Advanced Regression Techniques: GLM and Validation
Advanced statistics, mastering generalized linear models, implementing robust regression techniques, and validating statistical frameworks in R programming.
Advanced statistics, mastering generalized linear models, implementing robust regression techniques, and validating statistical frameworks in R programming.
This course delves into advanced regression techniques, focusing on generalized linear models (GLM), robust regression, and model validation. Students will learn logistic and Poisson regression, understand the GLM framework, and explore techniques for handling outliers. The curriculum covers maximum likelihood estimation, variable selection, and model validation methods. Practical applications using R programming are emphasized throughout. By the end, participants will be able to select appropriate regression models, perform robust analyses, and validate their results, preparing them for data-driven roles across various industries.
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What you'll learn
Determine appropriate regression models based on response variable characteristics
Implement generalized linear models using R, including logistic and Poisson regression
Apply robust regression techniques to handle outliers in data
Perform model validation for GLMs, particularly logistic regression
Interpret regression results and draw meaningful conclusions
Use R for statistical inference based on regression models
Skills you'll gain
This course includes:
2.75 Hours PreRecorded video
6 quizzes, 4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides an in-depth exploration of advanced regression techniques, focusing on generalized linear models (GLM) and model validation. Students will learn about logistic and Poisson regression, understanding their differences from ordinary linear regression. The course covers the GLM framework, illustrating how various regression models fit within this structure. Additionally, students will explore robust regression techniques to handle outliers effectively. The curriculum emphasizes practical application using R, teaching students how to implement these models, interpret results, and perform statistical inference. Model validation techniques are also covered, ensuring students can assess and improve their regression models.
Logistic Regression
Module 1 · 6 Hours to complete
Poisson Regression and Generalized Linear Model
Module 2 · 5 Hours to complete
Robust Regression and Model Validation
Module 3 · 6 Hours to complete
Summative Course Assessment
Module 4 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Associate Chair and Director of Undergraduate Studies in Applied Mathematics at Illinois Tech
Kiah Ong is the Associate Chair and Director of Undergraduate Studies in the Department of Applied Mathematics at Illinois Tech. He teaches several courses, including "Linear Regression," "Model Diagnostics and Remedial Measures," and "Variable Selection, Model Validation, Nonlinear Regression." His courses focus on statistical modeling techniques, providing students with the necessary skills to analyze data effectively and make informed decisions based on their findings. Through a combination of theoretical concepts and practical applications, Kiah Ong prepares students for advanced studies and careers in data analysis and applied mathematics.
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