Insegnamento a.a. 2022-2023


Dipartimento di Scienze delle Decisioni / Department of Decision Sciences

Per la lingua del corso verificare le informazioni sulle classi/
For the instruction language of the course see class group/s below
Vai alle classi / Go to class group/s: 15 - 16 - 17 - 18
BIEM (8 cfu - I sem. - OBBC  |  SECS-S/01)
Docente responsabile dell'insegnamento / Course Director:

Classes: 15 (I sem.) - 16 (I sem.) - 17 (I sem.) - 18 (I sem.)

Class group/s taught in English

Lezioni della classe erogate in presenza

Suggested background knowledge

For a fruitful attendance, students are strongly advised to have a basic understanding of the concepts of probability theory and random variables. Notes on these arguments (taught in course cod. 30063, Mathematics, Module 2 (Applied)) can be found in chapters 3, 4 and 5 of the course textbook. In particular it is suggested to look carefully at the topics covered in paragraphs 4.3 and 4.7 .


For BIEM students the exam code 30001 STATISTICA / STATISTICS is a prerequisite of the exam code 30280 Applications for management For BIEF students the exam code 30001 STATISTICA / STATISTICS is a prerequisite of the exam code 30284 EMPIRICAL METHODS FOR ECONOMICS (INTRODUCTION TO ECONOMETRICS) and of the exam code 30285 EMPIRICAL METHODS FOR FINANCE (INTRODUCTION TO ECONOMETRICS FOR FINANCE)

Mission & Content Summary


In the last decade an unprecedented revolution has taken place in the collection of and accessibility to all types of data. Exploratory data analysis, inference and prediction are becoming more and more important in almost every field. The reliability of the conclusions drawn based on the analysis of data relies on the suitability of the applied procedures, as well as on the appropriate communication of results. This course aims at providing the basic theoretical and applied tools for a rigorous statistical analysis. Specifically, the course focuses on techniques to summarize and visualize data of different types and their possible relations, as well as on basic sampling and inferential procedures, and on the assessment of the risk associated to extrapolation and inference. In particular, students will learn how to extract information from data and how to assess the reliability of such information.


The course covers the following topics:

  • Collection, management and summary of data using frequency distributions, graphical representations and summaries.
  • Study of the relationship between two variables.
  • Statistical inference and sampling variability.
  • Theory of point estimation and confidence intervals.
  • Hypothesis testing.
  • Simple regression model and brief introduction to the multiple regression model.


Intended Learning Outcomes (ILO)


At the end of the course student will be able to...
  • Recognize different types of data.
  • Understand the difference between the tools of descriptive and inferential statistics, and identify the most suitable approach for the problem at hand.
  • Recognize simple statistical models.



At the end of the course student will be able to...
  • Properly summarize a dataset.
  • Estimate, and test hypotheses on, the unknown parameters of a population on the basis of sample data.
  • Build simple statistical models, as regression models, aimed at studying the relationships  between variables of interest.
  • Use the R software to address the aformentioned issues.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)


Beyond traditional classes, the course features hands-on classes, where the statistical software R - and in particular the integrated development environment (IDE) RStudio - is used to apply basic statistical analyses to data. More specifically, during these sessions students will use their laptop to address specific issues, and to interpret the obtained results.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  x x


The assessment method, both for attending and not-attending students, consists of 1) two midterm exams or 2) a general exam.


The two midterms are articulated into two parts. The first part, consisting in a traditional written exam with theoretical questions and traditional “paper and pencil” derivation exercises, is graded 26 points maximum. The second part consists of the analysis of a dataset using R/Rstudio (installed on each student’s laptop), and is graded 5 points maximum. The maximum grade in each midterm is 31/30.

To pass the exam, a grade higher than or equal to 15 is required in both midterms, and an average of at least 18 points. A final grade equal to 31 is rewarded cum laude.


The general exam is organized as the midterms, and consists of a first traditional part (theoretical questions and “paper and pencil” exercises) graded 26 points maximum, and of a second part with problems to be solved using R/Rstudio (installed on each student’s laptop), graded 5 points maximum. The maximum grade in the exam is 31/30; The exam is passed with a grade higher than or equal to 18. A final grade equal to 31 is rewarded cum laude.


The exam aims at assessing:

  • The ability to identify the proper methodology to solve a given problem.
  • The understanding of the logic underlying a certain procedure.
  • The ability to compute appropriate statistical measures with both a pocket calculator and a statistical software.
  • The ability of suggesting and implementing with R a statistical model, consistent with both the assumptions stated and the data at hand.
  • The ability to understand the software output.

Teaching materials


  • P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics, Pearson/Prentice Hall, 9th global edition (2019). 
  • Additional teaching note on Frequency Distributions, available on the Bboard platform.

  • A specific manual on the use of R/Studio available on the Bboard platform.


Last change 26/04/2023 19:40