Chapter 4 Nonlinear relationships

Read through the list of goals at the end of the chapter frequently.

In this chapter you will work with bivariate quantitative data and relationships between two categorical variables. For the quantitative part, you will learn to "straighten" x-y data, that is to use a transformation function to create a new relationship between f(x) and g(y) that is approximately linear. Find the least-squares relationship between the transformed data, then find the inverse of the original transformation function to transform the model into a curve which passes through your original x-y data. It's pretty cool to accomplish this and magnificently powerful math.

The second part, the categorical part, covers conditional and marginal probabilities. For instance, break the class into m/f and soph/jun/sen identifiers. Each person falls into exactly one of the gender groups and exactly one of the class year groups. Overall, what is the likelihood that a randomly-selected person is in a particular class? What is the probability that they are a particular gender? If they are a girl, then what is the probability that they are a senior? If they are a senior, what is the probability that they are a guy? Also, if guys do better than girls in 1st period and guys do better than girls in 5th period, how could the combination of the two classes indicate that girls are doing better than boys?

Dress appropriately for the weather and for doing activities that involve sitting on the floor this week. See you 10/8 at CiCi's???