#Correlation and Regression: Datacamp
#3rd Course in Statistics with R Track
#browseURL("https://www.openintro.org/stat/data/")
if(!require("openintro")){
install.packages("openintro")
library(openintro)
}
## Loading required package: openintro
## Please visit openintro.org for free statistics materials
##
## Attaching package: 'openintro'
## The following objects are masked from 'package:datasets':
##
## cars, trees
if(!require("HistData")){
install.packages("HistData")
library(HistData)
}
## Loading required package: HistData
data(anscombe)
data(mammals)
data(mlbBat10)
data(possum)
data(ncbirths)
data(bdims)
data(GaltonFamilies)
library(data.table)
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:openintro':
##
## diamonds
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:data.table':
##
## between, first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#bdims dataset
#download.file("http://www.openintro.org/stat/data/bdims.RData",destfile="bdims.RData")
#load("bdims.RData")
###################Part 1: VISUALIZING 2 VARIABLES###############
#Anscombe's quartet comprises four datasets that have nearly
#identical simple descriptive statistics, yet appear very
#different when graphed.
# str(anscombe)
#
# ggplot(data = anscombe, aes(x = x, y = y)) +
# geom_point() +
# facet_wrap(~ set)
#
# # Compute properties of Anscombe
# anscombe %>%
# group_by(set) %>%
# summarize(N = n(), mean(x), sd(x),
# mean(y), sd(y), cor(x,y))
###################1.8 Outliers###################
###################1.9 Identifying Outliers###################
# In Chapter 5, we will discuss how outliers can affect the results
# of a linear regression model and how we can deal with them.
# For now, it is enough to simply identify them and note how the
# relationship between two variables may change as a result of
# removing outliers.
#
# Recall that in the baseball example earlier in the chapter,
# most of the points were clustered in the lower left corner
# of the plot, making it difficult to see the general pattern
# of the majority of the data. This difficulty was caused by a
# few outlying players whose on-base percentages (OBPs) were
# exceptionally high. These values are present in our dataset
# only because these players had very few batting opportunities.
#
# Both OBP and SLG are known as rate statistics, since they
# measure the frequency of certain events (as opposed to their
# count). In order to compare these rates sensibly, it makes
# sense to include only players with a reasonable number of
# opportunities, so that these observed rates have the chance
# to approach their long-run frequencies.
#
# In Major League Baseball, batters qualify for the batting
# title only if they have 3.1 plate appearances per game
# . This translates into roughly 502 plate appearances in a
# 162-game season. The mlbBat10 dataset does not include plate
# appearances as a variable, but we can use at-bats (AB) --
# which constitute a subset of plate appearances -- as a proxy.
# Use filter() to create a scatterplot for SLG as a function
# of OBP among players who had at least 200 at-bats.
mlbBat10 %>%
filter(AB >= 200) %>%
ggplot(aes(x = OBP, y = SLG)) +
geom_point()
#Find the row of mlbBat10 corresponding to the one player with at least 200 at-bats whose OBP was below 0.200.
mlbBat10 %>%
filter(AB >= 200, OBP < 0.200)
## name team position G AB R H 2B 3B HR RBI TB BB SO SB CS OBP
## 1 B Wood LAA 3B 81 226 20 33 2 0 4 14 47 6 71 1 0 0.174
## SLG AVG
## 1 0.208 0.146
###################Part 2: CORRELATION#######################
##############2.4 Computing correlation###############
# The cor(x, y) function will compute the Pearson product-momen
# t correlation between variables, x and y. Since this quantity is
# symmetric with respect to x and y, it doesn't matter in which order
# you put the variables.
#
# At the same time, the cor() function is very conservative when it
# encounters missing data (e.g. NAs). The use argument allows you to
# override the default behavior of returning NA whenever any of the values
# encountered is NA. Setting the use argument to "pairwise.complete.obs"
# allows cor() to compute the correlation coefficient for those observations
# where the values of x and y are both not missing.
# Use cor() to compute the correlation between the birthweight of
# babies in the ncbirths dataset and their mother's age. There is
# no missing data in either variable.
ncbirths %>%
summarize(N = n(), r = cor(mage, weight))
## N r
## 1 1000 0.05506589
# Compute the correlation between the birthweight and the number
# of weeks of gestation for all non-missing pairs.
ncbirths %>%
summarize(N = n(), r = cor(weight, weeks, use = "pairwise.complete.obs"))
## N r
## 1 1000 0.6701013
##############2.8 Perception of correlation (2)###############
# Correlation for all baseball players
mlbBat10 %>%
summarize(N = n(), r = cor(OBP, SLG))
## N r
## 1 1199 0.8145628
# Correlation for all players with at least 200 ABs
mlbBat10 %>%
filter(AB >= 200) %>%
summarize(N = n(), r = cor(OBP, SLG))
## N r
## 1 329 0.6855364
# Correlation of body dimensions
bdims %>%
group_by(sex) %>%
summarize(N = n(), r = cor(wgt,hgt))
## # A tibble: 2 x 3
## sex N r
## <int> <int> <dbl>
## 1 0 260 0.4310593
## 2 1 247 0.5347418
# Correlation among mammals, with and without log
mammals %>%
summarize(N = n(),
r = cor(BodyWt, BrainWt),
r_log = cor(log(BodyWt), log(BrainWt)))
## N r r_log
## 1 62 0.9341638 0.9595748
#CORRELATION AND CAUSATION
#In the San Francisco Bay Area from 1960-1967, the correlation between the birthweight of 1,236 babies and the length of their gestational period was 0.408. Which of the following conclusions is not a valid statistical interpretation of these results.
#Staying in the womb longer causes babies to be heavier when they are born.
########PART 3: SIMPLE LINEAR REGRESSION########
########3.1 Visualization of linear models###########
ggplot(data = possum, aes(y = totalL, x = tailL)) +
geom_point()
#through the origin--this doesn't capture the general trend
ggplot(data = possum, aes(y = totalL, x = tailL)) +
geom_point() + geom_abline(intercept = 0, slope = 2.5)
#not through the origin
ggplot(data = possum, aes(y = totalL, x = tailL)) +
geom_point() + geom_abline(intercept = 40, slope = 1.3)
#Which line fits better? Use the least squares criteria to get the best fit line.
ggplot(data = possum, aes(y = totalL, x = tailL)) +
geom_point() + geom_smooth(method = "lm")
#this will automatically draw the regression line in blue with the grey
#shading representing the standard error of the line
ggplot(data = possum, aes(y = totalL, x = tailL)) +
geom_point() + geom_smooth(method = "lm", se = FALSE) #to turn off grey shading
########3.2 The "best fit" line###########
# Scatterplot with regression line
# Scatterplot with regression line
ggplot(data = bdims, aes(x = hgt, y = wgt)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
ggplot(bdims, aes(x=hgt, y=wgt)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm, # Add linear regression line
se=FALSE) # Don't add shaded confidence region
#############3.3 Uniqueness of least squares regression line########
bdims.lm<-lm(wgt~hgt, data=bdims)
summary(bdims.lm)
##
## Call:
## lm(formula = wgt ~ hgt, data = bdims)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.743 -6.402 -1.231 5.059 41.103
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -105.01125 7.53941 -13.93 <2e-16 ***
## hgt 1.01762 0.04399 23.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.308 on 505 degrees of freedom
## Multiple R-squared: 0.5145, Adjusted R-squared: 0.5136
## F-statistic: 535.2 on 1 and 505 DF, p-value: < 2.2e-16
# The least squares criterion implies that the slope of the
# regression line is unique. In practice, the slope is computed
# by R. In this exercise, you will experiment with trying to find
# the optimal value for the regression slope for weight as a
# function of height in the bdims dataset via trial-and-error.
#
# To help, we've built a custom function for you called
# add_line(), which takes a single argument: the proposed
# slope coefficient.
# Estimate optimal value of my_slope
#ANSWER: add_line(my_slope = 1)
################3.4 Understanding the linear model##################
#Generic statistical model
#response = f(explanatory) + noise
################3.6 Regression model output terminology##################
#The fitted model for the poverty rate of U.S. counties as a function of high school graduation rate is:
#poverty^=64.594−0.591⋅hs_grad
#In Hampshire County in western Massachusetts, the high school
#graduation rate is 92.4%. These two facts imply that the poverty
#rate in Hampshire County is ___.
#ANSWER: EXPECTED TO BE ABOUT 10%
###############3.7 Fitting a linear model "by hand"####################
data("trees")
reg1<-lm(Height ~ Girth, data = trees) #linear regression model--
#girth to predict height
reg1
##
## Call:
## lm(formula = Height ~ Girth, data = trees)
##
## Coefficients:
## (Intercept) Girth
## 62.031 1.054
summary(reg1)
##
## Call:
## lm(formula = Height ~ Girth, data = trees)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.5816 -2.7686 0.3163 2.4728 9.9456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.0313 4.3833 14.152 1.49e-14 ***
## Girth 1.0544 0.3222 3.272 0.00276 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.538 on 29 degrees of freedom
## Multiple R-squared: 0.2697, Adjusted R-squared: 0.2445
## F-statistic: 10.71 on 1 and 29 DF, p-value: 0.002758
confint(reg1) #confidence intervals for coefficients
## 2.5 % 97.5 %
## (Intercept) 53.0664541 70.996174
## Girth 0.3953483 1.713389
trees%>%
mutate(slope = cor(Height, Girth),
intercept = sd(Height, Girth))
## Warning in if (na.rm) "na.or.complete" else "everything": the condition has
## length > 1 and only the first element will be used
## Girth Height Volume slope intercept
## 1 8.3 70 10.3 0.5192801 6.371813
## 2 8.6 65 10.3 0.5192801 6.371813
## 3 8.8 63 10.2 0.5192801 6.371813
## 4 10.5 72 16.4 0.5192801 6.371813
## 5 10.7 81 18.8 0.5192801 6.371813
## 6 10.8 83 19.7 0.5192801 6.371813
## 7 11.0 66 15.6 0.5192801 6.371813
## 8 11.0 75 18.2 0.5192801 6.371813
## 9 11.1 80 22.6 0.5192801 6.371813
## 10 11.2 75 19.9 0.5192801 6.371813
## 11 11.3 79 24.2 0.5192801 6.371813
## 12 11.4 76 21.0 0.5192801 6.371813
## 13 11.4 76 21.4 0.5192801 6.371813
## 14 11.7 69 21.3 0.5192801 6.371813
## 15 12.0 75 19.1 0.5192801 6.371813
## 16 12.9 74 22.2 0.5192801 6.371813
## 17 12.9 85 33.8 0.5192801 6.371813
## 18 13.3 86 27.4 0.5192801 6.371813
## 19 13.7 71 25.7 0.5192801 6.371813
## 20 13.8 64 24.9 0.5192801 6.371813
## 21 14.0 78 34.5 0.5192801 6.371813
## 22 14.2 80 31.7 0.5192801 6.371813
## 23 14.5 74 36.3 0.5192801 6.371813
## 24 16.0 72 38.3 0.5192801 6.371813
## 25 16.3 77 42.6 0.5192801 6.371813
## 26 17.3 81 55.4 0.5192801 6.371813
## 27 17.5 82 55.7 0.5192801 6.371813
## 28 17.9 80 58.3 0.5192801 6.371813
## 29 18.0 80 51.5 0.5192801 6.371813
## 30 18.0 80 51.0 0.5192801 6.371813
## 31 20.6 87 77.0 0.5192801 6.371813
ggplot(trees, aes(x=Girth, y=Height)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm, # Add linear regression line
se=TRUE) # add shaded confidence region
# Recall the simple linear regression model:
# Y=b0+b1⋅X
# Y=b0+b1⋅X
# Two facts enable you to compute the slope b1b1 and
# intercept b0b0 of a simple linear regression model
# from some basic summary statistics.
#
# First, the slope can be defined as:
#
# b1=rX,Y⋅sYsX
# b1=rX,Y⋅sYsX
# where rX,YrX,Y represents the correlation (cor())
# of XX and YY and sXsX and sYsY represent the standard deviation
# (sd()) of XX and YY, respectively.
#
# Second, the point (x¯,y¯)(x¯,y¯) is always on the least squares
# regression line, where x¯x¯ and y¯y¯ denote the average of xx and yy,
# respectively.
#
# The bdims_summary data frame contains all of the information you need
# to compute the slope and intercept of the least squares regression line
# for body weight (YY) as a function of height (XX). You might need to do some
# algebra to solve for b0b0!
#
# Print the bdims_summary data frame.
bdims_summary <-bdims
# Hint
# To get intercept, first calculate slope, then solve
# Y=b0+b1⋅X
# Y=b0+b1⋅X
# for b0b0. Finally, plug in mean_wgt for YY, mean_hgt for XX, and slope for b1b1.
#Use mutate() to add the slope and intercept to the bdims_summary data frame.
#bdims_summary %>%
# mutate(slope =,
# intercept =)
#####Answer
# Print bdims_summary
bdims_summary
## bia.di bii.di bit.di che.de che.di elb.di wri.di kne.di ank.di sho.gi
## 1 42.9 26.0 31.5 17.7 28.0 13.1 10.4 18.8 14.1 106.2
## 2 43.7 28.5 33.5 16.9 30.8 14.0 11.8 20.6 15.1 110.5
## 3 40.1 28.2 33.3 20.9 31.7 13.9 10.9 19.7 14.1 115.1
## 4 44.3 29.9 34.0 18.4 28.2 13.9 11.2 20.9 15.0 104.5
## 5 42.5 29.9 34.0 21.5 29.4 15.2 11.6 20.7 14.9 107.5
## 6 43.3 27.0 31.5 19.6 31.3 14.0 11.5 18.8 13.9 119.8
## 7 43.5 30.0 34.0 21.9 31.7 16.1 12.5 20.8 15.6 123.5
## 8 44.4 29.8 33.2 21.8 28.8 15.1 11.9 21.0 14.6 120.4
## 9 43.5 26.5 32.1 15.5 27.5 14.1 11.2 18.9 13.2 111.0
## 10 42.0 28.0 34.0 22.5 28.0 15.6 12.0 21.1 15.0 119.5
## 11 40.3 29.0 33.0 20.1 30.3 13.4 10.4 19.4 14.5 117.1
## 12 43.7 29.0 31.3 20.5 29.7 15.0 11.7 20.9 16.0 123.5
## 13 47.4 29.6 35.7 20.8 31.4 16.1 11.3 21.5 15.4 116.5
## 14 40.3 27.5 31.4 21.7 28.0 13.3 10.3 18.8 13.2 113.0
## 15 41.0 26.8 32.2 21.9 28.6 14.9 10.6 17.8 14.0 107.5
## 16 45.0 27.0 33.2 21.7 30.6 13.7 11.1 20.7 14.0 112.0
## 17 39.9 30.0 34.5 21.0 29.4 15.6 11.9 21.2 16.0 112.2
## 18 43.0 26.5 30.3 19.3 30.0 14.8 11.2 19.7 14.7 120.0
## 19 43.1 28.6 33.4 22.2 29.5 14.9 12.2 20.8 14.8 109.0
## 20 43.6 29.3 34.4 20.2 32.6 15.4 10.9 20.7 15.5 118.5
## 21 42.0 27.5 30.7 21.3 32.0 13.1 11.1 19.2 13.9 116.0
## 22 43.8 28.0 33.3 20.0 32.0 15.0 11.5 20.4 14.4 111.0
## 23 42.3 26.4 31.2 18.0 30.9 14.6 10.8 18.6 13.8 117.7
## 24 42.7 29.9 35.0 21.8 32.8 14.3 11.2 19.8 14.1 123.9
## 25 44.8 27.8 32.2 18.3 31.5 15.2 11.6 19.4 14.7 120.6
## 26 46.0 30.1 34.5 20.2 31.1 16.4 13.3 22.2 14.9 129.5
## 27 45.4 31.8 35.2 20.2 32.3 14.6 10.5 20.2 15.3 115.0
## 28 40.5 28.3 33.4 19.2 28.8 14.6 11.1 20.8 14.5 116.0
## 29 39.4 25.5 30.2 17.6 27.7 13.0 10.2 18.9 13.2 107.8
## 30 40.2 27.2 31.7 18.1 26.5 13.3 10.1 18.6 13.2 100.2
## 31 44.2 30.3 34.7 19.4 30.0 14.9 11.0 19.1 15.8 113.0
## 32 41.0 23.6 30.2 22.9 28.0 14.3 11.2 18.2 14.0 117.9
## 33 44.0 31.0 35.3 19.2 31.0 15.2 11.4 21.2 15.1 112.5
## 34 41.6 32.0 35.3 23.6 27.0 15.5 11.3 20.9 15.0 110.5
## 35 41.0 25.1 31.9 20.8 27.9 13.6 10.8 18.8 12.9 112.0
## 36 41.5 24.5 30.5 17.7 26.7 13.3 10.8 18.6 14.0 104.0
## 37 41.1 27.8 31.4 19.0 31.5 14.5 11.9 18.5 13.0 114.8
## 38 38.8 27.2 31.6 18.5 25.5 13.4 10.8 19.0 14.0 108.0
## 39 36.2 27.5 30.4 18.7 28.0 13.6 10.8 19.0 15.4 111.2
## 40 42.1 27.5 32.4 18.2 28.0 16.2 12.0 21.0 16.4 118.3
## 41 40.3 29.4 32.9 23.7 31.5 14.6 11.3 19.8 15.2 115.2
## 42 41.7 27.1 32.6 21.6 28.0 14.1 11.5 19.7 13.8 129.9
## 43 37.8 27.1 31.5 18.5 27.3 14.6 10.8 19.5 14.9 112.9
## 44 39.2 26.1 30.8 19.4 29.9 14.3 11.2 20.0 16.0 112.2
## 45 41.5 30.8 33.3 19.4 30.6 14.8 11.3 20.2 16.0 117.1
## 46 42.5 27.8 33.5 20.6 30.2 15.9 12.8 22.4 16.3 118.7
## 47 39.4 26.1 34.4 20.4 27.3 15.1 10.6 20.0 15.3 109.2
## 48 43.6 33.1 33.5 21.6 33.1 15.6 12.0 20.7 16.5 128.1
## 49 38.9 24.9 28.7 19.7 26.8 14.2 10.2 18.0 14.4 113.3
## 50 37.6 24.4 28.0 18.0 26.4 14.2 10.6 17.3 13.4 108.4
## 51 39.4 28.3 30.6 20.2 28.7 15.0 11.5 18.4 14.4 118.7
## 52 38.5 26.1 30.8 20.6 30.8 15.1 11.4 19.8 14.2 126.3
## 53 40.1 27.8 33.1 19.2 31.3 15.4 11.5 20.6 15.4 124.2
## 54 40.3 28.0 32.0 20.9 31.7 14.8 10.6 19.4 15.0 126.7
## 55 37.6 26.6 29.9 17.3 25.6 12.8 10.0 17.0 13.0 103.3
## 56 38.3 25.2 30.2 17.0 26.4 13.2 10.4 18.8 13.0 101.2
## 57 39.7 28.6 32.1 19.1 27.1 13.4 10.0 18.2 14.8 104.3
## 58 42.2 29.0 33.7 22.5 30.4 15.6 12.0 19.8 16.2 113.2
## 59 41.1 30.4 35.1 23.2 32.6 15.5 11.6 21.5 15.4 121.9
## 60 40.5 29.3 33.7 19.6 29.8 13.8 11.7 19.7 14.4 113.1
## 61 41.5 28.6 30.4 20.8 26.9 14.8 11.2 20.7 16.5 108.5
## 62 43.4 32.4 36.4 20.3 32.1 15.6 12.0 20.8 16.3 113.9
## 63 43.5 26.0 31.6 19.1 30.9 14.3 11.4 19.5 14.6 112.6
## 64 41.3 27.1 32.4 17.5 27.6 14.1 10.8 20.2 15.5 110.2
## 65 40.3 29.5 33.3 18.4 26.2 14.0 11.0 19.4 14.8 108.7
## 66 36.3 29.2 33.0 20.0 29.0 14.1 11.7 20.4 14.3 104.0
## 67 39.9 28.3 32.0 18.3 31.4 13.5 11.4 18.9 14.4 115.2
## 68 39.8 28.8 33.0 19.7 28.7 12.4 10.7 18.5 13.2 111.9
## 69 43.5 33.2 34.0 23.9 34.3 15.8 12.0 18.6 13.2 127.0
## 70 41.2 26.6 30.6 19.5 28.0 13.1 10.4 19.0 13.8 111.2
## 71 44.0 28.4 32.0 22.5 29.7 14.9 10.9 21.0 14.8 122.0
## 72 41.8 28.5 31.6 21.6 31.5 13.3 10.3 18.9 14.3 114.5
## 73 42.9 27.5 30.3 18.9 29.6 12.6 10.4 19.2 13.8 109.5
## 74 38.7 24.6 28.5 18.3 29.8 14.0 11.2 18.9 13.6 110.8
## 75 41.4 26.4 32.3 18.6 31.3 14.9 11.5 18.9 14.6 118.8
## 76 39.6 27.5 30.2 19.2 28.9 13.5 10.4 19.3 14.2 108.0
## 77 40.5 27.5 32.3 19.4 28.8 12.6 10.6 18.4 14.0 114.3
## 78 34.1 28.1 30.1 21.8 25.8 12.9 9.9 18.6 12.3 105.4
## 79 43.5 28.8 34.0 20.6 29.0 14.3 10.5 19.8 14.2 115.0
## 80 44.1 29.2 35.3 23.6 30.9 15.8 12.5 20.2 15.2 119.5
## 81 42.2 32.6 36.6 22.4 34.5 14.1 11.1 18.2 13.9 130.0
## 82 42.2 30.1 31.4 21.2 29.7 14.0 11.6 21.6 14.1 113.3
## 83 43.0 26.5 31.6 20.6 29.5 13.4 10.4 18.8 13.6 113.2
## 84 39.8 28.7 33.3 19.3 29.2 13.5 11.6 19.5 14.6 106.9
## 85 37.7 29.7 32.7 20.2 28.8 13.3 11.1 18.3 13.2 113.8
## 86 39.6 27.9 33.3 20.2 29.5 12.6 10.7 18.5 12.9 117.3
## 87 43.2 26.3 30.5 19.7 30.6 14.4 12.3 20.2 13.6 124.2
## 88 44.3 28.2 32.2 21.2 31.8 14.2 11.6 20.0 14.4 123.0
## 89 43.3 28.2 33.0 19.4 31.6 13.8 11.1 17.8 13.2 117.8
## 90 42.8 27.5 31.5 19.2 31.8 14.1 11.1 19.1 14.7 118.8
## 91 41.5 30.0 33.4 19.1 29.4 14.8 11.0 19.8 13.8 112.0
## 92 42.0 27.6 32.2 19.7 29.4 13.9 10.0 18.7 13.8 113.0
## 93 41.2 27.1 29.8 20.1 31.0 12.9 11.6 18.8 13.5 116.0
## 94 43.8 29.5 31.2 18.2 29.5 13.1 10.3 19.1 13.2 112.8
## 95 46.2 31.0 36.0 25.0 33.1 14.6 12.0 20.9 15.1 125.0
## 96 40.4 28.6 31.4 19.8 27.6 13.9 10.1 20.0 13.4 108.3
## 97 40.8 27.1 29.4 17.8 29.4 13.3 10.4 18.5 12.8 108.2
## 98 43.9 27.0 33.5 22.3 31.0 13.2 10.4 19.1 13.1 113.0
## 99 44.2 27.9 32.0 21.6 32.9 14.3 11.0 21.1 14.9 115.0
## 100 41.6 28.0 35.0 24.2 31.0 13.4 11.2 20.6 14.4 123.0
## 101 38.1 30.1 33.2 21.6 31.3 14.2 12.3 19.2 15.2 120.2
## 102 42.0 28.0 33.0 18.1 28.4 14.3 11.1 20.2 15.2 114.0
## 103 37.0 27.3 31.1 18.2 25.0 13.2 10.5 18.7 13.4 102.9
## 104 41.6 27.5 32.0 18.1 29.5 13.8 10.7 19.0 13.9 112.5
## 105 40.1 19.4 28.0 17.1 26.8 13.0 10.6 16.9 12.6 104.5
## 106 38.7 25.2 28.8 19.1 25.6 13.0 10.2 17.9 13.5 111.3
## 107 37.4 29.9 33.5 22.3 30.8 14.4 11.5 20.5 16.8 117.2
## 108 41.7 28.0 32.9 19.4 29.7 14.6 11.0 19.5 15.3 112.8
## 109 38.0 27.1 28.3 18.2 25.9 13.8 11.0 18.9 14.8 104.8
## 110 40.5 24.9 29.7 19.0 30.2 14.4 11.8 19.5 14.9 117.7
## 111 35.6 28.5 29.4 17.7 25.2 14.0 10.8 19.1 15.0 107.7
## 112 43.6 30.2 32.4 21.8 33.1 15.2 11.3 19.8 15.2 125.2
## 113 37.6 24.4 28.3 17.7 24.7 12.9 10.8 18.0 14.3 109.1
## 114 41.1 31.7 34.2 22.8 34.0 13.8 11.8 19.4 15.4 122.6
## 115 42.1 30.6 34.0 22.1 30.6 15.0 11.4 20.2 15.4 117.3
## 116 40.5 28.3 32.4 19.4 27.8 13.4 11.0 19.0 14.5 109.1
## 117 40.9 28.5 31.3 21.1 29.7 14.3 11.7 19.0 15.6 116.7
## 118 43.0 30.6 33.8 23.3 35.3 15.6 12.0 21.6 16.4 124.9
## 119 40.5 27.8 31.1 21.8 30.6 15.0 11.6 20.4 15.2 118.6
## 120 41.9 25.4 30.2 14.4 26.8 12.6 9.8 18.8 13.6 108.8
## 121 42.1 28.5 33.1 20.2 30.6 15.6 12.2 19.7 15.6 121.9
## 122 43.8 29.2 32.6 18.7 30.4 14.6 11.7 20.0 15.2 117.3
## 123 42.1 28.5 31.7 19.4 28.0 14.0 11.3 19.0 14.4 115.0
## 124 43.4 32.0 36.2 23.5 35.6 16.1 12.6 23.0 16.3 134.8
## 125 38.7 26.8 31.5 21.4 27.8 13.8 10.8 18.2 13.3 113.8
## 126 39.6 28.7 32.4 18.2 28.3 15.2 11.8 19.6 14.8 119.4
## 127 43.4 30.6 32.9 21.6 28.3 15.0 12.0 20.5 17.2 117.9
## 128 40.5 29.7 31.7 22.1 32.6 15.2 11.3 21.2 15.2 127.3
## 129 40.3 30.4 34.2 21.1 34.0 13.6 12.0 19.2 13.8 118.8
## 130 44.2 30.6 33.8 22.1 32.4 15.3 11.5 20.9 16.5 122.4
## 131 41.3 26.8 32.2 21.4 31.1 13.6 11.0 19.1 15.0 111.5
## 132 39.8 25.6 31.3 23.5 32.0 14.0 11.2 21.2 16.4 113.2
## 133 41.3 29.0 32.2 25.2 30.8 14.4 11.0 19.7 15.8 115.3
## 134 38.9 27.5 32.9 22.5 33.3 14.6 11.0 20.5 15.3 122.5
## 135 41.1 25.6 29.9 23.3 25.2 14.1 10.7 19.0 15.1 114.4
## 136 41.5 30.6 35.8 21.1 28.0 15.0 11.8 21.0 15.6 112.8
## 137 38.5 27.8 31.7 19.7 26.4 13.1 11.0 18.4 14.8 112.2
## 138 39.4 29.7 33.1 23.0 30.4 14.2 11.6 20.4 15.0 119.4
## 139 40.9 26.1 27.5 20.2 28.0 13.2 10.4 18.6 14.8 108.4
## 140 41.1 23.0 29.4 21.8 30.6 15.0 10.8 19.3 14.5 122.4
## 141 43.6 28.0 32.4 27.5 33.5 14.6 11.7 21.4 15.1 128.8
## 142 39.8 29.0 34.9 22.5 28.3 14.3 11.7 19.8 15.4 118.0
## 143 42.1 27.8 31.7 20.2 28.7 14.3 11.5 19.6 15.6 116.5
## 144 41.1 27.1 33.8 24.9 29.4 14.4 12.4 18.0 15.1 120.4
## 145 44.2 30.4 36.5 21.6 31.5 15.4 11.6 20.4 15.4 123.1
## 146 38.9 26.8 31.5 20.4 29.0 13.6 10.8 18.9 15.2 117.2
## 147 40.1 28.7 32.2 18.0 29.4 15.2 11.8 20.7 15.4 113.0
## 148 38.7 26.8 31.5 18.0 27.8 12.9 10.4 18.0 14.3 109.4
## 149 35.6 26.4 30.8 19.2 29.4 14.6 11.5 19.6 15.3 105.7
## 150 40.5 26.8 30.6 21.4 32.4 15.0 11.8 20.4 15.8 119.7
## 151 41.1 25.4 32.0 21.6 28.7 14.3 12.4 19.6 14.3 118.0
## 152 38.7 26.1 29.2 18.2 24.9 13.6 10.4 17.6 14.2 104.3
## 153 38.9 27.1 30.4 20.4 28.7 14.8 11.7 19.4 14.6 114.1
## 154 39.4 29.7 33.1 22.3 31.5 15.6 12.0 19.5 14.8 119.0
## 155 37.6 27.8 32.2 20.2 31.3 14.6 11.0 21.5 15.8 113.8
## 156 39.8 25.9 31.3 19.4 29.2 14.3 11.2 18.7 14.3 118.0
## 157 40.3 27.3 30.4 20.4 29.0 15.0 11.3 19.1 14.6 116.0
## 158 40.3 25.2 29.2 18.7 28.5 13.2 10.2 18.9 14.3 108.3
## 159 43.8 30.4 34.9 24.0 33.3 15.4 11.6 17.8 14.6 129.2
## 160 41.1 27.8 32.9 18.0 26.8 14.6 11.2 19.5 15.8 109.2
## 161 41.7 30.6 34.7 24.9 32.0 14.9 10.8 18.9 14.1 124.3
## 162 45.4 30.8 35.6 20.9 29.7 15.2 10.8 18.6 15.0 122.6
## 163 43.0 30.8 34.7 22.1 32.2 16.0 13.2 19.5 16.1 122.4
## 164 41.1 25.4 30.4 19.2 29.9 14.8 10.6 18.7 15.2 114.9
## 165 41.1 29.2 31.5 19.7 29.9 14.8 11.0 18.0 15.0 111.1
## 166 45.2 32.2 36.0 22.5 33.5 15.8 11.3 20.5 14.8 126.0
## 167 39.8 34.7 34.7 23.5 30.2 14.8 10.6 20.6 14.3 115.5
## 168 39.6 28.7 32.0 20.2 32.9 14.3 11.5 19.6 15.1 124.7
## 169 42.3 30.2 34.4 25.4 32.2 15.2 10.7 18.8 14.8 125.3
## 170 40.9 29.0 32.2 20.2 29.2 13.8 10.4 18.4 14.4 118.2
## 171 39.8 28.0 34.2 23.3 31.5 14.0 11.0 19.7 13.8 124.3
## 172 42.3 27.1 31.7 17.3 29.4 14.4 11.6 21.2 15.8 118.3
## 173 42.3 26.8 32.0 25.4 28.7 15.0 11.2 18.4 14.4 122.3
## 174 40.5 28.7 30.4 22.1 29.2 14.3 11.2 18.5 14.5 116.5
## 175 42.7 28.5 36.7 23.7 30.8 15.8 12.9 19.3 16.0 123.0
## 176 43.6 30.8 33.3 20.4 29.7 14.3 10.9 