Regression
Least squares, regression diagnostics, regularization, logistic regression and generalized linear models.
14 planned
No page in this subject is open yet. The planned list below shows what is coming, in learning order.
Planned
In learning order. These are not pages yet.
- Least squares: which line, and why squaresIntro
- Regression to the meanIntro
- OLS as a projection: the geometry of regressionAdvanced undergrad
- Omitted variable biasAdvanced undergrad
- Leverage and influential pointsAdvanced undergrad
- Fitting curves with linear regression: basis functionsAdvanced undergrad
- Analysis of variance: splitting variation between and within groupsAdvanced undergrad
- Logistic regression: fitting probabilities to 0/1 dataAdvanced undergrad
- Standard errors of regression coefficientsAdvanced undergrad
- Generalized linear models: link functions and Poisson regressionAdvanced undergrad
- Multicollinearity: why coefficients go wild when predictors agreeAdvanced undergrad
- Heteroskedasticity and robust standard errorsAdvanced undergrad
- The Gauss-Markov theorem: when OLS is the best linear estimatorAdvanced undergrad
- Ridge and lasso: shrinking coefficients with a penaltyAdvanced undergrad
How it connects
Builds on Probability Statistical inference Linear algebra Numerical methods Optimization
Leads to Equities and portfolios Optimization Statistical inference Time series Bayesian statistics Computational statistics Monte Carlo and numerical pricing
Subjects marked “connection” are linked by a related idea, not by a prerequisite.