# 回归模型

## Regression Models

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Coursera
• 完成时间大约为 17 个小时
• 混合难度
• 英语, 韩语, 其他

### 你将学到什么

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

### 课程概况

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.

### 课程大纲

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

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

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

Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.

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