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.
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课程信息
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100% 在线
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第 3 门课程（共 3 门）
可灵活调整截止日期
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中级
Completion of the first two courses in this specialization; high school-level algebra
完成时间大约为15 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 韩语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 3 门）
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15 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 韩语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
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.
完成时间为 3 小时
8 个视频 （总计 73 分钟）, 6 个阅读材料, 1 个测验
8 个视频
Fitting Statistical Models to Data with Python Guidelines5分钟
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
完成时间为 5 小时
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.
完成时间为 5 小时
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
完成时间为 4 小时
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.
完成时间为 4 小时
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
完成时间为 3 小时
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.
完成时间为 3 小时
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
审阅
来自FITTING STATISTICAL MODELS TO DATA WITH PYTHON的热门评论
由 BS 提供Jan 17, 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.
由 SM 提供Jun 15, 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.
由 AF 提供Mar 11, 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.
由 JJ 提供May 23, 2020
Very informative. But had few confusions in the last course. Also the python code explanations were not good as the instructor was rushing through it without explaining.
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.
常见问题
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讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程，则可以免费查看大多数课程资料。要访问评分作业并获得证书，您需要在旁听期间或之后购买证书体验。如果看不到旁听选项：
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我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
Is financial aid available?
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。
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此课程不提供大学学分，但部分大学可能会选择接受课程证书作为学分。查看您的合作院校，了解详情。Coursera 上的在线学位和 Mastertrack™ 证书提供获得大学学分的机会。
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