INFERENTIAL STATISTICS | Università degli studi di Bergamo - Didattica e Rubrica

INFERENTIAL STATISTICS

Attività formativa monodisciplinare
Codice dell'attività formativa: 
86059

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2021/2022
Insegnamento (nome in italiano): 
INFERENTIAL STATISTICS
Insegnamento (nome in inglese): 
INFERENTIAL STATISTICS
Tipo di attività formativa: 
Attività formativa Caratterizzante
Tipo di insegnamento: 
Opzionale
Settore disciplinare: 
STATISTICA (SECS-S/01)
Anno di corso: 
2
Anno accademico di offerta: 
2022/2023
Crediti: 
6
Responsabile della didattica: 

Altre informazioni sull'insegnamento

Modalità di erogazione: 
Didattica Convenzionale
Lingua: 
Inglese
Ciclo: 
Primo Semestre
Obbligo di frequenza: 
No
Ore di attività frontale: 
48
Ore di studio individuale: 
102
Ambito: 
Statistico-matematico
Prerequisites

Good knowledge of the main concepts of descriptive statistics: how to organize and describe data via tables, graphs and indices. Basic knowledge of Probability Theory.

Compulsory prerequisites required (Propedeuticità) are published on the web site: https://lt-eco.unibg.it/it/node/119

Compulsory: Statistica I

Educational goals

This course aim at presenting statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures, but also to understand why these procedures should be used.
Concepts will be motivated, illustrated, and explained in a way that attempts to increase one’s intuition.
To illustrate the diverse applications of statistics (with a particular focus on the Economics applications) and to offer students different perspectives about the use of statistics, the course will provide a wide variety of examples and problems, mostly referring to real-world issues.
Moreover, the use of a Software to apply the theoretical models and obtain concrete results in real data analysis will be largely discussed.

At the end of the course the students will gain the ability to:
a) perform point and interval estimation in univariate statistical model.
b) Perform a test of hypothesis and interpret the relative p-value.
c) Perform a linear regression model by using a statistical software, Interpret the results of the analysis.

Course content

PROBABILITY: Conditional probability. Bayes formula. Distributions. Random variables. Expected value. Note on subjective probability. Sequences of random variables and stochastic convergences. Multiple normal. Linear combinations of normals.
STATISTICAL INFERENCE. Point parametric estimation, estimation methods, efficient estimation. Interval estimation; Parametric and non-parametric hypothesis tests. Linear regression.

Teaching methods

The course is taught through class lectures.

Assessment and Evaluation

The exam is in written form. It consists in several exercises. The student must identify proper techniques to be used, among those learned in the course. One exercise will be devoted to the use or the interpretation of a linear regression model fitted with a statistical software.

Further information

- Attending classes is strongly recommended.
Non-attending students are strongly invited
to contact the teachers to get help for
preparing the exam.
- In case of specific directives by the
authorities required for the management of
epidemiological emergencies, the course
could undergo changes compared to what is
stated in the syllabus