30457 - STATISTICS - MODULE 2 (APPLIED STATISTICS)

The empirical study of economic and social phenomena relies on the collection of significant amounts of data to describe the relationships among customers and firms on the markets. Similarly, managerial decision making needs to explicitly take into account data to identify the most profitable action in a business problem. Appropriate summary and organization of the data collected are crucial steps in a proper description of the phenomenon of interest. Nonetheless, by its very nature, available data represents only incomplete information about the subject matter; consequently conclusions drawn from it are affected by uncertainty, and decisions based on these conclusions might be subject to errors. The course aims at providing the student with appropriate methods and procedures to assess the reliability of the conclusions drawn from data and to monitor and guide the decision making process, avoiding gross mistakes and unpleasant consequences. These methods and procedures are widely known as Statistical Inference.

The course covers the following broad areas:

• Statistical inference and sampling variability.
• Theory of interval estimation and confidence intervals.
• Hypothesis testing.
• Simple linear regression model.

• Recognize different types of inferential problems.
• Identify the appropriate inferential tool to solve the problem.
• Recognize different types of statistical models underlying the problems.

• Build simple statistical models.
• Provide interval estimates and test hypotheses on the unknown parameters of a population on the basis of sample data.
• Use the R software to implement statistical models with data.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)

• Face-to-face lectures with use of software.

The exam can be taken in two alternative ways:

• Two partial written exams (one in the middle and one at the end of the course), with exercises and questions about theory.
• A written general exam with exercises and questions about theory.

Both formats might require the use of the computer (R statistical software) for the exercise questions.

• M. W. TROSSET, An Introduction to Statistical Inference and Its Applications with R, Chapman and Hall/CRC, 2009.
• Y. PAWITAN, In All Likelihood: Statistical Modelling and Inference using Likelihood, Oxford Science Publications, 2013.

• Additional material provided on the e-learning platform.