Overview of Data Analysis

The Roles of Data and Predictive Analytics in Business

  • Explain how predictive analytics can help in business strategy formulation.
  • Distinguish structured from unstructured data
  • Recognize questions pertaining to business strategy that may utilize (active) predictive analytics.

 Reasoning with Data

  • Explain how inductive reasoning can be used to evaluate an assumption.
  • Explain an empirically testable conclusion
  • Explain how inductive reasoning can be used to evaluate an assumption.

Reasoning from Sample to Population

  • Explain the reasoning inherent in a confidence interval.
  • Explain the reasoning inherent in a hypothesis test.

Linear Regression as a Fundamental Descriptive Tool

  • Explain both intuitively and formally the formulas generating a regression line for a single treatment.
  • Explain the difference between linear regression and a regression line.

 Correlation vs. Causality in Regression Analysis

  • How to calculate partial and semi-partial correlations.
  • How to execute active prediction using regression analysis.

Advanced Methods for Establishing Causal Inference

  • Explain how instrumental variables can improve causal inference in regression analysis.
  • How execute two-stage least squares regression.

Prediction for a Dichotomous Variable

  • Describe the linear probability model.
  • Calculate marginal effects for logit and probit models.

Identification and Data Assessment

  • Explain what it means for a variable’s effect to be identified in a model.
  • Explain extrapolation and interpolation and how each inherently suffers from an identification problem.
  • Distinguish between functional form assumptions and enhanced data coverage as remedies for identification problems stemming from extrapolation and interpolation.
  • Differentiate between endogeneity and types of multicollinearity as identification problems due to variable co-movement.
  • Articulate remedies for identification problems and inference challenges due to variable co-movement.
  • Solve for the direction of bias in cases of variable co-movement.

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