Discrete Data Analysis with R Techniques

Master statistical methods for categorical and count data with our in-depth Discrete data analysis course. This course is designed to help you analyze discrete datasets using R, covering essential techniques such as contingency tables, logistic regression, and Poisson regression. Whether you are a researcher, data analyst, or statistician, this course will equip you with the skills to handle real-world discrete data challenges efficiently.

What You’ll Learn

• Understanding the fundamentals of discrete data analysis
• Using R for categorical and count data modeling
• Exploring contingency tables and chi-square tests
• Applying logistic regression for binary outcomes
• Implementing Poisson regression for count data
• Performing goodness-of-fit tests and model selection
• Visualizing discrete data with R packages
• Interpreting statistical outputs and making data-driven decisions

Requirements

• Basic knowledge of R programming
• Familiarity with fundamental statistics
• Interest in data analysis and statistical modeling

Course Description

This Discrete data analysis course is tailored for individuals looking to enhance their ability to analyze categorical and count data using R. Discrete data plays a critical role in various fields such as healthcare, social sciences, and business analytics, making it essential for analysts to master relevant techniques.

Through hands-on coding exercises and case studies, you’ll explore contingency tables, hypothesis testing, logistic regression, and Poisson regression. You’ll also learn to evaluate models using goodness-of-fit tests and visualize results for effective communication. By the end of the course, you’ll have the confidence to work with discrete data in R and apply statistical models to real-world datasets.

About the Publication

This course is developed by an experienced statistician and R programming expert with years of teaching and industry experience. The curriculum is structured to cater to both beginners and professionals seeking a deeper understanding of discrete data analysis.

Explore These Valuable Resources

• The Comprehensive R Archive Network (CRAN)
• Logistic Regression in R – Towards Data Science
• Statistical Methods in R – Quick Reference

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