Statistics Lab.

- Brief introduction to R (
- Creating random variables.
- Applications to the central limit theorem and the law of large numbers
- Descriptive statistics: (i) Representing probability and cumulative distribution functions in discrete and continuous cases; (ii) calculating mean, variance, concentration indexes, covariance and correlation coeff.
- Statistical inference: (i) Point estimation and properties; (ii) interval estimation and properties; (iii) hypothesis testing and properties.
- Theory and applications of simple regression model (model, assumptions, estimation methods, residual diagnostics).
- If time permits:
Theory and applications of Bootstrap and Jacknife elements for simple parameters and for the regression model parameters.

Prerequisites: The topics of ?Foundations of Probability Theory and Statistical inference? are
supposed known.