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There are two traditional ways to learn statistics. One way is to pass over the mathematical underpinnings and focus on developing relatively shallow knowledge about a wide variety of statistical procedures. Another is to spend years learning the mathematics necessary for traditional mathematical approaches to statistics. For many people who need to analyze data, neither of these paths is sufficient. The shallow-but-wide approach fails to provide students with the foundation that allows for confidence and creativity in analyzing modern datasets, and many researchers—though possibly motivated to learn math—do not have the background to start immediately on a traditional mathematical approach. This book exists to help researchers jump between tracks, providing motivated students whose knowledge of mathematics may be incomplete or rusty with a serious introduction to statistics that allows further study from more mathematical sources. This is done by focusing on a single statistical technique that is fundamental to statistical practice—simple linear regression—and supplementing the exposition with ample simulations conducted in the statistical programming language R. The first half of the book focuses on preliminaries, including the use of R and probability theory, whereas the second half covers statistical estimation and inference from semiparametric, parametric, and Bayesian perspectives. |
