Regression Modeling: Poisson & Negative Binomial Techniques
What you'll learn
- Understand the problem statement and data requirements.
- Explore and analyze datasets effectively.
- Fit Poisson regression models to data.
- Fit Negative Binomial regression models to data.
- Interpret the results of Poisson and Negative Binomial regression models.
- Apply advanced techniques for model evaluation and selection.
Requirements
- Poisson regression is based on the concept of Poisson distribution
Description
Welcome to the course on Poisson and Negative Binomial Regression Modeling! In this course, you'll delve into the fascinating world of regression analysis, focusing specifically on Poisson and Negative Binomial regression models.
Section 1: Introduction
In this section, we'll kick things off with a comprehensive introduction to the course objectives and the problem statement we aim to address. By understanding the context and purpose of our analysis, you'll be better equipped to navigate the subsequent sections effectively.
Section 2: Dataset
Before diving into regression modeling, it's essential to familiarize ourselves with the dataset we'll be working with. In this section, we'll explore the characteristics and structure of the dataset, laying the groundwork for our modeling journey.
Section 3: Exploring Dataset
A crucial step in any data analysis process is exploring the dataset to uncover meaningful insights. In this section, we'll embark on a journey of exploration, using various techniques to understand the underlying patterns and relationships within the data.
Section 4: Fitting Poisson Regression Model
Poisson regression is a powerful tool for modeling count data, commonly encountered in fields such as epidemiology, finance, and more. Here, we'll delve into the theory and application of Poisson regression, learning how to fit models, interpret results, and assess model performance.
Section 5: Fitting Negative Binomial Model
While Poisson regression is valuable, it has limitations, particularly when dealing with overdispersed count data. Enter the Negative Binomial regression model, which addresses these limitations by allowing for greater flexibility in modeling variance. In this section, we'll explore the intricacies of Negative Binomial regression, from model fitting to interpretation.
Throughout the course, you'll engage in hands-on exercises, practical examples, and real-world case studies to reinforce your learning and develop a solid understanding of Poisson and Negative Binomial regression modeling techniques. Get ready to unlock new insights and enhance your data analysis skills!
Who this course is for:
- The course is suitable for data analysts, statisticians, researchers, and anyone interested in learning advanced regression modeling techniques using Poisson and Negative Binomial regression. It is also beneficial for professionals working with count data in fields such as healthcare, finance, marketing, and social sciences.
Instructor
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