Linear regression is a common statistical data analysis technique.

It is used to determine the extent to which there is a linear relationship
between a dependent variable and one or more independent variables, and is
widely used in machine learning applications.

There are two types of linear regression, simple linear regression and multiple
linear regression.

In simple linear regression a single independent variable is used to predict
the value of a dependent variable. In multiple linear regression two or more
independent variables are used to predict the value of a dependent variable.
The difference between the two is the number of independent variables. In both
cases there is only a single dependent variable.

In this post I’m going to introduce basic concepts on linear regression and build a simple linear regression example using go and
spago.

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