PROBABILITY AND STATISTICS

The course is one of the quantitative training activities of the Bachelor degree in Digital Management. The aim is to get the student familiar with
the main statistical tools for use in management, economics and finance, under conditions of uncertainty.
The course provides knowledge of descriptive statistics, probability and inference, as well as skills in the use of specific programs for analysing data and reporting.
At the end of the course, the student will be able to identify suitable models and methodologies in the context of interest; moreover he will learn to interpret and communicate the obtained results, with the aim of driving appropriate decisions.

1. Knowledge and understanding:
- to know the main tools for graphical representation and summary of a dataset
- to know the basic concepts of probability calculus and distributions for inference
- to know the basic methodologies of statistical inference

2. Ability to apply knowledge and understanding:
- to use specific programs for data analysis and reporting
- to use the appropriate terminology in all the processes of application and communication of the acquired knowledge

3. Ability to judge:
- to apply the acquired knowledge in a specific context, identifying the most appropriate models and methods

4. Communication skills:
- to present in a clear and exhaustive way the results obtained from a statistical analysis, both in written and oral form
- to know how to interact with the other students and with the instructor during the classes and on the virtual forum

5. Learning skills:
- to use and integrate information from notes, books, slides and practical lab sessions
- to assess the achieved knowledge through quizzes, exercises and assignments during the course

Mathematics for decision sciences is propaedeutic.

The course provides a practical introduction to probability and statistics. The aim of the first part of the course is to get the students familiar with the most useful statistical techniques for summarising and representing datasets. Then some basic concepts about elementary probability and distributions are presented. The last part is devoted to statistical inference methods for estimation, testing and prediction. Theoretical presentations are always motivated by practical examples and applications to economic and business problems. The use of the statistical package R (http://cran.r-project.org/ ) is also introduced for data analysis, simulation and inference.

Descriptive statistics: population and samples; types of variables; basic graphical representations and summaries for numerical variables and factors; relationship between two factors and the Chi-squared statistics; relationship between two numerical variables, correlation and regression.
Probability: sample space, events and probability; independence; discrete and continuous random variables; the binomial and the normal distributions.
Inference: sample distributions; estimation of the mean and the standard deviation of a population; confidence intervals; hypotheses testing and p-values.

Robinson, R. and White, H. (2016) Elementary Statistics with R. Available at http://homerhanumat.github.io/elemStats/

The achievement of the course objectives is assessed through participation in activities and assignments during the course (30%) and a final exam (70%). The use of the software R is part of the program of the course and the main tool for solving the assignments and for the exam. Examples of quizzes and exercises will be available in Moodle.

Activities and assignments consist of discussions about different topics on the Moodle forum, solution of quizzes and exercises in Moodle, group projects and analyses of real datasets.

The final exam is composed of quizzes and exercises to be solved with R. The exercises are similar to those assigned in Moodle during the course.

Interactive approach based on lectures, practical lab sessions using R, case studies to be analysed and presented to the class. Use of e-learning platforms for discussions and learning assessment. Open-source programs for data analysis and reporting.

English

written