Statistics for Compensation – A Practical Guide to Compensation Analysis

JOHN H. DAVIS, PhD , is a Certified Compensation Professional and President of Davis Consulting, where he has consulted on salary surveys, statistics, base pay programs, incentive programs, and performance management programs for numerous Fortune 1000-size organizations. He has taught undergraduate and graduate statistics courses and, for the past three decades, has taught thousands of compensation and human resources professionals statistics and its application to common problems in their fields.

Preface. Chapter 1 Introduction. 1.1 Why Do Statistical Analysis? 1.2 Statistics. 1.3 Numbers Raise Issues. 1.4 Behind Every Data Point, There is a Story. 1.5 Aggressive Inquisitiveness. 1.6 Model Building Framework. 1.7 Data Sets. 1.8 Prerequisites. Chapter 2 Basic Notions. 2.1 Percent. 2.2 Percent Difference. 2.3 Compound Interest. Practice Problems. Chapter 3 Frequency Distributions and Histograms. 3.1 Definitions and Construction. 3.2 Comparing Distributions. 3.3 Information Loss and Comprehensive Gain. 3.4 Category Selection. 3.5 Distribution Shapes. Practice Problems. Chapter 4 Measures of Location. 4.1 Mode. 4.2 Median. 4.3 Mean. 4.4 Trimmed Mean. 4.5 Overall Example and Comparison. 4.6 Weighted and Unweighted Average. 4.7 Simpson's Paradox. 4.8 Percentile. 4.9 Percentile Bars. Practice Problems. Chapter 5 Measures of Variability. 5.1 Importance of Knowing Variability. 5.2 Population and Sample. 5.3 Types of Samples. 5.4 Standard Deviation. 5.5 Coefficient f Variation. 5.6 Range. 5.7 P90/P10. 5.8 Comparison and Summary. Practice Problems. Chapter 6 Model Building. 6.1 Prelude to Models. 6.2 Introduction. 6.3 Scientific Method. 6.4 Models. 6.5 Model Building Process. Practice Problems. Chapter 7 Linear Model. 7.1 Examples. 7.2 Straight Line Basics. 7.3 Fitting the Line to the Data. 7.4 Model Evaluation. 7.5 Summary of Interpretations and Evaluation. 7.6 Cautions. 7.7 Digging Deeper. 7.8 Keep the Horse Before the Cart. Practice Problems. Chapter 8 Exponential Model. 8.1 Examples. 8.2 Logarithms. 8.3 Exponential Model. 8.4 Model Evaluation. Practice Problems. Chapter 9 Maturity Curve Model. 9.1 Maturity Curves. 9.2 Building the Model. 9.3 Comparison of Models. Practice Problems. Chapter 10 Power Model. 10.1 Building the Model. 10.2 Model Evaluation. Practice Problems. Chapter 11 Market Models and Salary Survey Analysis. 11.1 Introduction. 11.2 Commonalities of Approaches. 11.3 Final Market-Based Salary Increase Budget. 11.4 Other Factors Influencing the Final Salary Increase Budget Recommendation. 11.5 Salary Structure. Practice Problems. Chapter 12 Integrated Market Model -- Linear. 12.1 Gather Market Data. 12.2 Age Data to a Common Date. 12.3 Create an Integrated Market Model Interpretations. 12.4 Compare Employee Pay with Market Model. Practice Problems. Chapter 13 Integrated Market Model -- Exponential. Practice Problems. Chapter 14 Integrated Market Model -- Maturity Curve. Practice Problems. Chapter 15 Job Pricing Market Model -- Group of Jobs. Practice Problems. Chapter 16 Job Pricing Market Model -- Power Model . Practice Problems. Chapter 17 Multiple Linear Regression. 17.1 What It Is. 17.2 Similarities and Differences with Simple Linear Regression. 17.3 Building the Model. 17.4 Model Evaluation. 17.5 Mixed Messages in Evaluating a Model. 17.6 Summary of Regressions. 17.7 Digging Deeper. Practice Problems. Appendix. A.1 Value Exchange Theory. A.2 Factors Determining a Person's Pay. A.3 Types of Numbers. A.4 Significant Figures. A.5 Scientific Notation. A.6 Accuracy and Precision. A.7 Compound Interest -- Additional. A.8 Rule of 72. A.9 Normal Distribution. A.10 Linear Regression Technical Note. A.11 Formulas for Regression Terms. A.12 Logarithmic Conversion. A.13 Range Spread Relationships. A.14 Statistical Inference in Regression. A.15 Additional Multiple Linear Regression Topics. Glossary. References. Answers to Practice Problems. Index.