Master in Business and Finance (Plan: 362 - Estudio: 69)
EPE
Coordinating teacher: VELILLA CERDAN, SANTIAGO
Department assigned to the subject: Statistics Department
Type: Compulsory
ECTS Credits: 5.0 ECTS
Course: 1º
Semester: 1º
Requirements (Subjects that are assumed to be known)
Foundations of Statistics
Objectives
The aim of the course is to review, at an intermediate level, the basic concepts and methods of Linear Regression. Emphasis is both in theory and applications.
Description of contents: programme
1. INTRODUCTION.
** 1.1 Formulation and meaning of a statistical regression problem.
** 1.2 Regression models. Goals of a regression analysis.
** 1.3 Data in a regression analysis.
** 1.4 Regression software.
2. THE MULTIPLE LINEAR REGRESSION MODEL: ESTIMATION.
** 2.1 Definition and matrix expression.
** 2.2 Least squares estimation.
** 2.3 Analysis of variance.
Appendix:
** A.1 The multivariate normal distribution.
3.THE MULTIPLE LINEAR REGRESSION MODEL: HYPOTHESIS
TESTING AND CONFIDENCE REGIONS.
** 3.1 The F-test for the linear general hypothesis.
** 3.2 Confidence regions.
** 3.3 Prediction intervals.
Appendix:
** A.1 Indicator variables.
4. MULTICOLLINEARITY, RESIDUAL ANALYSIS, AND DIAGNOSTIC
TECHNIQUES.
** 4.1 Multicollinearity: description and consequences.
** 4.2 Residual analysis.
** 4.3 Outliers and extreme cases.
5. GENERALIZED LEAST SQUARES THEORY.
** 5.1 Cases of known and unknown covariance matrix.
** 5.2 Heteroscedasticity.
** 5.3 Transformations.
** 5.4 Serial correlation.
6. TIME SERIES MODELS
** 6.1 Autoregressive (AR) and moving average (MA) models.
** 6.2 ARMA and ARIMA models.
Learning activities and methodology
There will be computer classes, in which several statistical packages will be used, Excel, Matlab, R, SAS, SPSS, Statgraphics, ..., with the purpose of illustrating the derivations of the theoretical classes.
Assessment System
% end-of-term-examination 50
% of continuous assessment (assigments, laboratory, practicals...) 50
Basic Bibliography
Brockwell, P. J. and Davis, R. A. . Introduction to Time Series and Forecasting, 3rd Edn.. Springer Verlag. 2016
Chatterjee, S. and Hadi, A. . Regression Analysis by Example, 5th Edn. John Wiley. 2012
James, G., Witten, D., Hastie, T. and Tibshirani, R. . An Introduction to Statistical Learning with Applications in R . Springer Verlag. 2013
Weisberg, S.. Applied Linear Regression, 4th Edition. Wiley. 2014
Additional Bibliography
Kabacoff, R. L. . R in action: Data analysis and graphics with R, 2nd Edn. . Manning Publications . 2015
Kutner, M. H., Nachtsheim, C., and Neter, J.. Applied Linear Statistical Models 4th Edition. . McGraw Hill. 2004
Rawlings, J. O., Pantula, S. G. and Dickey, D. A. . Applied Regression Analysis: A Research Tool, 2nd Edn.. Springer Verlag. 1998
The course syllabus may change due academic events or other reasons.
