Discriminant Analysis

Data can be extremely valuable if we are able to extract information from them. This is why multivariate data analysis is essential for business and science. This book offers an easy-to-understand introduction to the most relevant methods of multivariate data analysis. It is strictly application-oriented, requires little knowledge of mathematics and statistics, demonstrates the procedures with numerical examples and illustrates each method via a case study solved with IBM’s statistical software package SPSS.

Data can be extremely valuable if we are able to extract information from them. This is why multivariate data analysis is essential for business and science. This book offers an easy-to-understand introduction to the most relevant methods of multivariate data analysis. It is strictly application-oriented, requires little knowledge of mathematics and statistics, demonstrates the procedures with numerical examples and illustrates each method via a case study solved with IBM’s statistical software package SPSS.

Master: Association rules (MBA) & it's usage, Linear Discriminant Analysis (LDA) for classification & variable selection

Learn to apply Fisher Discriminant Analysis on Python from a Data Science expert. Code templates included.

This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks.

This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD.

This book highlights major problems in the statistical analysis of compositions that have been known for over a century, as well as the corresponding solutions that have been put forward by specialists over the past 30 years. The basic assumptions of normality or multi-normality are pointed out and methods to test and achieve them are also covered. The conventional major and trace element geochemistry and modeling equations are discussed, and are followed by a more sophisticated multidimensional approach to data handling.