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.
Этот курс входит в специализацию ''Специализация Statistics with Python'
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Специализация Statistics with PythonМичиганский университетОб этом курсе
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Курс 3 из 3 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Средний уровень
Completion of the first two courses in this specialization; high school-level algebra
Прибл. 15 часов на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Турецкий, Английский, Испанский
Приобретаемые навыки
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 3 из 3 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Средний уровень
Completion of the first two courses in this specialization; high school-level algebra
Прибл. 15 часов на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Турецкий, Английский, Испанский
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Мичиганский университет
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.
Программа курса: что вы изучите
3 ч. на завершение
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 ч. на завершение
8 видео ((всего 73 мин.)), 6 материалов для самостоятельного изучения, 1 тест
8 видео
Fitting Statistical Models to Data with Python Guidelines5мин
What Do We Mean by Fitting Models to Data?18мин
Types of Variables in Statistical Modeling13мин
Different Study Designs Generate Different Types of Data: Implications for Modeling9мин
Objectives of Model Fitting: Inference vs. Prediction11мин
Plotting Predictions and Prediction Uncertainty8мин
Python Statistics Landscape2мин
6 материалов для самостоятельного изучения
Course Syllabus5мин
Meet the Course Team!10мин
Help Us Learn More About You!10мин
About Our Datasets2мин
Mixed effects models: Is it time to go Bayesian by default?15мин
Python Statistics Landscape1мин
1 практическое упражнение
Week 1 Assessment15мин
5 ч. на завершение
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 ч. на завершение
6 видео ((всего 85 мин.)), 4 материалов для самостоятельного изучения, 3 тестов
6 видео
Linear Regression Inference15мин
Interview: Causation vs Correlation18мин
Logistic Regression Introduction15мин
Logistic Regression Inference7мин
NHANES Case Study Tutorial (Linear and Logistic Regression)17мин
4 материала для самостоятельного изучения
Linear Regression Models: Notation, Parameters, Estimation Methods30мин
Try It Out: Continuous Data Scatterplot App15мин
Importance of Data Visualization: The Datasaurus Dozen10мин
Logistic Regression Models: Notation, Parameters, Estimation Methods30мин
3 практических упражнения
Linear Regression Quiz20мин
Logistic Regression Quiz15мин
Week 2 Python Assessment20мин
4 ч. на завершение
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 ч. на завершение
8 видео ((всего 121 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
8 видео
Multilevel Linear Regression Models21мин
Multilevel Logistic Regression models14мин
Practice with Multilevel Modeling: The Cal Poly App12мин
What are Marginal Models and Why Do We Fit Them?13мин
Marginal Linear Regression Models19мин
Marginal Logistic Regression11мин
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10мин
2 материала для самостоятельного изучения
Visualizing Multilevel Models10мин
Likelihood Ratio Tests for Fixed Effects and Variance Components10мин
2 практических упражнения
Name That Model15мин
Week 3 Python Assessment20мин
3 ч. на завершение
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 ч. на завершение
6 видео ((всего 105 мин.)), 4 материалов для самостоятельного изучения, 1 тест
6 видео
Bayesian Approaches to Statistics and Modeling15мин
Bayesian Approaches Case Study: Part I13мин
Bayesian Approaches Case Study: Part II19мин
Bayesian Approaches Case Study - Part III23мин
Bayesian in Python19мин
4 материала для самостоятельного изучения
Other Types of Dependent Variables20мин
Optional: A Visual Introduction to Machine Learning20мин
Course Feedback10мин
Keep Learning with Michigan Online10мин
1 практическое упражнение
Week 4 Python Assessment20мин
Рецензии
Лучшие отзывы о курсе FITTING STATISTICAL MODELS TO DATA WITH PYTHON
от партнера BS17 янв. 2020 г.
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.
от партнера FS20 окт. 2020 г.
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.
от партнера SM15 июня 2020 г.
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.
от партнера YT21 сент. 2020 г.
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.
Специализация Statistics with Python: общие сведения
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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