How do you define a regression line using the least squares?

Daily Dose 015 | Probability and Statistics

How do you define a regression line using the least squares?

I know for me, PROBABILITY AND STATISTICS had me scratching my head in college.

I didn’t really get the need for the tables and strict formulas, I was more comfortable in the realm of derivations and hard science.

But I was wrong, much of what we do as engineers in the real world couldn’t be done without relying on the workings within this discipline. We use those tables and strict formulas to make better decisions. We use them to strengthen our analysis, test data and assess risk.

We use them to design and manufacture robust products, detect problems and understand how variations affect performance.

All this to say, there’s a reason we as engineers must be comfortable in this realm.

In this video, we jump in to a problem that is covered in the subject of ENGINEERING PROBABILITY & STATISTICS, specifically, we will be working on a problem dealing with the LEAST SQUARES LINEAR REGRESSION LINE.

Key Definition

What is LEAST SQUARES LINEAR REGRESSION?

The method of LEAST SQUARES is a standard approach in REGRESSION ANALYSIS, or LINEAR REGRESSION ANALYSIS, used to approximate the solution of sets of equations in which there are more equations than unknowns.

LEAST SQUARES means that the overall solution minimizes the sum of the squares of the residuals made in the results of every single equation.

Check out the video and see how we can go about solving this type of problem in the most efficient manner.

As always, with Love, Prepineer

Video Review

How to define a least squares linear regression line