Learn Count Regression Models with R: Poisson, negative binomial, zero inflated and hurdle model

Learn count regression models with this comprehensive tutorial. Learn how to effectively handle count data scenarios like insect counts on plants, traffic accidents, or hospital admissions using R programming. This video explains the differences between linear, non-linear, and count regression models, and demonstrates the application of Poisson, negative binomial, zero-inflated, and hurdle models. Perfect for statisticians, data scientists, and anyone involved in data analysis where count data is prevalent. Enhance your understanding and skills in count regression analysis with step-by-step instructions and real-world examples. Hashtags: #CountRegression #RProgramming #DataScience #Statistics #Biostatistics #PoissonModel #NegativeBinomial #ZeroInflatedModels #HurdleModels #StatisticalModeling #DataAnalysis Timestamps: 00:00 - Introduction to Count Regression 01:30 - Differences Between Linear and Count Regression 04:00 - Overview of Count Data and Distribution Assumptions 06:30 - Exploring the Insect Spray Dataset 08:50 - Visualizing Data with Box Plots 10:15 - Fitting and Interpreting a Poisson Model 14:50 - Adjusting for Overdispersion with Negative Binomial Regression 18:25 - Addressing Excess Zeros with Zero-Inflated Models 21:40 - Understanding and Applying Hurdle Models 25:05 - Summary of Model Comparisons and Selection 27:30 - Conclusion and Final Thoughts Facebook page: https://www.facebook.com/RajendraChoureISC Mail Id: [email protected] youtube playlist: https://www.youtube.com/playlist?list=PLfAzV0jqypOjX2h3YkeETd5RRO6f3VXpE