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.
提供方
課程信息
142,106 次近期查看
您將獲得的技能
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
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7 小時
建議：4 weeks; 4-6 hours/week
英語（English）
字幕：英語（English）, 韓語
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
4.5
來自FITTING STATISTICAL MODELS TO DATA WITH PYTHON的熱門評論
由 BS 提供Jan 17, 2020
I am very thankful to you sir.. i have learned so much great things through this course.\n\nthis course is very helpful for my career. i would like to learn more courses from you. thank you so much.
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 NA 提供Dec 19, 2019
Challenging but excellent course, especially how content was organized and examples used to explain concepts
由 JX 提供Jun 29, 2019
Really thorough and in-depth material about statistical models with python.
由 NG 提供May 24, 2019
Very informative and the example\n\napplications are extremely detailed
由 ND 提供Jan 22, 2020
Excellent course, really enjoyed the section on Bayesian statistics.
由 PC 提供Mar 16, 2020
Have given me CLearcut idea about Mixed-effects and Marginal Models
關於 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.
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？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
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。
退款政策是如何规定的？
有助学金吗？
還有其他問題嗎？請訪問 學生幫助中心。
