---
title: "HW01 - Statistical Modeling"
author: "STUDENT NAME"
format: html
execute:
    warning: false
    message: false
---


```{r}
# load appropriate libraries and import data here
library(tidyverse)
library(performance)

# This code requires you to have the data in the `data` folder, and that
# you are using an R project to manage your files. 
fev <- read.delim(here::here("data/Lung.txt"))
hiv <- read.delim(here::here("data/Parhiv.txt"))
dep <- read.delim(here::here("data/Depress.txt"))
```

# Part I: Statistical Modeling

## 1. Fit a linear regression model

```{r}

```


### a. Analyze the residuals



### b. Interpret each coefficient



## 2. Test gender as a moderator using a) using a stratified model




### b) using an interaction model.



## 3. Which of the two models in question 2 assumes that the affect of income on depression is constant (does not change) between males and females?




## 4. Determine whether the regression plane can be improved by also including `weight`. Use two measures of model fit to justify your answer to this question

   

    
## 5. Does weight _confound_ the relationship between age or height and FEV1?



## 6. Fit a model of income using age, sex, educational level and religion as predictors.



### a. Use a general F test to determine whether religion has an effect on income.


### b. State the reference categories for both relition and educational level.


### c. Interpret the coefficient for each level of educational level



\newpage



# Part II: Variable Selection

## 1. PMA6 9.9


## 2. PMA6 9.11


## 3. PMA6 9.12


## 4. PMA6 9.13


## 4. PMA6 9.14



   