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Course information comes from the public UCLA General Catalog. Requisites are read from UCLA’s published wording and can be incomplete or out of date — check the official catalog listing and your department adviser before you enroll.

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BIOSTAT 250BLinear Statistical Models

Biostatistics · 4 units · Graduate courses (200-299)

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Theoretical foundation for linear models with applications to different types of problems in biomedical field. Emphasis on mathematical training and understanding of theory of linear models, including linear mixed models and topics that may include theory and tests for various types of model misspecification, such as heteroscedasticity and outliers. Other selected topics may include ridge regression, Bayesian estimation in linear models, REML, prediction, and model selection issues. Some data analysis, instructions for STATA provided.

Letter grading.

When it runs

  • Winter 2026
  • Winter 2027

Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.

Requisites

Official UCLA wording

Requisites: courses 200A, 200B, 200C, 250A.

BruinTree reads · Prerequisite

confidence 1.00 · from text
all of
  • BIOSTAT 200A
  • BIOSTAT 200B
  • BIOSTAT 200C
  • BIOSTAT 250A

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About BIOSTAT 250B. We read UCLA’s requisite wording by machine, and it gets things wrong.

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Requires

Everything that has to come before this course, not just the courses named in the requisite above.

BIOSTAT 250B

  • BIOSTAT 200AMethods in Biostatistics A
  • BIOSTAT 200BMethods in Biostatistics B
    • BIOSTAT 200AMethods in Biostatistics Aanother path to it
  • BIOSTAT 200CMethods in Biostatistics C
  • BIOSTAT 250ALinear Statistical Models

4 direct requisites. The whole upstream is here — 5 courses over 2 levels. Every course here opens its own tree.

Unlocks

What this course is a requisite for, and what those courses lead to in turn.

BIOSTAT 250B

  • BIOSTAT 250CMultivariate Biostatistics

1 course lists this as a requisite. The whole downstream is here — 1 course over 1 level. Every course here opens its own tree.