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 observation

• 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.