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.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technicaladministrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period  From  To 

primo semestre (lauree)  Sep 28, 2020  Dec 23, 2020 
secondo semestre (lauree)  Feb 15, 2021  Jun 1, 2021 
Session  From  To 

sessione invernale  Jan 11, 2021  Feb 12, 2021 
sessione estiva  Jun 7, 2021  Jul 23, 2021 
sessione autunnale  Aug 23, 2021  Sep 17, 2021 
Session  From  To 

sessione autunnale (validità a.a. 2019/20)  Dec 9, 2020  Dec 11, 2020 
sessione invernale (validità a.a. 2019/20)  Apr 7, 2021  Apr 9, 2021 
sessione estiva (validità a.a. 2020/21)  Sep 6, 2021  Sep 8, 2021 
Period  From  To 

Vacanze di Natale  Dec 24, 2020  Jan 6, 2021 
Vacanze di Pasqua  Apr 3, 2021  Apr 6, 2021 
Vacanze estive  Aug 9, 2021  Aug 15, 2021 
Exam calendar
Exam dates and rounds are managed by the relevant Economics Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Should you have any doubts or questions, please check the Enrollment FAQs
Academic staff
Cicogna Veronica
veronica.cicogna@univr.it 045 802 8246Vannucci Virginia
virginia.vannucci@univr.itStudy 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
Modules  Credits  TAF  SSD 

2° Year activated in the A.Y. 2021/2022
Modules  Credits  TAF  SSD 

3° Year activated in the A.Y. 2022/2023
Modules  Credits  TAF  SSD 

Modules  Credits  TAF  SSD 

Modules  Credits  TAF  SSD 

Modules  Credits  TAF  SSD 

Modules  Credits  TAF  SSD 

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.
Statistics (2021/2022)
Teaching code
4S00121
Academic staff
Coordinator
Credits
9
Language
Italian
Scientific Disciplinary Sector (SSD)
SECSS/01  STATISTICS
Period
primo semestre (lauree) dal Sep 20, 2021 al Jan 14, 2022.
Location
VERONA
Learning outcomes
The course aims to provide the basic techniques of descriptive statistics, probability calculus and statistical inference for undergraduate students in business and economic sciences, who have acquired the necessary preliminary mathematical notions. Overall, these techniques provide the necessary toolkit for the quantitative analysis of processes related to the observation of collective phenomena. From a practical point of view, these techniques are necessary for descriptive, interpretative and decisionmaking purposes for conducting statistical surveys related to economic and social phenomena. In addition to providing the necessary mathematical statistics apparatus, the course aims at providing conceptual tools for a critical evaluation of the methodologies considered. At the end of the lessons, the student must be able to use the tools learned to conduct statistical analyses relating to economic and social phenomena.
Program
a) DESCRIPTIVE STATISTICS
• Data collection and classification; data types.
• Frequency distributions; histograms and charts.
• Measures of central tendency; arithmetic mean, geometric mean and harmonic mean; median; quartiles and
percentiles.
• Variability and measures of dispersion; variance and standard deviation; coefficient of variation.
• Moments; indices of skewness and kurtosis.
• Multivariate distributions; scatterplots; covariance; variance of the sum of more variables.
• Multivariate frequency distributions; conditional distributions; chisquared index of dependence; Simpson’s
paradox.
• Method of least squares; leastsquares regression line; Pearson’s coefficient of linear correlation; CauchySchwarz
inequality; R^2 coefficient; total, explained and residual deviance.
b) PROBABILITY
• Random experiments; sample space; random events and operations; combinatorics.
• Conditional probability; independence; Bayes' theorem.
• Discrete and continuous random variables; distribution function; expectation and variance; Markov and
Tchebycheff's inequalities. Special discrete distributions: uniform, Bernoulli, Binomial, Poisson and geometric.
Special continuous distributions: continuous uniform, Gaussian, exponential.
• Multivariate discrete random variables; joint probability distribution; marginal and conditional probability
distributions; independence; covariance; correlation coefficient.
• Linear combinations of random variables; average of independent random variables; sum of independent, Gaussian
random variables.
• Weak law of large numbers; Bernoulli’s law of large numbers for relative frequencies; central limit theorem.
c) INFERENTIAL STATISTICS
• Sample statistics and sampling distributions; Chisquare distribution; Student's t distribution; Snedecor's F
distribution.
• Point estimates and estimators; unbiasedness, efficiency, consistency; estimate of a mean, of a proportion, of a
variance.
• Confidence intervals for a mean, for a proportion (large samples) and for a variance.
• Hypothesis testing; one and two tails tests for a mean, for a proportion (large samples) and for a variance;
hypothesis testing for differences between two means, two proportions (large samples) and two variances.
Textbooks
 A. AZZALINI (2001) Inferenza statistica: una presentazione basata sul concetto di verosimiglianza, 2nd Ed.,
Springer Verlag Italia.
 E. BATTISTINI (2004) Probabilità e statistica: un approccio interattivo con Excel. McGrawHill, Milano.
 S. BERNSTEIN, R. BERNSTEIN (2003) Statistica descrittiva, Collana Schaum's, numero 109. McGrawHill, Milano.
 S. BERNSTEIN, R. BERNSTEIN (2003) Calcolo delle probabilita', Collana Schaum's, numero 110. McGrawHill, Milano.
 S. BERNSTEIN, R. BERNSTEIN (2003) Statistica inferenziale, Collana Schaum's, numero 111. McGrawHill, Milano.
 F. P. BORAZZO, P. PERCHINUNNO (2007) Analisi statistiche con Excel. Pearson, Education.
 D. GIULIANI, M. M. DICKSON (2015) Analisi statistica con Excel. Maggioli Editore.
 P. KLIBANOFF, A. SANDRONI, B. MODELLE, B. SARANITI (2010) Statistica per manager, 1st Ed., Egea.
 D. M. LEVINE, D. F. STEPHAN, K. A. SZABAT (2014) Statistics for Managers Using Microsoft Excel, 7th Ed.,
Global Edition. Pearson.
 M. R. MIDDLETON (2004) Analisi statistica con Excel. Apogeo.
 D. PICCOLO (1998) Statistica, 2nd Ed. 2000. Il Mulino, Bologna.
 D. PICCOLO (2010) Statistica per le decisioni, New Ed. Il Mulino, Bologna.
Teaching methods
Course load is equal to 84 hours: the course consists of 48 lecture hours (equal to 6 ECTS credits) and of 36 exercise hours (equal to 3 ECTS credits).
Study Guide
A detailed syllabus will be made available at the end of the course on the elearning platform.
Prerequisites
Students are supposed to have acquired math knowledge of basic concepts like limit, derivative and integral.
Exercise sessions
Exercise sessions are integral part of the course and necessary to adequate understanding of the topics.
Tutoring activities
There will be optional tutoring hours devoted to exercises during the course and before exam sessions. More detailed information will be made available in due course.
Bibliography
Examination Methods
The exam can be passed through two partial written tests or one general written test.
Contents, assessment methods and criteria for partial written tests
The first partial written test focuses on the part of the program explained until the break at the end of October, typically on Descriptive Statistics and part of Probability (cf. the program of the course). The second partial written test focuses on the rest of the program. The topics of the program on which each partial written test is based will be defined in detail in due course. The bonus earned by the student when passing the first partial written test can be used to access the second partial written test in ONLY ONE of the two exam sessions of January and February. In case of
 student's withdrawal during the second partial written test
 failing of the second partial written test
 failing of the total exam due to a final exam grade less than 18/30 (cf. assessment methods and criteria for partial written tests)
the exam can be subsequently taken ONLY through a general written test.
Both partial written tests include exercises and theoretical questions. Each partial written test is passed if the score is greater than or equal to 15/30. If both partial written tests are passed, the final exam mark results from the weighted average (eventually rounded up to the least greater integer) of the grades obtained in the single partial written tests. The exam is passed if this average is greater than or equal to 18/30. If the total score obtained is greater than or equal to 15 and less than 18, the student may take an additional oral test. There is the possibility to take an optional oral test for those who have obtained a score greater than or equal to 18/30. Date and time of the oral test will be promptly communicated after the written test.
Contents, assessment methods and criteria for general written test
The general written test covers all topics of the program and includes exercises and theoretical questions. The exam is passed if the score is greater than or equal to 18/30. If the grade earned is greater than or equal to 15 and less than 18, the student may take an additional oral test. There is the possibility to take an optional oral test for those who have obtained a score greater than or equal to 18/30. Date and time of the oral test will be promptly communicated after the written test.
Contents, assessment methods and criteria are the same for attending and nonattending students.
Type D and Type F activities
years  Modules  TAF  Teacher 

