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Python에서 statsmodels로 살펴보는 회귀 소개
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업데이트됨 2026. 6.
PythonProbability & Statistics4시간14 동영상53 연습 문제4,150 XP60,132성취 증명서
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선수 조건
Introduction to Data Visualization with SeabornIntroduction to Statistics in Python1
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.
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.
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.
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.
Python에서 statsmodels로 살펴보는 회귀 소개
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19백만 명 이상의 학습자와 함께 Python에서 statsmodels로 살펴보는 회귀 소개을(를) 시작하세요!
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