19.6 15.4 117.6
## 177 42.5 31.3 33.1 19.4 32.0 14.8 11.3 20.1 15.5 117.5
## 178 41.3 30.8 33.3 22.5 28.3 13.0 10.5 19.7 13.4 117.3
## 179 41.3 24.7 35.4 22.5 30.2 14.8 11.3 20.4 16.0 114.5
## 180 42.3 26.6 33.3 23.3 28.7 14.2 11.1 19.5 14.3 122.9
## 181 36.9 25.9 31.7 19.9 27.3 14.8 10.6 19.4 14.3 115.5
## 182 40.3 28.5 35.1 19.2 32.0 14.8 12.0 21.2 16.2 116.1
## 183 40.1 26.4 32.0 21.4 32.6 14.8 12.0 20.0 15.2 121.6
## 184 43.2 31.3 34.0 23.0 32.6 15.7 11.5 20.5 15.2 131.6
## 185 45.0 29.0 33.3 25.4 30.8 15.4 11.0 18.8 15.0 124.5
## 186 42.1 29.7 35.6 23.5 31.3 14.3 11.0 18.2 14.8 115.6
## 187 40.9 28.7 33.5 23.7 30.2 15.8 11.6 19.3 16.8 117.0
## 188 41.3 27.3 32.2 20.2 28.3 13.8 11.4 18.0 13.0 108.6
## 189 42.1 29.7 32.9 24.9 30.8 15.3 11.5 19.2 14.6 120.2
## 190 45.2 29.7 33.8 19.9 32.0 15.5 11.8 19.6 16.1 116.2
## 191 42.1 31.1 34.9 22.1 31.3 15.2 11.0 19.0 14.4 115.4
## 192 41.2 29.8 32.2 22.4 29.8 15.6 11.2 19.6 14.8 120.0
## 193 41.7 29.2 33.8 19.2 29.7 13.8 10.7 18.6 14.2 110.3
## 194 43.0 29.4 33.8 24.7 31.5 14.2 11.2 19.4 14.2 119.7
## 195 41.7 28.5 33.8 23.5 32.9 15.6 12.0 18.4 16.3 123.5
## 196 38.0 29.7 34.0 22.8 27.3 13.1 10.8 17.3 15.5 107.8
## 197 41.9 27.8 33.3 19.0 28.7 15.1 11.3 19.2 14.9 118.7
## 198 40.7 28.0 35.3 19.4 31.7 14.4 10.7 18.6 14.4 120.7
## 199 41.3 29.7 34.7 22.8 32.0 15.5 11.2 18.4 15.6 118.7
## 200 40.9 28.7 32.9 21.6 30.6 15.0 11.0 18.7 13.8 118.0
## 201 41.7 26.8 32.0 19.7 27.8 14.0 10.5 18.4 14.6 105.0
## 202 41.3 31.1 34.9 26.4 30.2 15.0 11.6 18.8 15.8 110.6
## 203 41.1 27.8 32.0 21.1 30.6 14.8 11.4 17.6 14.2 123.1
## 204 43.4 27.3 34.7 19.9 31.3 14.0 11.2 20.2 14.3 121.0
## 205 40.7 27.8 34.0 21.1 29.4 15.6 12.0 21.2 16.4 111.7
## 206 42.5 25.2 30.6 20.9 30.4 15.3 11.4 18.9 13.8 111.0
## 207 40.3 28.3 30.6 18.2 29.2 12.9 10.6 20.2 14.2 112.0
## 208 39.8 27.8 32.9 22.3 29.7 16.6 11.8 20.8 15.3 115.9
## 209 39.8 28.3 30.4 19.7 30.2 12.9 11.0 18.6 12.7 121.0
## 210 40.5 29.9 34.9 21.4 32.9 14.5 11.7 20.4 15.0 123.6
## 211 41.1 26.8 32.4 20.2 31.1 14.4 11.8 20.4 15.2 120.0
## 212 42.5 29.4 34.2 23.5 34.7 15.1 11.8 21.8 15.8 128.7
## 213 42.5 29.4 34.4 19.9 34.0 14.5 11.0 21.3 14.4 124.5
## 214 38.5 24.4 30.4 18.0 29.9 14.3 10.1 18.3 13.2 112.4
## 215 43.8 29.2 35.6 19.9 28.3 14.8 12.8 20.7 14.3 112.2
## 216 41.5 29.0 33.3 21.4 32.4 15.3 11.0 20.6 15.0 127.0
## 217 40.3 27.8 33.5 20.6 29.9 15.3 11.2 20.4 13.8 115.3
## 218 40.3 30.2 32.2 20.6 29.2 14.5 11.5 20.0 16.9 111.5
## 219 42.1 27.1 32.2 20.2 30.6 13.7 10.8 18.9 14.3 118.3
## 220 42.1 31.3 35.1 20.6 31.1 16.7 12.0 21.0 15.1 118.0
## 221 38.9 29.4 33.3 24.2 33.5 14.2 12.8 20.6 15.5 127.0
## 222 44.4 24.0 30.2 20.6 32.0 14.2 11.2 20.2 14.5 124.4
## 223 40.9 24.4 28.7 18.7 29.0 14.3 11.4 17.8 14.6 112.4
## 224 42.1 28.7 33.5 25.2 29.4 14.3 11.5 18.0 13.8 125.8
## 225 43.0 27.1 33.5 25.2 31.5 15.2 11.8 19.3 15.1 131.7
## 226 38.0 27.1 32.2 20.6 28.0 13.9 10.3 19.1 14.0 117.3
## 227 37.1 24.2 30.6 17.7 27.8 13.8 11.0 17.8 14.6 107.6
## 228 41.1 24.0 29.4 21.6 30.8 13.6 11.7 18.8 14.6 122.4
## 229 40.9 24.4 30.6 19.7 29.9 13.8 11.5 19.0 15.1 117.0
## 230 38.9 27.1 31.7 21.6 29.7 15.0 12.2 20.5 15.2 119.0
## 231 40.9 28.3 34.0 23.7 28.5 14.3 12.0 19.0 14.3 123.5
## 232 39.2 26.8 34.0 23.7 30.8 14.2 11.2 19.1 14.5 121.4
## 233 41.7 26.6 31.5 19.9 29.9 14.6 11.2 19.2 14.8 112.3
## 234 39.4 26.6 31.7 24.0 31.1 13.8 11.8 20.0 15.8 113.2
## 235 40.1 26.4 32.0 21.8 30.2 15.8 12.4 20.7 15.8 121.7
## 236 38.9 25.6 32.9 21.1 29.0 15.6 10.6 20.2 14.8 116.6
## 237 38.9 26.4 31.7 21.6 27.8 14.4 11.3 19.6 14.8 117.8
## 238 41.7 26.4 31.1 20.2 28.3 14.6 10.2 18.4 15.3 118.0
## 239 43.2 26.8 32.6 22.1 32.9 15.4 12.0 20.5 16.8 131.1
## 240 40.5 29.2 33.5 23.7 31.1 15.2 11.3 20.0 16.1 121.4
## 241 39.2 23.5 29.9 19.7 29.0 15.4 11.5 19.0 14.8 116.3
## 242 40.9 25.4 32.0 20.6 30.2 16.1 11.5 19.3 15.8 121.8
## 243 41.7 27.3 31.5 21.8 29.7 14.9 11.8 18.9 13.6 118.2
## 244 43.8 32.2 38.0 25.4 32.0 16.0 10.7 21.0 16.8 126.3
## 245 41.9 28.0 33.1 26.4 29.9 15.6 11.5 21.2 15.9 121.0
## 246 43.0 27.8 34.2 21.4 31.5 14.3 11.1 21.0 14.8 123.1
## 247 41.5 28.5 33.5 19.7 29.4 14.5 10.5 19.4 15.3 114.9
## 248 37.6 25.0 31.3 16.2 24.9 11.2 9.2 17.0 12.3 95.0
## 249 36.7 26.4 31.0 16.8 24.5 12.1 9.9 19.3 12.8 99.5
## 250 34.8 25.9 30.2 16.4 24.2 11.3 8.9 17.0 12.2 88.0
## 251 36.6 27.9 31.8 19.3 24.9 12.3 9.5 18.6 13.0 97.0
## 252 35.5 28.2 31.0 18.2 26.2 11.5 9.1 17.2 12.4 103.3
## 253 37.0 28.0 32.0 15.1 25.7 12.5 10.0 17.2 13.2 93.5
## 254 35.5 26.5 29.2 15.4 24.5 12.3 9.4 17.2 12.0 93.3
## 255 37.4 30.2 33.2 18.8 26.6 13.3 10.7 19.8 13.8 94.5
## 256 37.8 29.0 32.6 18.6 25.0 12.1 9.8 17.8 12.7 98.6
## 257 38.6 28.8 33.2 19.7 29.4 13.4 11.5 20.9 13.2 115.5
## 258 37.6 28.5 32.2 15.5 24.3 11.8 8.6 17.1 11.9 97.9
## 259 36.0 25.6 31.5 15.4 25.5 12.8 9.7 17.6 13.2 97.7
## 260 39.5 30.0 31.7 17.3 27.3 12.8 9.2 18.1 12.4 100.5
## 261 34.0 25.0 27.0 16.9 22.6 10.6 8.3 15.9 11.6 88.7
## 262 35.0 26.5 31.6 18.3 23.7 11.5 8.6 16.8 12.2 96.6
## 263 35.6 25.8 32.0 16.2 25.7 11.5 9.0 17.2 11.8 92.0
## 264 36.2 27.4 29.5 14.6 23.9 11.2 9.6 16.7 12.6 90.0
## 265 39.0 28.4 34.9 19.6 26.7 13.4 11.0 18.9 13.6 104.0
## 266 32.6 25.6 30.0 15.3 22.6 10.3 8.1 16.2 11.6 90.1
## 267 37.6 30.0 33.9 19.1 28.8 13.4 10.5 19.2 13.2 104.0
## 268 36.3 27.5 31.0 15.4 24.0 11.1 9.4 16.4 12.3 91.0
## 269 38.0 29.6 32.7 21.3 28.7 13.7 10.6 21.5 14.4 111.4
## 270 37.0 27.9 30.8 15.1 26.8 13.2 8.9 18.1 12.3 96.2
## 271 42.6 31.8 34.8 19.9 30.4 12.5 11.0 21.0 14.2 117.1
## 272 37.5 29.2 35.1 19.6 27.5 13.1 10.2 20.0 13.1 101.5
## 273 36.1 28.2 32.3 15.8 25.0 12.0 10.2 18.4 12.9 95.1
## 274 39.1 27.5 34.0 17.2 25.6 12.6 10.0 18.0 12.7 97.1
## 275 39.6 28.6 31.2 18.0 27.2 12.4 10.1 19.3 13.8 101.4
## 276 40.5 29.6 34.9 17.4 27.5 12.6 10.4 18.8 13.1 103.7
## 277 35.0 26.0 29.8 16.1 22.3 12.4 9.5 17.2 12.3 89.2
## 278 34.5 26.4 30.1 18.6 23.5 11.3 8.9 17.2 12.3 86.0
## 279 35.6 27.9 32.0 15.6 23.9 11.9 9.6 18.2 12.7 98.1
## 280 39.1 29.7 33.7 20.1 29.5 13.2 10.0 20.3 13.6 114.0
## 281 36.6 28.2 32.0 14.3 26.0 11.8 9.9 18.0 12.7 95.0
## 282 34.8 26.3 29.9 16.0 22.9 11.3 9.2 16.5 11.8 87.0
## 283 36.7 22.1 28.3 16.7 27.5 12.3 10.1 16.8 12.4 100.8
## 284 39.7 31.6 34.5 19.0 24.9 12.7 10.4 18.2 13.1 112.2
## 285 35.5 24.3 29.5 15.0 26.0 11.5 9.4 17.2 12.1 98.7
## 286 36.0 28.0 30.8 17.3 25.9 12.9 9.8 17.8 13.8 100.1
## 287 35.8 26.4 32.2 17.3 24.9 12.7 10.4 18.9 14.0 100.3
## 288 37.6 27.3 32.0 18.0 25.6 12.4 9.4 18.8 13.6 100.5
## 289 37.6 28.7 30.8 19.0 25.2 13.1 11.5 18.6 13.6 101.2
## 290 34.9 28.0 30.8 15.6 24.2 11.2 9.2 17.9 13.2 96.0
## 291 33.3 24.9 27.8 17.3 23.3 12.8 9.8 17.4 12.9 92.8
## 292 36.2 27.1 29.9 16.0 24.2 11.8 9.2 17.3 12.6 95.5
## 293 33.3 29.2 32.0 14.8 25.9 12.6 10.4 18.4 13.0 98.7
## 294 36.0 25.2 30.8 16.5 24.2 12.6 9.2 17.6 13.2 96.9
## 295 34.0 27.3 29.9 17.0 24.0 12.0 9.2 16.8 12.4 91.0
## 296 36.2 28.7 31.3 19.2 27.1 12.8 10.5 18.0 14.4 106.3
## 297 34.9 27.1 31.5 19.0 24.9 12.4 9.8 18.0 13.8 99.8
## 298 37.4 28.3 31.3 16.3 25.6 12.9 11.0 18.4 14.3 105.0
## 299 33.8 28.7 32.2 18.7 25.4 12.0 8.9 17.4 13.0 106.2
## 300 34.0 27.3 32.2 19.0 24.2 12.9 10.6 17.9 13.4 97.7
## 301 34.4 23.5 26.8 16.5 24.4 11.7 9.2 16.8 12.4 97.4
## 302 34.7 27.8 32.6 16.5 24.2 12.6 9.8 17.6 12.6 96.3
## 303 35.6 26.4 32.2 15.1 26.6 13.0 10.0 17.6 13.8 98.0
## 304 33.8 29.7 32.4 17.3 25.4 13.4 9.6 18.4 13.4 94.5
## 305 37.8 26.6 33.1 16.5 27.1 12.4 9.8 18.3 13.0 101.4
## 306 38.3 30.4 32.2 18.7 26.4 12.4 9.4 18.1 12.7 95.1
## 307 40.3 29.2 32.9 16.5 26.4 13.0 9.4 18.6 12.9 101.1
## 308 38.9 27.5 31.1 15.6 26.8 12.4 10.0 18.4 13.6 97.4
## 309 39.4 29.2 34.7 18.0 28.3 13.4 10.5 19.8 13.1 104.8
## 310 35.0 27.0 33.0 19.3 25.0 11.5 9.9 17.1 13.0 94.5
## 311 40.7 29.0 35.3 17.7 28.5 13.8 10.8 19.8 14.3 103.2
## 312 39.6 30.4 34.2 21.4 26.8 14.2 10.6 20.7 13.1 110.8
## 313 36.9 28.3 32.0 17.0 25.6 10.9 9.4 17.3 12.6 94.6
## 314 35.6 25.7 29.1 15.5 26.0 11.5 9.5 17.8 12.1 96.6
## 315 38.2 30.0 35.5 19.4 26.7 12.6 10.8 19.5 13.6 102.8
## 316 36.9 29.0 33.7 15.6 27.0 11.5 10.2 18.4 13.8 96.9
## 317 36.9 26.9 31.9 16.1 25.2 13.4 10.0 18.8 14.3 95.6
## 318 34.8 26.4 29.8 14.9 25.2 12.2 9.1 18.0 12.4 92.4
## 319 36.6 27.5 30.0 17.7 24.1 12.0 9.3 17.7 13.2 93.7
## 320 36.9 27.4 31.0 18.2 25.7 12.8 10.4 17.8 14.1 96.1
## 321 36.9 26.4 32.4 16.6 26.0 12.6 10.5 18.9 14.4 100.1
## 322 36.9 30.2 34.6 17.5 26.2 13.2 10.4 19.4 14.3 98.8
## 323 36.0 28.6 32.0 17.5 24.1 12.8 9.8 18.7 13.8 92.2
## 324 36.2 30.2 31.2 16.8 27.7 11.5 9.4 17.4 13.1 97.3
## 325 35.1 28.1 30.0 17.1 25.2 12.0 9.6 19.0 14.2 90.5
## 326 38.8 27.2 33.4 17.2 31.6 12.9 11.0 18.2 13.4 110.5
## 327 36.4 26.2 32.2 17.3 25.8 13.1 10.1 17.9 12.6 103.0
## 328 38.6 24.2 34.4 15.3 29.0 13.1 10.4 22.1 14.4 109.0
## 329 36.5 26.0 30.7 15.9 26.8 12.1 10.3 17.0 12.4 104.8
## 330 37.5 27.4 31.2 18.9 26.5 14.0 11.0 19.1 13.5 109.2
## 331 39.9 20.0 32.4 17.6 31.9 13.6 10.4 18.5 13.4 117.0
## 332 34.4 26.1 30.5 18.8 25.2 12.0 9.9 17.5 12.7 93.9
## 333 38.7 23.5 33.0 16.4 27.6 12.6 9.9 17.5 12.6 105.5
## 334 32.6 24.6 29.4 15.8 25.0 11.3 9.2 16.9 11.9 99.4
## 335 34.5 25.6 29.2 18.0 24.2 10.1 9.0 16.3 11.5 89.5
## 336 36.7 28.2 33.0 17.5 25.6 12.2 10.1 17.8 13.5 98.5
## 337 36.5 27.0 31.6 16.6 24.4 11.9 9.7 18.0 12.5 94.9
## 338 34.2 28.0 31.0 20.4 25.6 12.4 9.7 18.5 13.0 96.3
## 339 37.8 28.6 30.7 17.3 25.4 11.8 10.6 18.8 13.3 96.8
## 340 35.7 25.5 32.5 16.0 27.2 11.9 10.0 18.6 13.6 102.5
## 341 33.9 24.3 29.5 15.8 26.0 11.6 9.4 16.3 11.2 95.2
## 342 35.2 24.1 26.9 17.9 23.6 12.0 9.5 17.5 11.5 103.0
## 343 37.1 24.3 28.4 16.2 25.8 12.1 9.8 16.8 12.2 99.1
## 344 37.7 26.2 30.8 17.8 25.2 12.4 10.2 17.2 11.8 104.1
## 345 37.8 28.1 30.0 17.9 24.9 11.1 9.6 18.0 13.2 99.5
## 346 39.3 24.5 32.3 21.4 29.5 13.4 10.9 18.2 13.0 110.2
## 347 37.2 24.4 29.4 18.1 27.3 12.3 9.9 17.1 12.2 107.1
## 348 36.1 26.8 31.0 16.2 24.0 11.0 8.9 17.8 12.2 95.7
## 349 35.2 25.1 33.0 20.0 26.6 11.6 9.1 21.0 12.6 103.4
## 350 35.8 27.7 28.9 16.4 25.6 11.5 9.1 17.1 12.3 94.6
## 351 35.0 25.6 32.5 17.9 27.0 12.6 9.8 18.6 12.9 104.5
## 352 37.8 29.9 31.1 17.3 25.8 10.9 9.4 15.7 11.5 97.7
## 353 35.7 27.0 29.8 16.5 26.2 12.1 9.6 18.0 13.6 96.4
## 354 39.5 29.8 31.5 19.2 25.4 13.0 10.6 19.1 13.4 103.0
## 355 38.8 26.3 31.5 19.0 25.9 11.6 9.9 18.8 13.1 103.4
## 356 37.9 29.0 32.9 18.6 27.0 12.7 10.6 18.2 12.1 109.5
## 357 37.7 25.0 30.4 16.6 29.3 12.4 10.2 18.6 12.7 110.7
## 358 38.2 25.9 30.2 16.9 24.8 11.7 9.4 16.9 11.7 102.2
## 359 40.0 31.0 35.8 21.3 33.2 14.1 12.2 24.3 12.9 129.5
## 360 35.7 28.3 29.6 17.4 23.3 10.7 8.7 16.8 11.3 99.2
## 361 35.7 25.9 29.3 14.7 29.1 13.2 10.9 18.1 13.1 104.7
## 362 36.0 27.6 29.0 18.6 25.0 11.8 9.2 18.0 11.4 98.9
## 363 35.0 25.0 27.2 15.4 23.4 10.8 8.7 16.8 12.2 98.5
## 364 37.0 26.9 32.4 16.6 25.4 12.9 10.0 18.1 13.1 102.2
## 365 36.7 23.5 29.5 17.2 28.3 11.5 9.5 16.1 9.9 100.2
## 366 32.8 26.2 26.3 16.1 23.6 10.6 9.2 16.4 11.7 85.9
## 367 34.8 20.3 31.0 17.3 26.8 11.6 9.8 17.9 12.4 99.8
## 368 35.0 23.7 29.0 15.4 23.6 11.5 9.6 16.6 11.6 93.3
## 369 38.7 27.1 31.4 16.5 27.5 12.8 9.2 18.8 12.5 107.3
## 370 32.8 20.9 28.2 17.2 22.2 11.3 9.5 16.0 11.2 90.0
## 371 36.7 23.0 30.8 17.0 27.6 13.1 11.0 17.0 12.9 101.5
## 372 38.8 26.5 29.8 17.4 28.2 12.0 9.4 17.7 12.6 105.1
## 373 36.4 24.9 30.8 16.5 26.7 12.4 10.5 18.4 12.8 101.0
## 374 34.0 27.8 29.6 16.5 24.4 9.9 8.9 17.0 11.5 91.9
## 375 38.2 27.5 30.0 20.4 29.2 11.8 9.2 17.8 11.5 112.1
## 376 35.1 23.4 30.6 16.2 26.8 12.1 9.4 16.1 12.2 98.2
## 377 36.0 25.2 28.4 15.4 26.9 11.2 9.9 16.8 12.1 102.5
## 378 35.9 25.1 28.2 18.1 26.1 11.6 8.5 16.8 11.0 98.4
## 379 35.0 27.1 30.1 19.5 24.9 12.4 9.9 18.2 12.5 96.8
## 380 35.2 24.4 29.8 15.1 26.0 13.0 9.2 17.3 12.2 103.9
## 381 34.9 26.2 28.5 14.6 23.5 10.4 8.7 16.0 11.1 92.7
## 382 36.0 28.1 31.0 18.8 27.4 11.5 9.7 17.5 12.8 105.0
## 383 36.4 27.7 29.8 17.0 25.0 11.2 9.0 16.9 12.2 94.0
## 384 35.5 27.7 30.7 20.6 24.9 11.6 8.9 17.9 11.5 101.5
## 385 34.1 18.7 27.2 16.1 24.6 11.2 9.5 16.1 11.9 92.0
## 386 37.1 25.0 29.3 17.1 28.1 13.0 10.9 17.5 13.0 108.5
## 387 35.2 26.0 30.4 17.3 25.5 11.1 9.0 17.1 12.0 93.8
## 388 37.5 26.0 29.4 17.6 26.5 12.3 10.4 18.3 12.7 99.8
## 389 36.3 24.9 31.0 16.6 27.7 12.5 10.0 18.1 13.0 100.3
## 390 37.2 26.0 28.5 18.8 25.0 12.3 9.7 18.4 13.0 101.5
## 391 37.6 27.7 30.2 17.4 25.7 12.0 9.1 17.5 12.1 94.7
## 392 33.6 24.3 27.9 18.2 24.7 11.0 8.4 16.6 11.1 103.1
## 393 34.5 29.2 31.9 18.3 23.6 11.0 9.3 18.7 12.4 94.5
## 394 36.0 27.1 31.4 18.5 24.6 11.9 9.4 15.9 11.9 97.1
## 395 35.4 27.0 29.0 17.8 25.0 11.6 9.2 18.2 12.1 98.0
## 396 35.8 28.3 30.2 19.2 24.9 12.0 9.2 18.4 14.2 102.4
## 397 38.3 28.3 32.4 18.7 28.3 13.9 10.6 18.9 14.0 109.1
## 398 33.8 28.7 32.9 17.7 23.5 12.6 9.4 18.2 13.2 94.7
## 399 36.9 31.1 33.8 16.8 26.6 13.0 9.8 18.4 13.8 98.2
## 400 34.9 28.0 33.1 18.0 26.1 12.6 9.6 18.8 12.2 101.5
## 401 35.1 28.3 29.7 18.2 27.3 13.0 9.3 17.7 13.8 96.6
## 402 37.4 30.2 34.2 21.6 28.3 13.2 10.0 19.6 14.1 106.1
## 403 36.9 27.5 31.1 16.3 26.6 12.8 9.8 18.0 14.0 100.1
## 404 36.0 28.0 30.4 16.3 26.4 12.2 9.2 17.6 12.8 105.3
## 405 32.9 26.6 32.2 18.5 25.2 12.0 8.4 19.4 12.0 99.8
## 406 38.3 31.7 37.4 19.2 28.5 13.2 9.3 18.6 13.8 103.9
## 407 37.6 30.7 35.6 24.7 32.2 14.0 10.6 21.2 14.5 111.0
## 408 36.7 28.0 31.7 17.3 27.3 13.1 10.6 18.4 13.4 99.0
## 409 38.9 25.2 31.5 17.3 24.2 12.9 9.8 18.0 12.8 102.5
## 410 37.8 28.0 32.0 16.8 28.3 12.7 9.8 18.1 13.2 105.6
## 411 38.0 31.7 32.9 22.3 25.2 12.4 10.0 19.6 13.8 99.5
## 412 38.7 30.4 31.3 18.7 26.8 12.4 10.0 19.4 13.6 106.5
## 413 37.1 27.1 32.6 17.3 26.6 13.2 10.3 19.0 14.4 104.1
## 414 35.8 30.2 33.5 20.6 27.5 12.4 9.4 18.4 13.0 99.7
## 415 38.5 30.4 35.1 18.2 27.3 13.8 10.7 19.0 14.4 106.6
## 416 38.5 30.8 35.1 15.8 27.3 13.1 10.6 18.6 14.2 97.2
## 417 36.0 30.2 32.9 18.7 27.8 12.4 9.6 18.4 14.2 100.8
## 418 34.0 26.4 29.9 19.2 27.5 11.8 9.4 17.3 12.4 100.7
## 419 38.3 26.6 33.3 18.2 26.4 13.1 10.8 20.0 13.8 99.1
## 420 38.7 33.3 33.8 20.9 30.2 14.0 10.5 18.9 14.8 108.9
## 421 36.9 29.0 33.5 20.9 30.2 12.7 9.6 18.6 14.2 109.8
## 422 35.8 26.6 31.1 18.5 24.7 12.4 9.6 17.9 13.8 99.5
## 423 36.7 29.0 32.0 19.7 25.6 12.4 10.0 18.6 13.0 100.5
## 424 36.7 28.0 31.1 19.4 27.5 12.9 10.0 17.9 14.0 97.4
## 425 37.6 31.3 33.5 17.0 25.4 12.4 9.4 19.2 11.5 105.0
## 426 35.1 23.7 31.1 18.5 25.9 12.8 10.0 17.7 12.8 102.9
## 427 35.3 23.3 28.3 15.3 24.7 12.4 10.2 17.1 13.4 93.9
## 428 38.5 26.6 34.0 22.3 26.6 13.8 10.7 20.0 15.5 114.2
## 429 37.8 29.4 32.2 15.8 27.5 11.8 9.8 18.2 13.8 95.9
## 430 35.1 25.6 29.9 16.8 24.9 12.6 9.6 18.2 12.5 98.7
## 431 32.4 30.2 33.8 15.3 25.6 12.2 10.2 17.9 13.4 91.2
## 432 39.2 31.3 33.8 17.7 29.7 13.2 9.6 17.8 14.4 105.1
## 433 38.3 30.8 35.6 17.0 26.8 13.6 10.4 19.0 13.6 102.3
## 434 32.9 20.9 28.5 19.0 23.0 12.2 9.1 16.0 12.7 93.1
## 435 38.0 28.3 31.1 17.7 26.1 12.2 10.2 17.3 13.0 101.7
## 436 37.8 32.0 33.8 16.8 26.6 13.4 10.4 18.8 13.4 105.0
## 437 34.2 29.2 31.1 18.0 25.9 12.8 9.8 17.6 13.4 96.4
## 438 37.6 31.3 35.3 20.2 27.3 11.3 9.4 18.9 13.8 103.5
## 439 35.3 25.4 31.3 17.3 25.4 12.4 10.0 17.5 13.6 99.8
## 440 34.9 27.3 32.2 17.7 25.4 12.6 9.0 17.4 12.2 87.0
## 441 37.4 32.0 34.7 19.7 25.9 13.0 10.0 18.4 13.4 105.2
## 442 36.2 31.1 34.7 21.1 26.8 12.9 10.4 18.6 14.0 102.1
## 443 38.9 29.0 33.1 16.3 25.9 12.2 10.2 18.8 13.8 101.3
## 444 36.0 30.4 32.9 18.2 24.7 13.4 10.5 19.5 13.6 99.6
## 445 33.3 25.9 29.9 18.0 25.6 12.0 9.2 17.6 12.9 99.4
## 446 37.4 29.4 32.6 19.9 27.1 13.4 10.2 18.4 14.1 99.4
## 447 34.7 29.4 31.5 17.7 24.9 13.4 9.6 18.4 13.4 99.0
## 448 38.7 28.0 34.7 16.3 28.5 13.6 10.4 20.4 14.3 108.2
## 449 36.2 28.3 31.3 16.3 25.4 12.2 9.6 16.8 12.9 96.4
## 450 37.6 27.8 31.1 19.9 25.9 12.9 9.6 17.2 13.8 101.4
## 451 34.9 26.1 29.0 16.5 25.6 12.0 9.2 17.3 13.2 98.2
## 452 34.7 24.7 32.0 17.7 25.2 12.8 10.4 18.4 13.8 100.6
## 453 34.0 26.8 32.2 17.3 28.7 11.2 9.1 17.4 12.9 108.4
## 454 37.8 28.5 33.5 18.0 27.1 12.9 10.4 18.6 13.4 100.8
## 455 35.1 27.8 30.6 16.3 26.8 11.6 9.6 17.2 12.0 94.8
## 456 39.2 29.9 34.9 18.5 26.4 14.3 10.8 19.6 14.4 108.2
## 457 35.1 29.9 35.3 18.0 28.5 13.8 10.5 20.0 14.3 110.7
## 458 37.1 29.2 32.0 18.0 26.1 13.2 10.2 19.2 14.4 102.6
## 459 35.8 29.4 30.8 17.0 26.6 12.2 10.8 17.6 13.4 98.1
## 460 35.3 26.8 31.7 18.5 23.3 11.5 9.2 17.8 12.6 92.0
## 461 37.6 26.8 31.1 16.5 24.0 11.8 10.2 18.4 12.8 98.6
## 462 35.8 29.0 30.6 18.0 24.2 12.4 10.5 17.9 13.4 96.1
## 463 36.0 29.2 33.5 17.5 27.8 11.6 10.5 18.7 14.2 107.7
## 464 35.6 28.3 28.0 14.4 23.7 11.7 9.6 16.4 13.0 95.4
## 465 37.1 27.8 31.7 20.6 26.4 12.4 10.4 18.2 13.2 99.3
## 466 35.6 30.2 32.9 21.8 26.8 13.0 11.0 20.6 14.3 115.2
## 467 35.1 29.4 33.1 17.7 27.3 12.8 10.3 18.7 13.6 96.7
## 468 35.8 26.8 30.4 23.3 25.6 12.4 10.3 17.5 13.0 109.0
## 469 37.4 29.4 31.7 16.5 25.9 12.2 10.8 19.4 14.3 95.1
## 470 38.9 28.5 34.0 19.2 27.5 13.3 10.3 19.1 14.2 108.4
## 471 37.4 29.0 32.9 17.7 23.5 12.7 10.0 18.2 13.8 98.2
## 472 37.6 28.3 29.4 16.8 25.4 13.2 10.0 17.8 14.2 99.7
## 473 35.1 26.6 29.2 17.0 24.4 11.8 9.4 17.0 12.2 94.4
## 474 38.0 33.3 37.8 20.6 28.3 15.0 11.5 22.6 13.8 127.1
## 475 36.7 25.2 29.2 18.5 24.2 12.0 9.0 17.0 11.8 94.9
## 476 35.6 29.9 33.5 18.2 24.9 13.2 11.0 19.2 13.8 98.9
## 477 35.6 30.8 33.8 26.8 27.1 12.4 10.2 18.2 14.0 106.4
## 478 35.6 27.3 31.7 17.5 27.3 12.4 9.8 17.6 12.7 102.2
## 479 37.8 29.0 32.4 20.6 28.5 12.7 9.1 18.2 13.2 110.3
## 480 40.9 28.7 29.2 18.2 27.3 12.9 9.6 16.8 13.4 107.3
## 481 34.0 25.9 29.4 20.2 24.2 11.4 9.6 16.6 12.6 95.0
## 482 34.9 31.3 31.7 20.9 31.5 13.0 9.6 17.8 13.7 115.6
## 483 35.8 31.5 32.6 17.5 25.2 12.9 9.8 17.9 13.4 99.3
## 484 36.5 27.5 30.8 18.5 24.5 11.6 9.6 17.0 11.7 93.1
## 485 32.9 26.8 28.7 16.3 24.4 11.0 8.6 16.4 12.6 94.9
## 486 36.9 28.5 31.5 15.6 27.8 11.6 9.8 17.2 12.9 100.0
## 487 35.8 29.4 32.0 18.0 25.9 11.8 9.8 18.0 12.7 98.6
## 488 39.4 28.3 30.2 21.6 29.7 12.8 10.5 19.5 14.4 107.2
## 489 36.5 28.3 29.7 17.7 26.4 12.0 10.0 17.1 12.8 98.3
## 490 35.8 27.3 28.5 18.2 23.5 11.2 10.2 17.5 12.9 92.4
## 491 34.4 29.7 31.7 17.5 24.7 12.6 10.2 17.7 12.4 93.0
## 492 36.5 26.6 30.8 16.0 24.9 12.6 10.7 17.0 13.0 99.5
## 493 38.5 25.6 31.7 17.0 25.6 11.8 10.2 16.8 12.9 92.8
## 494 35.3 25.4 28.5 15.1 24.9 10.6 10.4 17.0 12.6 95.9
## 495 36.2 28.7 27.8 16.0 23.7 12.4 10.2 17.9 13.2 96.1
## 496 34.9 28.7 32.9 16.8 26.8 13.4 10.8 17.9 12.9 98.5
## 497 38.7 29.7 32.0 15.8 26.4 12.8 10.5 18.4 13.8 103.8
## 498 37.6 25.2 29.0 19.2 27.1 14.0 11.0 18.9 13.2 105.0
## 499 34.7 29.4 33.5 17.7 25.4 12.4 10.8 20.2 12.8 100.2
## 500 37.8 29.7 31.3 18.5 26.6 13.4 11.2 18.4 14.2 99.1
## 501 39.8 27.5 31.5 19.4 29.0 13.1 10.4 17.6 13.8 107.6
## 502 36.5 29.7 34.0 20.2 28.5 13.3 9.8 18.9 12.4 104.0
## 503 38.0 30.4 32.9 17.0 27.1 12.9 10.4 19.5 14.4 108.4
## 504 35.3 28.7 30.4 17.7 25.6 12.4 9.8 17.3 13.6 99.3
## 505 34.7 24.9 24.7 17.3 24.2 12.0 10.2 18.0 13.6 91.9
## 506 38.5 29.0 32.9 15.3 25.6 12.0 9.8 18.6 13.3 107.1
## 507 35.6 29.0 29.0 20.4 26.8 13.4 10.8 18.7 13.8 100.5
## che.gi wai.gi nav.gi hip.gi thi.gi bic.gi for.gi kne.gi cal.gi ank.gi
## 1 89.5 71.5 74.5 93.5 51.5 32.5 26.0 34.5 36.5 23.5
## 2 97.0 79.0 86.5 94.8 51.5 34.4 28.0 36.5 37.5 24.5
## 3 97.5 83.2 82.9 95.0 57.3 33.4 28.8 37.0 37.3 21.9
## 4 97.0 77.8 78.8 94.0 53.0 31.0 26.2 37.0 34.8 23.0
## 5 97.5 80.0 82.5 98.5 55.4 32.0 28.4 37.7 38.6 24.4
## 6 99.9 82.5 80.1 95.3 57.5 33.0 28.0 36.6 36.1 23.5
## 7 106.9 82.0 84.0 101.0 60.9 42.4 32.3 40.1 40.3 23.6
## 8 102.5 76.8 80.5 98.0 56.0 34.1 28.0 39.2 36.7 22.5
## 9 91.0 68.5 69.0 89.5 50.0 33.0 26.0 35.5 35.0 22.0
## 10 93.5 77.5 81.5 99.8 59.8 36.5 29.2 38.3 38.6 22.2
## 11 97.7 81.9 81.0 98.4 60.5 34.6 27.9 38.9 40.1 23.2
## 12 99.5 82.6 82.5 95.0 58.5 38.5 30.4 39.0 38.4 24.3
## 13 103.0 85.0 94.5 103.0 59.0 33.5 29.0 40.5 40.0 26.0
## 14 99.6 85.6 89.2 98.0 59.1 35.6 29.0 35.8 36.0 21.5
## 15 101.5 78.0 89.5 95.0 57.0 36.0 29.0 34.5 35.0 22.0
## 16 104.1 82.0 84.0 97.0 56.0 34.5 29.5 39.0 35.7 24.0
## 17 100.0 88.3 93.5 105.0 65.8 37.0 28.8 40.9 41.7 24.2
## 18 93.8 73.6 74.9 90.1 54.1 31.2 26.9 36.4 35.6 22.0
## 19 98.5 78.5 86.0 94.5 55.0 34.5 28.5 38.0 36.5 23.0
## 20 104.0 87.3 88.0 101.1 59.5 37.0 30.5 39.8 42.0 26.5
## 21 100.0 92.0 91.0 98.0 57.5 32.0 27.6 37.5 35.2 21.0
## 22 100.0 80.0 83.7 99.5 57.0 37.0 30.0 37.5 35.5 23.0
## 23 99.0 74.5 75.9 92.2 53.4 31.2 26.9 36.2 33.3 23.5
## 24 101.0 90.6 89.6 101.2 59.5 37.0 28.3 35.4 40.6 22.9
## 25 101.6 81.4 81.6 98.8 61.3 39.4 31.9 38.5 41.2 22.8
## 26 108.8 89.5 89.5 106.0 59.5 37.5 30.1 39.9 41.5 23.5
## 27 100.0 85.0 94.5 105.0 62.0 35.5 28.5 38.0 40.0 23.5
## 28 88.0 73.5 77.7 97.0 56.3 32.5 27.8 39.0 38.2 23.5
## 29 88.7 75.8 83.0 89.0 52.6 31.2 26.5 37.0 37.4 21.5
## 30 84.5 74.0 81.0 93.5 50.5 27.5 24.8 34.0 32.8 21.0
## 31 93.6 77.5 82.1 95.0 56.5 32.8 26.2 37.6 36.3 21.0
## 32 105.0 74.0 72.0 90.0 54.2 34.1 28.6 36.2 36.6 22.4
## 33 90.9 74.0 78.8 96.4 51.8 29.8 27.0 36.4 34.6 22.9
## 34 91.2 82.0 89.5 100.0 57.5 32.8 28.0 40.7 40.1 24.3
## 35 98.4 73.0 83.0 95.4 56.3 36.4 27.5 37.2 34.5 21.8
## 36 85.0 70.5 84.0 90.0 50.0 29.0 26.0 36.0 34.5 21.5
## 37 97.2 75.0 77.2 91.3 49.5 31.0 26.1 36.3 35.1 21.0
## 38 91.5 72.1 79.2 91.0 54.9 29.5 24.5 36.1 37.2 22.9
## 39 91.2 78.8 78.0 93.2 55.8 31.9 27.4 36.4 35.1 23.0
## 40 101.1 77.5 78.0 97.0 55.0 37.7 29.9 38.3 39.6 23.3
## 41 104.3 91.5 93.2 103.9 62.0 36.3 29.0 36.7 39.4 23.1
## 42 110.8 84.9 83.0 102.6 66.4 42.3 30.9 37.0 37.7 22.6
## 43 96.3 79.1 78.3 97.1 60.1 35.5 28.8 36.9 38.2 23.4
## 44 102.7 77.9 77.9 90.7 56.7 35.4 28.3 35.6 35.5 22.9
## 45 103.9 91.7 89.4 101.8 61.0 35.7 29.4 37.7 40.0 22.2
## 46 105.6 86.6 87.3 103.9 63.2 37.8 29.7 39.0 40.2 24.3
## 47 95.8 84.7 84.0 101.4 60.0 35.0 28.5 38.4 37.9 23.2
## 48 111.2 90.3 93.5 108.7 66.9 40.2 32.4 39.2 40.1 25.7
## 49 100.0 79.7 87.1 98.4 61.1 36.3 28.6 34.5 36.1 21.4
## 50 91.6 73.1 75.4 86.5 50.6 30.8 26.1 31.7 33.6 20.3
## 51 108.0 79.8 82.5 94.8 58.3 39.8 29.6 34.2 38.1 21.1
## 52 109.6 81.6 86.5 100.9 61.7 39.5 31.7 38.8 36.5 22.7
## 53 105.7 76.8 83.4 98.0 56.8 37.9 30.9 35.9 38.3 23.4
## 54 109.1 85.9 90.4 100.9 61.3 40.1 30.0 36.8 38.6 21.9
## 55 88.8 73.3 77.9 85.7 46.9 30.5 24.8 31.1 30.5 19.0
## 56 86.1 69.9 67.4 84.1 50.8 31.5 26.6 32.8 36.3 20.0
## 57 91.3 72.7 83.2 91.4 51.2 27.8 26.0 34.8 34.7 21.1
## 58 100.6 82.7 83.5 98.0 55.8 33.1 28.0 37.9 39.1 23.2
## 59 105.5 90.1 89.2 104.5 62.7 36.3 29.6 38.4 42.4 25.3
## 60 97.5 82.9 83.6 95.8 52.6 34.5 27.0 35.2 35.2 21.4
## 61 94.4 77.9 79.0 91.7 57.1 31.2 27.5 36.6 37.5 21.6
## 62 99.6 92.5 96.2 103.4 58.5 34.5 28.4 38.4 38.0 22.4
## 63 98.1 77.8 77.2 90.0 52.4 33.2 26.4 34.2 36.0 21.8
## 64 93.6 72.7 77.3 91.7 51.9 32.1 27.4 