STAT 530, Fall 2014
--------------------

Homework 2
-----------

*******************************************************************
The second extra problem is mandatory for graduate students and optional
(extra credit) for undergraduate students.
*******************************************************************

NOTE:  The air pollution data set (from chapter 2) is given on the course web page.
You should use the FULL data set for the problems given below.
You can read the data into R (as a data frame) with the code:

airpol.full <- read.table("http://www.stat.sc.edu/~hitchcock/airpoll.txt", header=T)
city.names <- as.character(airpol.full[,1])
airpol.data <- airpol.full[,2:8]


Do the following 3 problems from the textbook, and the other problems given below:

2.1**, 2.2***, 2.6

NOTE: For EACH of these problems, also write several sentences 
explaining in words what substantive conclusions about the data
that you can draw from the plots.

** Do Problem 2.1 from the textbook, but simply do a regular star plot for all 7 variables like we discussed in class, 
not the kind the book describes where they add the stars to a scatterplot.
And also do a plot using Chernoff Faces.  Write a short paragraph explaining what the plots tell you about the cities.
(Check out the "labels" argument for both the stars function and the faces function.)

*** For 2.2:  Do problem 2.2 from the textbook, but just do the ordinary scatterplot matrix for this data set.  Write a short 
paragraph explaining the main conclusions from the scatterplot matrix.


EXTRA PROBLEM 1:  Do a bivariate boxplot of the pair of variables "Education" and "Mortality" 
from the air pollution data set.  Explain what the plot tells you about the relationship 
between the two variables.  Do you see any outliers?  If so, which cities are they?

Mandatory for Graduate Students:
EXTRA PROBLEM 2:  Do a bubble plot with "Education" and "Mortality" on the axes and "Population Density" 
represented by the bubbles.  Explain what the plot tells you about the relationships 
among the three variables.  Comment on any notable cities.