課程信息
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專項課程

第 3 門課程(共 3 門)

100% 在線

100% 在線

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

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

完成時間(小時)

完成時間大約為14 小時

建議:4 weeks; 4-6 hours/week...
可選語言

英語(English)

字幕:英語(English)

您將獲得的技能

Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
專項課程

第 3 門課程(共 3 門)

100% 在線

100% 在線

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

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

完成時間(小時)

完成時間大約為14 小時

建議:4 weeks; 4-6 hours/week...
可選語言

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
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....
Reading
7 個視頻 (總計 67 分鐘), 6 個閱讀材料, 1 個測驗
Video7 個視頻
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
Reading6 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
Quiz1 個練習
Week 1 Assessment15分鐘
2
完成時間(小時)
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....
Reading
6 個視頻 (總計 85 分鐘), 4 個閱讀材料, 3 個測驗
Video6 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
Reading4 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
Quiz3 個練習
Linear Regression Quiz20分鐘
Logistic Regression Quiz15分鐘
Week 2 Python Assessment20分鐘
3
完成時間(小時)
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. ...
Reading
8 個視頻 (總計 121 分鐘), 2 個閱讀材料, 2 個測驗
Video8 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
Reading2 個閱讀材料
Visualizing Multilevel Models10分鐘
Likelihood Ratio Tests for Fixed Effects and Variance Components10分鐘
Quiz2 個練習
Name That Model15分鐘
Week 3 Python Assessment20分鐘
4
完成時間(小時)
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....
Reading
6 個視頻 (總計 105 分鐘), 3 個閱讀材料, 1 個測驗
Video6 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
Reading3 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
Quiz1 個練習
Week 4 Python Assessment20分鐘

講師

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Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
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Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

關於 University of Michigan

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, and use of the Python programming language to conduct data analyses. Learners will learn where data come from, what types of data can be collected, how to effectively summarize and visualize data, how to utilize data for estimation and assessing theories, proper interpretations of inferential results, and how to apply more advanced statistical modeling procedures....
Statistics with Python

常見問題

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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