33.5 33.8 21.1
## 65 93.4 75.0 79.2 94.0 53.8 34.2 27.9 36.1 36.2 22.0
## 66 92.0 76.0 83.0 93.0 54.5 29.5 26.0 37.0 34.5 22.8
## 67 99.2 82.7 84.2 93.0 56.6 32.4 27.6 35.8 36.3 21.8
## 68 97.6 80.0 85.7 97.4 57.8 33.8 28.6 36.2 37.4 22.0
## 69 108.8 107.1 107.2 108.3 67.0 39.6 30.6 40.0 39.6 24.6
## 70 91.9 76.2 78.1 90.0 52.0 30.7 25.8 34.8 32.6 21.0
## 71 105.2 90.2 88.6 100.2 60.8 35.7 29.4 39.2 39.1 24.5
## 72 98.3 89.4 87.4 97.7 54.8 31.0 26.0 36.4 35.6 21.6
## 73 92.5 80.9 78.5 96.0 59.0 31.5 26.3 36.1 39.0 21.2
## 74 92.5 73.5 76.4 92.0 53.1 30.6 27.1 36.0 36.0 23.8
## 75 101.6 70.9 76.7 95.3 56.0 36.0 28.6 36.0 34.0 22.0
## 76 94.6 76.1 78.0 86.3 52.4 28.6 23.9 34.5 37.9 22.7
## 77 92.5 81.0 85.2 92.5 54.7 32.3 26.8 35.8 37.6 21.1
## 78 88.2 72.0 72.0 85.5 50.2 28.6 24.8 34.9 35.1 20.1
## 79 91.0 76.8 80.0 94.5 54.6 33.2 28.0 37.5 35.6 22.1
## 80 106.0 86.0 92.0 103.0 60.6 34.0 29.8 38.8 39.5 23.6
## 81 115.0 98.5 106.6 116.5 67.8 35.8 27.2 38.0 41.2 23.3
## 82 100.0 79.0 82.5 98.5 62.1 34.0 28.8 39.6 40.8 25.9
## 83 94.7 77.5 80.5 92.0 54.2 30.9 26.6 36.5 35.8 21.3
## 84 92.5 75.2 80.2 91.6 49.6 29.2 26.1 36.2 35.7 22.1
## 85 96.7 82.0 82.2 92.7 54.6 32.0 27.1 35.6 36.4 20.7
## 86 97.4 79.6 80.8 95.0 54.2 32.6 27.4 36.5 38.0 21.6
## 87 106.7 75.2 77.8 94.5 57.4 36.7 29.9 38.1 36.0 22.6
## 88 106.2 88.6 88.3 100.5 63.4 36.9 29.4 38.4 38.6 23.1
## 89 103.6 81.5 83.3 91.8 55.0 33.0 27.8 35.4 36.5 21.9
## 90 98.3 79.9 82.4 87.5 54.4 33.5 27.3 36.8 37.9 22.3
## 91 87.8 73.5 77.5 94.9 53.5 34.3 28.5 36.5 35.2 22.0
## 92 99.8 80.3 80.8 93.0 55.4 33.3 28.0 36.0 37.8 20.3
## 93 104.6 81.5 85.0 92.0 54.1 33.0 28.0 35.1 35.2 21.1
## 94 86.5 74.0 76.5 91.3 53.5 30.5 26.1 36.6 38.6 21.2
## 95 110.0 104.0 99.0 111.7 63.2 37.5 29.0 41.2 39.3 24.6
## 96 93.2 76.2 83.8 92.8 55.2 31.2 26.2 36.8 37.7 22.7
## 97 90.0 76.5 77.7 91.2 54.2 33.1 27.2 35.5 35.3 21.5
## 98 98.4 81.0 80.5 96.2 56.0 32.0 27.4 37.0 35.5 24.0
## 99 107.2 88.8 86.8 100.0 61.0 34.6 27.9 38.0 39.4 23.2
## 100 108.3 94.0 98.0 108.2 66.8 35.6 27.3 39.5 43.0 25.3
## 101 105.7 83.4 86.5 101.1 61.3 34.7 29.4 39.4 41.8 24.0
## 102 88.5 77.0 79.0 93.0 51.7 33.5 27.9 38.4 38.5 22.5
## 103 79.3 75.4 78.0 88.6 50.0 25.6 22.7 33.8 32.5 21.2
## 104 90.9 80.3 80.8 92.8 53.9 32.5 28.0 36.5 35.0 21.0
## 105 90.2 68.0 67.0 81.5 49.5 27.0 23.6 34.0 34.5 20.9
## 106 91.6 80.6 78.0 91.3 55.0 30.7 25.3 35.5 34.0 20.8
## 107 105.2 88.6 94.7 94.7 58.3 36.9 28.8 40.3 39.7 26.3
## 108 97.0 81.1 88.2 93.9 53.5 33.7 28.6 35.0 37.3 23.1
## 109 85.3 70.8 84.9 89.4 55.8 28.7 25.5 38.5 34.2 21.3
## 110 99.6 73.3 82.1 89.3 55.4 36.3 32.5 34.3 34.3 22.3
## 111 94.3 75.9 79.1 92.6 54.4 33.2 27.9 34.8 35.5 23.0
## 112 111.8 86.2 93.5 96.3 59.1 36.3 28.0 38.3 34.7 23.0
## 113 94.3 75.0 82.2 88.0 53.8 36.3 28.9 34.5 33.5 23.0
## 114 106.1 101.0 99.7 105.5 60.2 38.6 30.3 39.5 39.4 25.6
## 115 102.0 90.0 92.3 102.3 60.0 34.6 29.7 38.2 38.3 23.7
## 116 94.8 75.0 79.6 91.6 49.4 30.0 26.5 31.7 30.2 16.4
## 117 102.9 75.9 77.0 93.4 55.0 35.2 28.7 37.0 37.7 24.5
## 118 115.8 96.0 95.9 103.6 62.2 38.2 30.1 41.2 39.4 25.1
## 119 105.4 84.0 90.4 94.8 57.6 38.7 30.2 38.6 38.2 22.8
## 120 87.1 67.1 80.4 85.9 46.8 30.3 25.4 32.7 32.1 20.0
## 121 104.1 82.5 90.1 98.4 57.7 37.9 31.6 37.8 38.3 24.6
## 122 95.8 83.7 84.2 98.4 56.2 33.7 28.0 38.0 39.6 25.8
## 123 98.6 76.7 85.8 93.3 56.0 35.7 27.6 34.7 34.6 20.6
## 124 118.7 105.2 105.0 115.5 69.9 39.4 32.1 42.2 47.7 27.0
## 125 100.9 90.6 93.3 97.7 58.0 34.8 28.0 34.1 35.8 22.2
## 126 98.5 85.7 92.9 98.6 55.5 35.3 28.7 39.3 35.9 23.0
## 127 96.9 82.5 90.8 94.9 54.4 32.8 28.7 39.2 37.0 27.5
## 128 110.7 94.7 92.0 101.3 60.1 37.2 30.9 40.5 40.0 24.2
## 129 108.0 105.2 103.4 108.1 60.5 38.0 30.2 36.9 37.7 21.6
## 130 109.0 86.0 90.2 98.0 59.5 40.0 31.2 38.3 39.0 25.8
## 131 104.2 90.9 92.7 100.2 51.8 30.1 26.8 38.1 36.4 23.2
## 132 100.5 92.4 92.4 97.0 50.9 32.9 29.0 37.7 37.7 23.4
## 133 105.7 96.5 98.2 97.4 54.3 31.9 28.5 37.7 39.3 24.5
## 134 112.4 98.4 101.5 107.9 67.4 39.2 30.5 42.6 40.7 25.3
## 135 96.2 76.7 83.5 93.9 50.4 32.1 27.7 36.1 32.9 23.2
## 136 97.5 94.8 98.2 98.6 48.3 31.1 27.0 37.7 36.8 24.6
## 137 90.9 80.1 79.8 91.3 56.2 32.9 27.2 36.2 33.0 23.0
## 138 108.4 97.4 103.7 105.3 55.6 36.6 28.4 38.2 36.6 22.9
## 139 94.3 73.7 74.5 88.2 52.3 29.6 26.2 35.2 36.2 21.2
## 140 109.1 76.1 90.1 93.3 51.7 37.0 30.6 36.8 37.7 23.6
## 141 115.0 95.6 101.9 107.9 64.6 37.1 30.0 41.8 39.6 24.7
## 142 104.4 101.0 98.9 103.3 54.4 38.1 29.8 39.7 41.8 25.0
## 143 100.1 84.5 84.5 94.4 54.7 33.9 28.6 38.5 37.6 25.0
## 144 108.4 98.0 101.8 101.5 56.9 38.2 29.9 37.7 39.2 24.9
## 145 107.3 101.6 103.8 110.0 57.8 34.9 28.9 40.3 40.0 23.7
## 146 101.8 87.8 90.2 98.4 55.6 33.1 28.4 38.2 37.7 24.5
## 147 94.5 80.0 85.0 95.0 52.0 31.5 26.5 36.9 36.4 22.9
## 148 91.7 81.8 82.9 98.3 56.3 31.0 25.7 35.0 33.0 22.0
## 149 95.9 84.4 86.8 99.0 55.0 30.5 26.4 36.1 38.4 21.3
## 150 110.5 85.0 83.5 95.7 59.0 39.2 29.9 37.9 37.7 23.8
## 151 104.0 90.0 86.0 96.0 52.5 33.5 29.1 36.0 36.9 23.0
## 152 86.8 72.9 73.4 89.5 51.0 29.8 24.8 32.6 33.1 22.1
## 153 106.7 81.0 80.2 93.7 54.8 35.5 30.6 36.9 37.3 22.7
## 154 102.5 86.5 89.0 97.0 57.0 34.0 28.4 38.0 37.0 22.5
## 155 103.0 93.9 98.6 103.6 60.5 34.4 28.5 40.9 40.8 24.6
## 156 95.0 77.0 78.0 93.0 52.0 32.6 28.4 34.4 34.4 20.0
## 157 99.0 75.0 75.0 90.0 50.6 32.0 27.3 33.8 34.0 22.0
## 158 89.7 80.6 80.8 90.0 55.5 28.9 25.0 34.6 37.4 23.0
## 159 111.5 100.5 107.3 109.5 61.8 37.4 31.6 41.0 39.7 25.4
## 160 94.1 81.2 84.0 91.6 51.5 33.0 27.0 35.2 35.5 23.1
## 161 118.3 103.4 106.2 108.5 60.5 35.4 29.7 42.3 40.8 24.8
## 162 106.5 90.3 101.1 101.6 57.2 35.4 28.6 40.4 37.8 24.9
## 163 110.4 98.0 98.0 99.6 56.7 36.4 29.2 40.9 42.1 26.1
## 164 102.3 86.5 87.7 91.9 55.0 35.0 28.9 38.3 37.8 24.0
## 165 98.5 77.9 87.3 90.8 50.8 35.0 28.4 35.5 35.0 21.0
## 166 111.6 89.1 95.1 104.8 62.7 37.9 31.2 41.1 41.2 27.7
## 167 106.7 93.9 111.8 111.4 62.8 36.2 29.7 42.8 39.3 23.5
## 168 110.4 85.3 82.9 96.5 57.0 39.0 29.8 36.8 36.0 21.6
## 169 114.0 98.5 103.8 108.1 61.3 37.2 31.4 41.9 42.1 26.4
## 170 101.8 79.5 90.1 95.3 54.8 34.2 28.5 36.6 36.2 22.8
## 171 109.6 94.9 94.7 104.3 59.0 35.9 27.8 37.7 36.8 23.2
## 172 100.7 76.5 87.2 96.3 54.2 33.8 27.7 36.4 38.2 23.8
## 173 104.3 88.4 89.6 98.8 54.8 35.5 29.7 37.7 37.0 23.7
## 174 104.2 84.2 84.0 93.2 55.0 33.0 25.4 35.6 36.4 22.8
## 175 107.4 87.6 89.4 106.7 60.9 38.3 31.2 39.0 42.6 25.8
## 176 101.0 83.7 91.1 99.9 56.8 33.5 27.7 38.7 41.8 29.3
## 177 103.0 92.1 91.3 103.8 56.6 33.3 27.7 37.1 37.4 22.6
## 178 107.2 89.9 94.7 107.1 59.2 35.3 26.9 36.6 32.3 22.0
## 179 99.0 88.7 91.0 100.0 57.5 34.0 28.3 40.9 38.8 26.4
## 180 100.3 83.9 89.4 103.9 59.8 36.1 29.4 37.0 36.5 24.3
## 181 100.2 79.5 88.7 95.3 52.5 34.6 25.8 35.6 35.1 21.8
## 182 99.8 84.5 92.6 99.5 59.2 34.3 29.0 36.5 38.5 24.5
## 183 107.5 89.2 88.4 107.0 56.9 35.6 28.5 37.0 37.6 23.0
## 184 110.1 90.7 91.9 101.7 58.0 36.8 29.0 36.9 38.9 24.2
## 185 107.0 88.8 97.5 103.8 61.0 36.7 28.6 38.4 39.5 24.4
## 186 105.6 103.6 100.7 100.6 55.3 33.6 26.9 37.8 37.9 23.9
## 187 103.6 98.5 99.9 103.6 57.5 33.7 29.0 38.7 36.7 24.3
## 188 97.1 82.9 88.1 91.2 51.7 30.7 25.7 33.9 33.4 21.2
## 189 109.8 90.5 92.3 95.2 52.4 35.8 28.7 32.5 36.5 23.7
## 190 103.3 84.5 94.5 98.2 53.7 32.5 27.8 36.0 36.3 22.0
## 191 103.7 86.0 93.8 97.1 53.1 33.9 27.3 35.7 36.6 22.6
## 192 108.5 84.2 88.9 97.5 58.8 36.6 29.9 34.2 34.8 22.0
## 193 97.8 85.7 89.5 94.9 51.7 32.2 26.4 34.4 32.6 22.0
## 194 112.7 112.1 105.9 106.3 56.9 35.7 27.8 37.3 36.6 22.7
## 195 111.4 99.7 102.9 105.8 57.8 36.5 30.5 39.0 41.2 25.7
## 196 95.1 84.7 92.9 96.7 54.6 32.8 25.3 36.6 35.0 23.5
## 197 100.2 80.3 92.4 96.4 53.8 35.7 29.2 38.4 37.0 25.6
## 198 106.3 98.6 111.7 118.7 70.0 37.1 27.8 37.5 39.2 25.7
## 199 109.5 90.0 96.2 104.3 63.5 39.4 29.9 37.4 37.3 24.3
## 200 106.3 86.6 93.9 95.9 53.6 34.4 28.6 35.0 34.1 21.7
## 201 91.5 80.2 85.7 89.2 48.5 28.7 25.0 35.4 32.3 23.0
## 202 104.9 109.2 104.4 101.7 56.4 34.0 29.2 39.3 38.5 24.3
## 203 109.6 88.4 92.0 95.1 52.5 39.4 29.8 34.5 34.0 22.0
## 204 102.5 81.0 84.5 105.0 56.0 31.5 26.5 38.5 36.9 21.3
## 205 101.2 88.8 90.8 101.3 62.5 35.2 28.9 40.6 39.2 23.0
## 206 98.7 78.1 78.6 92.0 56.5 35.7 29.2 36.4 37.0 22.0
## 207 96.0 81.5 80.5 89.5 52.0 30.3 25.0 36.0 35.0 22.4
## 208 104.2 87.6 89.6 100.5 60.7 36.5 31.1 40.7 38.4 22.9
## 209 103.0 92.5 88.5 94.0 55.0 36.0 28.0 36.5 37.0 21.8
## 210 107.0 97.0 95.5 104.5 56.6 34.4 28.9 40.0 38.5 24.0
## 211 101.0 82.0 82.0 98.0 59.0 35.4 29.0 38.0 42.0 23.6
## 212 112.1 99.5 97.8 107.4 60.5 35.5 28.8 42.9 38.7 25.7
## 213 103.4 93.8 92.7 112.2 66.2 34.8 30.1 45.7 43.6 26.9
## 214 100.3 79.1 80.4 94.3 51.0 32.6 27.2 36.1 32.7 22.5
## 215 95.7 85.8 94.7 106.8 60.6 33.6 27.3 37.5 41.8 26.2
## 216 108.0 90.8 92.4 100.3 56.5 36.8 29.3 37.5 36.3 23.4
## 217 101.0 85.4 86.9 101.8 57.0 34.0 27.1 39.4 37.1 23.7
## 218 98.6 91.6 102.1 106.7 57.8 32.3 27.9 41.8 41.5 27.7
## 219 101.6 79.7 82.0 98.4 58.1 36.5 31.0 38.0 38.1 25.3
## 220 99.0 86.1 90.8 101.3 56.0 33.5 28.3 37.7 38.3 25.2
## 221 116.7 113.2 102.9 107.9 57.7 37.4 28.9 37.8 37.4 24.1
## 222 105.0 79.5 85.8 92.7 51.8 36.2 27.0 33.9 28.9 20.3
## 223 95.1 85.4 84.1 94.3 52.7 31.2 25.9 37.4 34.4 24.5
## 224 106.7 97.9 94.7 104.6 60.8 36.2 28.7 37.7 39.4 22.6
## 225 116.6 94.7 93.1 103.3 58.0 42.3 31.9 39.9 39.6 25.5
## 226 103.5 86.1 90.5 96.7 55.2 34.5 27.7 37.3 34.8 22.5
## 227 90.4 84.9 83.7 97.9 51.8 28.0 25.2 37.5 36.0 21.3
## 228 107.5 92.2 88.6 97.5 53.7 34.3 27.6 36.0 37.5 23.3
## 229 100.8 83.7 82.5 97.7 53.3 32.5 27.1 35.4 37.0 22.7
## 230 104.2 97.8 93.6 102.5 58.0 35.8 29.4 39.0 38.1 23.0
## 231 104.9 98.6 99.2 103.3 55.3 35.0 29.3 35.9 36.0 23.5
## 232 112.8 89.5 96.9 100.9 54.9 38.5 28.7 35.9 33.6 22.1
## 233 95.7 82.9 89.6 94.8 51.9 32.2 25.3 33.4 32.8 21.4
## 234 104.9 90.1 90.8 96.6 55.0 32.9 26.5 35.6 37.9 22.5
## 235 109.1 78.9 91.0 98.1 55.7 38.5 30.1 37.2 36.8 23.4
## 236 105.4 80.7 87.4 95.5 56.8 38.3 28.6 35.0 34.3 23.3
## 237 104.3 94.3 99.4 100.4 59.1 35.0 26.7 35.5 35.9 22.6
## 238 100.5 70.2 79.3 92.0 51.7 32.1 26.7 33.2 34.9 23.7
## 239 111.8 83.6 92.7 101.5 59.5 40.4 31.7 36.7 38.3 25.5
## 240 109.5 91.9 96.5 103.3 57.7 36.5 29.1 34.6 38.8 25.1
## 241 101.2 71.8 82.3 87.6 50.1 34.2 29.2 34.1 33.4 23.1
## 242 103.3 85.0 90.8 97.9 55.2 35.2 29.4 34.9 37.3 23.5
## 243 101.6 85.7 91.0 95.9 50.9 34.0 28.4 35.0 34.3 21.1
## 244 103.1 96.5 99.0 111.8 62.3 34.8 27.5 41.7 37.0 24.3
## 245 104.6 82.4 85.7 99.9 63.3 38.6 32.0 38.4 39.8 25.4
## 246 104.3 86.3 87.8 103.3 59.7 36.4 30.4 39.3 42.0 27.7
## 247 95.9 83.2 88.0 100.6 57.8 34.0 28.2 36.3 39.6 25.2
## 248 83.0 66.5 79.0 92.0 53.5 24.3 20.5 32.0 32.2 21.0
## 249 78.5 61.5 70.5 90.5 57.7 27.8 24.0 38.5 38.5 22.5
## 250 75.0 61.2 66.5 91.0 53.0 24.0 22.0 32.5 32.5 19.0
## 251 86.5 78.0 91.0 99.5 61.5 28.0 24.0 35.2 36.7 23.0
## 252 91.0 70.5 80.5 91.5 55.0 26.9 22.7 33.0 33.3 19.9
## 253 79.5 66.5 78.5 94.0 54.0 26.5 22.5 34.0 35.0 23.0
## 254 77.0 58.0 64.0 85.5 49.5 24.1 22.0 32.5 32.0 19.0
## 255 88.0 74.5 87.0 104.0 64.0 29.2 26.2 38.5 38.0 22.0
## 256 85.0 73.5 92.0 104.1 65.3 29.0 23.4 35.3 37.4 21.6
## 257 98.8 90.5 103.5 108.1 61.1 33.6 26.6 37.2 35.8 22.6
## 258 79.0 66.5 74.0 90.3 52.0 24.8 21.0 32.2 32.5 19.9
## 259 77.6 61.0 71.8 91.6 53.0 25.4 22.6 34.0 34.5 20.5
## 260 85.0 69.5 81.5 94.4 55.8 25.9 22.9 36.1 35.3 20.9
## 261 76.7 62.0 74.1 80.9 48.8 24.0 20.5 30.8 30.4 17.9
## 262 76.7 63.4 69.0 87.7 54.0 25.6 21.6 34.4 32.8 19.1
## 263 82.0 71.0 69.0 88.5 54.5 26.0 21.8 33.5 35.0 21.0
## 264 79.0 59.0 79.0 88.5 51.2 23.5 21.0 32.5 29.6 18.5
## 265 89.5 74.0 92.0 101.0 61.5 31.0 26.5 38.6 38.6 23.8
## 266 73.5 60.5 68.3 88.5 54.0 24.6 20.6 29.0 33.0 19.0
## 267 90.0 75.0 80.5 99.0 59.0 28.7 24.9 37.0 36.5 20.6
## 268 80.0 65.0 78.3 91.0 53.5 27.0 23.0 32.0 31.5 22.0
## 269 94.5 82.2 94.2 110.2 69.0 32.5 26.8 40.5 38.6 23.0
## 270 83.8 70.2 82.5 94.2 54.1 25.9 23.2 34.3 30.9 19.4
## 271 95.2 85.0 91.2 111.0 68.0 34.4 28.3 40.1 40.0 24.2
## 272 91.0 75.0 85.0 103.0 62.0 31.5 26.5 38.2 40.0 23.0
## 273 80.0 63.0 76.5 90.0 51.1 24.6 22.3 35.2 33.7 20.9
## 274 81.7 70.0 81.5 96.0 57.9 26.3 22.6 37.2 36.0 21.0
## 275 89.6 72.6 86.3 93.7 56.4 27.4 23.4 37.0 38.4 21.3
## 276 90.9 78.7 83.5 101.5 59.9 29.5 25.3 35.4 36.7 21.3
## 277 78.5 61.0 73.0 82.5 49.5 24.5 22.5 32.5 33.0 20.5
## 278 78.0 64.2 69.8 89.0 53.0 23.2 21.2 33.0 31.5 20.5
## 279 80.2 63.1 78.9 93.9 57.1 26.4 23.1 35.0 35.0 20.1
## 280 99.0 85.0 92.5 106.4 65.6 31.6 26.6 39.6 38.9 23.3
## 281 80.0 66.0 78.0 92.0 52.5 25.0 22.2 33.0 34.8 20.5
## 282 75.5 65.0 76.7 86.0 51.0 24.5 20.5 31.5 31.0 20.0
## 283 82.6 63.2 70.5 89.1 55.8 27.0 23.6 33.6 33.4 20.0
## 284 94.5 83.6 94.2 104.1 61.5 31.0 25.4 37.2 35.2 22.3
## 285 83.9 68.0 76.1 88.0 52.2 26.2 23.2 33.2 35.3 21.2
## 286 92.8 75.7 88.6 102.6 64.3 31.0 24.9 35.5 38.0 21.5
## 287 85.3 65.5 74.4 95.9 53.7 27.8 23.9 35.6 33.4 21.9
## 288 84.4 66.9 79.7 98.8 59.2 28.5 23.7 35.9 34.6 20.0
## 289 87.6 67.9 82.7 93.5 57.0 29.6 24.5 37.1 36.3 20.6
## 290 81.0 67.8 73.0 92.9 55.8 26.3 23.0 32.3 30.0 18.8
## 291 84.1 63.2 75.0 86.9 52.1 26.5 23.5 32.3 31.6 18.9
## 292 88.2 73.1 80.0 94.9 57.2 27.0 24.3 34.9 35.7 22.0
## 293 88.1 65.3 75.8 95.3 55.7 27.7 24.5 33.1 35.4 21.2
## 294 84.9 65.6 75.5 93.3 57.4 27.7 24.2 35.1 35.6 21.0
## 295 81.2 62.7 75.4 90.0 54.0 25.3 22.7 32.6 32.9 19.8
## 296 91.6 81.8 96.5 99.3 60.7 28.3 25.2 35.0 36.3 21.4
## 297 88.4 72.1 87.3 95.6 59.2 29.0 24.8 34.3 37.1 21.9
## 298 87.6 68.6 80.7 95.8 56.6 28.5 24.1 35.6 34.0 20.7
## 299 91.3 80.0 97.9 104.6 62.7 30.0 23.1 36.3 36.0 21.0
## 300 87.2 64.9 75.9 92.2 53.1 28.0 24.2 34.0 35.4 20.7
## 301 82.2 61.7 66.0 80.7 50.3 23.7 21.6 30.5 33.8 20.7
## 302 82.1 64.0 73.6 98.1 56.8 25.8 23.9 33.7 34.0 20.3
## 303 82.6 58.6 73.0 89.6 54.9 27.1 23.1 33.2 35.8 20.7
## 304 86.5 68.3 89.2 94.0 57.0 26.0 22.6 35.9 32.5 19.7
## 305 85.3 69.5 74.9 94.7 55.7 24.9 22.8 34.9 37.1 20.1
## 306 84.7 70.5 86.1 94.1 54.6 28.0 23.0 33.5 33.5 20.0
## 307 86.8 69.8 76.5 93.7 55.7 25.4 23.4 35.9 34.2 21.5
## 308 85.2 69.0 76.3 86.7 51.5 26.6 22.7 31.6 33.3 19.6
## 309 93.9 81.0 93.2 105.9 65.5 31.3 26.8 38.1 37.7 21.7
## 310 84.5 71.0 80.5 97.5 59.5 28.0 23.0 34.0 34.2 20.6
## 311 90.7 73.6 79.8 105.3 65.8 29.5 26.6 38.8 39.7 24.3
## 312 97.4 96.3 104.2 102.6 57.5 34.3 26.5 38.3 41.1 22.9
## 313 83.3 65.0 68.2 91.6 55.3 27.7 23.2 33.0 31.5 18.9
## 314 82.1 62.8 70.6 83.9 51.5 24.6 21.4 34.0 34.8 20.9
## 315 92.2 83.0 101.4 108.1 65.0 29.8 24.6 38.1 37.8 20.7
## 316 83.6 67.6 86.2 99.1 57.5 26.4 23.3 38.5 35.0 22.1
## 317 82.6 65.4 74.8 89.3 53.7 26.4 23.8 34.0 33.7 19.1
## 318 77.0 61.6 74.0 89.9 53.6 26.3 23.1 33.3 32.4 19.0
## 319 79.3 63.2 75.5 85.8 51.6 24.2 22.4 32.5 31.3 18.4
## 320 84.3 59.4 74.0 85.8 51.0 26.9 23.0 32.8 32.1 19.7
## 321 85.6 62.7 74.0 96.0 61.5 28.0 23.6 35.4 38.5 21.3
## 322 85.0 66.6 82.0 98.7 60.3 30.4 25.2 35.7 38.2 21.8
## 323 79.1 64.6 82.5 94.9 54.0 24.7 21.7 37.4 35.5 21.0
## 324 85.0 65.7 77.7 90.5 56.1 29.4 23.1 33.2 34.4 20.3
## 325 79.0 63.0 79.2 86.6 48.5 26.4 22.5 32.2 30.3 20.2
## 326 96.2 78.5 93.4 103.0 59.6 30.2 25.9 37.0 35.2 23.2
## 327 86.0 68.2 76.2 97.9 59.8 29.8 25.0 36.8 38.1 21.8
## 328 89.8 68.5 80.2 98.1 62.0 30.1 26.2 40.9 40.4 23.3
## 329 84.5 68.8 70.6 94.8 56.5 27.0 23.4 34.4 35.2 21.6
## 330 88.2 69.0 77.6 96.5 57.6 28.6 25.2 37.0 35.0 22.2
## 331 99.5 78.0 87.5 103.0 64.8 31.1 26.6 35.2 36.7 21.8
## 332 76.1 62.0 70.8 86.8 52.6 24.3 22.3 34.2 34.8 20.2
## 333 85.8 65.8 70.5 89.3 57.4 27.0 23.2 36.8 36.2 22.1
## 334 82.0 65.0 77.2 89.5 53.4 26.0 21.4 34.0 33.9 21.4
## 335 77.5 64.9 74.5 88.4 53.4 25.6 22.4 30.9 32.6 19.1
## 336 85.7 71.5 82.0 100.5 60.4 28.0 24.2 36.6 36.4 21.8
## 337 78.8 61.4 78.8 94.0 56.4 27.1 22.8 35.4 34.8 21.0
## 338 88.0 75.4 90.0 101.3 63.1 28.4 22.6 37.6 34.3 21.7
## 339 80.2 67.2 78.3 95.5 54.7 27.0 22.2 36.0 35.4 22.2
## 340 85.6 69.5 75.5 95.3 57.6 28.5 24.4 35.2 35.4 21.8
## 341 81.3 57.9 66.4 84.1 49.0 26.6 22.2 33.6 33.0 19.6
## 342 84.6 64.4 77.9 91.0 53.4 26.6 23.3 32.7 33.0 19.4
## 343 82.2 62.3 68.7 83.9 52.8 24.4 22.1 32.4 32.5 21.0
## 344 84.9 68.7 78.6 93.1 55.2 28.2 23.8 34.6 34.3 21.5
## 345 81.5 68.0 84.6 92.3 55.4 27.4 23.0 34.5 35.3 21.6
## 346 89.9 66.0 79.9 97.1 59.0 27.0 24.2 37.2 39.0 23.2
## 347 89.8 71.2 85.7 97.5 58.2 28.2 25.2 34.2 34.0 20.4
## 348 80.8 64.8 79.8 92.0 57.4 26.5 22.6 31.0 35.2 20.4
## 349 88.4 78.0 85.6 112.1 75.7 33.8 24.6 38.2 39.8 22.0
## 350 80.2 60.7 77.0 89.9 53.3 25.2 22.4 34.2 32.2 19.0
## 351 89.5 75.0 95.0 104.9 63.2 29.6 23.9 37.6 36.6 22.0
## 352 83.7 68.6 83.0 94.2 58.0 26.2 22.1 32.2 35.9 19.9
## 353 82.5 67.1 75.0 90.4 53.2 27.3 23.6 35.9 36.6 23.8
## 354 85.5 68.0 84.5 99.5 60.8 26.5 24.2 40.5 39.4 23.2
## 355 88.3 71.0 87.1 98.7 59.2 28.2 23.6 35.6 35.8 20.8
## 356 91.5 76.0 82.5 103.5 64.8 29.5 25.2 39.2 37.0 21.8
## 357 92.2 70.3 82.8 100.0 59.6 30.8 25.4 37.0 37.0 23.0
## 358 80.5 69.5 81.0 95.2 56.2 27.4 23.8 35.4 34.0 21.3
## 359 108.0 101.5 105.5 114.0 70.0 40.3 30.8 45.6 45.0 25.8
## 360 80.0 67.2 75.2 91.2 53.6 27.1 23.2 34.6 34.2 20.3
## 361 89.5 68.7 81.0 93.8 58.0 30.0 24.4 35.9 37.4 22.0
## 362 84.9 66.0 81.0 92.0 56.2 26.4 22.6 35.4 35.6 21.0
## 363 80.8 67.5 75.4 85.8 53.8 24.8 21.5 33.0 33.0 20.0
## 364 81.0 65.2 75.9 95.4 58.5 27.2 25.3 37.5 38.4 23.2
## 365 88.7 64.2 76.8 88.4 56.0 29.0 23.8 33.9 33.2 20.5
## 366 74.5 61.0 65.3 84.5 51.5 22.4 19.6 30.4 28.4 17.4
## 367 81.9 60.2 64.2 91.7 53.3 26.0 23.2 36.0 35.8 21.2
## 368 75.8 62.6 72.9 87.4 54.0 26.4 21.8 34.5 33.0 20.7
## 369 88.0 72.7 76.7 95.9 56.6 29.5 25.3 36.8 37.6 22.4
## 370 72.6 58.5 67.2 84.3 52.2 25.0 20.7 31.7 32.0 20.1
## 371 88.0 67.5 79.5 96.5 56.3 28.4 24.6 35.6 33.5 21.8
## 372 86.0 66.1 73.2 91.8 54.4 27.4 23.8 35.0 33.0 20.2
## 373 85.5 68.0 86.1 101.0 59.6 28.0 23.3 36.1 35.1 21.6
## 374 80.4 64.0 85.0 92.6 54.8 25.6 20.7 32.7 31.5 18.9
## 375 98.1 90.1 90.1 95.2 53.8 32.1 25.8 34.7 37.2 21.5
## 376 82.0 64.1 74.1 96.0 53.7 26.6 23.2 35.2 35.0 21.0
## 377 89.0 72.4 80.3 89.9 52.6 27.6 23.8 33.2 34.4 21.3
## 378 85.8 71.0 85.0 92.7 57.3 28.4 23.6 34.2 34.0 19.8
## 379 85.9 69.1 77.2 94.1 56.2 28.9 23.1 35.0 34.4 20.0
## 380 82.5 66.1 74.9 93.8 54.2 27.8 24.0 35.3 33.7 22.0
## 381 74.5 59.5 65.5 78.8 46.3 23.2 20.8 31.6 29.6 18.6
## 382 89.4 71.5 80.6 96.0 56.9 26.8 22.0 33.8 33.5 21.1
## 383 80.3 65.0 73.3 84.9 50.8 25.0 22.2 31.6 31.6 18.8
## 384 85.6 71.5 82.0 97.5 63.0 28.4 23.5 39.1 38.2 20.4
## 385 83.0 63.0 72.0 86.0 53.0 24.1 22.0 30.0 31.2 20.4
## 386 88.2 70.0 81.2 96.8 59.8 30.1 25.2 35.8 35.0 21.7
## 387 85.5 70.4 82.4 93.5 57.5 28.3 21.9 33.1 32.8 19.0
## 388 80.6 64.3 75.0 93.0 55.6 28.2 23.8 35.6 35.5 22.0
## 389 81.0 66.0 73.2 90.2 54.8 26.8 23.8 34.7 34.5 21.4
## 390 83.6 70.5 78.5 91.9 56.8 30.5 24.3 35.8 36.9 20.4
## 391 79.4 62.0 73.0 88.5 53.6 24.7 22.4 33.5 33.0 19.4
## 392 82.9 67.6 73.9 90.2 56.4 30.2 24.2 34.1 33.8 20.6
## 393 83.2 65.0 80.9 97.6 62.2 27.2 22.8 38.6 37.4 20.6
## 394 78.1 64.0 75.9 95.0 55.9 25.2 23.0 32.3 34.8 20.3
## 395 82.4 70.5 81.6 98.0 61.4 27.2 23.2 35.6 30.5 20.1
## 396 90.0 79.2 95.2 97.4 61.7 27.8 23.4 36.0 35.7 23.8
## 397 92.4 71.1 91.5 98.2 55.3 29.6 24.6 34.3 36.6 20.7
## 398 86.7 74.7 90.8 100.0 59.4 31.0 23.7 35.7 34.0 20.9
## 399 84.0 62.9 76.8 95.5 52.8 26.4 23.1 34.6 32.3 20.4
## 400 86.0 70.6 91.9 101.9 62.0 29.1 24.2 37.2 33.1 19.9
## 401 84.5 61.6 81.0 87.6 51.6 27.4 23.7 32.4 32.9 20.3
## 402 91.2 74.2 98.2 102.6 61.5 31.4 25.0 37.6 41.9 24.3
## 403 81.1 65.5 82.3 90.4 53.3 26.8 22.3 34.2 33.4 21.5
## 404 87.6 67.7 84.2 92.0 54.3 28.2 23.8 33.0 32.7 18.8
## 405 88.2 72.9 97.4 102.2 60.1 29.9 24.1 33.8 33.5 21.0
## 406 88.2 72.1 96.3 106.7 62.0 27.3 23.1 34.6 35.6 23.5
## 407 104.2 93.4 111.1 109.8 67.7 35.7 28.6 41.5 38.6 22.3
## 408 84.8 60.7 78.0 89.1 52.6 26.6 22.8 32.5 34.6 21.2
## 409 84.3 69.2 85.3 93.0 53.9 27.9 23.7 35.5 33.1 21.9
## 410 91.0 75.5 90.7 98.6 62.7 31.5 24.8 37.2 35.4 20.3
## 411 90.5 85.6 111.7 112.1 64.3 31.3 25.0 35.5 38.0 21.0
## 412 89.6 71.6 92.7 95.0 56.8 27.2 24.3 36.2 35.8 20.1
## 413 89.3 75.5 98.9 100.2 57.8 30.3 23.8 37.3 37.0 22.5
## 414 90.0 73.7 92.7 101.2 57.4 28.1 23.3 33.9 33.3 21.2
## 415 88.2 67.9 86.9 100.8 60.2 31.0 26.6 36.6 38.5 22.2
## 416 83.5 71.2 83.0 96.5 52.4 25.2 21.7 33.7 32.1 21.0
## 417 91.0 69.3 85.9 93.2 55.4 29.7 24.1 34.8 34.6 21.5
## 418 88.2 75.6 94.7 95.8 58.5 30.0 22.2 34.6 32.6 19.9
## 419 82.2 63.8 81.8 97.6 59.0 27.8 23.6 35.9 35.5 22.6
## 420 98.9 83.6 108.6 108.3 63.0 31.9 26.0 38.1 36.4 24.5
## 421 97.9 84.9 98.0 102.4 59.9 32.0 25.7 37.6 33.7 21.6
## 422 81.7 66.0 85.0 94.3 56.4 28.0 21.1 32.1 34.3 21.0
## 423 86.1 68.3 85.3 96.5 55.1 26.0 22.6 33.9 33.7 21.8
## 424 87.1 63.8 88.3 94.0 56.1 28.1 24.0 33.1 35.2 22.1
## 425 76.8 73.6 106.3 106.2 61.7 31.4 26.2 39.6 39.1 24.8
## 426 87.9 76.3 93.8 98.6 55.5 31.2 23.0 32.3 31.3 20.3
## 427 81.6 58.8 77.4 89.3 51.6 25.4 22.4 33.8 32.4 21.0
## 428 92.3 78.0 100.9 106.9 65.5 31.7 26.3 38.5 39.7 22.8
## 429 81.3 64.5 80.8 91.9 53.4 25.1 23.1 34.6 32.7 21.4
## 430 82.2 65.8 80.2 89.1 55.4 28.4 22.9 33.6 35.0 19.7
## 431 78.6 62.5 84.5 95.5 57.3 27.4 22.4 35.1 35.7 20.4
## 432 94.3 77.5 93.9 101.1 61.6 28.3 23.0 36.5 34.6 21.9
## 433 89.5 70.0 89.6 98.9 55.0 29.3 25.0 35.6 36.6 22.5
## 434 81.0 59.4 73.7 83.3 50.2 26.9 22.5 31.7 30.7 20.5
## 435 85.4 68.3 84.0 94.0 57.3 29.8 24.7 32.7 34.3 21.7
## 436 86.2 77.9 96.0 100.1 60.7 28.4 24.3 34.7 34.5 20.9
## 437 85.3 68.3 88.0 93.8 54.7 27.7 24.4 32.7 33.0 19.6
## 438 86.1 72.0 91.9 103.9 57.5 27.4 22.4 36.3 37.9 23.4
## 439 84.5 62.7 78.3 93.5 52.5 24.2 23.3 32.8 36.1 22.0
## 440 72.6 58.0 82.9 90.2 52.5 23.1 21.0 35.5 36.4 20.3
## 441 90.0 72.7 97.2 100.2 57.9 30.5 23.7 36.3 34.3 20.5
## 442 95.8 85.4 105.2 109.5 68.3 29.7 24.5 40.3 37.0 22.4
## 443 85.5 67.3 86.9 97.6 58.8 27.2 22.6 38.3 35.6 21.0
## 444 85.7 70.8 92.1 96.9 57.9 29.3 24.2 36.5 35.3 20.3
## 445 85.4 67.4 85.9 90.3 54.7 26.9 22.3 32.8 29.5 18.7
## 446 79.1 66.0 85.0 94.8 57.4 26.8 22.9 32.2 32.6 19.2
## 447 84.4 63.6 84.9 92.7 54.4 26.7 23.7 33.5 33.6 20.2
## 448 90.0 71.7 95.8 108.4 68.5 28.7 25.9 40.5 39.5 23.3
## 449 82.9 73.6 86.9 94.7 52.9 25.0 21.0 31.6 30.2 19.3
## 450 87.3 66.9 83.3 90.6 54.4 28.9 23.8 33.9 33.9 20.8
## 451 85.3 65.5 76.7 90.8 56.4 27.3 24.3 33.1 35.0 20.7
## 452 87.3 70.4 91.1 96.7 55.6 29.9 23.7 35.3 33.8 23.0
## 453 93.2 80.3 98.0 101.2 61.2 31.0 23.6 34.6 35.6 20.0
## 454 84.7 65.0 84.5 94.7 54.8 28.9 24.4 34.7 34.8 22.5
## 455 78.0 60.4 79.2 87.0 49.5 26.8 22.7 33.2 31.5 18.8
## 456 90.7 75.3 96.3 103.6 59.9 31.5 25.4 35.7 35.8 21.1
## 457 