Predict and Validate Regression Models in R

本课程是多个项目的一部分。

位教师：LearningMate

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2个模块

深入了解一个主题并学习基础知识。

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2 小时完成

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2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Build and validate linear regression models in R, using diagnostics and cross-validation to ensure robust, reliable business predictions.

您将获得的技能

您将学习的工具

R Programming

要了解的详细信息

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作业

4 任务¹

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授课语言：英语（English）

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该课程共有2个模块

This course is your entry point into the world of predictive analytics with R. Designed for aspiring data analysts and business professionals, this course empowers you to build and interpret multiple linear regression models from the ground up. You will move beyond simply running code and learn to critically evaluate your model's performance. Through a series of hands-on learnings and real-world case studies, you will master the techniques to diagnose your model's statistical assumptions using residual plots and assess its reliability with k-fold cross-validation.

This module introduces the fundamentals of predictive modeling with multiple linear regression. You will learn how to formulate, build, and interpret a regression model in R to predict outcomes like housing prices or customer churn. More importantly, you will learn to look beyond surface-level accuracy by generating and analyzing key diagnostic plots to ensure your model is statistically sound and free of common pitfalls such as nonlinearity or heteroscedasticity.

涵盖的内容

2个视频2篇阅读材料2个作业

2个视频总计13分钟

Beyond Accuracy: The Danger of a "Wrong" Model7分钟
Building and Diagnosing a Regression Model in R7分钟

2篇阅读材料总计11分钟

The Anatomy of a Multiple Regression Model8分钟
Connecting Your Skills to Your Career3分钟

2个作业总计35分钟

Hands-On Learning: Build and Diagnose a Predictive Regression Model30分钟
Knowledge Check: Interpreting Model Output and Diagnostics5分钟

In this module, you will learn that a model is only useful if its performance is reliable. You will move beyond single-score accuracy to master k-fold cross-validation—a powerful technique for ensuring your model's stability and ensuring that it generalizes to new, unseen data. You will implement this technique in R, analyze the variance in performance across folds, and learn how to confidently report on your model's robustness, a key skill for any data professional.