Info
Logo Bocconi

Course 2017-2018 a.y.

30401 - MATHEMATICS AND STATISTICS - MODULE 2 (STATISTICS)


BEMACS
Department of Decision Sciences

Course taught in English


Go to class group/s: 25

BEMACS (8 credits - II sem. - OB  |  SECS-S/01)
Course Director:
ISADORA ANTONIANO VILLALOBOS

Classes: 25 (II sem.)
Instructors:
Class 25: ISADORA ANTONIANO VILLALOBOS


Course Objectives
The course explores techniques for collecting and analyzing data. Concepts of statistical thinking, both descriptive and inferential, are covered. The course introduces the fundamental principles of probability theory and random variables, as a basis for the better understanding of inferential techniques. The focus is on analyzing real data, illustrating some of the methods and concepts with the help of the statistical software R.

Intended Learning Outcomes
Click here to see the ILOs of the course

Course Content Summary
The course focuses on the following main points
  • Introduction to probability: basic definitions and properties.
  • Random variables: discrete and continuous models and their properties.
  • Data collection and description through frequency distributions, graphical representation methods, and measures of location and spread.
  • The study of the relationship existing between two variables using two-way frequency tables, scatterplots, and measures of dependence (covariance and linear correlation coefficient). Linear interpolation.
  • Inferential statistics, population, sampling, sampling variability and sample statistics.
  • Point and interval estimation.
  • Parametric hypothesis testing for the population mean and the proportion of successes.
  • Nonparametric hypothesis testing for two-way tables.
  • Simple linear regression model: explanatory power of the model, parameter estimation, forecasting.

Teaching methods
Click here to see the teaching methods

Assessment methods
Click here to see the assessment methods

Detailed Description of Assessment Methods
The exam can be taken in two alternative ways.
  • Two partial written exams (one in the middle and one at the end of the course), with exercises and questions about theory.
  • A written general exam with exercises and questions about theory.
Both formats may require the use of the computer (R statistical software) for the exercise questions.

Textbooks
  • M. W. TROSSET, An Introduction to Statistical Inference and Its Applications with R, Chapman and Hall/CRC, 2009.

Prerequisites
Knowledge of methods and concepts introduced in the course of mathematics and basic computer skills. It is advisable to have passed the course Mathematics & Statistics - Module 1.
Last change 13/06/2017 12:27