Grading the professor:Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously. However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related characteristics, such as the physical appearance of the instructor. The article titled, "Beauty in the classroom: instructors' pulchritude and putative pedagogical productivity"(Hamermesh and Parker 2005)found that instructors who are viewed to be better looking receive higher instructional ratings.

In this homework assignment, you need to perform various descriptive statistics to visualize the data from this study in order to learn what goes into a positive professor evaluation.

The data: The data were gathered from end of semester student evaluations for a large sample of professors from the University of Texas at Austin. In addition, six students rated the professors' physical appearance. (This is a slightly modified version of the original data set that was released as part of the replication data forData Analysis Using Regression and Multilevel/Hierarchical Models(Gelman and Hill 2007).) The result is a data frame where each row contains a different course and columns represent variables about the courses and professors.

Download the "evals.csv" file located in this module. Save it to a convenient folder on your computer and load it into R. Change the file location (stuff in " " below) to match where this file is located!

evals <- read.csv("D:/SJU/DSS 665/evals.csv",header=T)

Variable	Description of Variable
score	average professor evaluation score: (1) very unsatisfactory - (5) excellent.
rank	rank of professor: teaching, tenure track, tenured.
ethnicity	ethnicity of professor: not minority, minority.
gender	gender of professor: female, male.
language	language of school where professor received education: english or non-english.
age	age of professor.
cls_perc_eval	percent of students in class who completed evaluation.
cls_did_eval	number of students in class who completed evaluation.
cls_students	total number of students in class.
cls_level	class level: lower, upper.
cls_profs	number of professors teaching sections in course in sample: single, multiple.
bty_avg	average beauty rating of professor.
pic_outfit	outfit of professor in picture: not formal, formal.
pic_color	color of professor's picture: color, black & white.

To access variables from the dataset, we have to attach the data. Type attach(evals). Now try typing one of the variable names into the console - you should see it!

attach(evals)

Problem 1: Describe the course evaluation scores. Calculate some summary statistics and display the data using a histogram (with proper colors, labels, titles, etc.)

Problem 2: Visualize the relationship between course evaluation scores and the "beauty" of the professor using a scatterplot. Make the graph "pretty" by changing the type of point, the xlim to be 1-5 and ylim to be 0-10, the colors, the axis labels, and the title of the graph. Does there appear to be an association? Find the correlation coefficient.

Problem 3: Describe the gender of professors using a table and a bar plot. Make sure the table and plot are properly labeled and titled!

Problem 4: Describe the relationship between course evaluation scores and gender of the professor by creating a side-by-side boxplot.Also visualize the distributions of course evaluation scores by gender using two histograms in the same graphics window. For both the histograms and boxplot, change the color and titles of the graphs. Do males or females get higher course evaluations, on average? Is it a statistically significant difference?

Problem 5: Describe the relationship between rank of the professor and the level of the course by generating a two-way table and a clustered bar plot. Add a title to the clustered bar plot. Add a legend to the plot describing the bar colors of each group.

Problem 6: Create at least one other data visualization using the ggplot2 package on variables not used in the previous problems.Write a few sentences on what the viewer should take away from the visualization (patterns, outliers, variable relationships, etc.) Be creative on this one.