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Statistics for Compensation

A practical guide teaching compensation and HR professionals the descriptive statistical and modeling techniques needed to analyze pay data and make sound organizational decisions.

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What it’s about

Statistics for Compensation demystifies the numbers behind pay decisions, giving compensation and human resources professionals a hands-on toolkit of descriptive statistics and model-building techniques grounded in real-world case studies from a fictitious company, BPD. Author John H. Davis draws on decades of practitioner, consulting, and teaching experience to walk readers from basic notions of percent and compound interest through frequency distributions, measures of location and variability, and into powerful regression-based market models—linear, exponential, maturity curve, power, and multiple linear regression. The book's central message is that statistics do not simply answer questions; they raise issues, challenge assumptions, and require aggressive inquisitiveness because behind every data point there is a story. With worked examples, practice problems, and a disciplined five-step model-building framework, the book equips professionals to identify market positions, build salary structures, set salary increase budgets, and defend recommendations with data—all in service of helping organizations attract, retain, motivate, and align the people they need.

The through-line

Who it’s for
A compensation or human resources professional responsible for pay who wants to make sound, data-backed decisions and confidently defend recommendations to executives.
The problem
They must analyze internal and external pay data to determine market positions, build salary structures, and set salary increase budgets—often under time pressure and uncertainty. They feel intimidated by statistics and worried that they lack the analytical competence to prove recommendations with data when executives say 'prove it to us.'
The plan
  1. Learn the basic notions—percent, compound interest, and how numbers raise issues.
  2. Summarize and describe data with frequency distributions, measures of location, and measures of variability.
  3. Follow the five-step model-building process to relate pay to grade, experience, or company size.
  4. Apply market models to identify market position, build salary structures, and set salary increase budgets.
  5. Use multiple linear regression to uncover the real drivers of pay and other outcomes while checking assumptions.
The payoff
The professional confidently makes data-backed recommendations, defends them to executives, and helps the organization attract, retain, motivate, and align the right people.

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