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STAT 360
Statistical Inference Spring 2021
Division III Quantative/Formal Reasoning

Class Details

How do we estimate unknown parameters and express the uncertainty we have in our estimate? Is there an estimator that works best? Many topics from introductory statistics such as random variables, the central limit theorem, point and interval estimation and hypotheses testing will be revisited and put on a more rigorous mathematical footing. The focus is on maximum likelihood estimators and their properties. Bayesian and computer intensive resampling techniques (e.g., the bootstrap) will also be considered.
The Class: Format: lecture
Limit: 30
Expected: 30
Class#: 4209
Grading: no pass/fail option, yes fifth course option
Requirements/Evaluation: problem sets and exams
Prerequisites: MATH 250, STAT 201 or 202, STAT 341
Enrollment Preferences: Statistics majors
Distributions: Division III Quantative/Formal Reasoning

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