What you'll get
- 2+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Comprehensive understanding of the theoretical principles behind Poisson and Negative Binomial regression models.
- Methods for exploring and analyzing complex datasets commonly encountered in count data studies.
- Stepwise guidance for fitting Poisson regression models to count-based datasets.
- Approaches to interpreting regression outputs and evaluating model performance.
- Practical implementation of Poisson regression in real-world predictive scenarios.
- In-depth coverage of both theoretical and applied aspects of Negative Binomial regression modeling.
- Hands-on experience in applying Negative Binomial models to count data.
- Techniques for selecting the most suitable model between the Poisson and the Negative Binomial approaches.
- Advanced strategies for diagnosing model assumptions and managing issues such as overdispersion.
- Expertise in applying Poisson and Negative Binomial regression models to extract meaningful insights and guide informed, data-driven decisions.
- Application of Poisson regression for predictive analytics, such as forecasting lead-to-customer conversion within specific time frames.
- Utilization of SAS Stat's built-in tools to calculate, implement, and evaluate Poisson regression models.
- Understanding the foundational concept of the Poisson distribution underlying Poisson regression analysis.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Poisson Regression Project using SAS Stat | 2h 21m | ✔ | View Curriculum |
Description
This course provides an in-depth understanding of Poisson and Negative Binomial regression modeling, enabling participants to analyze count data and make accurate predictions using advanced statistical techniques.
Participants will explore the course dataset to understand its structure and key characteristics. Through hands-on exercises, they will thoroughly examine and prepare the data for regression analysis.
The course guides participants through the process of fitting Poisson regression models, covering step-by-step procedures such as data preparation, variable selection, and model interpretation. Each module highlights a distinct component of Poisson regression, ensuring a thorough and well-rounded learning experience.
Participants will also learn to fit Negative Binomial regression models, following a similar approach. Emphasis is placed on model fitting, result interpretation, and understanding when to apply this model to count data, including comparisons with Poisson regression to select the most appropriate method.
By the conclusion of the course, participants will be confident in applying both Poisson and Negative Binomial regression techniques, analyzing and interpreting count data effectively, and generating actionable insights for data-driven decision-making.
Requirements
- Poisson regression operates on the principles of the Poisson distribution, which is used to model event counts within a fixed interval.
- Learners should have a basic understanding of statistical concepts to grasp how the Poisson distribution supports this modeling approach.
- A foundational knowledge of data analysis will help participants apply Poisson regression effectively to real-world count data.
Target Audience
- Ideal for Data Engineers, Analysts, Architects, Software Engineers, IT Operations teams, and Technical Managers working with data systems.
- Suitable for anyone looking to develop or strengthen their knowledge in data and analytics.
