Solution PreviewSolution Preview

This material may consist of step-by-step explanations on how to solve a problem or examples of proper writing, including the use of citations, references, bibliographies, and formatting. This material is made available for the sole purpose of studying and learning - misuse is strictly forbidden.

# 1.

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

ncaa2018 <- read.csv("ncaa2018.csv", header=TRUE)
names(ncaa2018) # 766 x 6
ELO <- ncaa2018$ELO
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....
$54.00 for this solution

PayPal, G Pay, ApplePay, Amazon Pay, and all major credit cards accepted.

Find A Tutor

View available Statistics-R Programming Tutors

Get College Homework Help.

Are you sure you don't want to upload any files?

Fast tutor response requires as much info as possible.

Upload a file
Continue without uploading

We couldn't find that subject.
Please select the best match from the list below.

We'll send you an email right away. If it's not in your inbox, check your spam folder.

  • 1
  • 2
  • 3
Live Chats