Web page of F. Tusell

Statistics: Linear Models

This 6-credits course runs for a quarter, and deals with linear models (Regression and Analysis of Variance). 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 present academic year . 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.

• NUÑEZ, V. y TUSELL, F. (2004)   Regresión y Análisis de Varianza. Size: 1223824 bytes.

Different versions of this document have been used over the years. Parts of it have evolved into a new set of notes for a course based on the statistical and graphics package R . These are the notes referenced next, used from 2011 onwards.

• TUSELL, F. (2011)   Análisis de Regresión. Introducción teórica y práctica basada en R. Updated: 14-12-2017. Size: 1394583 bytes.

In

• TUSELL, F. (2011)   Análisis de Regresión. Introducción teórica y práctica basada en R. Formato e-libro 6 pulgadas. Updated: 14-10-2011. Size: 1642606 bytes.

you can find the same notes in e-book format (optimized for 6'' screens), for the benefit of those having e-book readers or tablets. This format has beed discontinued and will not be updated.

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.
• TUSELL, F.(2008)   Herramientas para la elaboración de tareas y trabajos de curso. Updated: 03-11-2011. Size: 650125 bytes.

Problem sets

In a normal quarter, students do between 10 and 12 problem sets, which are assigned approximately with a weekly periodicity. Usually, some computing is required, for which R is a perfect tool.

• Problem set 1. Repaso álgebra lineal y matricial.
• Problem set 2. Proyecciones y matrices de proyección.
• Problem set 3. Multicolinealidad.
Datos:  choco.dat.
• Problem set 4. Simulación comportamiento estimadores MCO.
• Problem set 5. Uso elemental de regresión (I).
Datos:  presis.dat. Datos:  geyser.dat.
• Problem set 6. Uso elemental de regresión (II).
Datos:  camionero.dat. Datos:  Bolsa.dge. Datos:  vida.dge.
• Problem set 7. Ridge regression y regresión sesgada.
Datos:  longley.dat. Datos:  clouds.dge. Datos:  Rentabilidades.csv.
• Problem set 8. Criterios de Especificación.
• Problem set 9. Modelos de valoración.
Datos:  pisos.dge.   descrip.R.
• Problem set 10. Regresión logística.
• Problem set 11. Introducción al Análisis de Varianza.
• Problem set 12. Regresión no lineal.

Previous years exams

This course is usually offered in the first quarter, and the final exam can be taken in January/February and/or June. Students who do not pass the exam in January/February can take it again in June.

Customarily, the January/February exam is (basically) multiple choice, while in June it is an essay type of exam. This is so in order to cater for students who feel extremely uneasy with either of the two types of exam.

• 1998: February ,  June
• 1999: June
• 2000: January ,  June
• 2002: January ,  June
• 2003: January ,  June
• 2004: January ,  June
• 2005: January
• 2006: January
• 2007: January ,  June
• 2008: January
• 2009: January