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SAS: Apply & Evaluate Poisson & Negative Binomial Models

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SAS: Apply & Evaluate Poisson & Negative Binomial Models

Dozent: EDUCBA

Bei enthalten

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2 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

5 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

2 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

5 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Build Poisson regression models in SAS using PROC GENMOD and log link.
Differentiate Poisson vs. negative binomial models and assess dispersion.
Compare models using AIC, deviance, and diagnostics to optimize accuracy.

Kompetenzen, die Sie erwerben

Kategorie: Predictive Modeling
Kategorie: Statistical Programming
Kategorie: Model Evaluation
Kategorie: Probability Distribution
Kategorie: Statistical Analysis
Kategorie: Statistical Methods
Kategorie: Statistical Modeling
Kategorie: Regression Analysis

Werkzeuge, die Sie lernen werden

Kategorie: Statistical Software
Kategorie: SAS (Software)

Wichtige Details

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7 Aufgaben

Unterrichtet in Englisch

91%

of learners achieved a positive career outcome

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In diesem Kurs gibt es 2 Module

This course equips learners with the knowledge and practical skills to analyze, construct, and evaluate statistical models for count data using SAS. Beginning with Poisson regression, learners will identify appropriate datasets, assess distributional assumptions, and build models using PROC GENMOD with the log link function. They will then examine model diagnostics to detect issues such as overdispersion and refine models for better accuracy.

Building on these foundations, learners will differentiate between Poisson and negative binomial regression approaches, interpret the role of the dispersion parameter, and compare models using statistical criteria like AIC and deviance. Real-world examples and guided SAS implementations will allow learners to apply these techniques effectively, justify model selection decisions, and optimize predictive performance for diverse count data scenarios. By the end of the course, participants will be able to select, implement, and critique regression models that best fit the characteristics of their datasets, enhancing their analytical capabilities in statistical modeling with SAS.

Moduldetails

This module introduces learners to the principles and application of Poisson regression using SAS. It covers the fundamentals of modeling count data, exploring datasets for suitability, fitting models using PROC GENMOD, and interpreting results. Learners will progress from understanding the problem context and dataset structure to building and refining Poisson regression models, diagnosing potential issues like overdispersion, and improving model accuracy through variable selection and statistical analysis techniques.

Das ist alles enthalten

11 Videos4 Aufgaben

11 VideosInsgesamt 91 Minuten

Course Introduction7 Minuten
Problem Statement7 Minuten
Dataset Explanation6 Minuten
Exploring Dataset7 Minuten
Exploring Dataset Continue8 Minuten
Fitting Poisson Regression Model Part 110 Minuten
Fitting Poisson Regression Model Part 29 Minuten
Fitting Poisson Regression Model Part 38 Minuten
Fitting Poisson Regression Model Part 47 Minuten
Fitting Poisson Regression Model Part 511 Minuten
Fitting Poisson Regression Model Part 611 Minuten

4 AufgabenInsgesamt 60 Minuten

Poisson Regression Fundamentals30 Minuten
Course Overview and Data Exploration10 Minuten
Building the Poisson Regression Model10 Minuten
Advanced Poisson Model Implementation10 Minuten

This module builds on the foundations of Poisson regression by introducing the negative binomial model for count data exhibiting overdispersion. Learners will explore when and why to choose the negative binomial approach, understand the role of the dispersion parameter, and evaluate model outputs using statistical diagnostics and information criteria. Through practical SAS implementations, learners will gain the skills to refine models, address influential observations, and compare performance against Poisson regression to select the most appropriate modeling strategy.