Statistics - Multivariate Analysis I
Details
| ID | 2858611 |
| Duration | 3.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | General knowledge of math |
| Target group | Data Analysts |
Overview
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.
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Services
- Lunch / catering
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- Flexible: free cancellation up to one day before
Content
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.Instructor
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 (Comelio Medien)
978-3-939701-23-1 - System und Systematik von Fragebögen (Comelio Medien)
978-3-939701-26-2 - Oracle PL/SQL (Comelio Medien)
978-3-939701-40-8 - MS SQL Server - T-SQL Programmierung und Abfragen (Comelio Medien)
978-3-939701-69-9