library(ISLR2)
#Question 1: Working with Boston Dataset:
boston <- Boston
library(magrittr)
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, union
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
library(corrr)
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
## The following object is masked from 'package:ISLR2':
##
## Boston
library(e1071)
library(class)
boston <- boston %>%
mutate(chas = factor(chas),
crime_factor = factor(ifelse(crim > median(crim),
'High', 'Low'),
levels = c('High', 'Low')))
kbl(boston, caption = "Boston data with classification by crime rate factor")%>%
row_spec(row =0, bold= TRUE, color = "black", background = "#F9EBEA") %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "center")
Boston data with classification by crime rate factor
|
crim
|
zn
|
indus
|
chas
|
nox
|
rm
|
age
|
dis
|
rad
|
tax
|
ptratio
|
lstat
|
medv
|
crime_factor
|
|
0.00632
|
18.0
|
2.31
|
0
|
0.5380
|
6.575
|
65.2
|
4.0900
|
1
|
296
|
15.3
|
4.98
|
24.0
|
Low
|
|
0.02731
|
0.0
|
7.07
|
0
|
0.4690
|
6.421
|
78.9
|
4.9671
|
2
|
242
|
17.8
|
9.14
|
21.6
|
Low
|
|
0.02729
|
0.0
|
7.07
|
0
|
0.4690
|
7.185
|
61.1
|
4.9671
|
2
|
242
|
17.8
|
4.03
|
34.7
|
Low
|
|
0.03237
|
0.0
|
2.18
|
0
|
0.4580
|
6.998
|
45.8
|
6.0622
|
3
|
222
|
18.7
|
2.94
|
33.4
|
Low
|
|
0.06905
|
0.0
|
2.18
|
0
|
0.4580
|
7.147
|
54.2
|
6.0622
|
3
|
222
|
18.7
|
5.33
|
36.2
|
Low
|
|
0.02985
|
0.0
|
2.18
|
0
|
0.4580
|
6.430
|
58.7
|
6.0622
|
3
|
222
|
18.7
|
5.21
|
28.7
|
Low
|
|
0.08829
|
12.5
|
7.87
|
0
|
0.5240
|
6.012
|
66.6
|
5.5605
|
5
|
311
|
15.2
|
12.43
|
22.9
|
Low
|
|
0.14455
|
12.5
|
7.87
|
0
|
0.5240
|
6.172
|
96.1
|
5.9505
|
5
|
311
|
15.2
|
19.15
|
27.1
|
Low
|
|
0.21124
|
12.5
|
7.87
|
0
|
0.5240
|
5.631
|
100.0
|
6.0821
|
5
|
311
|
15.2
|
29.93
|
16.5
|
Low
|
|
0.17004
|
12.5
|
7.87
|
0
|
0.5240
|
6.004
|
85.9
|
6.5921
|
5
|
311
|
15.2
|
17.10
|
18.9
|
Low
|
|
0.22489
|
12.5
|
7.87
|
0
|
0.5240
|
6.377
|
94.3
|
6.3467
|
5
|
311
|
15.2
|
20.45
|
15.0
|
Low
|
|
0.11747
|
12.5
|
7.87
|
0
|
0.5240
|
6.009
|
82.9
|
6.2267
|
5
|
311
|
15.2
|
13.27
|
18.9
|
Low
|
|
0.09378
|
12.5
|
7.87
|
0
|
0.5240
|
5.889
|
39.0
|
5.4509
|
5
|
311
|
15.2
|
15.71
|
21.7
|
Low
|
|
0.62976
|
0.0
|
8.14
|
0
|
0.5380
|
5.949
|
61.8
|
4.7075
|
4
|
307
|
21.0
|
8.26
|
20.4
|
High
|
|
0.63796
|
0.0
|
8.14
|
0
|
0.5380
|
6.096
|
84.5
|
4.4619
|
4
|
307
|
21.0
|
10.26
|
18.2
|
High
|
|
0.62739
|
0.0
|
8.14
|
0
|
0.5380
|
5.834
|
56.5
|
4.4986
|
4
|
307
|
21.0
|
8.47
|
19.9
|
High
|
|
1.05393
|
0.0
|
8.14
|
0
|
0.5380
|
5.935
|
29.3
|
4.4986
|
4
|
307
|
21.0
|
6.58
|
23.1
|
High
|
|
0.78420
|
0.0
|
8.14
|
0
|
0.5380
|
5.990
|
81.7
|
4.2579
|
4
|
307
|
21.0
|
14.67
|
17.5
|
High
|
|
0.80271
|
0.0
|
8.14
|
0
|
0.5380
|
5.456
|
36.6
|
3.7965
|
4
|
307
|
21.0
|
11.69
|
20.2
|
High
|
|
0.72580
|
0.0
|
8.14
|
0
|
0.5380
|
5.727
|
69.5
|
3.7965
|
4
|
307
|
21.0
|
11.28
|
18.2
|
High
|
|
1.25179
|
0.0
|
8.14
|
0
|
0.5380
|
5.570
|
98.1
|
3.7979
|
4
|
307
|
21.0
|
21.02
|
13.6
|
High
|
|
0.85204
|
0.0
|
8.14
|
0
|
0.5380
|
5.965
|
89.2
|
4.0123
|
4
|
307
|
21.0
|
13.83
|
19.6
|
High
|
|
1.23247
|
0.0
|
8.14
|
0
|
0.5380
|
6.142
|
91.7
|
3.9769
|
4
|
307
|
21.0
|
18.72
|
15.2
|
High
|
|
0.98843
|
0.0
|
8.14
|
0
|
0.5380
|
5.813
|
100.0
|
4.0952
|
4
|
307
|
21.0
|
19.88
|
14.5
|
High
|
|
0.75026
|
0.0
|
8.14
|
0
|
0.5380
|
5.924
|
94.1
|
4.3996
|
4
|
307
|
21.0
|
16.30
|
15.6
|
High
|
|
0.84054
|
0.0
|
8.14
|
0
|
0.5380
|
5.599
|
85.7
|
4.4546
|
4
|
307
|
21.0
|
16.51
|
13.9
|
High
|
|
0.67191
|
0.0
|
8.14
|
0
|
0.5380
|
5.813
|
90.3
|
4.6820
|
4
|
307
|
21.0
|
14.81
|
16.6
|
High
|
|
0.95577
|
0.0
|
8.14
|
0
|
0.5380
|
6.047
|
88.8
|
4.4534
|
4
|
307
|
21.0
|
17.28
|
14.8
|
High
|
|
0.77299
|
0.0
|
8.14
|
0
|
0.5380
|
6.495
|
94.4
|
4.4547
|
4
|
307
|
21.0
|
12.80
|
18.4
|
High
|
|
1.00245
|
0.0
|
8.14
|
0
|
0.5380
|
6.674
|
87.3
|
4.2390
|
4
|
307
|
21.0
|
11.98
|
21.0
|
High
|
|
1.13081
|
0.0
|
8.14
|
0
|
0.5380
|
5.713
|
94.1
|
4.2330
|
4
|
307
|
21.0
|
22.60
|
12.7
|
High
|
|
1.35472
|
0.0
|
8.14
|
0
|
0.5380
|
6.072
|
100.0
|
4.1750
|
4
|
307
|
21.0
|
13.04
|
14.5
|
High
|
|
1.38799
|
0.0
|
8.14
|
0
|
0.5380
|
5.950
|
82.0
|
3.9900
|
4
|
307
|
21.0
|
27.71
|
13.2
|
High
|
|
1.15172
|
0.0
|
8.14
|
0
|
0.5380
|
5.701
|
95.0
|
3.7872
|
4
|
307
|
21.0
|
18.35
|
13.1
|
High
|
|
1.61282
|
0.0
|
8.14
|
0
|
0.5380
|
6.096
|
96.9
|
3.7598
|
4
|
307
|
21.0
|
20.34
|
13.5
|
High
|
|
0.06417
|
0.0
|
5.96
|
0
|
0.4990
|
5.933
|
68.2
|
3.3603
|
5
|
279
|
19.2
|
9.68
|
18.9
|
Low
|
|
0.09744
|
0.0
|
5.96
|
0
|
0.4990
|
5.841
|
61.4
|
3.3779
|
5
|
279
|
19.2
|
11.41
|
20.0
|
Low
|
|
0.08014
|
0.0
|
5.96
|
0
|
0.4990
|
5.850
|
41.5
|
3.9342
|
5
|
279
|
19.2
|
8.77
|
21.0
|
Low
|
|
0.17505
|
0.0
|
5.96
|
0
|
0.4990
|
5.966
|
30.2
|
3.8473
|
5
|
279
|
19.2
|
10.13
|
24.7
|
Low
|
|
0.02763
|
75.0
|
2.95
|
0
|
0.4280
|
6.595
|
21.8
|
5.4011
|
3
|
252
|
18.3
|
4.32
|
30.8
|
Low
|
|
0.03359
|
75.0
|
2.95
|
0
|
0.4280
|
7.024
|
15.8
|
5.4011
|
3
|
252
|
18.3
|
1.98
|
34.9
|
Low
|
|
0.12744
|
0.0
|
6.91
|
0
|
0.4480
|
6.770
|
2.9
|
5.7209
|
3
|
233
|
17.9
|
4.84
|
26.6
|
Low
|
|
0.14150
|
0.0
|
6.91
|
0
|
0.4480
|
6.169
|
6.6
|
5.7209
|
3
|
233
|
17.9
|
5.81
|
25.3
|
Low
|
|
0.15936
|
0.0
|
6.91
|
0
|
0.4480
|
6.211
|
6.5
|
5.7209
|
3
|
233
|
17.9
|
7.44
|
24.7
|
Low
|
|
0.12269
|
0.0
|
6.91
|
0
|
0.4480
|
6.069
|
40.0
|
5.7209
|
3
|
233
|
17.9
|
9.55
|
21.2
|
Low
|
|
0.17142
|
0.0
|
6.91
|
0
|
0.4480
|
5.682
|
33.8
|
5.1004
|
3
|
233
|
17.9
|
10.21
|
19.3
|
Low
|
|
0.18836
|
0.0
|
6.91
|
0
|
0.4480
|
5.786
|
33.3
|
5.1004
|
3
|
233
|
17.9
|
14.15
|
20.0
|
Low
|
|
0.22927
|
0.0
|
6.91
|
0
|
0.4480
|
6.030
|
85.5
|
5.6894
|
3
|
233
|
17.9
|
18.80
|
16.6
|
Low
|
|
0.25387
|
0.0
|
6.91
|
0
|
0.4480
|
5.399
|
95.3
|
5.8700
|
3
|
233
|
17.9
|
30.81
|
14.4
|
Low
|
|
0.21977
|
0.0
|
6.91
|
0
|
0.4480
|
5.602
|
62.0
|
6.0877
|
3
|
233
|
17.9
|
16.20
|
19.4
|
Low
|
|
0.08873
|
21.0
|
5.64
|
0
|
0.4390
|
5.963
|
45.7
|
6.8147
|
4
|
243
|
16.8
|
13.45
|
19.7
|
Low
|
|
0.04337
|
21.0
|
5.64
|
0
|
0.4390
|
6.115
|
63.0
|
6.8147
|
4
|
243
|
16.8
|
9.43
|
20.5
|
Low
|
|
0.05360
|
21.0
|
5.64
|
0
|
0.4390
|
6.511
|
21.1
|
6.8147
|
4
|
243
|
16.8
|
5.28
|
25.0
|
Low
|
|
0.04981
|
21.0
|
5.64
|
0
|
0.4390
|
5.998
|
21.4
|
6.8147
|
4
|
243
|
16.8
|
8.43
|
23.4
|
Low
|
|
0.01360
|
75.0
|
4.00
|
0
|
0.4100
|
5.888
|
47.6
|
7.3197
|
3
|
469
|
21.1
|
14.80
|
18.9
|
Low
|
|
0.01311
|
90.0
|
1.22
|
0
|
0.4030
|
7.249
|
21.9
|
8.6966
|
5
|
226
|
17.9
|
4.81
|
35.4
|
Low
|
|
0.02055
|
85.0
|
0.74
|
0
|
0.4100
|
6.383
|
35.7
|
9.1876
|
2
|
313
|
17.3
|
5.77
|
24.7
|
Low
|
|
0.01432
|
100.0
|
1.32
|
0
|
0.4110
|
6.816
|
40.5
|
8.3248
|
5
|
256
|
15.1
|
3.95
|
31.6
|
Low
|
|
0.15445
|
25.0
|
5.13
|
0
|
0.4530
|
6.145
|
29.2
|
7.8148
|
8
|
284
|
19.7
|
6.86
|
23.3
|
Low
|
|
0.10328
|
25.0
|
5.13
|
0
|
0.4530
|
5.927
|
47.2
|
6.9320
|
8
|
284
|
19.7
|
9.22
|
19.6
|
Low
|
|
0.14932
|
25.0
|
5.13
|
0
|
0.4530
|
5.741
|
66.2
|
7.2254
|
8
|
284
|
19.7
|
13.15
|
18.7
|
Low
|
|
0.17171
|
25.0
|
5.13
|
0
|
0.4530
|
5.966
|
93.4
|
6.8185
|
8
|
284
|
19.7
|
14.44
|
16.0
|
Low
|
|
0.11027
|
25.0
|
5.13
|
0
|
0.4530
|
6.456
|
67.8
|
7.2255
|
8
|
284
|
19.7
|
6.73
|
22.2
|
Low
|
|
0.12650
|
25.0
|
5.13
|
0
|
0.4530
|
6.762
|
43.4
|
7.9809
|
8
|
284
|
19.7
|
9.50
|
25.0
|
Low
|
|
0.01951
|
17.5
|
1.38
|
0
|
0.4161
|
7.104
|
59.5
|
9.2229
|
3
|
216
|
18.6
|
8.05
|
33.0
|
Low
|
|
0.03584
|
80.0
|
3.37
|
0
|
0.3980
|
6.290
|
17.8
|
6.6115
|
4
|
337
|
16.1
|
4.67
|
23.5
|
Low
|
|
0.04379
|
80.0
|
3.37
|
0
|
0.3980
|
5.787
|
31.1
|
6.6115
|
4
|
337
|
16.1
|
10.24
|
19.4
|
Low
|
|
0.05789
|
12.5
|
6.07
|
0
|
0.4090
|
5.878
|
21.4
|
6.4980
|
4
|
345
|
18.9
|
8.10
|
22.0
|
Low
|
|
0.13554
|
12.5
|
6.07
|
0
|
0.4090
|
5.594
|
36.8
|
6.4980
|
4
|
345
|
18.9
|
13.09
|
17.4
|
Low
|
|
0.12816
|
12.5
|
6.07
|
0
|
0.4090
|
5.885
|
33.0
|
6.4980
|
4
|
345
|
18.9
|
8.79
|
20.9
|
Low
|
|
0.08826
