# The Linear Regression Equation in a Nutshell Regression Analysis is about predicting a value or attribute of a variable based on some other variables. And linear regression is when there is only one variable you want to predict based on another single variable.

What really is regression? Regression Analysis is about predicting a value or attribute of a variable based on some other variables. And linear regression is when there is only one variable you want to predict based on another single variable.

## The Definition

Actually, the formal definition of simple linear regression that I learned at school is, by regression of a variable y on another variable x, we mean the dependence of y on x, on the average. This is more of the answer you can write in your exam sheet. The concept of regression is actually a lot easier than that.

Regression is actually about finding the trend of a data set plotted in a graph. If you draw a regression line ( buzzword alert! ) through a scatter plot, by just looking at the line you can tell if the trend is increasing or decreasing with respect to something!! A matplotlib scatterplot | Image by author

*You should know that regression analysis is the way of calculating and formulating the equation of the line ( do not worry we will get to it ) while the regression line is the line itself. While the equation of simple regression is the *equation of a line.

Y = mX + b

## Intuition

While going around the internet you will find two types of an intuitive approach to linear regression. One is where people will tell you regression is the way you can predict a value of a variable say, y with an input of x which you may already have and that is all right! ( That is the best approach of ML)

The other approach is what I defined in the definition. Think about it, you can draw like thousands of lines through a scatterplot but the line which fits the best and is unique from all every other line is the regression line. The telling it out is in the next paragraph!

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