Some code and problems solved in Mathematica and R in relation to:
- Probability, random variables, probability distributions and probability models, and their relevance to statistical inference.
- Standard probability models formulation.
- Properties of probability distributions, moment generating functions, variable transformations and conditional expectations to analyse common random variables and probability models.
- Basic ideas of estimation and hypothesis testing
- fit probability models to data by both estimating and testing hypotheses about model parameters.
- Underlying statistical theory of linear models and the limitations of such models.
- Fit linear models to data using R and interpret the results.
- Prediction (or classification) qualitative responses.
- Bayesian statistics.
- EM algorithm in simple settings.