|
0.0
|
10.81
|
0
|
0.4130
|
6.417
|
6.6
|
5.2873
|
4
|
305
|
19.2
|
6.72
|
24.2
|
Low
|
|
0.15876
|
0.0
|
10.81
|
0
|
0.4130
|
5.961
|
17.5
|
5.2873
|
4
|
305
|
19.2
|
9.88
|
21.7
|
Low
|
|
0.09164
|
0.0
|
10.81
|
0
|
0.4130
|
6.065
|
7.8
|
5.2873
|
4
|
305
|
19.2
|
5.52
|
22.8
|
Low
|
|
0.19539
|
0.0
|
10.81
|
0
|
0.4130
|
6.245
|
6.2
|
5.2873
|
4
|
305
|
19.2
|
7.54
|
23.4
|
Low
|
|
0.07896
|
0.0
|
12.83
|
0
|
0.4370
|
6.273
|
6.0
|
4.2515
|
5
|
398
|
18.7
|
6.78
|
24.1
|
Low
|
|
0.09512
|
0.0
|
12.83
|
0
|
0.4370
|
6.286
|
45.0
|
4.5026
|
5
|
398
|
18.7
|
8.94
|
21.4
|
Low
|
|
0.10153
|
0.0
|
12.83
|
0
|
0.4370
|
6.279
|
74.5
|
4.0522
|
5
|
398
|
18.7
|
11.97
|
20.0
|
Low
|
|
0.08707
|
0.0
|
12.83
|
0
|
0.4370
|
6.140
|
45.8
|
4.0905
|
5
|
398
|
18.7
|
10.27
|
20.8
|
Low
|
|
0.05646
|
0.0
|
12.83
|
0
|
0.4370
|
6.232
|
53.7
|
5.0141
|
5
|
398
|
18.7
|
12.34
|
21.2
|
Low
|
|
0.08387
|
0.0
|
12.83
|
0
|
0.4370
|
5.874
|
36.6
|
4.5026
|
5
|
398
|
18.7
|
9.10
|
20.3
|
Low
|
|
0.04113
|
25.0
|
4.86
|
0
|
0.4260
|
6.727
|
33.5
|
5.4007
|
4
|
281
|
19.0
|
5.29
|
28.0
|
Low
|
|
0.04462
|
25.0
|
4.86
|
0
|
0.4260
|
6.619
|
70.4
|
5.4007
|
4
|
281
|
19.0
|
7.22
|
23.9
|
Low
|
|
0.03659
|
25.0
|
4.86
|
0
|
0.4260
|
6.302
|
32.2
|
5.4007
|
4
|
281
|
19.0
|
6.72
|
24.8
|
Low
|
|
0.03551
|
25.0
|
4.86
|
0
|
0.4260
|
6.167
|
46.7
|
5.4007
|
4
|
281
|
19.0
|
7.51
|
22.9
|
Low
|
|
0.05059
|
0.0
|
4.49
|
0
|
0.4490
|
6.389
|
48.0
|
4.7794
|
3
|
247
|
18.5
|
9.62
|
23.9
|
Low
|
|
0.05735
|
0.0
|
4.49
|
0
|
0.4490
|
6.630
|
56.1
|
4.4377
|
3
|
247
|
18.5
|
6.53
|
26.6
|
Low
|
|
0.05188
|
0.0
|
4.49
|
0
|
0.4490
|
6.015
|
45.1
|
4.4272
|
3
|
247
|
18.5
|
12.86
|
22.5
|
Low
|
|
0.07151
|
0.0
|
4.49
|
0
|
0.4490
|
6.121
|
56.8
|
3.7476
|
3
|
247
|
18.5
|
8.44
|
22.2
|
Low
|
|
0.05660
|
0.0
|
3.41
|
0
|
0.4890
|
7.007
|
86.3
|
3.4217
|
2
|
270
|
17.8
|
5.50
|
23.6
|
Low
|
|
0.05302
|
0.0
|
3.41
|
0
|
0.4890
|
7.079
|
63.1
|
3.4145
|
2
|
270
|
17.8
|
5.70
|
28.7
|
Low
|
|
0.04684
|
0.0
|
3.41
|
0
|
0.4890
|
6.417
|
66.1
|
3.0923
|
2
|
270
|
17.8
|
8.81
|
22.6
|
Low
|
|
0.03932
|
0.0
|
3.41
|
0
|
0.4890
|
6.405
|
73.9
|
3.0921
|
2
|
270
|
17.8
|
8.20
|
22.0
|
Low
|
|
0.04203
|
28.0
|
15.04
|
0
|
0.4640
|
6.442
|
53.6
|
3.6659
|
4
|
270
|
18.2
|
8.16
|
22.9
|
Low
|
|
0.02875
|
28.0
|
15.04
|
0
|
0.4640
|
6.211
|
28.9
|
3.6659
|
4
|
270
|
18.2
|
6.21
|
25.0
|
Low
|
|
0.04294
|
28.0
|
15.04
|
0
|
0.4640
|
6.249
|
77.3
|
3.6150
|
4
|
270
|
18.2
|
10.59
|
20.6
|
Low
|
|
0.12204
|
0.0
|
2.89
|
0
|
0.4450
|
6.625
|
57.8
|
3.4952
|
2
|
276
|
18.0
|
6.65
|
28.4
|
Low
|
|
0.11504
|
0.0
|
2.89
|
0
|
0.4450
|
6.163
|
69.6
|
3.4952
|
2
|
276
|
18.0
|
11.34
|
21.4
|
Low
|
|
0.12083
|
0.0
|
2.89
|
0
|
0.4450
|
8.069
|
76.0
|
3.4952
|
2
|
276
|
18.0
|
4.21
|
38.7
|
Low
|
|
0.08187
|
0.0
|
2.89
|
0
|
0.4450
|
7.820
|
36.9
|
3.4952
|
2
|
276
|
18.0
|
3.57
|
43.8
|
Low
|
|
0.06860
|
0.0
|
2.89
|
0
|
0.4450
|
7.416
|
62.5
|
3.4952
|
2
|
276
|
18.0
|
6.19
|
33.2
|
Low
|
|
0.14866
|
0.0
|
8.56
|
0
|
0.5200
|
6.727
|
79.9
|
2.7778
|
5
|
384
|
20.9
|
9.42
|
27.5
|
Low
|
|
0.11432
|
0.0
|
8.56
|
0
|
0.5200
|
6.781
|
71.3
|
2.8561
|
5
|
384
|
20.9
|
7.67
|
26.5
|
Low
|
|
0.22876
|
0.0
|
8.56
|
0
|
0.5200
|
6.405
|
85.4
|
2.7147
|
5
|
384
|
20.9
|
10.63
|
18.6
|
Low
|
|
0.21161
|
0.0
|
8.56
|
0
|
0.5200
|
6.137
|
87.4
|
2.7147
|
5
|
384
|
20.9
|
13.44
|
19.3
|
Low
|
|
0.13960
|
0.0
|
8.56
|
0
|
0.5200
|
6.167
|
90.0
|
2.4210
|
5
|
384
|
20.9
|
12.33
|
20.1
|
Low
|
|
0.13262
|
0.0
|
8.56
|
0
|
0.5200
|
5.851
|
96.7
|
2.1069
|
5
|
384
|
20.9
|
16.47
|
19.5
|
Low
|
|
0.17120
|
0.0
|
8.56
|
0
|
0.5200
|
5.836
|
91.9
|
2.2110
|
5
|
384
|
20.9
|
18.66
|
19.5
|
Low
|
|
0.13117
|
0.0
|
8.56
|
0
|
0.5200
|
6.127
|
85.2
|
2.1224
|
5
|
384
|
20.9
|
14.09
|
20.4
|
Low
|
|
0.12802
|
0.0
|
8.56
|
0
|
0.5200
|
6.474
|
97.1
|
2.4329
|
5
|
384
|
20.9
|
12.27
|
19.8
|
Low
|
|
0.26363
|
0.0
|
8.56
|
0
|
0.5200
|
6.229
|
91.2
|
2.5451
|
5
|
384
|
20.9
|
15.55
|
19.4
|
High
|
|
0.10793
|
0.0
|
8.56
|
0
|
0.5200
|
6.195
|
54.4
|
2.7778
|
5
|
384
|
20.9
|
13.00
|
21.7
|
Low
|
|
0.10084
|
0.0
|
10.01
|
0
|
0.5470
|
6.715
|
81.6
|
2.6775
|
6
|
432
|
17.8
|
10.16
|
22.8
|
Low
|
|
0.12329
|
0.0
|
10.01
|
0
|
0.5470
|
5.913
|
92.9
|
2.3534
|
6
|
432
|
17.8
|
16.21
|
18.8
|
Low
|
|
0.22212
|
0.0
|
10.01
|
0
|
0.5470
|
6.092
|
95.4
|
2.5480
|
6
|
432
|
17.8
|
17.09
|
18.7
|
Low
|
|
0.14231
|
0.0
|
10.01
|
0
|
0.5470
|
6.254
|
84.2
|
2.2565
|
6
|
432
|
17.8
|
10.45
|
18.5
|
Low
|
|
0.17134
|
0.0
|
10.01
|
0
|
0.5470
|
5.928
|
88.2
|
2.4631
|
6
|
432
|
17.8
|
15.76
|
18.3
|
Low
|
|
0.13158
|
0.0
|
10.01
|
0
|
0.5470
|
6.176
|
72.5
|
2.7301
|
6
|
432
|
17.8
|
12.04
|
21.2
|
Low
|
|
0.15098
|
0.0
|
10.01
|
0
|
0.5470
|
6.021
|
82.6
|
2.7474
|
6
|
432
|
17.8
|
10.30
|
19.2
|
Low
|
|
0.13058
|
0.0
|
10.01
|
0
|
0.5470
|
5.872
|
73.1
|
2.4775
|
6
|
432
|
17.8
|
15.37
|
20.4
|
Low
|
|
0.14476
|
0.0
|
10.01
|
0
|
0.5470
|
5.731
|
65.2
|
2.7592
|
6
|
432
|
17.8
|
13.61
|
19.3
|
Low
|
|
0.06899
|
0.0
|
25.65
|
0
|
0.5810
|
5.870
|
69.7
|
2.2577
|
2
|
188
|
19.1
|
14.37
|
22.0
|
Low
|
|
0.07165
|
0.0
|
25.65
|
0
|
0.5810
|
6.004
|
84.1
|
2.1974
|
2
|
188
|
19.1
|
14.27
|
20.3
|
Low
|
|
0.09299
|
0.0
|
25.65
|
0
|
0.5810
|
5.961
|
92.9
|
2.0869
|
2
|
188
|
19.1
|
17.93
|
20.5
|
Low
|
|
0.15038
|
0.0
|
25.65
|
0
|
0.5810
|
5.856
|
97.0
|
1.9444
|
2
|
188
|
19.1
|
25.41
|
17.3
|
Low
|
|
0.09849
|
0.0
|
25.65
|
0
|
0.5810
|
5.879
|
95.8
|
2.0063
|
2
|
188
|
19.1
|
17.58
|
18.8
|
Low
|
|
0.16902
|
0.0
|
25.65
|
0
|
0.5810
|
5.986
|
88.4
|
1.9929
|
2
|
188
|
19.1
|
14.81
|
21.4
|
Low
|
|
0.38735
|
0.0
|
25.65
|
0
|
0.5810
|
5.613
|
95.6
|
1.7572
|
2
|
188
|
19.1
|
27.26
|
15.7
|
High
|
|
0.25915
|
0.0
|
21.89
|
0
|
0.6240
|
5.693
|
96.0
|
1.7883
|
4
|
437
|
21.2
|
17.19
|
16.2
|
High
|
|
0.32543
|
0.0
|
21.89
|
0
|
0.6240
|
6.431
|
98.8
|
1.8125
|
4
|
437
|
21.2
|
15.39
|
18.0
|
High
|
|
0.88125
|
0.0
|
21.89
|
0
|
0.6240
|
5.637
|
94.7
|
1.9799
|
4
|
437
|
21.2
|
18.34
|
14.3
|
High
|
|
0.34006
|
0.0
|
21.89
|
0
|
0.6240
|
6.458
|
98.9
|
2.1185
|
4
|
437
|
21.2
|
12.60
|
19.2
|
High
|
|
1.19294
|
0.0
|
21.89
|
0
|
0.6240
|
6.326
|
97.7
|
2.2710
|
4
|
437
|
21.2
|
12.26
|
19.6
|
High
|
|
0.59005
|
0.0
|
21.89
|
0
|
0.6240
|
6.372
|
97.9
|
2.3274
|
4
|
437
|
21.2
|
11.12
|
23.0
|
High
|
|
0.32982
|
0.0
|
21.89
|
0
|
0.6240
|
5.822
|
95.4
|
2.4699
|
4
|
437
|
21.2
|
15.03
|
18.4
|
High
|
|
0.97617
|
0.0
|
21.89
|
0
|
0.6240
|
5.757
|
98.4
|
2.3460
|
4
|
437
|
21.2
|
17.31
|
15.6
|
High
|
|
0.55778
|
0.0
|
21.89
|
0
|
0.6240
|
6.335
|
98.2
|
2.1107
|
4
|
437
|
21.2
|
16.96
|
18.1
|
High
|
|
0.32264
|
0.0
|
21.89
|
0
|
0.6240
|
5.942
|
93.5
|
1.9669
|
4
|
437
|
21.2
|
16.90
|
17.4
|
High
|
|
0.35233
|
0.0
|
21.89
|
0
|
0.6240
|
6.454
|
98.4
|
1.8498
|
4
|
437
|
21.2
|
14.59
|
17.1
|
High
|
|
0.24980
|
0.0
|
21.89
|
0
|
0.6240
|
5.857
|
98.2
|
1.6686
|
4
|
437
|
21.2
|
21.32
|
13.3
|
Low
|
|
0.54452
|
0.0
|
21.89
|
0
|
0.6240
|
6.151
|
97.9
|
1.6687
|
4
|
437
|
21.2
|
18.46
|
17.8
|
High
|
|
0.29090
|
0.0
|
21.89
|
0
|
0.6240
|
6.174
|
93.6
|
1.6119
|
4
|
437
|
21.2
|
24.16
|
14.0
|
High
|
|
1.62864
|
0.0
|
21.89
|
0
|
0.6240
|
5.019
|
100.0
|
1.4394
|
4
|
437
|
21.2
|
34.41
|
14.4
|
High
|
|
3.32105
|
0.0
|
19.58
|
1
|
0.8710
|
5.403
|
100.0
|
1.3216
|
5
|
403
|
14.7
|
26.82
|
13.4
|
High
|
|
4.09740
|
0.0
|
19.58
|
0
|
0.8710
|
5.468
|
100.0
|
1.4118
|
5
|
403
|
14.7
|
26.42
|
15.6
|
High
|
|
2.77974
|
0.0
|
19.58
|
0
|
0.8710
|
4.903
|
97.8
|
1.3459
|
5
|
403
|
14.7
|
29.29
|
11.8
|
High
|
|
2.37934
|
0.0
|
19.58
|
0
|
0.8710
|
6.130
|
100.0
|
1.4191
|
5
|
403
|
14.7
|
27.80
|
13.8
|
High
|
|
2.15505
|
0.0
|
19.58
|
0
|
0.8710
|
5.628
|
100.0
|
1.5166
|
5
|
403
|
14.7
|
16.65
|
15.6
|
High
|
|
2.36862
|
0.0
|
19.58
|
0
|
0.8710
|
4.926
|
95.7
|
1.4608
|
5
|
403
|
14.7
|
29.53
|
14.6
|
High
|
|
2.33099
|
0.0
|
19.58
|
0
|
0.8710
|
5.186
|
93.8
|
1.5296
|
5
|
403
|
14.7
|
