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 faz parte do Programa de cursos integrados Statistics with Python
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Programa de cursos integrados Statistics with PythonUniversidade de MichiganInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Completion of the first two courses in this specialization; high school-level algebra
Aprox. 15 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
Habilidades que você terá
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Completion of the first two courses in this specialization; high school-level algebra
Aprox. 15 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
oferecido por
Universidade de Michigan
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 - O que você aprenderá com este curso
3 horas para concluir
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.
3 horas para concluir
8 vídeos (Total 73 mín.), 6 leituras, 1 teste
8 videos
Fitting Statistical Models to Data with Python Guidelines5min
What Do We Mean by Fitting Models to Data?18min
Types of Variables in Statistical Modeling13min
Different Study Designs Generate Different Types of Data: Implications for Modeling9min
Objectives of Model Fitting: Inference vs. Prediction11min
Plotting Predictions and Prediction Uncertainty8min
Python Statistics Landscape2min
6 leituras
Course Syllabus5min
Meet the Course Team!10min
Help Us Learn More About You!10min
About Our Datasets2min
Mixed effects models: Is it time to go Bayesian by default?15min
Python Statistics Landscape1min
1 exercício prático
Week 1 Assessment15min
5 horas para concluir
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.
5 horas para concluir
6 vídeos (Total 85 mín.), 4 leituras, 3 testes
6 videos
Linear Regression Inference15min
Interview: Causation vs Correlation18min
Logistic Regression Introduction15min
Logistic Regression Inference7min
NHANES Case Study Tutorial (Linear and Logistic Regression)17min
4 leituras
Linear Regression Models: Notation, Parameters, Estimation Methods30min
Try It Out: Continuous Data Scatterplot App15min
Importance of Data Visualization: The Datasaurus Dozen10min
Logistic Regression Models: Notation, Parameters, Estimation Methods30min
3 exercícios práticos
Linear Regression Quiz20min
Logistic Regression Quiz15min
Week 2 Python Assessment20min
4 horas para concluir
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.
4 horas para concluir
8 vídeos (Total 121 mín.), 2 leituras, 2 testes
8 videos
Multilevel Linear Regression Models21min
Multilevel Logistic Regression models14min
Practice with Multilevel Modeling: The Cal Poly App12min
What are Marginal Models and Why Do We Fit Them?13min
Marginal Linear Regression Models19min
Marginal Logistic Regression11min
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10min
2 leituras
Visualizing Multilevel Models10min
Likelihood Ratio Tests for Fixed Effects and Variance Components10min
2 exercícios práticos
Name That Model15min
Week 3 Python Assessment20min
3 horas para concluir
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.
3 horas para concluir
6 vídeos (Total 105 mín.), 4 leituras, 1 teste
6 videos
Bayesian Approaches to Statistics and Modeling15min
Bayesian Approaches Case Study: Part I13min
Bayesian Approaches Case Study: Part II19min
Bayesian Approaches Case Study - Part III23min
Bayesian in Python19min
4 leituras
Other Types of Dependent Variables20min
Optional: A Visual Introduction to Machine Learning20min
Course Feedback10min
Keep Learning with Michigan Online10min
1 exercício prático
Week 4 Python Assessment20min
Avaliações
Principais avaliações do FITTING STATISTICAL MODELS TO DATA WITH PYTHON
por BS17 de Jan de 2020
I am very thankful to you sir.. i have learned so much great things through this course. this course is very helpful for my career. i would like to learn more courses from you. thank you so much.
por FS20 de Out de 2020
Overall, the course was a great refresher of statistical theory and application with some great Python exercises. However, some of the Python coding instruction itself could have been more detailed.
por SM15 de Jun de 2020
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.
por YT21 de Set de 2020
The code examples may be more precise with detailed comments. Some codes are not understood, in other words codes can be refactored in a way that can be more suitable for reproducible studies.
Sobre Programa de cursos integrados 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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