Scribd is the world’s largest social reading and publishing site. Daniel Peña of University Carlos III de Madrid, Getafe (UC3M) with expertise in: Álgebra matricial — Descripción de datos multivariantes — Análisis gráfico y. Comentaris de llibres: Peña, Daniel. “Análisis de datos multivariantes”. Madrid: Editorial McGraw Hill, pp. Thumbnail.
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Johnson y Dean W. Learning activities and methodology. If you try to connect from outside of the University you will need to set up a VPN.
Multivariate Analysis Course Outline Daniel Peña / ppt download
Books Kruskal, Joseph B. Theoretical classes with support material taken from the web. Capacity for identifying problems associated with statistical data in several variables.
Modeling and solving problems. Tutorial classes before the midterm exam.
Contenidos del curso
Knowing what is expected in each of your courses reduces misunderstandings. Share buttons are a little bit lower. An introduction to multivariate statistical analysis. Editorial Universitaria de Barcelona. My presentations Profile Feedback Log out. Acquire skills in analieis component analysis. To make this website work, we log user data and share it with processors. Department assigned to the subject: Capacity of analysis and synthesis. Published by Modified over 3 years ago.
Tutorial classes during the week Further information on this link. Oral presentations and debates. Know the properties of multivariate distributions. Theoretical classes muotivariantes support material taken from the web.
Multivariate Analysis Course Outline Daniel Peña 2007/08.
Acquire skills in heterogeneity problems such as outlier detection, hypothesis testing for different means and classification. Model based clustering and canonical correlation Dcember 18th: Introduction to the multivariate data analysis. We think you have liked multivariante presentation. Tutorial classes during the week Multidimensional Scaling November 13th: Bachelor in Statistics and Business Introduction to the multivariate data analysis.
To pass the course is necessary to obtain a minimum of 4 points over a total of 10 points in the final exam. Acquire skills in heterogeneity problems such as outlier detection, hypothesis testing for different pra and classification.
Students are expected to have completed. Acquire skills in multivariate data description.