课程

使用 Python 中的 statsmodels 进行回归入门

中级技能水平

更新时间 2026年6月

用 Python 中的 statsmodels 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

PythonProbability & Statistics

4小时

14 视频

53 道练习

4,150 XP

60,156

成就证明

深受数千家公司学习者的喜爱

需要团队培训？

企业版试用

课程描述

使用 Python statsmodels 进行线性回归和逻辑回归

线性回归和逻辑回归是两种应用最广泛的统计模型。它们就像万能钥匙，解锁隐藏在数据中的秘密。在本课程中，你将掌握拟合简单线性回归和逻辑回归的技能。

通过动手练习，你将探索真实世界数据集中变量之间的关系，包括机动车保险理赔、台湾房价、鱼类尺寸等。

了解如何进行预测并评估模型拟合度

你将从这门4小时的课程开始，了解什么是回归，以及线性回归和逻辑回归有何不同，并学习如何应用这两种方法。接下来，你将学习如何使用线性回归模型对数据进行预测，同时了解模型对象。

随着学习的深入，你将学会如何评估模型的拟合情况，以及如何判断你的线性回归模型拟合得有多好。最后，你将更深入地学习逻辑回归模型，以便对真实数据进行预测。

学习 Python 回归分析基础

在本课程结束时，你将学会如何根据数据进行预测、量化模型性能，并诊断模型拟合中的问题。你将了解如何使用 Python 的 statsmodels 进行回归分析，并能够将这些技能应用于真实数据集。

先决条件

Introduction to Data Visualization with Seaborn Introduction to Statistics in Python

1

Simple Linear Regression Modeling

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 numeric variables

Fitting a linear regression

Estimate the intercept

Estimate the slope

Linear regression with ols()

Categorical explanatory variables

Visualizing numeric vs. categorical

Calculating means by category

Linear regression 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

Regression to the mean

Plotting consecutive portfolio returns

Modeling consecutive returns

Transforming variables

Transforming the explanatory variable

Transforming the response variable too

Back transformation

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 each observation's leverage and influence 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 Modeling

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, and quantify model performance using confusion matrices.

Why you need logistic regression

Exploring the explanatory variables

Visualizing linear and logistic models

Logistic regression with logit()

Predictions and odds

Probabilities

Most likely outcome

Odds

Quantifying logistic regression fit

Calculating the confusion matrix

Drawing a mosaic plot of the confusion matrix

Accuracy, sensitivity, specificity

Measuring logistic model performance

Congratulations

使用 Python 中的 statsmodels 进行回归入门

课程完成

获得成就证明

将此证书添加到您的 LinkedIn 档案、简历或履历中
在社交媒体和绩效评估中分享立即注册

加入超过19百万学习者，今天就开始使用 Python 中的 statsmodels 进行回归入门！

通过 DataCamp for Mobile 提升您的数据技能

随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。