# Multivariate Techniques for Data Analysis

## Course Description

• ### Course Name

Multivariate Techniques for Data Analysis

• ### Area of Study

Accounting, Business Administration, Business Management, Economics, Finance, Financial Management, Mathematics, Statistics

• ### Language Level

Taught In English

• ### Prerequisites

STUDENTS ARE EXPECTED TO HAVE COMPLETED
Sequences:
Statistics I-II
Mathematics for Economics I-II
In general: Fundamentals of Statistics, Linear Algebra, and Mathematical Analysis.

• ### Course Level Recommendations

Upper

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

COMPETENCES AND SKILLS THAT WILL BE ACQUIRED AND LEARNING RESULTS.
Knowledge of basic statistical techniques of Multivariate Analysis.
Use of Multivariate Analisys statistical software.

DESCRIPTION OF CONTENTS: PROGRAMME
The course is an introduction to the basic techniques of Multivariate Analysis, with particular emphasis in computer and data applications.
1. Introduction. The data matrix. Mean vector. Covariance and correlation matrices. Graphical methods. Linear combinations.
2. Principal components. Motivation and construction. Data examples. Standardized case.
3. The multivariate normal distribution. Main properties.
4. Factor analysis. The orthogonal factor model. Factor estimation and rotation. Examples of application.

Appendix:
[I] Matrix algebra. Eigenvalues and eigenvectors.
[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 have two parts: [1] A preliminary exam (20%); [2] Completion of a Practice Workbook with a collection of computer and data analysis activities (30%). Final exam: 50%.
50 % end-of-term-examination: 50% of continuous assessment (assigments, laboratory, practicals?):

BASIC BIBLIOGRAPHY
- JOHNSON, R.A. and WICHERN, D.W. Applied Multivariate Statistical Analysis, 6th Edn, Prentice Hall., 2007
- EVERITT, B. and HOTHORN, T. An Introduction to Applied Multivariate Analysis with R., Springer, 2011
- JOHNSON, D. E. Applied Multivariate Methods for Data Analysts, Duxbury Press, 1998
- MANLY, B. F. J. Multivariate Statistical Methods: A Primer, Third Edition., Chapman and Hall/CRC, 2004

### Course Disclaimer

Please note that there are no beginning level Spanish courses offered in this program.

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

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.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Availability of courses is based on enrollment numbers. All students should seek pre-approval for alternate courses in the event of last minute class cancellations

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.

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