Across various industries, compensation professionals work toorganize and analyze aspects of employment that deal with elementsof pay, such as deciding base salary, bonus, and commissionprovided by an employer to its employees for work performed.Acknowledging the numerous quantitative analyses of data that are apart of this everyday work, Statistics for Compensation provides acomprehensive guide to the key statistical tools and techniquesneeded to perform those analyses and to help organizations makefully informed compensation decisions.
This self-contained book is the first of its kind to explore theuse of various quantitative methods—from basic notions aboutpercents to multiple linear regression—that are used in themanagement, design, and implementation of powerful compensationstrategies. Drawing upon his extensive experience as a consultant,practitioner, and teacher of both statistics and compensation, theauthor focuses on the usefulness of the techniques and theirimmediate application to everyday compensation work, thoroughlyexplaining major areas such as:
Frequency distributions and histograms
Measures of location and variability
Exponential curve models
Maturity curve models
Market models and salary survey analysis
Linear and exponential integrated market models
Job pricing market models
Throughout the book, rigorous definitions and step-by-stepprocedures clearly explain and demonstrate how to apply thepresented statistical techniques. Each chapter concludes with a setof exercises, and various case studies showcase the topic'sreal-world relevance. The book also features an extensive glossaryof key statistical terms and an appendix with technical details.Data for the examples and practice problems are available in thebook and on a related FTP site.
Statistics for Compensation is an excellent reference forcompensation professionals, human resources professionals, andother practitioners responsible for any aspect of base pay,incentive pay, sales compensation, and executive compensation intheir organizations. It can also serve as a supplement forcompensation courses at the upper-undergraduate and graduatelevels.
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Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.