Homework One
Kymmi
9/27/2017
Exercise 2
Data1-House Prices
For the housing prices, I wanted to look at how the distribution of prices varied between neighborhoods. First I looked at the price summary as a whole, then broke it down into the individual neighborhoods. From the graphs, you can see that houses in the West neighborhood tend to be higher prices than the North and East neighborhoods.
library(lattice)
hp <- read.csv("/Users/kimberlyhatlestad/Data Mining/HousePrices.csv")
head(hp)## HomeID Price SqFt Bedrooms Bathrooms Offers Brick Neighborhood
## 1 1 114300 1790 2 2 2 No East
## 2 2 114200 2030 4 2 3 No East
## 3 3 114800 1740 3 2 1 No East
## 4 4 94700 1980 3 2 3 No East
## 5 5 119800 2130 3 3 3 No East
## 6 6 114600 1780 3 2 2 No Northsummary(hp$Price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 69100 111300 126000 130400 148200 211200sd(hp$Price)## [1] 26868.77barchart(~Price|Neighborhood,data=hp,layout=c(1,5),col="black")
Data2- Direct Marketing
I wanted to see how the spending of the two genders varied. I also wanted to take into account different salaries of the two genders, because theoretically if you are receiving more money from your job, then you have more money to spent. Based on the graphs, I found that while female salaries and spending were slightly lower, there was also more outliers in the spending in the female group as compared to the male group.
dm <- read.csv("/Users/kimberlyhatlestad/Data Mining/DirectMarketing.csv")
head(dm)## Age Gender OwnHome Married Location Salary Children History Catalogs
## 1 Old Female Own Single Far 47500 0 High 6
## 2 Middle Male Rent Single Close 63600 0 High 6
## 3 Young Female Rent Single Close 13500 0 Low 18
## 4 Middle Male Own Married Close 85600 1 High 18
## 5 Middle Female Own Single Close 68400 0 High 12
## 6 Young Male Own Married Close 30400 0 Low 6
## AmountSpent
## 1 755
## 2 1318
## 3 296
## 4 2436
## 5 1304
## 6 495summary(dm$AmountSpent)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 38.0 488.2 962.0 1217.0 1688.0 6217.0sd(dm$AmountSpent)## [1] 961.0686summary(dm$Gender)## Female Male
## 506 494fac=factor(dm$Gender)
res=dm$AmountSpent
t4=tapply(res,fac,mean,na.rm=TRUE)
t4## Female Male
## 1025.34 1412.85boxplot(Salary~Gender,data=dm,ylab="Salary",xlab="Gender")
boxplot(AmountSpent~Gender,data=dm,ylab="Amount Spent",xlab="Gender")
Data3- Gender Discrimintation
For this set of data, I wanted to look at how much influence gender had on Salaries, while also taking experience into account. From the graphs I created, it looks as though yes, Males are receiving higher salaries, but they also tend to have more experience. I would also say that maybe that is because Males have a better chance of getting higher salary jobs, have a longer history of being in higher salary positions, and tend to apply for higher salary positions more than females do.
gd <- read.csv("/Users/kimberlyhatlestad/Data Mining/GenderDiscrimination.csv")
summary(gd)## Gender Experience Salary
## Female:140 Min. : 2.00 Min. : 53400
## Male : 68 1st Qu.: 7.00 1st Qu.: 66000
## Median :10.00 Median : 74000
## Mean :12.05 Mean : 79844
## 3rd Qu.:16.00 3rd Qu.: 88000
## Max. :39.00 Max. :194000fac=factor(gd$Salary)
res=gd$Experience
t4=tapply(res,fac,mean,na.rm=TRUE)
t4## 53400 53600 54000 57000 57200 57520 58000
## 7.000000 11.000000 8.000000 5.500000 4.000000 11.000000 3.000000
## 58200 58400 59000 59200 59300 59600 59800
## 7.000000 16.000000 7.500000 11.000000 8.000000 9.000000 10.500000
## 60000 60900 61000 61200 61500 61600 62000
## 11.250000 2.000000 6.666667 15.000000 4.000000 9.000000 6.666667
## 62400 62600 63000 63400 63800 64000 65000
## 10.666667 16.000000 19.000000 19.000000 13.000000 9.000000 13.000000
## 65200 65480 66000 66400 67000 67400 67600
## 7.500000 22.000000 8.600000 12.000000 9.000000 9.000000 8.000000
## 67800 68000 68200 68400 68600 69000 69200
## 8.000000 10.100000 14.000000 6.000000 14.000000 5.000000 7.500000
## 69400 69800 69840 70000 70600 70800 71000
## 16.000000 11.000000 30.000000 9.750000 15.000000 11.000000 11.000000
## 71200 72000 72200 72400 72600 72800 73000
## 10.000000 5.000000 20.000000 9.000000 10.500000 5.000000 3.000000
## 74000 74500 74600 75000 75200 76000 76400
## 11.000000 11.000000 8.000000 7.000000 13.000000 9.000000 16.000000
## 77600 78000 78040 78200 79000 79400 79600
## 20.000000 10.000000 8.000000 15.000000 4.500000 14.000000 10.000000
## 80000 80400 80520 80600 81320 82000 82600
## 6.000000 5.000000 10.000000 30.000000 27.000000 10.500000 15.000000
## 82800 83000 83600 84000 84400 84800 85000
## 9.000000 4.000000 4.000000 9.666667 22.000000 22.000000 10.000000
## 85400 86000 86200 86400 87000 87200 87600
## 9.000000 16.000000 21.000000 9.000000 35.000000 11.500000 15.000000
## 87800 88000 88800 89000 89600 90000 90600
## 7.000000 9.600000 19.000000 8.250000 26.000000 10.666667 18.000000
## 91000 91600 93000 94000 95000 96000 97000
## 20.000000 12.000000 12.000000 11.500000 7.500000 11.500000 11.500000
## 98000 100000 102400 104000 105000 108600 110000
## 10.000000 11.000000 15.000000 21.000000 21.000000 27.000000 23.000000
## 114000 116000 117000 118000 120000 123000 123600
## 22.500000 22.000000 15.000000 23.000000 15.000000 14.000000 14.000000
## 130000 148000 176000 188000 190000 194000
## 20.000000 39.000000 32.000000 35.000000 34.000000 36.000000t5=tapply(gd$Experience,INDEX=list(gd$Salary,gd$Gender),FUN=mean,na.rm=TRUE)
t5## Female Male
## 53400 NA 7.000000
## 53600 11.000000 NA
## 54000 6.000000 10.000000
## 57000 5.500000 NA
## 57200 4.000000 NA
## 57520 11.000000 NA
## 58000 NA 3.000000
## 58200 7.000000 NA
## 58400 16.000000 NA
## 59000 8.666667 4.000000
## 59200 11.000000 NA
## 59300 8.000000 NA
## 59600 9.000000 NA
## 59800 10.000000 11.000000
## 60000 13.666667 4.000000
## 60900 2.000000 NA
## 61000 7.500000 5.000000
## 61200 15.000000 NA
## 61500 NA 4.000000
## 61600 9.000000 NA
## 62000 3.000000 8.500000
## 62400 8.500000 15.000000
## 62600 16.000000 NA
## 63000 19.000000 NA
## 63400 19.000000 NA
## 63800 13.000000 NA
## 64000 14.000000 4.000000
## 65000 13.000000 NA
## 65200 7.500000 NA
## 65480 22.000000 NA
## 66000 10.000000 6.500000
## 66400 12.000000 NA
## 67000 11.500000 4.000000
## 67400 9.000000 NA
## 67600 8.000000 NA
## 67800 8.000000 NA
## 68000 11.625000 4.000000
## 68200 14.000000 NA
## 68400 6.000000 NA
## 68600 14.000000 NA
## 69000 NA 5.000000
## 69200 7.500000 NA
## 69400 16.000000 NA
## 69800 11.000000 NA
## 69840 NA 30.000000
## 70000 9.750000 NA
## 70600 15.000000 NA
## 70800 11.000000 NA
## 71000 22.000000 5.500000
## 71200 10.000000 NA
## 72000 5.000000 5.000000
## 72200 20.000000 NA
## 72400 9.000000 NA
## 72600 12.000000 9.000000
## 72800 5.000000 NA
## 73000 NA 3.000000
## 74000 18.000000 4.000000
## 74500 11.000000 NA
## 74600 8.000000 NA
## 75000 7.000000 NA
## 75200 13.000000 NA
## 76000 9.000000 NA
## 76400 16.000000 NA
## 77600 20.000000 20.000000
## 78000 12.000000 8.000000
## 78040 8.000000 NA
## 78200 15.000000 NA
## 79000 4.000000 5.000000
## 79400 14.000000 NA
## 79600 10.000000 NA
## 80000 6.000000 NA
## 80400 5.000000 NA
## 80520 10.000000 NA
## 80600 30.000000 NA
## 81320 27.000000 NA
## 82000 16.000000 5.000000
## 82600 15.000000 NA
## 82800 9.000000 NA
## 83000 NA 4.000000
## 83600 NA 4.000000
## 84000 11.000000 7.000000
## 84400 22.000000 NA
## 84800 22.000000 NA
## 85000 12.500000 7.500000
## 85400 9.000000 NA
## 86000 16.000000 NA
## 86200 21.000000 NA
## 86400 9.000000 NA
## 87000 35.000000 NA
## 87200 11.500000 NA
## 87600 15.000000 NA
## 87800 7.000000 NA
## 88000 11.000000 8.666667
## 88800 19.000000 NA
## 89000 9.500000 7.000000
## 89600 26.000000 NA
## 90000 12.000000 8.000000
## 90600 18.000000 NA
## 91000 17.000000 23.000000
## 91600 12.000000 NA
## 93000 NA 12.000000
## 94000 NA 11.500000
## 95000 NA 7.500000
## 96000 16.000000 7.000000
## 97000 15.000000 8.000000
## 98000 NA 10.000000
## 100000 11.000000 NA
## 102400 15.000000 NA
## 104000 NA 21.000000
## 105000 21.000000 NA
## 108600 27.000000 NA
## 110000 NA 23.000000
## 114000 NA 22.500000
## 116000 NA 22.000000
## 117000 15.000000 NA
## 118000 NA 23.000000
## 120000 NA 15.000000
## 123000 14.000000 NA
## 123600 14.000000 NA
## 130000 NA 20.000000
## 148000 NA 39.000000
## 176000 NA 32.000000
## 188000 NA 35.000000
## 190000 NA 34.000000
## 194000 NA 36.000000levelplot(Salary~Gender*Experience,data=gd,xlab="Gender",ylab="Experience")
Data4- Loan Data
For Loan Data I wanted to look at how borrowing and age were connected. I looked both at how often each age group borrowed and how much each age group borrowed. It is understandable that the younger ages borrowed more and borrowed at a higher rate seeing as how younger people tend to have less money saved up, lower incomes, and more things to pay for.
