2nd Edition
Handbook of Regression Modeling in People Analytics With Examples in R and Python, Second Edition
Foreword by Alexis Fink Introduction 1 The Importance of Regression in People Analytics 2 The Basics of the R Programming Language 3 Statistics Foundations 4 Linear Regression for Continuous Outcomes 5 Binomial Logistic Regression for Binary Outcomes 6 Multinomial Logistic Regression for Nominal Category Out-comes
7 Proportional Odds Logistic Regression for Ordered Category Outcomes 8 Poisson, Quasi-Poisson and Negative Binomial Regression for Count Outcomes 9 Modeling Explicit and Latent Hierarchy in Data 10 Survival Analysis for Modeling Singular Events Over Time 11 Power Analysis for Estimating Required Sample Sizes for Modeling 12 Bayesian Inference - A Modern Alternative to Classical Statistical Methods 13 Linear Regression Using Bayesian Inference 14 Fitting Other Regression Models Using Bayesian Inference 15 Causal Inference - Moving From Association to Causation 16 Further Exercises for Practice References Glossary Index
Biography
Keith McNulty, PhD is a leading practitioner of applied statistics, psychometrics and people analytics. He has spent over 25 years developing data-driven frameworks to solve critical talent and organizational challenges in major organizations. He is currently Head of Talent Assessment at Citadel.
