STATISTICS
Instructional goals
The course introduces the main concepts of probability and statistics, providing the methodological foundations for data collection and analysis.
Intended learning outcomes
Knowledge and understanding:
The course develops students' ability in collecting, analyzing and critically interpreting data related to economics, finance, management as well as everyday situations. The course also contributes to students' mathematical skills.
Applying knowledge and understanding:
The course provides the student the knowledge of a series of tools such as probabilistic models to model phenomena whose outcomes are uncertain, estimation techniques for understanding and prediction, hypothesis testing for decision making.
During the final exam, students are required to use mathematical thinking to formalize complex problems and to apply analytical tools to solve them.
Making judgements:
When facing complex problems, students are encouraged to apply analytical tools in an independent way and to give original interpretations to the results they obtain. This is also a requirement for the final exam.
Communication skill:
The course contributes to students' mathematical reasoning and ability to communicate in mathematical language.
Learning skills:
The knowledge in Probability and Statistics acquired during the course will
allow the student to autonomously understand and interpret new more advanced techniques and adapt them to the specific reference context.
Course Contents
Elements of probability theory and main probability models.
Descriptive statistics: data sets description and summarization; correlation in bivariate data sets.
Statistical inference: sampling statistics; point estimation and confidence intervals; hypothesis testing.
Reference Books
Suggested readings:
Agresti, Franklin and Klingenberg. Statistics: The Art and Science of Learning from Data. Pearson.
Dekking et al. A Modern Introduction to Probability and Statistics. Springer.
Ross, Sheldon M. Introduction to probability and statistics for engineers and scientists. Elsevier.
Teaching Methods
Online and on campus lectures with at least three intermediate tests.
Assessment Method
Students will be evaluated on the basis of the score obtained in several intermediate tests and in the final exam.
Intermediate tests will be given during classes, at least three times during the semester. Students will be required to answer some multiple choice and some open questions.
During the final exam students will have to demonstrate their knowledge of the theoretical notions as well as their ability in using them for problem solving and result interpretation. The final exam will be written, but the teacher might require an oral integration.
The final exam will be evaulated in the usual 0-30 scale. intermediate tests can add up to 5 bonus points.
Does the syllabus cover sustainability topics?
No
Week 1 Contenuto sessioni on line e on campus
Descriptive statistics.
Week 2 Contenuto sessioni on line e on campus
Basic probability: events, experiments, conditional probability, independence.
Week 3 Contenuto sessioni on line e on campus
Bayes' formula.
Introduction to random variables.
Bernoulli sequences.
Week 4 Contenuto sessioni on line e on campus
Discrete and continuous random variables.
Introduction to multivariate random variables.
Week 5 Contenuto sessioni on line e on campus
Independent random variables.
Properties of expectations.
Week 6 Contenuto sessioni on line e on campus
Variance and covariance.
Main probability models.
The normal distribution.
Week 7 Contenuto sessioni on line e on campus
Lections suspended for midterm exams.
Week 8 Contenuto sessioni on line e on campus
Distribution of sample statistics.
Main convergence theorems.
Meximum likelihood estimation.
Week 9 Contenuto sessioni on line e on campus
Sampling properties of estimators (MSE and bias).
Confidence intervals for the parameters of a normal population.
Week 10 Contenuto sessioni on line e on campus
Confidence intervals for the difference of expectations.
Asymptotic confidence intervals based on Central Limit Theorem approximation.
Week 11 Contenuto sessioni on line e on campus
Hypothesis testing.
Week 12 Contenuto sessioni on line e on campus
Recap.