# 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 variableâ€™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.