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R in Action by Robert Kabacoff

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Part 4. Advanced methods

In this final section, we consider advanced methods of statistical and graphical analysis to round out your data analysis toolkit. Chapter 13 expands on the regression methods in chapter 8 to cover parametric approaches to data that aren’t normally distributed. The chapter starts with a discussion of the generalized linear model, and then focuses on cases where we’re trying to predict an outcome variable that’s either categorical (logistic regression) or a count (poisson regression).

Dealing with a large number of variables can be challenging, due to the complexity inherent in multivariate data. Chapter 14 describes two popular methods for exploring and simplifying multivariate data. Principal components analysis can ...

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