Inferential Statistical Analysis with Python

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.

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.

Dr. Kerby Shedden is a Professor at the University of Michigan, specializing in the application of statistical models and data analysis techniques. His research and teaching focus on using Python as a tool for statistical analysis, making complex data accessible and interpretable.Dr. Shedden offers courses that equip students with essential skills in statistical modeling and data science, including Fitting Statistical Models to Data with Python, Inferential Statistical Analysis with Python, and Understanding and Visualizing Data with Python. These courses provide a comprehensive understanding of how to apply Python programming to real-world data analysis challenges, empowering students to make informed decisions based on statistical evidence.Through his teaching, Dr. Shedden is helping shape the next generation of data scientists, ensuring they are proficient in both statistical theory and the practical tools required to analyze and visualize data effectively.