'Original variables' diaporamas de présentation

Factor and Component Analysis

Factor and Component Analysis. esp. Principal Component Analysis (PCA). Why Factor or Component Analysis?. We study phenomena that can not be directly observed ego, personality, intelligence in psychology Underlying factors that govern the observed data

1.28k views • 99 slides

Instructor: Prof. Louis Chauvel

Instructor: Prof. Louis Chauvel. Statistical Analysis Multivariate descriptive analysis Factor analysis and clustering (PCA and H CA) + kmeans. Principal components analysis Hierarchic cluster analysis. This session: descriptive multidimensional analysis.

462 views • 46 slides

Principal Component Analysis (Dimensionality Reduction)

Principal Component Analysis (Dimensionality Reduction). By: Tarun Bhatia Y7475. Overview:. What is Principal Component Analysis Computing the compnents in PCA Dimensionality Reduction using PCA A 2D example in PCA Applications of PCA in computer vision

455 views • 33 slides

Analisi fattoriale

291 views • 28 slides

Chapter 8: Advanced Method Concepts

Chapter 8: Advanced Method Concepts. Understanding Parameter Types. Mandatory parameter An argument for it is required in every method call Four types of mandatory parameters: Value parameters Declared without any modifiers Reference parameters Declared with the ref modifier

489 views • 27 slides

381 views • 23 slides

Sparse, Flexible and Efficient Modeling using L 1 -Regularization

351 views • 21 slides

Multivariate statistical methods

488 views • 19 slides

Measurement

229 views • 15 slides

Multiple Regression

Multiple Regression. What is multiple regression?. What is multiple regression? Predicting a score on Y based upon several predictors. Why is this important?

152 views • 15 slides

Section 6.4

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Chapter 6: Eigenvalue Problems

Chapter 6: Eigenvalue Problems. Motivation Differential Equations Other Equations. 6.1 Motivation. 6.1 Motivation. Physical Systems are commonly described by ( usually differential) equations involving sets of coupled variables.

302 views • 13 slides

Feature Selection

Feature Selection.

272 views • 12 slides

Cluster Analysis

Objectives ADDRESS HETEROGENEITY Combine observations into groups or clusters such that groups formed are homogeneous (similar) within the group and heterogeneous (different) from other groups on some variables (?).

390 views • 10 slides

Factor Analysis Basics

Factor Analysis Basics. Why Factor?. Combine similar variables into more meaningful factors. Reduce the number of variables dramatically while retaining most of the explanatory power, for subsequent analyses, such as Clustering. Factor Types, Variables. R-type (commonly used)

429 views • 8 slides