Course 2026-2027 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 (through Blackboard, without Respondus) that are carried out during the lessons of the first part of the course (statistical tools for data analysis) and a final written exam.

Each in-class test has a maximum time of 30 minutes and consists of  8 multiple choice questions, whose answers require the use of the statistical software (R/RStudio). The first test concerns the linear regression models, the second test concerns the logistic regression models, the third test concerns the time series analysis. The dates for the tests can be seen on the detailed timetable of each class, published on Blackboard. The score obtained in the in-class tests is valid only for the first exam session scheduled in the calendar (December 2026).

This   score varies from 0 to 4 and is determined as follows  based on the total number of correct answers obtained in the three tests:  

The final written exam covers the material presented in both parts of the course: statistical tools for data analysis and mathematical tools for data analysis. It consists of questions and exercises, whose solution  requires the use of the software presented during the course. The questions mainly aim to test the knowledge  of the mathematical and statistical tools for the analysis of economic and business data. The exercises  mainly aim to test the  ability to apply the acquired knowledge.

The part of the written exam related to statistical tools for data analysis has a maximum score of 18, while the part related to mathematical tools for data analysis has a maximum score of 9.

The written exam has a total duration of 2 hours.

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

V=max{S+M+T, 22/18*S+M},

where:

T=score of the three in-class tests (maximum 4)

S=score of the statistics part of the final written exam (maximum 18)

M=score of the mathematics part of the final written exam (maximum 9)

The final grade V 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 T is equal to 0.

The exam is passed if the final grade V, possibly rounded as described above, is not less than 18; no minimum score is required for the individual parts that make up the exam.

Of course, for both the in-class tests and the final exam the students must have the personal laptop.

• 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).