Linear Regression with R: Build & Optimize
About this course
Build a strong foundation in Linear Regression with R and learn how to develop, evaluate, and optimize predictive models for data-driven decision-making. This course takes you through a structured learning journey, beginning with the fundamentals of regression concepts and progressing to advanced regression techniques used in supervised machine learning. You will learn how to define the relationship between dependent and independent variables, construct simple and multiple linear regression models, apply dummy variables for categorical data, and interpret regression equations and outputs. As you advance, you will evaluate model performance using statistical tests, validate predictive accuracy on new datasets, and improve model quality through backward elimination. Throughout the course, you will work with real-world datasets to build, visualize, and refine regression models using R, strengthening both your conceptual understanding and practical skills. Designed for students, analysts, and professionals, this course combines theory with hands-on application, making complex regression concepts accessible while providing practical experience. Its clear progression from foundational models to advanced optimization techniques helps you confidently build, assess, and improve regression models for predictive analytics and supervised machine learning applications.
68/100
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- What the provider tells you
- 32/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Understand the relationship between dependent and independent variables
- Construct simple and multiple linear regression models
- Apply dummy variables for categorical data
- Evaluate model performance using statistical tests
- Validate predictive accuracy on new datasets
- Improve model quality through backward elimination
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