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# 1.

# install.packages("moments")
# library(moments)

ncaa2018 <- read.csv("ncaa2018.csv", header=TRUE)
names(ncaa2018) # 766 x 6
ELO <- ncaa2018$ELO
par(mfrow=c(2,3))
hist(ELO, main="Histogram of ELO")
qqnorm(ELO, main="Quantile-Quantile Plot for EOL ")
qqline(ELO, datax = FALSE, distribution = qnorm, probs = c(0.25, 0.75), qtype = 7)
boxplot(ELO, main="Boxplot of ELO", xlab="ELO")

# part b
logELO <- log(ELO)
hist(logELO, main="Histogram of log(ELO)", xlab="log(ELO)")
qqnorm(logELO, main="Quantile-Quantile Plot for log(ELO) ")
qqline(logELO, datax = FALSE, distribution = qnorm, probs = c(0.25, 0.75), qtype = 7)
boxplot(logELO, main="Boxplot of log(ELO)", xlab="log(ELO)")

# The log-transformation on ELO does not seem to make it look more like a
# normal distribution. All of histograms, qqplots, and boxplots show very
# similar distributions. Using different base for the logarithem instead of e
# could be usesful attempts to make the distributio of ELO look more normal....
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