Training and Research
PhD school courses/classes - 2025/2026
Cose che devi assolutamente sapere
1. I dottorandi devono ottenere un determinato numero di CFU ogni anno frequentando le attività didattiche offerte dal proprio corso di dottorato e dalla Scuola di Dottorato. Per ulteriori informazioni, consulta il Vademecum del Dottorando: https://www.univr.it/phd-vademecum
2. Non è richiesta l'iscrizione ai corsi, a meno che non sia esplicitamente indicato; si prega di consultare le informazioni sul corso per verificare se l'iscrizione è richiesta o meno. Quando l'iscrizione è effettivamente richiesta, non verrà inviata alcuna e-mail di conferma dopo la registrazione. Si prega di non inviare richieste di conferma: se sono state inserite le informazioni richieste, l'iscrizione è andata a buon fine.
3. Quando i link Zoom delle lezioni non sono esplicitamente indicati, si intende che i corsi sono organizzati per essere erogati solo in presenza. Non è quindi possibile in questi casi richiedere un link Zoom.
4. Tutte le informazioni sui corsi in nostro possesso vengono pubblicate qui. Si prega di non richiedere informazioni mancanti o link Zoom: non appena riceveremo nuove informazioni, le pubblicheremo e comunicheremo tempestivamente.
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
INTRODUCTION TO PROBABILITY (MODULE I)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO PROBABILITY (MODULE II)
Credits: 1
Language: English
Teacher: Marco Minozzo
STATISTICAL ANALYSIS WITH R - MODULE 1
Credits: 1
Language: English
Teacher: Michele Scandola
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Teacher: Marco Minozzo
BASIC LEVEL STATISTICS
Credits: 2.5
Language: English
Teacher: Margherita Brondino
GENERALIZED LINEAR MODELS: LOGISTIC REGRESSION, LOGLINEAR MODEL, POISSON MODEL
Credits: 1
Language: English
Teacher: Lucia Cazzoletti
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 0.5
Language: inglese
Teacher: Giuseppe Verlato
ANALISI DI SOPRAVVIVENZA: TEST LOG-RANK, CURVE DI SOPRAVVIVENZA DI KAPLAN-MEIER, MODELLO DI REGRESSIONE DI COX
Credits: 1.5
Language: English
Teacher: Simone Accordini
STATISTICAL ANALYSIS WITH R - MODULE 2
Credits: 0.8
Language: Inglese/Italiano
Teacher: Alessandro Mantovani
INTERMEDIATE LEVEL STATISTICS
Credits: 2.5
Language: Inglese
Teacher: Margherita Brondino
INTRODUCTION TO STATISTICAL INFERENCE (2025/2026)
Teacher
Referent
Credits
1
Language
English
Class attendance
Free Choice
Location
VERONA
Prerequisites and basic notions
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.
Program
- 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.
Bibliography
Didactic methods
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.
Learning assessment procedures
The final assessment will be through a written paper. Alternatively, there will be a Moodle QUIZ.
Scheduled Lessons
| When | Classroom | Teacher | topics |
|---|---|---|---|
|
Monday 23 February 2026 14:00 - 17:00 Duration: 3:00 AM |
To be defined | Marco Minozzo | Expectation of transformations of multidimensional random variables. Linear combinations of random variables, arithmetic mean of i.i.d. random variables. Weak law of large numbers. Introduction to classical statistical inference, random samples, point estimation, sampling distributions. STATISTICAL INFERENCE (Minozzo) - 23 February 2026 https://univr.zoom.us/j/89460085364 |
|
Tuesday 24 February 2026 14:00 - 17:00 Duration: 3:00 AM |
To be defined | Marco Minozzo | Estimation of the mean of a normal population, properties of estimators, unbiasedness, mean square error, efficiency, consistency. Confidence interval for the mean of a normal population with known variance, pivotal quantity. Introduction to hypothesis testing, type I and type II errors. STATISTICAL INFERENCE (Minozzo) - 24 February 2026 https://univr.zoom.us/j/81848602155 |
|
Wednesday 25 February 2026 14:00 - 16:00 Duration: 2:00 AM |
To be defined | Marco Minozzo | Hypothesis testing for the mean of a normal population with variance known, probability of type I and type II errors, critical value, power, test statistics, observed significance level. STATISTICAL INFERENCE (Minozzo) - 25 February 2026 https://univr.zoom.us/j/88097081470 |
