Description: The data `Evals` are gathered from end of semester student evaluations for a large sample of professors from a university. In addition, six students rate the professors’ physical appearance. The result is a data frame where each row contains a different professor and each column has information on the professor. Please load in the data by copy/pasting the relevant code below:
R:
evals<-read.csv(“https://raw.githubusercontent.com/nazzstat/AppliedData/master/PracticeData/Evals.csv”, stringsAsFactors = FALSE)
SAS:
LIBNAME myFolder “P:\QAC\QAC201\PracticeData”;
data new; set myFolder.Evals; run;
Stata:
use “https://github.com/nazzstat/AppliedData/blob/master/PracticeData/Evals.dta?raw=true”, clear
| Variable | Description |
| Score | Average professor evaluation score (1) very unsatisfactory – (5) excellent |
| Rank | Rank of professor, (1) teaching, (2) tenure track, (3) tenured |
| Ethnicity | Minority (1), Not Minority (0) |
| Gender | Female (0), Male (1) |
| Age | Age of professor |
| bty_r1 | Beauty rating of professor from 1st semester female: (1) lowest – (10) highest. |
| bty_r2 | Beauty rating of professor from 3rd semester female: (1) lowest – (10) highest. |
| bty_r3 | Beauty rating of professor from 7th semester female: (1) lowest – (10) highest. |
| bty_r4 | Beauty rating of professor from 1st semester male: (1) lowest – (10) highest. |
| bty_r5 | Beauty rating of professor from 3rd semester male: (1) lowest – (10) highest. |
| bty_r6 | Beauty rating of professor from 7th semester male: (1) lowest – (10) highest. |
Instructions:
- Call in the data set.
- Make an appropriate plot that describes the distribution of the scores. Answer Questions #1 and #2 on moodle.
- Make a frequency table with cumulative percents of bty_r1. Answer Questions #3 and #4.
- Make a frequency table with cumulative percents of bty_r4. Answer Questions #5 and #6.
- Create a new variable bty_total that sums up the beauty scores for each professor.
- Evaluate the relationship between bty_total and score using the pearson correlation test. Answer Questions #7 and #8.
- Find the mean evaluation score based on gender. Answer Question #9.
- Determine whether there is a significant relationship between score and gender. Answer Question #10.
- Create a new categorical variable junior from the quantitative variable age by dummy coding professors under 38 years old with a 1 (junior faculty), and professors at least 38 years old with a 0 (not junior faculty).
- Determine what percent of faculty is considered junior and not junior based on your new variable. Answer Question #11.
- Determine whether there is a significant relationship between gender and rank. Answer Questions #12 and #13.
- Run two multiple regression analyses, first examining the association between bty_total (explanatory variable) and score (response variable) (MODEL1) and then examining the association between bty_total (explanatory variable) and score (response variable), controlling for gender. Answer Questions #14 and #15.
- Submit your program.
Check your answers by submitting them on moodle under Practice B for Exam 4.