- Simple and multiple linear regression; correlation.
- The use of dummy variables.
- Residuals analysis and diagnostics assessment of model assumptions.
- Model building/variable selection, regression models and methods in matrix form.
- Generalized linear models.
- Basic knowledge of weighted least squares, regression with correlated errors, and nonlinear regression.
- Programming in R or Python: functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction, simulations, parallel data analyses, optimization, large data set handling, commenting and organizing code.