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.
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约翰霍普金斯大学课程信息
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完成时间大约为54 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将学到的内容有
Use regression analysis, least squares and inference
Understand ANOVA and ANCOVA model cases
Investigate analysis of residuals and variability
Describe novel uses of regression models such as scatterplot smoothing
您将获得的技能
- Model Selection
- Generalized Linear Model
- Linear Regression
- Regression Analysis
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
完成时间大约为54 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
授课大纲 - 您将从这门课程中学到什么
完成时间为 13 小时
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
完成时间为 13 小时
9 个视频 （总计 74 分钟）, 11 个阅读材料, 4 个测验
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.
完成时间为 11 小时
10 个视频 （总计 70 分钟）, 5 个阅读材料, 4 个测验
完成时间为 14 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.
完成时间为 14 小时
14 个视频 （总计 168 分钟）, 5 个阅读材料, 5 个测验
完成时间为 16 小时
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
完成时间为 16 小时
7 个视频 （总计 95 分钟）, 6 个阅读材料, 6 个测验
审阅
- 5 stars64.26%
- 4 stars23.06%
- 3 stars7.55%
- 2 stars2.96%
- 1 star2.14%
来自回归模型的热门评论
由 CJ 提供Jan 3, 2018
The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!! I wonder where it will take us..
由 BK 提供Feb 9, 2016
This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.
由 JV 提供Oct 15, 2017
It is very interesting, however is difficult to follow the math explanations, it could be more easy with practical examples.... like the final assignment, it was difficult to me.
由 AK 提供Dec 9, 2016
I was hoping to learn about PROBIT models. I know they are very similar to LOGIT ones, but still... the pace is a little bit too fast and I think it requires more time than what it says.
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