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.
teacher profile teaching materials
The course is divided into two modules A first module is about multivariate statistics, i.e. it aims to illustrate some multivariate statistics methods and - above all - some applications.
The second module (which takes place in parallel to the first one) aims to teach how to apply these methods by means of software R
Topics dealt with in the first module (prof. S.Terzi):
The first lessons will be dedicated to the 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 syntheses of the data matrix: principal component analysis; cluster analysis.
Finally, after a brief summary of simple bivariate regression, we will move on to the study of multiple regression.
Much emphasis will be placed on case studies.
Topics dealt with in the second module (prof. F. Fortuna):
Introduction to the main commands of R and R-studio;
principal component analysis, cluster analysis and linear regression in R. Discussion of study cases.
https://cran.r-project.org/doc/contrib/DellOmodarme-esercitazioni-R.pdf
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)
Programme
The course is divided into two modules A first module is about multivariate statistics, i.e. it aims to illustrate some multivariate statistics methods and - above all - some applications.
The second module (which takes place in parallel to the first one) aims to teach how to apply these methods by means of software R
Topics dealt with in the first module (prof. S.Terzi):
The first lessons will be dedicated to the 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 syntheses of the data matrix: principal component analysis; cluster analysis.
Finally, after a brief summary of simple bivariate regression, we will move on to the study of multiple regression.
Much emphasis will be placed on case studies.
Topics dealt with in the second module (prof. F. Fortuna):
Introduction to the main commands of R and R-studio;
principal component analysis, cluster analysis and linear regression in R. Discussion of study cases.
Core Documentation
https://cran.r-project.org/doc/contrib/DellOmodarme-esercitazioni-R.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 exercises in the laboratory whether in classroom or on-line.Type of evaluation
-The evaluation takes place by means of a practical test (data set elaboration by means of R) For the students regularly attending the course, there is the possibility to take the exam through two exemption tests, to be passed before the end of th course, consisting in the elaboration af a short report ad a written open answer test. In the event of an extension of the health emergency from COVID-19, all the provisions that regulate the methods of carrying out the teaching activities and student assessment will be implemented. In particular, the following procedures will apply: the exam will take place in oral form at distance by eans of screen sharing. teacher profile teaching materials
The second module (which takes place in parallel to the first one) aims to teach how to apply these methods by means of software R.
Topics dealt with in the first module:
The first lessons will be dedicated to the 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 syntheses of the data matrix: principal component analysis; cluster analysis.
Finally, after a brief summary of simple bivariate regression, we will move on to the study of multiple regression.
Much emphasis will be placed on case studies.
Topics dealt with in the second module:
Introduction to the main commands of R and R-studio;
principal component analysis, cluster analysis and linear regression in R. Discussion of study cases.
https://cran.r-project.org/doc/contrib/DellOmodarme-esercitazioni-R.pdf
Lecture notes and slides will also be provided by the teachers.
Programme
The course is divided into two modules. A first module is about multivariate statistics, i.e. it aims to illustrate some multivariate statistics methods and - above all - some applications.The second module (which takes place in parallel to the first one) aims to teach how to apply these methods by means of software R.
Topics dealt with in the first module:
The first lessons will be dedicated to the 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 syntheses of the data matrix: principal component analysis; cluster analysis.
Finally, after a brief summary of simple bivariate regression, we will move on to the study of multiple regression.
Much emphasis will be placed on case studies.
Topics dealt with in the second module:
Introduction to the main commands of R and R-studio;
principal component analysis, cluster analysis and linear regression in R. Discussion of study cases.
Core Documentation
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016)https://cran.r-project.org/doc/contrib/DellOmodarme-esercitazioni-R.pdf
Lecture notes and slides will also be provided by the teachers.
Reference Bibliography
B.F.J. Manly and J.A. Navarro Alberto. Multivariate Statistical Methods: A Primer, Fourth Edition. Taylor & Francis (2016) https://cran.r-project.org/doc/contrib/DellOmodarme-esercitazioni-R.pdfType of delivery of the course
The course normally includes classroom lectures. In the event of an extension of the health emergency from COVID-19, all the provisions that regulate the methods of carrying out the teaching activities and student assessment will be implemented. In particular, the following methods will apply: remote mode, through the distribution of handouts, live and deferred audio recordings.Attendance
Frequency, although not strictly mandatory, is strongly recommended. In fact, there are no textbooks that follow the lectures and the practical lessons in the laboratory whether in classroom or on-line.Type of evaluation
The evaluation takes place by means of a practical test (data set elaboration by means of R). The exams usually take place in the computer room. For the students regularly attending the course, there is the possibility to take the exam through two exemption tests, to be passed before the end of the course. In the event of an extension of the health emergency from COVID-19, all the provisions that regulate the methods of carrying out the teaching activities and student assessment will be implemented. In particular, the following procedures will apply: the exam will take place in oral form at distance.