Data Science / Statistics / R Foundation / R / Multivariate Analysis using R

R - Multivariate Analysis using R

Details

ID 2858611
Classroom 3 days
Webinar 5 days
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Data Analysts

Services:

  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training

Summary

Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one variable. The application of multivariate statistics is multivariate analysis. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical implementation of multivariate statistics to a particular problem may involve several types of univariate and multivariate analysis in order to understand the relationships between variables and their relevance to the actual problem being studied. This training is one part of a pair of courses on multivariate statistics. It helps you understand the techniques of complex and more advanced data analysis for marketing, controlling and engineering.

Training Dates

  • 2020-Oct-19 - Oct-23
  • 2020-Dec-28 - Jan-01
  • 2021-Mar-08 - Mar-12
  • 2021-May-17 - May-21

950 EUR +VAT

Location | Enrollment


Agenda

Multivariate Regression Analysis

Determination of a formula that can describe how elements in a vector of variables respond simultaneously to changes in others.

Multivariate Analysis of Variance (ANOVA and MANOVA)

Comparing multivariate means of several groups using the variance-covariance between variables in testing the statistical significance of the mean differences.

Discriminant Analysis

Examination whether a set of variables can be used to distinguish between two or more groups of cases.

Logistic Regression

Prediction of the outcome of a categorical dependent variable based on one or more predictor variables.

Factor Analysis

Extraction of a specified number of synthetic variables (latent variables or factors), fewer than the original set, leaving the remaining unexplained variation as error.

Clustering

Assignment of objects into groups (clusters) so that objects (cases) from the same cluster are more similar to each other than objects from different clusters.

Trainer

Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

Publications
  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9
Projects

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.

Research

He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.