95.0 83.4 100.5 107.5 67.4 34.2 28.3 40.9 41.2 23.4
## 458 88.7 72.2 87.8 96.1 61.1 29.5 24.6 39.1 38.7 26.0
## 459 86.6 70.1 86.2 97.6 55.6 27.6 23.7 34.9 32.0 21.1
## 460 82.5 69.8 86.4 100.2 59.0 26.7 23.3 33.5 33.1 19.6
## 461 81.4 66.4 78.5 91.6 54.6 24.2 21.2 33.0 34.3 20.2
## 462 79.9 61.7 79.7 94.7 56.5 25.5 23.2 36.7 36.2 22.0
## 463 91.1 76.4 98.3 107.0 64.5 32.8 24.8 36.5 36.2 23.9
## 464 80.1 63.8 72.5 86.3 51.5 24.4 22.0 32.0 32.6 21.0
## 465 86.4 70.2 86.7 97.8 58.5 28.3 24.9 35.5 36.3 21.4
## 466 105.2 88.2 106.5 107.9 63.1 30.3 25.2 39.2 38.4 23.1
## 467 91.1 75.8 94.4 103.3 60.5 30.4 24.8 36.8 35.9 21.8
## 468 96.7 81.5 94.1 94.9 54.8 31.2 25.8 34.4 37.3 21.8
## 469 80.5 67.4 77.7 95.1 56.3 27.0 24.3 36.2 36.7 23.7
## 470 90.4 78.3 95.4 104.9 63.1 32.8 27.0 39.4 39.1 23.0
## 471 84.1 65.9 83.7 96.3 57.0 26.6 23.7 35.6 34.0 21.7
## 472 84.2 62.4 81.4 90.4 53.2 25.8 23.0 34.5 31.0 19.2
## 473 82.5 65.6 81.8 92.8 56.2 28.8 24.7 33.0 34.6 21.7
## 474 106.9 96.2 121.1 128.3 72.3 35.9 30.6 49.0 45.4 24.1
## 475 79.9 62.5 74.0 87.6 51.6 25.7 21.9 31.7 34.7 18.5
## 476 93.0 80.5 98.8 107.2 63.0 34.1 27.6 38.8 38.2 22.2
## 477 96.3 86.1 107.4 112.0 69.4 31.7 25.5 39.4 38.7 23.7
## 478 86.8 71.3 85.7 94.0 59.2 31.1 24.4 35.9 34.4 21.2
## 479 95.3 79.4 90.9 102.7 63.0 29.9 24.9 38.7 36.3 21.3
## 480 93.0 72.3 89.0 92.0 54.7 28.8 23.2 35.0 34.2 20.5
## 481 86.4 67.4 86.7 98.4 59.1 29.3 23.1 35.6 37.4 22.5
## 482 109.0 94.2 110.5 103.5 57.8 35.7 25.6 34.8 35.5 19.5
## 483 86.2 67.0 85.3 96.7 56.2 27.0 22.3 37.0 33.0 21.6
## 484 85.3 69.5 86.2 90.6 51.7 24.4 22.1 33.9 30.8 19.9
## 485 84.2 66.4 79.3 92.0 53.4 24.9 22.3 32.5 31.7 21.7
## 486 83.7 67.6 84.1 90.4 52.4 25.6 23.0 34.5 32.5 21.7
## 487 86.5 66.6 87.5 96.7 59.3 27.3 24.0 36.0 34.9 21.4
## 488 98.2 69.7 86.0 94.9 59.1 29.9 25.0 37.7 37.7 23.7
## 489 88.0 72.0 86.0 95.5 57.3 27.6 23.3 35.2 31.9 19.6
## 490 80.4 64.5 81.7 88.4 52.4 25.5 23.0 34.5 32.7 20.3
## 491 86.0 72.3 91.7 97.8 59.1 31.0 26.3 39.9 39.7 21.6
## 492 86.4 69.1 87.8 95.4 59.5 28.3 24.7 35.6 34.6 23.0
## 493 87.6 74.7 77.0 97.7 57.0 28.1 23.1 34.4 32.7 20.9
## 494 80.9 60.8 77.4 84.6 51.0 26.8 22.8 34.4 33.1 21.0
## 495 82.1 66.9 82.4 91.6 53.4 26.2 22.6 35.0 35.2 22.4
## 496 89.3 68.8 90.8 103.3 63.0 29.6 25.1 34.4 35.0 21.0
## 497 85.3 63.7 85.3 96.3 55.5 26.4 23.0 36.5 34.1 21.3
## 498 89.0 71.2 86.1 93.5 56.1 30.2 24.4 37.0 37.5 22.6
## 499 94.1 79.6 103.5 104.3 66.5 36.9 29.0 40.3 37.9 24.6
## 500 90.8 77.9 90.6 96.3 56.2 28.1 24.3 38.2 36.6 22.3
## 501 97.0 69.6 90.2 98.3 56.3 32.0 25.1 32.3 33.8 20.8
## 502 95.4 86.0 107.1 112.1 64.4 32.3 26.4 35.7 37.0 21.4
## 503 91.8 69.9 90.4 101.0 60.6 30.3 25.4 37.7 37.9 22.4
## 504 87.3 63.5 79.2 89.5 55.2 30.1 23.6 35.6 33.3 22.4
## 505 78.1 57.9 75.1 86.9 51.8 27.4 24.0 34.4 34.1 21.2
## 506 90.9 72.2 89.4 98.6 59.0 30.6 24.9 38.4 36.6 22.0
## 507 97.1 80.4 100.8 102.2 57.4 33.2 25.5 39.6 35.9 23.0
## wri.gi age wgt hgt sex
## 1 16.5 21 65.6 174.0 1
## 2 17.0 23 71.8 175.3 1
## 3 16.9 28 80.7 193.5 1
## 4 16.6 23 72.6 186.5 1
## 5 18.0 22 78.8 187.2 1
## 6 16.9 21 74.8 181.5 1
## 7 18.8 26 86.4 184.0 1
## 8 18.0 27 78.4 184.5 1
## 9 16.5 23 62.0 175.0 1
## 10 16.9 21 81.6 184.0 1
## 11 16.2 23 76.6 180.0 1
## 12 18.2 22 83.6 177.8 1
## 13 18.0 20 90.0 192.0 1
## 14 16.6 26 74.6 176.0 1
## 15 16.5 23 71.0 174.0 1
## 16 17.5 22 79.6 184.0 1
## 17 17.8 30 93.8 192.7 1
## 18 17.1 22 70.0 171.5 1
## 19 18.5 29 72.4 173.0 1
## 20 18.8 22 85.9 176.0 1
## 21 17.3 22 78.8 176.0 1
## 22 18.0 20 77.8 180.5 1
## 23 16.0 22 66.2 172.7 1
## 24 17.0 24 86.4 176.0 1
## 25 18.2 26 81.8 173.5 1
## 26 19.0 24 89.6 178.0 1
## 27 16.7 21 82.8 180.3 1
## 28 17.8 24 76.4 180.3 1
## 29 16.5 23 63.2 164.5 1
## 30 15.6 19 60.9 173.0 1
## 31 16.6 23 74.8 183.5 1
## 32 17.2 25 70.0 175.5 1
## 33 17.0 23 72.4 188.0 1
## 34 17.2 23 84.1 189.2 1
## 35 16.9 23 69.1 172.8 1
## 36 17.0 20 59.5 170.0 1
## 37 17.0 22 67.2 182.0 1
## 38 16.0 24 61.3 170.0 1
## 39 16.8 22 68.6 177.8 1
## 40 18.7 24 80.1 184.2 1
## 41 17.6 21 87.8 186.7 1
## 42 17.5 23 84.7 171.4 1
## 43 16.7 24 73.4 172.7 1
## 44 17.0 35 72.1 175.3 1
## 45 17.9 29 82.6 180.3 1
## 46 18.9 25 88.7 182.9 1
## 47 16.8 23 84.1 188.0 1
## 48 17.7 20 94.1 177.2 1
## 49 16.3 25 74.9 172.1 1
## 50 15.8 29 59.1 167.0 1
## 51 16.9 23 75.6 169.5 1
## 52 17.9 23 86.2 174.0 1
## 53 17.3 36 75.3 172.7 1
## 54 16.4 25 87.1 182.2 1
## 55 15.0 24 55.2 164.1 1
## 56 15.8 20 57.0 163.0 1
## 57 15.3 52 61.4 171.5 1
## 58 17.7 50 76.8 184.2 1
## 59 17.5 46 86.8 174.0 1
## 60 16.5 51 72.2 174.0 1
## 61 16.1 28 71.6 177.0 1
## 62 17.2 48 84.8 186.0 1
## 63 17.5 35 68.2 167.0 1
## 64 16.9 23 66.1 171.8 1
## 65 17.5 23 72.0 182.0 1
## 66 17.4 62 64.6 167.0 1
## 67 16.7 21 74.8 177.8 1
## 68 17.2 26 70.0 164.5 1
## 69 17.7 33 101.6 192.0 1
## 70 16.1 36 63.2 175.5 1
## 71 17.9 41 79.1 171.2 1
## 72 16.6 40 78.9 181.6 1
## 73 15.6 27 67.7 167.4 1
## 74 17.5 27 66.0 181.1 1
## 75 17.0 23 68.2 177.0 1
## 76 15.8 31 63.9 174.5 1
## 77 15.8 26 72.0 177.5 1
## 78 15.4 23 56.8 170.5 1
## 79 16.3 24 74.5 182.4 1
## 80 18.0 24 90.9 197.1 1
## 81 16.0 34 93.0 180.1 1
## 82 18.0 21 80.9 175.5 1
## 83 16.5 25 72.7 180.6 1
## 84 16.7 34 68.0 184.4 1
## 85 16.2 31 70.9 175.5 1
## 86 16.6 40 72.5 180.6 1
## 87 17.3 21 72.5 177.0 1
## 88 17.3 33 83.4 177.1 1
## 89 16.4 25 75.5 181.6 1
## 90 16.6 29 73.0 176.5 1
## 91 17.0 27 70.2 175.0 1
## 92 16.5 44 73.4 174.0 1
## 93 16.3 26 70.5 165.1 1
## 94 16.0 22 68.9 177.0 1
## 95 18.6 37 102.3 192.0 1
## 96 16.2 38 68.4 176.5 1
## 97 16.2 20 65.9 169.4 1
## 98 16.5 21 75.7 182.1 1
## 99 17.0 24 84.5 179.8 1
## 100 17.7 45 87.7 175.3 1
## 101 17.8 25 86.4 184.9 1
## 102 17.5 22 73.2 177.3 1
## 103 14.6 29 53.9 167.4 1
## 104 16.4 37 72.0 178.1 1
## 105 16.0 20 55.5 168.9 1
## 106 16.4 20 58.4 157.2 1
## 107 18.1 32 83.2 180.3 1
## 108 16.7 23 72.7 170.2 1
## 109 15.6 25 64.1 177.8 1
## 110 18.4 27 72.3 172.7 1
## 111 16.3 21 65.0 165.1 1
## 112 16.1 27 86.4 186.7 1
## 113 17.1 25 65.0 165.1 1
## 114 18.3 38 88.6 174.0 1
## 115 18.0 44 84.1 175.3 1
## 116 16.1 27 66.8 185.4 1
## 117 17.3 37 75.5 177.8 1
## 118 17.7 28 93.2 180.3 1
## 119 18.2 33 82.7 180.3 1
## 120 15.3 25 58.0 177.8 1
## 121 18.1 21 79.5 177.8 1
## 122 17.6 30 78.6 177.8 1
## 123 16.0 26 71.8 177.8 1
## 124 19.2 27 116.4 177.8 1
## 125 16.3 33 72.2 163.8 1
## 126 17.4 29 83.6 188.0 1
## 127 17.9 27 85.5 198.1 1
## 128 17.6 34 90.9 175.3 1
## 129 18.3 42 85.9 166.4 1
## 130 19.5 29 89.1 190.5 1
## 131 16.3 41 75.0 166.4 1
## 132 17.0 43 77.7 177.8 1
## 133 17.1 43 86.4 179.7 1
## 134 17.5 29 90.9 172.7 1
## 135 17.0 27 73.6 190.5 1
## 136 18.4 62 76.4 185.4 1
## 137 16.8 33 69.1 168.9 1
## 138 18.1 45 84.5 167.6 1
## 139 16.2 30 64.5 175.3 1
## 140 18.5 20 69.1 170.2 1
## 141 18.2 22 108.6 190.5 1
## 142 19.6 51 86.4 177.8 1
## 143 17.7 34 80.9 190.5 1
## 144 17.6 44 87.7 177.8 1
## 145 18.1 46 94.5 184.2 1
## 146 17.1 34 80.2 176.5 1
## 147 17.0 32 72.0 177.8 1
## 148 15.5 28 71.4 180.3 1
## 149 16.4 31 72.7 171.4 1
## 150 17.1 29 84.1 172.7 1
## 151 18.0 42 76.8 172.7 1
## 152 16.3 29 63.6 177.8 1
## 153 16.8 31 80.9 177.8 1
## 154 17.4 30 80.9 182.9 1
## 155 17.1 27 85.5 170.2 1
## 156 16.5 25 68.6 167.6 1
## 157 17.0 24 67.7 175.3 1
## 158 16.4 33 66.4 165.1 1
## 159 18.4 45 102.3 185.4 1
## 160 16.3 37 70.5 181.6 1
## 161 17.1 44 95.9 172.7 1
## 162 17.9 34 84.1 190.5 1
## 163 19.5 55 87.3 179.1 1
## 164 17.3 43 71.8 175.3 1
## 165 16.6 24 65.9 170.2 1
## 166 18.1 22 95.9 193.0 1
## 167 16.8 38 91.4 171.4 1
## 168 17.3 24 81.8 177.8 1
## 169 17.9 29 96.8 177.8 1
## 170 17.3 25 69.1 167.6 1
## 171 16.5 37 82.7 167.6 1
## 172 18.2 30 75.5 180.3 1
## 173 18.3 26 79.5 182.9 1
## 174 15.9 35 73.6 176.5 1
## 175 18.8 29 91.8 186.7 1
## 176 18.0 30 84.1 188.0 1
## 177 17.1 37 85.9 188.0 1
## 178 17.1 34 81.8 177.8 1
## 179 18.1 28 82.5 174.0 1
## 180 18.7 27 80.5 177.8 1
## 181 16.3 32 70.0 171.4 1
## 182 17.3 28 81.8 185.4 1
## 183 17.5 22 84.1 185.4 1
## 184 17.9 44 90.5 188.0 1
## 185 16.8 25 91.4 188.0 1
## 186 17.1 49 89.1 182.9 1
## 187 17.7 54 85.0 176.5 1
## 188 16.7 49 69.1 175.3 1
## 189 17.5 60 73.6 175.3 1
## 190 17.7 42 80.5 188.0 1
## 191 17.5 52 82.7 188.0 1
## 192 18.1 23 86.4 175.3 1
## 193 16.4 33 67.7 170.5 1
## 194 16.4 46 92.7 179.1 1
## 195 18.8 43 93.6 177.8 1
## 196 17.1 56 70.9 175.3 1
## 197 17.9 21 75.0 182.9 1
## 198 15.9 18 93.2 170.8 1
## 199 17.2 21 93.2 188.0 1
## 200 17.1 45 77.7 180.3 1
## 201 16.2 22 61.4 177.8 1
## 202 17.4 55 94.1 185.4 1
## 203 17.0 42 75.0 168.9 1
## 204 16.6 29 83.6 185.4 1
## 205 17.5 40 85.5 180.3 1
## 206 17.2 24 73.9 174.0 1
## 207 16.7 62 66.8 167.6 1
## 208 17.9 26 87.3 182.9 1
## 209 17.5 35 72.3 160.0 1
## 210 17.0 37 88.6 180.3 1
## 211 17.5 34 75.5 167.6 1
## 212 17.1 25 101.4 186.7 1
## 213 17.0 30 91.1 175.3 1
## 214 17.9 32 67.3 175.3 1
## 215 17.8 27 77.7 175.9 1
## 216 16.7 42 81.8 175.3 1
## 217 16.6 44 75.5 179.1 1
## 218 18.1 46 84.5 181.6 1
## 219 17.7 19 76.6 177.8 1
## 220 17.4 43 85.0 182.9 1
## 221 17.1 28 102.5 177.8 1
## 222 15.8 39 77.3 184.2 1
## 223 17.1 30 71.8 179.1 1
## 224 17.5 36 87.9 176.5 1
## 225 18.9 48 94.3 188.0 1
## 226 16.4 48 70.9 174.0 1
## 227 15.1 53 64.5 167.6 1
## 228 16.7 45 77.3 170.2 1
## 229 16.6 39 72.3 167.6 1
## 230 17.0 43 87.3 188.0 1
## 231 18.1 65 80.0 174.0 1
## 232 17.1 45 82.3 176.5 1
## 233 16.3 37 73.6 180.3 1
## 234 17.8 55 74.1 167.6 1
## 235 19.6 33 85.9 188.0 1
## 236 17.3 25 73.2 180.3 1
## 237 17.3 35 76.3 167.6 1
## 238 18.4 28 65.9 183.0 1
## 239 19.4 26 90.9 183.0 1
## 240 18.9 43 89.1 179.1 1
## 241 18.3 30 62.3 170.2 1
## 242 17.3 26 82.7 177.8 1
## 243 16.3 51 79.1 179.1 1
## 244 16.7 30 98.2 190.5 1
## 245 18.1 24 84.1 177.8 1
## 246 18.4 35 83.2 180.3 1
## 247 17.0 37 83.2 180.3 1
## 248 13.5 22 51.6 161.2 0
## 249 15.0 20 59.0 167.5 0
## 250 14.0 19 49.2 159.5 0
## 251 15.0 25 63.0 157.0 0
## 252 14.5 21 53.6 155.8 0
## 253 14.5 23 59.0 170.0 0
## 254 13.9 26 47.6 159.1 0
## 255 16.8 22 69.8 166.0 0
## 256 15.2 28 66.8 176.2 0
## 257 16.3 40 75.2 160.2 0
## 258 13.8 32 55.2 172.5 0
## 259 15.3 25 54.2 170.9 0
## 260 14.4 25 62.5 172.9 0
## 261 13.2 29 42.0 153.4 0
## 262 13.8 22 50.0 160.0 0
## 263 14.0 25 49.8 147.2 0
## 264 14.5 23 49.2 168.2 0
## 265 17.0 37 73.2 175.0 0
## 266 13.2 19 47.8 157.0 0
## 267 15.9 23 68.8 167.6 0
## 268 13.8 25 50.6 159.5 0
## 269 16.6 26 82.5 175.0 0
## 270 14.5 24 57.2 166.8 0
## 271 17.6 29 87.8 176.5 0
## 272 16.0 22 72.8 170.2 0
## 273 15.2 30 54.5 174.0 0
## 274 15.0 23 59.8 173.0 0
## 275 15.6 38 67.3 179.9 0
## 276 15.6 23 67.8 170.5 0
## 277 14.5 19 47.0 160.0 0
## 278 14.0 46 46.2 154.4 0
## 279 14.9 20 55.0 162.0 0
## 280 15.9 22 83.0 176.5 0
## 281 14.4 25 54.4 160.0 0
## 282 14.0 21 45.8 152.0 0
## 283 15.6 23 53.6 162.1 0
## 284 16.1 31 73.2 170.0 0
## 285 14.6 29 52.1 160.2 0
## 286 15.3 19 67.9 161.3 0
## 287 15.2 21 56.6 166.4 0
## 288 15.1 23 62.3 168.9 0
## 289 15.2 24 58.5 163.8 0
## 290 14.3 20 54.5 167.6 0
## 291 15.0 19 50.2 160.0 0
## 292 15.1 20 60.3 161.3 0
## 293 15.7 19 58.3 167.6 0
## 294 14.7 20 56.2 165.1 0
## 295 13.8 19 50.2 160.0 0
## 296 15.3 22 72.9 170.0 0
## 297 14.3 39 59.8 157.5 0
## 298 15.5 18 61.0 167.6 0
## 299 14.6 19 69.1 160.7 0
## 300 15.5 26 55.9 163.2 0
## 301 14.6 20 46.5 152.4 0
## 302 14.6 20 54.3 157.5 0
## 303 14.4 26 54.8 168.3 0
## 304 14.6 21 60.7 180.3 0
## 305 14.9 21 60.0 165.5 0
## 306 14.6 38 62.0 165.0 0
## 307 15.1 23 60.3 164.5 0
## 308 15.3 37 52.7 156.0 0
## 309 15.9 19 74.3 160.0 0
## 310 15.0 25 62.0 163.0 0
## 311 15.9 20 73.1 165.7 0
## 312 16.4 41 80.0 161.0 0
## 313 14.1 26 54.7 162.0 0
## 314 14.3 21 53.2 166.0 0
## 315 16.6 47 75.7 174.0 0
## 316 15.6 19 61.1 172.7 0
## 317 14.7 44 55.7 167.6 0
## 318 14.2 35 48.7 151.1 0
## 319 14.7 32 52.3 164.5 0
## 320 15.0 46 50.0 163.5 0
## 321 15.4 22 59.3 152.0 0
## 322 15.9 49 62.5 169.0 0
## 323 15.0 52 55.7 164.0 0
## 324 14.4 25 54.8 161.2 0
## 325 15.0 48 45.9 155.0 0
## 326 16.2 41 70.6 170.0 0
## 327 16.2 18 67.2 176.2 0
## 328 16.4 30 69.4 170.0 0
## 329 14.6 20 58.2 162.5 0
## 330 16.2 24 64.8 170.3 0
## 331 16.2 23 71.6 164.1 0
## 332 14.4 30 52.8 169.5 0
## 333 15.8 23 59.8 163.2 0
## 334 14.4 45 49.0 154.5 0
## 335 14.0 20 50.0 159.8 0
## 336 15.6 20 69.2 173.2 0
## 337 15.0 23 55.9 170.0 0
## 338 14.8 21 63.4 161.4 0
## 339 15.6 28 58.2 169.0 0
## 340 15.4 45 58.6 166.2 0
## 341 14.0 24 45.7 159.4 0
## 342 14.2 25 52.2 162.5 0
## 343 14.8 19 48.6 159.0 0
## 344 15.0 20 57.8 162.8 0
## 345 15.6 29 55.6 159.0 0
## 346 16.2 24 66.8 179.8 0
## 347 15.4 24 59.4 162.9 0
## 348 14.4 25 53.6 161.0 0
## 349 14.4 31 73.2 151.1 0
## 350 14.6 22 53.4 168.2 0
## 351 15.1 20 69.0 168.9 0
## 352 14.6 32 58.4 173.2 0
## 353 15.3 25 56.2 171.8 0
## 354 15.8 19 70.6 178.0 0
## 355 15.5 23 59.8 164.3 0
## 356 15.8 22 72.0 163.0 0
## 357 16.0 20 65.2 168.5 0
## 358 15.2 27 56.6 166.8 0
## 359 18.2 34 105.2 172.7 0
## 360 14.2 25 51.8 163.5 0
## 361 16.0 26 63.4 169.4 0
## 362 14.5 19 59.0 167.8 0
## 363 14.0 26 47.6 159.5 0
## 364 15.8 25 63.0 167.6 0
## 365 15.0 20 55.2 161.2 0
## 366 13.2 21 45.0 160.0 0
## 367 15.2 18 54.0 163.2 0
## 368 14.4 19 50.2 162.2 0
## 369 16.6 27 60.2 161.3 0
## 370 13.1 26 44.8 149.5 0
## 371 16.0 36 58.8 157.5 0
## 372 14.9 20 56.4 163.2 0
## 373 15.6 28 62.0 172.7 0
## 374 13.4 32 49.2 155.0 0
## 375 15.8 32 67.2 156.5 0
## 376 14.8 23 53.8 164.0 0
## 377 14.6 20 54.4 160.9 0
## 378 13.8 20 58.0 162.8 0
## 379 15.0 20 59.8 167.0 0
## 380 14.6 23 54.8 160.0 0
## 381 13.8 20 43.2 160.0 0
## 382 15.3 28 60.5 168.9 0
## 383 14.2 23 46.4 158.2 0
## 384 14.4 19 64.4 156.0 0
## 385 14.4 28 48.8 160.0 0
## 386 16.0 19 62.2 167.1 0
## 387 13.9 29 55.5 158.0 0
## 388 15.5 32 57.8 167.6 0
## 389 15.2 20 54.6 156.0 0
## 390 15.7 28 59.2 162.1 0
## 391 14.5 36 52.7 173.4 0
## 392 14.8 22 53.2 159.8 0
## 393 14.8 20 64.5 170.5 0
## 394 13.4 22 51.8 159.2 0
## 395 14.6 32 56.0 157.5 0
## 396 14.1 40 63.6 161.3 0
## 397 15.6 40 63.2 162.6 0
## 398 14.2 42 59.5 160.0 0
## 399 14.7 40 56.8 168.9 0
## 400 15.0 44 64.1 165.1 0
## 401 14.6 30 50.0 162.6 0
## 402 15.4 28 72.3 165.1 0
## 403 14.5 37 55.0 166.4 0
## 404 14.7 40 55.9 160.0 0
## 405 15.1 45 60.4 152.4 0
## 406 14.7 35 69.1 170.2 0
## 407 17.3 41 84.5 162.6 0
## 408 14.1 27 55.9 170.2 0
## 409 15.1 20 55.5 158.8 0
## 410 14.6 24 69.5 172.7 0
## 411 14.6 36 76.4 167.6 0
## 412 14.6 27 61.4 162.6 0
## 413 15.1 32 65.9 167.6 0
## 414 14.9 64 58.6 156.2 0
## 415 16.0 21 66.8 175.2 0
## 416 15.0 32 56.6 172.1 0
## 417 14.6 35 58.6 162.6 0
## 418 13.7 41 55.9 160.0 0
## 419 15.4 40 59.1 165.1 0
## 420 16.0 29 81.8 182.9 0
## 421 15.5 40 70.7 166.4 0
## 422 14.4 24 56.8 165.1 0
## 423 15.0 23 60.0 177.8 0
## 424 16.0 41 58.2 165.1 0
## 425 16.2 44 72.7 175.3 0
## 426 15.1 53 54.1 154.9 0
## 427 14.8 19 49.1 158.8 0
## 428 15.6 24 75.9 172.7 0
## 429 14.6 25 55.0 168.9 0
## 430 14.8 20 57.3 161.3 0
## 431 14.8 34 55.0 167.6 0
## 432 14.7 32 65.5 165.1 0
## 433 16.4 24 65.5 175.3 0
## 434 15.2 29 48.6 157.5 0
## 435 15.4 31 58.6 163.8 0
## 436 14.9 34 63.6 167.6 0
## 437 14.9 36 55.2 165.1 0
## 438 14.5 32 62.7 165.1 0
## 439 14.8 39 56.6 168.9 0
## 440 13.0 37 53.9 162.6 0
## 441 14.8 52 63.2 164.5 0
## 442 15.3 24 73.6 176.5 0
## 443 14.6 33 62.0 168.9 0
## 444 15.4 42 63.6 175.3 0
## 445 13.7 34 53.2 159.4 0
## 446 14.6 37 53.4 160.0 0
## 447 14.6 39 55.0 170.2 0
## 448 15.6 41 70.5 162.6 0
## 449 13.6 36 54.5 167.6 0
## 450 15.5 19 54.5 162.6 0
## 451 14.2 22 55.9 160.7 0
## 452 16.3 23 59.0 160.0 0
## 453 14.3 36 63.6 157.5 0
## 454 15.3 45 54.5 162.6 0
## 455 14.7 25 47.3 152.4 0
## 456 16.3 67 67.7 170.2 0
## 457 15.6 26 80.9 165.1 0
## 458 15.5 21 70.5 172.7 0
## 459 15.3 33 60.9 165.1 0
## 460 14.2 25 63.6 170.2 0
## 461 15.0 24 54.5 170.2 0
## 462 14.8 21 59.1 170.2 0
## 463 15.5 35 70.5 161.3 0
## 464 14.0 27 52.7 167.6 0
## 465 15.3 27 62.7 167.6 0
## 466 16.4 26 86.3 165.1 0
## 467 15.5 25 66.4 162.6 0
## 468 14.6 44 67.3 152.4 0
## 469 15.9 29 63.0 168.9 0
## 470 16.5 26 73.6 170.2 0
## 471 15.1 23 62.3 175.2 0
## 472 14.7 32 57.7 175.2 0
## 473 15.8 32 55.4 160.0 0
## 474 17.8 43 104.1 165.1 0
## 475 13.9 32 55.5 174.0 0
## 476 16.3 41 77.3 170.2 0
## 477 16.3 33 80.5 160.0 0
## 478 14.2 28 64.5 167.6 0
## 479 15.5 28 72.3 167.6 0
## 480 14.8 25 61.4 167.6 0
## 481 14.4 38 58.2 154.9 0
## 482 15.0 37 81.8 162.6 0
## 483 15.0 25 63.6 175.3 0
## 484 14.1 37 53.4 171.4 0
## 485 13.4 27 54.5 157.5 0
## 486 14.6 27 53.6 165.1 0
## 487 14.6 20 60.0 160.0 0
## 488 16.2 19 73.6 174.0 0
## 489 15.4 32 61.4 162.6 0
## 490 14.9 26 55.5 174.0 0
## 491 15.4 56 63.6 162.6 0
## 492 16.6 23 60.9 161.3 0
## 493 15.2 19 60.0 156.2 0
## 494 15.4 31 46.8 149.9 0
## 495 15.2 34 57.3 169.5 0
## 496 15.9 34 64.1 160.0 0
## 497 15.0 24 63.6 175.3 0
## 498 15.7 22 67.3 169.5 0
## 499 16.4 34 75.5 160.0 0
## 500 15.4 30 68.2 172.7 0
## 501 15.9 32 61.4 162.6 0
## 502 15.8 40 76.8 157.5 0
## 503 15.4 29 71.8 176.5 0
## 504 15.2 21 55.5 164.4 0
## 505 15.5 33 48.6 160.7 0
## 506 15.5 33 66.4 174.0 0
## 507 16.4 38 67.3 163.8 0
r<-cor(bdims_summary$wgt, bdims_summary$hgt)
sd_wgt<-sd(bdims_summary$wgt)
sd_hgt<-sd(bdims_summary$hgt)
mean_wgt<-mean(bdims_summary$wgt)
mean_hgt<-mean(bdims_summary$hgt)
# Add slope and intercept
bdims_summary %>%
mutate(slope = r * sd_wgt / sd_hgt,
intercept = mean_wgt - slope * mean_hgt)
## bia.di bii.di bit.di che.de che.di elb.di wri.di kne.di ank.di sho.gi
## 1 42.9 26.0 31.5 17.7 28.0 13.1 10.4 18.8 14.1 106.2
## 2 43.7 28.5 33.5 16.9 30.8 14.0 11.8 20.6 15.1 110.5
## 3 40.1 28.2 33.3 20.9 31.7 13.9 10.9 19.7 14.1 115.1
## 4 44.3 29.9 34.0 18.4 28.2 13.9 11.2 20.9 15.0 104.5
## 5 42.5 29.9 34.0 21.5 29.4 15.2 11.6 20.7 14.9 107.5
## 6 43.3 27.0 31.5 19.6 31.3 14.0 11.5 18.8 13.9 119.8
## 7 43.5 30.0 34.0 21.9 31.7 16.1 12.5 20.8 15.6 123.5
## 8 44.4 29.8 33.2 21.8 28.8 15.1 11.9 21.0 14.6 120.4
## 9 43.5 26.5 32.1 15.5 27.5 14.1 11.2 18.9 13.2 111.0
## 10 42.0 28.0 34.0 22.5 28.0 15.6 12.0 21.1 15.0 119.5
## 11 40.3 29.0 33.0 20.1 30.3 13.4 10.4 19.4 14.5 117.1
## 12 43.7 29.0 31.3 20.5 29.7 15.0 11.7 20.9 16.0 123.5
## 13 47.4 29.6 35.7 20.8 31.4 16.1 11.3 21.5 15.4 116.5
## 14 40.3 27.5 31.4 21.7 28.0 13.3 10.3 18.8 13.2 113.0
## 15 41.0 26.8 32.2 21.9 28.6 14.9 10.6 17.8 14.0 107.5
## 16 45.0 27.0 33.2 21.7 30.6 13.7 11.1 20.7 14.0 112.0
## 17 39.9 30.0 34.5 21.0 29.4 15.6 11.9 21.2 16.0 112.2
## 18 43.0 26.5 30.3 19.3 30.0 14.8 11.2 19.7 14.7 120.0
## 19 43.1 28.6 33.4 22.2 29.5 14.9 12.2 20.8 14.8 109.0
## 20 43.6 29.3 34.4 20.2 32.6 15.4 10.9 20.7 15.5 118.5
## 21 42.0 27.5 30.7 21.3 32.0 13.1 11.1 19.2 13.9 116.0
## 22 43.8 28.0 33.3 20.0 32.0 15.0 11.5 20.4 14.4 111.0
## 23 42.3 26.4 31.2 18.0 30.9 14.6 10.8 18.6 13.8 117.7
## 24 42.7 29.9 35.0 21.8 32.8 14.3 11.2 19.8 14.1 123.9
## 25 44.8 27.8 32.2 18.3 31.5 15.2 11.6 19.4 14.7 120.6
## 26 46.0 30.1 34.5 20.2 31.1 16.4 13.3 22.2 14.9 129.5
## 27 45.4 31.8 35.2 20.2 32.3 14.6 10.5 20.2 15.3 115.0
## 28 40.5 28.3 33.4 19.2 28.8 14.6 11.1 20.8 14.5 116.0
## 29 39.4 25.5 30.2 17.6 27.7 13.0 10.2 18.9 13.2 107.8
## 30 40.2 27.2 31.7 18.1 26.5 13.3 10.1 18.6 13.2 100.2
## 31 44.2 30.3 34.7 19.4 30.0 14.9 11.0 19.1 15.8 113.0
## 32 41.0 23.6 30.2 22.9 28.0 14.3 11.2 18.2 14.0 117.9
## 33 44.0 31.0 35.3 19.2 31.0 15.2 11.4 21.2 15.1 112.5
## 34 41.6 32.0 35.3 23.6 27.0 15.5 11.3 20.9 15.0 110.5
## 35 41.0 25.1 31.9 20.8 27.9 13.6 10.8 18.8 12.9 112.0
## 36 41.5 24.5 30.5 17.7 26.7 13.3 10.8 18.6 14.0 104.0
## 37 41.1 27.8 31.4 19.0 31.5 14.5 11.9 18.5 13.0 114.8
## 38 38.8 27.2 31.6 18.5 25.5 13.4 10.8 19.0 14.0 108.0
## 39 36.2 27.5 30.4 18.7 28.0 13.6 10.8 19.0 15.4 111.2
## 40 42.1 27.5 32.4 18.2 28.0 16.2 12.0 21.0 16.4 118.3
## 41 40.3 29.4 32.9 23.7 31.5 14.6 11.3 19.8 15.2 115.2
## 42 41.7 27.1 32.6 21.6 28.0 14.1 11.5 19.7 13.8 129.9
## 43 37.8 27.1 31.5 18.5 27.3 14.6 10.8 19.5 14.9 112.9
## 44 39.2 26.1 30.8 19.4 29.9 14.3 11.2 20.0 16.0 112.2
## 45 41.5 30.8 33.3 19.4 30.6 14.8 11.3 20.2 16.0 117.1
## 46 42.5 27.8 33.5 20.6 30.2 15.9 12.8 22.4 16.3 118.7
## 47 39.4 26.1 34.4 20.4 27.3 15.1 10.6 20.0 15.3 109.2
## 48 43.6 33.1 33.5 21.6 33.1 15.6 12.0 20.7 16.5 128.1
## 49 38.9 24.9 28.7 19.7 26.8 14.2 10.2 18.0 14.4 113.3
## 50 37.6 24.4 28.0 18.0 26.4 14.2 10.6 17.3 13.4 108.4
## 51 39.4 28.3 30.6 20.2 28.7 15.0 11.5 18.4 14.4 118.7
## 52 38.5 26.1 30.8 20.6 30.8 15.1 11.4 19.8 14.2 126.3
## 53 40.1 27.8 33.1 19.2 31.3 15.4 11.5 20.6 15.4 124.2
## 54 40.3 28.0 32.0 20.9 31.7 14.8 10.6 19.4 15.0 126.7
## 55 37.6 26.6 29.9 17.3 25.6 12.8 10.0 17.0 13.0 103.3
## 56 38.3 25.2 30.2 17.0 26.4 13.2 10.4 18.8 13.0 101.2
## 57 39.7 28.6 32.1 19.1 27.1 13.4 10.0 18.2 14.8 104.3
## 58 42.2 29.0 33.7 22.5 30.4 15.6 12.0 19.8 16.2 113.2
## 59 41.1 30.4 35.1 23.2 32.6 15.5 11.6 21.5 15.4 121.9
## 60 40.5 29.3 33.7 19.6 29.8 13.8 11.7 19.7 14.4 113.1
## 61 41.5 28.6 30.4 20.8 26.9 14.8 11.2 20.7 16.5 108.5
## 62 43.4 32.4 36.4 20.3 32.1 15.6 12.0 20.8 16.3 113.9
## 63 43.5 26.0 31.6 19.1 30.9 14.3 11.4 19.5 14.6 112.6
## 64 41.3 27.1 32.4 17.5 27.6 14.1 10.8 20.2 15.5 110.2
## 65 40.3 29.5 33.3 18.4 26.2 14.0 11.0 19.4 14.8 108.7
## 66 36.3 29.2 33.0 20.0 29.0 14.1 11.7 20.4 14.3 104.0
## 67 39.9 28.3 32.0 18.3 31.4 13.5 11.4 18.9 14.4 115.2
## 68 39.8 28.8 33.0 19.7 28.7 12.4 10.7 18.5 13.2 111.9
## 69 43.5 33.2 34.0 23.9 34.3 15.8 12.0 18.6 13.2 127.0
## 70 41.2 26.6 30.6 19.5 28.0 13.1 10.4 19.0 13.8 111.2
## 71 44.0 28.4 32.0 22.5 29.7 14.9 10.9 21.0 14.8 122.0
## 72 41.8 28.5 31.6 21.6 31.5 13.3 10.3 18.9 14.3 114.5
## 73 42.9 27.5 30.3 18.9 29.6 12.6 10.4 19.2 13.8 109.5
## 74 38.7 24.6 28.5 18.3 29.8 14.0 11.2 18.9 13.6 110.8
## 75 41.4 26.4 32.3 18.6 31.3 14.9 11.5 18.9 14.6 118.8
## 76 39.6 27.5 30.2 19.2 28.9 13.5 10.4 19.3 14.2 108.0
## 77 40.5 27.5 32.3 19.4 28.8 12.6 10.6 18.4 14.0 114.3
## 78 34.1 28.1 30.1 21.8 25.8 12.9 9.9 18.6 12.3 105.4
## 79 43.5 28.8 34.0 20.6 29.0 14.3 10.5 19.8 14.2 115.0
## 80 44.1 29.2 35.3 23.6 30.9 15.8 12.5 20.2 15.2 119.5
## 81 42.2 32.6 36.6 22.4 34.5 14.1 11.1 18.2 13.9 130.0
## 82 42.2 30.1 31.4 21.2 29.7 14.0 11.6 21.6 14.1 113.3
## 83 43.0 26.5 31.6 20.6 29.5 13.4 10.4 18.8 13.6 113.2
## 84 39.8 28.7 33.3 19.3 29.2 13.5 11.6 19.5 14.6 106.9
## 85 37.7 29.7 32.7 20.2 28.8 13.3 11.1 18.3 13.2 113.8
## 86 39.6 27.9 33.3 20.2 29.5 12.6 10.7 18.5 12.9 117.3
## 87 43.2 26.3 30.5 19.7 30.6 14.4 12.3 20.2 13.6 124.2
## 88 44.3 28.2 32.2 21.2 31.8 14.2 11.6 20.0 14.4 123.0
## 89 43.3 28.2 33.0 19.4 31.6 13.8 11.1 17.8 13.2 117.8
## 90 42.8 27.5 31.5 19.2 31.8 14.1 11.1 19.1 14.7 118.8
## 91 41.5 30.0 33.4 19.1 29.4 14.8 11.0 19.8 13.8 112.0
## 92 42.0 27.6 