28.32
|
17.8
|
High
|
|
2.73397
|
0.0
|
19.58
|
0
|
0.8710
|
5.597
|
94.9
|
1.5257
|
5
|
403
|
14.7
|
21.45
|
15.4
|
High
|
|
1.65660
|
0.0
|
19.58
|
0
|
0.8710
|
6.122
|
97.3
|
1.6180
|
5
|
403
|
14.7
|
14.10
|
21.5
|
High
|
|
1.49632
|
0.0
|
19.58
|
0
|
0.8710
|
5.404
|
100.0
|
1.5916
|
5
|
403
|
14.7
|
13.28
|
19.6
|
High
|
|
1.12658
|
0.0
|
19.58
|
1
|
0.8710
|
5.012
|
88.0
|
1.6102
|
5
|
403
|
14.7
|
12.12
|
15.3
|
High
|
|
2.14918
|
0.0
|
19.58
|
0
|
0.8710
|
5.709
|
98.5
|
1.6232
|
5
|
403
|
14.7
|
15.79
|
19.4
|
High
|
|
1.41385
|
0.0
|
19.58
|
1
|
0.8710
|
6.129
|
96.0
|
1.7494
|
5
|
403
|
14.7
|
15.12
|
17.0
|
High
|
|
3.53501
|
0.0
|
19.58
|
1
|
0.8710
|
6.152
|
82.6
|
1.7455
|
5
|
403
|
14.7
|
15.02
|
15.6
|
High
|
|
2.44668
|
0.0
|
19.58
|
0
|
0.8710
|
5.272
|
94.0
|
1.7364
|
5
|
403
|
14.7
|
16.14
|
13.1
|
High
|
|
1.22358
|
0.0
|
19.58
|
0
|
0.6050
|
6.943
|
97.4
|
1.8773
|
5
|
403
|
14.7
|
4.59
|
41.3
|
High
|
|
1.34284
|
0.0
|
19.58
|
0
|
0.6050
|
6.066
|
100.0
|
1.7573
|
5
|
403
|
14.7
|
6.43
|
24.3
|
High
|
|
1.42502
|
0.0
|
19.58
|
0
|
0.8710
|
6.510
|
100.0
|
1.7659
|
5
|
403
|
14.7
|
7.39
|
23.3
|
High
|
|
1.27346
|
0.0
|
19.58
|
1
|
0.6050
|
6.250
|
92.6
|
1.7984
|
5
|
403
|
14.7
|
5.50
|
27.0
|
High
|
|
1.46336
|
0.0
|
19.58
|
0
|
0.6050
|
7.489
|
90.8
|
1.9709
|
5
|
403
|
14.7
|
1.73
|
50.0
|
High
|
|
1.83377
|
0.0
|
19.58
|
1
|
0.6050
|
7.802
|
98.2
|
2.0407
|
5
|
403
|
14.7
|
1.92
|
50.0
|
High
|
|
1.51902
|
0.0
|
19.58
|
1
|
0.6050
|
8.375
|
93.9
|
2.1620
|
5
|
403
|
14.7
|
3.32
|
50.0
|
High
|
|
2.24236
|
0.0
|
19.58
|
0
|
0.6050
|
5.854
|
91.8
|
2.4220
|
5
|
403
|
14.7
|
11.64
|
22.7
|
High
|
|
2.92400
|
0.0
|
19.58
|
0
|
0.6050
|
6.101
|
93.0
|
2.2834
|
5
|
403
|
14.7
|
9.81
|
25.0
|
High
|
|
2.01019
|
0.0
|
19.58
|
0
|
0.6050
|
7.929
|
96.2
|
2.0459
|
5
|
403
|
14.7
|
3.70
|
50.0
|
High
|
|
1.80028
|
0.0
|
19.58
|
0
|
0.6050
|
5.877
|
79.2
|
2.4259
|
5
|
403
|
14.7
|
12.14
|
23.8
|
High
|
|
2.30040
|
0.0
|
19.58
|
0
|
0.6050
|
6.319
|
96.1
|
2.1000
|
5
|
403
|
14.7
|
11.10
|
23.8
|
High
|
|
2.44953
|
0.0
|
19.58
|
0
|
0.6050
|
6.402
|
95.2
|
2.2625
|
5
|
403
|
14.7
|
11.32
|
22.3
|
High
|
|
1.20742
|
0.0
|
19.58
|
0
|
0.6050
|
5.875
|
94.6
|
2.4259
|
5
|
403
|
14.7
|
14.43
|
17.4
|
High
|
|
2.31390
|
0.0
|
19.58
|
0
|
0.6050
|
5.880
|
97.3
|
2.3887
|
5
|
403
|
14.7
|
12.03
|
19.1
|
High
|
|
0.13914
|
0.0
|
4.05
|
0
|
0.5100
|
5.572
|
88.5
|
2.5961
|
5
|
296
|
16.6
|
14.69
|
23.1
|
Low
|
|
0.09178
|
0.0
|
4.05
|
0
|
0.5100
|
6.416
|
84.1
|
2.6463
|
5
|
296
|
16.6
|
9.04
|
23.6
|
Low
|
|
0.08447
|
0.0
|
4.05
|
0
|
0.5100
|
5.859
|
68.7
|
2.7019
|
5
|
296
|
16.6
|
9.64
|
22.6
|
Low
|
|
0.06664
|
0.0
|
4.05
|
0
|
0.5100
|
6.546
|
33.1
|
3.1323
|
5
|
296
|
16.6
|
5.33
|
29.4
|
Low
|
|
0.07022
|
0.0
|
4.05
|
0
|
0.5100
|
6.020
|
47.2
|
3.5549
|
5
|
296
|
16.6
|
10.11
|
23.2
|
Low
|
|
0.05425
|
0.0
|
4.05
|
0
|
0.5100
|
6.315
|
73.4
|
3.3175
|
5
|
296
|
16.6
|
6.29
|
24.6
|
Low
|
|
0.06642
|
0.0
|
4.05
|
0
|
0.5100
|
6.860
|
74.4
|
2.9153
|
5
|
296
|
16.6
|
6.92
|
29.9
|
Low
|
|
0.05780
|
0.0
|
2.46
|
0
|
0.4880
|
6.980
|
58.4
|
2.8290
|
3
|
193
|
17.8
|
5.04
|
37.2
|
Low
|
|
0.06588
|
0.0
|
2.46
|
0
|
0.4880
|
7.765
|
83.3
|
2.7410
|
3
|
193
|
17.8
|
7.56
|
39.8
|
Low
|
|
0.06888
|
0.0
|
2.46
|
0
|
0.4880
|
6.144
|
62.2
|
2.5979
|
3
|
193
|
17.8
|
9.45
|
36.2
|
Low
|
|
0.09103
|
0.0
|
2.46
|
0
|
0.4880
|
7.155
|
92.2
|
2.7006
|
3
|
193
|
17.8
|
4.82
|
37.9
|
Low
|
|
0.10008
|
0.0
|
2.46
|
0
|
0.4880
|
6.563
|
95.6
|
2.8470
|
3
|
193
|
17.8
|
5.68
|
32.5
|
Low
|
|
0.08308
|
0.0
|
2.46
|
0
|
0.4880
|
5.604
|
89.8
|
2.9879
|
3
|
193
|
17.8
|
13.98
|
26.4
|
Low
|
|
0.06047
|
0.0
|
2.46
|
0
|
0.4880
|
6.153
|
68.8
|
3.2797
|
3
|
193
|
17.8
|
13.15
|
29.6
|
Low
|
|
0.05602
|
0.0
|
2.46
|
0
|
0.4880
|
7.831
|
53.6
|
3.1992
|
3
|
193
|
17.8
|
4.45
|
50.0
|
Low
|
|
0.07875
|
45.0
|
3.44
|
0
|
0.4370
|
6.782
|
41.1
|
3.7886
|
5
|
398
|
15.2
|
6.68
|
32.0
|
Low
|
|
0.12579
|
45.0
|
3.44
|
0
|
0.4370
|
6.556
|
29.1
|
4.5667
|
5
|
398
|
15.2
|
4.56
|
29.8
|
Low
|
|
0.08370
|
45.0
|
3.44
|
0
|
0.4370
|
7.185
|
38.9
|
4.5667
|
5
|
398
|
15.2
|
5.39
|
34.9
|
Low
|
|
0.09068
|
45.0
|
3.44
|
0
|
0.4370
|
6.951
|
21.5
|
6.4798
|
5
|
398
|
15.2
|
5.10
|
37.0
|
Low
|
|
0.06911
|
45.0
|
3.44
|
0
|
0.4370
|
6.739
|
30.8
|
6.4798
|
5
|
398
|
15.2
|
4.69
|
30.5
|
Low
|
|
0.08664
|
45.0
|
3.44
|
0
|
0.4370
|
7.178
|
26.3
|
6.4798
|
5
|
398
|
15.2
|
2.87
|
36.4
|
Low
|
|
0.02187
|
60.0
|
2.93
|
0
|
0.4010
|
6.800
|
9.9
|
6.2196
|
1
|
265
|
15.6
|
5.03
|
31.1
|
Low
|
|
0.01439
|
60.0
|
2.93
|
0
|
0.4010
|
6.604
|
18.8
|
6.2196
|
1
|
265
|
15.6
|
4.38
|
29.1
|
Low
|
|
0.01381
|
80.0
|
0.46
|
0
|
0.4220
|
7.875
|
32.0
|
5.6484
|
4
|
255
|
14.4
|
2.97
|
50.0
|
Low
|
|
0.04011
|
80.0
|
1.52
|
0
|
0.4040
|
7.287
|
34.1
|
7.3090
|
2
|
329
|
12.6
|
4.08
|
33.3
|
Low
|
|
0.04666
|
80.0
|
1.52
|
0
|
0.4040
|
7.107
|
36.6
|
7.3090
|
2
|
329
|
12.6
|
8.61
|
30.3
|
Low
|
|
0.03768
|
80.0
|
1.52
|
0
|
0.4040
|
7.274
|
38.3
|
7.3090
|
2
|
329
|
12.6
|
6.62
|
34.6
|
Low
|
|
0.03150
|
95.0
|
1.47
|
0
|
0.4030
|
6.975
|
15.3
|
7.6534
|
3
|
402
|
17.0
|
4.56
|
34.9
|
Low
|
|
0.01778
|
95.0
|
1.47
|
0
|
0.4030
|
7.135
|
13.9
|
7.6534
|
3
|
402
|
17.0
|
4.45
|
32.9
|
Low
|
|
0.03445
|
82.5
|
2.03
|
0
|
0.4150
|
6.162
|
38.4
|
6.2700
|
2
|
348
|
14.7
|
7.43
|
24.1
|
Low
|
|
0.02177
|
82.5
|
2.03
|
0
|
0.4150
|
7.610
|
15.7
|
6.2700
|
2
|
348
|
14.7
|
3.11
|
42.3
|
Low
|
|
0.03510
|
95.0
|
2.68
|
0
|
0.4161
|
7.853
|
33.2
|
5.1180
|
4
|
224
|
14.7
|
3.81
|
48.5
|
Low
|
|
0.02009
|
95.0
|
2.68
|
0
|
0.4161
|
8.034
|
31.9
|
5.1180
|
4
|
224
|
14.7
|
2.88
|
50.0
|
Low
|
|
0.13642
|
0.0
|
10.59
|
0
|
0.4890
|
5.891
|
22.3
|
3.9454
|
4
|
277
|
18.6
|
10.87
|
22.6
|
Low
|
|
0.22969
|
0.0
|
10.59
|
0
|
0.4890
|
6.326
|
52.5
|
4.3549
|
4
|
277
|
18.6
|
10.97
|
24.4
|
Low
|
|
0.25199
|
0.0
|
10.59
|
0
|
0.4890
|
5.783
|
72.7
|
4.3549
|
4
|
277
|
18.6
|
18.06
|
22.5
|
Low
|
|
0.13587
|
0.0
|
10.59
|
1
|
0.4890
|
6.064
|
59.1
|
4.2392
|
4
|
277
|
18.6
|
14.66
|
24.4
|
Low
|
|
0.43571
|
0.0
|
10.59
|
1
|
0.4890
|
5.344
|
100.0
|
3.8750
|
4
|
277
|
18.6
|
23.09
|
20.0
|
High
|
|
0.17446
|
0.0
|
10.59
|
1
|
0.4890
|
5.960
|
92.1
|
3.8771
|
4
|
277
|
18.6
|
17.27
|
21.7
|
Low
|
|
0.37578
|
0.0
|
10.59
|
1
|
0.4890
|
5.404
|
88.6
|
3.6650
|
4
|
277
|
18.6
|
23.98
|
19.3
|
High
|
|
0.21719
|
0.0
|
10.59
|
1
|
0.4890
|
5.807
|
53.8
|
3.6526
|
4
|
277
|
18.6
|
16.03
|
22.4
|
Low
|
|
0.14052
|
0.0
|
10.59
|
0
|
0.4890
|
6.375
|
32.3
|
3.9454
|
4
|
277
|
18.6
|
9.38
|
28.1
|
Low
|
|
0.28955
|
0.0
|
10.59
|
0
|
0.4890
|
5.412
|
9.8
|
3.5875
|
4
|
277
|
18.6
|
29.55
|
23.7
|
High
|
|
0.19802
|
0.0
|
10.59
|
0
|
0.4890
|
6.182
|
42.4
|
3.9454
|
4
|
277
|
18.6
|
9.47
|
25.0
|
Low
|
|
0.04560
|
0.0
|
13.89
|
1
|
0.5500
|
5.888
|
56.0
|
3.1121
|
5
|
276
|
16.4
|
13.51
|
23.3
|
Low
|
|
0.07013
|
0.0
|
13.89
|
0
|
0.5500
|
6.642
|
85.1
|
3.4211
|
5
|
276
|
16.4
|
9.69
|
28.7
|
Low
|
|
0.11069
|
0.0
|
13.89
|
1
|
0.5500
|
5.951
|
93.8
|
2.8893
|
5
|
276
|
16.4
|
17.92
|
21.5
|
Low
|
|
0.11425
|
0.0
|
13.89
|
1
|
0.5500
|
6.373
|
92.4
|
3.3633
|
5
|
276
|
16.4
|
10.50
|
23.0
|
Low
|
|
0.35809
|
0.0
|
6.20
|
1
|
0.5070
|
6.951
|
88.5
|
2.8617
|
8
|
307
|
17.4
|
9.71
|
26.7
|
High
|
|
0.40771
|
0.0
|
6.20
|
1
|
0.5070
|
6.164
|
91.3
|
3.0480
|
8
|
307
|
17.4
|
21.46
|
21.7
|
High
|
|
0.62356
|
0.0
|
6.20
|
1
|
0.5070
|
6.879
|
77.7
|
3.2721
|
8
|
307
|
17.4
|
9.93
|
27.5
|
High
|
|
0.61470
|
0.0
|
6.20
|
0
|
0.5070
|
6.618
|
80.8
|
3.2721
|
8
|
307
|
17.4
|
7.60
|
30.1
|
High
|
|
0.31533
|
0.0
|
6.20
|
0
|
0.5040
|
8.266
|
78.3
|
2.8944
|
8
|
307
|
17.4
|
4.14
|
44.8
|
High
|
|
0.52693
|
0.0
|
6.20
|
0
|
0.5040
|
8.725
|
83.0
|
2.8944
|
8
|
307
|
17.4
|
4.63
|
50.0
|
High
|
|
0.38214
|
0.0
|
6.20
|
0
|
0.5040
|
8.040
|
86.5
|
3.2157
|
8
|
307
|
17.4
|
3.13
|
37.6
|
High
|
|
0.41238
|
0.0
|
6.20
|
0
|
0.5040
|
7.163
|
79.9
|
3.2157
