STAT 210 Lecture Notes - Lecture 10: Confounding, Marginal Distribution, Scatter Plot
Document Summary
Everything to this point has assumed two quantitative variables: independent (or explanatory) variable x, dependent (or response) variable y. We have talked about how to describe the relationship between the two variables (direction, form and strength) and how the scatterplot, correlation coefficient and regression line can be used to help do this. Now suppose we have two qualitative or categorical variables: the variables vary in name, but not in magnitude, implying that they cannot be ranked. All we can do is name the categories and count the number of observations falling in each category. With two variables, we can count the number of observations that fall in each pair of categories. The counts are displayed in a two-way table. There exists a marginal distribution for each variable. A marginal distribution lists the categories of the variable together with the frequency (count) or relative frequency (percentage) of observations in each category.