ld <- read.csv("/Users/kimberlyhatlestad/Data Mining/LoanData.csv")
summary(ld)## Status Credit.Grade Amount Age
## Current:5186 HR :1217 Min. : 1000 Min. : 0.000
## Default: 75 E :1129 1st Qu.: 2025 1st Qu.: 2.000
## Late : 350 D : 927 Median : 3001 Median : 4.000
## C : 843 Mean : 4817 Mean : 4.504
## B : 553 3rd Qu.: 6000 3rd Qu.: 7.000
## AA : 451 Max. :25000 Max. :14.000
## (Other): 491
## Borrower.Rate Debt.To.Income.Ratio
## Min. :0.0000 Min. : 0.00
## 1st Qu.:0.1425 1st Qu.: 0.09
## Median :0.1950 Median : 0.16
## Mean :0.1937 Mean : 45.38
## 3rd Qu.:0.2500 3rd Qu.: 0.25
## Max. :0.4975 Max. :51280.07
## dob.dm.tbl=table(WK=ld$Age,MM=ld$Borrower.Rate)
dob.dm.tbl## MM
## WK 0 0.0021 0.0099 0.01 0.015 0.0295 0.03 0.04 0.045 0.05 0.0575 0.06
## 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1 1 0 0 1 0 0 0 0 0 1 0 1
## 2 0 0 0 0 0 0 0 1 1 5 0 0
## 3 1 1 1 0 1 0 0 0 0 1 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 1
## 5 0 0 0 0 0 0 1 0 0 0 0 1
## 6 0 0 0 0 0 1 0 0 0 1 2 0
## 7 0 0 0 0 0 0 0 0 0 0 0 1
## 8 0 0 0 0 0 0 1 0 0 1 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 1 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 1 1
## 12 0 0 0 0 0 0 0 0 0 1 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 2
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0612 0.062 0.0625 0.065 0.0655 0.068 0.0685 0.069 0.0695 0.07
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 1 0 1 1 0 1 0 9
## 2 0 0 0 1 0 0 0 0 0 3
## 3 0 0 0 0 0 1 0 0 0 4
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 1
## 6 0 0 0 0 0 0 0 0 0 1
## 7 0 0 0 1 1 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 1 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 1 0 0 1 0 0 1 0 2 4
## 11 0 0 1 1 0 0 0 0 0 5
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 1
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0712 0.0715 0.072 0.0725 0.0735 0.0739 0.074 0.0749 0.075 0.0752
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 0 3 1 0 0 0 7 0
## 2 0 0 0 1 0 0 0 0 3 0
## 3 0 0 0 1 0 0 0 0 1 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 2 0
## 6 0 0 0 0 0 1 1 0 2 0
## 7 0 0 1 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 1 1 0
## 9 0 1 0 0 0 0 0 0 1 0
## 10 1 0 1 3 0 0 0 0 3 1
## 11 0 0 0 0 0 0 0 0 1 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0755 0.0762 0.0765 0.077 0.0774 0.0775 0.078 0.0782 0.0784 0.0785
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 1 3 5 1 0 1 3
## 2 0 0 0 0 0 0 1 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 1 0 1 0 0 2 0 0 0 0
## 7 0 0 1 0 0 1 1 0 0 0
## 8 0 0 0 0 0 2 0 0 0 0
## 9 0 0 0 0 0 1 1 0 0 0
## 10 0 1 0 1 0 2 0 1 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0788 0.079 0.0793 0.0795 0.0799 0.08 0.0803 0.0805 0.081 0.0812
## 0 0 0 0 0 0 0 0 0 1 0
## 1 1 3 1 1 3 10 1 0 2 0
## 2 0 1 0 2 1 7 0 0 2 0
## 3 1 0 0 3 0 7 0 0 0 0
## 4 0 0 0 2 0 5 0 0 0 1
## 5 0 0 0 0 0 5 0 1 0 1
## 6 0 0 0 1 0 5 0 0 0 1
## 7 0 0 0 0 0 5 0 0 1 0
## 8 0 0 0 1 0 5 0 0 0 0
## 9 0 0 0 0 0 5 0 0 1 0
## 10 0 1 0 1 0 2 0 0 0 0
## 11 0 0 0 0 0 5 0 0 0 0
## 12 0 0 0 0 0 2 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0815 0.082 0.0822 0.0824 0.0825 0.0826 0.0829 0.083 0.0834 0.0835
## 0 0 0 0 0 0 0 0 0 0 0
## 1 2 5 0 1 5 0 1 0 0 0
## 2 0 2 0 0 1 0 0 0 0 0
## 3 0 1 0 0 2 0 0 0 1 0
## 4 0 1 0 0 1 0 0 1 0 0
## 5 1 1 0 0 1 0 0 0 0 0
## 6 0 2 1 0 0 1 0 0 0 0
## 7 0 1 0 0 0 0 0 0 0 1
## 8 0 0 0 0 1 0 0 0 0 0
## 9 0 0 0 0 2 0 0 1 0 0
## 10 0 0 0 0 1 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0838 0.0839 0.084 0.0844 0.0845 0.0846 0.0847 0.0849 0.085 0.0854
## 0 0 0 0 0 0 0 0 0 1 0
## 1 0 1 1 0 1 1 0 0 5 1
## 2 0 0 1 0 1 0 0 1 1 0
## 3 0 0 1 0 0 0 0 0 4 0
## 4 1 0 1 0 2 0 1 1 3 0
## 5 0 0 1 1 2 0 0 0 5 0
## 6 0 0 1 0 1 0 0 1 0 0
## 7 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 1 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 1 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0855 0.086 0.0862 0.0865 0.087 0.0872 0.0873 0.0874 0.0875 0.0879
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 2 0 0 0 0 1 0 5 1
## 2 0 1 0 0 1 0 0 1 1 0
## 3 0 1 0 0 0 0 0 1 1 0
## 4 0 0 1 0 0 0 0 0 0 0
## 5 0 1 1 0 1 0 0 0 4 0
## 6 0 0 1 0 1 1 0 0 4 0
## 7 0 1 0 0 1 0 0 0 5 0
## 8 0 0 0 1 0 0 0 0 3 0
## 9 1 0 0 0 0 0 0 0 2 0
## 10 0 1 0 0 0 0 0 0 2 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.088 0.0883 0.0884 0.0885 0.0889 0.089 0.0892 0.0894 0.0895 0.0899
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 1 0 1 1 0 0 2 0
## 2 0 0 0 1 0 1 0 1 0 0
## 3 0 0 0 0 2 0 0 0 1 0
## 4 0 0 0 1 0 1 0 0 1 0
## 5 0 0 1 1 0 1 0 0 1 1
## 6 0 1 0 0 0 0 0 2 0 1
## 7 1 0 0 0 0 1 1 0 0 2
## 8 0 1 0 0 0 0 0 0 0 0
## 9 0 0 0 1 0 0 0 0 1 0
## 10 0 0 0 2 0 1 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.09 0.0904 0.091 0.0911 0.0912 0.0915 0.092 0.0922 0.0924 0.0925
## 0 0 0 0 0 0 0 0 0 0 0
## 1 17 0 1 0 0 1 0 0 1 3
## 2 9 0 0 0 0 0 2 0 0 3
## 3 9 0 0 0 0 0 0 0 0 2
## 4 2 0 0 0 2 0 0 0 0 1
## 5 7 0 0 0 1 0 1 0 0 2
## 6 5 1 0 0 0 0 0 0 0 1
## 7 4 0 0 1 0 0 0 0 0 1
## 8 2 0 0 0 0 0 0 0 0 0
## 9 3 0 0 0 0 1 0 1 0 0
## 10 3 0 0 0 0 0 0 0 0 1
## 11 2 0 0 0 0 0 0 0 0 0
## 12 1 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.093 0.0937 0.0938 0.094 0.0944 0.0945 0.0949 0.095 0.0952 0.0958
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 3 0 0
## 2 0 0 0 0 1 1 0 3 0 0
## 3 1 0 0 1 0 0 0 3 0 0
## 4 0 0 0 0 0 0 1 4 0 0
## 5 0 1 0 0 0 1 0 6 0 0
## 6 0 0 0 0 0 0 0 1 1 0
## 7 1 0 1 0 0 1 0 0 0 0
## 8 0 0 1 0 0 0 0 0 0 1
## 9 0 0 1 0 0 0 0 3 0 0
## 10 0 0 0 1 0 1 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 1 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0959 0.096 0.0962 0.0963 0.0965 0.0969 0.097 0.0972 0.0975 0.098
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 1 0 1 1
