Statistics for Compensation
is the first book of its kind to focus on the quantitative methodologies that are utilized by compensation and human resources professionals in their everyday work. The book outlines those descriptive statistics that are most needed and used in compensation, ranging from basic notions about percents to multiple linear regression. Although the methods described in the book have a theoretical basis, the focus is on practical applications and what the author has found works- based on his experience as a practitioner, consultant, and teacher. Topics of coverage include: frequency distributions and histograms; measures of location; measures of variability; model building; linear models; exponential model; maturity curve model; power models; market models and salary survey analysis; linear and exponential integrated market models; job pricing market models; and multiple linear regression. The author has more than thirty years of experience teaching thousands of compensation and human resources professionals the practical statistics needed to analyze everyday problems in their fields. This book reflects what has been taught in and learned from those courses, and the writing itself reflects feedback from the students on making the subject matter understandable in their terms. Each chapter ends with a set of exercise problems which allows readers to test their comprehension of the presented material. Throughout the book, numerous examples of analyses, clear explanations and step-by-step procedures demonstrate how to apply the presented statistical techniques, and various case studies throughout showcase the topic’s relevance in today’s society. The book also features an extensive glossary of terms and an appendix with technical details, ensuring that the book can serve as a stand-alone reference for practitioners.
Keywords: john h. davis, davis consulting, statistics for compensation,  , measures of location, measures of variability, linear models, exponential models, maturity curve models, power models, market models, salary survey analysis, multiple linear regression