课程信息
17,573 次近期查看

第 3 门课程(共 3 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

Completion of the first two courses in this specialization; high school-level algebra

完成时间大约为13 小时

建议:4 weeks; 4-6 hours/week...

英语(English)

字幕:英语(English), 韩语

您将获得的技能

Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression

第 3 门课程(共 3 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

Completion of the first two courses in this specialization; high school-level algebra

完成时间大约为13 小时

建议:4 weeks; 4-6 hours/week...

英语(English)

字幕:英语(English), 韩语

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 3 小时

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

7 个视频 (总计 67 分钟), 6 个阅读材料, 1 个测验
7 个视频
What Do We Mean by Fitting Models to Data'?18分钟
Types of Variables in Statistical Modeling13分钟
Different Study Designs Generate Different Types of Data: Implications for Modeling9分钟
Objectives of Model Fitting: Inference vs. Prediction11分钟
Plotting Predictions and Prediction Uncertainty8分钟
Python Statistics Landscape2分钟
6 个阅读材料
Course Syllabus5分钟
Meet the Course Team!10分钟
Help Us Learn More About You!10分钟
About Our Datasets2分钟
Mixed effects models: Is it time to go Bayesian by default?15分钟
Python Statistics Landscape1分钟
1 个练习
Week 1 Assessment15分钟
2
完成时间为 5 小时

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

6 个视频 (总计 85 分钟), 4 个阅读材料, 3 个测验
6 个视频
Linear Regression Inference15分钟
Interview: Causation vs Correlation18分钟
Logistic Regression Introduction15分钟
Logistic Regression Inference7分钟
NHANES Case Study Tutorial (Linear and Logistic Regression)17分钟
4 个阅读材料
Linear Regression Models: Notation, Parameters, Estimation Methods30分钟
Try It Out: Continuous Data Scatterplot App15分钟
Importance of Data Visualization: The Datasaurus Dozen10分钟
Logistic Regression Models: Notation, Parameters, Estimation Methods30分钟
3 个练习
Linear Regression Quiz20分钟
Logistic Regression Quiz15分钟
Week 2 Python Assessment20分钟
3
完成时间为 4 小时

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

8 个视频 (总计 121 分钟), 2 个阅读材料, 2 个测验
8 个视频
Multilevel Linear Regression Models21分钟
Multilevel Logistic Regression models14分钟
Practice with Multilevel Modeling: The Cal Poly App12分钟
What are Marginal Models and Why Do We Fit Them?13分钟
Marginal Linear Regression Models19分钟
Marginal Logistic Regression11分钟
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10分钟
2 个阅读材料
Visualizing Multilevel Models10分钟
Likelihood Ratio Tests for Fixed Effects and Variance Components10分钟
2 个练习
Name That Model15分钟
Week 3 Python Assessment20分钟
4
完成时间为 3 小时

WEEK 4: Special Topics

6 个视频 (总计 105 分钟), 3 个阅读材料, 1 个测验
6 个视频
Bayesian Approaches to Statistics and Modeling15分钟
Bayesian Approaches Case Study: Part I13分钟
Bayesian Approaches Case Study: Part II19分钟
Bayesian Approaches Case Study - Part III23分钟
Bayesian in Python19分钟
3 个阅读材料
Other Types of Dependent Variables20分钟
Optional: A Visual Introduction to Machine Learning20分钟
Course Feedback10分钟
1 个练习
Week 4 Python Assessment20分钟
4.4
15 个审阅Chevron Right

来自Fitting Statistical Models to Data with Python的热门评论

创建者 AFMar 12th 2019

The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.

创建者 JXJun 30th 2019

Really thorough and in-depth material about statistical models with python.

讲师

Avatar

Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
Avatar

Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

关于 密歇根大学

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....

关于 Statistics with Python 专项课程

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....
Statistics with Python

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