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# Linear Regression Fundamentals

Master the fundamentals and get hands-on experience in applying linear regression when working on people analytics challenges. Explore best practices, understand key principles, learn how to assess statistical assumptions — and apply your new skills and knowledge working on a real-life scenario, in the R environment.

Online

1h 40minutes

on-demand video

5 modules

Paid

member access

\$49.00

Save 65% with Full Access

Linear regression is one of the most useful tools in any people analytics professional’s toolkit, and so with this course, you’ll get a practical dive into linear regression fundamentals needed for people analytics, and how to best use it your day-to-day job.

After defining linear regression, you will have a chance to learn about the best ways to use linear regression and apply it in R for your analysis on a concrete practical example.

### Who is this course for?

This course is for those interested in hands-on experience with linear regression and also those with previous knowledge and experience working in R.

• People Analysts
• Data Analysts looking to tackle people data
• HRs starting their people analytics journey

Key takeaways

## What you’ll learn

Fundamentals of linear regression and its importance in people analytics

The mechanics and principles of linear regression, and practical application in R

Sample size consideration and the role of statistical power

Importance of evaluating statistical assumptions

Modules

## Topics covered

This course also includes a multiple-choice quiz and additional resources that lead you to a certificate.

What is linear regression and how does it work?

Linear regression allows us to understand the individual and combined effects that predictors have on an outcome by statistically controlling for alternative influences. Learn how to estimate values of an outcome variable based on linear (straight line) relationships with one or more predictors.

Linear regression formulae & statistical power

Learn about the differences between simple and multiple linear regressions and the impact of statistical power. Craig provides hands-on examples and general rule-of-thumb advice.

Assumptions & diagnostics

A Really important part in linear regression is a set of assumptions in diagnostics which are a prerequisite for fitting a regression model. Learn which assumptions to take into account and what to look out for.

Application of linear regression in R

Go through a concrete example of applications of both simple and multiple linear regressions in R on a simulated people analytics data.

Instructor