Behavioral Data Analysis with R and Python

Index

A

• A/B tests
  • about causal analytics, The Different Types of Analytics
  • complier average causal effect (CACE) for encouragement design, Complier Average Causal Estimate for Mandatory Intervention
  • powerful but narrow, Intervention
    • (see also experimental design)
• achieved significance level (ASL), The Bootstrap for Regression Analysis
• actions
  • about, Actions
  • causal diagram variable selection, Actions
  • data and ethical considerations, Collecting data and ethical considerations
  • intention-action gap, Intentions
  • modeling human behavior, Actions
  • uplift analysis, Segmenting experimental data
• age as behavioral determinant, Personal Characteristics
• aggregate metrics, Refine Behavioral Variables
  • linear algebra and aggregating variables, Aggregating Variables
  • transformations of causal diagrams
    • aggregating variables, Aggregating Variables
    • slicing or disaggregating variables, Slicing/Disaggregating Variables
• AirCnC (Air Coach and Couch)
  • call center problem-solving mode
    • about business problem, Cluster Randomization and Hierarchical Modeling
    • analyzing the experiment, Analyzing the Experiment
    • data and packages for example, Data and Packages
    • hierarchical modeling, Definition of the Intervention, Introduction to Hierarchical Modeling-Python Code
    • planning the experiment, Planning the Experiment-Behavioral Logic
    • power analysis, Power Analysis-Power curve
    • power curve, Power curve
  • connecting data and behavior, How to Connect Behaviors and Data, Identify the Category, Identify the Category, Understand the Context
  • customer satisfaction ...