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Statistics

Description

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).

Basic knowledge of the freeware statistical software R Project

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 used.

Description / Program
See attached pdf.

Learning Method / Style of Lessons

There will be theoretical and applied frontal lectures.

Compliant with COVID-19 guidelines

Exam Style

Class participation is a mandatory component of the course grade.

There will be a final exam in the form of a multiple choice quiz that will comprise 100% of the final grade.

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.

People

 

Mira A.

Course director

Ebert A.

Assistant

Ghiringhelli C.

Assistant

Peluso S.

Assistant

Additional information

Semester
Fall
Academic year
2020-2021
ECTS
6
Language
English
Education
Master of Science in Economics in Finance, Core course, Minor in Quantitative Finance, 1st year
Master of Science in Economics in Finance, Core course, Minor in Banking and Finance, 1st year
Master of Science in Economics in Finance, Core course, Minor in Digital Finance, 1st year