"Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide i…
"ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and the researcher with a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics, t…
The complexity of large-scale data sets (Big Data) has stimulated the development of advanced computational methods for analysing them. There are two different kinds of methods to aid this. The model-based method uses probability models and likelihood and Bayesian theory, while …
Applied Regression Including Computing and Graphics; Contents; Preface; PART I INTRODUCTION; 1 Looking Forward and Back; 2 Introduction to Regression; 3 Introduction to Smoothing; 4 Bivariate Distributions; 5 Two-Dimensional Plots; PART II. TOOLS; 6 Simple Linear Regression; 7 I…
Packed with fresh and practical examples appropriate for a range of degree-seeking students, this easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), drawing test conclusions w…
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the gg…