Università Roma Tre

This graduate level course provides an introduction to the basic concepts of probability, common distributions, statistical methods, and data analysis. It is intended for graduate students who have one undergraduate statistics course. Upon completion of this course students will:

- Appreciate and understand the role of statistics in their own field of study.
- Develop an ability to apply appropriate statistical methods to summarize and analyse data for some of the more routine experimental settings.

- Make sense of data and be able to report the results in appropriate table or statistical terms for inclusion in your thesis or paper.

- Perform appropriate statistical techniques using Minitab and Hugin and interpret the results/outputs.

Strengths of the course:
Software: R, Minitab, Hugin
Project work on a data set relative to your studies.

Topics:
- Introduction to sampling techniques;
- Simple and multiple linear model;
- Generalized linear models (logistic and linear-logistic models);
- Analysis of variance (ANOVA);
- Contingency tables and tests for Independence and Goodness-of-Fit;
- Decision support methods. Decision trees. Bayesian networks and decision networks. Learning networks. Applications to real cases.
-Methods for exploratory multivariate statistics such as factor analysis, cluster analysis.

Agresti A, Finlay B. (2007) Statistical Methods for the Social Sciences, Pearson College Div; 4th edition
T. W. Anderson (2003) An Introduction to Multivariate Statistical Analysis, 3rd Edition. ISBN: 978-0-471-36091-9

Other course material will be available on the course pages on the School's website and on the Moodle platform.