logo-polimi
Loading...
Degree programme
Programme Structure
Show/Search Programme
Course Details
Save Document
Degree Programme
Read Degree Programme
Faculty
Infrastructures
Quantitative data
International context
Customized Schedule
Your customized time schedule has been disabled
Enable
Search
Search a Professor
Search a Course
Search a Course (system prior D.M. n. 509)
Search Lessons taught in English

Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2019/2020
School School of Design
Name (Master of Science degree)(ord. 270) - BV (1260) Interior and Spatial Design
Track *** - Non diversificato
Programme Year 2

Course Details
ID Code 053791
Course Title APPLIED STATISTICS
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 6.0
Semester First Semester
Course Description Exploring a multivariate dataset: descriptive statistics and graphical displays. The geometry of a multivariate sample. The distance induced by the covariance matrix. Analysis of covariance structure: principal components and dimensional reduction. Inferences about a mean vector: Hotelling T^2 test. Confidence regions and simultaneous comparisons of component means. Multiple comparisons methods. ANOVA and MANOVA. Discrimination, classification, clustering: Statistical classification: model, miscalassification costs and prior probability. Bayesian supervised classification and the Fisher approach to discriminant analysis. Alternative approaches to classification: logistic regression, CART. Similarity measures. Unsupervised classification; hierarchical and nonhierarchical methods. Multidimensional scaling. Functional data Analysis: smoothing and representing functional data. Dimensional reduction: functional principal components. Allignment of functional data: amplitude and phase variability. Registration procedures. Classiffication with functional data. Mixed effects models: introduction to linear and generalized multilevel models. Multivariate logistic regression models for binary data: classification and prediction. Multilevel models for longitudinal data. Growth curves with autocorrelated residuals. Models for multivariate repente measures. Introduction to statistical models for the analysis of spatial data.
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
--
SECS-S/01
STATISTICS
6.0
Innovative teaching The course includes  1.0  credits in Innovative Teaching as follows:
  • Blended Learning & Flipped Classroom

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZSecchi Piercesare
manifesti v. 3.1.9 / 3.1.9
Area Servizi ICT
12/12/2019