## 2 0 0 0 0 1 0 1 0 0 0
## 3 0 1 0 0 0 0 0 0 0 1
## 4 1 0 1 1 0 0 0 0 1 1
## 5 0 0 0 0 0 1 0 0 1 0
## 6 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 1 2 0
## 8 0 0 0 0 0 0 0 0 1 0
## 9 0 0 0 0 0 0 0 0 1 0
## 10 0 0 0 0 0 0 0 0 2 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0981 0.0982 0.0985 0.0987 0.0989 0.099 0.0992 0.0993 0.0995 0.0997
## 0 0 0 0 0 0 1 0 0 0 0
## 1 0 0 0 0 0 1 0 0 1 1
## 2 0 0 0 0 0 1 0 0 0 0
## 3 0 0 0 0 0 1 0 0 1 0
## 4 1 0 0 0 1 1 0 0 1 0
## 5 0 0 0 0 0 1 0 0 0 0
## 6 0 0 0 0 0 0 0 0 1 0
## 7 0 0 0 1 0 1 1 1 0 0
## 8 0 1 2 0 0 0 0 0 1 0
## 9 0 0 0 0 0 0 0 0 1 0
## 10 0 0 0 0 0 0 0 0 1 1
## 11 0 0 0 0 0 0 0 0 0 1
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.0999 0.1 0.1002 0.1007 0.1009 0.101 0.1012 0.1015 0.1018 0.1019
## 0 0 1 0 0 0 0 0 0 0 0
## 1 1 6 0 0 0 1 0 0 0 0
## 2 1 6 0 0 0 0 0 0 1 0
## 3 2 7 0 1 1 0 1 0 0 1
## 4 0 4 0 0 0 0 1 0 0 0
## 5 0 6 1 0 0 0 1 1 0 1
## 6 1 7 0 0 0 0 0 1 0 0
## 7 0 6 0 0 0 0 0 0 0 0
## 8 0 3 0 0 0 0 0 0 0 0
## 9 0 7 0 0 0 2 0 0 0 0
## 10 0 5 0 0 0 0 0 0 0 0
## 11 0 5 0 0 0 0 0 0 0 0
## 12 0 1 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.102 0.1023 0.1025 0.103 0.1033 0.1035 0.1038 0.104 0.1043 0.1045
## 0 0 0 0 0 0 0 0 0 0 1
## 1 0 0 4 0 0 0 1 0 1 0
## 2 0 0 1 0 0 0 0 0 0 1
## 3 0 0 1 0 0 1 1 0 0 0
## 4 0 0 0 1 0 1 0 0 0 0
## 5 1 0 2 0 0 0 1 2 0 0
## 6 0 1 2 0 0 0 0 0 0 0
## 7 0 0 4 0 0 0 1 0 0 0
## 8 0 0 0 0 1 0 0 0 0 0
## 9 0 0 1 0 0 0 0 0 0 1
## 10 1 0 1 0 0 0 0 1 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1049 0.105 0.1053 0.1055 0.1061 0.1062 0.1065 0.107 0.1071 0.1074
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 1 0 0 0 0 2 1 0
## 2 0 2 0 1 0 0 0 0 0 1
## 3 0 2 0 0 0 0 0 0 0 0
## 4 0 0 0 1 0 0 0 0 0 0
## 5 0 1 0 0 1 0 2 1 0 0
## 6 1 3 0 0 0 0 1 1 0 0
## 7 0 3 1 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 1 0 0 0 1 0 0 0 0
## 10 0 2 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1075 0.1076 0.1079 0.108 0.1083 0.1085 0.1086 0.1089 0.109 0.1091
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 0 1 2 0 1 0 0 0 0
## 2 3 0 0 1 0 0 0 0 0 0
## 3 0 0 0 2 0 1 0 0 3 0
## 4 0 0 0 0 0 0 1 1 0 0
## 5 2 0 0 1 1 0 0 0 1 0
## 6 0 1 0 1 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 1 0
## 8 1 0 0 1 0 0 0 0 0 1
## 9 1 0 0 0 0 0 0 0 1 0
## 10 1 0 0 0 0 0 0 0 0 0
## 11 1 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1094 0.1095 0.1097 0.1098 0.11 0.1104 0.1105 0.111 0.1111 0.1114
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 13 0 0 0 0 1
## 2 0 0 0 0 9 0 1 0 0 0
## 3 0 0 0 0 12 0 0 0 0 0
## 4 0 0 0 0 14 0 0 0 1 0
## 5 1 1 0 1 12 0 0 1 0 0
## 6 0 1 0 0 0 0 0 0 0 0
## 7 0 0 0 0 4 0 0 0 0 0
## 8 0 2 0 0 0 0 0 0 1 0
## 9 0 0 1 0 4 0 0 1 0 0
## 10 0 2 0 0 1 1 0 1 0 0
## 11 0 0 0 0 4 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1115 0.1119 0.112 0.1121 0.1122 0.1124 0.1125 0.113 0.1135 0.1137
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 1 1 1 0 0 1
## 4 1 1 0 0 0 0 0 0 0 0
## 5 0 0 0 1 0 0 1 0 0 0
## 6 0 0 1 0 0 0 1 0 1 0
## 7 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 1 0 0
## 9 0 0 0 0 0 0 1 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 1 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.114 0.1141 0.1143 0.1144 0.1145 0.1148 0.1149 0.115 0.1155 0.1159
## 0 0 0 0 0 0 0 0 0 0 0
## 1 2 0 0 0 0 0 0 1 1 0
## 2 0 0 0 1 0 0 0 3 0 1
## 3 0 0 0 0 1 0 0 2 0 0
## 4 1 0 0 0 2 1 0 3 0 0
## 5 0 1 0 0 0 1 0 3 0 0
## 6 0 0 1 0 0 0 1 2 0 0
## 7 0 0 0 0 0 0 0 3 0 0
## 8 1 0 0 0 0 0 0 4 0 0
## 9 1 0 0 0 0 0 0 2 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 1 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1162 0.1163 0.1169 0.117 0.1172 0.1175 0.118 0.1183 0.1185 0.1189
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 1 0 4 2 0 0 0
## 2 0 0 0 1 0 0 0 0 0 0
## 3 0 0 0 0 0 1 1 0 0 0
## 4 0 0 0 0 0 0 2 0 0 1
## 5 0 0 0 0 0 0 1 0 0 0
## 6 0 0 0 0 0 6 0 0 0 0
## 7 1 0 1 1 0 5 1 0 0 0
## 8 0 2 0 0 1 2 1 1 0 0
## 9 0 0 0 0 0 2 0 0 0 0
## 10 0 0 0 0 0 1 0 0 1 0
## 11 0 0 0 1 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.119 0.1195 0.1197 0.1199 0.12 0.1201 0.1205 0.1208 0.121 0.122
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 6 0 0 0 0 0
## 2 0 0 0 0 7 0 0 0 0 0
## 3 0 1 1 0 4 0 0 0 1 1
## 4 1 3 0 0 5 0 0 1 1 1
## 5 0 0 0 2 6 0 0 0 1 0
## 6 1 0 0 0 4 0 0 0 0 0
## 7 0 1 0 0 3 0 0 0 0 0
## 8 2 0 0 0 2 0 0 0 0 0
## 9 0 0 0 0 5 0 1 0 0 0
## 10 1 1 0 0 3 0 0 0 0 0
## 11 0 0 0 0 4 1 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1222 0.1225 0.123 0.1235 0.1238 0.1239 0.124 0.1241 0.1242 0.1245
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 0 0 0 1 1 0 1 0
## 2 0 0 0 0 0 1 0 1 0 1
## 3 0 0 0 1 0 0 0 0 0 0
## 4 0 0 1 0 1 0 0 0 0 1
## 5 0 3 0 0 0 0 1 0 0 0
## 6 1 1 0 0 0 0 2 0 0 0
## 7 0 2 0 0 0 0 0 0 0 0
## 8 0 1 0 0 1 0 0 0 0 0
## 9 0 1 0 1 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1248 0.1249 0.125 0.1255 0.1259 0.126 0.1262 0.1264 0.1265 0.1269
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 6 0 0 0 0 0 1 0
## 2 0 0 1 0 0 0 0 0 0 0
## 3 1 0 5 1 0 0 0 0 0 2
## 4 0 1 4 1 1 1 1 0 0 0
## 5 0 0 1 0 0 0 0 0 0 0
## 6 0 0 2 0 0 0 0 1 0 0
## 7 0 0 4 1 0 0 0 0 0 0
## 8 0 0 1 0 0 1 0 0 0 0
## 9 0 0 2 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.127 0.1272 0.1275 0.128 0.1285 0.1288 0.129 0.1293 0.1295 0.1298
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 0 2 0 0 0 1 0 1 0