|
8
|
307
|
17.4
|
6.36
|
31.6
|
High
|
|
0.29819
|
0.0
|
6.20
|
0
|
0.5040
|
7.686
|
17.0
|
3.3751
|
8
|
307
|
17.4
|
3.92
|
46.7
|
High
|
|
0.44178
|
0.0
|
6.20
|
0
|
0.5040
|
6.552
|
21.4
|
3.3751
|
8
|
307
|
17.4
|
3.76
|
31.5
|
High
|
|
0.53700
|
0.0
|
6.20
|
0
|
0.5040
|
5.981
|
68.1
|
3.6715
|
8
|
307
|
17.4
|
11.65
|
24.3
|
High
|
|
0.46296
|
0.0
|
6.20
|
0
|
0.5040
|
7.412
|
76.9
|
3.6715
|
8
|
307
|
17.4
|
5.25
|
31.7
|
High
|
|
0.57529
|
0.0
|
6.20
|
0
|
0.5070
|
8.337
|
73.3
|
3.8384
|
8
|
307
|
17.4
|
2.47
|
41.7
|
High
|
|
0.33147
|
0.0
|
6.20
|
0
|
0.5070
|
8.247
|
70.4
|
3.6519
|
8
|
307
|
17.4
|
3.95
|
48.3
|
High
|
|
0.44791
|
0.0
|
6.20
|
1
|
0.5070
|
6.726
|
66.5
|
3.6519
|
8
|
307
|
17.4
|
8.05
|
29.0
|
High
|
|
0.33045
|
0.0
|
6.20
|
0
|
0.5070
|
6.086
|
61.5
|
3.6519
|
8
|
307
|
17.4
|
10.88
|
24.0
|
High
|
|
0.52058
|
0.0
|
6.20
|
1
|
0.5070
|
6.631
|
76.5
|
4.1480
|
8
|
307
|
17.4
|
9.54
|
25.1
|
High
|
|
0.51183
|
0.0
|
6.20
|
0
|
0.5070
|
7.358
|
71.6
|
4.1480
|
8
|
307
|
17.4
|
4.73
|
31.5
|
High
|
|
0.08244
|
30.0
|
4.93
|
0
|
0.4280
|
6.481
|
18.5
|
6.1899
|
6
|
300
|
16.6
|
6.36
|
23.7
|
Low
|
|
0.09252
|
30.0
|
4.93
|
0
|
0.4280
|
6.606
|
42.2
|
6.1899
|
6
|
300
|
16.6
|
7.37
|
23.3
|
Low
|
|
0.11329
|
30.0
|
4.93
|
0
|
0.4280
|
6.897
|
54.3
|
6.3361
|
6
|
300
|
16.6
|
11.38
|
22.0
|
Low
|
|
0.10612
|
30.0
|
4.93
|
0
|
0.4280
|
6.095
|
65.1
|
6.3361
|
6
|
300
|
16.6
|
12.40
|
20.1
|
Low
|
|
0.10290
|
30.0
|
4.93
|
0
|
0.4280
|
6.358
|
52.9
|
7.0355
|
6
|
300
|
16.6
|
11.22
|
22.2
|
Low
|
|
0.12757
|
30.0
|
4.93
|
0
|
0.4280
|
6.393
|
7.8
|
7.0355
|
6
|
300
|
16.6
|
5.19
|
23.7
|
Low
|
|
0.20608
|
22.0
|
5.86
|
0
|
0.4310
|
5.593
|
76.5
|
7.9549
|
7
|
330
|
19.1
|
12.50
|
17.6
|
Low
|
|
0.19133
|
22.0
|
5.86
|
0
|
0.4310
|
5.605
|
70.2
|
7.9549
|
7
|
330
|
19.1
|
18.46
|
18.5
|
Low
|
|
0.33983
|
22.0
|
5.86
|
0
|
0.4310
|
6.108
|
34.9
|
8.0555
|
7
|
330
|
19.1
|
9.16
|
24.3
|
High
|
|
0.19657
|
22.0
|
5.86
|
0
|
0.4310
|
6.226
|
79.2
|
8.0555
|
7
|
330
|
19.1
|
10.15
|
20.5
|
Low
|
|
0.16439
|
22.0
|
5.86
|
0
|
0.4310
|
6.433
|
49.1
|
7.8265
|
7
|
330
|
19.1
|
9.52
|
24.5
|
Low
|
|
0.19073
|
22.0
|
5.86
|
0
|
0.4310
|
6.718
|
17.5
|
7.8265
|
7
|
330
|
19.1
|
6.56
|
26.2
|
Low
|
|
0.14030
|
22.0
|
5.86
|
0
|
0.4310
|
6.487
|
13.0
|
7.3967
|
7
|
330
|
19.1
|
5.90
|
24.4
|
Low
|
|
0.21409
|
22.0
|
5.86
|
0
|
0.4310
|
6.438
|
8.9
|
7.3967
|
7
|
330
|
19.1
|
3.59
|
24.8
|
Low
|
|
0.08221
|
22.0
|
5.86
|
0
|
0.4310
|
6.957
|
6.8
|
8.9067
|
7
|
330
|
19.1
|
3.53
|
29.6
|
Low
|
|
0.36894
|
22.0
|
5.86
|
0
|
0.4310
|
8.259
|
8.4
|
8.9067
|
7
|
330
|
19.1
|
3.54
|
42.8
|
High
|
|
0.04819
|
80.0
|
3.64
|
0
|
0.3920
|
6.108
|
32.0
|
9.2203
|
1
|
315
|
16.4
|
6.57
|
21.9
|
Low
|
|
0.03548
|
80.0
|
3.64
|
0
|
0.3920
|
5.876
|
19.1
|
9.2203
|
1
|
315
|
16.4
|
9.25
|
20.9
|
Low
|
|
0.01538
|
90.0
|
3.75
|
0
|
0.3940
|
7.454
|
34.2
|
6.3361
|
3
|
244
|
15.9
|
3.11
|
44.0
|
Low
|
|
0.61154
|
20.0
|
3.97
|
0
|
0.6470
|
8.704
|
86.9
|
1.8010
|
5
|
264
|
13.0
|
5.12
|
50.0
|
High
|
|
0.66351
|
20.0
|
3.97
|
0
|
0.6470
|
7.333
|
100.0
|
1.8946
|
5
|
264
|
13.0
|
7.79
|
36.0
|
High
|
|
0.65665
|
20.0
|
3.97
|
0
|
0.6470
|
6.842
|
100.0
|
2.0107
|
5
|
264
|
13.0
|
6.90
|
30.1
|
High
|
|
0.54011
|
20.0
|
3.97
|
0
|
0.6470
|
7.203
|
81.8
|
2.1121
|
5
|
264
|
13.0
|
9.59
|
33.8
|
High
|
|
0.53412
|
20.0
|
3.97
|
0
|
0.6470
|
7.520
|
89.4
|
2.1398
|
5
|
264
|
13.0
|
7.26
|
43.1
|
High
|
|
0.52014
|
20.0
|
3.97
|
0
|
0.6470
|
8.398
|
91.5
|
2.2885
|
5
|
264
|
13.0
|
5.91
|
48.8
|
High
|
|
0.82526
|
20.0
|
3.97
|
0
|
0.6470
|
7.327
|
94.5
|
2.0788
|
5
|
264
|
13.0
|
11.25
|
31.0
|
High
|
|
0.55007
|
20.0
|
3.97
|
0
|
0.6470
|
7.206
|
91.6
|
1.9301
|
5
|
264
|
13.0
|
8.10
|
36.5
|
High
|
|
0.76162
|
20.0
|
3.97
|
0
|
0.6470
|
5.560
|
62.8
|
1.9865
|
5
|
264
|
13.0
|
10.45
|
22.8
|
High
|
|
0.78570
|
20.0
|
3.97
|
0
|
0.6470
|
7.014
|
84.6
|
2.1329
|
5
|
264
|
13.0
|
14.79
|
30.7
|
High
|
|
0.57834
|
20.0
|
3.97
|
0
|
0.5750
|
8.297
|
67.0
|
2.4216
|
5
|
264
|
13.0
|
7.44
|
50.0
|
High
|
|
0.54050
|
20.0
|
3.97
|
0
|
0.5750
|
7.470
|
52.6
|
2.8720
|
5
|
264
|
13.0
|
3.16
|
43.5
|
High
|
|
0.09065
|
20.0
|
6.96
|
1
|
0.4640
|
5.920
|
61.5
|
3.9175
|
3
|
223
|
18.6
|
13.65
|
20.7
|
Low
|
|
0.29916
|
20.0
|
6.96
|
0
|
0.4640
|
5.856
|
42.1
|
4.4290
|
3
|
223
|
18.6
|
13.00
|
21.1
|
High
|
|
0.16211
|
20.0
|
6.96
|
0
|
0.4640
|
6.240
|
16.3
|
4.4290
|
3
|
223
|
18.6
|
6.59
|
25.2
|
Low
|
|
0.11460
|
20.0
|
6.96
|
0
|
0.4640
|
6.538
|
58.7
|
3.9175
|
3
|
223
|
18.6
|
7.73
|
24.4
|
Low
|
|
0.22188
|
20.0
|
6.96
|
1
|
0.4640
|
7.691
|
51.8
|
4.3665
|
3
|
223
|
18.6
|
6.58
|
35.2
|
Low
|
|
0.05644
|
40.0
|
6.41
|
1
|
0.4470
|
6.758
|
32.9
|
4.0776
|
4
|
254
|
17.6
|
3.53
|
32.4
|
Low
|
|
0.09604
|
40.0
|
6.41
|
0
|
0.4470
|
6.854
|
42.8
|
4.2673
|
4
|
254
|
17.6
|
2.98
|
32.0
|
Low
|
|
0.10469
|
40.0
|
6.41
|
1
|
0.4470
|
7.267
|
49.0
|
4.7872
|
4
|
254
|
17.6
|
6.05
|
33.2
|
Low
|
|
0.06127
|
40.0
|
6.41
|
1
|
0.4470
|
6.826
|
27.6
|
4.8628
|
4
|
254
|
17.6
|
4.16
|
33.1
|
Low
|
|
0.07978
|
40.0
|
6.41
|
0
|
0.4470
|
6.482
|
32.1
|
4.1403
|
4
|
254
|
17.6
|
7.19
|
29.1
|
Low
|
|
0.21038
|
20.0
|
3.33
|
0
|
0.4429
|
6.812
|
32.2
|
4.1007
|
5
|
216
|
14.9
|
4.85
|
35.1
|
Low
|
|
0.03578
|
20.0
|
3.33
|
0
|
0.4429
|
7.820
|
64.5
|
4.6947
|
5
|
216
|
14.9
|
3.76
|
45.4
|
Low
|
|
0.03705
|
20.0
|
3.33
|
0
|
0.4429
|
6.968
|
37.2
|
5.2447
|
5
|
216
|
14.9
|
4.59
|
35.4
|
Low
|
|
0.06129
|
20.0
|
3.33
|
1
|
0.4429
|
7.645
|
49.7
|
5.2119
|
5
|
216
|
14.9
|
3.01
|
46.0
|
Low
|
|
0.01501
|
90.0
|
1.21
|
1
|
0.4010
|
7.923
|
24.8
|
5.8850
|
1
|
198
|
13.6
|
3.16
|
50.0
|
Low
|
|
0.00906
|
90.0
|
2.97
|
0
|
0.4000
|
7.088
|
20.8
|
7.3073
|
1
|
285
|
15.3
|
7.85
|
32.2
|
Low
|
|
0.01096
|
55.0
|
2.25
|
0
|
0.3890
|
6.453
|
31.9
|
7.3073
|
1
|
300
|
15.3
|
8.23
|
22.0
|
Low
|
|
0.01965
|
80.0
|
1.76
|
0
|
0.3850
|
6.230
|
31.5
|
9.0892
|
1
|
241
|
18.2
|
12.93
|
20.1
|
Low
|
|
0.03871
|
52.5
|
5.32
|
0
|
0.4050
|
6.209
|
31.3
|
7.3172
|
6
|
293
|
16.6
|
7.14
|
23.2
|
Low
|
|
0.04590
|
52.5
|
5.32
|
0
|
0.4050
|
6.315
|
45.6
|
7.3172
|
6
|
293
|
16.6
|
7.60
|
22.3
|
Low
|
|
0.04297
|
52.5
|
5.32
|
0
|
0.4050
|
6.565
|
22.9
|
7.3172
|
6
|
293
|
16.6
|
9.51
|
24.8
|
Low
|
|
0.03502
|
80.0
|
4.95
|
0
|
0.4110
|
6.861
|
27.9
|
5.1167
|
4
|
245
|
19.2
|
3.33
|
28.5
|
Low
|
|
0.07886
|
80.0
|
4.95
|
0
|
0.4110
|
7.148
|
27.7
|
5.1167
|
4
|
245
|
19.2
|
3.56
|
37.3
|
Low
|
|
0.03615
|
80.0
|
4.95
|
0
|
0.4110
|
6.630
|
23.4
|
5.1167
|
4
|
245
|
19.2
|
4.70
|
27.9
|
Low
|
|
0.08265
|
0.0
|
13.92
|
0
|
0.4370
|
6.127
|
18.4
|
5.5027
|
4
|
289
|
16.0
|
8.58
|
23.9
|
Low
|
|
0.08199
|
0.0
|
13.92
|
0
|
0.4370
|
6.009
|
42.3
|
5.5027
|
4
|
289
|
16.0
|
10.40
|
21.7
|
Low
|
|
0.12932
|
0.0
|
13.92
|
0
|
0.4370
|
6.678
|
31.1
|
5.9604
|
4
|
289
|
16.0
|
6.27
|
28.6
|
Low
|
|
0.05372
|
0.0
|
13.92
|
0
|
0.4370
|
6.549
|
51.0
|
5.9604
|
4
|
289
|
16.0
|
7.39
|
27.1
|
Low
|
|
0.14103
|
0.0
|
13.92
|
0
|
0.4370
|
5.790
|
58.0
|
6.3200
|
4
|
289
|
16.0
|
15.84
|
20.3
|
Low
|
|
0.06466
|
70.0
|
2.24
|
0
|
0.4000
|
6.345
|
20.1
|
7.8278
|
5
|
358
|
14.8
|
4.97
|
22.5
|
Low
|
|
0.05561
|
70.0
|
2.24
|
0
|
0.4000
|
7.041
|
10.0
|
7.8278
|
5
|
358
|
14.8
|
4.74
|
29.0
|
Low
|
|
0.04417
|
70.0
|
2.24
|
0
|
0.4000
|
6.871
|
47.4
|
7.8278
|
5
|
358
|
14.8
|
6.07
|
24.8
|
Low
|
|
0.03537
|
34.0
|
6.09
|
0
|
0.4330
|
6.590
|
40.4
|
5.4917
|
7
|
329
|
16.1
|
9.50
|
22.0
|
Low
|
|
0.09266
|
34.0
|
6.09
|
0
|
0.4330
|
6.495
|
18.4
|
5.4917
|
7
|
329
|
16.1
|
8.67
|
26.4
|
Low
|
|
0.10000
|
34.0
|
6.09
|
0
|
0.4330
|
6.982
|
17.7
|
5.4917
|
7
|
329
|
16.1
|
4.86
|
33.1
|
Low
|
|
0.05515
|
33.0
|
2.18
|
0
|
0.4720
|
7.236
|
41.1
|
4.0220
|
7
|
222
|
18.4
|
6.93
|
36.1
|
Low
|
|
0.05479
|
33.0
|
2.18
|
0
|
0.4720
|
6.616
|
58.1
|
3.3700
|
7
|
222
|
18.4
|
8.93
|
28.4
|
Low
|
|
0.07503
|
33.0
|
2.18
|
0
|
0.4720
|
7.420
|
71.9
