Course 2017-2018 a.y.



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:

Classes: 25 (II sem.)

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
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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
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Assessment methods
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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.


  • M. W. TROSSET, An Introduction to Statistical Inference and Its Applications with R, Chapman and Hall/CRC, 2009.
Exam textbooks & Online Articles (check availability at the Library)


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