## 2 0 0 0 1 0 0 1 0 2 0
## 3 0 0 1 2 0 0 2 0 1 0
## 4 0 0 2 1 0 0 0 0 1 1
## 5 1 1 0 1 1 1 0 1 1 0
## 6 2 0 1 0 0 0 0 0 1 0
## 7 0 0 1 0 0 0 2 0 0 0
## 8 0 0 1 0 0 0 1 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 1 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1299 0.13 0.1308 0.1309 0.131 0.1312 0.1313 0.1315 0.1318 0.132
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 11 0 1 0 0 0 1 1 1
## 2 0 3 0 0 0 0 0 1 0 1
## 3 1 11 0 0 0 0 0 0 0 0
## 4 0 4 0 0 0 1 0 0 0 0
## 5 2 3 1 0 0 0 0 0 0 0
## 6 0 2 0 0 0 0 0 0 0 0
## 7 0 3 0 0 0 0 0 0 0 1
## 8 0 5 0 0 1 0 1 0 0 0
## 9 0 1 0 0 0 0 0 0 0 2
## 10 0 3 0 0 0 0 0 0 0 1
## 11 0 4 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 1 0 0 0 0 0 0 0 0
## MM
## WK 0.1324 0.1325 0.133 0.1333 0.1335 0.1339 0.134 0.1342 0.1344 0.1345
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 1 0 1 0 0 0 1 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 1 1 1 0 0 0 0 0 0
## 4 1 1 2 0 0 0 1 0 0 0
## 5 0 3 1 0 0 0 1 0 0 1
## 6 0 0 1 0 0 0 0 1 0 0
## 7 0 1 2 0 0 0 0 0 0 0
## 8 0 2 0 0 0 1 0 0 0 0
## 9 0 2 1 0 0 0 0 0 0 1
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 1 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.135 0.1353 0.1354 0.1355 0.1365 0.1366 0.1367 0.137 0.1371 0.1373
## 0 0 0 0 0 0 0 0 0 0 0
## 1 3 0 0 1 0 0 1 2 0 0
## 2 8 0 0 0 0 1 0 0 0 0
## 3 3 0 0 0 1 0 0 1 0 0
## 4 2 0 0 0 0 0 0 0 1 1
## 5 3 0 1 1 1 0 0 0 0 0
## 6 2 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 2 0 0
## 8 2 2 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 2 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1375 0.138 0.1385 0.1386 0.1387 0.1388 0.1389 0.139 0.1393 0.1394
## 0 0 1 0 0 0 0 0 0 0 0
## 1 1 3 0 1 0 0 0 3 0 0
## 2 2 0 0 0 0 0 0 0 0 0
## 3 1 2 1 0 0 0 1 0 1 0
## 4 2 1 0 0 0 1 0 0 0 1
## 5 2 3 0 0 1 0 0 1 0 0
## 6 1 3 1 0 0 0 0 1 0 0
## 7 0 0 2 0 0 0 0 0 0 0
## 8 1 0 1 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 1 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 2 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1395 0.1398 0.1399 0.14 0.1401 0.1403 0.1405 0.141 0.1415 0.142
## 0 0 0 0 1 0 0 0 0 0 0
## 1 1 0 0 9 0 0 2 0 0 1
## 2 2 0 1 11 0 0 0 0 0 0
## 3 0 1 2 4 0 0 0 1 0 0
## 4 0 0 0 9 0 0 0 1 0 0
## 5 2 0 1 5 0 2 0 0 0 0
## 6 0 0 0 9 1 0 0 0 0 0
## 7 0 0 0 6 0 0 0 0 0 0
## 8 1 0 0 3 0 0 0 1 0 2
## 9 0 0 0 5 0 0 0 0 1 0
## 10 0 0 0 0 0 0 1 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 1 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1423 0.1424 0.1425 0.143 0.1435 0.1438 0.144 0.1444 0.1445 0.1448
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 2 0 0 0 1 0 1 1
## 2 0 0 1 2 1 0 0 0 0 0
## 3 0 0 0 1 0 0 1 0 0 0
## 4 1 0 3 2 0 0 1 1 0 0
## 5 0 0 2 2 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## 7 0 1 1 0 0 0 0 1 0 0
## 8 0 0 2 0 0 1 0 0 0 0
## 9 0 0 3 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.145 0.1455 0.146 0.1466 0.1468 0.1469 0.147 0.1471 0.1474 0.1475
## 0 0 0 0 0 0 0 0 0 0 0
## 1 4 0 0 0 0 0 1 0 0 1
## 2 0 0 1 0 0 0 1 0 0 1
## 3 3 0 1 0 0 0 1 0 0 0
## 4 5 0 0 1 0 1 1 0 0 2
## 5 6 0 1 0 1 0 1 0 0 3
## 6 4 0 0 0 0 0 0 1 0 1
## 7 5 1 0 0 0 0 0 0 1 0
## 8 2 0 0 0 0 0 0 0 0 1
## 9 3 0 0 0 0 0 0 0 0 1
## 10 1 0 1 0 0 0 0 0 0 0
## 11 1 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1479 0.148 0.1483 0.1484 0.1485 0.1488 0.1489 0.149 0.1492 0.1493
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 1
## 3 0 3 0 0 0 0 0 1 0 0
## 4 0 1 0 0 0 1 0 2 0 0
## 5 0 0 0 0 1 0 0 1 0 0
## 6 1 0 0 0 0 0 0 0 0 0
## 7 0 1 1 1 0 0 1 2 0 1
## 8 0 0 0 0 1 0 0 0 0 0
## 9 0 0 0 0 0 0 0 2 1 0
## 10 0 0 0 0 0 0 0 1 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1495 0.1497 0.1498 0.1499 0.15 0.1505 0.151 0.1514 0.1515 0.1519
## 0 0 0 0 0 1 0 0 0 0 0
## 1 2 1 0 1 43 0 0 1 0 1
## 2 0 0 1 0 38 0 0 0 0 0
## 3 0 0 0 0 43 0 1 0 0 0
## 4 0 0 0 0 31 0 0 0 0 0
## 5 3 0 0 3 35 2 0 0 2 1
## 6 0 0 0 0 14 0 0 0 0 0
## 7 2 0 1 0 8 0 1 0 0 0
## 8 0 0 0 0 23 0 0 0 0 0
## 9 1 0 0 0 11 2 0 0 0 0
## 10 1 0 0 0 15 0 0 0 0 0
## 11 0 0 0 0 4 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.152 0.1525 0.1528 0.153 0.1538 0.1539 0.154 0.1543 0.1544 0.1545
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 0 0 0 0 0 0 0 2
## 2 0 1 0 0 0 0 0 0 2 0
## 3 1 0 0 2 0 0 0 1 0 0
## 4 0 1 0 0 0 0 0 0 0 0
## 5 0 0 0 1 0 0 2 0 0 1
## 6 1 1 0 0 0 0 1 0 1 2
## 7 2 2 0 0 0 1 1 0 0 0
## 8 0 2 1 1 1 0 0 0 0 1
## 9 0 0 0 1 0 0 0 0 0 0
## 10 0 1 0 0 0 0 1 0 0 0
## 11 0 1 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1547 0.1549 0.155 0.1552 0.1555 0.1556 0.1561 0.1562 0.1565 0.1567
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 1 1 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 1 5 0 0 0 1 0 0 0
## 4 0 0 4 0 0 0 0 0 2 1
## 5 1 1 2 0 0 1 0 0 0 0
## 6 0 0 3 0 0 0 0 1 0 0
## 7 0 0 2 0 0 0 0 0 0 0
## 8 0 0 6 0 0 0 0 0 2 0
## 9 0 0 0 0 1 0 0 0 0 0
## 10 0 0 3 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1568 0.157 0.1575 0.1577 0.158 0.1585 0.1588 0.159 0.1593 0.1595
## 0 0 0 0 0 0 0 0 0 0 0
## 1 2 0 1 0 1 0 0 0 0 0
## 2 0 0 0 0 0 1 0 1 1 1
## 3 0 0 0 0 1 0 0 1 0 0
## 4 0 0 0 0 1 0 0 0 0 2
## 5 0 0 1 1 2 0 0 0 0 2
## 6 0 2 18 0 3 0 1 0 1 0
## 7 0 0 16 0 1 0 0 0 0 0
## 8 0 0 14 0 0 0 0 1 0 0
## 9 0 0 5 0 0 0 0 0 0 0
## 10 0 0 9 0 0 0 0 1 0 0
## 11 0 0 3 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1599 0.16 0.1605 0.1614 0.1615 0.162 0.1623 0.1624 0.1625 0.163
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 12 0 1 1 0 0 0 2 0
## 2 0 10 1 0 1 0 0 0 0 0
## 3 0 9 0 0 1 0 0 1 1 1
## 4 1 6 0 0 0 1 1 0 0 0
## 5 0 7 0 0 0 2 0 0 0 3
## 6 0 11 0 0 0 0 0 0 1 0
## 7 0 2 0 1 0 0 0 0 2 1
## 8 0 7 0 0 0 0 0 0 1 0
## 9 0 2 0 0 0 0 0 0 1 0
## 10 0 3 0 0 0 0 0 0 1 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1639 0.164 0.1644 0.1645 0.1649 0.165 0.1655 0.166 0.1665 0.167
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 1 2 0 6 1 0 1 2
