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Statistics

Descrizione

Prerequisites

The course assumes prior knowledge of the following topics:

 Probability of an event; Discrete and continuous random variables;

 Probability distribution function, density function and cumulative distribution function;

Conditional probability and distribution. Law of total probability, Independence of events, Bayes Theorem. Expectation and variance of a random variable; Some specific random variables (Bernoulli, Binomial, Uniform, Gaussian).

Objectives
The course aims to deepen notions of descriptive and inferencial statistics both from a theoretical and an applied point of view. The students will be able to analyze a given data set. The freeware statistical software R Project will be introduced.

Description / Program
See attached pdf.

Learning Method / Style of Lessons
There will be theoretical and applied frontal lectures.

Exam Style
There will be graded homework assignments and a final exam. The exam (that will count for 70% of the final grade), will comprise two parts: a theory part and a practical part that will be held in the computer lab (to this aim students are requested to get familiar with the computers available in the USI computer lab).

Requested Material
Students are requested to bring to class their own laptop, if available.

Readings/Textbooks

There is no specific text book. Class notes and slides will be distributed during the course. For the theory part of the course, a good reference book is Introduction to the Theory of Statistics by A. M. Mood, F. A. Graybill, D. C. Boes; Publisher: McGraw-Hill 1974. The book is out of print and can be downloaded here http://www.ebooksdirectory.com/details.php?ebook=3627

For the applied part of the course students are referred to the online material available available here https://www.r-project.org

Lecture notes will also be provided.

Persone

 

Mira A.

Docente titolare del corso

Denti F.

Assistente

Ghiringhelli C.

Assistente

Peluso S.

Assistente

Pisati M. M.

Assistente

Informazioni aggiuntive

Semestre
Autunnale
Anno accademico
2019-2020
ECTS
6
Lingua
Inglese
Offerta formativa
Master of Science in Economics in Finance, Corso obbligatorio, Minor in Digital Finance, 1° anno

Master of Science in Economics in Finance, Corso obbligatorio, Minor in Banking and Finance, 1° anno

Master of Science in Economics in Finance, Corso obbligatorio, Minor in Quantitative Finance, 1° anno