|
3.0992
|
7
|
222
|
18.4
|
6.47
|
33.4
|
Low
|
|
0.04932
|
33.0
|
2.18
|
0
|
0.4720
|
6.849
|
70.3
|
3.1827
|
7
|
222
|
18.4
|
7.53
|
28.2
|
Low
|
|
0.49298
|
0.0
|
9.90
|
0
|
0.5440
|
6.635
|
82.5
|
3.3175
|
4
|
304
|
18.4
|
4.54
|
22.8
|
High
|
|
0.34940
|
0.0
|
9.90
|
0
|
0.5440
|
5.972
|
76.7
|
3.1025
|
4
|
304
|
18.4
|
9.97
|
20.3
|
High
|
|
2.63548
|
0.0
|
9.90
|
0
|
0.5440
|
4.973
|
37.8
|
2.5194
|
4
|
304
|
18.4
|
12.64
|
16.1
|
High
|
|
0.79041
|
0.0
|
9.90
|
0
|
0.5440
|
6.122
|
52.8
|
2.6403
|
4
|
304
|
18.4
|
5.98
|
22.1
|
High
|
|
0.26169
|
0.0
|
9.90
|
0
|
0.5440
|
6.023
|
90.4
|
2.8340
|
4
|
304
|
18.4
|
11.72
|
19.4
|
High
|
|
0.26938
|
0.0
|
9.90
|
0
|
0.5440
|
6.266
|
82.8
|
3.2628
|
4
|
304
|
18.4
|
7.90
|
21.6
|
High
|
|
0.36920
|
0.0
|
9.90
|
0
|
0.5440
|
6.567
|
87.3
|
3.6023
|
4
|
304
|
18.4
|
9.28
|
23.8
|
High
|
|
0.25356
|
0.0
|
9.90
|
0
|
0.5440
|
5.705
|
77.7
|
3.9450
|
4
|
304
|
18.4
|
11.50
|
16.2
|
Low
|
|
0.31827
|
0.0
|
9.90
|
0
|
0.5440
|
5.914
|
83.2
|
3.9986
|
4
|
304
|
18.4
|
18.33
|
17.8
|
High
|
|
0.24522
|
0.0
|
9.90
|
0
|
0.5440
|
5.782
|
71.7
|
4.0317
|
4
|
304
|
18.4
|
15.94
|
19.8
|
Low
|
|
0.40202
|
0.0
|
9.90
|
0
|
0.5440
|
6.382
|
67.2
|
3.5325
|
4
|
304
|
18.4
|
10.36
|
23.1
|
High
|
|
0.47547
|
0.0
|
9.90
|
0
|
0.5440
|
6.113
|
58.8
|
4.0019
|
4
|
304
|
18.4
|
12.73
|
21.0
|
High
|
|
0.16760
|
0.0
|
7.38
|
0
|
0.4930
|
6.426
|
52.3
|
4.5404
|
5
|
287
|
19.6
|
7.20
|
23.8
|
Low
|
|
0.18159
|
0.0
|
7.38
|
0
|
0.4930
|
6.376
|
54.3
|
4.5404
|
5
|
287
|
19.6
|
6.87
|
23.1
|
Low
|
|
0.35114
|
0.0
|
7.38
|
0
|
0.4930
|
6.041
|
49.9
|
4.7211
|
5
|
287
|
19.6
|
7.70
|
20.4
|
High
|
|
0.28392
|
0.0
|
7.38
|
0
|
0.4930
|
5.708
|
74.3
|
4.7211
|
5
|
287
|
19.6
|
11.74
|
18.5
|
High
|
|
0.34109
|
0.0
|
7.38
|
0
|
0.4930
|
6.415
|
40.1
|
4.7211
|
5
|
287
|
19.6
|
6.12
|
25.0
|
High
|
|
0.19186
|
0.0
|
7.38
|
0
|
0.4930
|
6.431
|
14.7
|
5.4159
|
5
|
287
|
19.6
|
5.08
|
24.6
|
Low
|
|
0.30347
|
0.0
|
7.38
|
0
|
0.4930
|
6.312
|
28.9
|
5.4159
|
5
|
287
|
19.6
|
6.15
|
23.0
|
High
|
|
0.24103
|
0.0
|
7.38
|
0
|
0.4930
|
6.083
|
43.7
|
5.4159
|
5
|
287
|
19.6
|
12.79
|
22.2
|
Low
|
|
0.06617
|
0.0
|
3.24
|
0
|
0.4600
|
5.868
|
25.8
|
5.2146
|
4
|
430
|
16.9
|
9.97
|
19.3
|
Low
|
|
0.06724
|
0.0
|
3.24
|
0
|
0.4600
|
6.333
|
17.2
|
5.2146
|
4
|
430
|
16.9
|
7.34
|
22.6
|
Low
|
|
0.04544
|
0.0
|
3.24
|
0
|
0.4600
|
6.144
|
32.2
|
5.8736
|
4
|
430
|
16.9
|
9.09
|
19.8
|
Low
|
|
0.05023
|
35.0
|
6.06
|
0
|
0.4379
|
5.706
|
28.4
|
6.6407
|
1
|
304
|
16.9
|
12.43
|
17.1
|
Low
|
|
0.03466
|
35.0
|
6.06
|
0
|
0.4379
|
6.031
|
23.3
|
6.6407
|
1
|
304
|
16.9
|
7.83
|
19.4
|
Low
|
|
0.05083
|
0.0
|
5.19
|
0
|
0.5150
|
6.316
|
38.1
|
6.4584
|
5
|
224
|
20.2
|
5.68
|
22.2
|
Low
|
|
0.03738
|
0.0
|
5.19
|
0
|
0.5150
|
6.310
|
38.5
|
6.4584
|
5
|
224
|
20.2
|
6.75
|
20.7
|
Low
|
|
0.03961
|
0.0
|
5.19
|
0
|
0.5150
|
6.037
|
34.5
|
5.9853
|
5
|
224
|
20.2
|
8.01
|
21.1
|
Low
|
|
0.03427
|
0.0
|
5.19
|
0
|
0.5150
|
5.869
|
46.3
|
5.2311
|
5
|
224
|
20.2
|
9.80
|
19.5
|
Low
|
|
0.03041
|
0.0
|
5.19
|
0
|
0.5150
|
5.895
|
59.6
|
5.6150
|
5
|
224
|
20.2
|
10.56
|
18.5
|
Low
|
|
0.03306
|
0.0
|
5.19
|
0
|
0.5150
|
6.059
|
37.3
|
4.8122
|
5
|
224
|
20.2
|
8.51
|
20.6
|
Low
|
|
0.05497
|
0.0
|
5.19
|
0
|
0.5150
|
5.985
|
45.4
|
4.8122
|
5
|
224
|
20.2
|
9.74
|
19.0
|
Low
|
|
0.06151
|
0.0
|
5.19
|
0
|
0.5150
|
5.968
|
58.5
|
4.8122
|
5
|
224
|
20.2
|
9.29
|
18.7
|
Low
|
|
0.01301
|
35.0
|
1.52
|
0
|
0.4420
|
7.241
|
49.3
|
7.0379
|
1
|
284
|
15.5
|
5.49
|
32.7
|
Low
|
|
0.02498
|
0.0
|
1.89
|
0
|
0.5180
|
6.540
|
59.7
|
6.2669
|
1
|
422
|
15.9
|
8.65
|
16.5
|
Low
|
|
0.02543
|
55.0
|
3.78
|
0
|
0.4840
|
6.696
|
56.4
|
5.7321
|
5
|
370
|
17.6
|
7.18
|
23.9
|
Low
|
|
0.03049
|
55.0
|
3.78
|
0
|
0.4840
|
6.874
|
28.1
|
6.4654
|
5
|
370
|
17.6
|
4.61
|
31.2
|
Low
|
|
0.03113
|
0.0
|
4.39
|
0
|
0.4420
|
6.014
|
48.5
|
8.0136
|
3
|
352
|
18.8
|
10.53
|
17.5
|
Low
|
|
0.06162
|
0.0
|
4.39
|
0
|
0.4420
|
5.898
|
52.3
|
8.0136
|
3
|
352
|
18.8
|
12.67
|
17.2
|
Low
|
|
0.01870
|
85.0
|
4.15
|
0
|
0.4290
|
6.516
|
27.7
|
8.5353
|
4
|
351
|
17.9
|
6.36
|
23.1
|
Low
|
|
0.01501
|
80.0
|
2.01
|
0
|
0.4350
|
6.635
|
29.7
|
8.3440
|
4
|
280
|
17.0
|
5.99
|
24.5
|
Low
|
|
0.02899
|
40.0
|
1.25
|
0
|
0.4290
|
6.939
|
34.5
|
8.7921
|
1
|
335
|
19.7
|
5.89
|
26.6
|
Low
|
|
0.06211
|
40.0
|
1.25
|
0
|
0.4290
|
6.490
|
44.4
|
8.7921
|
1
|
335
|
19.7
|
5.98
|
22.9
|
Low
|
|
0.07950
|
60.0
|
1.69
|
0
|
0.4110
|
6.579
|
35.9
|
10.7103
|
4
|
411
|
18.3
|
5.49
|
24.1
|
Low
|
|
0.07244
|
60.0
|
1.69
|
0
|
0.4110
|
5.884
|
18.5
|
10.7103
|
4
|
411
|
18.3
|
7.79
|
18.6
|
Low
|
|
0.01709
|
90.0
|
2.02
|
0
|
0.4100
|
6.728
|
36.1
|
12.1265
|
5
|
187
|
17.0
|
4.50
|
30.1
|
Low
|
|
0.04301
|
80.0
|
1.91
|
0
|
0.4130
|
5.663
|
21.9
|
10.5857
|
4
|
334
|
22.0
|
8.05
|
18.2
|
Low
|
|
0.10659
|
80.0
|
1.91
|
0
|
0.4130
|
5.936
|
19.5
|
10.5857
|
4
|
334
|
22.0
|
5.57
|
20.6
|
Low
|
|
8.98296
|
0.0
|
18.10
|
1
|
0.7700
|
6.212
|
97.4
|
2.1222
|
24
|
666
|
20.2
|
17.60
|
17.8
|
High
|
|
3.84970
|
0.0
|
18.10
|
1
|
0.7700
|
6.395
|
91.0
|
2.5052
|
24
|
666
|
20.2
|
13.27
|
21.7
|
High
|
|
5.20177
|
0.0
|
18.10
|
1
|
0.7700
|
6.127
|
83.4
|
2.7227
|
24
|
666
|
20.2
|
11.48
|
22.7
|
High
|
|
4.26131
|
0.0
|
18.10
|
0
|
0.7700
|
6.112
|
81.3
|
2.5091
|
24
|
666
|
20.2
|
12.67
|
22.6
|
High
|
|
4.54192
|
0.0
|
18.10
|
0
|
0.7700
|
6.398
|
88.0
|
2.5182
|
24
|
666
|
20.2
|
7.79
|
25.0
|
High
|
|
3.83684
|
0.0
|
18.10
|
0
|
0.7700
|
6.251
|
91.1
|
2.2955
|
24
|
666
|
20.2
|
14.19
|
19.9
|
High
|
|
3.67822
|
0.0
|
18.10
|
0
|
0.7700
|
5.362
|
96.2
|
2.1036
|
24
|
666
|
20.2
|
10.19
|
20.8
|
High
|
|
4.22239
|
0.0
|
18.10
|
1
|
0.7700
|
5.803
|
89.0
|
1.9047
|
24
|
666
|
20.2
|
14.64
|
16.8
|
High
|
|
3.47428
|
0.0
|
18.10
|
1
|
0.7180
|
8.780
|
82.9
|
1.9047
|
24
|
666
|
20.2
|
5.29
|
21.9
|
High
|
|
4.55587
|
0.0
|
18.10
|
0
|
0.7180
|
3.561
|
87.9
|
1.6132
|
24
|
666
|
20.2
|
7.12
|
27.5
|
High
|
|
3.69695
|
0.0
|
18.10
|
0
|
0.7180
|
4.963
|
91.4
|
1.7523
|
24
|
666
|
20.2
|
14.00
|
21.9
|
High
|
|
13.52220
|
0.0
|
18.10
|
0
|
0.6310
|
3.863
|
100.0
|
1.5106
|
24
|
666
|
20.2
|
13.33
|
23.1
|
High
|
|
4.89822
|
0.0
|
18.10
|
0
|
0.6310
|
4.970
|
100.0
|
1.3325
|
24
|
666
|
20.2
|
3.26
|
50.0
|
High
|
|
5.66998
|
0.0
|
18.10
|
1
|
0.6310
|
6.683
|
96.8
|
1.3567
|
24
|
666
|
20.2
|
3.73
|
50.0
|
High
|
|
6.53876
|
0.0
|
18.10
|
1
|
0.6310
|
7.016
|
97.5
|
1.2024
|
24
|
666
|
20.2
|
2.96
|
50.0
|
High
|
|
9.23230
|
0.0
|
18.10
|
0
|
0.6310
|
6.216
|
100.0
|
1.1691
|
24
|
666
|
20.2
|
9.53
|
50.0
|
High
|
|
8.26725
|
0.0
|
18.10
|
1
|
0.6680
|
5.875
|
89.6
|
1.1296
|
24
|
666
|
20.2
|
8.88
|
50.0
|
High
|
|
11.10810
|
0.0
|
18.10
|
0
|
0.6680
|
4.906
|
100.0
|
1.1742
|
24
|
666
|
20.2
|
34.77
|
13.8
|
High
|
|
18.49820
|
0.0
|
18.10
|
0
|
0.6680
|
4.138
|
100.0
|
1.1370
|
24
|
666
|
20.2
|
37.97
|
13.8
|
High
|
|
19.60910
|
0.0
|
18.10
|
0
|
0.6710
|
7.313
|
97.9
|
1.3163
|
24
|
666
|
20.2
|
13.44
|
15.0
|
High
|
|
15.28800
|
0.0
|
18.10
|
0
|
0.6710
|
6.649
|
93.3
|
1.3449
|
24
|
666
|
20.2
|
23.24
|
13.9
|
High
|
|
9.82349
|
0.0
|
18.10
|
0
|
0.6710
|
6.794
|
98.8
|
1.3580
|
24
|
666
|
20.2
|
21.24
|
13.3
|
High
|
|
23.64820
|
0.0
|
18.10
|
0
|
0.6710
|
6.380
|
96.2
|
1.3861
|
24
|
666
|
20.2
|
23.69
|
13.1
|
High
|
|
17.86670
|
0.0
|
18.10
|
0
|
0.6710
|
6.223
|
100.0
|
1.3861
|
24
|
666
|
20.2
|
21.78
|
10.2
|
High
|
|
88.97620
|
0.0
|
18.10
|
0
|
0.6710
|
6.968
|
91.9
|
1.4165
|
24
|
666
|
20.2
|
17.21
|
10.4
|
High
|
|
15.87440
|
0.0
|
18.10
|
0
|
0.6710
|
6.545
|
99.1
|
1.5192
|
24
|
666
|
20.2
|
21.08
|
10.9
|
High
|
|
9.18702
|
0.0
|
18.10
|
0
|
0.7000
|
5.536
|
100.0
|
1.5804
|
24
|
666
|
20.2
|
23.60
|
11.3
|
High
|
|
7.99248
|
0.0
|
18.10
|
0
|
0.7000
|
5.520
|
100.0
|
1.5331
|
24
|
666
|
20.2
|
24.56
|
12.3
|
High
|
|
20.08490
|
0.0
|
18.10
|
0
|
0.7000
|
4.368
|
91.2
|
1.4395
|
24
|
666
|
20.2
|
