Fitting Statistical Models to Data with Python

О курсе

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.

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Учитесь у экспертов

Brenda Gunderson

Lecturer IV Emerita in Statistics

Brady T. West

Collegiate Research Professor, Faculty Associate, Population Studies Center, Research Professor, Survey Research Center, Institute for Social Research, Adjunct Lecturer in Quantitative Methods and Social Sciences Program, College of Literature, Science, and the Arts and Research Professor, Biostatistics, School of Public Health

Kerby Shedden

Associate Chair and Professor of Statistics