---
title: "Lab 11"
author: "Your Name Here"
date: ""
output: html_document
---

##### Remember to change the `author: ` field on this Rmd file to your own name.

### Learning objectives

> In today's Lab you will gain practice with the following concepts from today's class:

>- Interpreting linear regression coefficients of numeric covariates
- Interpreting linear regression coefficients of categorical variables
- Fitting linear regression models with interaction terms
- Interpreting linear regression coefficients of interaction terms

We'll begin by loading some packages and importing the data.
```{r}
library(tidyverse)
```

```{r, echo = FALSE}
# Load data from MASS into a tibble and do pre-processing
birthwt <- as_tibble(MASS::birthwt) %>%
  rename(birthwt.below.2500 = low, 
         mother.age = age,
         mother.weight = lwt,
         mother.smokes = smoke,
         previous.prem.labor = ptl,
         hypertension = ht,
         uterine.irr = ui,
         physician.visits = ftv,
         birthwt.grams = bwt) %>%
  mutate(race = recode_factor(race, `1` = "white", `2` = "black", `3` = "other")) %>%
  mutate_at(c("mother.smokes", "hypertension", "uterine.irr", "birthwt.below.2500"),
            ~ recode_factor(.x, `0` = "no", `1` = "yes"))
```

### Interaction terms in regression


**(a)** Run a linear regression to better understand how birthweight varies with the mother's age and smoking status (do not include interaction terms).

```{r}
# Edit me
```

**(b)** What is the coefficient of mother.age in your regression?  How do you interpret this coefficient?

```{r}
# Edit me
```

**(c)** How many coefficients are estimated for the mother's smoking status variable?  How do you interpret these coefficients?

```{r}
# Edit me
```

**(d)** What does the intercept mean in this model?

**(e)** Using ggplot, construct a scatterplot with birthweight on the y-axis and mother's age on the x-axis.  Color the points by mother's smoking status, and add smoking status-specific linear regression lines using the `stat_smooth` layer.

```{r}
# Edit me
```

**(f)** Do the regression lines plotted in part (e) correspond to the model you fit in part (a)?  How can you tell?

**(g)** Fit a linear regression model that now models potential interactions between mother's age and smoking status in their effect on birthweight.

```{r}
# Edit me
```

**(h)** Interpret your model.  Is the interaction term statistically significant?  What does it mean?

```{r}
# Edit me
```
