Scheda insegnamento

Anno Accademico 2022/2023

Conoscenze e abilità da conseguire

This course introduces students to the study of the main applied statistical methods to extract useful information from business databases and to support the management decision process. At the end of the course students are able (a) to select the most appropriate methodology to quantitatively analyse the business phenomena and (b) to critically interpret empirical results.


The course is designed to provide students with a working knowledge of hypothesis testing and regression analysis for understanding and interpreting multivariate data in business.

Special focus will be put on interpreting estimation results. At the end of the course students are able to:

  • understand and use properly hypothesis testing and some extensions of multiple linear regression
  • critically interpret empirical results obtained using the above tools
  • correctly communicate the information contained in empirical results (special emphasis will be placed on this skill)

The following contents will be covered:

  • Recap on the following key concepts/tools: random variables, Probability Density functions and Cumulative Distribution Function, Normal and Standard Normal Distribution, Use of the Standard Normal Table
  • Recap of Mulitple Linear Regression, Hypothesys testing / significance test
  • Some useful extensions of the linear regression:
    • Log transformations (log-log, log-linear and linear-log models)
    • Binary variables in regressions (intercept shift and interaction terms)
  • In the lab:
    • Introduction to Stata
    • Descriptive analysis and graphs in Stata
    • Hypothesis testing in Stata
    • Estimating multiple linear regressions (and extensions) in Stata


  • R. C. Hill, W. E. Griffiths and G. C. Lim, "Principles of Econometircs", 4th edition, New York: John Wiley and Sons
  • Stock, James H., and Mark W. Watson. "Introduction to econometrics" Pearson (any edition)

Metodi didattici

During the course theoretical and practical sessions will be held.

During practical sessions empirical knowledge of the proposed methods will be reached through real-world case studies performed using Stata.

Stata is available in all the computer labs in the Campus. Moreover, a Campus licence of Stata is available to all students enrolled in the course.

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students.

Home assignments will serve to reinforce class concepts and get familiarity with the software. Students are allowed and encouraged to work together on home assignments. However, a separate write-up is expected from each student, in his/her own words. Home assignments will not be graded, solutions will be provided for self-assessment.

Modalità di verifica e valutazione dell'apprendimento

The course has a required cumulative final examination. You must take, and pass, the final examination to receive a passing grade in the course. The final exam will be a written test in computer lab. Students are required to enrol using Almaesami.

The test contains three sections: 1) multiple choice/short-answers section (35% of Exam Score, 6 questions); 2) free response section on regression outputs (20% of Exam Score, 2 questions); 3) practical section with Stata (45% of Exam Score, 5 questions). The test duration is 75 minutes. (Please notice that the test structure might change; any modifications will be communicated in class).

Work on a group project: groups of 2-3 students can work in teams to present the results of a published journal article to their classmates. This activity allows students to get up to extra credits. Details about group projects will be provided in class.

Details will be discusses in class.

The grading system is on a 0-30 range, the following grid applies:

  • <18 failed
  • 18-23 sufficient
  • 24-27 good
  • 28-30 very good
  • 30 cum laude honors

Strumenti a supporto della didattica

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students. The teaching material is particularly rich, and it is composed of:

  • Slides/Lecture notes: summarising theoretical concepts shown in class
  • Stata datasets (named “Example 1”, “Example 2”, etc) used to formulate examples described in the slides (students can use these datasets to replicate examples discussed in class)
  • Do files, lecture notes and Stata datasets: with these tools students are able to follow the practical sessions step by step and to completely replicate them at home.
  • Stata Assignments and Solutions which will be regularly proposed to students
  • Miscellanea: exercises, focus notes, sample tests will be uploaded when needed

Orario di ricevimento

Consulta il sito web di Sara Capacci