People Analytics Learning Hub
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.
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
What you’ll learn
This course also includes a multiple-choice quiz and additional resources that lead you to a certificate.
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.
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.
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.
Go through a concrete example of applications of both simple and multiple linear regressions in R on a simulated people analytics data.