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Regression Models

Learn how to use regression models, the most important statistical analysis tool in the data scientist’s toolkit. This is the seventh course in the Johns Hopkins Data Science Specialization.

约翰霍普金斯大学

Coursera

计算机

简单(初级)

17 小时

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  • 英语
  • 624

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.

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

课程大纲

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

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

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

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.

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