Other Useful Kinds of Regression
In This Chapter
Using Poisson regression to analyze counts and event rates
Getting a grip on nonlinear regression
Smoothing data without making any assumptions
This chapter covers some other kinds of regression you’re likely to encounter in biostatistical work. They’re not quite as ubiquitous as the types described in Chapters 18–20 (straight-line regression, multiple regression, and logistic regression), but you should be aware of them, so I collect them here. I don’t go into a lot of detail, but I describe what they are, when you may want to use them, how to run them and interpret the output, and what special situations you should watch out for.
Note: I don’t cover survival regression in this chapter, even though it’s one of the most important kinds of regression analysis in biostatistics. It has its own chapter (Chapter 24), in Part V of this book, which deals with the analysis of survival data.
Analyzing Counts and Rates with Poisson Regression
Statisticians often have to analyze outcomes consisting of the number of occurrences of an event over some interval of time, like the number of fatal highway accidents in a city in a year. ...