EXST 4002 - Principles and Theory of Statistics

Spring 2000

Course Description: EXST 4002, Probability distributions as models for real-world processes; sampling distributions and the central limit theorem; estimation and confidence region methods; principles of hypothesis testing; and modeling; links between theory, methodology, and application emphasized.

Course Prerequisites: EXST 4001 or equivalent and MATH 1020/1021 or equivalent.

Course Objectives: General objectives include the following:

Give student insight into and practice with the statistical principles underlying many of the common statistical methods.

Familiarize the student with the terminology and mathematical language used in mathematical statistics.

Develop the likelihood principle and how it can be used in estimation and testing.

Develop basic skills with symbolic computing software that can be used to perform the mathematical analyses discussed.

Develop critical thinking skills to see characteristics of new problems, and then to place them into the context of statistical models.

Stimulate the student's general interest in the principles and mathematics of statistics.

Instructor: Dr. E. Barry Moser, 149A Ag. Administration, 388-8376, bmoser@lsu.edu, http://www.stat.lsu.edu/faculty/moser/

Office Hours: TBA and by appointment

Text: Devore, J.L. 2000. Probability and Statistics for Engineering and the Sciences, 5^th edition. Duxbury, Pacific Grove, CA, 775pp.

Assignments and Grading: There will generally be weekly graded assignments and short quizzes, and a final examination. The assignments and quizzes will cover material from both the lecture and lab portions of the course. The final examination will be worth 25% of the grade and the graded assignments and quizzes will be equally weighted and compose the remainder of the grade.

Grading Scale: Letter grade assignments will be made according to the scale below.

Score	Minimum Grade
90-100	A
80-89.9	B
70-79.9	C
60-69.9	D
0-59.9	F

EXST 4002 - Principles and Theory of Statistics

CONTENT OUTLINE

Chapter¹	Topic
2	Basics of probability; sample spaces, events, permutations, combinations, counting rules, conditional probability
3	Discrete random variables: Bernoulli, binomial, Poisson, hypergeometric, geometric, negative binomial; distributions and expectation of random variables
4	Continuous random variables: normal, gamma, beta, uniform, exponential, chi-squared, Weibull, lognormal; exponential family; probability plots
5	Joint probability distributions and sampling; marginal and conditional distributions; independence; covariance and correlation; sampling distributions of statistics; linear combinations of random variables
6	Point estimation; properties of estimators; methods of estimation
7	Interval estimation; large-sample methods, bootstrap and Monte Carlo methods
8	Hypothesis testing principles; Type I and II errors, p-values, power; likelihood ratio principle
14,15	Applications with generalized linear models; parametric survival analysis; operating characteristic curves

¹Devore, J.L. 2000. Probability and Statistics for Engineering and the Sciences, 5^th edition. Duxbury, Pacific Grove, CA, 775pp.

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