title: “Multiple linear regression” author: “Jagdish Chhabria”" date: “May 2, 2019” output: html_document: css: ./lab.css highlight: pygments theme: cerulean df_print: paged toc: yes toc_depth: 2 toc_float: yes |

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. (Daniel S. Hamermesh, Amy Parker, Beauty in the classroom: instructors pulchritude and putative pedagogical productivity, *Economics of Education Review*, Volume 24, Issue 4, August 2005, Pages 369-376, ISSN 0272-7757, 10.1016/j.econedurev.2004.07.013. http://www.sciencedirect.com/science/article/pii/S0272775704001165.)

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 aslightly 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.

```
load("more/evals.RData")
require(ggplot2)
```

`## Loading required package: ggplot2`

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.

This is an observational study. It is incorrect to pose this question as a causality statement, because there is no separation of the sample into a treatment group and a control group. Given that this is not an experimental study, the question could be re-phrased as whether there is a correlation between beauty and course evaluations.

- 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?

`hist(evals$score)`

The distribution is left-skewed, with a center between 4 and 4.5. This tells me that more students tend to rate coureses/professors highly more often than not. This is in line with my expectations on account of the following reasons:

Even if the survey granted anonymity, I’d expect students to not be very forthcoming about providing low scores to professors since they’re perceived to be in a position of power with the ability to influence student grades.

Survivorship bias: Typically if professors consistently receive a poor rating, they would likely be replaced. That implies that only professors who had received an above average rating in the past would be still teaching. And it can be assumed that there is a correlation between professor’s past performance and performance in this current course i.e. that typically there would not be big jumps in the way professors teach courses and how they’re generally perceived by the students.

- Excluding
`score`

, select two other variables and describe their relationship using an appropriate visualization (scatterplot, side-by-side boxplots, or mosaic plot).

`plot(evals$age, evals$bty_avg)`

`boxplot(evals$bty_avg ~ evals$age)`

There seems to be a relationship between the professor’s age and average beauty rating i.e. older professors seem to be getting lower beauty scores. However the variability in beauty score does not seem to depend on the professor’s change as can be seen from the boxplot.

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:

`plot(evals$score ~ evals$bty_avg)`

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?

`nrow(evals)`

`## [1] 463`

The approximate number of points on the scatterplot seem to be much less than the number of observations in the dataset i.e. 463

- Replot the scatterplot, but this time use the function
`jitter()`

on the \(y\)- or the \(x\)-coordinate. (Use`?jitter`

to learn more.) What was misleading about the initial scatterplot?

`plot(jitter(evals$score) ~ evals$bty_avg)`

The initial scatterplot showed only one point (points plotted on top of each other) for the score regardless of its frequency. Given the relatively narrow range of possible scores, there are multiple cases of a professor being given the same score by students.

- 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 line to your plot using`abline(m_bty)`

. Write out the equation for the linear model and interpret the slope. Is average beauty score a statistically significant predictor? Does it appear to be a practically significant predictor?

```
m_bty<-lm(evals$score~evals$bty_avg)
plot(evals$score ~ evals$bty_avg)
abline(m_bty)
```

`summary(m_bty)`

```
##
## Call:
## lm(formula = evals$score ~ evals$bty_avg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9246 -0.3690 0.1420 0.3977 0.9309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.88034 0.07614 50.96 < 2e-16 ***
## evals$bty_avg 0.06664 0.01629 4.09 5.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5348 on 461 degrees of freedom
## Multiple R-squared: 0.03502, Adjusted R-squared: 0.03293
## F-statistic: 16.73 on 1 and 461 DF, p-value: 5.083e-05
```

\[\hat { score } =\quad 3.88034\quad +\quad 0.06664\quad \times \quad beauty\_ avg\] The linear model states that for every point increase in average beauty rating, there is a 1 point increase in professor score. Since the p-value is tiny, this does seem like a statistically significant relationship. However given the very small value of the slope i.e. 0.066, the practical significance is much lower since the actual impact is much lower. Given the R-squared value, the beauty average explains only 3.5% of the variability in scores.

