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This document outlines the methodology and statistical analysis for the Factorial Correspondence Analysis (AFC) applied to two qualitative variables. It includes contingency tables representing the distribution of employees by age and category. The document further explores the relationships between these variables and tests for independence using the chi-squared test.

This document appears to focus on the systematic observation and decomposition of tasks into measurable elements for time study. The methodology involves recording operations performed by machines and operators at different stages, calculating time averages, maximums, minimums, and variations. It aims to analyze workflow inefficiencies and propose improvements. Additionally, tabulated data structures are likely utilized for performance assessments and enhancements.

This document provides a practical demonstration of Correspondence Analysis (AFC) using R. A contingency table is created from qualitative variables, followed by row and column profile analysis using proportions. Chi-square tests are conducted to assess the independence of variables, and theoretical and empirical contingency tables are computed. The correspondence analysis (CA) is performed with two principal components for data visualization and interpretation.

This document details a practical session on Principal Component Analysis (PCA) using automotive data from cars.xls containing characteristics of 394 vehicles. Key steps include data preprocessing, such as handling missing values and variable standardization, followed by generating a correlation matrix and performing PCA. Analyses reveal significant correlations between variables like 'cylinders', 'horsepower', and 'weight', as well as trends observed through PCA plotting. Scree plots and correlation circles illustrate the relationships among variables and their contributions to principal c...