Chapter 1 Preface
This book will cover the basics of Inferential Statistics. We will assume that the reader has a strong working knowledge of probability and calculus; the key topics that are crucial to this book are:
- Joint and conditional probabilities and distributions; strong familiarity with Bayes’ Rule.
- The famous distributions (i.e., Normal, Exponential, Poisson, Binomial, Beta, Gamma, Uniform, etc.).
- Derivation and integration, as well as derivative-based optimization.
For a background on Probability, you can refer to this book; reading through this work on Probability will prepare you sufficiently well for this book on Inference.
We will also be reinforcing and exploring our findings with the statistical programming language R. Please refer to the first chapter of the book linked above for an introduction to R.
This book is a part of a series of works dedicated to providing free and open access to educational resources for topics in Statistics. You can reach the author at
firstname.lastname@example.org with any questions or comments, as well as (please!) any typos you find. If you like this book, you can find further study in Introductory Statistics and Stochastic Processes, in addition to the Probability book linked above. The full YouTube channel is available here.