Objectives

Explore visualization methods in R using

  • using data we collected in class (standing on one foot, pulse rates)
  • a data set of your choice in the Lock5Data

Plots and Descriptive Statistics

Combination of continuous and categorical variables

  • Side-by-side boxplots
  • Density plots (overlayed)
  • Difference in means

Two quantitative variables

  • Scaterplots
  • Correlation coefficient

Objectives (continued)

Explore how resistant (mean, median, sd) are to outliers

Explore sampling distributions (and standard errors) for varying sample sizes

Setting the seed

R has built in capabilities for generating random numbers

Similar to a random number table, they require a “starting” point, determined by the “seed”

The seed (i.e., starting value and all random numbers) will differ each time you run your code, unless you set the seed before generating random numbers.

set.seed(*number*) will allow you to produce the same output every time you run your code.