Essential Topics in Multivariate Data Analysis
Date:
12/07/2017 - 13/07/2017
Organised by:
University of Hertfordshire
Presenter:
Dr Neil H. Spencer
Level:
Intermediate (some prior knowledge)
Contact:
Dr Neil Spencer
01707 285529
statistics@herts.ac.uk
Map:
View in Google Maps (AL10 9EU)
Venue:
de Havilland campus,
University of Hertfordshire,
Hatfield,
Herts.
Description:
This course is about some of the most commonly used multivariate data analysis techniques (factor, correspondence, cluster and discriminant analysis), focusing on the practical application of the techniques rather than their mathematical complexities.This course is aimed at those who want to gain an understanding of some of the most commonly used multivariate analysis methods, namely factor analysis, correspondence analysis, cluster analysis and discriminant analysis. These techniques are used in a range of disciplines and examples used in the course will be accessible to all audiences including PhD students, researchers and those who need to use these techniques in the workplace. The use of a custom-built add-in for Microsoft Excel makes the analyses possible for all with even basic tools at their disposal.
The topics covered in this course are factor analysis (including principal components analysis), correspondence analysis, cluster analysis, discriminant analysis. These topics will be demonstrated using SPSS and a custom-built add-in for Microsoft Excel.
Topics on this course are also available as part of our “Pick & Mix” range. Please note that by doing this course, you are obtaining a discount on the fees for undertaking the individual “Pick & Mix” elements.
See our full range of courses (including our “Pick & Mix” selection) at http://go.herts.ac.uk/sscu or contact the course organiser, Dr Neil Spencer, on 01707 285529, statistics@herts.ac.uk.
Prior knowledge of hypothesis testing and regression is essential.
Cost:
£399
Website and registration:
Region:
East of England
Keywords:
Principal components analysis, Factor analysis, Confirmatory factor analysis, Correspondence analysis, Cluster analysis, SPSS, Discriminant analysis , Statistics
Related publications and presentations:
Principal components analysis
Factor analysis
Confirmatory factor analysis
Correspondence analysis
Cluster analysis
SPSS