## 2 0 1 0 0 0 5 0 0 0 0
## 3 0 1 0 0 0 3 0 0 0 0
## 4 0 0 0 0 0 3 0 0 0 1
## 5 1 0 0 1 0 2 1 1 1 0
## 6 0 0 0 0 0 3 0 0 0 1
## 7 0 0 0 0 0 1 0 0 0 1
## 8 0 0 0 0 1 3 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1675 0.168 0.1685 0.1687 0.1688 0.169 0.1692 0.1694 0.1695 0.1697
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 2 1 0 0 0 1 1 0 0
## 2 0 1 0 1 0 1 0 0 4 1
## 3 1 0 0 0 0 3 0 0 1 0
## 4 2 1 0 0 1 1 0 0 1 0
## 5 1 1 1 0 0 1 0 0 1 0
## 6 0 1 1 0 0 0 0 0 1 0
## 7 1 0 0 0 0 1 0 1 0 0
## 8 1 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1699 0.17 0.1714 0.1715 0.1721 0.1724 0.1725 0.173 0.1735 0.174
## 0 0 3 0 0 0 0 0 0 0 0
## 1 0 38 1 0 0 0 1 1 0 1
## 2 0 41 0 0 0 1 1 0 0 0
## 3 0 28 0 0 0 0 1 0 0 1
## 4 0 26 1 1 1 0 2 0 2 3
## 5 1 23 0 0 0 0 1 1 0 0
## 6 0 12 0 0 0 0 0 0 0 1
## 7 2 5 0 0 0 0 2 1 0 1
## 8 0 8 0 0 0 0 0 0 1 0
## 9 0 3 0 0 0 0 1 0 0 0
## 10 0 2 0 0 0 0 1 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1742 0.1744 0.1745 0.1749 0.175 0.176 0.1761 0.1765 0.177 0.1775
## 0 1 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 6 2 0 0 0 3
## 2 0 0 0 0 1 0 0 0 0 0
## 3 0 0 2 0 3 0 0 0 0 2
## 4 1 0 1 0 2 0 0 0 0 2
## 5 0 1 0 1 2 0 1 0 0 0
## 6 0 0 0 0 2 1 0 0 0 23
## 7 0 0 0 0 6 0 0 1 0 11
## 8 0 0 0 0 3 0 0 0 0 10
## 9 0 0 0 0 3 1 0 0 0 10
## 10 0 0 0 0 1 0 0 0 1 6
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.178 0.1784 0.1785 0.1788 0.1789 0.179 0.1793 0.1795 0.1797 0.1798
## 0 0 0 0 0 0 0 0 1 0 0
## 1 0 0 1 0 0 1 0 2 1 0
## 2 0 0 0 0 0 0 1 0 0 0
## 3 1 0 0 0 0 0 0 2 0 1
## 4 1 1 0 1 2 1 0 1 0 0
## 5 0 0 0 0 0 1 0 1 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## 7 1 0 0 0 0 0 0 2 0 0
## 8 0 0 0 0 0 0 0 1 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1799 0.18 0.1805 0.181 0.1815 0.1819 0.182 0.1825 0.1826 0.183
## 0 0 1 0 0 0 0 0 0 0 0
## 1 2 19 0 0 0 0 1 0 1 1
## 2 0 11 1 0 1 0 0 0 0 1
## 3 0 19 0 0 0 0 0 1 0 2
## 4 0 5 0 0 0 1 0 0 0 0
## 5 0 12 0 1 0 0 1 0 0 1
## 6 1 5 0 0 0 0 0 0 0 0
## 7 0 7 0 0 0 0 0 1 0 1
## 8 0 5 0 0 0 0 1 3 0 0
## 9 0 2 0 1 0 0 0 0 0 0
## 10 0 2 0 0 0 0 0 0 0 0
## 11 0 1 0 0 0 0 0 1 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1838 0.184 0.1844 0.1845 0.1849 0.185 0.1855 0.1865 0.1866 0.187
## 0 0 0 0 0 0 1 0 0 0 0
## 1 1 0 1 0 1 8 0 0 0 2
## 2 0 0 0 3 0 4 1 1 0 0
## 3 0 0 0 1 0 4 1 0 0 1
## 4 0 0 0 0 0 3 0 0 0 0
## 5 0 1 0 0 1 2 0 0 1 0
## 6 0 1 0 0 0 1 0 0 0 0
## 7 0 0 0 0 0 2 0 0 0 1
## 8 0 0 0 1 0 3 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1871 0.1875 0.188 0.1883 0.1885 0.1888 0.1889 0.189 0.1894 0.1895
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 1 0 1 0 0 0
## 2 0 0 1 1 0 0 0 1 1 0
## 3 0 5 0 0 0 0 0 0 0 1
## 4 1 1 1 0 0 0 0 1 0 0
## 5 0 1 0 0 0 0 0 1 0 1
## 6 0 0 0 0 0 0 0 0 0 0
## 7 0 2 0 0 0 1 0 2 0 0
## 8 0 0 0 0 0 0 0 0 0 1
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1897 0.1898 0.1899 0.19 0.1902 0.1905 0.1911 0.1914 0.1915 0.1919
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 9 1 0 0 0 0 0
## 2 0 0 1 10 0 0 0 1 0 0
## 3 0 0 0 18 0 1 1 0 0 1
## 4 0 0 0 6 0 1 0 0 0 0
## 5 0 1 0 7 0 0 0 0 0 0
## 6 0 0 1 4 0 0 0 0 1 0
## 7 1 0 0 2 0 0 0 0 0 0
## 8 0 0 0 4 0 0 0 0 1 0
## 9 0 0 0 4 0 0 0 0 0 0
## 10 0 0 0 2 0 0 0 0 0 0
## 11 0 0 0 1 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.192 0.1925 0.193 0.1938 0.1939 0.194 0.1945 0.1948 0.1949 0.195
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 1 0 0 0 0 2 1 0 5
## 2 1 0 1 1 1 0 0 0 0 10
## 3 0 3 0 0 0 2 1 0 0 4
## 4 1 1 0 0 0 0 1 0 0 2
## 5 3 1 0 0 0 0 1 0 1 2
## 6 0 1 0 0 0 0 0 0 0 2
## 7 1 2 0 0 0 0 0 0 0 1
## 8 1 1 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.1954 0.1955 0.196 0.1969 0.197 0.1975 0.198 0.1985 0.1988 0.1989
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 2 0 1 1 0 1 0 0
## 2 0 2 1 1 0 0 0 0 0 0
## 3 0 0 0 0 0 3 0 0 1 2
## 4 0 0 0 0 1 1 0 0 2 0
## 5 1 0 0 0 2 1 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 1
## 7 0 0 0 0 0 0 0 1 0 0
## 8 0 0 0 0 1 3 0 0 0 0
## 9 0 0 0 0 0 0 1 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.199 0.1995 0.1997 0.1998 0.1999 0.2 0.2014 0.2015 0.2019 0.202
## 0 0 0 0 0 0 2 0 0 0 0
## 1 2 4 0 1 0 56 0 0 1 0
## 2 0 1 0 1 2 54 0 0 0 0
## 3 1 1 0 0 2 33 1 0 0 1
## 4 1 1 0 0 0 22 0 0 0 0
## 5 1 2 1 0 0 31 0 1 0 0
## 6 2 1 0 0 1 15 0 0 0 0
## 7 3 0 0 0 0 16 0 0 0 0
## 8 0 1 0 0 0 12 0 0 0 1
## 9 0 3 0 0 0 14 0 0 0 0
## 10 0 0 0 0 0 6 0 0 0 0
## 11 0 0 0 0 0 1 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2025 0.2034 0.204 0.2044 0.2045 0.205 0.2055 0.2059 0.206 0.2065
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 1 1 0 0 5 0 0 0 0
## 2 0 0 0 0 0 4 0 0 0 0
## 3 0 0 1 0 0 1 1 0 0 0
## 4 0 0 0 1 0 0 0 0 0 0
## 5 3 0 0 0 1 0 0 0 1 0
## 6 0 0 1 0 0 1 0 1 0 0
## 7 2 0 0 0 0 0 0 0 0 0
## 8 1 0 0 0 0 2 0 0 0 1
## 9 0 0 0 0 0 1 0 0 0 0
## 10 1 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2069 0.207 0.2075 0.208 0.2088 0.2089 0.2095 0.2098 0.2099 0.21
## 0 0 0 0 0 0 0 0 0 1 0
## 1 1 1 1 0 0 1 0 0 1 14
## 2 0 0 0 0 0 0 0 0 0 7
## 3 0 0 0 0 1 0 0 1 0 4
## 4 0 0 1 0 0 0 0 0 0 3
## 5 0 0 1 0 0 1 0 1 0 8
## 6 0 0 12 0 0 0 0 0 0 5
## 7 0 1 16 1 0 0 2 0 0 9
## 8 0 0 12 0 0 0 0 0 0 5
## 9 0 0 2 0 0 0 1 0 0 5
## 10 0 0 2 0 0 0 0 0 0 3
## 11 0 0 0 0 0 0 0 0 0 1
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2105 0.211 0.212 0.2123 0.2125 0.2129 0.2134 0.2135 0.214 0.2144
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 2 0 0 0 0 0 2 0
