Analyze the summary of linear model
Analyzing the performance of the different imputed models is one of the most significant tasks in dealing with missing data. It determines, the type of imputed DataFrame you can rely upon. For analysis, you can fit a linear regression model on the imputed DataFrame and check for various parameters that impact the selection of the imputation type.
In this exercise, you have already been loaded with the DataFrame diabetes_cc which is the complete case of diabetes DataFrame. The complete case acts as a base for comparison against other imputed DataFrames. You will use the package statsmodels.api loaded as sm for creating a linear regression model and generating summaries.
Deze oefening maakt deel uit van de cursus
Dealing with Missing Data in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Add constant to X and set X & y values to fit linear model
X = sm.add_constant(___)
y = ___
lm = sm.OLS(y, X).fit()