**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 Final Exam.