1°  Future matters  D 
Alessandro Bucciol
(Coordinator)

1°  Future matters  D 
Alessandro Bucciol
(Coordinator)

years  Modules  TAF  Teacher 

1°  Discovering fair trade  D 
Angelo Bonfanti
(Coordinator)

1°  Business skills in action  D 
Angelo Bonfanti
(Coordinator)

1°  Design and Evaluation of Economic and Social Policies  D 
Federico Perali
(Coordinator)

1°  Public debate and scientific writing  2020/2021  D 
Martina Menon
(Coordinator)

1°  Wake up Italia  2020/2021  D 
Sergio Noto
(Coordinator)

years  Modules  TAF  Teacher  

1°  Ciclo di video conferenze: "L’economia del Covid, Verona e l’Italia. Una pandemia che viene da lontano?"  2020/21  D 
Sergio Noto
(Coordinator)


1°  Ciclo tematico di conferenze (online): “Come saremo? Ripensare il mondo dopo il 2020”  2020/21  D 
Federico Brunetti
(Coordinator)


1°  Marketing plan  2020/21  D 
Virginia Vannucci
(Coordinator)


1° 2°  Programming in Matlab  D 
Marco Minozzo
(Coordinator)


1° 2° 3°  Data Analysis Laboratory with R (Verona)  D 
Marco Minozzo
(Coordinator)


1° 2° 3°  Data Visualization Laboratory  D 
Marco Minozzo
(Coordinator)


1° 2° 3°  Python Laboratory  D 
Marco Minozzo
(Coordinator)


1° 2° 3°  Data Science Laboratory with SAP  D 
Marco Minozzo
(Coordinator)


1° 2° 3°  Advanced Excel Laboratory (Verona)  D 
Marco Minozzo
(Coordinator)


1° 2° 3°  Excel Laboratory (Verona)  D 
Marco Minozzo
(Coordinator)


1° 2° 3°  Programming in SAS  D 
Marco Minozzo
(Coordinator)

Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.
Graduation
Student mentoring
Gestione carriere
Linguistic training CLA
Internships
Student login and resources
Modalità di erogazione della didattica
Le lezioni di tutti gli insegnamenti del corso di studio, così come le relative prove d’esame, si svolgono in presenza.
Peraltro, come ulteriore servizio agli studenti, è altresì previsto che tali lezioni siano registrate e che le registrazioni vengano messe a disposizione sui relativi moodle degli insegnamenti, salvo diversa comunicazione del singolo docente.