applied statistics ii multivariable

This document introduces Principal Component Analysis as a dimensionality reduction technique used to simplify high-dimensional data while preserving most of its variance. It covers the motivation behind PCA, the underlying idea of principal components, the mathematical procedure involved, and the interpretation of results. The notes emphasize both theoretical understanding and practical application.

This document presents Factor Analysis as a statistical method for identifying latent variables that explain correlations among observed variables. It discusses the factor analysis model, parameter estimation, determination of the number of factors, factor rotation techniques, and estimation of factor scores. Emphasis is placed on interpretation, assumptions, and methodological choices in applied research.

This document provides an overview of Cluster Analysis as an unsupervised learning technique for discovering natural groupings in data. It introduces similarity measures, hierarchical and non-hierarchical clustering methods, cluster validation techniques, and strategies for interpreting clusters. The focus is on understanding methodological differences and evaluating clustering results effectively.

Test bank for applied statistics ii multivariable and multivariate techniques 3rd edition warner full chapter,.

Test Bank For Applied Statistics II Multivariable and Multivariate Techniques 3rd Edition By Rebecca M. Warner |All Chapters, Complete Q & A, Latest|

Test bank for applied statistics ii multivariable and multivariate techniques 3rd edition warner full chapter