Fitting Statistical Models to Data with Python

University of Michigan via Coursera

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Overview

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...

Syllabus

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

We begin this third course of the Statistics with Python specialization with an overview of what is meant by fitting statistical models to data. In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

In this second week, well introduce you to the basics of two types of regression: linear regression and logistic regression. Youll get the chance to think about how to fit models, how to assess how well those models fit, and to consider how to interpret those models in the context of the data. Youll also learn how to implement those models within Python.

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. Well be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.

WEEK 4: Special Topics

In this final week, we introduce special topics that extend the curriculum from previous weeks and courses further. We will cover a broad range of topics such as various types of dependent variables, exploring sampling methods and whether or not to use survey weights when fitting models, and in-depth case studies utilizing Bayesian techniques to derive insights from data. Youll also have the opportunity to apply Bayesian techniques in Python.