- Use residual plots to evaluate whether the conditions of least squares regression are reasonable. Provide plots and comments for each one (see the Simple Regression Lab for a reminder of how to make these).

```
plot(m_bty$residuals~evals$bty_avg)
abline(h = 0, lty = 3)
```

`hist(m_bty$residuals)`

```
qqnorm(m_bty$residuals)
qqline(m_bty$residuals)
```

There is no apparent pattern in the residuals, but they do seem to have lower variability at higher values of the average beauty rating. The histogram shows that the residuals are not nearly normal. Instead they are skewed to the left. The qqline confirms the lack of normality.

So the conditions for least squares regression don’t seem to be met.

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.

```
plot(evals$bty_avg ~ evals$bty_f1lower)
cor(evals$bty_avg, evals$bty_f1lower)
```

As expected the relationship 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 (columns 13 through 19) using the following command:

`plot(evals[,13:19])`

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 significant 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)
summary(m_bty_gen)
```

```
##
## Call:
## lm(formula = score ~ bty_avg + gender, data = evals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8305 -0.3625 0.1055 0.4213 0.9314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.74734 0.08466 44.266 < 2e-16 ***
## bty_avg 0.07416 0.01625 4.563 6.48e-06 ***
## gendermale 0.17239 0.05022 3.433 0.000652 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5287 on 460 degrees of freedom
## Multiple R-squared: 0.05912, Adjusted R-squared: 0.05503
## F-statistic: 14.45 on 2 and 460 DF, p-value: 8.177e-07
```

- P-values and parameter estimates should only be trusted if the conditions for the regression are reasonable. Verify that the conditions for this model are reasonable using diagnostic plots.

```
plot(m_bty_gen$residuals~evals$bty_avg)
abline(h = 0, lty = 3)
```

`hist(m_bty_gen$residuals)`

```
qqnorm(m_bty_gen$residuals)
qqline(m_bty_gen$residuals)
```

While the residuals still seem to be scattered randomly (some decrease in variability at higher beauty ratings), their distribution is still left-skewed, and the qqline shows a deviation from normality.

- Is
`bty_avg`

still a significant predictor of`score`

? Has the addition of`gender`

to the model changed the parameter estimate for`bty_avg`

?

The low p-value indicates that there is still a statistically significant relationship between beauty and score and gender of professor. While the addition of professor gender increases the adjusted R-squared, the 2 variables still account for only 5.5% of the variability in professor scores.

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.

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

We can plot this line and the line corresponding to males with the following custom function.

`multiLines(m_bty_gen)`

- 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?

\[\hat { score } =\quad 3.74734\quad +\quad 0.07416\quad \times \quad beauty\_ avg\quad +\quad 0.17239\quad \times \quad 1\] For two professors who received the same beauty rating, makes 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`

.

```
m_bty_rank <- lm(score ~ bty_avg + rank, data = evals)
summary(m_bty_rank)
```

```
##
## Call:
## lm(formula = score ~ bty_avg + rank, data = evals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8713 -0.3642 0.1489 0.4103 0.9525
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.98155 0.09078 43.860 < 2e-16 ***
## bty_avg 0.06783 0.01655 4.098 4.92e-05 ***
## ranktenure track -0.16070 0.07395 -2.173 0.0303 *
## ranktenured -0.12623 0.06266 -2.014 0.0445 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5328 on 459 degrees of freedom
## Multiple R-squared: 0.04652, Adjusted R-squared: 0.04029
## F-statistic: 7.465 on 3 and 459 DF, p-value: 6.88e-05
```

R re-codes the categorical variable with 3 levels, into 3 dummy variables, of which the reference variable has value 0 and the 2 other variables have value 1.

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 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 have the highest p-value in this model? Why?
*Hint:*Think about which variable would you expect to not have any association with the professor score.

I’d expect either rank to have a high p-value in this model. I’d think that picture color would not have any association with 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)
```

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

While rank does seem to have a non-trivial p-value, it is actually the variable indicating whether a single professor or multiple profeessors taught the course, that has the highest p-value.

