Content:
1. Basic concepts of probability and statistical inference.
Discrete and continuous random variables. Expected values, variability. Point estimation. Confidence intervals. Hypothesis test.
2. R statistical software. 3. Basic concepts of linear algebra: vectors and matrices; Determinant and inverse of a matrix.
4. The linear regression model
Simple linear regression. Multiple linear regression. Inference. Eteroschedasticity and Autocorrelation. Dummy variables. Instrumental variables.
5. The linear regression model and R.
Study cases.
6. The logistic regression model.
Definitions. Parameter Estimation and Interpretation.
7. The logistic regression model and R.