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Essential Topics in Multivariate Data Analysis
Prior knowledge of hypothesis testing and regression is essential.
About the course
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. See our full range of courses at http://go.herts.ac.uk/sscu or contact the course organiser, Prof Neil Spencer, at [email protected] .
Course fees
Dates can be organised if an organisation have a group of people wanting to take this course.
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Multivariate data analyses were and still an effective tool to solve problems related to several topics, however I wonder if anyone can use it in the field of artificial intelligence.
9.5.1 Multivariate Analysis. Multivariate analysis is a set of techniques used for analysis of data sets that contain more than one variable, and the techniques are especially valuable when working with correlated variables. The techniques provide an empirical method for information extraction, regression, or classification; some of these ...
IBMB 210: Multivariate Analysis. Scientific concepts, matrix theory, and computer techniques of multivariate analyses for clinical research and evaluation. Topics include cluster and factor analyses, multiple regression, and discriminant functions. Emphasizes research and clinical technology rather than mathematical theory. Prerequisite: IBMB 200.
Jan 1, 2017 · This thesis includes the study of three independent research problems in multivariate statistics. ^ The first part of the thesis studies additive principal components (APCs for short), a nonlinear ...
Multivariate analysis is concerned with the interrelationships among several variables. The data may be metrical, categorical, or a mixture of the two. Multivariate data may be, first, summarized by looking at the pair-wise associations. Beyond that, the different methods available are designed to explore and elucidate different features of the ...
In multivariate analysis, canonical correspondence analysis (CCA) is an ordination technique that determines axes from the response data as a linear combination of measured predictors. CCA is ...
Modern Statistics: Non parametric,multivariate Exploratory Analyses: Hypotheses generating. Projection Methods (new coordinates) Principal Component Analysis Principal Coordinate Analysis-Multidimensional Scaling (PCO,MDS) Correspondence Analysis Discriminant Analysis Tree based methods Phylogenetic Trees Clustering Trees
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.
to the topic of multivariate analysis. The content is broken down in to discussions on methods of classifying data in terms of increasing complexity, from a simple cut-based approach, through to the use of decision trees. There is also a section devoted to the topic of how to choose the classi cation method one should ultimately use. a.j ...
Multivariate data analysis has a wide range of applications in fields such as medical research, business analytics, social science, physics, and engineering. It is commonly used to identify relationships between variables, determine the relevance of variables, identify outliers and become more efficient in predicting outcomes.