Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
提供方
约翰霍普金斯大学
课程信息
62,513 次近期查看
您将学到的内容有
Describe novel uses of regression models such as scatterplot smoothing
Investigate analysis of residuals and variability
Understand ANOVA and ANCOVA model cases
Use regression analysis, least squares and inference
您将获得的技能
Model SelectionGeneralized Linear ModelLinear RegressionRegression Analysis
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 12 小时
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
9 个视频 （总计 74 分钟）, 11 个阅读材料, 4 个测验
9 个视频
Introduction: Basic Least Squares6分钟
Technical Details (Skip if you'd like)2分钟
Introductory Data Example12分钟
Notation and Background7分钟
Linear Least Squares6分钟
Linear Least Squares Coding Example7分钟
Technical Details (Skip if you'd like)11分钟
Regression to the Mean11分钟
11 个阅读材料
Welcome to Regression Models10分钟
Book: Regression Models for Data Science in R10分钟
Syllabus10分钟
Pre-Course Survey10分钟
Data Science Specialization Community Site10分钟
Where to get more advanced material10分钟
Regression10分钟
Technical details10分钟
Least squares10分钟
Regression to the mean10分钟
Practical R Exercises in swirl Part 110分钟
1 个练习
Quiz 120分钟
周
2完成时间为 11 小时
Week 2: Linear Regression & Multivariable Regression
This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.
10 个视频 （总计 70 分钟）, 5 个阅读材料, 4 个测验
10 个视频
Interpreting Coefficients3分钟
Linear Regression for Prediction10分钟
Residuals5分钟
Residuals, Coding Example14分钟
Residual Variance7分钟
Inference in Regression5分钟
Coding Example6分钟
Prediction9分钟
Really, really quick intro to knitr3分钟
5 个阅读材料
*Statistical* linear regression models10分钟
Residuals10分钟
Inference in regression10分钟
Looking ahead to the project10分钟
Practical R Exercises in swirl Part 210分钟
1 个练习
Quiz 220分钟
周
3完成时间为 13 小时
Week 3: Multivariable Regression, Residuals, & Diagnostics
This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.
14 个视频 （总计 168 分钟）, 5 个阅读材料, 5 个测验
14 个视频
Multivariable Regression part II10分钟
Multivariable Regression Continued8分钟
Multivariable Regression Examples part I19分钟
Multivariable Regression Examples part II22分钟
Multivariable Regression Examples part III7分钟
Multivariable Regression Examples part IV7分钟
Adjustment Examples17分钟
Residuals and Diagnostics part I5分钟
Residuals and Diagnostics part II9分钟
Residuals and Diagnostics part III9分钟
Model Selection part I7分钟
Model Selection part II22分钟
Model Selection part III12分钟
5 个阅读材料
Multivariable regression10分钟
Adjustment10分钟
Residuals10分钟
Model selection10分钟
Practical R Exercises in swirl Part 310分钟
2 个练习
Quiz 314分钟
(OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)8分钟
周
4完成时间为 17 小时
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
7 个视频 （总计 95 分钟）, 6 个阅读材料, 6 个测验
7 个视频
GLMs21分钟
Logistic Regression part I17分钟
Logistic Regression part II3分钟
Logistic Regression part III8分钟
Poisson Regression part I12分钟
Poisson Regression part II12分钟
Hodgepodge18分钟
6 个阅读材料
GLMs10分钟
Logistic regression10分钟
Count Data10分钟
Mishmash10分钟
Practical R Exercises in swirl Part 410分钟
Post-Course Survey10分钟
1 个练习
Quiz 412分钟
4.4
463 个审阅23%
完成这些课程后已开始新的职业生涯
25%
通过此课程获得实实在在的工作福利
14%
加薪或升职
来自回归模型的热门评论
Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.
It really helped me to have a better understanding of these Regression Models. However, I've noticed that there is a video recording repeated: Week 3, Model Selection. Part 3 is included in Part 2.
讲师
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
关于 约翰霍普金斯大学
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