Formazione e ricerca

Attività Formative del Corso di Dottorato - 2023/2024

L'offerta formativa viene gestita ed erogata dall'Univeristà di Trento

Offerta formativa da definire

Attività Formative della Scuola di Dottorato - 2023/2024

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.

Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, no confirmation e-mail will be sent after signing up.

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

Crediti

1

Lingua di erogazione

English

Frequenza alle lezioni

Scelta Libera

Obiettivi di apprendimento

The purpose of the module is to explain, at an elementary level, the conceptual basis of the classical (frequentist) approach to statistical inference. The topics will be illustrated and explained through many examples. Students are expected to acquire the language and the concepts needed to better understand the inferential procedures required for their subjects.

Prerequisiti e nozioni di base

There are no particular learning requirements. Students should have already been introduced (though at an elementary level) to probability and statistics. Students should also have some confidence in elementary set theory and mathematical calculus.

Programma

- Revision of limit theorems: weak law of large numbers; central limit theorem.
- Random samples, sample statistics and sampling distributions; normal and Bernoulli populations; sample mean, sample variance and sample proportion.
- Point estimation: estimators, unbiasedness, efficiency, mean square error, consistency.
- Interval estimation: pivotal quantity; paradigmatic examples.
- Hypothesis testing: type I and type II errors; critical value; confidence level; power; test statistic; observed significance level, paradigmatic examples.

Bibliografia

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Quando e Dove

Lessons will be delivered via Zoom; recordings will be made available by the lecturer. Attendance is not required, but passing a written test is required to obtain credits.
26 February 2024, 14:00-17:00
27 February 2024, 14:00-17:00
28 February 2024, 14:00-16:00
Moodle link

Modalità di verifica dell'apprendimento

The final assessment will be through a written paper. Alternatively, there will be a Moodle QUIZ.

Le/gli studentesse/studenti con disabilità o disturbi specifici di apprendimento (DSA), che intendano richiedere l'adattamento della prova d'esame, devono seguire le indicazioni riportate QUI

Lezioni Programmate

Quando Aula Docente Argomenti
lunedì 26 febbraio 2024
14:00 - 17:00
Durata: 3.00
Da definire Marco Minozzo INTRODUCTION TO STATISTICAL INFERENCE
martedì 27 febbraio 2024
14:00 - 17:00
Durata: 3.00
Da definire Marco Minozzo INTRODUCTION TO STATISTICAL INFERENCE
mercoledì 28 febbraio 2024
14:00 - 16:00
Durata: 2.00
Da definire Marco Minozzo INTRODUCTION TO STATISTICAL INFERENCE

Docenti

Dottorandi

Dottorandi presenti nel:

Non è presente alcuna persona. 40° Ciclo non iniziato.

Lezioni del Corso
Lezioni della Scuola di Dottorato

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