Chapter 2. Statistics, Probability, and Information Theory for Predictive Modeling

This chapter covers some statistical, probabilistic, and information theory concepts before getting started with predictive analytics. Some examples are random sampling, hypothesis testing, chi-square testing, correlation, expectation, variance, covariance and Bayes' rule, and so on. In the second part of this chapter, we will discuss probability and information theory for predictive analytics. Information theory studies the quantification, storage, and communication of information. Probability theory is the branch of mathematics concerned with probability, the analysis of random phenomena.

The central objects of probability theory are random variables, stochastic ...

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