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

Описание: 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

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