Senior Experience Machine Learning Section 4 - Simple Linear Regression

Simple Linear Regression Intuition

So simple linear regression creates a formula:

y = b₀ + b₁*x₁

y = dependent var, x = independent var (input),

b₁ = coefficient that will determine how a change in the unit X1 will affect the dependent variable

b₀ = constant

In short it’s finding the line of best fit.

ok so how does simple linear regression find the line of best fit?

It minimizes the sum of the distances.

Specifically, it wants to minimize (SUM (y_point - y_line)²), and whatever b’s it finds that minimize the most are the b’s you want!

And that’s called the Ordinary Least Squares method

OK i remember learning this before, but why is it the sum of the values squared and not just sum?

The sum of squares determines deviation from the line. That’s why it’s a sum of the differences squared and not just sums of differences.

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