Graduate Courses for MASDA Students

5304:  Introduction to Statistical Computing.  This course introduces students to statistical computing with an emphasis on SAS programming.  Students will learn how to read, write, and import data; prepare data for analysis; use SAS procedures; and create graphs.

5340 (CSE 5370): Probability and Statistics for Scientists and Engineers. Introduction to fundamentals of probability and distribution theory, statistical techniques used by engineers and physical scientists. Examples of tests of significance, operating characteristic curve, tests of hypothesis about one and two parameters, estimation, analysis of variance, and the choice of a particular experimental procedure and sample size. Prerequisites: MATH 1337, 1338, and 2339, or equivalent.

5344: Statistical Quality Control. Statistics and simple probability are introduced in terms of problems which arise in manufacturing; their application to control of manufacturing processes. Acceptance sampling in terms of standard sampling plans: MilStd 105, MilStd 414, Dodge-Romig plans, continuous sampling plans, etc. Prerequisites: Any one of STAT 4340, 5340, 5373, CSE 4340 or 5370.

5350 (ECO 5350): Introductory Econometrics. Basic concepts of econometrics, in particular, regression analysis, with topics geared to first-time regression users. Emphasis on application of regression analysis to inference and hypothesis testing, and on the consequences of making various types of specification errors. 

5370: Survey Sampling.  Principles of planning and conducting surveys.  Simple random sampling; stratified, systematic, subsampling; means, variances, confidence limits; finite population correction; sampling from binomial populations; margin of error and sample-size determination. 
Prerequisites:  Co-requisite:  STAT 5304.

5371: Experimental Statistics I.  Non-calculus development of fundamental statistical techniques, including hypothesis testing for population means and proportions, analysis of variance, factorial designs, and linear regression.  Obtaining sample sizes during the planning stages of research studies.  Emphasizes interpretation of results from analysis with SAS Statistical Software.  Co-requisite:  5304.

5372: Experimental Statistics II.  Extension of techniques in STAT 5371 to multivariate data.  Multiple linear regression, multivariate analysis of variance, canonical regression and principal components analysis.  Emphasizes interpretation of results from analysis with SAS.  Prerequisite: STAT 5371.

5373: Introduction to Mathematical Statistics I.  Topics include: probability; probability distributions; mathematical expectation; discrete and continuous random variables and their distributions; sampling distributions; moment generating function; functions of random variables; confidence intervals. Prerequisite: MATH 2339

5374: Introduction to Mathematical Statistics II: Second course in mathematical statistics.  Topics include: order statistics; limiting distributions; central limit theorem; point estimation; testing statistical hypotheses; Bayesian procedures; nonparametric methods. Prerequisite: STAT 5373

5375 (ECO 5375): Economic and Business Forecasting.  Presentation of methods used to forecast economic and business trends and methods for evaluating the usefulness of these methods.

5377 (CSE 5377): Statistical Design and Analysis of Experiments. Introduction to statistical principles in the design and analysis of industrial experiments. Completely randomized, randomized complete and incomplete block, Latin square, and Plackett-Burman screening designs. Complete and fractional factorial experiments. Descriptive and inferential statistics. Analysis of variance models. Mean comparisons. Prerequisite: Senior standing with a science or engineering major or permission of instructor.

5380 (ECO 5385) : Predictive Analytics/Data Mining.  A study of data mining techniques in fields of applied economics, marketing, finance, and statistics. Techniques include classification methods (logistic models, classification trees, neural networks), affinity analysis (association rules), and data reduction and exploration methods (principal components and k-means clustering).

5385: Introductory Nonparametric Statistics. Introduction to nonparametric statistics with examples in the behavioral sciences, including choice and use of rank tests, runs test and rank order correlation; tests for one-sample and two-sample cases. Prerequisite: STAT 5371, 5340/CSE 5370, or equivalent.

6304: Computational Statistics.  Designed to introduce students to the fundamentals of statistical computing.  Introduces computational methods in statistics with emphasis on the use of statistical software packages, statistical simulation, numerical methods, and related topics. Topics include introduction to R and other statistical software for statistical analysis and graphics; generating random deviates from various distributions; and the use of Monte Carlo methods for solving optimization problems.  Prerequisite: STAT 5373 or STAT 6327 or concurrent enrollment in these courses.

6308:  SAS II and Databases:  This second semester course in SAS covers topics in data management and statistical analysis techniques including data cleaning and verification; reading, writing, and manipulating data using DDE and SQL techniques; programming macros; combining, interleaving, stacking, and transposing data sets; customizing output using ODS; and advanced data analysis techniques.  Prerequisite:  STAT 5304 or permission of instructor.

6345: Linear Regression. The classical tools of linear regression based upon least squares estimation and inference through the assumption of normally distributed errors. Topics in model formulation, data transformations, variable selection, and regression diagnostics for influential observations. Collinear predictors and biased estimation. Survey of alternatives to least squares. Prerequisite: STAT 5372 or 6337.

STAT 6360: Statistical Methods in Epidemiology.  This course presents an introduction to epidemiologic principles and statistical methods used in biomedical research. Topics involve the design, analysis, and interpretation of biomedical study results.  Prerequisites:  5372, 5374, and 5304 or permission of instructor.

6363: Time Series Analysis. Statistical methods of analyzing time series. Autocorrelation function and spectrum. Autoregressive and moving average processes. More general models, forecasting, stochastic model building. Prerequisite: Permission of instructor.

6366: Statistical Consulting. Apprenticeship under an experienced consultant, with exposure to real problems handled by the Center for Statistical Consulting and Research. Between four to six hours per week will be spent in consultation sessions and seminars. In addition to a variety of technical statistical issues the class will study the existing literature on the non-statistical aspects of the consulting endeavor.

6395: Special Topics in Statistics.