Albert Y. Kim
Albert Y. Kim

Assistant Professor of Statistical & Data Sciences at Smith College

Albert completed his Ph.D. in Statistics at the University of Washington. He is a co-author of the fivethirtyeight R package as well as ModernDive, an online textbook for introductory statistics and data science using R. Previously he worked in the Search Ads Metrics Team at Google Inc. as well as at Reed, Middlebury, and Amherst Colleges. Find out more about his work on Twitter @rudeboybert and on his webpage.

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  • Chester Ismay

    Chester Ismay

  • Benjamin  Feder

    Benjamin Feder

Course Description

In this course you will learn to model with data. Models attempt to capture the relationship between an outcome variable of interest and a series of explanatory/predictor variables. Such models can be used for both explanatory purposes, e.g. "Does knowing professors ages explain their teaching evaluation scores as given by students?", and predictive purposes, e.g. "How well can we predict house price based on their size and condition?" You will leverage your tidyverse skills to construct and interpret such models. This course centers around the use of linear regression, one of the most commonly-used and easy to understand approaches to modeling. Such modeling and thinking is used in a wide variety of fields, including statistics, causal inference, machine learning, and artificial intelligence.

  1. 1

    Introduction to Modeling


    In this chapter you'll be introduced to some background theory and terminology for modeling, in particular the general modeling framework, modeling for explanation, modeling for prediction, and the modeling problem.

  2. Modeling with Basic Regression

    Equipped with your understanding of the general modeling framework, in this chapter you will be introduced to linear regression, one of the most widely-used and easy to understand approaches to data modeling. You'll keep things simple and model the outcome variable y as a function of a single explanatory/predictor variable x which can either be numerical or categorical. The outcome variable of interest in this chapter will be teaching evaluation scores of instructors at the University of Texas, Austin.

  3. Modeling with Multiple Regression

    In the previous chapter you learned about basic regression using either a single numerical or a categorical predictor. But why limit yourselves to using only one variable to inform your explanations/predictions? You will now extend basic regression to multiple regression which allows for more than one explanatory or one predictor variable in your models. You'll be modeling house prices using a dataset of houses in the Seattle, WA metropolitan area.

  4. Model Selection and Assessment

    In the previous chapters, you fit various models to explain or predict an outcome variable of interest. Model assessment techniques allow you to assess how well an explanatory model "fits" a set of data and how accurate and precise a predictive model is.