课程信息
4.3
225 个评分
43 个审阅
专项课程

第 3 门课程(共 5 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为15 小时

建议:4 weeks, 4 - 5 hours per week...
可选语言

英语(English)

字幕:英语(English)

您将获得的技能

Logistic RegressionData AnalysisPython ProgrammingRegression Analysis
专项课程

第 3 门课程(共 5 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为15 小时

建议:4 weeks, 4 - 5 hours per week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 3 小时

Introduction to Regression

This session starts where the Data Analysis Tools course left off. This first set of videos provides you with some conceptual background about the major types of data you may work with, which will increase your competence in choosing the statistical analysis that’s most appropriate given the structure of your data, and in understanding the limitations of your data set. We also introduce you to the concept of confounding variables, which are variables that may be the reason for the association between your explanatory and response variable. Finally, you will gain experience in describing your data by writing about your sample, the study data collection procedures, and your measures and data management steps. ...
Reading
4 个视频 (总计 25 分钟), 5 个阅读材料, 1 个测验
Video4 个视频
Lesson 2: Experimental Data6分钟
Lesson 3: Confounding Variables8分钟
Lesson 4: Introduction to Multivariate Methods6分钟
Reading5 个阅读材料
Some Guidance for Learners New to the Specialization10分钟
Getting Set up for Assignments10分钟
Tumblr Instructions10分钟
How to Write About Data10分钟
Writing About Your Data: Example Assignment10分钟
2
完成时间(小时)
完成时间为 4 小时

Basics of Linear Regression

In this session, we discuss more about the importance of testing for confounding, and provide examples of situations in which a confounding variable can explain the association between an explanatory and response variable. In addition, now that you have statistically tested the association between an explanatory variable and your response variable, you will test and interpret this association using basic linear regression analysis for a quantitative response variable. You will also learn about how the linear regression model can be used to predict your observed response variable. Finally, we will also discuss the statistical assumptions underlying the linear regression model, and show you some best practices for coding your explanatory variables Note that if your research question does not include one quantitative response variable, you can use one from your data set just to get some practice with the tool. ...
Reading
8 个视频 (总计 53 分钟), 9 个阅读材料, 1 个测验
Video8 个视频
SAS Lesson 2: Testing a Basic Linear Regression Mode6分钟
SAS Lesson 3: Categorical Explanatory Variables5分钟
Python Lesson 1: More on Confounding Variables6分钟
Python Lesson 2: Testing a Basic Linear Regression Model8分钟
Python Lesson 3: Categorical Explanatory Variables4分钟
Lesson 4: Linear Regression Assumptions12分钟
Lesson 5: Centering Explanatory Variables3分钟
Reading9 个阅读材料
SAS or Python - Which to Choose?10分钟
Getting Started with SAS10分钟
Getting Started with Python10分钟
Course Codebooks10分钟
Course Data Sets10分钟
Uploading Your Own Data to SAS10分钟
SAS Program Code for Video Examples10分钟
Python Program Code for Video Examples10分钟
Outlier Decision Tree10分钟
3
完成时间(小时)
完成时间为 3 小时

Multiple Regression

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. You will also learn how to account for nonlinear associations in a linear regression model. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable. Note that if you have not yet identified additional explanatory variables, you should choose at least one additional explanatory variable from your data set. When you go back to your codebooks, ask yourself a few questions like “What other variables might explain the association between my explanatory and response variable?”; “What other variables might explain more of the variability in my response variable?”, or even “What other explanatory variables might be interesting to explore?” Additional explanatory variables can be either quantitative, categorical, or both. Although you need only two explanatory variables to test a multiple regression model, we encourage you to identify more than one additional explanatory variable. Doing so will really allow you to experience the power of multiple regression analysis, and will increase your confidence in your ability to test and interpret more complex regression models. If your research question does not include one quantitative response variable, you can use the same quantitative response variable that you used in Module 2, or you may choose another one from your data set. ...
Reading
10 个视频 (总计 68 分钟), 2 个阅读材料, 1 个测验
Video10 个视频
SAS Lesson 2: Confidence Intervals3分钟
SAS Lesson 3: Polynomial Regression8分钟
SAS Lesson 4: Evaluating Model Fit, pt. 15分钟
SAS Lesson 5: Evaluating Model Fit, pt. 29分钟
Python Lesson 1: Multiple Regression6分钟
Python Lesson 2: Confidence Intervals3分钟
Python Lesson 3: Polynomial Regression9分钟
Python Lesson 4: Evaluating Model Fit, pt. 15分钟
Python Lesson 5: Evaluating Model Fit, pt. 210分钟
Reading2 个阅读材料
SAS Program Code for Video Examples10分钟
Python Program Code for Video Examples10分钟
4
完成时间(小时)
完成时间为 4 小时

Logistic Regression

In this session, we will discuss some things that you should keep in mind as you continue to use data analysis in the future. We will also teach also you how to test a categorical explanatory variable with more than two categories in a multiple regression analysis. Finally, we introduce you to logistic regression analysis for a binary response variable with multiple explanatory variables. Logistic regression is simply another form of the linear regression model, so the basic idea is the same as a multiple regression analysis. But, unlike the multiple regression model, the logistic regression model is designed to test binary response variables. You will gain experience testing and interpreting a logistic regression model, including using odds ratios and confidence intervals to determine the magnitude of the association between your explanatory variables and response variable. You can use the same explanatory variables that you used to test your multiple regression model with a quantitative outcome, but your response variable needs to be binary (categorical with 2 categories). If you have a quantitative response variable, you will have to bin it into 2 categories. Alternatively, you can choose a different binary response variable from your data set that you can use to test a logistic regression model. If you have a categorical response variable with more than two categories, you will need to collapse it into two categories. ...
Reading
7 个视频 (总计 38 分钟), 6 个阅读材料, 1 个测验
Video7 个视频
Python Lesson 1: Categorical Explanatory Variables with More Than Two Categories6分钟
Lesson 2: A Few Things to Keep in Mind2分钟
SAS Lesson 3: Logistic Regression for a Binary Response Variable, pt 17分钟
SAS Lesson 4: Logistic Regression for a Binary Response Variable, pt. 24分钟
Python Lesson 3: Logistic Regression for a Binary Response Variable, pt. 17分钟
Python Lesson 4: Logistic Regression for a Binary Response Variable, pt. 23分钟
Reading6 个阅读材料
SAS Program Code for Video Examples10分钟
Python Program Code for Video Examples10分钟
Week 1 Video Credits10分钟
Week 2 Video Credits10分钟
Week 3 Video Credits10分钟
Week 4 Video Credits10分钟
4.3
43 个审阅Chevron Right
职业方向

25%

完成这些课程后已开始新的职业生涯
工作福利

25%

通过此课程获得实实在在的工作福利

热门审阅

创建者 VMMar 7th 2017

Awesome course. More than regression generation, they have explained in details about how to interpret regression coefficients and results and how to make conclusions. 5 Stars

创建者 PCNov 28th 2016

This was a great course. I've done a few in the area of stats, regression and machine learning now and the Wesleyan ones are the most well-rounded of all of them

讲师

Avatar

Jen Rose

Research Professor
Psychology
Avatar

Lisa Dierker

Professor
Psychology

关于 Wesleyan University

At Wesleyan, distinguished scholar-teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit. ...

关于 Data Analysis and Interpretation 专项课程

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world's biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions....
Data Analysis and Interpretation

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