Unit Overview
   • In the first unit we explored tests of significance, 
    confidence intervals, generalization, and causation 
    mostly in terms of a single proportion.
   • In the second unit we compared two proportions, two 
    averages (independent), and paired data.  All of these 
    had a binary categorical explanatory variable.
   • In the third unit we will expand on this to compare 
    multiple proportions, multiple averages, and two 
    quantitative variables. The explanatory variable will be 
    categorical (not necessarily binary) or quantitative.
 Unit Overview
  Unit Overview
  • Throughout this unit we can still express the null 
   hypothesis in terms of no association between 
   the response and the explanatory variable and 
   the alternative hypothesis in terms of an 
   association between the response and the 
   explanatory variable. 
  • We can also be more specific in our hypotheses 
   to reflect the type of data (and hence 
   parameters) we are working with. 
    Comparing More Than 
    Two Proportions
    Chapter 8
   Chapter Overview
   • In chapter 5, our explanatory variable had two outcomes 
    (like parents smoke or not) and the response variable 
    also had two outcomes (like baby is boy or girl). 
    Remember these are call binary.
   • In this chapter we will allow the explanatory variable to 
    have more than two outcomes (like both parents smoke, 
    only mother smokes, only father smokes, or neither 
    parent smokes).
   • We will still focus on the response variable having two 
    outcomes, but it doesn’t have to.  It can have many as 
    well. We will explore this in Section 8.2.