# Introduction

The goal of this lab is to * Review **assumptions** of ANOVA/t-tests/regression * Review diagnostics plots for normality and constant variance * Introduce diagnostics plots for outliers * Investigate the role of **transformation** on diagnostic plots * Introduce how to model curved (non-linear) data with x^2 terms * BONUS: material on R^2 for tomorrow’s lecture occurs at the end

**Source Data**

Data on the relationship between the amount of pigment on lion snouts and their age from Whitman et al (2004). These data are featured in Chapter 17 of Whitlock & Shulter’s *Analysis of Biological Data, 2nd ed*.

The original data was presented in Figure 4, pg 2, of Whitman

**References:** Whitman, K, AM Starfield, HS Quadling and C Packer. 2004. Sustainable trophy hunting of African lions. Nature.

## Preliminaries

```
#The following sets up the data fro the analysis
#Set working directory
setwd("C:/Users/lisanjie2/Desktop/TEACHING/1_STATS_CalU/1_STAT_CalU_2016_by_NLB/Lecture/Unit3_regression/last_week")
```

**Load data**

`dat <- read.csv("lion_age_by_pop.csv")`

# Plot Raw Data

## Basic R plot

```
plot(age.years ~ portion.black,
data = dat)
```

## Change color w/ col =

```
plot(age.years ~ portion.black,
data = dat,
col = 2)
```