OVERVIEW

The course aims to introduce statistical methods for decision making both for public and private organisations in the environmental field. After recalling basic statistical concepts, we introduce in the first part the main techniques for the analysis of time series. The second part is devoted to methods for the evaluation of environmental goods, with a focus on experimental choice. We illustrate the methods using data from national and international sources.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims at giving useful tools for decision making both for public and private organisations in the environmental field. The class mainly emphasizes the applied aspects of data analysis methods. The first part of the class focuses on the main techniques for the analysis of time series, such as moving averages, exponential smoothing and autoregressive models. The second part is devoted to methods for the evaluation of environmental goods, with a focus on experimental choice.

AIMS AND LEARNING OUTCOMES

The course is divided in two parts:

1) main techniques for the analysis of time series

2) methods for the evaluation of environmental goods.

All the topics will be accompanied by practical exercises in R, so that the student can also combine the understanding of the theory with the ability to apply correct statistical analyses in real contexts and to read correctly the output of the statistical procedures.

Knowledge and understanding: Students will know the main techniques and the main tools for inferential statistics. They must be able to frame these tools in general terms (both theoretical and applied), and to analyze the underlying mathematical and statistical background.

Ability to apply knowledge and understanding: Students will be able to identify, when faced with problems from different contexts, the correct analysis. Moreover, they will be able to evaluate the results obtained through statistical software.

Making judgments: Students will have to become aware of the potential and limits of the statistical techniques, through the analysis of examples and case studies.

Communication skills: Students must be able to use the correct technical statistical language for the communication of the results and for the description of the techniques.

Learning skills: Students will develop adequate learning skills in order to continue with further studies about other aspects of the subject and different fields of application than those illustrated. Furthermore, they must also be able to use the R software in a general context.

PREREQUISITES

The typical skills of the introductory courses in Mathematics and Statistics for Economics and Business.

TEACHING METHODS

Lecture and computer laboratory tutorials with R. Discussion of case studies: 16 hours (about 1/3 of the total amount) will be held in the computer laboratory.

SYLLABUS/CONTENT

0. Introduction and basic recalls on estimation and hypothesis testing.

1. Regression

2. Time Series Data

3. Correlation

4. Forecasting strategies

5. Basic stochastic models

6. Stationary models

7. Non-stationary models

RECOMMENDED READING/BIBLIOGRAPHY

Paul S.P. Cowpertwait · Andrew V. Metcalfe (2009)  Introductory Time Series with R, Springer

Additional course materials will be available on AulaWeb.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

This class follows the Department calendar for the 1st semester.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS