University of Michigan
Fitting Statistical Models to Data with Python
University of Michigan

Fitting Statistical Models to Data with Python

Brenda Gunderson
Brady T. West
Kerby Shedden

Instructors: Brenda Gunderson

34,538 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.4

(689 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 14 hours
Learn at your own pace
88%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.4

(689 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 14 hours
Learn at your own pace
88%
Most learners liked this course

What you'll learn

  • Deepen your understanding of statistical inference techniques by mastering the art of fitting statistical models to data.

  • Connect research questions with data analysis methods, emphasizing objectives, relationships between variables, and making predictions.

  • Explore various statistical modeling techniques like linear regression, logistic regression, and Bayesian inference using real data sets.

  • Work through hands-on case studies in Python with libraries like Statsmodels, Pandas, and Seaborn in the Jupyter Notebook environment.

Details to know

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Assessments

7 assignments

Taught in English

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This course is part of the Statistics with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data.” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.

What's included

8 videos6 readings1 assignment2 ungraded labs

In this second week, we’ll introduce you to the basics of two types of regression: linear regression and logistic regression. You’ll get the chance to think about how to fit models, how to assess how well those models fit, and to consider how to interpret those models in the context of the data. You’ll also learn how to implement those models within Python.

What's included

5 videos4 readings3 assignments3 ungraded labs

In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. We’ll be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.

What's included

7 videos3 readings2 assignments4 ungraded labs

In this final week, we introduce special topics that extend the curriculum from previous weeks and courses further. We will cover a broad range of topics such as various types of dependent variables, exploring sampling methods and whether or not to use survey weights when fitting models, and in-depth case studies utilizing Bayesian techniques to derive insights from data. You’ll also have the opportunity to apply Bayesian techniques in Python.

What's included

6 videos4 readings1 assignment1 discussion prompt1 ungraded lab

Instructors

Instructor ratings
4.5 (102 ratings)
Brenda Gunderson
University of Michigan
3 Courses154,103 learners

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Recommended if you're interested in Probability and Statistics

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