University of Colorado Boulder
Generalized Linear Models and Nonparametric Regression
University of Colorado Boulder

Generalized Linear Models and Nonparametric Regression

This course is part of Statistical Modeling for Data Science Applications Specialization

Taught in English

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Brian Zaharatos

Instructor: Brian Zaharatos

3,743 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.4

(14 reviews)

Intermediate level

Recommended experience

42 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

What you'll learn

  • Describe how to generalize the linear model framework to accommodate data that is not suitable for the standard linear regression model.

  • State some advantages and disadvantages of (generalized) additive models.

  • Describe how an additive model can be generalized to incorporate non-normal response variables (i.e., define a generalized additive model).

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(14 reviews)

Intermediate level

Recommended experience

42 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

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This course is part of the Statistical Modeling for Data Science Applications Specialization
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There are 4 modules in this course

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, including the most common binomial link functions; correctly interpret the binomial regression model; and consider various methods for assessing the fit and predictive power of the binomial regression model.

What's included

7 videos4 readings3 quizzes2 programming assignments2 peer reviews1 discussion prompt2 ungraded labs

In this module, we will consider how to model count data. When the response variable is a count of some phenomenon, and when that count is thought to depend on a set of predictors, we can use Poisson regression as a model. We will describe the Poisson regression in some detail and use Poisson regression on real data. Then, we will describe situations in which Poisson regression is not appropriate, and briefly present solutions to those situations.

What's included

7 videos2 quizzes1 programming assignment1 peer review3 ungraded labs

In this module, we will introduce the concept of a nonparametric regression model. We will contrast this notion with the parametric models that we have studied so far. Then, we’ll study particular nonparametric regression models: kernel estimators and splines. Finally, we will introduce additive models as a blending of parametric and nonparametric methods.

What's included

6 videos1 quiz1 programming assignment1 peer review3 ungraded labs

Some models, such as linear regression, are easily interpretable, but inflexible, in that they don't capture many real-world relationships accurately. Other models, such as neural networks, are quite flexible, but very difficult to interpret. Generalized additive models (GAMs) are a nice balance between flexibility and interpretability. In this module, we will further motivate GAMs, learn the basic mathematics of fitting GAMs, and implementing them on simulated and real data in R.

What's included

6 videos1 reading2 quizzes1 programming assignment1 peer review3 ungraded labs

Instructor

Instructor ratings
4.7 (6 ratings)
Brian Zaharatos
University of Colorado Boulder
3 Courses10,751 learners

Offered by

Recommended if you're interested in Probability and Statistics

Get a head start on your degree

This course is part of the following degree programs offered by University of Colorado Boulder. If you are admitted and enroll, your coursework can count toward your degree learning and your progress can transfer with you.

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Reviewed on Jun 27, 2023

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