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Linear Regression in Python (statsmodels)
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Learn how to perform linear regression modeling in Python using the powerful statsmodels package in this comprehensive tutorial. We'll walk through the entire process step-by-step, from data preparation to model interpretation. First, we'll cover the basics of simple linear regression and discuss when it's appropriate to use. Then, we'll dive into a real-world dataset and use pandas, numpy, and seaborn to explore relationships between variables. Next, we'll learn how to specify a linear model using the OLS function and interpret the regression output. We'll cover key concepts like coefficients, residuals, R-squared, and model assumptions. Throughout the tutorial, we'll emphasize best practices for model building and discuss how to avoid common pitfalls. By the end, you'll have a solid understanding of linear regression and the ability to apply it to your own data science projects. Whether you're a beginner looking to learn regression or an experienced user wanting to take advantage of statsmodels, this video has something for you. The datasets are provided so you can follow along.
⭐️ Contents ⭐️
⌨️ (0:00) Introduction
⌨️ (0:15) Course overview
⌨️ (0:52) Concepts of modeling
⌨️ (10:27) Simple linear regression (Part I)
⌨️ (17:10) Simple linear regression (Part II)
⌨️ (20:54) Multiple linear regression
⌨️ (23:11) Model evaluation
⌨️ (31:53) Summary & conclusion
Datasets
Like, share, and subscribe for more R tutorials on statistics and machine learning!
#linearregression #python #statsmodels
⭐️ Contents ⭐️
⌨️ (0:00) Introduction
⌨️ (0:15) Course overview
⌨️ (0:52) Concepts of modeling
⌨️ (10:27) Simple linear regression (Part I)
⌨️ (17:10) Simple linear regression (Part II)
⌨️ (20:54) Multiple linear regression
⌨️ (23:11) Model evaluation
⌨️ (31:53) Summary & conclusion
Datasets
Like, share, and subscribe for more R tutorials on statistics and machine learning!
#linearregression #python #statsmodels
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