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This paper derives batch scaling laws for sketched linear regression under power-law spectra, analyzing one-pass and multi-pass mini-batch SGD. It provides explicit risk decompositions showing how batch size affects bias, variance, and fluctuation terms, and establishes that without-replacement sampling yields lower noise than with-replacement.