What you'll get
  • 2+ Hours
  • 1 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Develops a strong conceptual foundation in Poisson and Negative Binomial regression techniques.
  • Explains how to examine and prepare count-based datasets for statistical modeling.
  • Provides a structured, step-by-step approach to building Poisson regression models.
  • Demonstrates how to interpret model outputs and assess statistical performance.
  • Applies Poisson regression to real-world predictive use cases.
  • Explores the underlying theory alongside real-world applications of Negative Binomial regression.
  • Offers hands-on exposure to modeling count data using Negative Binomial methods.
  • Teaches how to choose the most appropriate model based on data characteristics.
  • Introduces advanced diagnostic techniques to address challenges such as overdispersion.
  • Builds the ability to extract insights from count data to support informed decision-making.
  • Showcases predictive analytics use cases, such as estimating lead-to-customer conversions.
  • Leverages SAS STAT tools for building, implementing, and evaluating regression models.
  • Clarifies the role of the Poisson distribution as the foundation of Poisson regression.

Content

Courses No. of Hours Certificates Details
Poisson Regression Project using SAS Stat2h 21m✔View Curriculum

Description

This course delivers a detailed exploration of Poisson and Negative Binomial regression models, equipping learners with the skills required to analyze count data and generate reliable predictions using advanced statistical methods. Participants begin by examining a structured dataset to understand its components and underlying patterns. Through guided, hands-on activities, they learn how to clean, explore, and prepare data for regression analysis.
The program then walks learners through the complete process of building Poisson regression models. Topics include data preprocessing, variable selection, model fitting, and result interpretation. Each module addresses a specific component of Poisson regression, helping learners build a strong and comprehensive understanding of the topic. The course also introduces Negative Binomial regression, following a parallel learning approach. Learners gain insight into when and why this model is preferable, particularly when Poisson assumptions are violated. Comparative analysis helps participants determine the most suitable modeling technique for different scenarios.
By the end of the course, learners are well prepared to apply Poisson and Negative Binomial regression models confidently, interpret outcomes accurately, and translate statistical findings into actionable business insights.

Goals

  • Build a solid understanding of regression techniques used for count data.
  • Enable learners to model, interpret, and evaluate Poisson and Negative Binomial regressions.
  • Strengthen analytical thinking for data-driven decision-making.

Objectives

By completing this course, learners will be able to:
  • Understand the theoretical principles behind Poisson and Negative Binomial regression.
  • Analyze and prepare datasets designed for count-based analysis.
  • Fit and interpret Poisson regression models using structured workflows.
  • Identify and manage overdispersion in count data.
  • Apply Negative Binomial regression when Poisson assumptions are not met.
  • Compare multiple regression models and select the most effective approach.
  • Use SAS STAT tools to implement and validate regression models.
  • Translate statistical outputs into practical, real-world insights.

Highlights

  • Comprehensive coverage of both theory and application.
  • Step-by-step guidance with practical exercises.
  • Real-world predictive analytics examples.
  • Hands-on use of SAS STAT for regression modeling.
  • Model diagnostics and performance evaluation techniques.
  • Clear comparison between Poisson and Negative Binomial approaches.

Requirements

  • Basic understanding of statistical concepts.
  • Familiarity with fundamental data analysis techniques.
  • General awareness of probability distributions is helpful but not mandatory.

Target Audience

This course is ideal for:
  • Data Analysts and Data Engineers
  • Data Architects and Software Engineers
  • IT Operations professionals
  • Technical Managers overseeing data-driven projects
  • Professionals seeking to strengthen their analytics and statistical modeling expertise

FAQ

Q1. Is prior experience with regression models required?
A basic understanding of statistics is sufficient. The course builds concepts progressively.
Q2. Does the course include hands-on practice?
Yes, learners work directly with datasets and apply models through guided exercises.
Q3. Which tools are used in the course?
The course uses SAS STAT to build and evaluate regression models.
Q4. Will this course help with real-world analytics problems?
Yes, the focus is on practical applications and real-world predictive scenarios.

Career Benefits

  • Enhances expertise in advanced statistical modeling for count data.
  • Improves the ability to solve complex analytical problems using regression techniques.
  • Strengthens credentials for data analytics, data science, and statistical roles.
  • Supports better decision-making through data-driven insights.
  • Increases proficiency with industry-relevant tools, such as SAS STAT.