Programme

- Introduction to Statistics.
- Parametric Inference.
- Maximum Likelihood Estimation.
- The Method of Moments.
- Parametric Hypothesis Testing.
- Testing Goodness of Fit.
- Regression.
- Bayesian Statistics.
- Principal Component Analysis.
- Autocorrelation Function and Spectrum of Stationary Processes.
- Linear Stationary Models.
- Linear Nonstationary Models.
- Forecasting.
- Model Identification.
- Parameter Estimation.
- Model Diagnostic Checking.
- Transfer Function Models.
- Identification, Fitting, and Checking of Transfer Function Models.
- Intervention Analysis, Outlier Detection, and Missing Values.
- Multivariate Time Series Analysis.

Core Documentation

Cox D.R., Donnelly C.A - Principles of Applied Statistics. Cambridge University Press, 2011 – 9781107644458.

Montgomery D.C., Runger G.C. - Applied Statistics and Probability for Engineers. Wiley, 2018 - ISBN: 9781119400363

Reference Bibliography

Box G.E.P., Jenkins G.M., Reinsel G.C., Ljung G.M. - Time Series Analysis: Forecasting and Control, 5th Edition. Wiley, 2015 - ISBN: 9781118674918.

Type of delivery of the course

The teaching methodology consists of lectures for acquiring fundamental knowledge to achieve the educational objectives scheduled. The attending Students to teaching activities is optional.

Attendance

Attendance is not mandatory, although strongly recommended in order to take advantage of all the technical information made available through theoretical lessons and practical exercises.

Type of evaluation

The verification of skills learning takes place through an oral test lasting about 1 hour, for verifying the level of effective understanding of the concepts and the students' ability to apply them in real contexts.