32.2 19.7 29.4 13.9 10.0 18.7 13.8 113.0
## 93 41.2 27.1 29.8 20.1 31.0 12.9 11.6 18.8 13.5 116.0
## 94 43.8 29.5 31.2 18.2 29.5 13.1 10.3 19.1 13.2 112.8
## 95 46.2 31.0 36.0 25.0 33.1 14.6 12.0 20.9 15.1 125.0
## 96 40.4 28.6 31.4 19.8 27.6 13.9 10.1 20.0 13.4 108.3
## 97 40.8 27.1 29.4 17.8 29.4 13.3 10.4 18.5 12.8 108.2
## 98 43.9 27.0 33.5 22.3 31.0 13.2 10.4 19.1 13.1 113.0
## 99 44.2 27.9 32.0 21.6 32.9 14.3 11.0 21.1 14.9 115.0
## 100 41.6 28.0 35.0 24.2 31.0 13.4 11.2 20.6 14.4 123.0
## 101 38.1 30.1 33.2 21.6 31.3 14.2 12.3 19.2 15.2 120.2
## 102 42.0 28.0 33.0 18.1 28.4 14.3 11.1 20.2 15.2 114.0
## 103 37.0 27.3 31.1 18.2 25.0 13.2 10.5 18.7 13.4 102.9
## 104 41.6 27.5 32.0 18.1 29.5 13.8 10.7 19.0 13.9 112.5
## 105 40.1 19.4 28.0 17.1 26.8 13.0 10.6 16.9 12.6 104.5
## 106 38.7 25.2 28.8 19.1 25.6 13.0 10.2 17.9 13.5 111.3
## 107 37.4 29.9 33.5 22.3 30.8 14.4 11.5 20.5 16.8 117.2
## 108 41.7 28.0 32.9 19.4 29.7 14.6 11.0 19.5 15.3 112.8
## 109 38.0 27.1 28.3 18.2 25.9 13.8 11.0 18.9 14.8 104.8
## 110 40.5 24.9 29.7 19.0 30.2 14.4 11.8 19.5 14.9 117.7
## 111 35.6 28.5 29.4 17.7 25.2 14.0 10.8 19.1 15.0 107.7
## 112 43.6 30.2 32.4 21.8 33.1 15.2 11.3 19.8 15.2 125.2
## 113 37.6 24.4 28.3 17.7 24.7 12.9 10.8 18.0 14.3 109.1
## 114 41.1 31.7 34.2 22.8 34.0 13.8 11.8 19.4 15.4 122.6
## 115 42.1 30.6 34.0 22.1 30.6 15.0 11.4 20.2 15.4 117.3
## 116 40.5 28.3 32.4 19.4 27.8 13.4 11.0 19.0 14.5 109.1
## 117 40.9 28.5 31.3 21.1 29.7 14.3 11.7 19.0 15.6 116.7
## 118 43.0 30.6 33.8 23.3 35.3 15.6 12.0 21.6 16.4 124.9
## 119 40.5 27.8 31.1 21.8 30.6 15.0 11.6 20.4 15.2 118.6
## 120 41.9 25.4 30.2 14.4 26.8 12.6 9.8 18.8 13.6 108.8
## 121 42.1 28.5 33.1 20.2 30.6 15.6 12.2 19.7 15.6 121.9
## 122 43.8 29.2 32.6 18.7 30.4 14.6 11.7 20.0 15.2 117.3
## 123 42.1 28.5 31.7 19.4 28.0 14.0 11.3 19.0 14.4 115.0
## 124 43.4 32.0 36.2 23.5 35.6 16.1 12.6 23.0 16.3 134.8
## 125 38.7 26.8 31.5 21.4 27.8 13.8 10.8 18.2 13.3 113.8
## 126 39.6 28.7 32.4 18.2 28.3 15.2 11.8 19.6 14.8 119.4
## 127 43.4 30.6 32.9 21.6 28.3 15.0 12.0 20.5 17.2 117.9
## 128 40.5 29.7 31.7 22.1 32.6 15.2 11.3 21.2 15.2 127.3
## 129 40.3 30.4 34.2 21.1 34.0 13.6 12.0 19.2 13.8 118.8
## 130 44.2 30.6 33.8 22.1 32.4 15.3 11.5 20.9 16.5 122.4
## 131 41.3 26.8 32.2 21.4 31.1 13.6 11.0 19.1 15.0 111.5
## 132 39.8 25.6 31.3 23.5 32.0 14.0 11.2 21.2 16.4 113.2
## 133 41.3 29.0 32.2 25.2 30.8 14.4 11.0 19.7 15.8 115.3
## 134 38.9 27.5 32.9 22.5 33.3 14.6 11.0 20.5 15.3 122.5
## 135 41.1 25.6 29.9 23.3 25.2 14.1 10.7 19.0 15.1 114.4
## 136 41.5 30.6 35.8 21.1 28.0 15.0 11.8 21.0 15.6 112.8
## 137 38.5 27.8 31.7 19.7 26.4 13.1 11.0 18.4 14.8 112.2
## 138 39.4 29.7 33.1 23.0 30.4 14.2 11.6 20.4 15.0 119.4
## 139 40.9 26.1 27.5 20.2 28.0 13.2 10.4 18.6 14.8 108.4
## 140 41.1 23.0 29.4 21.8 30.6 15.0 10.8 19.3 14.5 122.4
## 141 43.6 28.0 32.4 27.5 33.5 14.6 11.7 21.4 15.1 128.8
## 142 39.8 29.0 34.9 22.5 28.3 14.3 11.7 19.8 15.4 118.0
## 143 42.1 27.8 31.7 20.2 28.7 14.3 11.5 19.6 15.6 116.5
## 144 41.1 27.1 33.8 24.9 29.4 14.4 12.4 18.0 15.1 120.4
## 145 44.2 30.4 36.5 21.6 31.5 15.4 11.6 20.4 15.4 123.1
## 146 38.9 26.8 31.5 20.4 29.0 13.6 10.8 18.9 15.2 117.2
## 147 40.1 28.7 32.2 18.0 29.4 15.2 11.8 20.7 15.4 113.0
## 148 38.7 26.8 31.5 18.0 27.8 12.9 10.4 18.0 14.3 109.4
## 149 35.6 26.4 30.8 19.2 29.4 14.6 11.5 19.6 15.3 105.7
## 150 40.5 26.8 30.6 21.4 32.4 15.0 11.8 20.4 15.8 119.7
## 151 41.1 25.4 32.0 21.6 28.7 14.3 12.4 19.6 14.3 118.0
## 152 38.7 26.1 29.2 18.2 24.9 13.6 10.4 17.6 14.2 104.3
## 153 38.9 27.1 30.4 20.4 28.7 14.8 11.7 19.4 14.6 114.1
## 154 39.4 29.7 33.1 22.3 31.5 15.6 12.0 19.5 14.8 119.0
## 155 37.6 27.8 32.2 20.2 31.3 14.6 11.0 21.5 15.8 113.8
## 156 39.8 25.9 31.3 19.4 29.2 14.3 11.2 18.7 14.3 118.0
## 157 40.3 27.3 30.4 20.4 29.0 15.0 11.3 19.1 14.6 116.0
## 158 40.3 25.2 29.2 18.7 28.5 13.2 10.2 18.9 14.3 108.3
## 159 43.8 30.4 34.9 24.0 33.3 15.4 11.6 17.8 14.6 129.2
## 160 41.1 27.8 32.9 18.0 26.8 14.6 11.2 19.5 15.8 109.2
## 161 41.7 30.6 34.7 24.9 32.0 14.9 10.8 18.9 14.1 124.3
## 162 45.4 30.8 35.6 20.9 29.7 15.2 10.8 18.6 15.0 122.6
## 163 43.0 30.8 34.7 22.1 32.2 16.0 13.2 19.5 16.1 122.4
## 164 41.1 25.4 30.4 19.2 29.9 14.8 10.6 18.7 15.2 114.9
## 165 41.1 29.2 31.5 19.7 29.9 14.8 11.0 18.0 15.0 111.1
## 166 45.2 32.2 36.0 22.5 33.5 15.8 11.3 20.5 14.8 126.0
## 167 39.8 34.7 34.7 23.5 30.2 14.8 10.6 20.6 14.3 115.5
## 168 39.6 28.7 32.0 20.2 32.9 14.3 11.5 19.6 15.1 124.7
## 169 42.3 30.2 34.4 25.4 32.2 15.2 10.7 18.8 14.8 125.3
## 170 40.9 29.0 32.2 20.2 29.2 13.8 10.4 18.4 14.4 118.2
## 171 39.8 28.0 34.2 23.3 31.5 14.0 11.0 19.7 13.8 124.3
## 172 42.3 27.1 31.7 17.3 29.4 14.4 11.6 21.2 15.8 118.3
## 173 42.3 26.8 32.0 25.4 28.7 15.0 11.2 18.4 14.4 122.3
## 174 40.5 28.7 30.4 22.1 29.2 14.3 11.2 18.5 14.5 116.5
## 175 42.7 28.5 36.7 23.7 30.8 15.8 12.9 19.3 16.0 123.0
## 176 43.6 30.8 33.3 20.4 29.7 14.3 10.9 19.6 15.4 117.6
## 177 42.5 31.3 33.1 19.4 32.0 14.8 11.3 20.1 15.5 117.5
## 178 41.3 30.8 33.3 22.5 28.3 13.0 10.5 19.7 13.4 117.3
## 179 41.3 24.7 35.4 22.5 30.2 14.8 11.3 20.4 16.0 114.5
## 180 42.3 26.6 33.3 23.3 28.7 14.2 11.1 19.5 14.3 122.9
## 181 36.9 25.9 31.7 19.9 27.3 14.8 10.6 19.4 14.3 115.5
## 182 40.3 28.5 35.1 19.2 32.0 14.8 12.0 21.2 16.2 116.1
## 183 40.1 26.4 32.0 21.4 32.6 14.8 12.0 20.0 15.2 121.6
## 184 43.2 31.3 34.0 23.0 32.6 15.7 11.5 20.5 15.2 131.6
## 185 45.0 29.0 33.3 25.4 30.8 15.4 11.0 18.8 15.0 124.5
## 186 42.1 29.7 35.6 23.5 31.3 14.3 11.0 18.2 14.8 115.6
## 187 40.9 28.7 33.5 23.7 30.2 15.8 11.6 19.3 16.8 117.0
## 188 41.3 27.3 32.2 20.2 28.3 13.8 11.4 18.0 13.0 108.6
## 189 42.1 29.7 32.9 24.9 30.8 15.3 11.5 19.2 14.6 120.2
## 190 45.2 29.7 33.8 19.9 32.0 15.5 11.8 19.6 16.1 116.2
## 191 42.1 31.1 34.9 22.1 31.3 15.2 11.0 19.0 14.4 115.4
## 192 41.2 29.8 32.2 22.4 29.8 15.6 11.2 19.6 14.8 120.0
## 193 41.7 29.2 33.8 19.2 29.7 13.8 10.7 18.6 14.2 110.3
## 194 43.0 29.4 33.8 24.7 31.5 14.2 11.2 19.4 14.2 119.7
## 195 41.7 28.5 33.8 23.5 32.9 15.6 12.0 18.4 16.3 123.5
## 196 38.0 29.7 34.0 22.8 27.3 13.1 10.8 17.3 15.5 107.8
## 197 41.9 27.8 33.3 19.0 28.7 15.1 11.3 19.2 14.9 118.7
## 198 40.7 28.0 35.3 19.4 31.7 14.4 10.7 18.6 14.4 120.7
## 199 41.3 29.7 34.7 22.8 32.0 15.5 11.2 18.4 15.6 118.7
## 200 40.9 28.7 32.9 21.6 30.6 15.0 11.0 18.7 13.8 118.0
## 201 41.7 26.8 32.0 19.7 27.8 14.0 10.5 18.4 14.6 105.0
## 202 41.3 31.1 34.9 26.4 30.2 15.0 11.6 18.8 15.8 110.6
## 203 41.1 27.8 32.0 21.1 30.6 14.8 11.4 17.6 14.2 123.1
## 204 43.4 27.3 34.7 19.9 31.3 14.0 11.2 20.2 14.3 121.0
## 205 40.7 27.8 34.0 21.1 29.4 15.6 12.0 21.2 16.4 111.7
## 206 42.5 25.2 30.6 20.9 30.4 15.3 11.4 18.9 13.8 111.0
## 207 40.3 28.3 30.6 18.2 29.2 12.9 10.6 20.2 14.2 112.0
## 208 39.8 27.8 32.9 22.3 29.7 16.6 11.8 20.8 15.3 115.9
## 209 39.8 28.3 30.4 19.7 30.2 12.9 11.0 18.6 12.7 121.0
## 210 40.5 29.9 34.9 21.4 32.9 14.5 11.7 20.4 15.0 123.6
## 211 41.1 26.8 32.4 20.2 31.1 14.4 11.8 20.4 15.2 120.0
## 212 42.5 29.4 34.2 23.5 34.7 15.1 11.8 21.8 15.8 128.7
## 213 42.5 29.4 34.4 19.9 34.0 14.5 11.0 21.3 14.4 124.5
## 214 38.5 24.4 30.4 18.0 29.9 14.3 10.1 18.3 13.2 112.4
## 215 43.8 29.2 35.6 19.9 28.3 14.8 12.8 20.7 14.3 112.2
## 216 41.5 29.0 33.3 21.4 32.4 15.3 11.0 20.6 15.0 127.0
## 217 40.3 27.8 33.5 20.6 29.9 15.3 11.2 20.4 13.8 115.3
## 218 40.3 30.2 32.2 20.6 29.2 14.5 11.5 20.0 16.9 111.5
## 219 42.1 27.1 32.2 20.2 30.6 13.7 10.8 18.9 14.3 118.3
## 220 42.1 31.3 35.1 20.6 31.1 16.7 12.0 21.0 15.1 118.0
## 221 38.9 29.4 33.3 24.2 33.5 14.2 12.8 20.6 15.5 127.0
## 222 44.4 24.0 30.2 20.6 32.0 14.2 11.2 20.2 14.5 124.4
## 223 40.9 24.4 28.7 18.7 29.0 14.3 11.4 17.8 14.6 112.4
## 224 42.1 28.7 33.5 25.2 29.4 14.3 11.5 18.0 13.8 125.8
## 225 43.0 27.1 33.5 25.2 31.5 15.2 11.8 19.3 15.1 131.7
## 226 38.0 27.1 32.2 20.6 28.0 13.9 10.3 19.1 14.0 117.3
## 227 37.1 24.2 30.6 17.7 27.8 13.8 11.0 17.8 14.6 107.6
## 228 41.1 24.0 29.4 21.6 30.8 13.6 11.7 18.8 14.6 122.4
## 229 40.9 24.4 30.6 19.7 29.9 13.8 11.5 19.0 15.1 117.0
## 230 38.9 27.1 31.7 21.6 29.7 15.0 12.2 20.5 15.2 119.0
## 231 40.9 28.3 34.0 23.7 28.5 14.3 12.0 19.0 14.3 123.5
## 232 39.2 26.8 34.0 23.7 30.8 14.2 11.2 19.1 14.5 121.4
## 233 41.7 26.6 31.5 19.9 29.9 14.6 11.2 19.2 14.8 112.3
## 234 39.4 26.6 31.7 24.0 31.1 13.8 11.8 20.0 15.8 113.2
## 235 40.1 26.4 32.0 21.8 30.2 15.8 12.4 20.7 15.8 121.7
## 236 38.9 25.6 32.9 21.1 29.0 15.6 10.6 20.2 14.8 116.6
## 237 38.9 26.4 31.7 21.6 27.8 14.4 11.3 19.6 14.8 117.8
## 238 41.7 26.4 31.1 20.2 28.3 14.6 10.2 18.4 15.3 118.0
## 239 43.2 26.8 32.6 22.1 32.9 15.4 12.0 20.5 16.8 131.1
## 240 40.5 29.2 33.5 23.7 31.1 15.2 11.3 20.0 16.1 121.4
## 241 39.2 23.5 29.9 19.7 29.0 15.4 11.5 19.0 14.8 116.3
## 242 40.9 25.4 32.0 20.6 30.2 16.1 11.5 19.3 15.8 121.8
## 243 41.7 27.3 31.5 21.8 29.7 14.9 11.8 18.9 13.6 118.2
## 244 43.8 32.2 38.0 25.4 32.0 16.0 10.7 21.0 16.8 126.3
## 245 41.9 28.0 33.1 26.4 29.9 15.6 11.5 21.2 15.9 121.0
## 246 43.0 27.8 34.2 21.4 31.5 14.3 11.1 21.0 14.8 123.1
## 247 41.5 28.5 33.5 19.7 29.4 14.5 10.5 19.4 15.3 114.9
## 248 37.6 25.0 31.3 16.2 24.9 11.2 9.2 17.0 12.3 95.0
## 249 36.7 26.4 31.0 16.8 24.5 12.1 9.9 19.3 12.8 99.5
## 250 34.8 25.9 30.2 16.4 24.2 11.3 8.9 17.0 12.2 88.0
## 251 36.6 27.9 31.8 19.3 24.9 12.3 9.5 18.6 13.0 97.0
## 252 35.5 28.2 31.0 18.2 26.2 11.5 9.1 17.2 12.4 103.3
## 253 37.0 28.0 32.0 15.1 25.7 12.5 10.0 17.2 13.2 93.5
## 254 35.5 26.5 29.2 15.4 24.5 12.3 9.4 17.2 12.0 93.3
## 255 37.4 30.2 33.2 18.8 26.6 13.3 10.7 19.8 13.8 94.5
## 256 37.8 29.0 32.6 18.6 25.0 12.1 9.8 17.8 12.7 98.6
## 257 38.6 28.8 33.2 19.7 29.4 13.4 11.5 20.9 13.2 115.5
## 258 37.6 28.5 32.2 15.5 24.3 11.8 8.6 17.1 11.9 97.9
## 259 36.0 25.6 31.5 15.4 25.5 12.8 9.7 17.6 13.2 97.7
## 260 39.5 30.0 31.7 17.3 27.3 12.8 9.2 18.1 12.4 100.5
## 261 34.0 25.0 27.0 16.9 22.6 10.6 8.3 15.9 11.6 88.7
## 262 35.0 26.5 31.6 18.3 23.7 11.5 8.6 16.8 12.2 96.6
## 263 35.6 25.8 32.0 16.2 25.7 11.5 9.0 17.2 11.8 92.0
## 264 36.2 27.4 29.5 14.6 23.9 11.2 9.6 16.7 12.6 90.0
## 265 39.0 28.4 34.9 19.6 26.7 13.4 11.0 18.9 13.6 104.0
## 266 32.6 25.6 30.0 15.3 22.6 10.3 8.1 16.2 11.6 90.1
## 267 37.6 30.0 33.9 19.1 28.8 13.4 10.5 19.2 13.2 104.0
## 268 36.3 27.5 31.0 15.4 24.0 11.1 9.4 16.4 12.3 91.0
## 269 38.0 29.6 32.7 21.3 28.7 13.7 10.6 21.5 14.4 111.4
## 270 37.0 27.9 30.8 15.1 26.8 13.2 8.9 18.1 12.3 96.2
## 271 42.6 31.8 34.8 19.9 30.4 12.5 11.0 21.0 14.2 117.1
## 272 37.5 29.2 35.1 19.6 27.5 13.1 10.2 20.0 13.1 101.5
## 273 36.1 28.2 32.3 15.8 25.0 12.0 10.2 18.4 12.9 95.1
## 274 39.1 27.5 34.0 17.2 25.6 12.6 10.0 18.0 12.7 97.1
## 275 39.6 28.6 31.2 18.0 27.2 12.4 10.1 19.3 13.8 101.4
## 276 40.5 29.6 34.9 17.4 27.5 12.6 10.4 18.8 13.1 103.7
## 277 35.0 26.0 29.8 16.1 22.3 12.4 9.5 17.2 12.3 89.2
## 278 34.5 26.4 30.1 18.6 23.5 11.3 8.9 17.2 12.3 86.0
## 279 35.6 27.9 32.0 15.6 23.9 11.9 9.6 18.2 12.7 98.1
## 280 39.1 29.7 33.7 20.1 29.5 13.2 10.0 20.3 13.6 114.0
## 281 36.6 28.2 32.0 14.3 26.0 11.8 9.9 18.0 12.7 95.0
## 282 34.8 26.3 29.9 16.0 22.9 11.3 9.2 16.5 11.8 87.0
## 283 36.7 22.1 28.3 16.7 27.5 12.3 10.1 16.8 12.4 100.8
## 284 39.7 31.6 34.5 19.0 24.9 12.7 10.4 18.2 13.1 112.2
## 285 35.5 24.3 29.5 15.0 26.0 11.5 9.4 17.2 12.1 98.7
## 286 36.0 28.0 30.8 17.3 25.9 12.9 9.8 17.8 13.8 100.1
## 287 35.8 26.4 32.2 17.3 24.9 12.7 10.4 18.9 14.0 100.3
## 288 37.6 27.3 32.0 18.0 25.6 12.4 9.4 18.8 13.6 100.5
## 289 37.6 28.7 30.8 19.0 25.2 13.1 11.5 18.6 13.6 101.2
## 290 34.9 28.0 30.8 15.6 24.2 11.2 9.2 17.9 13.2 96.0
## 291 33.3 24.9 27.8 17.3 23.3 12.8 9.8 17.4 12.9 92.8
## 292 36.2 27.1 29.9 16.0 24.2 11.8 9.2 17.3 12.6 95.5
## 293 33.3 29.2 32.0 14.8 25.9 12.6 10.4 18.4 13.0 98.7
## 294 36.0 25.2 30.8 16.5 24.2 12.6 9.2 17.6 13.2 96.9
## 295 34.0 27.3 29.9 17.0 24.0 12.0 9.2 16.8 12.4 91.0
## 296 36.2 28.7 31.3 19.2 27.1 12.8 10.5 18.0 14.4 106.3
## 297 34.9 27.1 31.5 19.0 24.9 12.4 9.8 18.0 13.8 99.8
## 298 37.4 28.3 31.3 16.3 25.6 12.9 11.0 18.4 14.3 105.0
## 299 33.8 28.7 32.2 18.7 25.4 12.0 8.9 17.4 13.0 106.2
## 300 34.0 27.3 32.2 19.0 24.2 12.9 10.6 17.9 13.4 97.7
## 301 34.4 23.5 26.8 16.5 24.4 11.7 9.2 16.8 12.4 97.4
## 302 34.7 27.8 32.6 16.5 24.2 12.6 9.8 17.6 12.6 96.3
## 303 35.6 26.4 32.2 15.1 26.6 13.0 10.0 17.6 13.8 98.0
## 304 33.8 29.7 32.4 17.3 25.4 13.4 9.6 18.4 13.4 94.5
## 305 37.8 26.6 33.1 16.5 27.1 12.4 9.8 18.3 13.0 101.4
## 306 38.3 30.4 32.2 18.7 26.4 12.4 9.4 18.1 12.7 95.1
## 307 40.3 29.2 32.9 16.5 26.4 13.0 9.4 18.6 12.9 101.1
## 308 38.9 27.5 31.1 15.6 26.8 12.4 10.0 18.4 13.6 97.4
## 309 39.4 29.2 34.7 18.0 28.3 13.4 10.5 19.8 13.1 104.8
## 310 35.0 27.0 33.0 19.3 25.0 11.5 9.9 17.1 13.0 94.5
## 311 40.7 29.0 35.3 17.7 28.5 13.8 10.8 19.8 14.3 103.2
## 312 39.6 30.4 34.2 21.4 26.8 14.2 10.6 20.7 13.1 110.8
## 313 36.9 28.3 32.0 17.0 25.6 10.9 9.4 17.3 12.6 94.6
## 314 35.6 25.7 29.1 15.5 26.0 11.5 9.5 17.8 12.1 96.6
## 315 38.2 30.0 35.5 19.4 26.7 12.6 10.8 19.5 13.6 102.8
## 316 36.9 29.0 33.7 15.6 27.0 11.5 10.2 18.4 13.8 96.9
## 317 36.9 26.9 31.9 16.1 25.2 13.4 10.0 18.8 14.3 95.6
## 318 34.8 26.4 29.8 14.9 25.2 12.2 9.1 18.0 12.4 92.4
## 319 36.6 27.5 30.0 17.7 24.1 12.0 9.3 17.7 13.2 93.7
## 320 36.9 27.4 31.0 18.2 25.7 12.8 10.4 17.8 14.1 96.1
## 321 36.9 26.4 32.4 16.6 26.0 12.6 10.5 18.9 14.4 100.1
## 322 36.9 30.2 34.6 17.5 26.2 13.2 10.4 19.4 14.3 98.8
## 323 36.0 28.6 32.0 17.5 24.1 12.8 9.8 18.7 13.8 92.2
## 324 36.2 30.2 31.2 16.8 27.7 11.5 9.4 17.4 13.1 97.3
## 325 35.1 28.1 30.0 17.1 25.2 12.0 9.6 19.0 14.2 90.5
## 326 38.8 27.2 33.4 17.2 31.6 12.9 11.0 18.2 13.4 110.5
## 327 36.4 26.2 32.2 17.3 25.8 13.1 10.1 17.9 12.6 103.0
## 328 38.6 24.2 34.4 15.3 29.0 13.1 10.4 22.1 14.4 109.0
## 329 36.5 26.0 30.7 15.9 26.8 12.1 10.3 17.0 12.4 104.8
## 330 37.5 27.4 31.2 18.9 26.5 14.0 11.0 19.1 13.5 109.2
## 331 39.9 20.0 32.4 17.6 31.9 13.6 10.4 18.5 13.4 117.0
## 332 34.4 26.1 30.5 18.8 25.2 12.0 9.9 17.5 12.7 93.9
## 333 38.7 23.5 33.0 16.4 27.6 12.6 9.9 17.5 12.6 105.5
## 334 32.6 24.6 29.4 15.8 25.0 11.3 9.2 16.9 11.9 99.4
## 335 34.5 25.6 29.2 18.0 24.2 10.1 9.0 16.3 11.5 89.5
## 336 36.7 28.2 33.0 17.5 25.6 12.2 10.1 17.8 13.5 98.5
## 337 36.5 27.0 31.6 16.6 24.4 11.9 9.7 18.0 12.5 94.9
## 338 34.2 28.0 31.0 20.4 25.6 12.4 9.7 18.5 13.0 96.3
## 339 37.8 28.6 30.7 17.3 25.4 11.8 10.6 18.8 13.3 96.8
## 340 35.7 25.5 32.5 16.0 27.2 11.9 10.0 18.6 13.6 102.5
## 341 33.9 24.3 29.5 15.8 26.0 11.6 9.4 16.3 11.2 95.2
## 342 35.2 24.1 26.9 17.9 23.6 12.0 9.5 17.5 11.5 103.0
## 343 37.1 24.3 28.4 16.2 25.8 12.1 9.8 16.8 12.2 99.1
## 344 37.7 26.2 30.8 17.8 25.2 12.4 10.2 17.2 11.8 104.1
## 345 37.8 28.1 30.0 17.9 24.9 11.1 9.6 18.0 13.2 99.5
## 346 39.3 24.5 32.3 21.4 29.5 13.4 10.9 18.2 13.0 110.2
## 347 37.2 24.4 29.4 18.1 27.3 12.3 9.9 17.1 12.2 107.1
## 348 36.1 26.8 31.0 16.2 24.0 11.0 8.9 17.8 12.2 95.7
## 349 35.2 25.1 33.0 20.0 26.6 11.6 9.1 21.0 12.6 103.4
## 350 35.8 27.7 28.9 16.4 25.6 11.5 9.1 17.1 12.3 94.6
## 351 35.0 25.6 32.5 17.9 27.0 12.6 9.8 18.6 12.9 104.5
## 352 37.8 29.9 31.1 17.3 25.8 10.9 9.4 15.7 11.5 97.7
## 353 35.7 27.0 29.8 16.5 26.2 12.1 9.6 18.0 13.6 96.4
## 354 39.5 29.8 31.5 19.2 25.4 13.0 10.6 19.1 13.4 103.0
## 355 38.8 26.3 31.5 19.0 25.9 11.6 9.9 18.8 13.1 103.4
## 356 37.9 29.0 32.9 18.6 27.0 12.7 10.6 18.2 12.1 109.5
## 357 37.7 25.0 30.4 16.6 29.3 12.4 10.2 18.6 12.7 110.7
## 358 38.2 25.9 30.2 16.9 24.8 11.7 9.4 16.9 11.7 102.2
## 359 40.0 31.0 35.8 21.3 33.2 14.1 12.2 24.3 12.9 129.5
## 360 35.7 28.3 29.6 17.4 23.3 10.7 8.7 16.8 11.3 99.2
## 361 35.7 25.9 29.3 14.7 29.1 13.2 10.9 18.1 13.1 104.7
## 362 36.0 27.6 29.0 18.6 25.0 11.8 9.2 18.0 11.4 98.9
## 363 35.0 25.0 27.2 15.4 23.4 10.8 8.7 16.8 12.2 98.5
## 364 37.0 26.9 32.4 16.6 25.4 12.9 10.0 18.1 13.1 102.2
## 365 36.7 23.5 29.5 17.2 28.3 11.5 9.5 16.1 9.9 100.2
## 366 32.8 26.2 26.3 16.1 23.6 10.6 9.2 16.4 11.7 85.9
## 367 34.8 20.3 31.0 17.3 26.8 11.6 9.8 17.9 12.4 99.8
## 368 35.0 23.7 29.0 15.4 23.6 11.5 9.6 16.6 11.6 93.3
## 369 38.7 27.1 31.4 16.5 27.5 12.8 9.2 18.8 12.5 107.3
## 370 32.8 20.9 28.2 17.2 22.2 11.3 9.5 16.0 11.2 90.0
## 371 36.7 23.0 30.8 17.0 27.6 13.1 11.0 17.0 12.9 101.5
## 372 38.8 26.5 29.8 17.4 28.2 12.0 9.4 17.7 12.6 105.1
## 373 36.4 24.9 30.8 16.5 26.7 12.4 10.5 18.4 12.8 101.0
## 374 34.0 27.8 29.6 16.5 24.4 9.9 8.9 17.0 11.5 91.9
## 375 38.2 27.5 30.0 20.4 29.2 11.8 9.2 17.8 11.5 112.1
## 376 35.1 23.4 30.6 16.2 26.8 12.1 9.4 16.1 12.2 98.2
## 377 36.0 25.2 28.4 15.4 26.9 11.2 9.9 16.8 12.1 102.5
## 378 35.9 25.1 28.2 18.1 26.1 11.6 8.5 16.8 11.0 98.4
## 379 35.0 27.1 30.1 19.5 24.9 12.4 9.9 18.2 12.5 96.8
## 380 35.2 24.4 29.8 15.1 26.0 13.0 9.2 17.3 12.2 103.9
## 381 34.9 26.2 28.5 14.6 23.5 10.4 8.7 16.0 11.1 92.7
## 382 36.0 28.1 31.0 18.8 27.4 11.5 9.7 17.5 12.8 105.0
## 383 36.4 27.7 29.8 17.0 25.0 11.2 9.0 16.9 12.2 94.0
## 384 35.5 27.7 30.7 20.6 24.9 11.6 8.9 17.9 11.5 101.5
## 385 34.1 18.7 27.2 16.1 24.6 11.2 9.5 16.1 11.9 92.0
## 386 37.1 25.0 29.3 17.1 28.1 13.0 10.9 17.5 13.0 108.5
## 387 35.2 26.0 30.4 17.3 25.5 11.1 9.0 17.1 12.0 93.8
## 388 37.5 26.0 29.4 17.6 26.5 12.3 10.4 18.3 12.7 99.8
## 389 36.3 24.9 31.0 16.6 27.7 12.5 10.0 18.1 13.0 100.3
## 390 37.2 26.0 28.5 18.8 25.0 12.3 9.7 18.4 13.0 101.5
## 391 37.6 27.7 30.2 17.4 25.7 12.0 9.1 17.5 12.1 94.7
## 392 33.6 24.3 27.9 18.2 24.7 11.0 8.4 16.6 11.1 103.1
## 393 34.5 29.2 31.9 18.3 23.6 11.0 9.3 18.7 12.4 94.5
## 394 36.0 27.1 31.4 18.5 24.6 11.9 9.4 15.9 11.9 97.1
## 395 35.4 27.0 29.0 17.8 25.0 11.6 9.2 18.2 12.1 98.0
## 396 35.8 28.3 30.2 19.2 24.9 12.0 9.2 18.4 14.2 102.4
## 397 38.3 28.3 32.4 18.7 28.3 13.9 10.6 18.9 14.0 109.1
## 398 33.8 28.7 32.9 17.7 23.5 12.6 9.4 18.2 13.2 94.7
## 399 36.9 31.1 33.8 16.8 26.6 13.0 9.8 18.4 13.8 98.2
## 400 34.9 28.0 33.1 18.0 26.1 12.6 9.6 18.8 12.2 101.5
## 401 35.1 28.3 29.7 18.2 27.3 13.0 9.3 17.7 13.8 96.6
## 402 37.4 30.2 34.2 21.6 28.3 13.2 10.0 19.6 14.1 106.1
## 403 36.9 27.5 31.1 16.3 26.6 12.8 9.8 18.0 14.0 100.1
## 404 36.0 28.0 30.4 16.3 26.4 12.2 9.2 17.6 12.8 105.3
## 405 32.9 26.6 32.2 18.5 25.2 12.0 8.4 19.4 12.0 99.8
## 406 38.3 31.7 37.4 19.2 28.5 13.2 9.3 18.6 13.8 103.9
## 407 37.6 30.7 35.6 24.7 32.2 14.0 10.6 21.2 14.5 111.0
## 408 36.7 28.0 31.7 17.3 27.3 13.1 10.6 18.4 13.4 99.0
## 409 38.9 25.2 31.5 17.3 24.2 12.9 9.8 18.0 12.8 102.5
## 410 37.8 28.0 32.0 16.8 28.3 12.7 9.8 18.1 13.2 105.6
## 411 38.0 31.7 32.9 22.3 25.2 12.4 10.0 19.6 13.8 99.5
## 412 38.7 30.4 31.3 18.7 26.8 12.4 10.0 19.4 13.6 106.5
## 413 37.1 27.1 32.6 17.3 26.6 13.2 10.3 19.0 14.4 104.1
## 414 35.8 30.2 33.5 20.6 27.5 12.4 9.4 18.4 13.0 99.7
## 415 38.5 30.4 35.1 18.2 27.3 13.8 10.7 19.0 14.4 106.6
## 416 38.5 30.8 35.1 15.8 27.3 13.1 10.6 18.6 14.2 97.2
## 417 36.0 30.2 32.9 18.7 27.8 12.4 9.6 18.4 14.2 100.8
## 418 34.0 26.4 29.9 19.2 27.5 11.8 9.4 17.3 12.4 100.7
## 419 38.3 26.6 33.3 18.2 26.4 13.1 10.8 20.0 13.8 99.1
## 420 38.7 33.3 33.8 20.9 30.2 14.0 10.5 18.9 14.8 108.9
## 421 36.9 29.0 33.5 20.9 30.2 12.7 9.6 18.6 14.2 109.8
## 422 35.8 26.6 31.1 18.5 24.7 12.4 9.6 17.9 13.8 99.5
## 423 36.7 29.0 32.0 19.7 25.6 12.4 10.0 18.6 13.0 100.5
## 424 36.7 28.0 31.1 19.4 27.5 12.9 10.0 17.9 14.0 97.4
## 425 37.6 31.3 33.5 17.0 25.4 12.4 9.4 19.2 11.5 105.0
## 426 35.1 23.7 31.1 18.5 25.9 12.8 10.0 17.7 12.8 102.9
## 427 35.3 23.3 28.3 15.3 24.7 12.4 10.2 17.1 13.4 93.9
## 428 38.5 26.6 34.0 22.3 26.6 13.8 10.7 20.0 15.5 114.2
## 429 37.8 29.4 32.2 15.8 27.5 11.8 9.8 18.2 13.8 95.9
## 430 35.1 25.6 29.9 16.8 24.9 12.6 9.6 18.2 12.5 98.7
## 431 32.4 30.2 33.8 15.3 25.6 12.2 10.2 17.9 13.4 91.2
## 432 39.2 31.3 33.8 17.7 29.7 13.2 9.6 17.8 14.4 105.1
## 433 38.3 30.8 35.6 17.0 26.8 13.6 10.4 19.0 13.6 102.3
## 434 32.9 20.9 28.5 19.0 23.0 12.2 9.1 16.0 12.7 93.1
## 435 38.0 28.3 31.1 17.7 26.1 12.2 10.2 17.3 13.0 101.7
## 436 37.8 32.0 33.8 16.8 26.6 13.4 10.4 18.8 13.4 105.0
## 437 34.2 29.2 31.1 18.0 25.9 12.8 9.8 17.6 13.4 96.4
## 438 37.6 31.3 35.3 20.2 27.3 11.3 9.4 18.9 13.8 103.5
## 439 35.3 25.4 31.3 17.3 25.4 12.4 10.0 17.5 13.6 99.8
## 440 34.9 27.3 32.2 17.7 25.4 12.6 9.0 17.4 12.2 87.0
## 441 37.4 32.0 34.7 19.7 25.9 13.0 10.0 18.4 13.4 105.2
## 442 36.2 31.1 34.7 21.1 26.8 12.9 10.4 18.6 14.0 102.1
## 443 38.9 29.0 33.1 16.3 25.9 12.2 10.2 18.8 13.8 101.3
## 444 36.0 30.4 32.9 18.2 24.7 13.4 10.5 19.5 13.6 99.6
## 445 33.3 25.9 29.9 18.0 25.6 12.0 9.2 17.6 12.9 99.4
## 446 37.4 29.4 32.6 19.9 27.1 13.4 10.2 18.4 14.1 99.4
## 447 34.7 29.4 31.5 17.7 24.9 13.4 9.6 18.4 13.4 99.0
## 448 38.7 28.0 34.7 16.3 28.5 13.6 10.4 20.4 14.3 108.2
## 449 36.2 28.3 31.3 16.3 25.4 12.2 9.6 16.8 12.9 96.4
## 450 37.6 27.8 31.1 19.9 25.9 12.9 9.6 17.2 13.8 101.4
## 451 34.9 26.1 29.0 16.5 25.6 12.0 9.2 17.3 13.2 98.2
## 452 34.7 24.7 32.0 17.7 25.2 12.8 10.4 18.4 13.8 100.6
## 453 34.0 26.8 32.2 17.3 28.7 11.2 9.1 17.4 12.9 108.4
## 454 37.8 28.5 33.5 18.0 27.1 12.9 10.4 18.6 13.4 100.8
## 455 35.1 27.8 30.6 16.3 26.8 11.6 9.6 17.2 12.0 94.8
## 456 39.2 29.9 34.9 18.5 26.4 14.3 10.8 19.6 14.4 108.2
## 457 35.1 29.9 35.3 18.0 28.5 13.8 10.5 20.0 14.3 110.7
## 458 37.1 29.2 32.0 18.0 26.1 13.2 10.2 19.2 14.4 102.6
## 459 35.8 29.4 30.8 17.0 26.6 12.2 10.8 17.6 13.4 98.1
## 460 35.3 26.8 31.7 18.5 23.3 11.5 9.2 17.8 12.6 92.0
## 461 37.6 26.8 31.1 16.5 24.0 11.8 10.2 18.4 12.8 98.6
## 462 35.8 29.0 30.6 18.0 24.2 12.4 10.5 17.9 13.4 96.1
## 463 36.0 29.2 33.5 17.5 27.8 11.6 10.5 18.7 14.2 107.7
## 464 35.6 28.3 28.0 14.4 23.7 11.7 9.6 16.4 13.0 95.4
## 465 37.1 27.8 31.7 20.6 26.4 12.4 10.4 18.2 13.2 99.3
## 466 35.6 30.2 32.9 21.8 26.8 13.0 11.0 20.6 14.3 115.2
## 467 35.1 29.4 33.1 17.7 27.3 12.8 10.3 18.7 13.6 96.7
## 468 35.8 26.8 30.4 23.3 25.6 12.4 10.3 17.5 13.0 109.0
## 469 37.4 29.4 31.7 16.5 25.9 12.2 10.8 19.4 14.3 95.1
## 470 38.9 28.5 34.0 19.2 27.5 13.3 10.3 19.1 14.2 108.4
## 471 37.4 29.0 32.9 17.7 23.5 12.7 10.0 18.2 13.8 98.2
## 472 37.6 28.3 29.4 16.8 25.4 13.2 10.0 17.8 14.2 99.7
## 473 35.1 26.6 29.2 17.0 24.4 11.8 9.4 17.0 12.2 94.4
## 474 38.0 33.3 37.8 20.6 28.3 15.0 11.5 22.6 13.8 127.1
## 475 36.7 25.2 29.2 18.5 24.2 12.0 9.0 17.0 11.8 94.9
## 476 35.6 29.9 33.5 18.2 24.9 13.2 11.0 19.2 13.8 98.9
## 477 35.6 30.8 33.8 26.8 27.1 12.4 10.2 18.2 14.0 106.4
## 478 35.6 27.3 31.7 17.5 27.3 12.4 9.8 17.6 12.7 102.2
## 479 37.8 29.0 32.4 20.6 28.5 12.7 9.1 18.2 13.2 110.3
## 480 40.9 28.7 29.2 18.2 27.3 12.9 9.6 16.8 13.4 107.3
## 481 34.0 25.9 29.4 20.2 24.2 11.4 9.6 16.6 12.6 95.0
## 482 34.9 31.3 31.7 20.9 31.5 13.0 9.6 17.8 13.7 115.6
## 483 35.8 31.5 32.6 17.5 25.2 12.9 9.8 17.9 13.4 99.3
## 484 36.5 27.5 30.8 18.5 24.5 11.6 9.6 17.0 11.7 93.1
## 485 32.9 26.8 28.7 16.3 24.4 11.0 8.6 16.4 12.6 94.9
## 486 36.9 28.5 31.5 15.6 27.8 11.6 9.8 17.2 