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Quantitative Principles in Compensation Management Certification

What You Will Learn

  • See how to apply key concepts that are vital to compensation work, such as salary ranges and percents, individual and department compa-ratios, the time value of money and market index.
  • Learn to hone your decision making by applying key statistical tools, such as measures of variability, shapes of distributions, and regression analysis.
  • Learn about populations, samples, and frequency distributions.
  • Spot distorted data and recognize common mistakes that cause data distortion.
  • See how to effectively organize, group, and display data.
Who Will Benefit from This Course?

While designed for compensation and HR professionals seeking a foundational understanding of statistics to better manage compensation, this course will help anyone who works with statistics and numbers. Imagine the power of having an entire organization filled with employees who know how analyze and make sense of data!

Course Synopsis

This course will help you master an invaluable set of skills in applied statistics and analytics. Through hands-on Excel practice, learn to unlock data-driven insights that can lead to better, more strategic business outcomes.

In addition to compensation and HR professionals, this course can help anyone who works regularly with data.

Course Topics
Statistics — Data, Information and

Levels of Measurement

  • Why collect and use data
  • Five key questions to ask about the variable of interest
  • Levels of measurement


Percents and Related Issues

  • Percents
  • Compa-ratio (individual and department)
  • Market index
  • Percent difference
  • Developing salary ranges
  • Percents in compensation management


Time Value of Money

  • Present and future value
  • Compound interest and compound salary growth rate
  • Constant midpoint progression
  • Annuity payments


Statistics — Collecting, Organizing,

Grouping and Displaying Data

  • Populations and samples
  • Frequency distributions


Statistics — Lying with Statistics,

Graphs and Displays

  • Recognizing distorted data
  • Avoiding mistakes that distort data


Statistics — Measures of

Central Tendency

and/or Location

  • Measures of central tendency
  • Measures of location
  • Percentile bar


Measures of Variability

  • Measures of variability
  • Z-scores
Statistics — Shapes of Distributions

  • Interpreting distributions
  • Normal distribution


Regression Analysis

  • Regression models in a compensation environment
  • Developing a regression model
  • Cautions in the interpretation of correlations
  • Multiple regression