STAT 130. Statistics and Contemporary Life
(Class 3, Cr. 3)
Introduction to statistical ideas and their impact on various aspects of modern life. Topics will include the organization, manipulation, and understanding of numerical data, the art of data presentation, interpretation of statistical information as presented in the media, the concept of randomness in gambling and lotteries, and some discussion of statistical fallacies.
STAT 301. Elementary Statistical Methods I
(Class 3, Cr. 3)
Prerequisites: MA 147
A basic introductory statistics course with applications shown to various fields and emphasis placed on assumption, applicability, and interpretations, or various statistical techniques. Subject matter includes frequency distributions, descriptive statistics, elementary probability, normal analysis of variance, with emphasis on distribution applications, sampling distribution, estimation, hypothesis testing and linear regression.
STAT 345. Statistics
(Class 3, Cr. 3)
Prerequisites: MA 164
Topics from exploratory data analysis and inferential statistics will be covered, along with a necessary introduction to probability. Statistical and probabilistic simulations will be used to enhance students' understanding of randomness and variation. Extensive use of a statistical computer package will be required.
STAT 490. Topics In Statistics For Undergraduates
(Class 0 to 5, Cr. 1 to 5)
Supervised reading and reports in various fields. Open only to students with the consent of the department.
STAT 511. Statistical Methods
(Class 3, Cr. 3)
Prerequisites: MA 261
Descriptive statistics; elementary probability; sampling distributions; inference, testing hypotheses, and estimation; normal, binomial, poison, hypergeometric distributions; one way analysis of variance; contingency tables; regression.
STAT 512. Applied Regression Analysis
(Class 3, Cr. 3)
Prerequisites: STAT 511 or STAT 517
Inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. Use of existing statistical computer programs.
STAT 513. Statistical Quality Control
(Class 3, Cr. 3)
Prerequisites: STAT 511 or STAT 516
A strong background in control charts including adaptations, acceptance plans, sequential analysis, statistics of combinations, moments and probability distributions, applications.
STAT 514. Design Of Experiments
(Class 3, Cr. 3)
Prerequisites: STAT 511 or STAT 512
Fundamentals, completely randomized design; randomized complete blocks; latin square; multi-classification; nested factorial; incomplete block and fractional replications for 2n,3n,2m x 3n; confounding; lattice designs; general minded factorials; split plot; analysis of variance in regression models; optimum design. Use of existing statistical programs.
STAT 516. Basic Probability and Applications
(Class 3, Cr. 3)
Prerequisites: MA 164 or MA 224
Co-requisite: MA 261
A first course in probability intended to serve as a background for statistics and other applications. Sample spaces and axioms of probability, discrete and continuous random variables, conditional probability and Bayes' theorem, joint and conditional probability distributions, expectations, moments and moment generating functions, law of large numbers and central limit theorem. (The probability material in Course 1 of the Society of Actuaries and the Casualty Actuarial Society is covered in this course.)
STAT 517. Statistical Inference
(Class 3, Cr. 3)
Prerequisites: STAT 516 or STAT 519
A basic course in statistical theory covering standard statistical methods and their applications. Estimation including unbiased, maximum likelihood and moment estimation; testing hypothesis for standard distributions, and contingency tables; confidence intervals and regions; introduction to non-parametric tests and linear regression.
STAT 532. Elements Of Stochastic Processes
(Class 3, Cr. 3)
Prerequisites: STAT 519
A basic course in stochastic models, including discrete and continuous time Markov Chains and brownian motion, as well as an introduction to topics such as Gaussian processes, renewal processes, replacement, and reliability problems.
