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Variable Selection, Model Validation, Nonlinear Regression

Variable Selection, Model Validation, Nonlinear Regression

本课程是 Advanced Statistical Techniques for Data Science 专项课程的一部分

位教师：Kiah Ong

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

攻读学位

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

攻读学位

了解更多

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

10 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Advanced Statistical Techniques for Data Science 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.

This course will focus on getting you acquainted with the generalized linear model (GLM) through the examples of logistic and Poisson regression. You will also see how simple and multiple linear regression relates to GLM using the link function. We will also study a regression technique that is robust to having outliers in the data. Finally, we will learn how to perform model validation involving GLM. After this course, students will be able to: - Determine which regression models to use based on the nature of the response variable. - Use regression technique which is robust to the presence of outliers. - Perform generalized linear regression using R by identifying the correct link function. - Interpret and draw conclusions on the regression model. - Use R to perform statistical inference based on the regression models.

单元详情

In this module, you will learn the differences between logistic regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, and use R to compute the estimators of a linear regression model and give a probabilistic prediction of Y=1 given X=x’s. There is a lot to read, watch, and consume in this module so, let’s get started!

涵盖的内容

7个视频4篇阅读材料3个作业1个讨论话题

7个视频总计33分钟

Course Welcome2分钟
Module 1 Introduction1分钟
Lesson 1 Introduction1分钟
Logistic Regression - Part 111分钟
Lesson 2 Introduction1分钟
Logistic Regression Part II - Part 110分钟
Logistic Regression Part II - Part 28分钟

4篇阅读材料总计80分钟

Syllabus10分钟
Video 22 Slides - Introduction to Logistic Regression Part I (pdf)30分钟
Video 23 Slides - Introduction to Logistic Regression Part II (pdf)30分钟
Module 1 Summary10分钟

3个作业总计240分钟

Introduction to Logistic Regression Part I30分钟
Intro to Logistic Regression Part II30分钟
Module 1 Summative Assessment180分钟

1个讨论话题总计10分钟

Meet and Greet Discussion10分钟

In this module, you will learn the difference between Poisson regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, use R to compute the estimators of a Poisson regression model and the generalized linear model, and the similarities between the linear, logistic, and Poisson regressions. There is a lot to read, watch, and consume in this module so, let’s get started!

涵盖的内容

6个视频3篇阅读材料3个作业

6个视频总计26分钟

Module 2 Introduction1分钟
Lesson 3 Introduction1分钟
Poisson Regression - Part 19分钟
Poisson Regression - Part 24分钟
Lesson 4 Introduction1分钟
GLM10分钟

3篇阅读材料总计70分钟

Video 24 Slides - Poisson Regression (pdf)30分钟
Video 25 Slides - Generalized Linear Models (pdf)30分钟
Module 2 Summary10分钟

3个作业总计240分钟

Poisson Regression 30分钟
Generalized Linear Models30分钟
Module 2 Summative Assessment180分钟

In this module, you will learn how to modify the ordinary least squares method to make the regression model more robust to the effect of outliers and use R to compute the robust regression parameters using different M-estimators and perform model validations involving logistic regression. There is a lot to read, watch, and consume in this module so, let’s get started!

涵盖的内容

7个视频4篇阅读材料3个作业

7个视频总计45分钟

Module 3 Introduction1分钟
Lesson 5 Introduction1分钟
Robust Regression - Part 110分钟
Robust Regression - Part 212分钟
Lesson 6 Introduction1分钟
Model Validations - Part 111分钟
Model Validations - Part 210分钟

4篇阅读材料总计80分钟

Video 26 Slides - Robust Regression (pdf)30分钟
Video 27 Slides - Variable Selection and Model Validation (pdf)30分钟
Module 3 Summary10分钟
Insights from an Industry Leader: Learn More About Our Program10分钟

3个作业总计240分钟

Robust Regression30分钟
Variable Selection and Model Validation30分钟
Module 3 Summative Assessment180分钟

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.