Regression Analysis for Statistics & Machine Learning in R
Master statistical and machine learning regression techniques using R. Learn OLS, GLMs, and advanced modeling for data analysis.
Master statistical and machine learning regression techniques using R. Learn OLS, GLMs, and advanced modeling for data analysis.
This comprehensive course covers regression analysis techniques using R, combining statistical and machine learning approaches. Students learn everything from basic OLS regression to advanced topics like GLMs and non-parametric methods. The curriculum includes practical implementation in R, covering data cleaning, exploratory analysis, multicollinearity handling, and model selection. Advanced topics include robust regression, logistic regression, and machine learning techniques like random forests. The course emphasizes both theoretical understanding and hands-on application, making it ideal for data analysts and statisticians looking to enhance their regression analysis skills.
Instructors:
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What you'll learn
Master Ordinary Least Square (OLS) regression implementation in R
Handle multicollinearity using advanced regression techniques
Apply variable selection and model evaluation methods
Create and interpret Generalized Linear Models (GLMs)
Implement non-parametric and non-linear regression methods
Use machine learning approaches for complex regression problems
Skills you'll gain
This course includes:
437 Minutes PreRecorded video
8 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 7 modules in this course
This course provides a comprehensive introduction to regression analysis techniques using R, blending statistical and machine learning approaches. Students learn essential concepts from Ordinary Least Squares (OLS) regression to advanced topics like Generalized Linear Models (GLMs) and non-parametric methods. The curriculum covers practical aspects including data cleaning, exploratory analysis, handling multicollinearity, and model selection. Advanced topics include robust regression, logistic regression, and modern machine learning techniques such as random forests and gradient boosting.
Get Started with Practical Regression Analysis in R
Module 1 · 1 Hours to complete
Ordinary Least Square Regression Modelling
Module 2 · 1 Hours to complete
Deal with Multicollinearity in OLS Regression Models
Module 3 · 1 Hours to complete
Variable & Model Selection
Module 4 · 1 Hours to complete
Dealing with Other Violations of the OLS Regression Models
Module 5 · 42 Minutes to complete
Generalized Linear Models (GLMs)
Module 6 · 1 Hours to complete
Working with Non-Parametric and Non-Linear Data
Module 7 · 2 Hours to complete
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