![]() ![]() It is the same equation as your standard y= mx + b that you learned back in Algebra I, just written a little differently in statistics language. This equation may look familiar, and it should. x → the independent variable aka the variable we are using to predict y.In order for this line to be helpful, we need to find the equation of the line which is as following: We are trying to find a line that we can use to predict one variable by using another, all while minimizing error in this prediction. That was a pretty technical explanation, so let’s simplify. The goal of linear regression is to create a line of best fit that can predict the dependent variable with an independent variable while minimizing the squared error. This is a particularly useful tool for predictive modeling and forecasting, providing excellent insight on present data and predicting data in the future. Linear regression is a statistical modeling tool that we can use to predict one variable using another. Making Predictions Using Single Linear Regression Welcome back to our fourth and final installment of our series! In this post, we will be discussing single and multiple linear regression, so let’s jump right into it.
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