Who Should Attend
This course is intended for those individuals who are relatively new to the field as well as experienced practitioners who seek a basic treatment for applying statistics in total compensation management. Participants will benefit most from this course if they are proficient in the concepts covered in Course T1 – Total Rewards Management. It is assumed that participants have had no formal exposure to statistics, but have had a basic algebra course.

What You Will Learn

Statistics — Data, Information and Levels of Measurement
Introduces basic quantitative concepts and the four levels of measurement.

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

Percents and Related Issues
Defines and compares percents, market index and compa-ratios.

• Percents
• Individual compa-ratio
• Department compa-ratio
• Market index
• Percent difference
• Developing salary ranges
• Percents in benefits management

Time Value of Money
Addresses the time value of money, including compound interest and the compound salary growth rate.

• Time value of money
• Compound interest and compound salary growth rate
• Constant midpoint progression
• Annuity payments

Statistics — Collecting, Organizing, Grouping and Displaying Data
Introduces statistical analysis.

• Populations and samples
• Frequency distributions
• Organize, group and display data

Statistics — Lying with Statistics, Graphs and Displays
Explains how to accurately display data.

• Recognizing distorted data
• Mistakes that distort data

Statistics — Measures of Central Tendency and/or Location
Addresses measures of central tendency and measures of central location.

• Measures of central tendency
• Measures of location
• Percentile bars

Measures of Variability
Discusses measures of variability.

• Measures of variability
  • Range
  • Interquartile range
  • Standard deviation
• z-scores

Statistics — Shapes of Distributions
Addresses shapes of distributions.

• Interpreting distributions
• Normal distribution

Regression Analysis
Introduces modeling and regression analysis.

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