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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.

(REG-R.AJ1) / ISBN : 978-1-61691-689-3
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About This Course

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.

Skills You’ll Get

  • 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.
Download Course Outline

1

Preface

  • What this course covers
  • To get the most out of this course
  • Conventions used
2

Getting Started with Regression

  • 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
3

Basic Concepts – Simple Linear Regression

  • Association between variables – covariance and correlation
  • Searching linear relationships
  • Least squares regression
  • Creating a linear regression model
  • Modeling a perfect linear association
  • Summary
4

More Than Just One Predictor – MLR

  • Multiple linear regression concepts
  • Building a multiple linear regression model
  • Multiple linear regression with categorical predictor
  • Gradient Descent and linear regression
  • Polynomial regression
  • Summary
5

When the Response Falls into Two Categories – Logistic Regression

  • Understanding logistic regression
  • Generalized Linear Model
  • Multiple logistic regression
  • Multinomial logistic regression
  • Summary
6

Data Preparation Using R Tools

  • Data wrangling
  • Finding outliers in data
  • Scale of features
  • Discretization in R
  • Dimensionality reduction
  • Summary
7

Avoiding Overfitting Problems - Achieving Generalization

  • Understanding overfitting
  • Feature selection
  • Regularization
  • Summary
8

Going Further with Regression Models

  • Robust linear regression
  • Bayesian linear regression
  • Count data model
  • Summary
9

Beyond Linearity – When Curving Is Much Better

  • Nonlinear least squares
  • Multivariate Adaptive Regression Splines
  • Generalized Additive Model
  • Regression trees
  • Support Vector Regression
  • Summary
10

Regression Analysis in Practice

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

Any questions?
Check out the FAQs

Read answers to commonly asked questions about this certification exam.

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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.

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.

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.

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.

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