Data science enhances people’s decision making. Doctors and researchers are making critical decisions every day. Therefore, it is absolutely necessary for those people to have some basic knowledge of data science. This series aims to help people that are around medical field to enhance their data science skills.

We will work with a health related database the famous “Pima Indians Diabetes Database”. It was generously donated by Vincent Sigillito from Johns Hopkins University. Please find further information regarding the dataset here.

This is the second part of the series, it will contain the main descriptive statistics measures you will use most of the time. Those measures are divided in measures of central tendency and measures of spread. Moreover, most of the exercises can be solved with built-in functions, but I would encourage you to solve them “by hand”, because once you know the mechanics of the measures, then you are way more confident on using those measures. On the “solutions” page, I have both methods, so even if you didn’t solve them by hand, it would be nice if you check them out.

Before proceeding, it might be helpful to look over the help pages for the mean, median, sort , unique, tabulate, sd, var, IQR, mad, abs, cov, cor, summary, str, rcorr.

You also may need to load the Hmisc library.
install.packages('Hmisc')
library(Hmisc)

In case you haven’t solve the part 1, run the following script to load the prerequisites for this part.

Answers to the exercises are available here.

If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

Exercise 1

Find the mean of the mass variable.

Exercise 2

Find the median of the mass variable.

Exercise 3

Find the mode of the mass.

Exercise 4

Find the standard deviation of the age variable.

Exercise 5

Find the variance of the mass variable.

Unlike the popular mean/standard deviation combination,interquartile range and median/mean absolute deviation are not sensitive to the presence of outliers. Even though it is recommended to go for MAD because they can approximate the standard deviation.

Exercise 6

Find the interquartile range of the age variable.

Exercise 7

Find the median absolute deviation of age variable. Assume that the age follows a normal distribution.

Exercise 8
Find the covariance of the variables age, mass.

Exercise 9

Find the spearman and pearson correlations of the variables age, mass.

Exercise 10

Print the summary statistics, and the structure of the data set. Moreover construct the correlation matrix of the data set.