B000365 - STATISTICS

Italian

Is a basic course in Statistics that develops the fundamentals of Inference and Modelling. The main part of the course concerns the models for studying multivariate dependencies based on linear regression for continuous responses and on logistic models for binary responses. An essential part of the course is the study of several data sets providing real illustrations for the discussed methods.

Wasserman, L. (2004). All of statistics. Springer-Verlag, New York.

Marchetti, G. M (2018). All of Statistics: Note al testo, esercizi e soluzioni. Dispense.


http://local.disia.unifi.it/gmm/.

KNOWLEDGE AND UNDERSTANDINGWe present and discuss the basic models for the study of statistics. A central theme is the theory of statistical inference, both within the frequentist and the Bayesian approaches. Inference is applied to problems of random sampling from a population defined by single or multiple random variables. The study of dependence will be treated with some care distinguishing when the main goal is prediction or explanation. In this last case we study the process of building statistical models in the presence of mixed continuous and binary variables, including possibly quadratic terms and interactions.
APPLYING KNOWLEDGE AND UNDERSTANDINGIn statistics are crucial: the application of models to real problems, the model interpretation and the communication of results. Therefore, the course is based not only on theory but also on applications to data from various fields : Economics, Social Sciences, Biology or Engineering. Statistical computing will be carried out using one of the best statistical data analysis software, the language R. The student will be able to find the most appropriate models for given set of data with the capacity of defending the chosen methods. LEARNING OBJECTIVES• Develop model-building skills including evaluation of assumptions and interpretation of model-fitting• Learn and apply the basic mathematical theory of statistics• Develop verbal communication skills for discussing conclusions and limitations of statistical evidence; present data analysis appropriately. • Effectively use R, a widely-used statistical package, in data analysis

Calcolo delle Probabilità

Standard lectures, Exercises from the textbook, computer practicals on real problems using the R language

Frequency to lectures, practice and lab: Recommended

Teaching Tools:
http://local.disia.unifi.it/gmm
UniFi E-Learning: http://e-l.unifi.it

Office hours:
during the course: Wednesday: 14:00 to 16:00 or by appointment

Contact:
Dipartimento di Statistica Informatica e Applicazioni (DiSIA) in viale Morgagni, 65, 50134 Firenze (Ex-Farmacologia stanza 1/25)

email: giovanni.marchetti@unifi.it

Homework is assigned each week to the students mainly based on the exercises from the textbook. For the students attending regularly to classes the exercises. The final exam is oral.For students regularly attending the classes final score will be calculated based on a weighted average of the score for the homeworks (33%) and the oral (67%).If the students have not attended the lectures and not done the homeworks the oral includes questions concerning exercises from the textbook.

The lectures cover 12 weeks.

Data analysis in R (Rogantin)
Models and statistical Inference (Wasserman, chap. 6)
Estimation, empirical distribution function
(Wasserman, chap. 7)
Bootstrap (Wasserman, chap. 8)
Parametric Inference (Wasserman, chap. 9)
Test of hypotheses (Wasserman, chap. 10)
Linear and logistic regression (Wasserman, chap.13)
Multivariate models (Wasserman, chap. 14)
Inferenza sull’indipendenza (Wasserman, cap. 15)
Bayesian Inference (Wasserman, chap. 11)