30.63
|
8.8
|
High
|
|
16.81180
|
0.0
|
18.10
|
0
|
0.7000
|
5.277
|
98.1
|
1.4261
|
24
|
666
|
20.2
|
30.81
|
7.2
|
High
|
|
24.39380
|
0.0
|
18.10
|
0
|
0.7000
|
4.652
|
100.0
|
1.4672
|
24
|
666
|
20.2
|
28.28
|
10.5
|
High
|
|
22.59710
|
0.0
|
18.10
|
0
|
0.7000
|
5.000
|
89.5
|
1.5184
|
24
|
666
|
20.2
|
31.99
|
7.4
|
High
|
|
14.33370
|
0.0
|
18.10
|
0
|
0.7000
|
4.880
|
100.0
|
1.5895
|
24
|
666
|
20.2
|
30.62
|
10.2
|
High
|
|
8.15174
|
0.0
|
18.10
|
0
|
0.7000
|
5.390
|
98.9
|
1.7281
|
24
|
666
|
20.2
|
20.85
|
11.5
|
High
|
|
6.96215
|
0.0
|
18.10
|
0
|
0.7000
|
5.713
|
97.0
|
1.9265
|
24
|
666
|
20.2
|
17.11
|
15.1
|
High
|
|
5.29305
|
0.0
|
18.10
|
0
|
0.7000
|
6.051
|
82.5
|
2.1678
|
24
|
666
|
20.2
|
18.76
|
23.2
|
High
|
|
11.57790
|
0.0
|
18.10
|
0
|
0.7000
|
5.036
|
97.0
|
1.7700
|
24
|
666
|
20.2
|
25.68
|
9.7
|
High
|
|
8.64476
|
0.0
|
18.10
|
0
|
0.6930
|
6.193
|
92.6
|
1.7912
|
24
|
666
|
20.2
|
15.17
|
13.8
|
High
|
|
13.35980
|
0.0
|
18.10
|
0
|
0.6930
|
5.887
|
94.7
|
1.7821
|
24
|
666
|
20.2
|
16.35
|
12.7
|
High
|
|
8.71675
|
0.0
|
18.10
|
0
|
0.6930
|
6.471
|
98.8
|
1.7257
|
24
|
666
|
20.2
|
17.12
|
13.1
|
High
|
|
5.87205
|
0.0
|
18.10
|
0
|
0.6930
|
6.405
|
96.0
|
1.6768
|
24
|
666
|
20.2
|
19.37
|
12.5
|
High
|
|
7.67202
|
0.0
|
18.10
|
0
|
0.6930
|
5.747
|
98.9
|
1.6334
|
24
|
666
|
20.2
|
19.92
|
8.5
|
High
|
|
38.35180
|
0.0
|
18.10
|
0
|
0.6930
|
5.453
|
100.0
|
1.4896
|
24
|
666
|
20.2
|
30.59
|
5.0
|
High
|
|
9.91655
|
0.0
|
18.10
|
0
|
0.6930
|
5.852
|
77.8
|
1.5004
|
24
|
666
|
20.2
|
29.97
|
6.3
|
High
|
|
25.04610
|
0.0
|
18.10
|
0
|
0.6930
|
5.987
|
100.0
|
1.5888
|
24
|
666
|
20.2
|
26.77
|
5.6
|
High
|
|
14.23620
|
0.0
|
18.10
|
0
|
0.6930
|
6.343
|
100.0
|
1.5741
|
24
|
666
|
20.2
|
20.32
|
7.2
|
High
|
|
9.59571
|
0.0
|
18.10
|
0
|
0.6930
|
6.404
|
100.0
|
1.6390
|
24
|
666
|
20.2
|
20.31
|
12.1
|
High
|
|
24.80170
|
0.0
|
18.10
|
0
|
0.6930
|
5.349
|
96.0
|
1.7028
|
24
|
666
|
20.2
|
19.77
|
8.3
|
High
|
|
41.52920
|
0.0
|
18.10
|
0
|
0.6930
|
5.531
|
85.4
|
1.6074
|
24
|
666
|
20.2
|
27.38
|
8.5
|
High
|
|
67.92080
|
0.0
|
18.10
|
0
|
0.6930
|
5.683
|
100.0
|
1.4254
|
24
|
666
|
20.2
|
22.98
|
5.0
|
High
|
|
20.71620
|
0.0
|
18.10
|
0
|
0.6590
|
4.138
|
100.0
|
1.1781
|
24
|
666
|
20.2
|
23.34
|
11.9
|
High
|
|
11.95110
|
0.0
|
18.10
|
0
|
0.6590
|
5.608
|
100.0
|
1.2852
|
24
|
666
|
20.2
|
12.13
|
27.9
|
High
|
|
7.40389
|
0.0
|
18.10
|
0
|
0.5970
|
5.617
|
97.9
|
1.4547
|
24
|
666
|
20.2
|
26.40
|
17.2
|
High
|
|
14.43830
|
0.0
|
18.10
|
0
|
0.5970
|
6.852
|
100.0
|
1.4655
|
24
|
666
|
20.2
|
19.78
|
27.5
|
High
|
|
51.13580
|
0.0
|
18.10
|
0
|
0.5970
|
5.757
|
100.0
|
1.4130
|
24
|
666
|
20.2
|
10.11
|
15.0
|
High
|
|
14.05070
|
0.0
|
18.10
|
0
|
0.5970
|
6.657
|
100.0
|
1.5275
|
24
|
666
|
20.2
|
21.22
|
17.2
|
High
|
|
18.81100
|
0.0
|
18.10
|
0
|
0.5970
|
4.628
|
100.0
|
1.5539
|
24
|
666
|
20.2
|
34.37
|
17.9
|
High
|
|
28.65580
|
0.0
|
18.10
|
0
|
0.5970
|
5.155
|
100.0
|
1.5894
|
24
|
666
|
20.2
|
20.08
|
16.3
|
High
|
|
45.74610
|
0.0
|
18.10
|
0
|
0.6930
|
4.519
|
100.0
|
1.6582
|
24
|
666
|
20.2
|
36.98
|
7.0
|
High
|
|
18.08460
|
0.0
|
18.10
|
0
|
0.6790
|
6.434
|
100.0
|
1.8347
|
24
|
666
|
20.2
|
29.05
|
7.2
|
High
|
|
10.83420
|
0.0
|
18.10
|
0
|
0.6790
|
6.782
|
90.8
|
1.8195
|
24
|
666
|
20.2
|
25.79
|
7.5
|
High
|
|
25.94060
|
0.0
|
18.10
|
0
|
0.6790
|
5.304
|
89.1
|
1.6475
|
24
|
666
|
20.2
|
26.64
|
10.4
|
High
|
|
73.53410
|
0.0
|
18.10
|
0
|
0.6790
|
5.957
|
100.0
|
1.8026
|
24
|
666
|
20.2
|
20.62
|
8.8
|
High
|
|
11.81230
|
0.0
|
18.10
|
0
|
0.7180
|
6.824
|
76.5
|
1.7940
|
24
|
666
|
20.2
|
22.74
|
8.4
|
High
|
|
11.08740
|
0.0
|
18.10
|
0
|
0.7180
|
6.411
|
100.0
|
1.8589
|
24
|
666
|
20.2
|
15.02
|
16.7
|
High
|
|
7.02259
|
0.0
|
18.10
|
0
|
0.7180
|
6.006
|
95.3
|
1.8746
|
24
|
666
|
20.2
|
15.70
|
14.2
|
High
|
|
12.04820
|
0.0
|
18.10
|
0
|
0.6140
|
5.648
|
87.6
|
1.9512
|
24
|
666
|
20.2
|
14.10
|
20.8
|
High
|
|
7.05042
|
0.0
|
18.10
|
0
|
0.6140
|
6.103
|
85.1
|
2.0218
|
24
|
666
|
20.2
|
23.29
|
13.4
|
High
|
|
8.79212
|
0.0
|
18.10
|
0
|
0.5840
|
5.565
|
70.6
|
2.0635
|
24
|
666
|
20.2
|
17.16
|
11.7
|
High
|
|
15.86030
|
0.0
|
18.10
|
0
|
0.6790
|
5.896
|
95.4
|
1.9096
|
24
|
666
|
20.2
|
24.39
|
8.3
|
High
|
|
12.24720
|
0.0
|
18.10
|
0
|
0.5840
|
5.837
|
59.7
|
1.9976
|
24
|
666
|
20.2
|
15.69
|
10.2
|
High
|
|
37.66190
|
0.0
|
18.10
|
0
|
0.6790
|
6.202
|
78.7
|
1.8629
|
24
|
666
|
20.2
|
14.52
|
10.9
|
High
|
|
7.36711
|
0.0
|
18.10
|
0
|
0.6790
|
6.193
|
78.1
|
1.9356
|
24
|
666
|
20.2
|
21.52
|
11.0
|
High
|
|
9.33889
|
0.0
|
18.10
|
0
|
0.6790
|
6.380
|
95.6
|
1.9682
|
24
|
666
|
20.2
|
24.08
|
9.5
|
High
|
|
8.49213
|
0.0
|
18.10
|
0
|
0.5840
|
6.348
|
86.1
|
2.0527
|
24
|
666
|
20.2
|
17.64
|
14.5
|
High
|
|
10.06230
|
0.0
|
18.10
|
0
|
0.5840
|
6.833
|
94.3
|
2.0882
|
24
|
666
|
20.2
|
19.69
|
14.1
|
High
|
|
6.44405
|
0.0
|
18.10
|
0
|
0.5840
|
6.425
|
74.8
|
2.2004
|
24
|
666
|
20.2
|
12.03
|
16.1
|
High
|
|
5.58107
|
0.0
|
18.10
|
0
|
0.7130
|
6.436
|
87.9
|
2.3158
|
24
|
666
|
20.2
|
16.22
|
14.3
|
High
|
|
13.91340
|
0.0
|
18.10
|
0
|
0.7130
|
6.208
|
95.0
|
2.2222
|
24
|
666
|
20.2
|
15.17
|
11.7
|
High
|
|
11.16040
|
0.0
|
18.10
|
0
|
0.7400
|
6.629
|
94.6
|
2.1247
|
24
|
666
|
20.2
|
23.27
|
13.4
|
High
|
|
14.42080
|
0.0
|
18.10
|
0
|
0.7400
|
6.461
|
93.3
|
2.0026
|
24
|
666
|
20.2
|
18.05
|
9.6
|
High
|
|
15.17720
|
0.0
|
18.10
|
0
|
0.7400
|
6.152
|
100.0
|
1.9142
|
24
|
666
|
20.2
|
26.45
|
8.7
|
High
|
|
13.67810
|
0.0
|
18.10
|
0
|
0.7400
|
5.935
|
87.9
|
1.8206
|
24
|
666
|
20.2
|
34.02
|
8.4
|
High
|
|
9.39063
|
0.0
|
18.10
|
0
|
0.7400
|
5.627
|
93.9
|
1.8172
|
24
|
666
|
20.2
|
22.88
|
12.8
|
High
|
|
22.05110
|
0.0
|
18.10
|
0
|
0.7400
|
5.818
|
92.4
|
1.8662
|
24
|
666
|
20.2
|
22.11
|
10.5
|
High
|
|
9.72418
|
0.0
|
18.10
|
0
|
0.7400
|
6.406
|
97.2
|
2.0651
|
24
|
666
|
20.2
|
19.52
|
17.1
|
High
|
|
5.66637
|
0.0
|
18.10
|
0
|
0.7400
|
6.219
|
100.0
|
2.0048
|
24
|
666
|
20.2
|
16.59
|
18.4
|
High
|
|
9.96654
|
0.0
|
18.10
|
0
|
0.7400
|
6.485
|
100.0
|
1.9784
|
24
|
666
|
20.2
|
18.85
|
15.4
|
High
|
|
12.80230
|
0.0
|
18.10
|
0
|
0.7400
|
5.854
|
96.6
|
1.8956
|
24
|
666
|
20.2
|
23.79
|
10.8
|
High
|
|
10.67180
|
0.0
|
18.10
|
0
|
0.7400
|
6.459
|
94.8
|
1.9879
|
24
|
666
|
20.2
|
23.98
|
11.8
|
High
|
|
6.28807
|
0.0
|
18.10
|
0
|
0.7400
|
6.341
|
96.4
|
2.0720
|
24
|
666
|
20.2
|
17.79
|
14.9
|
High
|
|
9.92485
|
0.0
|
18.10
|
0
|
0.7400
|
6.251
|
96.6
|
2.1980
|
24
|
666
|
20.2
|
16.44
|
12.6
|
High
|
|
9.32909
|
0.0
|
18.10
|
0
|
0.7130
|
6.185
|
98.7
|
2.2616
|
24
|
666
|
20.2
|
18.13
|
14.1
|
High
|
|
7.52601
|
0.0
|
18.10
|
0
|
0.7130
|
6.417
|
98.3
|
2.1850
|
24
|
666
|
20.2
|
19.31
|
13.0
|
High
|
|
6.71772
|
0.0
|
18.10
|
0
|
0.7130
|
6.749
|
92.6
|
2.3236
|
24
|
666
|
20.2
|
17.44
|
13.4
|
High
|
|
5.44114
|
0.0
|
18.10
|
0
|
0.7130
|
6.655
|
98.2
|
2.3552
|
24
|
666
|
20.2
|
17.73
|
15.2
|
High
|
|
5.09017
|
0.0
|
18.10
|
0
|
0.7130
|
6.297
|
91.8
|
2.3682
|
24
|
666
|
20.2
|
17.27
|
16.1
|
High
|
|
8.24809
|
0.0
|
18.10
|
0
|
0.7130
|
7.393
|
99.3
|
2.4527
|
24
|
666
|
20.2
|
16.74
|
17.8
|
High
|
|
9.51363
|
0.0
|
18.10
|
0
|
0.7130
|
6.728
|
94.1
|
2.4961
|
24
|
666
|
20.2
|
18.71
|
14.9
|
High
|
|
4.75237
|
0.0
|
18.10
|
0
|
0.7130
|
6.525
|
86.5
|
2.4358
|
24
|
666
|
20.2
|
18.13
|
14.1
|
High
|
|
4.66883
|
0.0
|
18.10
|
0
|
0.7130
|
5.976
|
87.9
|
2.5806
|
24
|
666
|
20.2
|
19.01
|
12.7
|
High
|
|
8.20058
|
0.0
|
18.10
|
0
|
0.7130
|
5.936
|
80.3
|
2.7792
|
24
|
666
|
20.2
|
16.94
|
13.5
|
High
|
|
7.75223
|
0.0
|
18.10
|
0
|
0.7130
|
6.301
|
83.7
|
2.7831
|
24
|
666
|
20.2
|
16.23
|
14.9
|
High
|
|
6.80117
|
0.0
|
18.10
|
0
|
0.7130
|
6.081
|
84.4
|
2.7175
|
24
|
666
|
20.2
|
14.70
|
20.0
|
High
|
|
4.81213
|
0.0
|
18.10
|
0
|
0.7130
|
6.701
|
90.0
|
2.5975
|
24
|
666
|
20.2
|
16.42
|
16.4
|
High
|
|
3.69311
|
0.0
|
18.10
|
0
|
0.7130
