06445 - Business Statistics

Course Unit Page

Academic Year 2022/2023

Learning outcomes

This course introduce students to the study of some multidimensional statistical methods, among the most frequently used in the firm, and to the use of a statistical software. Prerequisites: knowledge of basic statistical methods. Expected learning outcomes: at the end of the course the student is able to analyze the relationships of interdependence between business phenomena, to critically interpret empirical results, to use these results in the business decision process.

Course contents

The course consists in two modules (30 hours each)

Contents of the First Module

  • Recap on the following key concepts/tools: random variables,, Normal and Standard Normal Distribution
  • Mulitple Linear Regression
  • Hypothesys testing / significance test
  • Elasticity and marginal effects
  • Non-linear realtions: Log transformations (log-log, log-linear and linear-log models), Binary variables in regressions (intercept shift and interaction terms)
  • Introduction to logistic regression

Contents of the Second Module

  • hypothesis tests for the difference between two means
  • Limited Dependent variable models: probability linear model, probit, logit
  • Panel data model (fixed-effects)
  • Reading of empirical academic papers


  • R.C.Hill, W.E. Griffiths, G.C.Lim «Principi di Econometria» (2012) Prima Edizione Italina, Zanichelli
  • J.H.Stock e M.W.Watson «Introduzione all’ Econometria» (2016) Quarta Edizione, Pearson

Teaching methods

The course is held in the computer lab. 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)

Assignments will be proposed regularly. They 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. Assignments will not be graded; solutions will be provided for self-assessment.

Assessment methods

The course has a required cumulative final examination. You must take, and pass, the final examination to receive a passing grade in the course.

In place of the cumulative final examination, two partial exams are available. At the end of the first part of the course (30 hours) students can take a first partial exam. A second partial exam will be reserved to those students who have passed the first partial exam.

All the exams (first partial, second partial and cumulative final exam) will consist of written test in the computer lab. Students are required to enrol using Almaesami.

All exams will contain an exercise section and a data analysis section with Stata.

During the course, it will be possible to earn extra credits, which will be added to the online test/s grade. Details will be provided 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

Teaching tools

The following tools will be available on the UNIBO e-learning platform (VIRTUALE)

  • Slides/lecture notes: summarising theoretical concepts shown 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

Office hours

See the website of Sara Capacci

See the website of Silvia De Nicolò