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The highly readable text captures the flavor of a course in mathematical statistics without imposing too much rigor; students can concentrate on the statistical strategies without getting lost in the theory. Students who use this book will be well on their way to thinking like a statistician. Practicing statisticians will find this book useful in that it is replete with statistical test procedures (both parametric and non-parametric) as well as numerous detailed examples.
Table of contents
- Regression Basics
- The Truth about Linear Regression
- Model Evaluation
- Smoothing in Regression
- Simulation
- The Bootstrap
- Splines
- Additive Models
- Testing Regression Specifications
- Weighting and Variance
- Logistic Regression
- Generalized Linear Models and Generalized Additive Models
- Classification and Regression Trees
II. Distributions and Latent Structure - Density Estimation
- Relative Distributions and Smooth Tests of Goodness-of-Fit
- Principal Components Analysis
- Factor Models
- Nonlinear Dimensionality Reduction
- Mixture Models
- Graphical Models
III. Causal Inference - Graphical Causal Models
- Identifying Causal Effects
- Estimating Causal Effects
- Discovering Causal Structure
IV. Dependent Data - Time Series
- Simulation-Based Inference
Appendices
- Data-Analysis Problem Sets
- Reminders from Linear Algebra
- Big O and Little o Notation
- Taylor Expansions
- Multivariate Distributions
- Algebra with Expectations and Variances
- Propagation of Error, and Standard Errors for Derived Quantities
- Optimization
- chi-squared and the Likelihood Ratio Test
- Rudimentary Graph Theory
- Writing R Functions
- Random Variable Generation
I. Regression and Its Generalizations
Advanced Data Analysis from an Elementary Point of View.pdf
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Data.rar
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Code.rar
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