The course builds on the coherence of the topics and their contextualization with the educational objectives of the degree in question.
The course in Applied Statistics aims at providing the necessary tools for designing, processing and analysing data on labour economics. The theoretical aspects will be accompanied by an applied part on real data and case studies coming from labour economics, industrial relations and welfare systems and from environmental economics. Various statistical software such as Minitab and Hugin will be used.
The student will be taught not only to apply statistical techniques but also to choose the most appropriate technique and to comment on the output of the analysis helping them understand how statistics can be useful for decision-making. Students will be taught not only the theoretical aspect of the models but also the main application contexts and their use through appropriate statistical software.
The course teaches students how to manage a statistical survey from its planning, to the analysis and to present a report commenting on the output of the data analysis.
The course in Applied Statistics aims at providing the necessary tools for designing, processing and analysing data on labour economics. The theoretical aspects will be accompanied by an applied part on real data and case studies coming from labour economics, industrial relations and welfare systems and from environmental economics. Various statistical software such as Minitab and Hugin will be used.
The student will be taught not only to apply statistical techniques but also to choose the most appropriate technique and to comment on the output of the analysis helping them understand how statistics can be useful for decision-making. Students will be taught not only the theoretical aspect of the models but also the main application contexts and their use through appropriate statistical software.
The course teaches students how to manage a statistical survey from its planning, to the analysis and to present a report commenting on the output of the data analysis.
teacher profile teaching materials
- 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.
T. W. Anderson (2003) An Introduction to Multivariate Statistical Analysis, 3rd Edition. ISBN: 978-0-471-36091-9
Programme
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.
Core Documentation
Agresti A, Finlay B. (2007) Statistical Methods for the Social Sciences, Pearson College Div; 4th editionT. W. Anderson (2003) An Introduction to Multivariate Statistical Analysis, 3rd Edition. ISBN: 978-0-471-36091-9
Reference Bibliography
Jensen, F. B., Graven-Nielsen, T. (2007) Bayesian Networks and Decision Graphs, 2nd ed., Springer Verlag.Type of delivery of the course
Lectures for a total of 30 hours: Tuesday, Wednesday and Thursday 12.30-14.30 Lessons will be held in the computer lab for 30 hours: Tuesdays and Thursdays 12.30-14.30 Reception: for the timetable please consult the web page: http://host.uniroma3.it/facolta/economia/economia.asp?contenuto=docenti&id=49 Tutorials and project: A tutor will guide the students in their project work.Type of evaluation
EVALUATION • For students that attend lectures, the evaluation is based on a written test and on the preparation and oral presentation of a project based on datasets relevant student’s studies. • For non-attending students the evaluation is based on a written test and an oral exam on the entire program.