Fitting Statistical Models to Data with Python
Master statistical modeling techniques using Python, from regression analysis to Bayesian inference.
Master statistical modeling techniques using Python, from regression analysis to Bayesian inference.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Statistics with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Apply statistical modeling techniques to real-world data
Implement linear and logistic regression models
Master multilevel and marginal modeling approaches
Use Bayesian inference techniques
Assess model fit and quality
Make data-driven predictions and inferences
Skills you'll gain
This course includes:
5.7 Hours PreRecorded video
7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores statistical modeling techniques using Python. Students learn to fit and interpret various statistical models, from basic linear and logistic regression to advanced multilevel and Bayesian models. The curriculum emphasizes connecting research questions with appropriate analysis methods, using real datasets and hands-on practice with Python libraries including Statsmodels, Pandas, and Seaborn in Jupyter Notebooks. Special attention is given to model assessment, variable relationships, and prediction techniques.
Overview & Considerations for Statistical Modeling
Module 1 · 3 Hours to complete
Fitting Models to Independent Data
Module 2 · 4 Hours to complete
Fitting Models to Dependent Data
Module 3 · 4 Hours to complete
Special Topics
Module 4 · 3 Hours to complete
Fee Structure
Instructors
Research Leader in Survey Methodology
Brady T. West serves as a Research Associate Professor in the Survey Methodology Program at the University of Michigan’s Survey Research Center, part of the Institute for Social Research. He earned his PhD in Survey Methodology from Michigan in 2011, following an MA in Applied Statistics in 2002 and a BS in Statistics with Highest Honors in 2001, both from the same institution. His research focuses on the implications of measurement error in auxiliary variables and survey paradata for survey estimation, along with survey nonresponse, interviewer effects, and multilevel regression models for clustered and longitudinal data. West is the lead author of Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition (2014, Chapman Hall/CRC Press) and co-author of Applied Survey Data Analysis (2017, Chapman Hill) with Steven Heeringa and Pat Berglund. Residing in Dexter, MI, he enjoys family life with his wife, Laura, their children, Carter and Everleigh, and their American Cocker Spaniel, Bailey.
Advocate for Statistical Education
Brenda Gunderson is a Senior Lecturer at the University of Michigan, where she received her PhD in Statistics in 1989. She coordinates and teaches the largest undergraduate statistics course, Statistics and Data Analysis, which accommodates approximately 1,800 students each term. In addition to her teaching role, Brenda serves as an undergraduate advisor for students pursuing a major or minor in Statistics. Her research focuses on enhancing statistical education, particularly through the integration of technology to improve teaching and learning outcomes.
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