Lecturer IV and Research Fellow
Fitting Statistical Models to Data with Python
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.
4 Modules
16 Hours
4 hrs per module (approx.)
Rating
About Fitting Statistical Models to Data with Python
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.
This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python).
During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.
Skills You'll Gain
- Bayesian Statistics
- Data Analysis
- Descriptive Statistics
- Linear Regression
- Probability Distribution
- Python For Data Analysis
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Fitting Statistical Models to Data with Python, a course that is part of the Statistics With Python series, builds on statistical inference foundations to help learners connect research questions with appropriate modeling techniques. You will explore linear and logistic regression, multilevel models, and Bayesian approaches using real-world datasets. Through hands-on labs and Python-based assessments, learners strengthen both conceptual understanding and applied data analysis skills.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Overview & Considerations for Statistical Modeling
- Video: Welcome to the Course!
- Reading: Course Syllabus
- Video: Fitting Statistical Models to Data with Python Guidelines
- Reading: Meet the Course Team!
- Reading: Help Us Learn More About You!
- Reading: About Our Datasets
- Video: What Do We Mean by Fitting Models to Data?
- Video: Types of Variables in Statistical Modeling
- Video: Different Study Designs Generate Different Types of Data: Implications for Modeling
- Video: Objectives of Model Fitting: Inference vs. Prediction
- Reading: Mixed effects models: Is it time to go Bayesian by default?
- Video: Plotting Predictions and Prediction Uncertainty
- Reading: Python Statistics Landscape
- Video: Python Statistics Landscape
- Ungraded Lab: Python Libraries
- Ungraded Lab: Getting Started with Modeling in Python
Module 2: Fitting Models to Independent Data
- Video: Linear Regression Introduction
- Video: Linear Regression Inference
- Reading: Linear Regression Models: Notation, Parameters, Estimation Methods
- Video: Interview: Causation vs Correlation
- Reading: Try It Out: Continuous Data Scatterplot App
- Reading: Importance of Data Visualization: The Datasaurus Dozen
- Video: Logistic Regression Introduction
- Video: Logistic Regression Inference
- Reading: Logistic Regression Models: Notation, Parameters, Estimation Methods
- Ungraded Lab: NHANES Case Study: Linear and Logistic Regression
- Ungraded Lab: Practice notebook for regression analysis with NHANES
- Ungraded Lab: Week 2 Python Assessment Notebook
Module 3: Fitting Models to Dependent Data
- Video: What are Multilevel Models and Why Do We Fit Them?
- Reading: Visualizing Multilevel Models
- Video: Multilevel Linear Regression Models
- Reading: Likelihood Ratio Tests for Fixed Effects and Variance Components
- Video: Multilevel Logistic Regression models
- Reading: Link to the Cal Poly App
- Video: Practice with Multilevel Modeling: The Cal Poly App
- Video: What are Marginal Models and Why Do We Fit Them?
- Video: Marginal Linear Regression Models
- Video: Marginal Logistic Regression
- Ungraded Lab: Fitting Multilevel and Marginal Models to Autism Data in Python
- Ungraded Lab: NHANES Case Study: Marginal and Multilevel Regression
- Ungraded Lab: Practice: Marginal and Multilevel Regression
- Ungraded Lab: Week 3 Python Assessment
Module 4: Special Topics
- Reading: Other Types of Dependent Variables
- Discussion Prompt: Your Turn: Other Types of Dependent Variables
- Video: Should We Use Survey Weights When Fitting Models?
- Video: Introduction to Bayesian
- Video: Bayesian Approaches to Statistics and Modeling
- Video: Bayesian Approaches Case Study: Part I
- Video: Bayesian Approaches Case Study: Part II
- Video: Bayesian Approaches Case Study - Part III
- Reading: Optional: A Visual Introduction to Machine Learning
- Ungraded Lab: Bayesian in Python
- Reading: Course Feedback
- Reading: Keep Learning with Michigan Online
Grading Policy
Grades are based on conceptual quizzes and Python assessments. Weights range from 10% to 20% per assessment, with Python assessments comprising the largest portion of the final grade.
Professor
Research Associate Professor
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Completion of the first two courses in this specialization; high school-level algebra
