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.
Este curso forma parte de Programa especializado: Statistics with Python
ofrecido por
Acerca de este Curso
Completion of the first two courses in this specialization; high school-level algebra
Habilidades que obtendrás
- Bayesian Statistics
- Python Programming
- Statistical Model
- statistical regression
Completion of the first two courses in this specialization; high school-level algebra
ofrecido por

Universidad de Míchigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Programa - Qué aprenderás en este curso
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, we’ll introduce you to the basics of two types of regression: linear regression and logistic regression. You’ll 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. You’ll 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. We’ll 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. You’ll also have the opportunity to apply Bayesian techniques in Python.
Reseñas
- 5 stars65,18 %
- 4 stars20,79 %
- 3 stars8,08 %
- 2 stars3,46 %
- 1 star2,47 %
Principales reseñas sobre FITTING STATISTICAL MODELS TO DATA WITH PYTHON
A great introduction to regression and bayesian analysis in python. I get that the content is hard, but they sum it all well. I would recommend for those who have prior knowledge of statistics.
Really thorough and in-depth material about statistical models with python.
The course was wonderful however, sometimes I felt that a little bit more details could be provided when python code was being explained for week 2.
Very informative. But had few confusions in the last course. Also the python code explanations were not good as the instructor was rushing through it without explaining.
Acerca de Programa especializado: Statistics with Python
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.

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