Modern Regression Analysis in R
This course is part of Statistical Modeling for Data Science Applications.
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.
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What you'll learn
Master linear regression model theory and implementation
Perform parameter estimation and statistical inference
Conduct model diagnostics and selection
Implement regression analysis in R
Understand statistical modeling ethics
Apply advanced regression techniques
Skills you'll gain
This course includes:
9.7 Hours PreRecorded video
11 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course covers modern regression analysis techniques using R programming. Students learn theoretical foundations and practical applications of linear statistical models, including parameter estimation, residual diagnostics, and model selection strategies. The curriculum emphasizes both mathematical understanding and hands-on implementation, with special attention to ethical considerations in statistical modeling and data science applications.
Introduction to Statistical Models
Module 1 · 8 Hours to complete
Linear Regression Parameter Estimation
Module 2 · 8 Hours to complete
Inference in Linear Regression
Module 3 · 8 Hours to complete
Prediction and Explanation in Linear Regression Analysis
Module 4 · 5 Hours to complete
Regression Diagnostics
Module 5 · 7 Hours to complete
Model Selection and Multicollinearity
Module 6 · 7 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Statistical Modeling for Data Science Applications
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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