- Interpret the coefficient associated with the ethnicity variable.

Holding all other variables constant, non-minority professors received a score that was 0.1234929 higher on average than their minority counterparts.

- Drop the variable with the highest p-value and re-fit the model. 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?

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

```
##
## Call:
## lm(formula = score ~ rank + ethnicity + gender + language + age +
## cls_perc_eval + cls_students + cls_level + cls_credits +
## bty_avg + pic_outfit + pic_color, data = evals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7836 -0.3257 0.0859 0.3513 0.9551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0872523 0.2888562 14.150 < 2e-16 ***
## ranktenure track -0.1476746 0.0819824 -1.801 0.072327 .
## ranktenured -0.0973829 0.0662614 -1.470 0.142349
## ethnicitynot minority 0.1274458 0.0772887 1.649 0.099856 .
## gendermale 0.2101231 0.0516873 4.065 5.66e-05 ***
## languagenon-english -0.2282894 0.1111305 -2.054 0.040530 *
## age -0.0089992 0.0031326 -2.873 0.004262 **
## cls_perc_eval 0.0052888 0.0015317 3.453 0.000607 ***
## cls_students 0.0004687 0.0003737 1.254 0.210384
## cls_levelupper 0.0606374 0.0575010 1.055 0.292200
## cls_creditsone credit 0.5061196 0.1149163 4.404 1.33e-05 ***
## bty_avg 0.0398629 0.0174780 2.281 0.023032 *
## pic_outfitnot formal -0.1083227 0.0721711 -1.501 0.134080
## pic_colorcolor -0.2190527 0.0711469 -3.079 0.002205 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4974 on 449 degrees of freedom
## Multiple R-squared: 0.187, Adjusted R-squared: 0.1634
## F-statistic: 7.943 on 13 and 449 DF, p-value: 2.336e-14
```

The co-efficients and significance of other variables did change after dropping the variable indicating single or multiple professors.

- Using backward-selection and p-value 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.

```
m_best <- lm(score ~ ethnicity + gender + language + age + cls_perc_eval
+ cls_credits + bty_avg
+ pic_color, data = evals)
summary(m_best)
```

```
##
## Call:
## lm(formula = score ~ ethnicity + gender + language + age + cls_perc_eval +
## cls_credits + bty_avg + pic_color, data = evals)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.85320 -0.32394 0.09984 0.37930 0.93610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.771922 0.232053 16.255 < 2e-16 ***
## ethnicitynot minority 0.167872 0.075275 2.230 0.02623 *
## gendermale 0.207112 0.050135 4.131 4.30e-05 ***
## languagenon-english -0.206178 0.103639 -1.989 0.04726 *
## age -0.006046 0.002612 -2.315 0.02108 *
## cls_perc_eval 0.004656 0.001435 3.244 0.00127 **
## cls_creditsone credit 0.505306 0.104119 4.853 1.67e-06 ***
## bty_avg 0.051069 0.016934 3.016 0.00271 **
## pic_colorcolor -0.190579 0.067351 -2.830 0.00487 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4992 on 454 degrees of freedom
## Multiple R-squared: 0.1722, Adjusted R-squared: 0.1576
## F-statistic: 11.8 on 8 and 454 DF, p-value: 2.58e-15
```

\[\hat { score } =\quad 3.771922\quad +\quad 0.167872\quad \times \quad ethnicity:not\_ minority\quad +\quad 0.207112\quad \times \quad gender:male\quad -\quad 0.206178\quad \times \quad lang:non\_ english\quad -\quad 0.006046\quad \times \quad age\quad +\quad 0.004656\quad \times \quad cls\_ percent\_ eval\quad +\quad 0.505306\quad \times \quad cls\_ credits:one\quad +\quad 0.051069\quad \times \quad beauty\_ average\quad -\quad 0.190579\quad \times \quad picture:color\]

- Verify that the conditions for this model are reasonable using diagnostic plots.

`hist(m_best$residuals)`

```
qqnorm(m_best$residuals)
qqline(m_best$residuals)
```