But before we dive into this math-y goodness, let me first explain why you should care.

First, it’s important because sometimes life gives us messy data and we need to find a way to clean it up. Maybe you have sales figures for the past year that look like they were thrown in a blender and then spilled all over your desk. Or maybe you’re trying to predict how many people will show up at an event based on historical attendance numbers, but those numbers are all over the place. Whatever the case may be, least squares can help us make sense of it all by finding that perfect line (or curve) that fits our data points as closely as possible.

Now let’s get into the details. The basic idea behind least squares is to find a line (or curve) that minimizes the sum of the squared differences between the actual values and the predicted values. This might sound like gibberish, but it basically means we want our line to be as close as possible to all those ***** data points without actually touching them.

To do this, we first calculate a value called “slope” (m) which tells us how steep the line is. The formula for calculating slope looks like this: m = y2 y1 / x2 x1. This basically means that if you have two points on your graph (x1,y1) and (x2,y2), then the slope of the line connecting those two points is equal to the change in y divided by the change in x.

Once we’ve found our slope, we can use it to calculate a value called “intercept” (b). This tells us where the line crosses the y-axis when x = 0. The formula for calculating intercept looks like this: b = y mx. So if you have a point on your graph with coordinates (x,y), then the intercept is equal to the value of y minus the product of slope and x.

Now that we’ve got our slope and intercept, we can use them to create our line! The formula for creating a line using least squares looks like this: y = mx + b. So if you have a data point with coordinates (x1,y1), then the predicted value of y based on your line would be equal to m times x plus b.

And that’s it! You now know how to use least squares to create a line that fits your data points as closely as possible. But remember, math is not always perfect and sometimes our lines won’t fit perfectly either. That’s okay though at least we tried!