Checking date: 14/01/2021

Course: 2020/2021

Statistics for Economics and Business
Study: Master in Business and Finance (69)


Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 5.0 ECTS


Competences and skills that will be acquired and learning results.
* To know exploratory data analysis. * To know concepts and properties of probability calculus and random variables. * To know the estimates construction methods and the estimates properties. * To understand the concept of confidence interval and its applications. * To know hypotheses testing, including the notion of p-value.
Description of contents: programme
1. Exploratory data analysis (EDO) 1.1 Descriptive measures. 1.2 Graphics and diagrams 2 Introduction to Probability calculus 2.1 Bases of Probability theory 2.2 Random variables. 2.3 Distributions. 2.4 Independence and transformations. 2.5 Expectation. 3 Point estimation and interval estimation. 3.1 Introduction: Estimation problems. 3.2 Examples. 3.3 Properties of estimators. 3.4 Construction of estimators. 4. Hypothesis tests 4.1 Introduction: hipothesis, errors and function of power. 4.2 Wald contrast. Fisher test. 4.3 p-value 4.4 Ratio of likelihood test.
Learning activities and methodology
The course will consist of lectures and problem-solving sessions. Tutorships will be scheduled according to the time of classes.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Basic Bibliography
  • Wasserman, L (2004). All of Statistics. Springer-Verlag. New York.
Additional Bibliography
  • Arnold, S.F. (1990). Mathematical Statistics. Prentice Hall. New York.
  • Bain, L.J. and Engelhardt, M. (2000). Introduction to Probability and Mathematical Statistics. Duxbury Classic. Boston.
  • Bickel, P.J. and Doksum, K.A. (2006). Mathematical Statistics- Second edition. Holden Day. San Francisco.
  • Casella, G. and Berger, R.L. (2012). Statistical Inference - Second edition. Wadsworth and Brooks/ Cole. San Francisco.
  • Dudewicz, E.J. and Mishra, S.N. (1988). Modern Mathematical Statistics. Wiley. New York.
  • Gibbons, J.D. and Chakraborti (2010). Nonparametric Statistical Inference. Fifth Edition. Marcel Dekker. New York.
  • Rice, J. (2006). Mathematical Statistics and Data Analysis. Third edition. Brooks and Cole. San Francisco.
  • Van der Vaart, A.W. (2001). Asymptotic Statistics. Cambridge University Press. Cambridge.

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