## 2 0 0 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 1 0 0 0
## 4 0 0 0 0 3 0 0 1 0 0
## 5 1 0 0 1 0 0 0 0 0 1
## 6 0 0 0 0 1 0 0 0 0 0
## 7 0 0 1 0 0 1 0 0 0 1
## 8 0 1 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2145 0.2149 0.215 0.2155 0.2175 0.218 0.2186 0.2188 0.2189 0.219
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 1 1 0 1 0 0 1 0
## 2 0 0 2 0 0 0 0 0 0 1
## 3 1 0 1 0 0 1 0 0 0 0
## 4 0 0 1 0 2 0 0 2 0 1
## 5 1 0 2 0 0 2 0 0 0 0
## 6 0 0 2 0 0 1 0 0 0 0
## 7 0 0 1 0 0 0 1 0 0 0
## 8 0 0 4 0 0 0 0 0 0 0
## 9 0 0 1 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2195 0.2198 0.22 0.2205 0.2208 0.221 0.2218 0.222 0.2223 0.2225
## 0 0 0 2 0 0 0 0 0 0 0
## 1 1 1 27 1 1 0 0 0 1 0
## 2 0 0 13 0 0 0 0 2 0 0
## 3 2 0 20 0 0 0 1 6 0 0
## 4 0 0 9 0 0 0 0 1 0 0
## 5 1 1 16 1 0 0 0 3 0 1
## 6 0 0 10 0 1 0 0 0 0 0
## 7 0 0 6 0 0 0 0 1 0 0
## 8 1 0 9 0 0 1 0 0 0 1
## 9 0 0 8 0 0 0 0 0 0 1
## 10 0 0 5 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2235 0.224 0.2244 0.2245 0.225 0.2253 0.226 0.2265 0.227 0.2275
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 0 0 1 4 0 1 0 1 0
## 2 0 0 0 1 2 1 0 0 0 0
## 3 0 0 0 0 2 0 0 0 1 0
## 4 0 0 0 0 4 0 0 0 1 0
## 5 0 0 1 0 3 0 0 0 0 0
## 6 0 1 0 0 1 0 0 0 0 3
## 7 0 0 0 0 2 0 0 1 0 2
## 8 0 0 0 0 5 0 0 0 0 2
## 9 0 0 0 0 2 0 0 0 0 3
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 1
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2279 0.228 0.229 0.2293 0.2294 0.2295 0.2298 0.23 0.2305 0.231
## 0 0 0 0 0 0 0 0 1 0 0
## 1 0 0 1 0 0 5 1 25 0 1
## 2 0 0 1 0 0 2 0 23 0 0
## 3 0 0 0 0 1 0 1 18 0 0
## 4 0 0 0 0 0 1 0 14 0 0
## 5 1 1 1 0 0 1 0 15 0 0
## 6 0 0 0 0 0 1 0 1 1 0
## 7 0 0 0 1 0 1 0 8 0 0
## 8 0 0 1 0 0 0 0 14 0 0
## 9 0 0 0 0 0 0 0 8 0 0
## 10 0 0 0 0 0 0 0 4 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2312 0.232 0.2321 0.2323 0.2325 0.233 0.2333 0.2335 0.234 0.2345
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 1 0 0 0 0 1 1
## 2 1 0 0 0 0 1 0 0 0 0
## 3 0 2 0 0 4 0 1 1 1 0
## 4 0 0 0 0 1 0 0 0 0 0
## 5 0 0 1 0 2 0 0 0 1 0
## 6 0 0 0 0 1 0 0 0 1 0
## 7 0 0 0 0 0 2 0 0 0 0
## 8 0 0 0 0 2 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2349 0.235 0.2355 0.236 0.2364 0.2365 0.237 0.2373 0.2374 0.2375
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 3 1 0 0 0 0 0 0 1
## 2 0 4 0 1 0 1 0 1 0 0
## 3 0 1 0 0 0 0 1 0 0 1
## 4 0 1 0 0 0 0 1 0 0 4
## 5 0 3 0 1 1 0 0 0 0 1
## 6 0 2 0 0 0 0 1 0 0 11
## 7 0 1 0 0 0 0 1 0 0 15
## 8 0 2 0 1 0 0 0 0 1 103
## 9 0 1 0 0 0 0 1 0 0 29
## 10 0 1 0 0 0 0 0 0 0 1
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.238 0.2385 0.239 0.2395 0.2399 0.24 0.2415 0.242 0.2421 0.2425
## 0 0 0 0 0 1 1 0 0 0 0
## 1 1 0 2 5 1 34 0 1 0 0
## 2 0 1 0 3 0 25 0 0 1 0
## 3 0 0 2 0 0 18 0 0 0 1
## 4 0 0 2 0 1 20 0 0 0 0
## 5 0 1 2 0 0 21 1 0 0 0
## 6 1 0 0 0 0 15 0 0 0 1
## 7 0 0 0 1 0 6 0 1 0 1
## 8 0 0 0 0 0 5 0 0 0 0
## 9 0 0 0 0 0 1 0 0 0 0
## 10 0 0 1 0 0 2 0 0 0 0
## 11 0 0 0 0 0 1 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2429 0.243 0.244 0.2443 0.2445 0.2449 0.245 0.2454 0.2459 0.246
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 0 0 0 0 2 0 1 0
## 2 0 0 0 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0 2 0 0 1
## 4 0 0 1 0 0 0 0 0 0 1
## 5 1 0 0 0 1 1 2 0 0 1
## 6 0 0 0 0 0 0 0 0 0 0
## 7 0 1 0 0 0 1 1 1 0 0
## 8 0 0 0 0 0 0 1 0 0 0
## 9 0 0 0 0 0 0 1 0 0 0
## 10 0 0 0 0 0 0 1 0 0 1
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2466 0.2469 0.247 0.2475 0.248 0.2484 0.2485 0.2489 0.249 0.2493
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 1 0 0 0 0 1 0 0 1
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 1 0 0 0 0 3 0
## 4 1 0 0 0 2 0 0 0 2 0
## 5 0 0 1 0 0 1 1 1 0 0
## 6 0 0 0 13 0 0 0 0 0 0
## 7 0 0 1 22 0 0 0 0 0 0
## 8 0 0 0 3 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 2 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2495 0.2499 0.25 0.251 0.2525 0.253 0.2539 0.254 0.2548 0.255
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 0 23 1 1 0 0 1 0 0
## 2 0 0 22 0 0 0 0 0 1 1
## 3 1 0 29 0 1 1 1 0 0 1
## 4 0 0 16 0 0 0 0 0 0 2
## 5 0 0 25 0 0 0 0 1 0 2
## 6 0 1 13 0 0 1 0 1 0 2
## 7 0 0 21 0 1 0 0 0 0 2
## 8 0 0 1 0 0 0 0 0 0 0
## 9 0 0 3 0 1 0 0 0 0 0
## 10 0 0 2 0 0 0 0 0 0 1
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2559 0.2565 0.2567 0.2575 0.2579 0.2583 0.2584 0.2585 0.259 0.2595
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 1 1 0 0 0 0 0 0
## 2 0 0 0 1 1 1 1 0 0 2
## 3 0 0 0 2 0 0 0 0 4 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 1 0 0 1 0 0 0 0 0 0
## 6 0 1 0 0 0 0 0 0 1 0
## 7 0 0 0 1 0 0 0 1 1 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2598 0.2599 0.26 0.2615 0.262 0.2621 0.2625 0.263 0.2639 0.264
## 0 0 0 1 0 0 0 0 0 0 0
## 1 1 1 12 0 0 0 1 0 1 0
## 2 0 1 11 0 0 1 0 0 0 1
## 3 0 0 5 1 0 0 0 1 0 1
## 4 0 1 6 0 0 0 0 0 0 0
## 5 0 1 13 0 0 0 2 0 0 1
## 6 0 0 12 0 2 0 0 0 0 0
## 7 0 0 6 0 2 0 2 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 1 0 0 0 1 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.265 0.2655 0.266 0.2665 0.2666 0.2675 0.2685 0.2689 0.269 0.2695
## 0 0 0 0 0 0 0 0 0 0 0
## 1 2 0 0 0 0 1 0 0 0 0
## 2 0 0 0 0 0 0 1 1 0 0
## 3 1 1 0 0 1 1 0 0 1 0
## 4 1 0 1 0 0 0 0 0 3 1
## 5 0 0 0 0 0 2 0 1 0 0
## 6 1 0 0 1 0 2 0 0 0 0
## 7 0 0 0 0 0 1 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2699 0.27 0.2715 0.2725 0.273 0.274 0.2743 0.275 0.276 0.277 0.2775
## 0 0 0 0 0 0 0 0 0 0 0 0
## 1 1 24 0 0 1 1 0 0 0 0 1
## 2 0 15 0 0 0 0 0 1 0 1 0
## 3 0 16 1 2 0 0 0 0 0 0 1
## 4 0 9 0 0 0 3 0 5 0 0 0
## 5 0 10 0 2 0 1 0 2 0 0 0
## 6 0 1 0 1 0 0 1 3 1 0 0
## 7 0 10 1 0 0 0 0 1 0 0 0
## 8 0 1 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 10 0 3 0 0 0 0 0 2 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.278 0.279 0.2793 0.2795 0.2799 0.28 0.2815 0.282 0.2825 0.283
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 1 16 0 0 0 0
