Course: 2022/2023

Statistics for Economics and Business

(17159)

* 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.
* To know basic concepts of non-parametric inference.

Skills and learning outcomes

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.
3.5 COnfidence intervals.
4. Hypothesis tests
4.1 Introduction: hypothesis, errors and function of power.
4.2 p-value.
4.3 Inference for one and two samples.
5. Non-parametric inference
5.1 Chi-squared test for distribution fitting.
5.2 Kolmogorov-Smirnov test for distribution fitting.

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

Basic Bibliography

- Wasserman, L (2004). All of Statistics. Springer-Verlag. New York.

Additional Bibliography

- Anderson, D. R., Sweeney, D. J., & Williams, T. A. . Statistics for business and economics . South-Western Cengage Learning. 2013
- 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.
- Wooldridge, J. M. . Introductory econometrics: a modern approach. Cengage Learning: South Western Educ. Pub. 2020

The course syllabus may change due academic events or other reasons.