Statistics for Social Sciences II: Multivariate Techniques

Statistics for Social Sciences II: Multivariate Techniques
Bachelor in International Studies
ECTS Credits: 6.0
Course: 2
Semester: 1

STUDENTS ARE EXPECTED TO HAVE COMPLETED

Statistics for Social Sciences I
(In general: Fundamentals of Statistics, Linear Algebra, and Mathematical Analysis)

COMPETENCES AND SKILLS THAT WILL BE ACQUIRED AND LEARNING RESULTS

Knowledge of basic statistical techniques of Multivariate Analysis and Linear Regression.

Use of Multivariate Analisys and Linear Regression statistical software.

DESCRIPTION OF CONTENTS: PROGRAMME

The course is an introduction to the basic techniques of Multivariate Analysis and Linear Regresion,
with particular emphasis in computer and data applications.

1. Introduction. The data matrix. Mean vector. Covariance and correlation matrices. Graphical
methods. Linear combinations.
2. Simple linear regression. Least squares estimation. Inference and prediction. Residuals.
Transformations.
3. Multiple Linear Regression. Analysis of variance. Indicator variables.

Appendix:

[I] Matrix algebra.

[II] Statistical software: Excel, Matlab, R, SAS, SPSS, Statgraphics,

LEARNING ACTIVITIES AND METHODOLOGY

Competences will be acquired by students from:

[I] Theory classes: one per week.

[II] Practical classes in the computer room: one per week.

Activities [I] and [II] will be devoted to exercises, problems, data examples, and case studies. Teaching will make intensive use of resources available in Aula Global. Some short reading notes will be also distributed for helping to understand specific parts of the course contents.

ASSESSMENT SYSTEM

Continuous evaluation: 50%. This will consist in the completion of a Practice Workbook with a collection of computer and Data Analysis activities (50%).
Final exam: 50%.

BASIC BIBLIOGRAPHY

JOHNSON, R. A. and BHATTACHARYYA, G. K. . Statistics: Principles and Methods, Seventh Edition.. Wiley.. 2014
JOHNSON, R. A. and WICHERN, D. W. . Applied Multivariate Statistical Analysis, Sixth Edition.. Prentice Hall.. 2007

ADDITIONAL BIBLIOGRAPHY

JAMES, G., WITTEN, D., HASTIE, T., and TIBSHIRANI, R.. An Introduction to Statistical Learning with Applications in R. Springer. 2013
KUTNER, M. H., NACHTSHEIM, C. J., NETER, J. and LI, W. . Applied Linear Statistical Models, Fifth Edition.. McGraw-Hill.. 2005