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3.19. Regression and Statistical Models (REG)

This knowledge area covers the construction, estimation, interpretation, and validation of statistical models for relationships between variables. It spans simple and multiple linear regression, generalized linear models, nonparametric regression, mixed models, and causal inference.

Knowledge Area (KA)	CS Core	KA Core
3.19.1 Simple Linear Regression	1	1

3.19.1. REG/Simple Linear Regression  (CS Core: 1 hr, KA Core: 1 hr) ↑ Back to top

The simple linear regression model: least squares estimation, inference for coefficients, model fit, and the assumptions of the classical linear model.
Topics:
Core

• Ordinary least squares estimation: derivation of coefficients and their properties
• Inference for regression coefficients: t-tests, confidence intervals, and p-values
• Coefficient of determination R-squared and analysis of variance decomposition
• Gauss-Markov theorem: BLUE property of OLS under classical assumptions

Learning Outcomes:
Core:

1. Fit a simple linear regression model and interpret the estimated coefficients [Usage]
2. Conduct hypothesis tests and construct confidence intervals for regression parameters [Usage]
3. Verify the Gauss-Markov assumptions and assess their consequences when violated [Assessment]