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Statistical data processing

02QUBRS

A.A. 2019/20

Course Language

Inglese

Course degree

Doctorate Research in Urban And Regional Development - Torino

Course structure
Teaching Hours
Lezioni 20
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Bellone Tamara     15 0 0 0 8
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
2019/20
PERIOD: NOVEMBER - DECEMBER The huge amount of data supplied by advanced technologies, requires a much needed treatment by new processes. So, an improved usage of classic methods, already learned in previous studies, is to be encouraged. When the investigated phenomenon is to be considered from a number of different viewpoints, it is hard the proper choice of a method for analysis. The said choice is linked to the scope of analysis: the multivariate analysis is the collective name for a melting pot of techniques: reduction and organization of the data, search of dependence among variables, statistical inferences. The course is intended a san offer of general ideas and methods at practicable level for people already in possess of basic knowledge of Statistics. Also, the course hopes to make immediately usable the explained methods as soon as possible.
PERIOD: NOVEMBER - DECEMBER The huge amount of data supplied by advanced technologies, requires a much needed treatment by new processes. So, an improved usage of classic methods, already learned in previous studies, is to be encouraged. When the investigated phenomenon is to be considered from a number of different viewpoints, it is hard the proper choice of a method for analysis. The said choice is linked to the scope of analysis: the multivariate analysis is the collective name for a melting pot of techniques: reduction and organization of the data, search of dependence among variables, statistical inferences. The course is intended a san offer of general ideas and methods at practicable level for people already in possess of basic knowledge of Statistics. Also, the course hopes to make immediately usable the explained methods as soon as possible.
Aspects of Multivariate Analysis and applications of Multivariate Techniques. Examples in the field of Engineering Natural Sciences and Human Sciences The organization and representation of the data. The geometry of the sample. The multivariate normal distribution. Inferences about a mean vector. Hotelling’s T2 test. Confidence regions and simultaneous comparisons of components means. Comparing mean vectors from two populations. Comparing mean vectors from several multivariate populations: Multivariate Analysis of Variance (MANOVA methods: one way, two way, one way with interaction, ...). Multivariate Analysis of Variance-Covariance (MANCOVA). The classic solution: multiple regression model. Inferences about the regression model. Multivariate multiple regression Data reduction and interpretation: Principal Components Analysis and Factor Analysis. Comparing Principal Components Analysis and Ridge Regression, Partial Least Square and Total Least Square. Comparing Factor Analysis and Principal Component Analysis. Discrimination and classification: Fisher’s discriminant function to separate two or several populations. Comparison with Logistic regression.
Aspects of Multivariate Analysis and applications of Multivariate Techniques. Examples in the field of Engineering Natural Sciences and Human Sciences The organization and representation of the data. The geometry of the sample. The multivariate normal distribution. Inferences about a mean vector. Hotelling’s T2 test. Confidence regions and simultaneous comparisons of components means. Comparing mean vectors from two populations. Comparing mean vectors from several multivariate populations: Multivariate Analysis of Variance (MANOVA methods: one way, two way, one way with interaction, ...). Multivariate Analysis of Variance-Covariance (MANCOVA). The classic solution: multiple regression model. Inferences about the regression model. Multivariate multiple regression Data reduction and interpretation: Principal Components Analysis and Factor Analysis. Comparing Principal Components Analysis and Ridge Regression, Partial Least Square and Total Least Square. Comparing Factor Analysis and Principal Component Analysis. Discrimination and classification: Fisher’s discriminant function to separate two or several populations. Comparison with Logistic regression.
Date: 15.11.19 - h 14.30-17.30 22, 29.11.19 and 6.12.19 - h 9.00-13.00 13.12.19 - 14.30-17.30 20.12.19 - h 17.30-19 10.01.20 - h 14.30-16.30 17.01.20 - h 14.30-16 Teachers: T. Bellone, I. Aicardi Location: BIBOLINI ROOM, DIATI
Date: 15.11.19 h 14.30-17.30 22, 29.11.19 and 6.12.19 h 9.00-13.00 13.12.19 14.30-17.30 20.12.19 h 17.30-19 10.01.20 h 14.30-16.30 17.01.20 h 14.30-16 Course: “Statistical data Processing (Program in attach) Teachers: T. Bellone, I. Aicardi Location: BIBOLINI ROOM, DIATI
Modalità di esame:
Exam:
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam:
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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