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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課程信息
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第 3 門課程（共 3 門）
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中級
Completion of the first two courses in this specialization; high school-level algebra
完成時間大約為15 小時
英語（English）
字幕：法語（French）, 巴西葡萄牙語, 韓語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 3 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
Completion of the first two courses in this specialization; high school-level algebra
完成時間大約為15 小時
英語（English）
字幕：法語（French）, 巴西葡萄牙語, 韓語, 俄語（Russian）, 英語（English）, 西班牙語（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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 3 小時
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.
完成時間為 3 小時
8 個視頻 （總計 73 分鐘）, 6 個閱讀材料, 1 個測驗
8 個視頻
Fitting Statistical Models to Data with Python Guidelines5分鐘
What Do We Mean by Fitting Models to Data?18分鐘
Types of Variables in Statistical Modeling13分鐘
Different Study Designs Generate Different Types of Data: Implications for Modeling9分鐘
Objectives of Model Fitting: Inference vs. Prediction11分鐘
Plotting Predictions and Prediction Uncertainty8分鐘
Python Statistics Landscape2分鐘
6 個閱讀材料
Course Syllabus5分鐘
Meet the Course Team!10分鐘
Help Us Learn More About You!10分鐘
About Our Datasets2分鐘
Mixed effects models: Is it time to go Bayesian by default?15分鐘
Python Statistics Landscape1分鐘
1 個練習
Week 1 Assessment15分鐘
完成時間為 5 小時
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.
完成時間為 5 小時
6 個視頻 （總計 85 分鐘）, 4 個閱讀材料, 3 個測驗
6 個視頻
Linear Regression Inference15分鐘
Interview: Causation vs Correlation18分鐘
Logistic Regression Introduction15分鐘
Logistic Regression Inference7分鐘
NHANES Case Study Tutorial (Linear and Logistic Regression)17分鐘
4 個閱讀材料
Linear Regression Models: Notation, Parameters, Estimation Methods30分鐘
Try It Out: Continuous Data Scatterplot App15分鐘
Importance of Data Visualization: The Datasaurus Dozen10分鐘
Logistic Regression Models: Notation, Parameters, Estimation Methods30分鐘
3 個練習
Linear Regression Quiz20分鐘
Logistic Regression Quiz15分鐘
Week 2 Python Assessment20分鐘
完成時間為 4 小時
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.
完成時間為 4 小時
8 個視頻 （總計 121 分鐘）, 2 個閱讀材料, 2 個測驗
8 個視頻
Multilevel Linear Regression Models21分鐘
Multilevel Logistic Regression models14分鐘
Practice with Multilevel Modeling: The Cal Poly App12分鐘
What are Marginal Models and Why Do We Fit Them?13分鐘
Marginal Linear Regression Models19分鐘
Marginal Logistic Regression11分鐘
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10分鐘
2 個閱讀材料
Visualizing Multilevel Models10分鐘
Likelihood Ratio Tests for Fixed Effects and Variance Components10分鐘
2 個練習
Name That Model15分鐘
Week 3 Python Assessment20分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
6 個視頻 （總計 105 分鐘）, 3 個閱讀材料, 1 個測驗
6 個視頻
Bayesian Approaches to Statistics and Modeling15分鐘
Bayesian Approaches Case Study: Part I13分鐘
Bayesian Approaches Case Study: Part II19分鐘
Bayesian Approaches Case Study - Part III23分鐘
Bayesian in Python19分鐘
3 個閱讀材料
Other Types of Dependent Variables20分鐘
Optional: A Visual Introduction to Machine Learning20分鐘
Course Feedback10分鐘
1 個練習
Week 4 Python Assessment20分鐘
審閱
來自FITTING STATISTICAL MODELS TO DATA WITH PYTHON的熱門評論
由 BS 提供2020年1月17日
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2020年6月15日
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 YT 提供2020年9月21日
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.
由 AF 提供2019年3月11日
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.
關於 Statistics with Python 專項課程
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.
常見問題
我什么时候能够访问课程视频和作业？
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：
- 课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
- 课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
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。
完成课程后，我会获得大学学分吗？
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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