Model Diagnostics: Enhancing Regression Analysis
Master regression diagnostics and remediation techniques. Learn to validate and improve statistical models.
Master regression diagnostics and remediation techniques. Learn to validate and improve statistical models.
This course focuses on advanced techniques for diagnosing and improving linear regression models. Students will learn to identify violations of model assumptions, apply appropriate transformations, and implement remedial measures. The curriculum covers regression diagnostics, variance stabilization, weighted least squares, autocorrelation, multicollinearity, and variable selection. Emphasis is placed on practical application using R programming. By the end, participants will be able to critically evaluate regression models, apply suitable remediation techniques, and perform effective model validation, preparing them for data-driven roles across various industries.
Instructors:
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What you'll learn
Describe the assumptions of linear regression models
Use diagnostic plots to detect violations of regression model assumptions
Apply variance stabilizing transformations, including Box-Cox transformation
Implement transformations to linearize regression models
Use weighted least squares to address heteroscedasticity
Identify and remediate autocorrelation in regression models
Skills you'll gain
This course includes:
5.46 Hours PreRecorded video
8 quizzes, 2 assignments
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FullTime access
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There are 2 modules in this course
This comprehensive course delves into advanced techniques for diagnosing and improving linear regression models. Students will learn to identify violations of model assumptions and apply appropriate remedial measures. The curriculum covers a wide range of topics including regression diagnostics, variance stabilization techniques, Box-Cox transformations, weighted least squares, autocorrelation, multicollinearity, and variable selection. Throughout the course, emphasis is placed on practical application using R programming. By the end, participants will be equipped with the skills to critically evaluate regression models, apply suitable remediation techniques, and perform effective model validation, preparing them for data-driven roles across various industries.
Model Diagnostics and Remediation Part II
Module 1 · 8 Hours to complete
Model Diagnostics and Remediation Part II
Module 2 · 8 Hours to complete
Fee Structure
Payment options
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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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