- Start with your repo for this assignment
- located at organization for this class
- name contains HW 2 and your name

Clone this repo in your local directory on `gort`

. (Remember, the address is http://gort.stat.duke.edu:8787/.)

Edit the README.md to include some relevant information about the repository, commit, and push. (This is just to check everything is working fine, and you know what you’re doing.)

Open a new R Markdown file, name it the same name as your repository, and save it.

Include answers to all exercises in your R Markdown file. Your answers should always include any summary and/or plot you use to answer that particular question.

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 lab we will analyze the data from this study in order to learn what goes into a positive professor evaluation.

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 for *Data 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.

Let’s first load the packages we’ll use for this exercise. Note that you might not have some of them installed. If that’s the case, use `install.packages`

to install them first.

```
library(dplyr)
library(ggplot2)
library(stringr)
library(GGally)
```

And let’s also load the data:

`load(url("http://www.openintro.org/stat/data/evals.RData"))`

variable | description |
---|---|

`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. |

`cls_credits` |
number of credits of class: one credit (lab, PE, etc.), multi credit. |

`bty_f1lower` |
beauty rating of professor from lower level female: (1) lowest - (10) highest. |

`bty_f1upper` |
beauty rating of professor from upper level female: (1) lowest - (10) highest. |

`bty_f2upper` |
beauty rating of professor from second upper level female: (1) lowest - (10) highest. |

`bty_m1lower` |
beauty rating of professor from lower level male: (1) lowest - (10) highest. |

`bty_m1upper` |
beauty rating of professor from upper level male: (1) lowest - (10) highest. |

`bty_m2upper` |
beauty rating of professor from second upper level male: (1) lowest - (10) highest. |

`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. |

Is this an observational study or an experiment? The original research question posed in the paper is whether beauty leads directly to the differences in course evaluations. Given the study design, is it possible to answer this question as it is phrased? If not, rephrase the question.

Describe the distribution of

`score`

. Is the distribution skewed? What does that tell you about how students rate courses? Is this what you expected to see? Why, or why not? Include any summary statistics and visualizations you use in your response.Excluding

`score`

, select two other variables and describe their relationship using an appropriate visualizations.

The fundamental phenomenon suggested by the study is that better looking teachers are evaluated more favorably. Let’s create a scatterplot to see if this appears to be the case:

```
ggplot(data = evals, aes(x = bty_avg, y = score)) +
geom_point()
```

Before we draw conclusions about the trend, compare the number of observations in the data frame with the approximate number of points on the scatterplot. Is anything awry?

Replot the scatterplot, but this time use

`geom_point(position = "jitter")`

. What does “jitter” mean? What was misleading about the initial scatterplot?Let’s see if the apparent trend in the plot is something more than natural variation. Fit a linear model called

`m_bty`

to predict average professor score by average beauty rating and add the regression line to your plot. Write out the equation for the linear model and interpret the slope. Does there appear to be a practically significant relationship between professor score and average beauty rating?

The data set contains several variables on the beauty score of the professor: individual ratings from each of the six students who were asked to score the physical appearance of the professors and the average of these six scores. Let’s take a look at the relationship between one of these scores and the average beauty score.

```
ggplot(data = evals, aes(x = bty_avg, y = bty_f1lower)) +
geom_point(position = "jitter")
evals %>%
summarise(cor(bty_avg, bty_f1lower))
```

As expected the relationship is quite strong. Note that the correlation coefficient measures the linear association between two variables, and ranges between -1 and 1, -1 indicating a perfect negative relationship and 1 indicating a perfect positive relationship. It is nor surprising that the correlation between these two variables is quite strong - after all, the average score is calculated using the individual scores. We can actually take a look at the relationships between all beauty variables (variables that have the character string `bty`

in them) using the following command:

```
bty_cols <- str_detect(names(evals), "bty_")
ggpairs(evals, columns = which(bty_cols == TRUE))
```

- Describe how the
`str_detect`

function works and how we use information resulting from that function to make the pairwise plot.

These variables are collinear (correlated), and adding more than one of these variables to the model would not add much value to the model. In this application and with these highly-correlated predictors, it is reasonable to use the average beauty score as the single representative of these variables.

In order to see if beauty is still a a good predictor of professor score after we’ve accounted for the gender of the professor, we can add the gender term into the model.

```
m_bty_gen <- lm(score ~ bty_avg + gender, data = evals)
m_bty_gen
```

- How do the AIC and the adjusted \(R^2\) of this model and the previous one compare? Has the addition of
`gender`

to the model changed the parameter estimate (slope) for`bty_avg`

?

