ambil data numerik saja (tanpa quality jika dianggap label)
data_fcm <- scaled_data6.2 Implementasi Fuzzy C-Means
set.seed(123)
# Kandidat parameter
k_values <- 2:6
m_values <- c(1.5, 2, 2.5)
best_sil <- -1
best_model <- NULL
best_cluster <- NULL
for(k in k_values){
for(m in m_values){
model <- cmeans(data_fcm, centers = k, m = m, iter.max = 200)
cluster <- model$cluster
if(length(unique(cluster)) > 1){
sil <- silhouette(cluster, dist(data_fcm))
avg_sil <- mean(sil[,3])
if(avg_sil > best_sil){
best_sil <- avg_sil
best_model <- model
best_cluster <- cluster
best_k <- k
best_m <- m
}
}
}
}
best_sil## [1] 0.1830426best_k## [1] 2best_m## [1] 1.56.3 Hasil Clustering
fcm_cluster <- best_cluster
membership <- best_model$membership
head(fcm_cluster)## [1] 1 1 1 2 1 1head(membership)## 1 2
## [1,] 0.9062826 0.09371744
## [2,] 0.6975809 0.30241908
## [3,] 0.8396916 0.16030842
## [4,] 0.1180551 0.88194488
## [5,] 0.9062826 0.09371744
## [6,] 0.9143523 0.085647736.4 Analisis Membership
membership[1:5, ]## 1 2
## [1,] 0.9062826 0.09371744
## [2,] 0.6975809 0.30241908
## [3,] 0.8396916 0.16030842
## [4,] 0.1180551 0.88194488
## [5,] 0.9062826 0.093717446.5 Visualisasi Cluster
pca <- prcomp(data_fcm)
pca_data <- data.frame(pca$x[,1:2])
pca_data$cluster <- as.factor(fcm_cluster)
ggplot(pca_data, aes(PC1, PC2, color = cluster)) +
geom_point(size = 2) +
scale_color_manual(values = c("#ADD8E6", "#FFB6C1")) +
ggtitle("Visualisasi Fuzzy C-Means (Optimized)") +
theme_minimal()
7. Evaluasi Clustering
Evaluasi dilakukan menggunakan Silhouette Score untuk mengukur kualitas hasil clustering dari masing-masing metode.
7.1 Perhitungan Silhouette Score
# Fungsi silhouette umum
hitung_silhouette <- function(data, cluster){
if(length(unique(cluster)) < 2) return(NA)
sil <- silhouette(cluster, dist(data))
return(mean(sil[,3]))
}
# Fungsi khusus DBSCAN
hitung_silhouette_dbscan <- function(data, cluster){
idx <- which(cluster != 0)
if(length(unique(cluster[idx])) < 2) return(NA)
sil <- silhouette(cluster[idx], dist(data[idx,]))
return(mean(sil[,3]))
}
# Hitung semua metode
sil_kmeans <- hitung_silhouette(scaled_data, kmeans_result$cluster)
sil_kmedian <- hitung_silhouette(scaled_data, cluster_kmedian)
sil_dbscan <- hitung_silhouette_dbscan(scaled_data, db_final$cluster)
sil_meanshift <- hitung_silhouette(scaled_data, ms_final$assignment)
sil_fcm <- best_sil
# Tabel hasil
silhouette_result <- data.frame(
Metode = c("K-Means", "K-Median", "DBSCAN", "Mean Shift", "Fuzzy C-Means"),
Silhouette = c(sil_kmeans, sil_kmedian, sil_dbscan, sil_meanshift, sil_fcm)
)
silhouette_result## Metode Silhouette
## 1 K-Means 0.1870031
## 2 K-Median 0.1803081
## 3 DBSCAN 0.2069663
## 4 Mean Shift 0.2215629
## 5 Fuzzy C-Means 0.18304267.2 Perbandingan Metode Clustering
ggplot(silhouette_result, aes(x = Metode, y = Silhouette, fill = Metode)) +
geom_bar(stat = "identity") +
theme_minimal() +
ggtitle("Perbandingan Silhouette Score Antar Metode")