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.
Ce cours fait partie de la Spécialisation Statistics with Python
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Spécialisation Statistics with PythonUniversité du MichiganÀ propos de ce cours
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Cours 3 sur 3 dans le
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Niveau intermédiaire
Completion of the first two courses in this specialization; high school-level algebra
Approx. 15 heures pour terminer
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Compétences que vous acquerrez
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Completion of the first two courses in this specialization; high school-level algebra
Approx. 15 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Offert par
Université du 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.
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
8 vidéos (Total 73 min), 6 lectures, 1 quiz
8 vidéos
Fitting Statistical Models to Data with Python Guidelines5 min
What Do We Mean by Fitting Models to Data?18 min
Types of Variables in Statistical Modeling13 min
Different Study Designs Generate Different Types of Data: Implications for Modeling9 min
Objectives of Model Fitting: Inference vs. Prediction11 min
Plotting Predictions and Prediction Uncertainty8 min
Python Statistics Landscape2 min
6 lectures
Course Syllabus5 min
Meet the Course Team!10 min
Help Us Learn More About You!10 min
About Our Datasets2 min
Mixed effects models: Is it time to go Bayesian by default?15 min
Python Statistics Landscape1 min
1 exercice pour s'entraîner
Week 1 Assessment15 min
5 heures pour terminer
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 heures pour terminer
6 vidéos (Total 85 min), 4 lectures, 3 quiz
6 vidéos
Linear Regression Inference15 min
Interview: Causation vs Correlation18 min
Logistic Regression Introduction15 min
Logistic Regression Inference7 min
NHANES Case Study Tutorial (Linear and Logistic Regression)17 min
4 lectures
Linear Regression Models: Notation, Parameters, Estimation Methods30 min
Try It Out: Continuous Data Scatterplot App15 min
Importance of Data Visualization: The Datasaurus Dozen10 min
Logistic Regression Models: Notation, Parameters, Estimation Methods30 min
3 exercices pour s'entraîner
Linear Regression Quiz20 min
Logistic Regression Quiz15 min
Week 2 Python Assessment20 min
4 heures pour terminer
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 heures pour terminer
8 vidéos (Total 121 min), 2 lectures, 2 quiz
8 vidéos
Multilevel Linear Regression Models21 min
Multilevel Logistic Regression models14 min
Practice with Multilevel Modeling: The Cal Poly App12 min
What are Marginal Models and Why Do We Fit Them?13 min
Marginal Linear Regression Models19 min
Marginal Logistic Regression11 min
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10 min
2 lectures
Visualizing Multilevel Models10 min
Likelihood Ratio Tests for Fixed Effects and Variance Components10 min
2 exercices pour s'entraîner
Name That Model15 min
Week 3 Python Assessment20 min
3 heures pour terminer
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 heures pour terminer
6 vidéos (Total 105 min), 4 lectures, 1 quiz
6 vidéos
Bayesian Approaches to Statistics and Modeling15 min
Bayesian Approaches Case Study: Part I13 min
Bayesian Approaches Case Study: Part II19 min
Bayesian Approaches Case Study - Part III23 min
Bayesian in Python19 min
4 lectures
Other Types of Dependent Variables20 min
Optional: A Visual Introduction to Machine Learning20 min
Course Feedback10 min
Keep Learning with Michigan Online10 min
1 exercice pour s'entraîner
Week 4 Python Assessment20 min
Avis
Meilleurs avis pour FITTING STATISTICAL MODELS TO DATA WITH PYTHON
par BS17 janv. 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.
par FS20 oct. 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.
par SM15 juin 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.
par YT21 sept. 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.
À propos du Spécialisation 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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