## 2 0 1 0 2 0 12 0 0 0 0
## 3 1 1 0 1 0 10 1 0 1 0
## 4 1 1 0 0 0 4 1 0 1 1
## 5 0 1 1 0 0 16 0 1 4 0
## 6 0 3 0 0 0 28 0 0 3 0
## 7 0 0 0 0 0 15 0 0 0 0
## 8 0 0 0 0 0 1 0 0 0 0
## 9 0 0 0 0 1 0 0 0 0 0
## 10 0 0 0 0 0 1 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2835 0.284 0.2844 0.2845 0.2848 0.285 0.2855 0.2865 0.287 0.2875
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 1 0 1 0 3 0 0 0 0
## 2 0 1 0 0 0 1 0 0 0 0
## 3 0 1 1 0 0 3 0 1 1 2
## 4 0 0 0 0 0 1 0 0 0 0
## 5 0 2 0 0 0 3 0 0 0 10
## 6 0 0 0 0 1 3 1 0 0 57
## 7 0 1 0 0 0 2 0 0 2 47
## 8 0 0 0 0 0 0 0 0 0 4
## 9 0 0 0 0 0 1 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2877 0.288 0.2881 0.2885 0.2888 0.2889 0.289 0.2892 0.2895 0.2898
## 0 0 0 0 0 0 0 0 0 0 0
## 1 1 0 0 0 0 0 3 0 3 0
## 2 0 0 0 0 0 0 0 0 1 0
## 3 0 0 1 1 1 1 0 0 2 0
## 4 0 1 0 0 0 0 2 1 2 1
## 5 0 0 0 0 0 2 1 0 1 0
## 6 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.2899 0.29 0.295 0.2963 0.2975 0.3 0.3025 0.31 0.3125 0.32 0.3295
## 0 0 1 0 0 0 0 0 0 0 0 0
## 1 0 133 0 0 0 2 0 0 0 0 0
## 2 3 102 1 0 0 2 0 0 0 0 0
## 3 0 97 0 0 1 2 0 0 0 0 0
## 4 0 115 0 0 0 3 0 0 0 0 0
## 5 0 128 0 1 0 1 0 0 0 0 0
## 6 0 5 0 0 0 0 0 0 0 0 0
## 7 1 1 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 3 1 1 0 0 0
## 10 0 2 0 0 0 7 0 0 1 3 1
## 11 0 0 0 0 0 1 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.33 0.3325 0.34 0.345 0.3475 0.348 0.35 0.357 0.3575 0.36 0.375 0.4
## 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 1 1 1 1 1 8 2 14 0 0 0
## 10 0 0 0 0 0 0 3 0 7 1 1 2
## 11 1 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## MM
## WK 0.45 0.48 0.4975
## 0 0 0 0
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 3 1 1
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0barchart(dob.dm.tbl,ylab="Age")
barchart(dob.dm.tbl,horizontal=TRUE,groups=TRUE,xlab="Borrower Rate")
densityplot(~Age|Amount,data=ld,layout=c(1,14),plot.points=FALSE,col="black")
Data5- Financial Indicators
For Financial Indicators I wanted to compare the US Market with Foreign Markets. I was trying to find something that the US did drastically different than the rest of the world, but it turns out our markets are very similar. The density of Net Income for the US and Foreign Markets is about the same, and the growth in revenue are pretty much the same. It does seem though that US Companies are more likely to have a higher Payout Ratio than Foreign Companies are.
fi <- read.csv("/Users/kimberlyhatlestad/Data Mining/FinancialIndicators.csv")
summary(fi)## Company.Name Industry.Name
## @Road Inc : 1 Bank : 487
## 1-800 Contacts Inc : 1 Computer Software/Svcs: 395
## 1-800-ATTORNEY Inc : 1 Drug : 306
## 1-800-FLOWERS.COM : 1 Internet : 306
## 1mage Software Inc : 1 Medical Supplies : 261
## 1st Centennial Bancorp: 1 Financial Svcs. (Div.): 244
## (Other) :7106 (Other) :5113
## SIC Exchange Country Stock.Price
## Min. : 0 0 : 6 Foreign:1783 Min. : 0.00
## 1st Qu.:3533 AMS : 431 US :5329 1st Qu.: 2.15
## Median :4911 INDE: 12 Median : 12.40
## Mean :5000 NDQ :4607 Mean : 37.63
## 3rd Qu.:6720 NYS :1806 3rd Qu.: 27.68
## Max. :9975 OTC : 48 Max. :88500.00
## TSE : 202
## X..Chg.in.last.year Trading.Volume X..of.shares.outstanding
## Min. :-0.920000 Min. :0.000e+00 Min. : 0.0
## 1st Qu.:-0.010000 1st Qu.:1.115e+04 1st Qu.: 7.2
## Median : 0.000000 Median :7.400e+04 Median : 22.3
## Mean : 0.003768 Mean :7.523e+05 Mean : 100.4
## 3rd Qu.: 0.000000 3rd Qu.:2.793e+05 3rd Qu.: 57.9
## Max. : 2.000000 Max. :1.677e+09 Max. :13073.5
##
## Market.Cap Total.Debt Firm.Value
## Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 12.9 1st Qu.: 0.0 1st Qu.: 18.7
## Median : 132.9 Median : 5.5 Median : 172.8
## Mean : 2823.3 Mean : 1000.7 Mean : 3824.0
## 3rd Qu.: 938.4 3rd Qu.: 148.0 3rd Qu.: 1174.7
## Max. :370978.1 Max. :478539.0 Max. :741342.1
##
## Enterprise.Value Cash Revenues..Last.yr
## Min. : -9671.9 Min. : 0.00 Min. : 0.1
## 1st Qu.: 13.6 1st Qu.: 0.80 1st Qu.: 20.0
## Median : 142.3 Median : 12.70 Median : 143.3
## Mean : 3520.7 Mean : 303.30 Mean : 2341.9
## 3rd Qu.: 1057.2 3rd Qu.: 79.92 3rd Qu.: 882.3
## Max. :726014.1 Max. :122152.00 Max. :285222.0
## NA's :1887
## Trailing.12.mth.Revenues Current.PE Trailing.PE
## Min. : 0.1 Min. : 0.00 Min. : 0.00
## 1st Qu.: 25.1 1st Qu.: 14.66 1st Qu.: 13.88
## Median : 174.8 Median : 20.50 Median : 19.04
## Mean : 2520.3 Mean : 40.08 Mean : 40.53
## 3rd Qu.: 968.4 3rd Qu.: 32.66 3rd Qu.: 29.45
## Max. :325891.0 Max. :5270.50 Max. :28518.28
## NA's :1857 NA's :3011 NA's :3165
## Forward.EPS Forward.PE PEG.Ratio PBV.Ratio
## Min. : -30.26 Min. : 4.38 Min. : 0.120 Min. : 0.000
## 1st Qu.: 0.50 1st Qu.: 14.54 1st Qu.: 1.200 1st Qu.: 1.430
## Median : 1.33 Median : 18.18 Median : 1.700 Median : 2.290
## Mean : 3.15 Mean : 29.93 Mean : 2.644 Mean : 5.338
## 3rd Qu.: 2.30 3rd Qu.: 25.53 3rd Qu.: 2.377 3rd Qu.: 4.020
## Max. :4000.00 Max. :2710.00 Max. :234.240 Max. :863.650
## NA's :4433 NA's :4715 NA's :4934 NA's :1469
## PS.Ratio EV.EBIT EV.EBITDA
## Min. : 0.00 Min. : 0.070 Min. : 0.1
## 1st Qu.: 0.59 1st Qu.: 7.225 1st Qu.: 5.3
## Median : 1.44 Median : 10.540 Median : 8.1
## Mean : 18.40 Mean : 25.108 Mean : 135.7
## 3rd Qu.: 3.82 3rd Qu.: 16.790 3rd Qu.: 13.2
## Max. :27304.00 Max. :10638.300 Max. :455000.0
## NA's :1887 NA's :3321 NA's :3127
## EV..Invested.Capital Value.BV.of.Capital EV.Sales
## Min. :0.000e+00 Min. : 0.000 Min. : 0.01
## 1st Qu.:1.000e+00 1st Qu.: 1.240 1st Qu.: 0.73
## Median :2.000e+00 Median : 1.830 Median : 1.63
## Mean :6.964e+12 Mean : 