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  3. Introduction to Regression with statsmodels in Python

Exercise

Linear regression with ols()

While sns.regplot() can display a linear regression trend line, it doesn't give you access to the intercept and slope as variables, or allow you to work with the model results as variables. That means that sometimes you'll need to run a linear regression yourself.

Time to run your first model!

taiwan_real_estate is available. TWD is an abbreviation for Taiwan dollars.

In addition, for this exercise and the remainder of the course, the following packages will be imported and aliased if necessary: matplotlib.pyplot as plt, seaborn as sns, and pandas as pd.

Instructions 1/3

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  • Import the ols() function from the statsmodels.formula.api package.
  • Run a linear regression with price_twd_msq as the response variable, n_convenience as the explanatory variable, and taiwan_real_estate as the dataset. Name it mdl_price_vs_conv.
  • Fit the model.
  • Print the parameters of the fitted model.