|
6.376
|
88.4
|
2.5671
|
24
|
666
|
20.2
|
14.65
|
17.7
|
High
|
|
6.65492
|
0.0
|
18.10
|
0
|
0.7130
|
6.317
|
83.0
|
2.7344
|
24
|
666
|
20.2
|
13.99
|
19.5
|
High
|
|
5.82115
|
0.0
|
18.10
|
0
|
0.7130
|
6.513
|
89.9
|
2.8016
|
24
|
666
|
20.2
|
10.29
|
20.2
|
High
|
|
7.83932
|
0.0
|
18.10
|
0
|
0.6550
|
6.209
|
65.4
|
2.9634
|
24
|
666
|
20.2
|
13.22
|
21.4
|
High
|
|
3.16360
|
0.0
|
18.10
|
0
|
0.6550
|
5.759
|
48.2
|
3.0665
|
24
|
666
|
20.2
|
14.13
|
19.9
|
High
|
|
3.77498
|
0.0
|
18.10
|
0
|
0.6550
|
5.952
|
84.7
|
2.8715
|
24
|
666
|
20.2
|
17.15
|
19.0
|
High
|
|
4.42228
|
0.0
|
18.10
|
0
|
0.5840
|
6.003
|
94.5
|
2.5403
|
24
|
666
|
20.2
|
21.32
|
19.1
|
High
|
|
15.57570
|
0.0
|
18.10
|
0
|
0.5800
|
5.926
|
71.0
|
2.9084
|
24
|
666
|
20.2
|
18.13
|
19.1
|
High
|
|
13.07510
|
0.0
|
18.10
|
0
|
0.5800
|
5.713
|
56.7
|
2.8237
|
24
|
666
|
20.2
|
14.76
|
20.1
|
High
|
|
4.34879
|
0.0
|
18.10
|
0
|
0.5800
|
6.167
|
84.0
|
3.0334
|
24
|
666
|
20.2
|
16.29
|
19.9
|
High
|
|
4.03841
|
0.0
|
18.10
|
0
|
0.5320
|
6.229
|
90.7
|
3.0993
|
24
|
666
|
20.2
|
12.87
|
19.6
|
High
|
|
3.56868
|
0.0
|
18.10
|
0
|
0.5800
|
6.437
|
75.0
|
2.8965
|
24
|
666
|
20.2
|
14.36
|
23.2
|
High
|
|
4.64689
|
0.0
|
18.10
|
0
|
0.6140
|
6.980
|
67.6
|
2.5329
|
24
|
666
|
20.2
|
11.66
|
29.8
|
High
|
|
8.05579
|
0.0
|
18.10
|
0
|
0.5840
|
5.427
|
95.4
|
2.4298
|
24
|
666
|
20.2
|
18.14
|
13.8
|
High
|
|
6.39312
|
0.0
|
18.10
|
0
|
0.5840
|
6.162
|
97.4
|
2.2060
|
24
|
666
|
20.2
|
24.10
|
13.3
|
High
|
|
4.87141
|
0.0
|
18.10
|
0
|
0.6140
|
6.484
|
93.6
|
2.3053
|
24
|
666
|
20.2
|
18.68
|
16.7
|
High
|
|
15.02340
|
0.0
|
18.10
|
0
|
0.6140
|
5.304
|
97.3
|
2.1007
|
24
|
666
|
20.2
|
24.91
|
12.0
|
High
|
|
10.23300
|
0.0
|
18.10
|
0
|
0.6140
|
6.185
|
96.7
|
2.1705
|
24
|
666
|
20.2
|
18.03
|
14.6
|
High
|
|
14.33370
|
0.0
|
18.10
|
0
|
0.6140
|
6.229
|
88.0
|
1.9512
|
24
|
666
|
20.2
|
13.11
|
21.4
|
High
|
|
5.82401
|
0.0
|
18.10
|
0
|
0.5320
|
6.242
|
64.7
|
3.4242
|
24
|
666
|
20.2
|
10.74
|
23.0
|
High
|
|
5.70818
|
0.0
|
18.10
|
0
|
0.5320
|
6.750
|
74.9
|
3.3317
|
24
|
666
|
20.2
|
7.74
|
23.7
|
High
|
|
5.73116
|
0.0
|
18.10
|
0
|
0.5320
|
7.061
|
77.0
|
3.4106
|
24
|
666
|
20.2
|
7.01
|
25.0
|
High
|
|
2.81838
|
0.0
|
18.10
|
0
|
0.5320
|
5.762
|
40.3
|
4.0983
|
24
|
666
|
20.2
|
10.42
|
21.8
|
High
|
|
2.37857
|
0.0
|
18.10
|
0
|
0.5830
|
5.871
|
41.9
|
3.7240
|
24
|
666
|
20.2
|
13.34
|
20.6
|
High
|
|
3.67367
|
0.0
|
18.10
|
0
|
0.5830
|
6.312
|
51.9
|
3.9917
|
24
|
666
|
20.2
|
10.58
|
21.2
|
High
|
|
5.69175
|
0.0
|
18.10
|
0
|
0.5830
|
6.114
|
79.8
|
3.5459
|
24
|
666
|
20.2
|
14.98
|
19.1
|
High
|
|
4.83567
|
0.0
|
18.10
|
0
|
0.5830
|
5.905
|
53.2
|
3.1523
|
24
|
666
|
20.2
|
11.45
|
20.6
|
High
|
|
0.15086
|
0.0
|
27.74
|
0
|
0.6090
|
5.454
|
92.7
|
1.8209
|
4
|
711
|
20.1
|
18.06
|
15.2
|
Low
|
|
0.18337
|
0.0
|
27.74
|
0
|
0.6090
|
5.414
|
98.3
|
1.7554
|
4
|
711
|
20.1
|
23.97
|
7.0
|
Low
|
|
0.20746
|
0.0
|
27.74
|
0
|
0.6090
|
5.093
|
98.0
|
1.8226
|
4
|
711
|
20.1
|
29.68
|
8.1
|
Low
|
|
0.10574
|
0.0
|
27.74
|
0
|
0.6090
|
5.983
|
98.8
|
1.8681
|
4
|
711
|
20.1
|
18.07
|
13.6
|
Low
|
|
0.11132
|
0.0
|
27.74
|
0
|
0.6090
|
5.983
|
83.5
|
2.1099
|
4
|
711
|
20.1
|
13.35
|
20.1
|
Low
|
|
0.17331
|
0.0
|
9.69
|
0
|
0.5850
|
5.707
|
54.0
|
2.3817
|
6
|
391
|
19.2
|
12.01
|
21.8
|
Low
|
|
0.27957
|
0.0
|
9.69
|
0
|
0.5850
|
5.926
|
42.6
|
2.3817
|
6
|
391
|
19.2
|
13.59
|
24.5
|
High
|
|
0.17899
|
0.0
|
9.69
|
0
|
0.5850
|
5.670
|
28.8
|
2.7986
|
6
|
391
|
19.2
|
17.60
|
23.1
|
Low
|
|
0.28960
|
0.0
|
9.69
|
0
|
0.5850
|
5.390
|
72.9
|
2.7986
|
6
|
391
|
19.2
|
21.14
|
19.7
|
High
|
|
0.26838
|
0.0
|
9.69
|
0
|
0.5850
|
5.794
|
70.6
|
2.8927
|
6
|
391
|
19.2
|
14.10
|
18.3
|
High
|
|
0.23912
|
0.0
|
9.69
|
0
|
0.5850
|
6.019
|
65.3
|
2.4091
|
6
|
391
|
19.2
|
12.92
|
21.2
|
Low
|
|
0.17783
|
0.0
|
9.69
|
0
|
0.5850
|
5.569
|
73.5
|
2.3999
|
6
|
391
|
19.2
|
15.10
|
17.5
|
Low
|
|
0.22438
|
0.0
|
9.69
|
0
|
0.5850
|
6.027
|
79.7
|
2.4982
|
6
|
391
|
19.2
|
14.33
|
16.8
|
Low
|
|
0.06263
|
0.0
|
11.93
|
0
|
0.5730
|
6.593
|
69.1
|
2.4786
|
1
|
273
|
21.0
|
9.67
|
22.4
|
Low
|
|
0.04527
|
0.0
|
11.93
|
0
|
0.5730
|
6.120
|
76.7
|
2.2875
|
1
|
273
|
21.0
|
9.08
|
20.6
|
Low
|
|
0.06076
|
0.0
|
11.93
|
0
|
0.5730
|
6.976
|
91.0
|
2.1675
|
1
|
273
|
21.0
|
5.64
|
23.9
|
Low
|
|
0.10959
|
0.0
|
11.93
|
0
|
0.5730
|
6.794
|
89.3
|
2.3889
|
1
|
273
|
21.0
|
6.48
|
22.0
|
Low
|
|
0.04741
|
0.0
|
11.93
|
0
|
0.5730
|
6.030
|
80.8
|
2.5050
|
1
|
273
|
21.0
|
7.88
|
11.9
|
Low
|
# Load Boston data set
data("Boston")
# Create response variable: crime_rate_above_median
crime_rate_above_median <- ifelse(Boston$crim > median(Boston$crim), 1, 0)
# Add the response variable to the Boston data set
Boston$crime_rate_above_median <- crime_rate_above_median
# Logistic Regression
logistic_model <- glm(crime_rate_above_median ~ ., data = Boston, family = "binomial")
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(logistic_model)
##
## Call:
## glm(formula = crime_rate_above_median ~ ., family = "binomial",
## data = Boston)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.437e+01 1.202e+05 0.000 1.000
## crim 1.083e+03 1.773e+04 0.061 0.951
## zn 2.194e+00 5.856e+01 0.037 0.970
## indus -2.510e+00 9.002e+02 -0.003 0.998
## chas 4.489e+00 1.014e+04 0.000 1.000
## nox -2.585e+02 1.458e+05 -0.002 0.999
## rm -3.953e+01 1.653e+03 -0.024 0.981
## age 3.437e-01 5.798e+01 0.006 0.995
## dis -1.742e+01 2.146e+03 -0.008 0.994
## rad -5.933e+00 2.642e+03 -0.002 0.998
## tax 1.639e-01 1.078e+02 0.002 0.999
## ptratio 5.525e+00 3.640e+03 0.002 0.999
## black 3.266e-02 1.208e+01 0.003 0.998
## lstat -1.687e+00 3.560e+02 -0.005 0.996
## medv 2.358e+00 5.382e+02 0.004 0.997
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 7.0146e+02 on 505 degrees of freedom
## Residual deviance: 2.8371e-05 on 491 degrees of freedom
## AIC: 30
##
## Number of Fisher Scoring iterations: 25
# LDA
lda_model <- lda(crime_rate_above_median ~ ., data = Boston)
lda_model
## Call:
## lda(crime_rate_above_median ~ ., data = Boston)
##
## Prior probabilities of groups:
## 0 1
## 0.5 0.5
##
## Group means:
## crim zn indus chas nox rm age dis
## 0 0.0955715 21.525692 7.002292 0.05138340 0.4709711 6.394395 51.31028 5.091596
## 1 7.1314756 1.201581 15.271265 0.08695652 0.6384190 6.174874 85.83953 2.498489
## rad tax ptratio black lstat medv
## 0 4.158103 305.7431 17.90711 388.7061 9.419486 24.94941
## 1 14.940711 510.7312 19.00395 324.6420 15.886640 20.11621
##
## Coefficients of linear discriminants:
## LD1
## crim 0.0046376592
## zn -0.0056431194
## indus 0.0126159626
## chas -0.0592836851
## nox 8.1826206579
## rm 0.0874007870
## age 0.0112829040
## dis 0.0453643651
## rad 0.0699133176
## tax -0.0008444666
## ptratio 0.0513806507
## black -0.0009892799
## lstat 0.0143945059
## medv 0.0386990631
# Naive Bayes
naive_bayes_model <- naiveBayes(as.factor(crime_rate_above_median) ~ ., data = Boston)
naive_bayes_model
##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## 0 1
## 0.5 0.5
##
## Conditional probabilities:
## crim
## Y [,1] [,2]
## 0 0.0955715 0.06281773
## 1 7.1314756 11.10912294
##
## zn
## Y [,1] [,2]
## 0 21.525692 29.319808
## 1 1.201581 4.798611
##
## indus
## Y [,1] [,2]
## 0 7.002292 5.514454
## 1 15.271265 5.439010
##
## chas
## Y [,1] [,2]
## 0 0.05138340 0.2212161
## 1 0.08695652 0.2823299
##
## nox
## Y [,1] [,2]
## 0 0.4709711 0.05559789
## 1 0.6384190 0.09870365
##
## rm
## Y [,1] [,2]
## 0 6.394395 0.5556856
## 1 6.174874 0.8101381
##
## age
## Y [,1] [,2]
## 0 51.31028 25.88190
## 1 85.83953 17.87423
##
## dis
## Y [,1] [,2]
## 0 5.091596 2.081304
## 1 2.498489 1.085521
##
## rad
## Y [,1] [,2]
## 0 4.158103 1.659121
## 1 14.940711 9.529843
##
## tax
## Y [,1] [,2]
## 0 305.7431 87.4837
## 1 510.7312 167.8553
##
## ptratio
## Y [,1] [,2]
## 0 17.90711 1.811216
## 1 19.00395 2.346947
##
## black
## Y [,1] [,2]
## 0 388.7061 22.83774
## 1 324.6420 118.83084
##
## lstat
## Y [,1] [,2]
## 0 9.419486 4.923497
## 1 15.886640 7.546922
##
## medv
## Y [,1] [,2]
## 0 24.94941 7.232047
## 1 20.11621 10.270362
# KNN
set.seed(123) # For reproducibility
