STAT 360
Statistical Inference Spring 2019 Division III; Quantative/Formal Reasoning;

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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: Type: lecture
Limit: 30
Expected: 30
Class#: 3755
Requirements/Evaluation: evaluation will be based on problem sets and exams
Extra Info: may not be taken on a pass/fail basis
Prerequisites: MATH 250, STAT 201 or 202, STAT 341
Enrollment Preference: Statistics majors
Distributions: Division III; Quantative/Formal Reasoning;

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