Handbook of Regression Modeling in People Analytics

Cover
Half Title
Title Page
Copyright Page
Contents
Foreword by Alexis Fink
Introduction
1. The Importance of Regression in People Analytics
	1.1. Why is regression modeling so important in people analytics?
	1.2. What do we mean by ‘modeling’ ?
		1.2.1. The theory of inferential modeling
		1.2.2. The process of inferential modeling
	1.3. The structure, system and organization of this book
2. The Basics of the R Programming Language
	2.1. What is R?
	2.2. How to start using R
	2.3. Data in R
		2.3.1. Data types
		2.3.2. Homogeneous data structures
		2.3.3. Heterogeneous data structures
	2.4. Working with dataframes
		2.4.1. Loading and tidying data in dataframes
		2.4.2. Manipulating dataframes
	2.5. Functions, packages and libraries
		2.5.1. Using functions
		2.5.2. Help with functions
		2.5.3. Writing your own functions
		2.5.4. Installing packages
		2.5.5. Using packages
		2.5.6. The pipe operator
	2.6. Errors, warnings and messages
	2.7. Plotting and graphing
		2.7.1. Plotting in base R
		2.7.2. Specialist plotting and graphing packages
	2.8. Documenting your work using R Markdown
	2.9. Learning exercises
		2.9.1. Discussion questions
		2.9.2. Data exercises
3. Statistics Foundations
	3.1. Elementary descriptive statistics of populations and samples
		3.1.1. Mean, variance and standard deviation
		3.1.2. Covariance and correlation
	3.2. Distribution of random variables
		3.2.1. Sampling of random variables
		3.2.2. Standard errors, the t-distribution and confidence intervals
	3.3. Hypothesis testing
		3.3.1. Testing for a difference in means (Welch’s t-test)
		3.3.2. Testing for a non-zero correlation between two variables t-test for correlation)
		3.3.3. Testing for a difference in frequency distribution between different categories in a data set (Chi-square test)
	3.4. Foundational statistics in Python
	3.5. Learning exercises
		3.5.1. Discussion questions
		3.5.2. Data exercises
4. Linear Regression for Continuous Outcomes
	4.1. When to use it
		4.1.1. Origins and intuition of linear regression
		4.1.2. Use cases for linear regression
		4.1.3. Walkthrough example
	4.2. Simple linear regression
		4.2.1. Linear relationship between a single input and an outcome
		4.2.2. Minimising the error
		4.2.3. Determining the best fit
		4.2.4. Measuring the fit of the model
	4.3. Multiple linear regression
		4.3.1. Running a multiple linear regression model and interpreting its coefficients
		4.3.2. Coefficient confidence
		4.3.3. Model ‘goodness-of-fit’
		4.3.4. Making predictions from your model
	4.4. Managing inputs in linear regression
		4.4.1. Relevance of input variables
		4.4.2. Sparseness (‘missingness’) of data
		4.4.3. Transforming categorical inputs to dummy variables
	4.5. Testing your model assumptions
		4.5.1. Assumption of linearity and additivity
		4.5.2. Assumption of constant error variance
		4.5.3. Assumption of normally distributed errors
		4.5.4. Avoiding high collinearity and multicollinearity between input variables
	4.6. Extending multiple linear regression
		4.6.1. Interactions between input variables
		4.6.2. Quadratic and higher-order polynomial terms
	4.7. Learning exercises
		4.7.1. Discussion questions
		4.7.2. Data exercises
5. Binomial Logistic Regression for Binary Outcomes
	5.1. When to use it
		5.1.1. Origins and intuition of binomial logistic regression
		5.1.2. Use cases for binomial logistic regression
		5.1.3. Walkthrough example
	5.2. Modeling probabilistic outcomes using a logistic function
		5.2.1. Deriving the concept of log odds
		5.2.2. Modeling the log odds and interpreting the coefficients
		5.2.3. Odds versus probability
	5.3. Running a multivariate binomial logistic regression model
		5.3.1. Running and interpreting a multivariate binomial logistic regression model
		5.3.2. Understanding the fit and goodness-of-fit of a binomial logistic regression model
		5.3.3. Model parsimony
	5.4. Other considerations in binomial logistic regression
	5.5. Learning exercises