Note that the estimate for `gender`

is now called `gendermale`

. You’ll see this name change whenever you introduce a categorical variable. The reason is that R recodes `gender`

from having the values of `female`

and `male`

to being an indicator variable called `gendermale`

that takes a value of \(0\) for females and a value of \(1\) for males. (Such variables are often referred to as “dummy” variables.)

As a result, for females, the parameter estimate is multiplied by zero, leaving the intercept and slope form familiar from simple regression.

\[ \begin{aligned} \widehat{score} &= \hat{\beta}_0 + \hat{\beta}_1 \times bty\_avg + \hat{\beta}_2 \times (0) \\ &= \hat{\beta}_0 + \hat{\beta}_1 \times bty\_avg\end{aligned} \]

- What is the equation of the line corresponding to males? (
*Hint:*For males, the parameter estimate is multiplied by 1.) For two professors who received the same beauty rating, which gender tends to have the higher course evaluation score?

The decision to call the indicator variable `gendermale`

instead of`genderfemale`

has no deeper meaning. R simply codes the category that comes first alphabetically as a \(0\). (You can change the reference level of a categorical variable, which is the level that is coded as a 0, using the`relevel`

function. Use `?relevel`

to learn more.)

- Create a new model called
`m_bty_rank`

with`gender`

removed and`rank`

added in. How does R appear to handle categorical variables that have more than two levels? Note that the rank variable has three levels:`teaching`

,`tenure track`

,`tenured`

.

The interpretation of the coefficients in multiple regression is slightly different from that of simple regression. The estimate for `bty_avg`

reflects how much higher a group of professors is expected to score if they have a beauty rating that is one point higher *while holding all other variables constant*. In this case, that translates into considering only professors of the same rank with `bty_avg`

scores that are one point apart.

We can go a step further and include an interaction variable between `bty_avg`

and `gender`

:

```
m_bty_gen_int <- lm(score ~ bty_avg * gender, data = evals)
m_bty_gen_int
```

We can also visualize this model with the following:

```
ggplot(data = evals, aes(x = bty_avg, y = score, color = gender)) +
geom_point() +
stat_smooth(method = "lm")
```

- How does the relationship between beauty and evaluation score vary between male and female professors?

We will start with a full model that predicts professor score based on rank, ethnicity, gender, language of the university where they got their degree, age, proportion of students that filled out evaluations, class size, course level, number of professors, number of credits, average beauty rating, outfit, and picture color.

- Which variable would you expect to be the worst predictor of evaluation scores? Why?
*Hint:*Think about which variable would you expect to not have any association with the professor score.

Let’s run the model…

```
m_full <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval
+ cls_students + cls_level + cls_profs + cls_credits + bty_avg
+ pic_outfit + pic_color, data = evals)
summary(m_full)
```

For simplicity we’ll work with a model that does not have interaction effects.

Check your suspicions from the previous exercise. Include the model output in your response.

Interpret the coefficient associated with the ethnicity variable.

Drop the variable that results in the highest gain of adjusted R-squared. Did the coefficients and significance of the other explanatory variables change? (One of the things that makes multiple regression interesting is that coefficient estimates depend on the other variables that are included in the model.) If not, what does this say about whether or not the dropped variable was collinear with the other explanatory variables?

Using backward-selection and either AIC or adjusted R-squared as the selection criterion, determine the best model. You do not need to show all steps in your answer, just the output for the final model. Also, write out the linear model for predicting score based on the final model you settle on.

Based on your final model, describe the characteristics of a professor and course at University of Texas at Austin that would be associated with a high evaluation score.

Would you be comfortable generalizing your conclusions to apply to professors generally (at any university)? Why or why not?

This is a product of OpenIntro that is released under a Creative Commons Attribution-ShareAlike 3.0 Unported. This lab was written by Mine Çetinkaya-Rundel and Andrew Bray.

Gelman, Andrew, and Jennifer Hill. 2007. *Data Analysis Using Regression and Multilevel/Hierarchical Models*. 1st ed. Cambridge University Press.

Hamermesh, Daniel S., and Amy Parker. 2005. “Beauty in the Classroom - Instructors’ Pulchritude and Putative Pedagogical Productivity” 24 (4). Economics of Education Review: 369–76. doi:10.1016/j.econedurev.2004.07.013.