12.9 100.0
## 487 35.8 29.4 32.0 18.0 25.9 11.8 9.8 18.0 12.7 98.6
## 488 39.4 28.3 30.2 21.6 29.7 12.8 10.5 19.5 14.4 107.2
## 489 36.5 28.3 29.7 17.7 26.4 12.0 10.0 17.1 12.8 98.3
## 490 35.8 27.3 28.5 18.2 23.5 11.2 10.2 17.5 12.9 92.4
## 491 34.4 29.7 31.7 17.5 24.7 12.6 10.2 17.7 12.4 93.0
## 492 36.5 26.6 30.8 16.0 24.9 12.6 10.7 17.0 13.0 99.5
## 493 38.5 25.6 31.7 17.0 25.6 11.8 10.2 16.8 12.9 92.8
## 494 35.3 25.4 28.5 15.1 24.9 10.6 10.4 17.0 12.6 95.9
## 495 36.2 28.7 27.8 16.0 23.7 12.4 10.2 17.9 13.2 96.1
## 496 34.9 28.7 32.9 16.8 26.8 13.4 10.8 17.9 12.9 98.5
## 497 38.7 29.7 32.0 15.8 26.4 12.8 10.5 18.4 13.8 103.8
## 498 37.6 25.2 29.0 19.2 27.1 14.0 11.0 18.9 13.2 105.0
## 499 34.7 29.4 33.5 17.7 25.4 12.4 10.8 20.2 12.8 100.2
## 500 37.8 29.7 31.3 18.5 26.6 13.4 11.2 18.4 14.2 99.1
## 501 39.8 27.5 31.5 19.4 29.0 13.1 10.4 17.6 13.8 107.6
## 502 36.5 29.7 34.0 20.2 28.5 13.3 9.8 18.9 12.4 104.0
## 503 38.0 30.4 32.9 17.0 27.1 12.9 10.4 19.5 14.4 108.4
## 504 35.3 28.7 30.4 17.7 25.6 12.4 9.8 17.3 13.6 99.3
## 505 34.7 24.9 24.7 17.3 24.2 12.0 10.2 18.0 13.6 91.9
## 506 38.5 29.0 32.9 15.3 25.6 12.0 9.8 18.6 13.3 107.1
## 507 35.6 29.0 29.0 20.4 26.8 13.4 10.8 18.7 13.8 100.5
## che.gi wai.gi nav.gi hip.gi thi.gi bic.gi for.gi kne.gi cal.gi ank.gi
## 1 89.5 71.5 74.5 93.5 51.5 32.5 26.0 34.5 36.5 23.5
## 2 97.0 79.0 86.5 94.8 51.5 34.4 28.0 36.5 37.5 24.5
## 3 97.5 83.2 82.9 95.0 57.3 33.4 28.8 37.0 37.3 21.9
## 4 97.0 77.8 78.8 94.0 53.0 31.0 26.2 37.0 34.8 23.0
## 5 97.5 80.0 82.5 98.5 55.4 32.0 28.4 37.7 38.6 24.4
## 6 99.9 82.5 80.1 95.3 57.5 33.0 28.0 36.6 36.1 23.5
## 7 106.9 82.0 84.0 101.0 60.9 42.4 32.3 40.1 40.3 23.6
## 8 102.5 76.8 80.5 98.0 56.0 34.1 28.0 39.2 36.7 22.5
## 9 91.0 68.5 69.0 89.5 50.0 33.0 26.0 35.5 35.0 22.0
## 10 93.5 77.5 81.5 99.8 59.8 36.5 29.2 38.3 38.6 22.2
## 11 97.7 81.9 81.0 98.4 60.5 34.6 27.9 38.9 40.1 23.2
## 12 99.5 82.6 82.5 95.0 58.5 38.5 30.4 39.0 38.4 24.3
## 13 103.0 85.0 94.5 103.0 59.0 33.5 29.0 40.5 40.0 26.0
## 14 99.6 85.6 89.2 98.0 59.1 35.6 29.0 35.8 36.0 21.5
## 15 101.5 78.0 89.5 95.0 57.0 36.0 29.0 34.5 35.0 22.0
## 16 104.1 82.0 84.0 97.0 56.0 34.5 29.5 39.0 35.7 24.0
## 17 100.0 88.3 93.5 105.0 65.8 37.0 28.8 40.9 41.7 24.2
## 18 93.8 73.6 74.9 90.1 54.1 31.2 26.9 36.4 35.6 22.0
## 19 98.5 78.5 86.0 94.5 55.0 34.5 28.5 38.0 36.5 23.0
## 20 104.0 87.3 88.0 101.1 59.5 37.0 30.5 39.8 42.0 26.5
## 21 100.0 92.0 91.0 98.0 57.5 32.0 27.6 37.5 35.2 21.0
## 22 100.0 80.0 83.7 99.5 57.0 37.0 30.0 37.5 35.5 23.0
## 23 99.0 74.5 75.9 92.2 53.4 31.2 26.9 36.2 33.3 23.5
## 24 101.0 90.6 89.6 101.2 59.5 37.0 28.3 35.4 40.6 22.9
## 25 101.6 81.4 81.6 98.8 61.3 39.4 31.9 38.5 41.2 22.8
## 26 108.8 89.5 89.5 106.0 59.5 37.5 30.1 39.9 41.5 23.5
## 27 100.0 85.0 94.5 105.0 62.0 35.5 28.5 38.0 40.0 23.5
## 28 88.0 73.5 77.7 97.0 56.3 32.5 27.8 39.0 38.2 23.5
## 29 88.7 75.8 83.0 89.0 52.6 31.2 26.5 37.0 37.4 21.5
## 30 84.5 74.0 81.0 93.5 50.5 27.5 24.8 34.0 32.8 21.0
## 31 93.6 77.5 82.1 95.0 56.5 32.8 26.2 37.6 36.3 21.0
## 32 105.0 74.0 72.0 90.0 54.2 34.1 28.6 36.2 36.6 22.4
## 33 90.9 74.0 78.8 96.4 51.8 29.8 27.0 36.4 34.6 22.9
## 34 91.2 82.0 89.5 100.0 57.5 32.8 28.0 40.7 40.1 24.3
## 35 98.4 73.0 83.0 95.4 56.3 36.4 27.5 37.2 34.5 21.8
## 36 85.0 70.5 84.0 90.0 50.0 29.0 26.0 36.0 34.5 21.5
## 37 97.2 75.0 77.2 91.3 49.5 31.0 26.1 36.3 35.1 21.0
## 38 91.5 72.1 79.2 91.0 54.9 29.5 24.5 36.1 37.2 22.9
## 39 91.2 78.8 78.0 93.2 55.8 31.9 27.4 36.4 35.1 23.0
## 40 101.1 77.5 78.0 97.0 55.0 37.7 29.9 38.3 39.6 23.3
## 41 104.3 91.5 93.2 103.9 62.0 36.3 29.0 36.7 39.4 23.1
## 42 110.8 84.9 83.0 102.6 66.4 42.3 30.9 37.0 37.7 22.6
## 43 96.3 79.1 78.3 97.1 60.1 35.5 28.8 36.9 38.2 23.4
## 44 102.7 77.9 77.9 90.7 56.7 35.4 28.3 35.6 35.5 22.9
## 45 103.9 91.7 89.4 101.8 61.0 35.7 29.4 37.7 40.0 22.2
## 46 105.6 86.6 87.3 103.9 63.2 37.8 29.7 39.0 40.2 24.3
## 47 95.8 84.7 84.0 101.4 60.0 35.0 28.5 38.4 37.9 23.2
## 48 111.2 90.3 93.5 108.7 66.9 40.2 32.4 39.2 40.1 25.7
## 49 100.0 79.7 87.1 98.4 61.1 36.3 28.6 34.5 36.1 21.4
## 50 91.6 73.1 75.4 86.5 50.6 30.8 26.1 31.7 33.6 20.3
## 51 108.0 79.8 82.5 94.8 58.3 39.8 29.6 34.2 38.1 21.1
## 52 109.6 81.6 86.5 100.9 61.7 39.5 31.7 38.8 36.5 22.7
## 53 105.7 76.8 83.4 98.0 56.8 37.9 30.9 35.9 38.3 23.4
## 54 109.1 85.9 90.4 100.9 61.3 40.1 30.0 36.8 38.6 21.9
## 55 88.8 73.3 77.9 85.7 46.9 30.5 24.8 31.1 30.5 19.0
## 56 86.1 69.9 67.4 84.1 50.8 31.5 26.6 32.8 36.3 20.0
## 57 91.3 72.7 83.2 91.4 51.2 27.8 26.0 34.8 34.7 21.1
## 58 100.6 82.7 83.5 98.0 55.8 33.1 28.0 37.9 39.1 23.2
## 59 105.5 90.1 89.2 104.5 62.7 36.3 29.6 38.4 42.4 25.3
## 60 97.5 82.9 83.6 95.8 52.6 34.5 27.0 35.2 35.2 21.4
## 61 94.4 77.9 79.0 91.7 57.1 31.2 27.5 36.6 37.5 21.6
## 62 99.6 92.5 96.2 103.4 58.5 34.5 28.4 38.4 38.0 22.4
## 63 98.1 77.8 77.2 90.0 52.4 33.2 26.4 34.2 36.0 21.8
## 64 93.6 72.7 77.3 91.7 51.9 32.1 27.4 33.5 33.8 21.1
## 65 93.4 75.0 79.2 94.0 53.8 34.2 27.9 36.1 36.2 22.0
## 66 92.0 76.0 83.0 93.0 54.5 29.5 26.0 37.0 34.5 22.8
## 67 99.2 82.7 84.2 93.0 56.6 32.4 27.6 35.8 36.3 21.8
## 68 97.6 80.0 85.7 97.4 57.8 33.8 28.6 36.2 37.4 22.0
## 69 108.8 107.1 107.2 108.3 67.0 39.6 30.6 40.0 39.6 24.6
## 70 91.9 76.2 78.1 90.0 52.0 30.7 25.8 34.8 32.6 21.0
## 71 105.2 90.2 88.6 100.2 60.8 35.7 29.4 39.2 39.1 24.5
## 72 98.3 89.4 87.4 97.7 54.8 31.0 26.0 36.4 35.6 21.6
## 73 92.5 80.9 78.5 96.0 59.0 31.5 26.3 36.1 39.0 21.2
## 74 92.5 73.5 76.4 92.0 53.1 30.6 27.1 36.0 36.0 23.8
## 75 101.6 70.9 76.7 95.3 56.0 36.0 28.6 36.0 34.0 22.0
## 76 94.6 76.1 78.0 86.3 52.4 28.6 23.9 34.5 37.9 22.7
## 77 92.5 81.0 85.2 92.5 54.7 32.3 26.8 35.8 37.6 21.1
## 78 88.2 72.0 72.0 85.5 50.2 28.6 24.8 34.9 35.1 20.1
## 79 91.0 76.8 80.0 94.5 54.6 33.2 28.0 37.5 35.6 22.1
## 80 106.0 86.0 92.0 103.0 60.6 34.0 29.8 38.8 39.5 23.6
## 81 115.0 98.5 106.6 116.5 67.8 35.8 27.2 38.0 41.2 23.3
## 82 100.0 79.0 82.5 98.5 62.1 34.0 28.8 39.6 40.8 25.9
## 83 94.7 77.5 80.5 92.0 54.2 30.9 26.6 36.5 35.8 21.3
## 84 92.5 75.2 80.2 91.6 49.6 29.2 26.1 36.2 35.7 22.1
## 85 96.7 82.0 82.2 92.7 54.6 32.0 27.1 35.6 36.4 20.7
## 86 97.4 79.6 80.8 95.0 54.2 32.6 27.4 36.5 38.0 21.6
## 87 106.7 75.2 77.8 94.5 57.4 36.7 29.9 38.1 36.0 22.6
## 88 106.2 88.6 88.3 100.5 63.4 36.9 29.4 38.4 38.6 23.1
## 89 103.6 81.5 83.3 91.8 55.0 33.0 27.8 35.4 36.5 21.9
## 90 98.3 79.9 82.4 87.5 54.4 33.5 27.3 36.8 37.9 22.3
## 91 87.8 73.5 77.5 94.9 53.5 34.3 28.5 36.5 35.2 22.0
## 92 99.8 80.3 80.8 93.0 55.4 33.3 28.0 36.0 37.8 20.3
## 93 104.6 81.5 85.0 92.0 54.1 33.0 28.0 35.1 35.2 21.1
## 94 86.5 74.0 76.5 91.3 53.5 30.5 26.1 36.6 38.6 21.2
## 95 110.0 104.0 99.0 111.7 63.2 37.5 29.0 41.2 39.3 24.6
## 96 93.2 76.2 83.8 92.8 55.2 31.2 26.2 36.8 37.7 22.7
## 97 90.0 76.5 77.7 91.2 54.2 33.1 27.2 35.5 35.3 21.5
## 98 98.4 81.0 80.5 96.2 56.0 32.0 27.4 37.0 35.5 24.0
## 99 107.2 88.8 86.8 100.0 61.0 34.6 27.9 38.0 39.4 23.2
## 100 108.3 94.0 98.0 108.2 66.8 35.6 27.3 39.5 43.0 25.3
## 101 105.7 83.4 86.5 101.1 61.3 34.7 29.4 39.4 41.8 24.0
## 102 88.5 77.0 79.0 93.0 51.7 33.5 27.9 38.4 38.5 22.5
## 103 79.3 75.4 78.0 88.6 50.0 25.6 22.7 33.8 32.5 21.2
## 104 90.9 80.3 80.8 92.8 53.9 32.5 28.0 36.5 35.0 21.0
## 105 90.2 68.0 67.0 81.5 49.5 27.0 23.6 34.0 34.5 20.9
## 106 91.6 80.6 78.0 91.3 55.0 30.7 25.3 35.5 34.0 20.8
## 107 105.2 88.6 94.7 94.7 58.3 36.9 28.8 40.3 39.7 26.3
## 108 97.0 81.1 88.2 93.9 53.5 33.7 28.6 35.0 37.3 23.1
## 109 85.3 70.8 84.9 89.4 55.8 28.7 25.5 38.5 34.2 21.3
## 110 99.6 73.3 82.1 89.3 55.4 36.3 32.5 34.3 34.3 22.3
## 111 94.3 75.9 79.1 92.6 54.4 33.2 27.9 34.8 35.5 23.0
## 112 111.8 86.2 93.5 96.3 59.1 36.3 28.0 38.3 34.7 23.0
## 113 94.3 75.0 82.2 88.0 53.8 36.3 28.9 34.5 33.5 23.0
## 114 106.1 101.0 99.7 105.5 60.2 38.6 30.3 39.5 39.4 25.6
## 115 102.0 90.0 92.3 102.3 60.0 34.6 29.7 38.2 38.3 23.7
## 116 94.8 75.0 79.6 91.6 49.4 30.0 26.5 31.7 30.2 16.4
## 117 102.9 75.9 77.0 93.4 55.0 35.2 28.7 37.0 37.7 24.5
## 118 115.8 96.0 95.9 103.6 62.2 38.2 30.1 41.2 39.4 25.1
## 119 105.4 84.0 90.4 94.8 57.6 38.7 30.2 38.6 38.2 22.8
## 120 87.1 67.1 80.4 85.9 46.8 30.3 25.4 32.7 32.1 20.0
## 121 104.1 82.5 90.1 98.4 57.7 37.9 31.6 37.8 38.3 24.6
## 122 95.8 83.7 84.2 98.4 56.2 33.7 28.0 38.0 39.6 25.8
## 123 98.6 76.7 85.8 93.3 56.0 35.7 27.6 34.7 34.6 20.6
## 124 118.7 105.2 105.0 115.5 69.9 39.4 32.1 42.2 47.7 27.0
## 125 100.9 90.6 93.3 97.7 58.0 34.8 28.0 34.1 35.8 22.2
## 126 98.5 85.7 92.9 98.6 55.5 35.3 28.7 39.3 35.9 23.0
## 127 96.9 82.5 90.8 94.9 54.4 32.8 28.7 39.2 37.0 27.5
## 128 110.7 94.7 92.0 101.3 60.1 37.2 30.9 40.5 40.0 24.2
## 129 108.0 105.2 103.4 108.1 60.5 38.0 30.2 36.9 37.7 21.6
## 130 109.0 86.0 90.2 98.0 59.5 40.0 31.2 38.3 39.0 25.8
## 131 104.2 90.9 92.7 100.2 51.8 30.1 26.8 38.1 36.4 23.2
## 132 100.5 92.4 92.4 97.0 50.9 32.9 29.0 37.7 37.7 23.4
## 133 105.7 96.5 98.2 97.4 54.3 31.9 28.5 37.7 39.3 24.5
## 134 112.4 98.4 101.5 107.9 67.4 39.2 30.5 42.6 40.7 25.3
## 135 96.2 76.7 83.5 93.9 50.4 32.1 27.7 36.1 32.9 23.2
## 136 97.5 94.8 98.2 98.6 48.3 31.1 27.0 37.7 36.8 24.6
## 137 90.9 80.1 79.8 91.3 56.2 32.9 27.2 36.2 33.0 23.0
## 138 108.4 97.4 103.7 105.3 55.6 36.6 28.4 38.2 36.6 22.9
## 139 94.3 73.7 74.5 88.2 52.3 29.6 26.2 35.2 36.2 21.2
## 140 109.1 76.1 90.1 93.3 51.7 37.0 30.6 36.8 37.7 23.6
## 141 115.0 95.6 101.9 107.9 64.6 37.1 30.0 41.8 39.6 24.7
## 142 104.4 101.0 98.9 103.3 54.4 38.1 29.8 39.7 41.8 25.0
## 143 100.1 84.5 84.5 94.4 54.7 33.9 28.6 38.5 37.6 25.0
## 144 108.4 98.0 101.8 101.5 56.9 38.2 29.9 37.7 39.2 24.9
## 145 107.3 101.6 103.8 110.0 57.8 34.9 28.9 40.3 40.0 23.7
## 146 101.8 87.8 90.2 98.4 55.6 33.1 28.4 38.2 37.7 24.5
## 147 94.5 80.0 85.0 95.0 52.0 31.5 26.5 36.9 36.4 22.9
## 148 91.7 81.8 82.9 98.3 56.3 31.0 25.7 35.0 33.0 22.0
## 149 95.9 84.4 86.8 99.0 55.0 30.5 26.4 36.1 38.4 21.3
## 150 110.5 85.0 83.5 95.7 59.0 39.2 29.9 37.9 37.7 23.8
## 151 104.0 90.0 86.0 96.0 52.5 33.5 29.1 36.0 36.9 23.0
## 152 86.8 72.9 73.4 89.5 51.0 29.8 24.8 32.6 33.1 22.1
## 153 106.7 81.0 80.2 93.7 54.8 35.5 30.6 36.9 37.3 22.7
## 154 102.5 86.5 89.0 97.0 57.0 34.0 28.4 38.0 37.0 22.5
## 155 103.0 93.9 98.6 103.6 60.5 34.4 28.5 40.9 40.8 24.6
## 156 95.0 77.0 78.0 93.0 52.0 32.6 28.4 34.4 34.4 20.0
## 157 99.0 75.0 75.0 90.0 50.6 32.0 27.3 33.8 34.0 22.0
## 158 89.7 80.6 80.8 90.0 55.5 28.9 25.0 34.6 37.4 23.0
## 159 111.5 100.5 107.3 109.5 61.8 37.4 31.6 41.0 39.7 25.4
## 160 94.1 81.2 84.0 91.6 51.5 33.0 27.0 35.2 35.5 23.1
## 161 118.3 103.4 106.2 108.5 60.5 35.4 29.7 42.3 40.8 24.8
## 162 106.5 90.3 101.1 101.6 57.2 35.4 28.6 40.4 37.8 24.9
## 163 110.4 98.0 98.0 99.6 56.7 36.4 29.2 40.9 42.1 26.1
## 164 102.3 86.5 87.7 91.9 55.0 35.0 28.9 38.3 37.8 24.0
## 165 98.5 77.9 87.3 90.8 50.8 35.0 28.4 35.5 35.0 21.0
## 166 111.6 89.1 95.1 104.8 62.7 37.9 31.2 41.1 41.2 27.7
## 167 106.7 93.9 111.8 111.4 62.8 36.2 29.7 42.8 39.3 23.5
## 168 110.4 85.3 82.9 96.5 57.0 39.0 29.8 36.8 36.0 21.6
## 169 114.0 98.5 103.8 108.1 61.3 37.2 31.4 41.9 42.1 26.4
## 170 101.8 79.5 90.1 95.3 54.8 34.2 28.5 36.6 36.2 22.8
## 171 109.6 94.9 94.7 104.3 59.0 35.9 27.8 37.7 36.8 23.2
## 172 100.7 76.5 87.2 96.3 54.2 33.8 27.7 36.4 38.2 23.8
## 173 104.3 88.4 89.6 98.8 54.8 35.5 29.7 37.7 37.0 23.7
## 174 104.2 84.2 84.0 93.2 55.0 33.0 25.4 35.6 36.4 22.8
## 175 107.4 87.6 89.4 106.7 60.9 38.3 31.2 39.0 42.6 25.8
## 176 101.0 83.7 91.1 99.9 56.8 33.5 27.7 38.7 41.8 29.3
## 177 103.0 92.1 91.3 103.8 56.6 33.3 27.7 37.1 37.4 22.6
## 178 107.2 89.9 94.7 107.1 59.2 35.3 26.9 36.6 32.3 22.0
## 179 99.0 88.7 91.0 100.0 57.5 34.0 28.3 40.9 38.8 26.4
## 180 100.3 83.9 89.4 103.9 59.8 36.1 29.4 37.0 36.5 24.3
## 181 100.2 79.5 88.7 95.3 52.5 34.6 25.8 35.6 35.1 21.8
## 182 99.8 84.5 92.6 99.5 59.2 34.3 29.0 36.5 38.5 24.5
## 183 107.5 89.2 88.4 107.0 56.9 35.6 28.5 37.0 37.6 23.0
## 184 110.1 90.7 91.9 101.7 58.0 36.8 29.0 36.9 38.9 24.2
## 185 107.0 88.8 97.5 103.8 61.0 36.7 28.6 38.4 39.5 24.4
## 186 105.6 103.6 100.7 100.6 55.3 33.6 26.9 37.8 37.9 23.9
## 187 103.6 98.5 99.9 103.6 57.5 33.7 29.0 38.7 36.7 24.3
## 188 97.1 82.9 88.1 91.2 51.7 30.7 25.7 33.9 33.4 21.2
## 189 109.8 90.5 92.3 95.2 52.4 35.8 28.7 32.5 36.5 23.7
## 190 103.3 84.5 94.5 98.2 53.7 32.5 27.8 36.0 36.3 22.0
## 191 103.7 86.0 93.8 97.1 53.1 33.9 27.3 35.7 36.6 22.6
## 192 108.5 84.2 88.9 97.5 58.8 36.6 29.9 34.2 34.8 22.0
## 193 97.8 85.7 89.5 94.9 51.7 32.2 26.4 34.4 32.6 22.0
## 194 112.7 112.1 105.9 106.3 56.9 35.7 27.8 37.3 36.6 22.7
## 195 111.4 99.7 102.9 105.8 57.8 36.5 30.5 39.0 41.2 25.7
## 196 95.1 84.7 92.9 96.7 54.6 32.8 25.3 36.6 35.0 23.5
## 197 100.2 80.3 92.4 96.4 53.8 35.7 29.2 38.4 37.0 25.6
## 198 106.3 98.6 111.7 118.7 70.0 37.1 27.8 37.5 39.2 25.7
## 199 109.5 90.0 96.2 104.3 63.5 39.4 29.9 37.4 37.3 24.3
## 200 106.3 86.6 93.9 95.9 53.6 34.4 28.6 35.0 34.1 21.7
## 201 91.5 80.2 85.7 89.2 48.5 28.7 25.0 35.4 32.3 23.0
## 202 104.9 109.2 104.4 101.7 56.4 34.0 29.2 39.3 38.5 24.3
## 203 109.6 88.4 92.0 95.1 52.5 39.4 29.8 34.5 34.0 22.0
## 204 102.5 81.0 84.5 105.0 56.0 31.5 26.5 38.5 36.9 21.3
## 205 101.2 88.8 90.8 101.3 62.5 35.2 28.9 40.6 39.2 23.0
## 206 98.7 78.1 78.6 92.0 56.5 35.7 29.2 36.4 37.0 22.0
## 207 96.0 81.5 80.5 89.5 52.0 30.3 25.0 36.0 35.0 22.4
## 208 104.2 87.6 89.6 100.5 60.7 36.5 31.1 40.7 38.4 22.9
## 209 103.0 92.5 88.5 94.0 55.0 36.0 28.0 36.5 37.0 21.8
## 210 107.0 97.0 95.5 104.5 56.6 34.4 28.9 40.0 38.5 24.0
## 211 101.0 82.0 82.0 98.0 59.0 35.4 29.0 38.0 42.0 23.6
## 212 112.1 99.5 97.8 107.4 60.5 35.5 28.8 42.9 38.7 25.7
## 213 103.4 93.8 92.7 112.2 66.2 34.8 30.1 45.7 43.6 26.9
## 214 100.3 79.1 80.4 94.3 51.0 32.6 27.2 36.1 32.7 22.5
## 215 95.7 85.8 94.7 106.8 60.6 33.6 27.3 37.5 41.8 26.2
## 216 108.0 90.8 92.4 100.3 56.5 36.8 29.3 37.5 36.3 23.4
## 217 101.0 85.4 86.9 101.8 57.0 34.0 27.1 39.4 37.1 23.7
## 218 98.6 91.6 102.1 106.7 57.8 32.3 27.9 41.8 41.5 27.7
## 219 101.6 79.7 82.0 98.4 58.1 36.5 31.0 38.0 38.1 25.3
## 220 99.0 86.1 90.8 101.3 56.0 33.5 28.3 37.7 38.3 25.2
## 221 116.7 113.2 102.9 107.9 57.7 37.4 28.9 37.8 37.4 24.1
## 222 105.0 79.5 85.8 92.7 51.8 36.2 27.0 33.9 28.9 20.3
## 223 95.1 85.4 84.1 94.3 52.7 31.2 25.9 37.4 34.4 24.5
## 224 106.7 97.9 94.7 104.6 60.8 36.2 28.7 37.7 39.4 22.6
## 225 116.6 94.7 93.1 103.3 58.0 42.3 31.9 39.9 39.6 25.5
## 226 103.5 86.1 90.5 96.7 55.2 34.5 27.7 37.3 34.8 22.5
## 227 90.4 84.9 83.7 97.9 51.8 28.0 25.2 37.5 36.0 21.3
## 228 107.5 92.2 88.6 97.5 53.7 34.3 27.6 36.0 37.5 23.3
## 229 100.8 83.7 82.5 97.7 53.3 32.5 27.1 35.4 37.0 22.7
## 230 104.2 97.8 93.6 102.5 58.0 35.8 29.4 39.0 38.1 23.0
## 231 104.9 98.6 99.2 103.3 55.3 35.0 29.3 35.9 36.0 23.5
## 232 112.8 89.5 96.9 100.9 54.9 38.5 28.7 35.9 33.6 22.1
## 233 95.7 82.9 89.6 94.8 51.9 32.2 25.3 33.4 32.8 21.4
## 234 104.9 90.1 90.8 96.6 55.0 32.9 26.5 35.6 37.9 22.5
## 235 109.1 78.9 91.0 98.1 55.7 38.5 30.1 37.2 36.8 23.4
## 236 105.4 80.7 87.4 95.5 56.8 38.3 28.6 35.0 34.3 23.3
## 237 104.3 94.3 99.4 100.4 59.1 35.0 26.7 35.5 35.9 22.6
## 238 100.5 70.2 79.3 92.0 51.7 32.1 26.7 33.2 34.9 23.7
## 239 111.8 83.6 92.7 101.5 59.5 40.4 31.7 36.7 38.3 25.5
## 240 109.5 91.9 96.5 103.3 57.7 36.5 29.1 34.6 38.8 25.1
## 241 101.2 71.8 82.3 87.6 50.1 34.2 29.2 34.1 33.4 23.1
## 242 103.3 85.0 90.8 97.9 55.2 35.2 29.4 34.9 37.3 23.5
## 243 101.6 85.7 91.0 95.9 50.9 34.0 28.4 35.0 34.3 21.1
## 244 103.1 96.5 99.0 111.8 62.3 34.8 27.5 41.7 37.0 24.3
## 245 104.6 82.4 85.7 99.9 63.3 38.6 32.0 38.4 39.8 25.4
## 246 104.3 86.3 87.8 103.3 59.7 36.4 30.4 39.3 42.0 27.7
## 247 95.9 83.2 88.0 100.6 57.8 34.0 28.2 36.3 39.6 25.2
## 248 83.0 66.5 79.0 92.0 53.5 24.3 20.5 32.0 32.2 21.0
## 249 78.5 61.5 70.5 90.5 57.7 27.8 24.0 38.5 38.5 22.5
## 250 75.0 61.2 66.5 91.0 53.0 24.0 22.0 32.5 32.5 19.0
## 251 86.5 78.0 91.0 99.5 61.5 28.0 24.0 35.2 36.7 23.0
## 252 91.0 70.5 80.5 91.5 55.0 26.9 22.7 33.0 33.3 19.9
## 253 79.5 66.5 78.5 94.0 54.0 26.5 22.5 34.0 35.0 23.0
## 254 77.0 58.0 64.0 85.5 49.5 24.1 22.0 32.5 32.0 19.0
## 255 88.0 74.5 87.0 104.0 64.0 29.2 26.2 38.5 38.0 22.0
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## 257 98.8 90.5 103.5 108.1 61.1 33.6 26.6 37.2 35.8 22.6
## 258 79.0 66.5 74.0 90.3 52.0 24.8 21.0 32.2 32.5 19.9
## 259 77.6 61.0 71.8 91.6 53.0 25.4 22.6 34.0 34.5 20.5
## 260 85.0 69.5 81.5 94.4 55.8 25.9 22.9 36.1 35.3 20.9
## 261 76.7 62.0 74.1 80.9 48.8 24.0 20.5 30.8 30.4 17.9
## 262 76.7 63.4 69.0 87.7 54.0 25.6 21.6 34.4 32.8 19.1
## 263 82.0 71.0 69.0 88.5 54.5 26.0 21.8 33.5 35.0 21.0
## 264 79.0 59.0 79.0 88.5 51.2 23.5 21.0 32.5 29.6 18.5
## 265 89.5 74.0 92.0 101.0 61.5 31.0 26.5 38.6 38.6 23.8
## 266 73.5 60.5 68.3 88.5 54.0 24.6 20.6 29.0 33.0 19.0
## 267 90.0 75.0 80.5 99.0 59.0 28.7 24.9 37.0 36.5 20.6
## 268 80.0 65.0 78.3 91.0 53.5 27.0 23.0 32.0 31.5 22.0
## 269 94.5 82.2 94.2 110.2 69.0 32.5 26.8 40.5 38.6 23.0
## 270 83.8 70.2 82.5 94.2 54.1 25.9 23.2 34.3 30.9 19.4
## 271 95.2 85.0 91.2 111.0 68.0 34.4 28.3 40.1 40.0 24.2
## 272 91.0 75.0 85.0 103.0 62.0 31.5 26.5 38.2 40.0 23.0
## 273 80.0 63.0 76.5 90.0 51.1 24.6 22.3 35.2 33.7 20.9
## 274 81.7 70.0 81.5 96.0 57.9 26.3 22.6 37.2 36.0 21.0
## 275 89.6 72.6 86.3 93.7 56.4 27.4 23.4 37.0 38.4 21.3
## 276 90.9 78.7 83.5 101.5 59.9 29.5 25.3 35.4 36.7 21.3
## 277 78.5 61.0 73.0 82.5 49.5 24.5 22.5 32.5 33.0 20.5
## 278 78.0 64.2 69.8 89.0 53.0 23.2 21.2 33.0 31.5 20.5
## 279 80.2 63.1 78.9 93.9 57.1 26.4 23.1 35.0 35.0 20.1
## 280 99.0 85.0 92.5 106.4 65.6 31.6 26.6 39.6 38.9 23.3
## 281 80.0 66.0 78.0 92.0 52.5 25.0 22.2 33.0 34.8 20.5
## 282 75.5 65.0 76.7 86.0 51.0 24.5 20.5 31.5 31.0 20.0
## 283 82.6 63.2 70.5 89.1 55.8 27.0 23.6 33.6 33.4 20.0
## 284 94.5 83.6 94.2 104.1 61.5 31.0 25.4 37.2 35.2 22.3
## 285 83.9 68.0 76.1 88.0 52.2 26.2 23.2 33.2 35.3 21.2
## 286 92.8 75.7 88.6 102.6 64.3 31.0 24.9 35.5 38.0 21.5
## 287 85.3 65.5 74.4 95.9 53.7 27.8 23.9 35.6 33.4 21.9
## 288 84.4 66.9 79.7 98.8 59.2 28.5 23.7 35.9 34.6 20.0
## 289 87.6 67.9 82.7 93.5 57.0 29.6 24.5 37.1 36.3 20.6
## 290 81.0 67.8 73.0 92.9 55.8 26.3 23.0 32.3 30.0 18.8
## 291 84.1 63.2 75.0 86.9 52.1 26.5 23.5 32.3 31.6 18.9
## 292 88.2 73.1 80.0 94.9 57.2 27.0 24.3 34.9 35.7 22.0
## 293 88.1 65.3 75.8 95.3 55.7 27.7 24.5 33.1 35.4 21.2
## 294 84.9 65.6 75.5 93.3 57.4 27.7 24.2 35.1 35.6 21.0
## 295 81.2 62.7 75.4 90.0 54.0 25.3 22.7 32.6 32.9 19.8
## 296 91.6 81.8 96.5 99.3 60.7 28.3 25.2 35.0 36.3 21.4
## 297 88.4 72.1 87.3 95.6 59.2 29.0 24.8 34.3 37.1 21.9
## 298 87.6 68.6 80.7 95.8 56.6 28.5 24.1 35.6 34.0 20.7
## 299 91.3 80.0 97.9 104.6 62.7 30.0 23.1 36.3 36.0 21.0
## 300 87.2 64.9 75.9 92.2 53.1 28.0 24.2 34.0 35.4 20.7
## 301 82.2 61.7 66.0 80.7 50.3 23.7 21.6 30.5 33.8 20.7
## 302 82.1 64.0 73.6 98.1 56.8 25.8 23.9 33.7 34.0 20.3
## 303 82.6 58.6 73.0 89.6 54.9 27.1 23.1 33.2 35.8 20.7
## 304 86.5 68.3 89.2 94.0 57.0 26.0 22.6 35.9 32.5 19.7
## 305 85.3 69.5 74.9 94.7 55.7 24.9 22.8 34.9 37.1 20.1
## 306 84.7 70.5 86.1 94.1 54.6 28.0 23.0 33.5 33.5 20.0
## 307 86.8 69.8 76.5 93.7 55.7 25.4 23.4 35.9 34.2 21.5
## 308 85.2 69.0 76.3 86.7 51.5 26.6 22.7 31.6 33.3 19.6
## 309 93.9 81.0 93.2 105.9 65.5 31.3 26.8 38.1 37.7 21.7
## 310 84.5 71.0 80.5 97.5 59.5 28.0 23.0 34.0 34.2 20.6
## 311 90.7 73.6 79.8 105.3 65.8 29.5 26.6 38.8 39.7 24.3
## 312 97.4 96.3 104.2 102.6 57.5 34.3 26.5 38.3 41.1 22.9
## 313 83.3 65.0 68.2 91.6 55.3 27.7 23.2 33.0 31.5 18.9
## 314 82.1 62.8 70.6 83.9 51.5 24.6 21.4 34.0 34.8 20.9
## 315 92.2 83.0 101.4 108.1 65.0 29.8 24.6 38.1 37.8 20.7
## 316 83.6 67.6 86.2 99.1 57.5 26.4 23.3 38.5 35.0 22.1
## 317 82.6 65.4 74.8 89.3 53.7 26.4 23.8 34.0 33.7 19.1
## 318 77.0 61.6 74.0 89.9 53.6 26.3 23.1 33.3 32.4 19.0
## 319 79.3 63.2 75.5 85.8 51.6 24.2 22.4 32.5 31.3 18.4
## 320 84.3 59.4 74.0 85.8 51.0 26.9 23.0 32.8 32.1 19.7
## 321 85.6 62.7 74.0 96.0 61.5 28.0 23.6 35.4 38.5 21.3
## 322 85.0 66.6 82.0 98.7 60.3 30.4 25.2 35.7 38.2 21.8
## 323 79.1 64.6 82.5 94.9 54.0 24.7 21.7 37.4 35.5 21.0
## 324 85.0 65.7 77.7 90.5 56.1 29.4 23.1 33.2 34.4 20.3
## 325 79.0 63.0 79.2 86.6 48.5 26.4 22.5 32.2 30.3 20.2
## 326 96.2 78.5 93.4 103.0 59.6 30.2 25.9 37.0 35.2 23.2
## 327 86.0 68.2 76.2 97.9 59.8 29.8 25.0 36.8 38.1 21.8
## 328 89.8 68.5 80.2 98.1 62.0 30.1 26.2 40.9 40.4 23.3
## 329 84.5 68.8 70.6 94.8 56.5 27.0 23.4 34.4 35.2 21.6
## 330 88.2 69.0 77.6 96.5 57.6 28.6 25.2 37.0 35.0 22.2
## 331 99.5 78.0 87.5 103.0 64.8 31.1 26.6 35.2 36.7 21.8
## 332 76.1 62.0 70.8 86.8 52.6 24.3 22.3 34.2 34.8 20.2
## 333 85.8 65.8 70.5 89.3 57.4 27.0 23.2 36.8 36.2 22.1
## 334 82.0 65.0 77.2 89.5 53.4 26.0 21.4 34.0 33.9 21.4
## 335 77.5 64.9 74.5 88.4 53.4 25.6 22.4 30.9 32.6 19.1
## 336 85.7 71.5 82.0 100.5 60.4 28.0 24.2 36.6 36.4 21.8
## 337 78.8 61.4 78.8 94.0 56.4 27.1 22.8 35.4 34.8 21.0
## 338 88.0 75.4 90.0 101.3 63.1 28.4 22.6 37.6 34.3 21.7
## 339 80.2 67.2 78.3 95.5 54.7 27.0 22.2 36.0 35.4 22.2
## 340 85.6 69.5 75.5 95.3 57.6 28.5 24.4 35.2 35.4 21.8
## 341 81.3 57.9 66.4 84.1 49.0 26.6 22.2 33.6 33.0 19.6
## 342 84.6 64.4 77.9 91.0 53.4 26.6 23.3 32.7 33.0 19.4
## 343 82.2 62.3 68.7 83.9 52.8 24.4 22.1 32.4 32.5 21.0
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## 345 81.5 68.0 84.6 92.3 55.4 27.4 23.0 34.5 35.3 21.6
## 346 89.9 66.0 79.9 97.1 59.0 27.0 24.2 37.2 39.0 23.2
## 347 89.8 71.2 85.7 97.5 58.2 28.2 25.2 34.2 34.0 20.4
## 348 80.8 64.8 79.8 92.0 57.4 26.5 22.6 31.0 35.2 20.4
## 349 88.4 78.0 85.6 112.1 75.7 33.8 24.6 38.2 39.8 22.0
## 350 80.2 60.7 77.0 89.9 53.3 25.2 22.4 34.2 32.2 19.0
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## 352 83.7 68.6 83.0 94.2 58.0 26.2 22.1 32.2 35.9 19.9
## 353 82.5 67.1 75.0 90.4 53.2 27.3 23.6 35.9 36.6 23.8
## 354 85.5 68.0 84.5 99.5 60.8 26.5 24.2 40.5 39.4 23.2
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## 357 92.2 70.3 82.8 100.0 59.6 30.8 25.4 37.0 37.0 23.0
## 358 80.5 69.5 81.0 95.2 56.2 27.4 23.8 35.4 34.0 21.3
## 359 108.0 101.5 105.5 114.0 70.0 40.3 30.8 45.6 45.0 25.8
## 360 80.0 67.2 75.2 91.2 53.6 27.1 23.2 34.6 34.2 20.3
## 361 89.5 68.7 81.0 93.8 58.0 30.0 24.4 35.9 37.4 22.0
## 362 84.9 66.0 81.0 92.0 56.2 26.4 22.6 35.4 35.6 21.0
## 363 80.8 67.5 75.4 85.8 53.8 24.8 21.5 33.0 33.0 20.0
## 364 81.0 65.2 75.9 95.4 58.5 27.2 25.3 37.5 38.4 23.2
## 365 88.7 64.2 76.8 88.4 56.0 29.0 23.8 33.9 33.2 20.5
## 366 74.5 61.0 65.3 84.5 51.5 22.4 19.6 30.4 28.4 17.4
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## 368 75.8 62.6 72.9 87.4 54.0 26.4 21.8 34.5 33.0 20.7
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## 370 72.6 58.5 67.2 84.3 52.2 25.0 20.7 31.7 32.0 20.1
## 371 88.0 67.5 79.5 96.5 56.3 28.4 24.6 35.6 33.5 21.8
## 372 86.0 66.1 73.2 91.8 54.4 27.4 23.8 35.0 33.0 20.2
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## 375 98.1 90.1 90.1 95.2 53.8 32.1 25.8 34.7 37.2 21.5
