+ \beta_n X_n\), where \(Y\) is the value of the response variable and \(X_i\) is the value of the explanatory variable(s). Recall the general format of the linear regression equation: But since this is a basic course, we will stick with ordinary least squares. There is a whole world of non-ordinary regression techniques out there intended to address this or that methodological problem or circumstance. The “ordinary” part of the name gives us the sense that the type of linear regression we are seeing here is just the tip of the methodological iceberg. OLS stands for “ordinary least squares”, which means the algorithm finds the best fit line my minimizing the squared residuals (this is “least squares”). The method we will use to create linear regression models in the Statsmodels library is OLS(). It has much more functionality than we need, but it provides nicely-formatted output similar to SAS Enterprise Guide. We have already used the heavyweight Statsmodels library, so we will continue to use it here. There are many ways to do linear regression in Python. Linear regression with a single explanatory variable ¶
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