Abstract
In the regression setting, the standard linear model \( Y = \beta_{0} + \beta_{1}X_{1} + \cdots + \beta_{p}X_{p} + \epsilon \) is commonly used to describe the relationship between a response Y and a set of variables X1, X2,…,Xp.
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James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). Linear Model Selection and Regularization. In: An Introduction to Statistical Learning. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-1418-1_6
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DOI: https://doi.org/10.1007/978-1-0716-1418-1_6
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