1. When you are trying to discover whether there is a relationship between two categorical variables, why is it useful to transform the counts in a crosstabs to percentages of row or column? Once you...

1. When you are trying to discover whether there is a relationship between two categorical variables, why is it useful to transform the counts in a crosstabs to percentages of row or column? Once you do this, how can you tell if the variables are related? 2. Suppose you have a crosstabs of two “Yes/No” categorical variables, with the counts shown as percentages of row. What will these percentages look like if there is absolutely no relationship between the variables? Besides this case, list all possible types of relationships that could occur. (There aren’t many.) 3. If you suspect that a company’s advertising expenditures in a given month affect its sales in future months, what correlations would you look at to confirm your suspicions? How would you find them? 4. Suppose you have customer data on whether they have bought your product in a given time period, along with various demographics on the customers. Explain how you could use pivot tables to see which demographics are the primary drivers of their “yes/no” buying behavior. 5. Suppose you have data on student achievement in high school for each of many school districts. In spreadsheet format, the school district is in column A, and various student achievement measures are in columns B, C, and so on. If you find fairly low correlations (magnitudes from 0 to 0.4, say) between the variables in these achievement columns, what exactly does this mean?