---
title: "Stat 140 Linear Regression Mini-Lab -- Practice with residual standard deviation and R^2"
author: "Who are you?"
date: "September 29, 2017"
output:
  html_document:
    fig_height: 3
    fig_width: 5
  pdf_document:
    fig_height: 2.8
    fig_width: 6
  word_document:
    fig_height: 3
    fig_width: 5
---


```{r include=FALSE}
require(readr)
```

## Anscombe's data

The following R chunk reads in a data set called `anscombe`.  It has four pairs of x variables and y variables: (x1, y1), (x2, y2), (x3, y3), and (x4, y4).  You will examine one of these pairs of variables, and then we will discuss them all as a class.

```{r, message=FALSE}
anscombe <- read_csv("https://mhc-stat140-2017.github.io/data/base_r/anscombe.csv")
```

#### 1) Fit a linear model 

SOLUTION:

```{r}
## use the lm() function to fit a linear model.
## It should look like this: anscombe_fit <- lm( ~, data = anscombe)
## You will need to fill in the proper formula with the
## response and explanatory variables you're using.

```

#### 2) Report and interpret the regression coefficients

SOLUTION:

```{r}
## Use the coef() function to print out the regression coefficients,
## or use summary(anscombe_fit) to print out more information, including the
## regression coefficients

```

#### 3) Report the residual standard deviation and interpret it using the "95" part of the 68-95-99.7 rule.

SOLUTION:

```{r}
## If you didn't already print out the summary(anscombe_fit) for part 2,
## you'll have to do it now

```


#### 4) Report and interpret $R^2$

SOLUTION:



#### 5) Put your regression coefficients, residual standard deviation, and $R^2$ on the board.
