Step 5 Now that we've set up the basic elements of the data, let's carry out the regression. Statsmodels works by creating objects that represent a model of the data: in this case, the model is OLS, or a least-squares straight-line fit to the data. The basic workflow is to first create an OLS object that contains the specific x and y data values of interest, and then to fit the model to the data, which is the process by which the best-fit model parameters are estimated. All model objects that one creates in statsmodels possess a fit method that carries out the process of fitting (parameter estimation). In the code cell below: Use the sm.OLS function to create a model object to fit y to x, and assign the result to the variable model. Also pass in the option missing='drop' to drop any observations with missing data in either x or y. As before, you might find it useful to consult the statsmodels documentation on OLS. Call the fit method on model to fit the model to the data, and store the output in results Print the results of the fit, by calling the results.summary method