Ordinary Least Squares

Junwei Lu
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Junwei Lu: Harvard University

Chapter Chapter 7 in Big Data Analysis, 2025, pp 41-46 from Springer

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

Date: 2025
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DOI: 10.1007/978-3-032-03161-7_7

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