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
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Programa Especializado - Statistics with PythonUniversidad de MíchiganAcerca de este Curso
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100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 3 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Completion of the first two courses in this specialization; high school-level algebra
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
Habilidades que obtendrás
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 3 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Completion of the first two courses in this specialization; high school-level algebra
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
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
3 horas para completar
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 completar
8 videos (Total 73 minutos), 6 lecturas, 1 cuestionario
8 videos
Fitting Statistical Models to Data with Python Guidelines5m
What Do We Mean by Fitting Models to Data?18m
Types of Variables in Statistical Modeling13m
Different Study Designs Generate Different Types of Data: Implications for Modeling9m
Objectives of Model Fitting: Inference vs. Prediction11m
Plotting Predictions and Prediction Uncertainty8m
Python Statistics Landscape2m
6 lecturas
Course Syllabus5m
Meet the Course Team!10m
Help Us Learn More About You!10m
About Our Datasets2m
Mixed effects models: Is it time to go Bayesian by default?15m
Python Statistics Landscape1m
1 ejercicio de práctica
Week 1 Assessment15m
5 horas para completar
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 completar
6 videos (Total 85 minutos), 4 lecturas, 3 cuestionarios
6 videos
Linear Regression Inference15m
Interview: Causation vs Correlation18m
Logistic Regression Introduction15m
Logistic Regression Inference7m
NHANES Case Study Tutorial (Linear and Logistic Regression)17m
4 lecturas
Linear Regression Models: Notation, Parameters, Estimation Methods30m
Try It Out: Continuous Data Scatterplot App15m
Importance of Data Visualization: The Datasaurus Dozen10m
Logistic Regression Models: Notation, Parameters, Estimation Methods30m
3 ejercicios de práctica
Linear Regression Quiz20m
Logistic Regression Quiz15m
Week 2 Python Assessment20m
4 horas para completar
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 completar
8 videos (Total 121 minutos), 2 lecturas, 2 cuestionarios
8 videos
Multilevel Linear Regression Models21m
Multilevel Logistic Regression models14m
Practice with Multilevel Modeling: The Cal Poly App12m
What are Marginal Models and Why Do We Fit Them?13m
Marginal Linear Regression Models19m
Marginal Logistic Regression11m
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10m
2 lecturas
Visualizing Multilevel Models10m
Likelihood Ratio Tests for Fixed Effects and Variance Components10m
2 ejercicios de práctica
Name That Model15m
Week 3 Python Assessment20m
3 horas para completar
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 completar
6 videos (Total 105 minutos), 4 lecturas, 1 cuestionario
6 videos
Bayesian Approaches to Statistics and Modeling15m
Bayesian Approaches Case Study: Part I13m
Bayesian Approaches Case Study: Part II19m
Bayesian Approaches Case Study - Part III23m
Bayesian in Python19m
4 lecturas
Other Types of Dependent Variables20m
Optional: A Visual Introduction to Machine Learning20m
Course Feedback10m
Keep Learning with Michigan Online10m
1 ejercicio de práctica
Week 4 Python Assessment20m
Reseñas
Principales reseñas sobre FITTING STATISTICAL MODELS TO DATA WITH PYTHON
por BS17 de ene. 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 oct. 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 sep. 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.
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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