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Advanced Count Data Analysis: Poisson and Negative Binomial Regression Modeling

Unlock the power of count data analysis with Poisson and Negative Binomial regression modeling in our advanced course. Learn to explore datasets, fit regression models, and interpret results effectively. Gain hands-on experience with practical exercises and step-by-step tutorials. Master advanced statistical techniques for insightful data analysis.

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What you'll get

  • 2+ Hours
  • 1 Courses
  • Course Completion Certificates
  • One year access
  • Self-paced Courses
  • Technical Support
  • Mobile App Access
  • Case Studies

Synopsis

  • Theoretical foundations of Poisson and Negative Binomial regression modeling.
  • Techniques for exploring and understanding complex datasets used in count data analysis.
  • Step-by-step procedures for fitting Poisson regression models to count data.
  • Strategies for interpreting model outputs and assessing model fit.
  • Practical applications of Poisson regression modeling in real-world scenarios.
  • Theoretical and practical aspects of Negative Binomial regression modeling.
  • Hands-on experience in fitting Negative Binomial regression models to count data.
  • Methods for comparing and selecting between Poisson and Negative Binomial models.
  • Advanced techniques for diagnosing model assumptions and addressing overdispersion.
  • Mastery of Poisson and Negative Binomial regression modeling for making informed decisions and deriving actionable insights from count data.
  • Poisson Regression analysis model is used for predictive analysis
  • SAS Stat provides built-in functions to calculate and evaluate the Poisson regression model
  • One of the use cases of a Poisson regression model would be predicting the number of leads that will convert to customers within a particular time frame
  • Poisson regression is based on the concept of Poisson distribution

Content

Description

Introduction In this course, participants will delve into the intricacies of Poisson and Negative Binomial regression modeling. They will learn how to analyze count data and make predictions using these advanced statistical techniques.

Dataset Exploration Participants will be introduced to the dataset used in the course and gain insights into its structure and characteristics. Through hands-on exercises, they will explore the dataset thoroughly, preparing it for regression analysis.

Fitting Poisson Regression Model This section focuses on fitting Poisson regression models to the dataset. Participants will learn the step-by-step process of model fitting, including data preparation, variable selection, and interpretation of model results. The course is divided into multiple parts, each covering different aspects of the Poisson regression modeling process.

Fitting Negative Binomial Model Similarly, participants will explore fitting Negative Binomial regression models to the dataset. This section follows a similar structure to the Poisson regression section, guiding participants through the process of model fitting and interpretation.

By the end of the course, participants will have a solid understanding of Poisson and Negative Binomial regression modeling techniques and be equipped with the skills to apply them to their own datasets for count data analysis.

Requirements

  • Poisson regression is based on the concept of Poisson distribution

Target Audience

  • Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers
  • Anyone who wants to learn about data and analytics

* One Time Payment & Get One year access

Offer ends in:

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