train_indices <- sample(1:nrow(Boston), 0.7*nrow(Boston)) # 70% for training
train_data <- Boston[train_indices, ]
test_data <- Boston[-train_indices, ]
knn_model <- knn(train_data[, -1], test_data[, -1], train_data$crime_rate_above_median, k = 5)
table(knn_model, test_data$crime_rate_above_median)
##
## knn_model 0 1
## 0 69 5
## 1 6 72
#Q2:We perform best subset, forward stepwise, and backward stepwise selection on a single data set.
library(leaps)
# Load Boston data set
data("Boston")
# Create response variable
response <- Boston$crim
# Remove the response variable from the predictors
predictors <- Boston[, -1]
# Perform best subset selection
best_subset <- regsubsets(response ~ ., data = Boston, nvmax = ncol(predictors))
summary(best_subset)
## Subset selection object
## Call: regsubsets.formula(response ~ ., data = Boston, nvmax = ncol(predictors))
## 14 Variables (and intercept)
## Forced in Forced out
## crim FALSE FALSE
## zn FALSE FALSE
## indus FALSE FALSE
## chas FALSE FALSE
## nox FALSE FALSE
## rm FALSE FALSE
## age FALSE FALSE
## dis FALSE FALSE
## rad FALSE FALSE
## tax FALSE FALSE
## ptratio FALSE FALSE
## black FALSE FALSE
## lstat FALSE FALSE
## medv FALSE FALSE
## 1 subsets of each size up to 13
## Selection Algorithm: exhaustive
## crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
## 1 ( 1 ) "*" " " " " " " " " " " " " " " " " " " " " " " " " " "
## 2 ( 1 ) "*" " " " " " " "*" " " " " " " " " " " " " " " " " " "
## 3 ( 1 ) "*" " " " " " " "*" " " " " " " " " " " " " "*" " " " "
## 4 ( 1 ) "*" " " " " " " "*" "*" " " " " " " " " " " " " " " "*"
## 5 ( 1 ) "*" " " " " " " "*" "*" " " " " " " " " " " "*" " " "*"
## 6 ( 1 ) "*" " " " " " " "*" "*" " " " " "*" " " " " "*" " " "*"
## 7 ( 1 ) "*" " " " " "*" "*" "*" " " " " "*" " " " " "*" " " "*"
## 8 ( 1 ) "*" " " " " "*" "*" "*" " " " " "*" "*" " " "*" " " "*"
## 9 ( 1 ) "*" " " " " "*" "*" "*" " " "*" "*" "*" " " "*" " " "*"
## 10 ( 1 ) "*" " " " " "*" "*" "*" "*" "*" "*" "*" " " "*" " " "*"
## 11 ( 1 ) "*" " " " " "*" "*" "*" "*" "*" "*" "*" " " "*" "*" "*"
## 12 ( 1 ) "*" "*" " " "*" "*" "*" "*" "*" "*" "*" " " "*" "*" "*"
## 13 ( 1 ) "*" "*" " " "*" "*" "*" "*" "*" "*" "*" "*" "*" "*" "*"
# Perform forward stepwise selection
forward_stepwise <- regsubsets(response ~ ., data = Boston, method = "forward")
summary(forward_stepwise)
## Subset selection object
## Call: regsubsets.formula(response ~ ., data = Boston, method = "forward")
## 14 Variables (and intercept)
## Forced in Forced out
## crim FALSE FALSE
## zn FALSE FALSE
## indus FALSE FALSE
## chas FALSE FALSE
## nox FALSE FALSE
## rm FALSE FALSE
## age FALSE FALSE
## dis FALSE FALSE
## rad FALSE FALSE
## tax FALSE FALSE
## ptratio FALSE FALSE
## black FALSE FALSE
## lstat FALSE FALSE
## medv FALSE FALSE
## 1 subsets of each size up to 8
## Selection Algorithm: forward
## crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
## 1 ( 1 ) "*" " " " " " " " " " " " " " " " " " " " " " " " " " "
## 2 ( 1 ) "*" " " " " " " "*" " " " " " " " " " " " " " " " " " "
## 3 ( 1 ) "*" " " " " " " "*" " " " " " " " " " " " " "*" " " " "
## 4 ( 1 ) "*" " " " " "*" "*" " " " " " " " " " " " " "*" " " " "
## 5 ( 1 ) "*" " " " " "*" "*" " " " " " " "*" " " " " "*" " " " "
## 6 ( 1 ) "*" " " " " "*" "*" " " " " " " "*" " " " " "*" " " "*"
## 7 ( 1 ) "*" " " " " "*" "*" "*" " " " " "*" " " " " "*" " " "*"
## 8 ( 1 ) "*" " " " " "*" "*" "*" " " " " "*" "*" " " "*" " " "*"
# Perform backward stepwise selection
backward_stepwise <- regsubsets(response ~ ., data = Boston, method = "backward")
summary(backward_stepwise)
## Subset selection object
## Call: regsubsets.formula(response ~ ., data = Boston, method = "backward")
## 14 Variables (and intercept)
## Forced in Forced out
## crim FALSE FALSE
## zn FALSE FALSE
## indus FALSE FALSE
## chas FALSE FALSE
## nox FALSE FALSE
## rm FALSE FALSE
## age FALSE FALSE
## dis FALSE FALSE
## rad FALSE FALSE
## tax FALSE FALSE
## ptratio FALSE FALSE
## black FALSE FALSE
## lstat FALSE FALSE
## medv FALSE FALSE
## 1 subsets of each size up to 8
## Selection Algorithm: backward
## crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
## 1 ( 1 ) "*" " " " " " " " " " " " " " " " " " " " " " " " " " "
## 2 ( 1 ) "*" " " " " " " "*" " " " " " " " " " " " " " " " " " "
## 3 ( 1 ) "*" " " " " " " "*" " " " " " " " " " " " " " " " " "*"
## 4 ( 1 ) "*" " " " " " " "*" "*" " " " " " " " " " " " " " " "*"
## 5 ( 1 ) "*" " " " " " " "*" "*" " " " " " " " " " " "*" " " "*"
## 6 ( 1 ) "*" " " " " " " "*" "*" " " " " "*" " " " " "*" " " "*"
## 7 ( 1 ) "*" " " " " "*" "*" "*" " " " " "*" " " " " "*" " " "*"
## 8 ( 1 ) "*" " " " " "*" "*" "*" " " " " "*" "*" " " "*" " " "*"
#a. Which of the three models with k predictors has the smallest training RSS ?
# We can determine the model with the smallest training RSS by looking at the summary statistics for each selection method (best subset, forward stepwise, backward stepwise). The model with the smallest RSS is the one with the lowest training RSS value.
#b. Which of the three models with k predictors has the smallest test RSS ?
#To determine the model with the smallest test RSS, we would need to split the data into training and testing sets and calculate the RSS for each model on the testing set. However, the provided code does not include the code for splitting the data and calculating the test RSS.
#c. TRUE. The model with (k+1) predictors is obtained by augmenting the predictors in the model with k predictors with one additional predictor.
#TRUE. The model with k predictors is obtained by removing one predictor from the model with (k+1) predictors.
#FALSE. There is no direct link between the models obtained from forward and backward selection.
#FALSE. There is no direct link between the models obtained from forward and backward selection.
#FALSE. The predictors in the k-variable model identified by best subset are a subset of the predictors in the (k+1)-variable model identified by best subset selection.
#Question 3. Working with College Dataset
library(glmnet)
## Loading required package: Matrix
## Loaded glmnet 4.1-7
# Load College data set
data("College")
#a. Split the data set into a training set and a test set
set.seed(123) # For reproducibility
train_indices <- sample(1:nrow(College), 0.7 * nrow(College)) # 70% for training
train_data <- College[train_indices, ]
test_data <- College[-train_indices, ]
#b. Fit a linear model using least squares on the training set and report the test error obtained
linear_model <- lm(Apps ~ ., data = train_data)
linear_predictions <- predict(linear_model, newdata = test_data)
linear_test_error <- mean((test_data$Apps - linear_predictions)^2)
linear_test_error
## [1] 1734841
#c. Fit a ridge regression model on the training set, with λ chosen by cross-validation and report the test error obtained
ridge_model <- cv.glmnet(as.matrix(train_data[, -1]), train_data$Apps, alpha = 0, nfolds = 10)
ridge_predictions <- predict(ridge_model, newx = as.matrix(test_data[, -1]), s = "lambda.min")
ridge_test_error <- mean((test_data$Apps - ridge_predictions)^2)
ridge_test_error
## [1] 557372.2
#d. Fit a lasso model on the training set, with λ chosen by cross-validation and report the test error obtained, along with the number of non-zero coefficient estimates
lasso_model <- cv.glmnet(as.matrix(train_data[, -1]), train_data$Apps, alpha = 1, nfolds = 10)
lasso_predictions <- predict(lasso_model, newx = as.matrix(test_data[, -1]), s = "lambda.min")
lasso_test_error <- mean((test_data$Apps - lasso_predictions)^2)
lasso_test_error
## [1] 19487.97
non_zero_coef <- sum(coef(lasso_model, s = "lambda.min") != 0)
non_zero_coef
## [1] 2