3.411 Mean : 18.89
## 3rd Qu.:4.000e+00 3rd Qu.: 3.150 3rd Qu.: 3.87
## Max. :3.363e+16 Max. :332.000 Max. :33657.50
## NA's :1410 NA's :1369 NA's :1977
## EV..Trailing.Sales Growth.in.EPS..Last.5.years
## Min. : 0.01 Min. :-0.500
## 1st Qu.: 0.70 1st Qu.:-0.030
## Median : 1.50 Median : 0.070
## Mean : 14.22 Mean : 0.073
## 3rd Qu.: 3.41 3rd Qu.: 0.170
## Max. :7667.55 Max. : 0.950
## NA's :2143 NA's :4898
## Expected.Growth.in.EPS..next.5.years
## Min. :-0.130
## 1st Qu.: 0.100
## Median : 0.140
## Mean : 0.167
## 3rd Qu.: 0.200
## Max. : 0.950
## NA's :4566
## Expected.Growth.in.Revenues..next.5.years Growth.in.Revenue..last.year
## Min. :-0.430 Min. :-0.7500
## 1st Qu.: 0.060 1st Qu.: 0.0000
## Median : 0.090 Median : 0.0000
## Mean : 0.096 Mean : 0.1041
## 3rd Qu.: 0.120 3rd Qu.: 0.1500
## Max. : 0.800 Max. : 4.8600
## NA's :5663
## X3.yr.Regression.Beta Value.Line.Beta HiLo.risk
## Min. :-9.4800 Min. :0.0000 Min. :0.0200
## 1st Qu.: 0.0000 1st Qu.:0.5000 1st Qu.:0.1700
## Median : 0.4400 Median :0.8000 Median :0.2700
## Mean : 0.7639 Mean :0.8231 Mean :0.3299
## 3rd Qu.: 1.1900 3rd Qu.:1.1000 3rd Qu.:0.4400
## Max. : 9.6100 Max. :7.9000 Max. :1.0000
## NA's :893
## X3.yr.Standard.Deviation..Stock.Price. Reinvestment
## Min. : 0.000 Min. :-108003.3
## 1st Qu.: 0.000 1st Qu.: -0.9
## Median : 0.280 Median : 0.0
## Mean : 0.437 Mean : 288.4
## 3rd Qu.: 0.560 3rd Qu.: 2.9
## Max. :29.190 Max. :1977007.2
##
## Correlation Payout.Ratio Reinvestment.Rate ROE
## Min. :-0.690 Min. : 0.0000 0 : 153 Min. :-97.0000
## 1st Qu.: 0.190 1st Qu.: 0.0000 0.06 : 85 1st Qu.: -0.0400
## Median : 0.440 Median : 0.0000 0.02 : 76 Median : 0.0800
## Mean : 0.445 Mean : 0.2212 0.04 : 74 Mean : -0.2009
## 3rd Qu.: 0.700 3rd Qu.: 0.2900 0.03 : 73 3rd Qu.: 0.1400
## Max. : 2.430 Max. :34.8300 (Other):4764 Max. :246.3500
## NA's :2090 NA's :3011 NA's :1887 NA's :1369
## ROC Net.Margin Pre.tax.Operating.Margin
## 0.1 : 182 Min. :-414.000 Min. :-99.6100
## 0.12 : 173 1st Qu.: -0.100 1st Qu.: -0.0200
## 0.14 : 167 Median : 0.030 Median : 0.0900
## 0.11 : 162 Mean : -1.882 Mean : -0.8673
## 0.13 : 162 3rd Qu.: 0.080 3rd Qu.: 0.1900
## (Other):4379 Max. : 19.670 Max. : 1.0000
## NA's :1887 NA's :1887 NA's :1887
## Invested.Capital BV.of.Assets Non.cash.WC
## Min. :-15212.5 Min. : 0.0 Min. :-12205.0
## 1st Qu.: 2.3 1st Qu.: 10.3 1st Qu.: -0.6
## Median : 48.0 Median : 146.8 Median : 0.0
## Mean : 1687.8 Mean : 4889.1 Mean : 247.0
## 3rd Qu.: 392.9 3rd Qu.: 925.1 3rd Qu.: 24.7
## Max. :503605.0 Max. :1484101.0 Max. :430192.0
##
## Chg.in.non.cash.WC Net.Income EBIT
## Min. :-109004.3 Min. :-13996.0 Min. : -925.44
## 1st Qu.: 0.0 1st Qu.: -1.2 1st Qu.: -0.19
## Median : 0.0 Median : 1.8 Median : 13.74
## Mean : 281.7 Mean : 133.7 Mean : 399.75
## 3rd Qu.: 1.1 3rd Qu.: 33.1 3rd Qu.: 130.29
## Max. :1978006.0 Max. : 25330.0 Max. :99485.19
## NA's :1887
## EBIT.1.t. EBITDA FCFF
## Min. : -925.44 Min. : -537.44 Min. :-1976050.3
## 1st Qu.: -0.19 1st Qu.: 0.33 1st Qu.: -0.9
## Median : 10.65 Median : 20.68 Median : 6.9
## Mean : 280.61 Mean : 522.56 Mean : -111.2
## 3rd Qu.: 91.56 3rd Qu.: 171.11 3rd Qu.: 71.4
## Max. :81701.22 Max. :107870.19 Max. : 108003.3
## NA's :1887 NA's :1887 NA's :1887
## Eff.Tax.Rate Non.cash.WC.as...of.Revenues Cash.as...of.Firm.Value
## Min. :0.0000 Min. :-241.0000 Min. : 0.0000
## 1st Qu.:0.0000 1st Qu.: -0.0700 1st Qu.: 0.0200
## Median :0.0300 Median : 0.0600 Median : 0.0700
## Mean :0.1627 Mean : -0.2979 Mean : 0.2539
## 3rd Qu.:0.3400 3rd Qu.: 0.1700 3rd Qu.: 0.1700
## Max. :0.6000 Max. : 29.6500 Max. :369.5000
## NA's :1887 NA's :553
## Cash.as...of.Revenues Cash.as...of.Total.Assets Capital.Expenditures
## Min. : 0.000 Min. :0.0000 Min. : 0.00
## 1st Qu.: 0.020 1st Qu.:0.0200 1st Qu.: 0.00
## Median : 0.100 Median :0.0900 Median : 1.20
## Mean : 2.089 Mean :0.1985 Mean : 96.90
## 3rd Qu.: 0.360 3rd Qu.:0.2900 3rd Qu.: 18.82
## Max. :1369.000 Max. :1.0000 Max. :17909.00
## NA's :1857 NA's :719
## Depreciation SG.A.Expenses Trailing.Revenues
## Min. : 0.00 Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 2.20 1st Qu.: 0.0
## Median : 1.60 Median : 13.90 Median : 48.5
## Mean : 90.23 Mean : 326.86 Mean : 1862.2
## 3rd Qu.: 19.00 3rd Qu.: 85.72 3rd Qu.: 516.4
## Max. :33750.00 Max. :51105.00 Max. :325891.0
##
## Trailing.Net.Income Dividends Intangible.Assets.Total.Assets
## Min. :-4365.0 Min. : 0.00 Min. :0.0000
## 1st Qu.: -0.6 1st Qu.: 0.00 1st Qu.:0.0000
## Median : 1.4 Median : 0.00 Median :0.0300
## Mean : 133.9 Mean : 46.41 Mean :0.1239
## 3rd Qu.: 30.9 3rd Qu.: 0.56 3rd Qu.:0.1900
## Max. :25330.0 Max. :10586.40 Max. :1.0000
## NA's :719
## Fixed.Assets.Total.Assets Market.D.E Market.Debt.to.Capital
## Min. :0.0000 Min. : 0.000 Min. :0.0000
## 1st Qu.:0.0200 1st Qu.: 0.000 1st Qu.:0.0000
## Median :0.1100 Median : 0.120 Median :0.1100
## Mean :0.2055 Mean : 6.249 Mean :0.1995
## 3rd Qu.:0.3100 3rd Qu.: 0.440 3rd Qu.:0.3000
## Max. :1.0000 Max. :8933.000 Max. :1.0000
## NA's :719 NA's :553 NA's :553
## Book.Debt.to.Capital Dividend.Yield Insider.Holdings
## Min. :0.0000 Min. :0.000000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.020
## Median :0.2100 Median :0.000000 Median :0.050
## Mean :0.2641 Mean :0.006947 Mean :0.102
## 3rd Qu.:0.4500 3rd Qu.:0.000000 3rd Qu.:0.110
## Max. :0.9900 Max. :3.540000 Max. :1.000
## NA's :1369 NA's :5673
## Institutional.Holdings
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.2000
## Mean :0.3185
## 3rd Qu.:0.6200
## Max. :1.0000
## densityplot(~Net.Income,groups=Country,data=fi,plot.points=FALSE)
xyplot(Payout.Ratio~Growth.in.Revenue..last.year|Country,data=fi,layout=c(1,2),col="black")