Statistics for Social Sciences II: Multivariate Techniques

Universidad Carlos III de Madrid

Course Description

  • Course Name

    Statistics for Social Sciences II: Multivariate Techniques

  • Host University

    Universidad Carlos III de Madrid

  • Location

    Madrid, Spain

  • Area of Study

    International Studies, Statistics

  • Language Level

    Taught In English

  • Prerequisites

    STUDENTS ARE EXPECTED TO HAVE COMPLETED

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

  • Course Level Recommendations

    Lower

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

    6
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    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

Course Disclaimer

Courses and course hours of instruction are subject to change.

ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.