```{r load-libraries, message=FALSE}
library(tidyverse)

theme_set(theme_classic(base_size = 12) + 
            theme(legend.position = "none"))
```

Read in data

```{r load-data, message = FALSE}
prof_data <- read_csv("https://dyurovsky.github.io/85309/data/demos/prof_data.csv") %>%
  select(-`...1`)

prof_data
```

Scatter plot of beauty vs. professor evaluation

```{r plot, fig.width = 4, fig.height= 3}
ggplot(prof_data, aes(x =  beauty, y = profevaluation)) +
  geom_point()
```

Full model

```{r full_model}
prof_lm <- lm(profevaluation ~ beauty + gender + age + formal + lower + 
                native + minority + students + tenure, data = prof_data)

summary(prof_lm)
```

Stepwise regression

```{r stepwise}
lm_null <- lm(profevaluation ~ 1, data = prof_data)

lm_all <- lm(profevaluation ~ ., data = prof_data)

# Backwards stepwise using defaults
lm_step <- step(lm_all)

# Forward stepwise starting from null model
lm_forward <- step(lm_null, scope = list(lower = lm_null, upper = lm_all), direction = "forward")

# Backward stepwise without defaults
lm_backward <- step(lm_all, scope = list(lower = lm_null, upper = lm_all), direction = "backward")
```
