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Given the outcome \(Y_i\) and the covariates \(X_i\) for \(i = 1, \ldots , n\) , a regression model assumes \(\displaystyle Y_i = f (X_i) + \varepsilon _i, \text{ for all } i = 1, \ldots , n, \) where \(\varepsilon _i\) is the error/noise. We typically assume that the error terms satisfy \(\mathbb {E} \varepsilon _i = 0\) and \(\epsilon _1, \ldots , \epsilon _n\) are independent.

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Ordinary Least Squares

  • 章节

  • 发表日期：

    2025年

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  • Junwei Lu

摘要

Given the outcome \(Y_i\) and the covariates \(X_i\) for \(i = 1, \ldots , n\) , a regression model assumes \(\displaystyle Y_i = f (X_i) + \varepsilon _i, \text{ for all } i = 1, \ldots , n, \) where \(\varepsilon _i\) is the error/noise. We typically assume that the error terms satisfy \(\mathbb {E} \varepsilon _i = 0\) and \(\epsilon _1, \ldots , \epsilon _n\) are independent.