Course 2025-2026 a.y.

Students without any background in basic statistics (elements of descriptive statistics, estimation, confidence intervals, tests, basic elements of the linear regression model) are advised to attend the preparatory statistics course.

Part I – Statistical tools for data analysis:

• Linear regression. Assumptions, estimates and their interpretation. Models with categorical covariates. Tests on the coefficients of the model. Interactions and transformations. Multicollinearity issues. Predictions. Residual analysis. Introduction to regression models for panel data. 
• Logistic regression. Interpretation of the coefficient estimates. Tests on the coefficients of the model. Evaluation of the quality of a model. Predictions. Multinomial logistic regression.
• Time serie analysis. Time series decomposition (trend, seasonality, error), autocorrelation function, stochastic models (ARMA, ARIMA), predictions.

Part II – Mathematical tools for data analysis:

• Valuation of investments.
• Valuation of financial transactions.
• Valuation of options (binomial model).

• Understand mathematical and statistical analyses of economic and business phenomena.
• Know the theoretical and operational tools required for the understanding and the implementation of such analyses.

• Deeply analyze and interpret economic and business phenomena, identifying and applying properly, even through the use of appropriate scientific software, suitable mathematical and statistical methodologies.

• Lectures
• Practical Exercises

Exercise sessions devoted to the analysis of economic and business data are proposed; to this aim the softwares presented during the course are  used. Students are invited to take an active part in the analysis.

The assessment for the course includes 3 in-class tests that are carried out during the exercise sessions in the first part of the course (statistics) and a final written exam.

The in-class tests consist of short tests, with closed-ended questions, aimed at checking students' understanding and their ability to apply (with the R/RStudio software) the concepts and techniques presented during the lectures. Each of the 3 tests has a maximum score of 2.

The final written exam covers the material presented in both parts of the course: statistics and mathematics. It consists of open-ended questions and exercises designed to test the students' ability to apply the knowledge acquired. Some questions require the use of the software presented during the course.

Each of the two parts of the written examination (the one relating to statistical tools and the one relating to mathematical tools) has a maximum mark of 31.

The final grade V for the examination is calculated as follows:

V=max{(2/3)·[I+(25/31)·S]+(1/3)·M , (2/3)·S+(1/3)·M},

where:

I=total score of the two in-class tests (maximum 6)

S=score of the statistics part of the final written exam (maximum 31)

M=score of the mathematics part of the final written exam (maximum 31)

The score is rounded up to the nearest integer number (upwards if the decimal point is 0.5).

If the in-class tests are not taken, the score I is equal to 0.

The closed-answer questions mainly aim to test the knowledge  of the mathematical and statistical tools for the analysis of economic and business data.

The open-answer questions mainly aim to test the  ability to apply the acquired knowledge.

• Part I: Lecture notes prepared by the teachers (available on Blackboard). Additional teaching material (exercises, dataset, ...) prepared by the teachers (available on Blackboard).
• Part II: Teaching material  prepared by the instructors (available on Blackboard).