		5.5.1. Discussion questions
		5.5.2. Data exercises
6. Multinomial Logistic Regression for Nominal Category Outcomes
	6.1. When to use it
		6.1.1. Intuition for multinomial logistic regression
		6.1.2. Use cases for multinomial logistic regression
		6.1.3. Walkthrough example
	6.2. Running stratified binomial models
		6.2.1. Modeling the choice of Product A versus other products
		6.2.2. Modeling other choices
	6.3. Running a multinomial regression model
		6.3.1. Defining a reference level and running the model
		6.3.2. Interpreting the model
		6.3.3. Changing the reference
	6.4. Model simplification, fit and goodness-of-fit for multinomial logistic regression models
		6.4.1. Gradual safe elimination of variables
		6.4.2. Model fit and goodness-of-fit
	6.5. Learning exercises
		6.5.1. Discussion questions
		6.5.2. Data exercises
7. Proportional Odds Logistic Regression for Ordered Category Outcomes
	7.1. When to use it
		7.1.1. Intuition for proportional odds logistic regression
		7.1.2. Use cases for proportional odds logistic regression
		7.1.3. Walkthrough example
	7.2. Modeling ordinal outcomes under the assumption of proportional odds
		7.2.1. Using a latent continuous outcome variable to derive a proportional odds model
		7.2.2. Running a proportional odds logistic regression model
		7.2.3. Calculating the likelihood of an observation being in a specific ordinal category
		7.2.4. Model diagnostics
	7.3. Testing the proportional odds assumption
		7.3.1. Sighting the coefficients of stratified binomial models
		7.3.2. The Brant-Wald test
		7.3.3. Alternatives to proportional odds models
	7.4. Learning exercises
		7.4.1. Discussion questions
		7.4.2. Data exercises
8. Modeling Explicit and Latent Hierarchy in Data
	8.1. Mixed models for explicit hierarchy in data
		8.1.1. Fixed and random effects
		8.1.2. Running a mixed model
	8.2. Structural equation models for latent hierarchy in data
		8.2.1. Running and assessing the measurement model
		8.2.2. Running and interpreting the structural model
	8.3. Learning exercises
		8.3.1. Discussion questions
		8.3.2. Data exercises
9. Survival Analysis for Modeling Singular Events Over Time
	9.1. Tracking and illustrating survival rates over the study period
	9.2. Cox proportional hazard regression models
		9.2.1. Running a Cox proportional hazard regression model
		9.2.2. Checking the proportional hazard assumption
	9.3. Frailty models
	9.4. Learning exercises
		9.4.1. Discussion questions
		9.4.2. Data exercises
10. Alternative Technical Approaches in R and Python
	10.1. ‘Tidier’ modeling approaches in R
		10.1.1. The broom package
		10.1.2. The parsnip package
	10.2. Inferential statistical modeling in Python
		10.2.1. Ordinary Least Squares (OLS) linear regression
		10.2.2. Binomial logistic regression
		10.2.3. Multinomial logistic regression
		10.2.4. Structural equation models
		10.2.5. Survival analysis
		10.2.6. Other model variants
11. Power Analysis to Estimate Required Sample Sizes for Modeling
	11.1. Errors, effect sizes and statistical power
	11.2. Power analysis for simple hypothesis tests
	11.3. Power analysis for linear regression models
	11.4. Power analysis for log-likelihood regression models
	11.5. Power analysis for hierarchical regression models
	11.6. Power analysis using Python
12. Further Exercises for Practice
	12.1. Analyzing graduate salaries
		12.1.1. The graduates data set
		12.1.2. Discussion questions
		12.1.3. Data exercises
	12.2. Analyzing a recruiting process
		12.2.1. The recruiting data set
		12.2.2. Discussion questions
		12.2.3. Data exercises
	12.3. Analyzing the drivers of performance ratings
		12.3.1. The employee_performance data set
		12.3.2. Discussion questions
		12.3.3. Data exercises
	12.4. Analyzing promotion differences between groups
		12.4.1. The promotion data set
		12.4.2. Discussion questions
		12.4.3. Data exercises
	12.5. Analyzing feedback on learning programs
		12.5.1. The learning data set
		12.5.2. Discussion questions
		12.5.3. Data exercises
References
Glossary
Index