When I say the “manual” approach in Python, I actually mean “quite a bit less manual” than Excel. Specifically: we typically need to change the granularity of the variable to provide more generalizable results. I am partial to the manual approach because dealing intelligently with categorical variables in real-world data almost always involves significant work. In Python, we can use either the manual approach (create a matrix of dummy variables ourselves) or the automatic approach (let the algorithm sort it out behind the scenes). R: We converted the variable to a factor data type and let R construct the n-1 dummy columns behind the scenes SAS Enterprise Guide: We used the recoding functionality in the query builder to add n-1 new columns to the data set Recall how we have dealt with categorical explanatory variables to this point:Įxcel: We used IF statements and other tricks to create n-1 new columns in the spreadsheet (where n is the number of values in the categorical variable) This might indicate that there are strong multicollinearity or other numerical problems. Notes: Standard Errors assume that the covariance matrix of the errors is correctly specified. Running the standardized regressionĩ.10.1. Creating standardized input matricesĩ.9.2. Standardized regression coefficientsĩ.9.1. Restricting variables in the scatterplot matrixĩ.9. Adding the dummy columns to the existingĩ.5.2.
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