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702Introduction to Statistical Theory I. (3)
(Prereq: MATH 241) Fundamental
theory of statistics and how it applies to industrial problems. Topics
include probability, random variables and vectors and their distributions,
sampling theory,
point and interval estimators, and application to the theory of
reliability,
regression, process control and quality issues. Not to be used for M.S. or
Ph.D. credit in statistics.
Course Homepage:
Fall 2006 (Past Pages:
Fall 2001
)
Usually Offered: Odd Numbered Falls
Purpose:
To expose the student to the basic concepts of theoretical statistics necessary
for the solid understanding of the statistical procedures and methods typically
used by practicing industrial personnel at an advanced level.
Current Textbook:
Mathematical Statistics and Data Analysis (2nd edition), by John A. Rice,
Duxbury Press, 1995.
| Topics Covered | Chapters | Time |
| Introduction to Probability: sample spaces, events, counting methods,
axioms, laws of probability, conditional probability, independence, Baye's rule
|
1 |
2 weeks |
| Random Variables and Distributions I - Discrete Distributions:
densities, distributions, expectation, generating functions, various discrete
distributions |
2.1, part of 4 |
1.5 weeks |
| Random Variables and Distributions II - Continuous Distributions:
densities, distributions, expectations, moment generating functions; gamma,
normal, Weibull distributions and applictions to life testing and
quality control, in particular; Chebyshev's inequality, normal approximations,
transformation of variables
|
2.2-2.3,
part of 4 |
3 weeks |
| Random Variables and Distributions III - Joint Distributions:
discrete and continuous cases; covariance, correlation and independence;
conditional distributions and expectations with applications |
3, part of 4 |
2 weeks |
| Sampling Distributions: central limit theorem; chi-square, t,
and F distributions; distribution of sample mean and sample variance |
5-6 |
2 weeks |
| Sampling and Estimation: random sampling and statistics, likelihood,
maximum likelihood estimation, method of moments estimation, bayes methods,
interval estimation |
7.2-7.3, 8.1-8.5 |
2.5 weeks |
The above textbook and course outline should correspond to the most
recent offering of the course by the Statistics Department. Please check
the current course homepage or with the instructor for
the course regulations, expectations, and operating procedures.
Contact Faculty: Brian Habing, Don Edwards
(Last Updated: July 11, 2008)
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