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  3. Introduction to Regression with statsmodels in Python
  • 1

    Simple Linear Regression Modeling

    Free

    You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

  • 2

    Predictions and model objects

    In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

  • 3

    Assessing model fit

    In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand each observation's leverage and influence to create the model.

  • 4

    Simple Logistic Regression Modeling

    Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Initializing

Exercise

Exploring the explanatory variables

When the response variable is logical, all the points lie on the \(y=0\) and \(y=1\) lines, making it difficult to see what is happening. In the video, until you saw the trend line, it wasn't clear how the explanatory variable was distributed on each line. This can be solved with a histogram of the explanatory variable, grouped by the response.

You will use these histograms to get to know the financial services churn dataset seen in the video.

churn is available as a pandas DataFrame.

Instructions 1/2

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  • 1

    In a sns.displot() call on the churn data, plot time_since_last_purchase as two histograms, split for each has_churned value.

  • 2

    Redraw the histograms using the time_since_first_purchase column, split for each has_churned value.