Checking date: 28/05/2021

Course: 2021/2022

Statistics, probability and multivariate analysis
Study: Master in Industrial Economics and Markets (270)

Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Department of Economics, Department of Statistics

Type: Compulsory
ECTS Credits: 6.0 ECTS


1. Capacity for modeling problems derived from data with many variables. 2. Acquire analytical skills to describe multivariate data. 3. Capacity for making and interpreting plots for data in high dimension. 4. Capacity for making statistical inference on a multivariate population. 5. Acquire skills in advanced statistical tools like principal component analysis, factorial analysis, classification and clustering. 6. Handle statistical software for multivariate analysis.
Skills and learning outcomes
Description of contents: programme
1. Descriptive analysis for univariate data 1.1 Introduction 1.2 Examples 2. Multivariate calculus 2.1 Vectors 2.2 Matrices 3. Descriptive analysis for multivariate data 3.1 Numerical analysis 3.2 Graphical analysis 4. Multivariate distributions and inference 4.1 Properties 4.2 Hypothesis tests 5. Principal component analysis 5.1 Introduction 5.2 Computation and interpretation 6. Factor analysis 6.1 Properties 6.2 Estimation and interpretation 7. Cluster analysis 7.1 Non-hierarchical models 7.2 Hierarchical models 8. Discriminant analysis and classification 8.1 Logistic regression 8.2 Bayes classifiers
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Michael Barrow. Statistics for Economics Accounting and Business Studies. Prentice Hall. 2010
  • Paul Newbold. Statistics for Business and Economics. Pearson. 2012
  • Richard A. Johnson and Dean W. Wichern. Applied multivariate statistical analysis. Prentice Hall. 2007
  • Theodor W. Anderson. An Introduction to Multivariate Statistical Analysis. Wiley. 2009
Additional Bibliography
  • Garrett Grolemund and Hadley Wickham. R for Data Science. O'Reilly, third edition. 2019

The course syllabus and the academic weekly planning may change due academic events or other reasons.

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