Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year It will be activated in the A.Y. 2026/2027
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One module between the followingTwo modules among the following| Modules | Credits | TAF | SSD |
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Statistical and econometric analysis for international business (2025/2026)
Teaching code
4S012305
Teacher
Coordinator
Credits
9
Language
English
Scientific Disciplinary Sector (SSD)
SECS-P/05 - ECONOMETRICS
Period
Primo semestre LM dal Sep 29, 2025 al Dec 18, 2025.
Courses Single
Authorized
Learning objectives
This module provides students with the statistical and econometrics tools to analyze economic and business phenomena quantitatively. In the first part of the module, students will be able to derive descriptive statistics and they will learn inferential procedures (e.g., point and interval estimation and simple hypothesis testing) for the unknown parameters of a normal distribution and a Bernoulli distribution. In the second part of the module, students will be introduced to the linear regression model and its assumptions. Students will learn how to estimate regression coefficients and how to test hypotheses of interest, i.e., how to evaluate alternative theories with quantitative evidence.
Prerequisites and basic notions
Basic knowledge of calculus, probability, and statistics is required.
Program
1. Probability and Statistics reiew
1.1. Probability
1.2. Statistics
2. The linear regression model
2.1. Linear regression with a single regressor and hypothesis testing
2.2. Linear regression with multiple regressions and hypothesis testing
2.3. Non Linear effects
2.4. Regression with binary dependent variable
Bibliography
Didactic methods
Lectures and lab sessions. During the module, we will extensively use the software R to illustrate examples and carry out simple empirical analyses.
Learning assessment procedures
The exam will be a 2-hour written examination. Students will have to answer questions on theory and practice related to the whole program. In addition, students will be asked to carry out a small empirical application by working in groups. Details of the group project will be extensively discussed at the beginning of the course.
Evaluation criteria
To obtain full marks, students should show knowledge of the various econometric methodologies to understand and solve the diverse issues posed by regression models.
Criteria for the composition of the final grade
The final grade will be a weighted average of the marks obtained in the written examination (75%) and in the group project (25%).
Exam language
English