## 376 82.0 64.1 74.1 96.0 53.7 26.6 23.2 35.2 35.0 21.0
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## 383 80.3 65.0 73.3 84.9 50.8 25.0 22.2 31.6 31.6 18.8
## 384 85.6 71.5 82.0 97.5 63.0 28.4 23.5 39.1 38.2 20.4
## 385 83.0 63.0 72.0 86.0 53.0 24.1 22.0 30.0 31.2 20.4
## 386 88.2 70.0 81.2 96.8 59.8 30.1 25.2 35.8 35.0 21.7
## 387 85.5 70.4 82.4 93.5 57.5 28.3 21.9 33.1 32.8 19.0
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## 394 78.1 64.0 75.9 95.0 55.9 25.2 23.0 32.3 34.8 20.3
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## 397 92.4 71.1 91.5 98.2 55.3 29.6 24.6 34.3 36.6 20.7
## 398 86.7 74.7 90.8 100.0 59.4 31.0 23.7 35.7 34.0 20.9
## 399 84.0 62.9 76.8 95.5 52.8 26.4 23.1 34.6 32.3 20.4
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## 401 84.5 61.6 81.0 87.6 51.6 27.4 23.7 32.4 32.9 20.3
## 402 91.2 74.2 98.2 102.6 61.5 31.4 25.0 37.6 41.9 24.3
## 403 81.1 65.5 82.3 90.4 53.3 26.8 22.3 34.2 33.4 21.5
## 404 87.6 67.7 84.2 92.0 54.3 28.2 23.8 33.0 32.7 18.8
## 405 88.2 72.9 97.4 102.2 60.1 29.9 24.1 33.8 33.5 21.0
## 406 88.2 72.1 96.3 106.7 62.0 27.3 23.1 34.6 35.6 23.5
## 407 104.2 93.4 111.1 109.8 67.7 35.7 28.6 41.5 38.6 22.3
## 408 84.8 60.7 78.0 89.1 52.6 26.6 22.8 32.5 34.6 21.2
## 409 84.3 69.2 85.3 93.0 53.9 27.9 23.7 35.5 33.1 21.9
## 410 91.0 75.5 90.7 98.6 62.7 31.5 24.8 37.2 35.4 20.3
## 411 90.5 85.6 111.7 112.1 64.3 31.3 25.0 35.5 38.0 21.0
## 412 89.6 71.6 92.7 95.0 56.8 27.2 24.3 36.2 35.8 20.1
## 413 89.3 75.5 98.9 100.2 57.8 30.3 23.8 37.3 37.0 22.5
## 414 90.0 73.7 92.7 101.2 57.4 28.1 23.3 33.9 33.3 21.2
## 415 88.2 67.9 86.9 100.8 60.2 31.0 26.6 36.6 38.5 22.2
## 416 83.5 71.2 83.0 96.5 52.4 25.2 21.7 33.7 32.1 21.0
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## 418 88.2 75.6 94.7 95.8 58.5 30.0 22.2 34.6 32.6 19.9
## 419 82.2 63.8 81.8 97.6 59.0 27.8 23.6 35.9 35.5 22.6
## 420 98.9 83.6 108.6 108.3 63.0 31.9 26.0 38.1 36.4 24.5
## 421 97.9 84.9 98.0 102.4 59.9 32.0 25.7 37.6 33.7 21.6
## 422 81.7 66.0 85.0 94.3 56.4 28.0 21.1 32.1 34.3 21.0
## 423 86.1 68.3 85.3 96.5 55.1 26.0 22.6 33.9 33.7 21.8
## 424 87.1 63.8 88.3 94.0 56.1 28.1 24.0 33.1 35.2 22.1
## 425 76.8 73.6 106.3 106.2 61.7 31.4 26.2 39.6 39.1 24.8
## 426 87.9 76.3 93.8 98.6 55.5 31.2 23.0 32.3 31.3 20.3
## 427 81.6 58.8 77.4 89.3 51.6 25.4 22.4 33.8 32.4 21.0
## 428 92.3 78.0 100.9 106.9 65.5 31.7 26.3 38.5 39.7 22.8
## 429 81.3 64.5 80.8 91.9 53.4 25.1 23.1 34.6 32.7 21.4
## 430 82.2 65.8 80.2 89.1 55.4 28.4 22.9 33.6 35.0 19.7
## 431 78.6 62.5 84.5 95.5 57.3 27.4 22.4 35.1 35.7 20.4
## 432 94.3 77.5 93.9 101.1 61.6 28.3 23.0 36.5 34.6 21.9
## 433 89.5 70.0 89.6 98.9 55.0 29.3 25.0 35.6 36.6 22.5
## 434 81.0 59.4 73.7 83.3 50.2 26.9 22.5 31.7 30.7 20.5
## 435 85.4 68.3 84.0 94.0 57.3 29.8 24.7 32.7 34.3 21.7
## 436 86.2 77.9 96.0 100.1 60.7 28.4 24.3 34.7 34.5 20.9
## 437 85.3 68.3 88.0 93.8 54.7 27.7 24.4 32.7 33.0 19.6
## 438 86.1 72.0 91.9 103.9 57.5 27.4 22.4 36.3 37.9 23.4
## 439 84.5 62.7 78.3 93.5 52.5 24.2 23.3 32.8 36.1 22.0
## 440 72.6 58.0 82.9 90.2 52.5 23.1 21.0 35.5 36.4 20.3
## 441 90.0 72.7 97.2 100.2 57.9 30.5 23.7 36.3 34.3 20.5
## 442 95.8 85.4 105.2 109.5 68.3 29.7 24.5 40.3 37.0 22.4
## 443 85.5 67.3 86.9 97.6 58.8 27.2 22.6 38.3 35.6 21.0
## 444 85.7 70.8 92.1 96.9 57.9 29.3 24.2 36.5 35.3 20.3
## 445 85.4 67.4 85.9 90.3 54.7 26.9 22.3 32.8 29.5 18.7
## 446 79.1 66.0 85.0 94.8 57.4 26.8 22.9 32.2 32.6 19.2
## 447 84.4 63.6 84.9 92.7 54.4 26.7 23.7 33.5 33.6 20.2
## 448 90.0 71.7 95.8 108.4 68.5 28.7 25.9 40.5 39.5 23.3
## 449 82.9 73.6 86.9 94.7 52.9 25.0 21.0 31.6 30.2 19.3
## 450 87.3 66.9 83.3 90.6 54.4 28.9 23.8 33.9 33.9 20.8
## 451 85.3 65.5 76.7 90.8 56.4 27.3 24.3 33.1 35.0 20.7
## 452 87.3 70.4 91.1 96.7 55.6 29.9 23.7 35.3 33.8 23.0
## 453 93.2 80.3 98.0 101.2 61.2 31.0 23.6 34.6 35.6 20.0
## 454 84.7 65.0 84.5 94.7 54.8 28.9 24.4 34.7 34.8 22.5
## 455 78.0 60.4 79.2 87.0 49.5 26.8 22.7 33.2 31.5 18.8
## 456 90.7 75.3 96.3 103.6 59.9 31.5 25.4 35.7 35.8 21.1
## 457 95.0 83.4 100.5 107.5 67.4 34.2 28.3 40.9 41.2 23.4
## 458 88.7 72.2 87.8 96.1 61.1 29.5 24.6 39.1 38.7 26.0
## 459 86.6 70.1 86.2 97.6 55.6 27.6 23.7 34.9 32.0 21.1
## 460 82.5 69.8 86.4 100.2 59.0 26.7 23.3 33.5 33.1 19.6
## 461 81.4 66.4 78.5 91.6 54.6 24.2 21.2 33.0 34.3 20.2
## 462 79.9 61.7 79.7 94.7 56.5 25.5 23.2 36.7 36.2 22.0
## 463 91.1 76.4 98.3 107.0 64.5 32.8 24.8 36.5 36.2 23.9
## 464 80.1 63.8 72.5 86.3 51.5 24.4 22.0 32.0 32.6 21.0
## 465 86.4 70.2 86.7 97.8 58.5 28.3 24.9 35.5 36.3 21.4
## 466 105.2 88.2 106.5 107.9 63.1 30.3 25.2 39.2 38.4 23.1
## 467 91.1 75.8 94.4 103.3 60.5 30.4 24.8 36.8 35.9 21.8
## 468 96.7 81.5 94.1 94.9 54.8 31.2 25.8 34.4 37.3 21.8
## 469 80.5 67.4 77.7 95.1 56.3 27.0 24.3 36.2 36.7 23.7
## 470 90.4 78.3 95.4 104.9 63.1 32.8 27.0 39.4 39.1 23.0
## 471 84.1 65.9 83.7 96.3 57.0 26.6 23.7 35.6 34.0 21.7
## 472 84.2 62.4 81.4 90.4 53.2 25.8 23.0 34.5 31.0 19.2
## 473 82.5 65.6 81.8 92.8 56.2 28.8 24.7 33.0 34.6 21.7
## 474 106.9 96.2 121.1 128.3 72.3 35.9 30.6 49.0 45.4 24.1
## 475 79.9 62.5 74.0 87.6 51.6 25.7 21.9 31.7 34.7 18.5
## 476 93.0 80.5 98.8 107.2 63.0 34.1 27.6 38.8 38.2 22.2
## 477 96.3 86.1 107.4 112.0 69.4 31.7 25.5 39.4 38.7 23.7
## 478 86.8 71.3 85.7 94.0 59.2 31.1 24.4 35.9 34.4 21.2
## 479 95.3 79.4 90.9 102.7 63.0 29.9 24.9 38.7 36.3 21.3
## 480 93.0 72.3 89.0 92.0 54.7 28.8 23.2 35.0 34.2 20.5
## 481 86.4 67.4 86.7 98.4 59.1 29.3 23.1 35.6 37.4 22.5
## 482 109.0 94.2 110.5 103.5 57.8 35.7 25.6 34.8 35.5 19.5
## 483 86.2 67.0 85.3 96.7 56.2 27.0 22.3 37.0 33.0 21.6
## 484 85.3 69.5 86.2 90.6 51.7 24.4 22.1 33.9 30.8 19.9
## 485 84.2 66.4 79.3 92.0 53.4 24.9 22.3 32.5 31.7 21.7
## 486 83.7 67.6 84.1 90.4 52.4 25.6 23.0 34.5 32.5 21.7
## 487 86.5 66.6 87.5 96.7 59.3 27.3 24.0 36.0 34.9 21.4
## 488 98.2 69.7 86.0 94.9 59.1 29.9 25.0 37.7 37.7 23.7
## 489 88.0 72.0 86.0 95.5 57.3 27.6 23.3 35.2 31.9 19.6
## 490 80.4 64.5 81.7 88.4 52.4 25.5 23.0 34.5 32.7 20.3
## 491 86.0 72.3 91.7 97.8 59.1 31.0 26.3 39.9 39.7 21.6
## 492 86.4 69.1 87.8 95.4 59.5 28.3 24.7 35.6 34.6 23.0
## 493 87.6 74.7 77.0 97.7 57.0 28.1 23.1 34.4 32.7 20.9
## 494 80.9 60.8 77.4 84.6 51.0 26.8 22.8 34.4 33.1 21.0
## 495 82.1 66.9 82.4 91.6 53.4 26.2 22.6 35.0 35.2 22.4
## 496 89.3 68.8 90.8 103.3 63.0 29.6 25.1 34.4 35.0 21.0
## 497 85.3 63.7 85.3 96.3 55.5 26.4 23.0 36.5 34.1 21.3
## 498 89.0 71.2 86.1 93.5 56.1 30.2 24.4 37.0 37.5 22.6
## 499 94.1 79.6 103.5 104.3 66.5 36.9 29.0 40.3 37.9 24.6
## 500 90.8 77.9 90.6 96.3 56.2 28.1 24.3 38.2 36.6 22.3
## 501 97.0 69.6 90.2 98.3 56.3 32.0 25.1 32.3 33.8 20.8
## 502 95.4 86.0 107.1 112.1 64.4 32.3 26.4 35.7 37.0 21.4
## 503 91.8 69.9 90.4 101.0 60.6 30.3 25.4 37.7 37.9 22.4
## 504 87.3 63.5 79.2 89.5 55.2 30.1 23.6 35.6 33.3 22.4
## 505 78.1 57.9 75.1 86.9 51.8 27.4 24.0 34.4 34.1 21.2
## 506 90.9 72.2 89.4 98.6 59.0 30.6 24.9 38.4 36.6 22.0
## 507 97.1 80.4 100.8 102.2 57.4 33.2 25.5 39.6 35.9 23.0
## wri.gi age wgt hgt sex slope intercept
## 1 16.5 21 65.6 174.0 1 1.017617 -105.0113
## 2 17.0 23 71.8 175.3 1 1.017617 -105.0113
## 3 16.9 28 80.7 193.5 1 1.017617 -105.0113
## 4 16.6 23 72.6 186.5 1 1.017617 -105.0113
## 5 18.0 22 78.8 187.2 1 1.017617 -105.0113
## 6 16.9 21 74.8 181.5 1 1.017617 -105.0113
## 7 18.8 26 86.4 184.0 1 1.017617 -105.0113
## 8 18.0 27 78.4 184.5 1 1.017617 -105.0113
## 9 16.5 23 62.0 175.0 1 1.017617 -105.0113
## 10 16.9 21 81.6 184.0 1 1.017617 -105.0113
## 11 16.2 23 76.6 180.0 1 1.017617 -105.0113
## 12 18.2 22 83.6 177.8 1 1.017617 -105.0113
## 13 18.0 20 90.0 192.0 1 1.017617 -105.0113
## 14 16.6 26 74.6 176.0 1 1.017617 -105.0113
## 15 16.5 23 71.0 174.0 1 1.017617 -105.0113
## 16 17.5 22 79.6 184.0 1 1.017617 -105.0113
## 17 17.8 30 93.8 192.7 1 1.017617 -105.0113
## 18 17.1 22 70.0 171.5 1 1.017617 -105.0113
## 19 18.5 29 72.4 173.0 1 1.017617 -105.0113
## 20 18.8 22 85.9 176.0 1 1.017617 -105.0113
## 21 17.3 22 78.8 176.0 1 1.017617 -105.0113
## 22 18.0 20 77.8 180.5 1 1.017617 -105.0113
## 23 16.0 22 66.2 172.7 1 1.017617 -105.0113
## 24 17.0 24 86.4 176.0 1 1.017617 -105.0113
## 25 18.2 26 81.8 173.5 1 1.017617 -105.0113
## 26 19.0 24 89.6 178.0 1 1.017617 -105.0113
## 27 16.7 21 82.8 180.3 1 1.017617 -105.0113
## 28 17.8 24 76.4 180.3 1 1.017617 -105.0113
## 29 16.5 23 63.2 164.5 1 1.017617 -105.0113
## 30 15.6 19 60.9 173.0 1 1.017617 -105.0113
## 31 16.6 23 74.8 183.5 1 1.017617 -105.0113
## 32 17.2 25 70.0 175.5 1 1.017617 -105.0113
## 33 17.0 23 72.4 188.0 1 1.017617 -105.0113
## 34 17.2 23 84.1 189.2 1 1.017617 -105.0113
## 35 16.9 23 69.1 172.8 1 1.017617 -105.0113
## 36 17.0 20 59.5 170.0 1 1.017617 -105.0113
## 37 17.0 22 67.2 182.0 1 1.017617 -105.0113
## 38 16.0 24 61.3 170.0 1 1.017617 -105.0113
## 39 16.8 22 68.6 177.8 1 1.017617 -105.0113
## 40 18.7 24 80.1 184.2 1 1.017617 -105.0113
## 41 17.6 21 87.8 186.7 1 1.017617 -105.0113
## 42 17.5 23 84.7 171.4 1 1.017617 -105.0113
## 43 16.7 24 73.4 172.7 1 1.017617 -105.0113
## 44 17.0 35 72.1 175.3 1 1.017617 -105.0113
## 45 17.9 29 82.6 180.3 1 1.017617 -105.0113
## 46 18.9 25 88.7 182.9 1 1.017617 -105.0113
## 47 16.8 23 84.1 188.0 1 1.017617 -105.0113
## 48 17.7 20 94.1 177.2 1 1.017617 -105.0113
## 49 16.3 25 74.9 172.1 1 1.017617 -105.0113
## 50 15.8 29 59.1 167.0 1 1.017617 -105.0113
## 51 16.9 23 75.6 169.5 1 1.017617 -105.0113
## 52 17.9 23 86.2 174.0 1 1.017617 -105.0113
## 53 17.3 36 75.3 172.7 1 1.017617 -105.0113
## 54 16.4 25 87.1 182.2 1 1.017617 -105.0113
## 55 15.0 24 55.2 164.1 1 1.017617 -105.0113
## 56 15.8 20 57.0 163.0 1 1.017617 -105.0113
## 57 15.3 52 61.4 171.5 1 1.017617 -105.0113
## 58 17.7 50 76.8 184.2 1 1.017617 -105.0113
## 59 17.5 46 86.8 174.0 1 1.017617 -105.0113
## 60 16.5 51 72.2 174.0 1 1.017617 -105.0113
## 61 16.1 28 71.6 177.0 1 1.017617 -105.0113
## 62 17.2 48 84.8 186.0 1 1.017617 -105.0113
## 63 17.5 35 68.2 167.0 1 1.017617 -105.0113
## 64 16.9 23 66.1 171.8 1 1.017617 -105.0113
## 65 17.5 23 72.0 182.0 1 1.017617 -105.0113
## 66 17.4 62 64.6 167.0 1 1.017617 -105.0113
## 67 16.7 21 74.8 177.8 1 1.017617 -105.0113
## 68 17.2 26 70.0 164.5 1 1.017617 -105.0113
## 69 17.7 33 101.6 192.0 1 1.017617 -105.0113
## 70 16.1 36 63.2 175.5 1 1.017617 -105.0113
## 71 17.9 41 79.1 171.2 1 1.017617 -105.0113
## 72 16.6 40 78.9 181.6 1 1.017617 -105.0113
## 73 15.6 27 67.7 167.4 1 1.017617 -105.0113
## 74 17.5 27 66.0 181.1 1 1.017617 -105.0113
## 75 17.0 23 68.2 177.0 1 1.017617 -105.0113
## 76 15.8 31 63.9 174.5 1 1.017617 -105.0113
## 77 15.8 26 72.0 177.5 1 1.017617 -105.0113
## 78 15.4 23 56.8 170.5 1 1.017617 -105.0113
## 79 16.3 24 74.5 182.4 1 1.017617 -105.0113
## 80 18.0 24 90.9 197.1 1 1.017617 -105.0113
## 81 16.0 34 93.0 180.1 1 1.017617 -105.0113
## 82 18.0 21 80.9 175.5 1 1.017617 -105.0113
## 83 16.5 25 72.7 180.6 1 1.017617 -105.0113
## 84 16.7 34 68.0 184.4 1 1.017617 -105.0113
## 85 16.2 31 70.9 175.5 1 1.017617 -105.0113
## 86 16.6 40 72.5 180.6 1 1.017617 -105.0113
## 87 17.3 21 72.5 177.0 1 1.017617 -105.0113
## 88 17.3 33 83.4 177.1 1 1.017617 -105.0113
## 89 16.4 25 75.5 181.6 1 1.017617 -105.0113
## 90 16.6 29 73.0 176.5 1 1.017617 -105.0113
## 91 17.0 27 70.2 175.0 1 1.017617 -105.0113
## 92 16.5 44 73.4 174.0 1 1.017617 -105.0113
## 93 16.3 26 70.5 165.1 1 1.017617 -105.0113
## 94 16.0 22 68.9 177.0 1 1.017617 -105.0113
## 95 18.6 37 102.3 192.0 1 1.017617 -105.0113
## 96 16.2 38 68.4 176.5 1 1.017617 -105.0113
## 97 16.2 20 65.9 169.4 1 1.017617 -105.0113
## 98 16.5 21 75.7 182.1 1 1.017617 -105.0113
## 99 17.0 24 84.5 179.8 1 1.017617 -105.0113
## 100 17.7 45 87.7 175.3 1 1.017617 -105.0113
## 101 17.8 25 86.4 184.9 1 1.017617 -105.0113
## 102 17.5 22 73.2 177.3 1 1.017617 -105.0113
## 103 14.6 29 53.9 167.4 1 1.017617 -105.0113
## 104 16.4 37 72.0 178.1 1 1.017617 -105.0113
## 105 16.0 20 55.5 168.9 1 1.017617 -105.0113
## 106 16.4 20 58.4 157.2 1 1.017617 -105.0113
## 107 18.1 32 83.2 180.3 1 1.017617 -105.0113
## 108 16.7 23 72.7 170.2 1 1.017617 -105.0113
## 109 15.6 25 64.1 177.8 1 1.017617 -105.0113
## 110 18.4 27 72.3 172.7 1 1.017617 -105.0113
## 111 16.3 21 65.0 165.1 1 1.017617 -105.0113
## 112 16.1 27 86.4 186.7 1 1.017617 -105.0113
## 113 17.1 25 65.0 165.1 1 1.017617 -105.0113
## 114 18.3 38 88.6 174.0 1 1.017617 -105.0113
## 115 18.0 44 84.1 175.3 1 1.017617 -105.0113
## 116 16.1 27 66.8 185.4 1 1.017617 -105.0113
## 117 17.3 37 75.5 177.8 1 1.017617 -105.0113
## 118 17.7 28 93.2 180.3 1 1.017617 -105.0113
## 119 18.2 33 82.7 180.3 1 1.017617 -105.0113
## 120 15.3 25 58.0 177.8 1 1.017617 -105.0113
## 121 18.1 21 79.5 177.8 1 1.017617 -105.0113
## 122 17.6 30 78.6 177.8 1 1.017617 -105.0113
## 123 16.0 26 71.8 177.8 1 1.017617 -105.0113
## 124 19.2 27 116.4 177.8 1 1.017617 -105.0113
## 125 16.3 33 72.2 163.8 1 1.017617 -105.0113
## 126 17.4 29 83.6 188.0 1 1.017617 -105.0113
## 127 17.9 27 85.5 198.1 1 1.017617 -105.0113
## 128 17.6 34 90.9 175.3 1 1.017617 -105.0113
## 129 18.3 42 85.9 166.4 1 1.017617 -105.0113
## 130 19.5 29 89.1 190.5 1 1.017617 -105.0113
## 131 16.3 41 75.0 166.4 1 1.017617 -105.0113
## 132 17.0 43 77.7 177.8 1 1.017617 -105.0113
## 133 17.1 43 86.4 179.7 1 1.017617 -105.0113
## 134 17.5 29 90.9 172.7 1 1.017617 -105.0113
## 135 17.0 27 73.6 190.5 1 1.017617 -105.0113
## 136 18.4 62 76.4 185.4 1 1.017617 -105.0113
## 137 16.8 33 69.1 168.9 1 1.017617 -105.0113
## 138 18.1 45 84.5 167.6 1 1.017617 -105.0113
## 139 16.2 30 64.5 175.3 1 1.017617 -105.0113
## 140 18.5 20 69.1 170.2 1 1.017617 -105.0113
## 141 18.2 22 108.6 190.5 1 1.017617 -105.0113
## 142 19.6 51 86.4 177.8 1 1.017617 -105.0113
## 143 17.7 34 80.9 190.5 1 1.017617 -105.0113
## 144 17.6 44 87.7 177.8 1 1.017617 -105.0113
## 145 18.1 46 94.5 184.2 1 1.017617 -105.0113
## 146 17.1 34 80.2 176.5 1 1.017617 -105.0113
## 147 17.0 32 72.0 177.8 1 1.017617 -105.0113
## 148 15.5 28 71.4 180.3 1 1.017617 -105.0113
## 149 16.4 31 72.7 171.4 1 1.017617 -105.0113
## 150 17.1 29 84.1 172.7 1 1.017617 -105.0113
## 151 18.0 42 76.8 172.7 1 1.017617 -105.0113
## 152 16.3 29 63.6 177.8 1 1.017617 -105.0113
## 153 16.8 31 80.9 177.8 1 1.017617 -105.0113
## 154 17.4 30 80.9 182.9 1 1.017617 -105.0113
## 155 17.1 27 85.5 170.2 1 1.017617 -105.0113
## 156 16.5 25 68.6 167.6 1 1.017617 -105.0113
## 157 17.0 24 67.7 175.3 1 1.017617 -105.0113
## 158 16.4 33 66.4 165.1 1 1.017617 -105.0113
## 159 18.4 45 102.3 185.4 1 1.017617 -105.0113
## 160 16.3 37 70.5 181.6 1 1.017617 -105.0113
## 161 17.1 44 95.9 172.7 1 1.017617 -105.0113
## 162 17.9 34 84.1 190.5 1 1.017617 -105.0113
## 163 19.5 55 87.3 179.1 1 1.017617 -105.0113
## 164 17.3 43 71.8 175.3 1 1.017617 -105.0113
## 165 16.6 24 65.9 170.2 1 1.017617 -105.0113
## 166 18.1 22 95.9 193.0 1 1.017617 -105.0113
## 167 16.8 38 91.4 171.4 1 1.017617 -105.0113
## 168 17.3 24 81.8 177.8 1 1.017617 -105.0113
## 169 17.9 29 96.8 177.8 1 1.017617 -105.0113
## 170 17.3 25 69.1 167.6 1 1.017617 -105.0113
## 171 16.5 37 82.7 167.6 1 1.017617 -105.0113
## 172 18.2 30 75.5 180.3 1 1.017617 -105.0113
## 173 18.3 26 79.5 182.9 1 1.017617 -105.0113
## 174 15.9 35 73.6 176.5 1 1.017617 -105.0113
## 175 18.8 29 91.8 186.7 1 1.017617 -105.0113
## 176 18.0 30 84.1 188.0 1 1.017617 -105.0113
## 177 17.1 37 85.9 188.0 1 1.017617 -105.0113
## 178 17.1 34 81.8 177.8 1 1.017617 -105.0113
## 179 18.1 28 82.5 174.0 1 1.017617 -105.0113
## 180 18.7 27 80.5 177.8 1 1.017617 -105.0113
## 181 16.3 32 70.0 171.4 1 1.017617 -105.0113
## 182 17.3 28 81.8 185.4 1 1.017617 -105.0113
## 183 17.5 22 84.1 185.4 1 1.017617 -105.0113
## 184 17.9 44 90.5 188.0 1 1.017617 -105.0113
## 185 16.8 25 91.4 188.0 1 1.017617 -105.0113
## 186 17.1 49 89.1 182.9 1 1.017617 -105.0113
## 187 17.7 54 85.0 176.5 1 1.017617 -105.0113
## 188 16.7 49 69.1 175.3 1 1.017617 -105.0113
## 189 17.5 60 73.6 175.3 1 1.017617 -105.0113
## 190 17.7 42 80.5 188.0 1 1.017617 -105.0113
## 191 17.5 52 82.7 188.0 1 1.017617 -105.0113
## 192 18.1 23 86.4 175.3 1 1.017617 -105.0113
## 193 16.4 33 67.7 170.5 1 1.017617 -105.0113
## 194 16.4 46 92.7 179.1 1 1.017617 -105.0113
## 195 18.8 43 93.6 177.8 1 1.017617 -105.0113
## 196 17.1 56 70.9 175.3 1 1.017617 -105.0113
## 197 17.9 21 75.0 182.9 1 1.017617 -105.0113
## 198 15.9 18 93.2 170.8 1 1.017617 -105.0113
## 199 17.2 21 93.2 188.0 1 1.017617 -105.0113
## 200 17.1 45 77.7 180.3 1 1.017617 -105.0113
## 201 16.2 22 61.4 177.8 1 1.017617 -105.0113
## 202 17.4 55 94.1 185.4 1 1.017617 -105.0113
## 203 17.0 42 75.0 168.9 1 1.017617 -105.0113
## 204 16.6 29 83.6 185.4 1 1.017617 -105.0113
## 205 17.5 40 85.5 180.3 1 1.017617 -105.0113
## 206 17.2 24 73.9 174.0 1 1.017617 -105.0113
## 207 16.7 62 66.8 167.6 1 1.017617 -105.0113
## 208 17.9 26 87.3 182.9 1 1.017617 -105.0113
## 209 17.5 35 72.3 160.0 1 1.017617 -105.0113
## 210 17.0 37 88.6 180.3 1 1.017617 -105.0113
## 211 17.5 34 75.5 167.6 1 1.017617 -105.0113
## 212 17.1 25 101.4 186.7 1 1.017617 -105.0113
## 213 17.0 30 91.1 175.3 1 1.017617 -105.0113
## 214 17.9 32 67.3 175.3 1 1.017617 -105.0113
## 215 17.8 27 77.7 175.9 1 1.017617 -105.0113
## 216 16.7 42 81.8 175.3 1 1.017617 -105.0113
## 217 16.6 44 75.5 179.1 1 1.017617 -105.0113
## 218 18.1 46 84.5 181.6 1 1.017617 -105.0113
## 219 17.7 19 76.6 177.8 1 1.017617 -105.0113
## 220 17.4 43 85.0 182.9 1 1.017617 -105.0113
## 221 17.1 28 102.5 177.8 1 1.017617 -105.0113
## 222 15.8 39 77.3 184.2 1 1.017617 -105.0113
## 223 17.1 30 71.8 179.1 1 1.017617 -105.0113
## 224 17.5 36 87.9 176.5 1 1.017617 -105.0113
## 225 18.9 48 94.3 188.0 1 1.017617 -105.0113
## 226 16.4 48 70.9 174.0 1 1.017617 -105.0113
## 227 15.1 53 64.5 167.6 1 1.017617 -105.0113
## 228 16.7 45 77.3 170.2 1 1.017617 -105.0113
## 229 16.6 39 72.3 167.6 1 1.017617 -105.0113
## 230 17.0 43 87.3 188.0 1 1.017617 -105.0113
## 231 18.1 65 80.0 174.0 1 1.017617 -105.0113
## 232 17.1 45 82.3 176.5 1 1.017617 -105.0113
## 233 16.3 37 73.6 180.3 1 1.017617 -105.0113
## 234 17.8 55 74.1 167.6 1 1.017617 -105.0113
## 235 19.6 33 85.9 188.0 1 1.017617 -105.0113
## 236 17.3 25 73.2 180.3 1 1.017617 -105.0113
## 237 17.3 35 76.3 167.6 1 1.017617 -105.0113
## 238 18.4 28 65.9 183.0 1 1.017617 -105.0113
## 239 19.4 26 90.9 183.0 1 1.017617 -105.0113
## 240 18.9 43 89.1 179.1 1 1.017617 -105.0113
## 241 18.3 30 62.3 170.2 1 1.017617 -105.0113
## 242 17.3 26 82.7 177.8 1 1.017617 -105.0113
## 243 16.3 51 79.1 179.1 1 1.017617 -105.0113
## 244 16.7 30 98.2 190.5 1 1.017617 -105.0113
## 245 18.1 24 84.1 177.8 1 1.017617 -105.0113
## 246 18.4 35 83.2 180.3 1 1.017617 -105.0113
## 247 17.0 37 83.2 180.3 1 1.017617 -105.0113
## 248 13.5 22 51.6 161.2 0 1.017617 -105.0113
## 249 15.0 20 59.0 167.5 0 1.017617 -105.0113
## 250 14.0 19 49.2 159.5 0 1.017617 -105.0113
## 251 15.0 25 63.0 157.0 0 1.017617 -105.0113
## 252 14.5 21 53.6 155.8 0 1.017617 -105.0113
## 253 14.5 23 59.0 170.0 0 1.017617 -105.0113
## 254 13.9 26 47.6 159.1 0 1.017617 -105.0113
## 255 16.8 22 69.8 166.0 0 1.017617 -105.0113
## 256 15.2 28 66.8 176.2 0 1.017617 -105.0113
## 257 16.3 40 75.2 160.2 0 1.017617 -105.0113
## 258 13.8 32 55.2 172.5 0 1.017617 -105.0113
## 259 15.3 25 54.2 170.9 0 1.017617 -105.0113
## 260 14.4 25 62.5 172.9 0 1.017617 -105.0113
## 261 13.2 29 42.0 153.4 0 1.017617 -105.0113
## 262 13.8 22 50.0 160.0 0 1.017617 -105.0113
## 263 14.0 25 49.8 147.2 0 1.017617 -105.0113
## 264 14.5 23 49.2 168.2 0 1.017617 -105.0113
## 265 17.0 37 73.2 175.0 0 1.017617 -105.0113
## 266 13.2 19 47.8 157.0 0 1.017617 -105.0113
## 267 15.9 23 68.8 167.6 0 1.017617 -105.0113
## 268 13.8 25 50.6 159.5 0 1.017617 -105.0113
## 269 16.6 26 82.5 175.0 0 1.017617 -105.0113
## 270 14.5 24 57.2 166.8 0 1.017617 -105.0113
## 271 17.6 29 87.8 176.5 0 1.017617 -105.0113
## 272 16.0 22 72.8 170.2 0 1.017617 -105.0113
## 273 15.2 30 54.5 174.0 0 1.017617 -105.0113
## 274 15.0 23 59.8 173.0 0 1.017617 -105.0113
## 275 15.6 38 67.3 179.9 0 1.017617 -105.0113
## 276 15.6 23 67.8 170.5 0 1.017617 -105.0113
## 277 14.5 19 47.0 160.0 0 1.017617 -105.0113
## 278 14.0 46 46.2 154.4 0 1.017617 -105.0113
## 279 14.9 20 55.0 162.0 0 1.017617 -105.0113
## 280 15.9 22 83.0 176.5 0 1.017617 -105.0113
## 281 14.4 25 54.4 160.0 0 1.017617 -105.0113
## 282 14.0 21 45.8 152.0 0 1.017617 -105.0113
## 283 15.6 23 53.6 162.1 0 1.017617 -105.0113
## 284 16.1 31 73.2 170.0 0 1.017617 -105.0113
## 285 14.6 29 52.1 160.2 0 1.017617 -105.0113
## 286 15.3 19 67.9 161.3 0 1.017617 -105.0113
## 287 15.2 21 56.6 166.4 0 1.017617 -105.0113
## 288 15.1 23 62.3 168.9 0 1.017617 -105.0113
## 289 15.2 24 58.5 163.8 0 1.017617 -105.0113
## 290 14.3 20 54.5 167.6 0 1.017617 -105.0113
## 291 15.0 19 50.2 160.0 0 1.017617 -105.0113
## 292 15.1 20 60.3 161.3 0 1.017617 -105.0113
## 293 15.7 19 58.3 167.6 0 1.017617 -105.0113
## 294 14.7 20 56.2 165.1 0 1.017617 -105.0113
## 295 13.8 19 50.2 160.0 0 1.017617 -105.0113
## 296 15.3 22 72.9 170.0 0 1.017617 -105.0113
## 297 14.3 39 59.8 157.5 0 1.017617 -105.0113
## 298 15.5 18 61.0 167.6 0 1.017617 -105.0113
## 299 14.6 19 69.1 160.7 0 1.017617 -105.0113
## 300 15.5 26 55.9 163.2 0 1.017617 -105.0113
## 301 14.6 20 46.5 152.4 0 1.017617 -105.0113
## 302 14.6 20 54.3 157.5 0 1.017617 -105.0113
## 303 14.4 26 54.8 168.3 0 1.017617 -105.0113
## 304 14.6 21 60.7 180.3 0 1.017617 -105.0113
## 305 14.9 21 60.0 165.5 0 1.017617 -105.0113
## 306 14.6 38 62.0 165.0 0 1.017617 -105.0113
## 307 15.1 23 60.3 164.5 0 1.017617 -105.0113
## 308 15.3 37 52.7 156.0 0 1.017617 -105.0113
## 309 15.9 19 74.3 160.0 0 1.017617 -105.0113
## 310 15.0 25 62.0 163.0 0 1.017617 -105.0113
## 311 15.9 20 73.1 165.7 0 1.017617 -105.0113
## 312 16.4 41 80.0 161.0 0 1.017617 -105.0113
## 313 14.1 26 54.7 162.0 0 1.017617 -105.0113
## 314 14.3 21 53.2 166.0 0 1.017617 -105.0113
## 315 16.6 47 75.7 174.0 0 1.017617 -105.0113
## 316 15.6 19 61.1 172.7 0 1.017617 -105.0113
## 317 14.7 44 55.7 167.6 0 1.017617 -105.0113
## 318 14.2 35 48.7 151.1 0 1.017617 -105.0113
## 319 14.7 32 52.3 164.5 0 1.017617 -105.0113
## 320 15.0 46 50.0 163.5 0 1.017617 -105.0113
## 321 15.4 22 59.3 152.0 0 1.017617 -105.0113
## 322 15.9 49 62.5 169.0 0 1.017617 -105.0113
## 323 15.0 52 55.7 164.0 0 1.017617 -105.0113
## 324 14.4 25 54.8 161.2 0 1.017617 -105.0113
## 325 15.0 48 45.9 155.0 0 1.017617 -105.0113
## 326 16.2 41 70.6 170.0 0 1.017617 -105.0113
## 327 16.2 18 67.2 176.2 0 1.017617 -105.0113
## 328 16.4 30 69.4 170.0 0 1.017617 -105.0113
## 329 14.6 20 58.2 162.5 0 1.017617 -105.0113
## 330 16.2 24 64.8 170.3 0 1.017617 -105.0113
## 331 16.2 23 71.6 164.1 0 1.017617 -105.0113
## 332 14.4 30 52.8 169.5 0 1.017617 -105.0113
## 333 15.8 23 59.8 163.2 0 1.017617 -105.0113
## 334 14.4 45 49.0 154.5 0 1.017617 -105.0113
## 335 14.0 20 50.0 159.8 0 1.017617 -105.0113
## 336 15.6 20 69.2 173.2 0 1.017617 -105.0113
## 337 15.0 23 55.9 170.0 0 1.017617 -105.0113
## 338 14.8 21 63.4 161.4 0 1.017617 -105.0113
## 339 15.6 28 58.2 169.0 0 1.017617 -105.0113
## 340 15.4 45 58.6 166.2 0 1.017617 -105.0113
## 341 14.0 24 45.7 159.4 0 1.017617 -105.0113
## 342 14.2 25 52.2 162.5 0 1.017617 -105.0113
## 343 14.8 19 48.6 159.0 0 1.017617 -105.0113
## 344 15.0 20 57.8 162.8 0 1.017617 -105.0113
## 345 15.6 29 55.6 159.0 0 1.017617 -105.0113
## 346 16.2 24 66.8 179.8 0 1.017617 -105.0113
## 347 15.4 24 59.4 162.9 0 1.017617 -105.0113
## 348 14.4 25 53.6 161.0 0 1.017617 -105.0113
## 349 14.4 31 73.2 151.1 0 1.017617 -105.0113
## 350 14.6 22 53.4 168.2 0 1.017617 -105.0113
## 351 15.1 20 69.0 168.9 0 1.017617 -105.0113
## 352 14.6 32 58.4 173.2 0 1.017617 -105.0113
## 353 15.3 25 56.2 171.8 0 1.017617 -105.0113
## 354 15.8 19 70.6 178.0 0 1.017617 -105.0113
## 355 15.5 23 59.8 164.3 0 1.017617 -105.0113
## 356 15.8 22 72.0 163.0 0 1.017617 -105.0113
## 357 16.0 20 65.2 168.5 0 1.017617 -105.0113
## 358 15.2 27 56.6 166.8 0 1.017617 -105.0113
## 359 18.2 34 105.2 172.7 0 1.017617 -105.0113
## 360 14.2 25 51.8 163.5 0 1.017617 -105.0113
## 361 16.0 26 63.4 169.4 0 1.017617 -105.0113
## 362 14.5 19 59.0 167.8 0 1.017617 -105.0113
## 363 14.0 26 47.6 159.5 0 1.017617 -105.0113
## 364 15.8 25 63.0 167.6 0 1.017617 -105.0113
## 365 15.0 20 55.2 161.2 0 1.017617 -105.0113
## 366 13.2 21 45.0 160.0 0 1.017617 -105.0113
## 367 15.2 18 54.0 163.2 0 1.017617 -105.0113
## 368 14.4 19 50.2 162.2 0 1.017617 -105.0113
## 369 16.6 27 60.2 161.3 0 1.017617 -105.0113
## 370 13.1 26 44.8 149.5 0 1.017617 -105.0113
## 371 16.0 36 58.8 157.5 0 1.017617 -105.0113
## 372 14.9 20 56.4 163.2 0 1.017617 -105.0113
## 373 15.6 28 62.0 172.7 0 1.017617 -105.0113
## 374 13.4 32 49.2 155.0 0 1.017617 -105.0113
## 375 15.8 32 67.2 156.5 0 1.017617 -105.0113
## 376 14.8 23 53.8 164.0 0 1.017617 -105.0113
## 377 14.6 20 54.4 160.9 0 1.017617 -105.0113
## 378 13.8 20 58.0 162.8 0 1.017617 -105.0113
## 379 15.0 20 59.8 167.0 0 1.017617 -105.0113
## 380 14.6 23 54.8 160.0 0 1.017617 -105.0113
## 381 13.8 20 43.2 160.0 0 1.017617 -105.0113
## 382 15.3 28 60.5 168.9 0 1.017617 -105.0113
## 383 14.2 23 46.4 158.2 0 1.017617 -105.0113
## 384 14.4 19 64.4 156.0 0 1.017617 -105.0113
## 385 14.4 28 48.8 160.0 0 1.017617 -105.0113
## 386 16.0 19 62.2 167.1 0 1.017617 -105.0113
## 387 13.9 29 55.5 158.0 0 1.017617 -105.0113
## 388 15.5 32 57.8 167.6 0 1.017617 -105.0113
## 389 15.2 20 54.6 156.0 0 1.017617 -105.0113
## 390 15.7 28 59.2 162.1 0 1.017617 -105.0113
## 391 14.5 36 52.7 173.4 0 1.017617 -105.0113
## 392 14.8 22 53.2 159.8 0 1.017617 -105.0113
## 393 14.8 20 64.5 170.5 0 1.017617 -105.0113
## 394 13.4 22 51.8 159.2 0 1.017617 -105.0113
## 395 14.6 32 56.0 157.5 0 1.017617 -105.0113
## 396 14.1 40 63.6 161.3 0 1.017617 -105.0113
## 397 15.6 40 63.2 162.6 0 1.017617 -105.0113
## 398 14.2 42 59.5 160.0 0 1.017617 -105.0113
## 399 14.7 40 56.8 168.9 0 1.017617 -105.0113
## 400 15.0 44 64.1 165.1 0 1.017617 -105.0113
## 401 14.6 30 50.0 162.6 0 1.017617 -105.0113
## 402 15.4 28 72.3 165.1 0 1.017617 -105.0113
## 403 14.5 37 55.0 166.4 0 1.017617 -105.0113
## 404 14.7 40 55.9 160.0 0 1.017617 -105.0113
## 405 15.1 45 60.4 152.4 0 1.017617 -105.0113
## 406 14.7 35 69.1 170.2 0 1.017617 -105.0113
## 407 17.3 41 84.5 162.6 0 1.017617 -105.0113
## 408 14.1 27 55.9 170.2 0 1.017617 -105.0113
## 409 15.1 20 55.5 158.8 0 1.017617 -105.0113
## 410 14.6 24 69.5 172.7 0 1.017617 -105.0113
## 411 14.6 36 76.4 167.6 0 1.017617 -105.0113
## 412 14.6 27 61.4 162.6 0 1.017617 -105.0113
## 413 15.1 32 65.9 167.6 0 1.017617 -105.0113
## 414 14.9 64 58.6 156.2 0 1.017617 -105.0113
## 415 16.0 21 66.8 175.2 0 1.017617 -105.0113
## 416 15.0 32 56.6 172.1 0 1.017617 -105.0113
## 417 14.6 35 58.6 162.6 0 1.017617 -105.0113
## 418 13.7 41 55.9 160.0 0 1.017617 -105.0113
## 419 15.4 40 59.1 165.1 0 1.017617 -105.0113
## 420 16.0 29 81.8 182.9 0 1.017617 -105.0113
## 421 15.5 40 70.7 166.4 0 1.017617 -105.0113
## 422 14.4 24 56.8 165.1 0 1.017617 -105.0113
## 423 15.0 23 60.0 177.8 0 1.017617 -105.0113
## 424 16.0 41 58.2 165.1 0 1.017617 -105.0113
## 425 16.2 44 72.7 175.3 0 1.017617 -105.0113
## 426 15.1 53 54.1 154.9 0 1.017617 -105.0113
## 427 14.8 19 49.1 158.8 0 1.017617 -105.0113
## 428 15.6 24 75.9 172.7 0 1.017617 -105.0113
## 429 14.6 25 55.0 168.9 0 1.017617 -105.0113
## 430 14.8 20 57.3 161.3 0 1.017617 -105.0113
## 431 14.8 34 55.0 167.6 0 1.017617 -105.0113
## 432 14.7 32 65.5 165.1 0 1.017617 -105.0113
## 433 16.4 24 65.5 175.3 0 1.017617 -105.0113
## 434 15.2 29 48.6 157.5 0 1.017617 -105.0113
## 435 15.4 31 58.6 163.8 0 1.017617 -105.0113
## 436 14.9 34 63.6 167.6 0 1.017617 -105.0113
## 437 14.9 36 55.2 165.1 0 1.017617 -105.0113
## 438 14.5 32 62.7 165.1 0 1.017617 -105.0113
## 439 14.8 39 56.6 168.9 0 1.017617 -105.0113
## 440 13.0 37 53.9 162.6 0 1.017617 -105.0113
## 441 14.8 52 63.2 164.5 0 1.017617 -105.0113
## 442 15.3 24 73.6 176.5 0 1.017617 -105.0113
## 443 14.6 33 62.0 168.9 0 1.017617 -105.0113
## 444 15.4 42 63.6 175.3 0 1.017617 -105.0113
## 445 13.7 34 53.2 159.4 0 1.017617 -105.0113
## 446 14.6 37 53.4 160.0 0 1.017617 -105.0113
## 447 14.6 39 55.0 170.2 0 1.017617 -105.0113
## 448 15.6 41 70.5 162.6 0 1.017617 -105.0113
## 449 13.6 36 54.5 167.6 0 1.017617 -105.0113
## 450 15.5 19 54.5 162.6 0 1.017617 -105.0113
## 451 14.2 22 55.9 160.7 0 1.017617 -105.0113
## 452 16.3 23 59.0 160.0 0 1.017617 -105.0113
## 453 14.3 36 63.6 157.5 0 1.017617 -105.0113
## 454 15.3 45 54.5 162.6 0 1.017617 -105.0113
## 455 14.7 25 47.3 152.4 0 1.017617 -105.0113
## 456 16.3 67 67.7 170.2 0 1.017617 -105.0113
## 457 15.6 26 80.9 165.1 0 1.017617 -105.0113
## 458 15.5 21 70.5 172.7 0 1.017617 -105.0113
## 459 15.3 33 60.9 165.1 0 1.017617 -105.0113
## 460 14.2 25 63.6 170.2 0 1.017617 -105.0113
## 461 15.0 24 54.5 170.2 0 1.017617 -105.0113
## 462 14.8 21 59.1 170.2 0 1.017617 -105.0113
## 463 15.5 35 70.5 161.3 0 1.017617 -105.0113
## 464 14.0 27 52.7 167.6 0 1.017617 -105.0113
## 465 15.3 27 62.7 167.6 0 1.017617 -105.0113
## 466 16.4 26 86.3 165.1 0 1.017617 -105.0113
## 467 15.5 25 66.4 162.6 0 1.017617 -105.0113
## 468 14.6 44 67.3 152.4 0 1.017617 -105.0113
## 469 15.9 29 63.0 168.9 0 1.017617 -105.0113
## 470 16.5 26 73.6 170.2 0 1.017617 -105.0113
## 471 15.1 23 62.3 175.2 0 1.017617 -105.0113
## 472 14.7 32 57.7 175.2 0 1.017617 -105.0113
## 473 15.8 32 55.4 160.0 0 1.017617 -105.0113
## 474 17.8 43 104.1 165.1 0 1.017617 -105.0113
## 475 13.9 32 55.5 174.0 0 1.017617 -105.0113
## 476 16.3 41 77.3 170.2 0 1.017617 -105.0113
## 477 16.3 33 80.5 160.0 0 1.017617 -105.0113
## 478 14.2 28 64.5 167.6 0 1.017617 -105.0113
## 479 15.5 28 72.3 167.6 0 1.017617 -105.0113
## 480 14.8 25 61.4 167.6 0 1.017617 -105.0113
## 481 14.4 38 58.2 154.9 0 1.017617 -105.0113
## 482 15.0 37 81.8 162.6 0 1.017617 -105.0113
## 483 15.0 25 63.6 175.3 0 1.017617 -105.0113
## 484 14.1 37 53.4 171.4 0 1.017617 -105.0113
## 485 13.4 27 54.5 157.5 0 1.017617 -105.0113
## 486 14.6 27 53.6 165.1 0 1.017617 -105.0113
## 487 14.6 20 60.0 160.0 0 1.017617 -105.0113
## 488 16.2 19 73.6 174.0 0 1.017617 -105.0113
## 489 15.4 32 61.4 162.6 0 1.017617 -105.0113
## 490 14.9 26 55.5 174.0 0 1.017617 -105.0113
## 491 15.4 56 63.6 162.6 0 1.017617 -105.0113
## 492 16.6 23 60.9 161.3 0 1.017617 -105.0113
## 493 15.2 19 60.0 156.2 0 1.017617 -105.0113
## 494 15.4 31 46.8 149.9 0 1.017617 -105.0113
## 495 15.2 34 57.3 169.5 0 1.017617 -105.0113
## 496 15.9 34 64.1 160.0 0 1.017617 -105.0113
## 497 15.0 24 63.6 175.3 0 1.017617 -105.0113
## 498 15.7 22 67.3 169.5 0 1.017617 -105.0113
## 499 16.4 34 75.5 160.0 0 1.017617 -105.0113
## 500 15.4 30 68.2 172.7 0 1.017617 -105.0113
## 501 15.9 32 61.4 162.6 0 1.017617 -105.0113
## 502 15.8 40 76.8 157.5 0 1.017617 -105.0113
## 503 15.4 29 71.8 176.5 0 1.017617 -105.0113
## 504 15.2 21 55.5 164.4 0 1.017617 -105.0113
## 505 15.5 33 48.6 160.7 0 1.017617 -105.0113
## 506 15.5 33 66.4 174.0 0 1.017617 -105.0113
## 507 16.4 38 67.3 163.8 0 1.017617 -105.0113
ggplot(bdims, aes(x=hgt, y=wgt)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm, # Add linear regression line
se=TRUE) # add shaded confidence region
###############3.8 Regression vs. regression to the mean####################
# Heredity
# Galton's "regression to the mean"
###############3.9 Regression to the mean###################################
Galton_men <- GaltonFamilies %>%
select(father, childHeight, gender) %>%
filter(gender == "male")
head(Galton_men)
## father childHeight gender
## 1 78.5 73.2 male
## 2 75.5 73.5 male
## 3 75.5 72.5 male
## 4 75.0 71.0 male
## 5 75.0 70.5 male
## 6 75.0 68.5 male
Galton_women <- GaltonFamilies %>%
select(mother, childHeight, gender) %>%
filter(gender == "female")
head(Galton_women)
## mother childHeight gender
## 1 67.0 69.2 female
## 2 67.0 69.0 female
## 3 67.0 69.0 female
## 4 66.5 65.5 female
## 5 66.5 65.5 female
## 6 64.0 68.0 female
# Regression to the mean is a concept attributed to Sir Francis Galton.
# The basic idea is that extreme random observations will tend to be less
# extreme upon a second trial. This is simply due to chance alone. While
# "regression to the mean" and "linear regression" are not the same thing,
# we will examine them together in this exercise.
#
# One way to see the effects of regression to the mean is to compare the
# heights of parents to their children's heights. While it is true that
# tall mothers and fathers tend to have tall children, those children tend to
# be less tall than their parents, relative to average. That is, fathers who
# are 3 inches taller than the average father tend to have children who may
# be taller than average, but by less than 3 inches.
#
# The Galton_men and Galton_women datasets contain data originally collected
# by Galton himself in the 1880s on the heights of men and women, respectively,
# along with their parents' heights.
#
# Compare the slope of the regression line to the slope of the diagonal
# line. What does this tell you?
# Height of children vs. height of father
ggplot(data = Galton_men, aes(x = father, y = childHeight)) +
geom_point() +
geom_abline(slope = 1, intercept = 0) +
geom_smooth(method = "lm", se = FALSE)
# Height of children vs. height of mother
ggplot(data = Galton_women, aes(x = mother, y = childHeight)) +
geom_point() +
geom_abline(slope = 1, intercept = 0) +
geom_smooth(method = "lm", se = FALSE)
plot(Galton_men$father, Galton_men$childHeight)
#Excellent! Because the slope of the regression line
#is smaller than 1 (the slope of the diagonal line)
#for both males and females, we can verify Sir Francis
#Galton's regression to the mean concept!
###############3.10 "Regression" in the parlance of our time####################
# In an opinion piece about nepotism published in The New York Times in 2015,
# economist Seth Stephens-Davidowitz wrote that:
#
# "Regression to the mean is so powerful that once-in-a-generation talent
# basically never sires once-in-a-generation talent. It explains why Michael
# Jordan’s sons were middling college basketball players and Jakob Dylan wrote
# two good songs. It is why there are no American parent-child pairs among
# Hall of Fame players in any major professional sports league."
#The author is arguing that...
# Because of regression to the mean, an outstanding basketball
# #player is likely to have sons that are good at basketball,
# #but not as good as him.
####################Part 4: Interpreting regression models###########################
####################4.1 Interpretation of regression coefficients####################
# The most import aspect of regression is interpreting the value
# of the coefficient.
#Is that texbook overpriced?
####################4.2 Interpretation of coefficients###############################
#Recall that the fitted model for the poverty rate of U.S. counties as a function
#of high school graduation rate is:
# povertyˆ=64.594−0.591⋅hs_grad
# poverty^=64.594−0.591⋅hs_grad
#Which of the following is the correct interpretation of the slope coefficient?
# Among U.S. counties, each additional percentage point increase in the high school graduation
# rate is associated with about a 0.591 percentage point decrease in the poverty rate.
####################4.3 Interpretation in context##############################
# A politician interpreting the relationship between poverty rates and high school graduation rates implores his constituents:
#
# If we can lower the poverty rate by 59%, we'll double the high school graduate rate in our county (i.e. raise it by 100%).
# Which of the following mistakes in interpretation has the politician made?
# 1. Implying that the regression model establishes a cause-and-effect relationship.
#
# 2. Switching the role of the response and explanatory variables.
#
# 3. Confusing percentage change with percentage point change.
#
#4. All of the above.
####################4.4 Fitting simple linear models##############################
# While the geom_smooth(method = "lm") function is useful for drawing linear models on a scatterplot,
# it doesn't actually return the characteristics of the model. As suggested by that syntax,
# however, the function that creates linear models is lm(). This function generally takes two arguments:
#
# A formula that specifies the model
# A data argument for the data frame that contains the data you want to use to fit
# the model
#
# The lm() function return a model object having class "lm". This object contains lots
# of information about your regression model, including the data used to fit the model,
# the specification of the model, the fitted values and residuals, etc.
#
# Using the bdims dataset, create a linear model for the weight of people as a function of their height.
lm(wgt ~ hgt, data = bdims)
##
## Call:
## lm(formula = wgt ~ hgt, data = bdims)
##
## Coefficients:
## (Intercept) hgt
## -105.011 1.018
# Linear model for SLG as a function of OBP
lm(SLG ~ OBP, data = mlbBat10)
##
## Call:
## lm(formula = SLG ~ OBP, data = mlbBat10)
##
## Coefficients:
## (Intercept) OBP
## 0.009407 1.110323
# Log-linear model for body weight as a function of brain weight
lm(log(BodyWt) ~ log(BrainWt), data = mammals)
##
## Call:
## lm(formula = log(BodyWt) ~ log(BrainWt), data = mammals)
##
## Coefficients:
## (Intercept) log(BrainWt)
## -2.509 1.225
####################4.5 Units and scale####################################
# In the previous examples, we fit two regression models:
# wgtˆ=−105.011+1.018⋅hgt
# wgt^=−105.011+1.018⋅hgt
# and
# SLGˆ=0.009+1.110⋅OBP.
# SLG^=0.009+1.110⋅OBP.
# Which of the following statements is incorrect?
# Because the slope coefficient for OBPOBP is larger (1.110)
# than the slope coefficient for hgthgt (1.018), we can conclude
# that the association between OBPOBP and SLGSLG is stronger than
# the association between height and weight.
####################4.6 Your linear model object ##########################
####################4.7 The lm summary output ##########################
####################4.8 Fitted values and residuals##########################
# Once you have fit a regression model, you are often interested in the fitted values (ŷ iy^i) and the residuals (eiei), where ii indexes the observations. Recall that:
#
# ei=yi−ŷ i
# ei=yi−y^i
# The least squares fitting procedure guarantees that the mean of the residuals is zero (n.b., numerical instability may result in the computed values not being exactly zero). At the same time, the mean of the fitted values must equal the mean of the response variable.
#
# In this exercise, we will confirm these two mathematical facts by accessing the fitted values and residuals with the fitted.values() and residuals() functions, respectively, for the following model:
#
#
mod <- lm(wgt ~ hgt, data = bdims)
#Confirm that the mean of the body weights equals the mean of the fitted values of mod.
mean(bdims$wgt) == mean(fitted.values(mod))
## [1] TRUE
#Compute the mean of the residuals of mod.
mean(residuals(mod))
## [1] -1.266971e-15
####################4.9 Tiding your linear model##########################
# As you fit a regression model, there are some quantities
# (e.g. R2R2) that apply to the model as a whole, while others
# apply to each observation (e.g. ŷ iy^i). If there are several
# of these per-observation quantities, it is sometimes
# convenient to attach them to the original data as new variables.
#
# The augment() function from the broom package does exactly this. It takes a model object as an argument and returns a data frame that contains the data on which the model was fit, along with several quantities specific to the regression model, including the fitted values, residuals, leverage scores, and standardized residuals.
# Load the broom package.
library(broom)
# Create bdims_tidy
#Create a new data frame called bdims_tidy that
#is the augmentation of the mod linear model.
bdims_tidy<-augment(mod)
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
# Glimpse the resulting data frame
glimpse(bdims_tidy)
## Observations: 507
## Variables: 9
## $ wgt <dbl> 65.6, 71.8, 80.7, 72.6, 78.8, 74.8, 86.4, 78.4, 62....
## $ hgt <dbl> 174.0, 175.3, 193.5, 186.5, 187.2, 181.5, 184.0, 18...
## $ .fitted <dbl> 72.05406, 73.37697, 91.89759, 84.77427, 85.48661, 7...
## $ .se.fit <dbl> 0.4320546, 0.4520060, 1.0667332, 0.7919264, 0.81834...
## $ .resid <dbl> -6.4540648, -1.5769666, -11.1975919, -12.1742745, -...
## $ .hat <dbl> 0.002154570, 0.002358152, 0.013133942, 0.007238576,...
## $ .sigma <dbl> 9.312824, 9.317005, 9.303732, 9.301360, 9.312471, 9...
## $ .cooksd <dbl> 5.201807e-04, 3.400330e-05, 9.758463e-03, 6.282074e...
## $ .std.resid <dbl> -0.69413418, -0.16961994, -1.21098084, -1.31269063,...
####################4.10 Using your linear model##########################
# Going back to the textbook example.
#
# By looking at the residuals you can look to see if any textbooks
# are under or over-priced.
#MAking predictions
#this a fundamental technique in machine learning.
# The predict function for the lm fitted values will return
# the fitted values for existing data.
#predict(lm, newdata) #this will make prediction for any values we want
#the newdata must be a data frame
#with a variable with the same names as the
#explanatory variable used to fit the model
#the result is a vector of fitted values
#Visualize new observations
#Example--single observation of the outlier book in red
# isrs <- broom::augment(mod, newdata = new_data)
# ggplot(data = textbooks, aes(x = amazNew, y = uclaNew)) +
# geom_point(data= isrs, aes(y= .fitted), size = 3, color = "red")
####################4.11 Making Predictions##########################
# The fitted.values() function or the augment()-ed data frame
# provides us with the fitted values for the observations that
# were in the original data. However, once we have fit the model,
# we may want to compute expected values for observations that
# were not present in the data on which the model was fit. These
# types of predictions are called out-of-sample.
#
# The ben data frame contains a height and weight observation
# for one person. The mod object contains the fitted model for
# weight as a function of height for the observations in the
# bdims dataset. We can use the predict() function to generate
# expected values for the weight of new individuals. We must
# pass the data frame of new observations through the newdata
# argument.
# Print ben--contains height & weight for 1 person
#ben
# Predict the weight of ben
#Use predict() with the newdata argument to compute the expected #height of the individual in the ben data frame.
#predict(mod, ben)
####################4.12 Adding a regression line to a plot manually##########################
# The geom_smooth() function makes it easy to add a simple linear
# regression line to a scatterplot of the corresponding variables.
# And in fact, there are more complicated regression models that
# can be visualized in the data space with geom_smooth(). However,
# there may still be times when we will want to add regression lines
# to our scatterplot manually. To do this, we will use the
# geom_abline() function, which takes slope and intercept arguments.
# Naturally, we have to compute those values ahead of time, but we
# already saw how to do this (e.g. using coef()).
#
# The coefs data frame contains the model estimates retrieved
# from coef(). Passing this to geom_abline() as the data argument
# will enable you to draw a straight line on your scatterplot.
coefs<-coef(mod)
# Add the line to the scatterplot
#Use geom_abline() to add a line defined in the coefs data frame to a scatterplot of weight vs. height for individuals in the bdims dataset
# ggplot(data = bdims, aes(x = hgt, y = wgt)) +
# geom_point() +
# geom_abline(data = coefs,
# aes(intercept = `(Intercept)`, slope = hgt),
# color = "dodgerblue")
####################Part 5 Model Fit #################################
####################5.1 Assessing Model Fit ##########################
# How well does our textbook model fit?
#
# Can you quantify your intuition about model fit? Yes
#
# Sums of squared deviations
# The regression line minimizes the sum of the least model squares.
# The residuals are the vertical points between that point and the regression lines.
#
# The positive and negative residuals cancel each other out
#
# SSE--captures how much our model missed
library(broom)
mod_possum <- lm(totalL ~ tailL, data = possum)
mod_possum %>%
augment() %>%
summarize(SSE = sum(.resid^2), #the SSE or sum squared residuals
SSE_also = (n()- 1) * var(.resid))
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## Warning: Deprecated: please use `purrr::possibly()` instead
## SSE SSE_also
## 1 1301.488 1301.488
summary(mod_possum)
##
## Call:
## lm(formula = totalL ~ tailL, data = possum)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.2100 -2.3265 0.1792 2.7765 6.7900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.0371 6.6568 6.165 1.43e-08 ***
## tailL 1.2443 0.1796 6.927 3.94e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.572 on 102 degrees of freedom
## Multiple R-squared: 0.32, Adjusted R-squared: 0.3133
## F-statistic: 47.99 on 1 and 102 DF, p-value: 3.935e-10
####################5.2 RMSE ##########################
#The residual standard error reported for the regression model
#for poverty rate of U.S. counties in terms of high school graduation
#rate is 4.67. What does this mean?