Aim of the course is to provide the student with an adequate preparation on the statistical concepts and methods that can be used to collect, elaborate and synthesize data concerning economical and social phenomena.
Much attention will be paid to conditions that need to be fulfilled for the different tecniques to be appliable, stressing their analytical potentialities. The course is focused mainly on applications of the different tecniques.
Much attention will be paid to conditions that need to be fulfilled for the different tecniques to be appliable, stressing their analytical potentialities. The course is focused mainly on applications of the different tecniques.
Curriculum
teacher profile teaching materials
The first lessons will be devoted to a summary of basic statistical concepts (such as variance, covariance, correlation, linear combinations) and matrix algebra.
We will then move on to the study of possible data matrix syntheses: composite indicators, principal component analysis; cluster analysis.
Finally, after a summary of simple regression (i.e. the straight line), we will move on to the study of multiple regression.
Emphasis will be placed on case studies .
https://romatrepress.uniroma3.it/wp-content/uploads/2021/03/open-togmp-2.pdf
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)
Programme
The first lessons will be devoted to a summary of basic statistical concepts (such as variance, covariance, correlation, linear combinations) and matrix algebra.
We will then move on to the study of possible data matrix syntheses: composite indicators, principal component analysis; cluster analysis.
Finally, after a summary of simple regression (i.e. the straight line), we will move on to the study of multiple regression.
Emphasis will be placed on case studies .
Core Documentation
Open issues in composite indicators. Terzi, Otoiu, Mazziotta, Pareto, Grimaccia. Downloadable from Roma TrE-presshttps://romatrepress.uniroma3.it/wp-content/uploads/2021/03/open-togmp-2.pdf
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)
Reference Bibliography
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)Type of delivery of the course
The course consists of both traditional face-to-face lectures and laboratory lessons and discussion of case studies. In the event of an extension of the health emergency from COVID-19, e-learning will consist in live and deferred audio recordings, as well as distribution of hand outs.Attendance
Frequency, although not strictly mandatory, is strongly recommended. In fact, there are no textbooks that follow the lessons and the discussion of case-studies .Type of evaluation
Assessment is based on a written examination lasting no more than two hours and consisting of approximately ten questions. Students are required to provide short answers to theoretical questions and to interpret and comment on the results of multivariate statistical analyses. The written examination is designed to assess both knowledge of the main models and methods covered in the course and the ability to interpret and apply them correctly in empirical contexts. The written examination is followed by a short oral interview aimed at further exploring the topics addressed in the written test and assessing the student's ability to discuss and critically evaluate the results of statistical analyses. Students attending the course may take two mid-term tests during the teaching period, on dates scheduled by the instructor. In this case, the assessment is completed through a final examination held at the end of the course, in December. teacher profile teaching materials
The first lessons will be devoted to a summary of basic statistical concepts (such as variance, covariance, correlation, linear combinations) and matrix algebra.
We will then move on to the study of possible data matrix syntheses: composite indicators, principal component analysis; cluster analysis.
Finally, after a summary of simple regression (i.e. the straight line), we will move on to the study of multiple regression.
Emphasis will be placed on case studies .
https://romatrepress.uniroma3.it/wp-content/uploads/2021/03/open-togmp-2.pdf
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)
Programme
The first lessons will be devoted to a summary of basic statistical concepts (such as variance, covariance, correlation, linear combinations) and matrix algebra.
We will then move on to the study of possible data matrix syntheses: composite indicators, principal component analysis; cluster analysis.
Finally, after a summary of simple regression (i.e. the straight line), we will move on to the study of multiple regression.
Emphasis will be placed on case studies .
Core Documentation
Open issues in composite indicators. Terzi, Otoiu, Mazziotta, Pareto, Grimaccia. Downloadable from Roma TrE-presshttps://romatrepress.uniroma3.it/wp-content/uploads/2021/03/open-togmp-2.pdf
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)
Reference Bibliography
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)Type of delivery of the course
The course consists of both traditional face-to-face lectures and laboratory lessons and discussion of case studies. In the event of an extension of the health emergency from COVID-19, e-learning will consist in live and deferred audio recordings, as well as distribution of hand outs.Attendance
Frequency, although not strictly mandatory, is strongly recommended. In fact, there are no textbooks that follow the lessons and the discussion of case-studies .Type of evaluation
Assessment is based on a written examination lasting no more than two hours and consisting of approximately ten questions. Students are required to provide short answers to theoretical questions and to interpret and comment on the results of multivariate statistical analyses. The written examination is designed to assess both knowledge of the main models and methods covered in the course and the ability to interpret and apply them correctly in empirical contexts. The written examination is followed by a short oral interview aimed at further exploring the topics addressed in the written test and assessing the student's ability to discuss and critically evaluate the results of statistical analyses. Students attending the course may take two mid-term tests during the teaching period, on dates scheduled by the instructor. In this case, the assessment is completed through a final examination held at the end of the course, in December.