Increasingly, predictive analytics is shaping companies' decisions about limited resources. In this project, you will build a regression model to make predictions. We will start this hands-on project by exploring the dataset and creating visualizations for the dataset. By the end of this 2-hour-long project, you will be able to build and interpret the result of a simple linear regression model in R. Also, you will learn how to perform model assessments and check for assumptions using diagnostic plots. By extension, you will learn how to build and interpret the result of a multiple linear regression model. To succeed in this project, you need to be familiar with using R to describe data. If you are unfamiliar with R and want to learn the basics, start with my previous guided project, "Getting Started with R." However, if you are comfortable using R, please join me on this beautiful and exciting ride! Let's get our hands dirty!
Data Analysis in R: Predictive Analysis with Regression
Instructor: Arimoro Olayinka Imisioluwa
Included with
(10 reviews)
Recommended experience
What you'll learn
Describe the data set (checking the structure of the dataset, checking for missing values, checking for correlations, basic data visualizations)
Build regression models and interpret the results
Predict new values using the regression model
Skills you'll practice
Details to know
Add to your LinkedIn profile
Only available on desktop
See how employees at top companies are mastering in-demand skills
Learn, practice, and apply job-ready skills in less than 2 hours
- Receive training from industry experts
- Gain hands-on experience solving real-world job tasks
- Build confidence using the latest tools and technologies
About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Import packages and dataset
Use R functions to describe the data
Create data visualization using ggplot
Practice Activity - Load and describe a dataset
Build a simple regression model
Perform diagnostic checks on fitted model
Perform model fit assessment - Part 1
Perform model fit assessment - Part 2
Build a simple regression model with transformation
Make predictions using fitted model
Build a multiple regression model
Portfolio Activity - Create a model to predict house prices in Iowa
Recommended experience
Familiar with R for describing data
12 project images
Instructor
Offered by
How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
Why people choose Coursera for their career
You might also like
University of Colorado Boulder
University of Minnesota
University of California San Diego
University of Michigan
New to Data Analysis? Start here.
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.