Generalized Linear Models and Nonparametric Regression
Master advanced statistical modeling with GLMs, nonparametric regression, and generalized additive models for data science.
Master advanced statistical modeling with GLMs, nonparametric regression, and generalized additive models for data science.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Statistical Modeling for Data Science Applications Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.4
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What you'll learn
Implement and interpret generalized linear models
Develop nonparametric regression solutions
Apply generalized additive models effectively
Assess model fit and predictive power
Handle non-normal response variables
Address ethical issues in statistical modeling
Skills you'll gain
This course includes:
5 Hours PreRecorded video
8 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This advanced statistical modeling course covers generalized linear models (GLMs), nonparametric regression techniques, and generalized additive models (GAMs). Students learn to implement these methods using R programming, with emphasis on both theoretical understanding and practical applications. The curriculum includes binomial and Poisson regression, kernel estimators, smoothing splines, and ethical considerations in complex statistical modeling.
An Introduction to Generalized Linear Models Through Binomial Regression
Module 1 · 12 Hours to complete
Models for Count Data
Module 2 · 9 Hours to complete
Introduction to Nonparametric Regression
Module 3 · 8 Hours to complete
Introduction to Generalized Additive Models
Module 4 · 11 Hours to complete
Fee Structure
Instructor
Director of Applied Mathematics Program and Statistical Modeling Educator
Brian Zaharatos is the Director of the Professional Master’s Degree in Applied Mathematics at the University of Colorado Boulder. He teaches several courses, including ANOVA and Experimental Design, Generalized Linear Models and Nonparametric Regression, and Modern Regression Analysis in R. His courses focus on statistical modeling techniques essential for data analysis and research applications.
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4.4 course rating
17 ratings
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