Aspectos Basicos del lenguaje R

Curso Inteligencia de Negocios

This project outlines a framework for handling missing data in applied research, moving from theory to practice with modern imputation techniques. It reviews the limits of traditional deletion methods, classifies missingness mechanisms (MCAR, MAR, MNAR), and applies screening tools such as Little’s MCAR test. Imputation strategies range from simple methods (mean substitution, hot-deck, regression) to advanced model-based approaches, maximum likelihood, and the EM algorithm for multiple imputation.

This case study aims to identify key factors that influence customer's likelihood to recommend the company after interacting with customer service. Methodology: The project utilizes a comprehensive analytical approach, including: - Data Simulation & Cleaning: Creating and preparing the dataset for analysis. - Exploratory Data Analysis: Using data visualization (e.g., heatmaps) and descriptive statistics to uncover patterns across multiple and interactive factors. - Statistical Modeling: Evaluating different regression models (linear, ordinal, binomial) to predict customer's likelihood to recommend the company. - Simulation Based Recommendations: Predictions to evaluate the impact of different actions. - Reusable Functions: The creation of functions to automate procedures. Tools & Libraries: R with a focus on libraries such as car, VGAM, ordinal, psych, vcd, coefplot, ggplot2, tidyr, dplyr, openxlsx, and readxl.

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