REG-R.AJ1 ISBN: 978-1-61691-689-3

Regression Analysis with R

With this Regression Analysis in R course, you’ll build predictive models, stand out in interviews, and secure high-paying roles in data science, analytics, and research.

What you will be able to do

Build, optimize, and validate predictive models using R, the language trusted by statisticians and data pros.
Uncover hidden patterns, measure relationships, and extract powerful insights that drive business impact.
Develop a problem-solving mindset to predict sales, analyze trends, and optimize outcomes.
Get hands-on with industry-grade libraries and tools likeggplot2, caret, lm(), and more.
Interpret model outputs, diagnose errors, and communicate findings.
Visualize and represent your data insights.

Intermediate Self-paced · 1 year access 4.5/5 (100 Reviews)

01 / About

About This Course

Try Free → No credit card required

Videos courses and tutorials teach you how to run regression models. This one teaches you how to think with them.

In this Regression Analysis with R course, you’ll learn how to uncover meaningful relationships in data, predict outcomes, and build models that can influence real-world decisions. Work with real datasets, tackle hands-on projects and build a job-ready portfolio. Go from simple linear regression to advanced techniques, all through practice, job-relevant scenarios.

No beating about the bush, just applied regression modeling in R, taught in a way that sticks.

02 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

10 Interactive Lessons · 54 topics

01 Preface 3 topics

What this course covers
To get the most out of this course
Conventions used

02 Getting Started with Regression 10 topics

Going back to the origin of regression
Regression in the real world
Understanding regression concepts
Regression versus correlation
Discovering different types of regression
The R environment
Installing R
RStudio
R packages for regression
Summary

03 Basic Concepts – Simple Linear Regression 6 topics

Association between variables – covariance and correlation
Searching linear relationships
Least squares regression
Creating a linear regression model
Modeling a perfect linear association
Summary

04 More Than Just One Predictor – MLR 6 topics

Multiple linear regression concepts
Building a multiple linear regression model
Multiple linear regression with categorical predictor
Gradient Descent and linear regression
Polynomial regression
Summary

05 When the Response Falls into Two Categories – Logistic Regression 5 topics

Understanding logistic regression
Generalized Linear Model
Multiple logistic regression
Multinomial logistic regression
Summary

06 Data Preparation Using R Tools 6 topics

Data wrangling
Finding outliers in data
Scale of features
Discretization in R
Dimensionality reduction
Summary

07 Avoiding Overfitting Problems - Achieving Generalization 4 topics

Understanding overfitting
Feature selection
Regularization
Summary

08 Going Further with Regression Models 4 topics

Robust linear regression
Bayesian linear regression
Count data model
Summary

09 Beyond Linearity – When Curving Is Much Better 6 topics

Nonlinear least squares
Multivariate Adaptive Regression Splines
Generalized Additive Model
Regression trees
Support Vector Regression
Summary

10 Regression Analysis in Practice 4 topics

Random forest regression with the Boston dataset
Classifying breast cancer using logistic regression
Regression with neural networks
Summary

03 / FAQs

Questions before you start

What exactly will I be able to do after finishing this course?

After you complete our Regression Analysis with R training, you will be able to build, interpret, and present regression models in R to solve real-world problems. Also, you'll have portfolio projects to show for it.

How is this course different from free tutorials?

This course is structured for outcomes, not just knowledge. You’ll get hands-on projects, real-world use cases, and skills you can actually use in a job.

Can I take this course if I’m switching to a data career?

Yes. This is a great starting point if you’re pivoting into data. It helps you build one of the most in-demand skills with immediate application.

What tools or software do I need?

All you need is R and RStudio. Both are free and open-source. We’ll guide you through setup at the beginning of the course.

Build a Portfolio Too Strong for Employers to Ignore

Start building the data career they said you needed experience for.

1 year of full access
Certificate of completion

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No credit card required