课程

R 中的回归入门

中级技能水平

更新时间 2024年8月

用 R 实现、分析并解读回归分析，预测房价和广告点击率。

免费开始课程

RProbability & Statistics

4小时

14 视频

52 道练习

4,050 XP

77,168

成就证明

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课程描述

线性回归和逻辑回归是最常用的两类统计模型，像万能钥匙一样，帮助揭示数据集中的规律。在本课程中，您将学会拟合简单的线性回归和逻辑回归。通过动手练习，您将探索真实世界数据集中变量之间的关系，例如机动车保险理赔、台湾房价、鱼类体长等。课程结束时，您将能够基于数据进行预测、量化模型性能，并诊断模型拟合中的问题。

先决条件

Introduction to Data Visualization with ggplot2 Introduction to Statistics in R

1

Simple Linear Regression

You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

A tale of two variables

Which one is the response variable?

Visualizing two variables

Fitting a linear regression

Estimate the intercept

Estimate the slope

Linear regression with lm()

Categorical explanatory variables

Visualizing numeric vs. categorical

Calculating means by category

lm() with a categorical explanatory variable

2

Predictions and model objects

In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

Making predictions

Predicting house prices

Visualizing predictions

The limits of prediction

Working with model objects

Extracting model elements

Manually predicting house prices

Using broom

Regression to the mean

Plotting consecutive portfolio returns

Modeling consecutive returns

Transforming variables

Transforming the explanatory variable

Transforming the response variable too

3

Assessing model fit

In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand the leverage and influence of each observation used to create the model.

Quantifying model fit

Coefficient of determination

Residual standard error

Visualizing model fit

Residuals vs. fitted values

Q-Q plot of residuals

Scale-location

Drawing diagnostic plots

Outliers, leverage, and influence

Extracting leverage and influence

4

Simple logistic regression

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Why you need logistic regression

Exploring the explanatory variables

Visualizing linear and logistic models

Logistic regression with glm()

Predictions and odds ratios

Probabilities

Most likely outcome

Log odds ratio

Quantifying logistic regression fit

Calculating the confusion matrix

Measuring logistic model performance

Accuracy, sensitivity, specificity

Congratulations

R 中的回归入门

课程完成

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