Multilevel models are statistical models that account for parameters that vary at different levels. They are particularly useful for analyzing nested data, where individuals are grouped within higher-level units. These models can be used to analyze repeated measures and provide an alternative to ANCOVA for adjusting for covariates.
UC Berkeley
Fall 2022
This course deepens students' understanding of computer architecture and the translation of high-level programs into machine language. Emphasis is on C and assembly language programming, computer organization, parallelism, CPU design, and warehouse-scale computing. Prerequisites include CS61A and CS61B or equivalent C-based programming experience.
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