本课程是 Statistics with Python 专项课程 专项课程的一部分

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Statistics with Python 专项课程

University of Michigan

课程信息

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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.
This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python).
During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

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

建议：4 weeks; 4-6 hours/week...

字幕：英语（English）

Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

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

建议：4 weeks; 4-6 hours/week...

字幕：英语（English）

周

1We 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....

7 个视频 （总计 67 分钟）, 6 个阅读材料, 1 个测验

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

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

Week 1 Assessment15分钟

周

2In 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....

6 个视频 （总计 85 分钟）, 4 个阅读材料, 3 个测验

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

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

Linear Regression Quiz20分钟

Logistic Regression Quiz15分钟

Week 2 Python Assessment20分钟

周

3In 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. ...

8 个视频 （总计 121 分钟）, 2 个阅读材料, 2 个测验

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

Visualizing Multilevel Models10分钟

Likelihood Ratio Tests for Fixed Effects and Variance Components10分钟

Name That Model15分钟

Week 3 Python Assessment20分钟

周

4In 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....

6 个视频 （总计 105 分钟）, 3 个阅读材料, 1 个测验

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

Other Types of Dependent Variables20分钟

Optional: A Visual Introduction to Machine Learning20分钟

Course Feedback10分钟

Week 4 Python Assessment20分钟

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....

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....

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Specialization?

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.

What is the refund policy?

Is financial aid available?

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