Checking date: 18/05/2022

Course: 2022/2023

Statistics for social sciences II
Study: Bachelor in Political Science (205)

Coordinating teacher: ALBARRAN LOZANO, IRENE

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Statistics for Social Sciences I or a similar introductory statistics course.
Specific competences: 1. Understanding the basic concepts of statistical inference and its applications in the social sciences. 2. Capacity for applying simple linear regression and interpreting the results. 3. Capacity for applying multiple linear regression and interpreting the results. 4. Effective use of statistical software. Transversal competences: 1. Capacity for analysis and synthesis. 2. Capacity for mathematical and statistical modeling. 3. Problem solving. 4. Critical reasoning. 5. Oral and written communication.
Skills and learning outcomes
Description of contents: programme
Topic 1. Advanced hypothesis testing 1.1.- Comparing two populations 1.2.- ANOVA Topic 2.Simple Linear Regression 2.1.- Motivation, examples and applications. Model formulation and parameter interpretation. 2.2.- Nonlinear relationships and linearizing transformations. 2.3.- Point and interval estimation of the model parameters. 2.4.- Hypothesis testing; statistical significance of estimated parameters. 2.5.- Model checking and residual analysis. Topic 3.Multiple Linear Regression 3.1.- Motivation, examples and applications. Model formulation and parameter interpretation. 3.2.- Inference on model parameters: confidence intervals; inference on the response. 3.3.- Marginal effects. Adjusted R-squared. 3.4.- Multicolinearity. Residual analysis. 3.5.- Variable selection methods. 3.6.- Considering categorical variables. Topic 4 Final Project
Learning activities and methodology
Theory (3 ECTS). Theory classes with supporting material available in the course's web page. Practical classes (3 ECTS). Problem-solving classes. Practical classes in computer rooms. Weekly individual tutoring sessions. The teaching methodology will be eminently practical, being based on the study of diverse data sets through inference and regression techniques, both in the theory and practical classes, as motivation and illustration of the theory.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Chatterjee, S.. Regression analysis by example. Wiley. 2000
  • J.F. Hair, W.C. Black, B.J. Babin, R.E. Anderson. Multivariate Data Analysis: A Global Perspective. 7th ed. , Pearson Education. 2010
  • Remenyi, D. et al.. . An introduction to statistics using Microsoft Excel.. Academic Publishing. . 2010
  • YOUNGER, M. S.. A First Course in Linear Regression. Duxbury Press. 1985
Additional Bibliography
  • D.J. Bartholomew, F. Steele, I. Moustaki, J. Galbraith. Analysis of Multivariate Social Science Data, 2nd ed.. Chapman & Hall/CRC. 2008

The course syllabus may change due academic events or other reasons.