Statistics: Multivariate Analysis
This 6-credits course runs for a quarter, and covers a selection of multivariate methods. It assumes an introduction roughly at the level of the course Estadística para Economistas in this School.Syllabus and bibliography
- Here it is the syllabus for the academic year 2012-2013, last time it was taught. . Syllabuses for previous academic years are also available (since 1.997-1.998).
Class notes and handouts
There is a very sketchy set of notes, that more or less cover the topics that we deal with in tha classroom. These notes are no subsititute for the familiarity with the bibliography cited in the syllabus.- TUSELL, F. (2005)   Análisis Multivariante. Updated: 26-12-2016. Size: 923355 bytes.
In a modern course on Multivariate Analisis, computation and visualization facilities are of tantamount importance. We use the statistical and graphics package R . Time permiting, we also devote some attention to the use of Ggobi , a graphics package accessible from R.
We use some handouts directly or indirectly related to the teaching of the course:
- TUSELL, F. (2000)   S-Plus, R y otros recursos. Updated: 19-10-2010. Size: 137934 bytes.
- TUSELL, F.(2000)   Funciones de avanzadas de regresión en S-Plus y R. Updated: 10-11-2011. Size: 243043 bytes.
- TUSELL, F.(2001)   Instalación y uso del CD-ROM cuanti-0.70. Updated: 02-10-2008. Size: 106389 bytes.
Problem sets
In a normal quarter, students do between 8 and 10 problem sets, which are assigned approximately with a weekly periodicity. Usually, some computing is required, for which R is a perfect tool.- Problem set 0. Repaso álgebra lineal y matricial .
- Problem set 1. Distribución normal multivariante .
- Problem set 2. Distribución Wishart y contraste T Hotelling . Ejemplo:  AnalPrev.R. Ejemplo:  Ttest.R.
- Problem set 3. Análisis de Varianza multivariante . Datos:  craneos.dat. Datos:  aritm.dat. Ejemplo:  aritm.R.
- Problem set 4. Componentes principales . Ejemplo:  prostituto.R. Datos:  MunG11_c.csv. Datos:  pxMunicipal_cc.csv.
- Problem set 5. Análisis factorial y correspondencias. Ejemplo:  ac3.R. Datos:  coches.frame.
- Problem set 6. Reescalado multidimensional.
- Problem set 7. Datos categóricos multivariantes.
- Problem set 8. Análisis discriminante.
- Problem set 9. Arboles de regresión y clasificación.
- Problem set 10. Análisis cluster.