Statistics Department

STAT 201. Basic Statistics for Social and Life Sciences (3)
Designed for undergraduates in the social sciences and life sciences who need to use statistical techniques in their fields. Descriptive statistics, probability models, sampling distributions. Point and confidence interval estimation, hypothesis testing. Elementary regression and analysis of variance. Not for credit toward major or minor in statistics.

STAT 207. Statistics for Business and Management Science I (3)
Organizing and summarizing data. Mean, variance, moments. Elementary probability, conditional probability. Commonly encountered distributions including binomial, Poisson, uniform, exponential, normal distributions. Central limit theorem. Sample quantities, empirical distributions. Reference distributions (chi-square, z-, t-, F-distributions). Point and interval estimation; hypothesis tests. Prerequisite: MATH 122 or MATH 126 or equivalent

STAT 208. Statistics for Business and Management Science II (3)
Hypothesis testing, analysis of variance. Simple linear regression and correlation; multiple linear regression. Analysis of contingency table data, goodness-of-fit tests. Nonparametric methods including sign, Wilcoxon, Kruskal-Wallis and runs tests. Introduction to time series analysis and forecasting. Prerequisite: STAT 207.

STAT 243. Statistical Theory with Application I (3)
Introduction to fundamental concepts of statistics through examples including design of an observational study, industrial simulation. Theoretical development motivated by sample survey methodology. Randomness, distribution functions, conditional probabilities. Derivation of common discrete distributions. Expectation operator. Statistics as random variables, point and interval extimation. Maximum likelihood estimators. Properties of estimators. Prerequisite: MATH 122 or MATH 126

STAT 244. Statistical Theory with Application II (3)
Extension of inferences to continuous-valued random variables. Common continuous-valued distributions. Expectation operator. Maximum likelihood estimators for the continuous case. Simple linear, multiple and polynomial regression. Properties of regression estimators when errors are Gaussian. Regression diagnostics. Class or student projects gathering real data or generating simulated data, fitting models and analyzing residuals from fit. Prerequisite: STAT 243 .

STAT 312. Basic Statistics for Engineering and Science (3)
For advanced undergraduate students in engineering, physical sciences, life sciences. Comprehensive introduction to probability models and statistical methods of analyzing data with the object of formulating statistical models and choosing appropriate methods for inference from experimental and observational data and for testing the model's validity. Balanced approach with equal emphasis on probability, fundamental concepts of statistics, point and interval estimation, hypothesis testing, analysis of variance, design of experiments, and regression modeling. Note: Credit given for only one (1)of STAT 312, 313, 333, 433. Prerequisite: MATH 122 or equiv.

STAT 313. Statistics for Experimenters (3)
For advanced undergraduates in engineering, physical sciences, life sciences. Comprehensive introduction to modeling data and statistical methods of analyzing data. General objective is to train students in formulating statistical models, in choosing appropriate methods for inference from experimental and observational data and to test the validity of these models. Focus on practicalities of inference from experimental data. Inference for curve and surface fitting to real data sets. Designs for experiments and simulations. Student generation of experimental data and application of statistical methods for analysis. Critique of model; use of regression diagnostics to analyze errors. Note: Credit given for only one (1) of STAT 312, 313, 333, 433. Prerequisite: MATH 122 or equivalent

STAT 317. Theory of Interest and Life Contingencies (3)
Mathematical formulation for calculation of compound interest, present and accumulated values of single investments and of portfolios. Life table analysis for simple and multiple decrement functions. Life and special annuities; life insurance and reserves for life insurance. Statistical issues for prediction from actuarial models. Problem solving using actual insurance record data. Topics covered include areas examined in the American Society of Actuaries examination over ASA courses 150 and 160. Prerequisite: MATH 223 and STAT 346 or STAT 446

STAT 325. Data Analysis and Linear Models (3)
Basic exploratory data analysis for univariate response with single or multiple covariates. Graphical methods and data summarization, model-fitting using S-plus computing language. Linear and multiple regression. Emphasis on model selection criteria, on diagnostics to assess goodness of fit and interpretation. Techniques include transformation, smoothing, median polish, robust/resistant methods. Case studies, and analysis of individual data sets. Notes or caution and some methods for handling bad data. Knowledge of regression is helpful. Prerequisite: Permission of Department.

STAT 326. Multivariate Analysis and Data Mining (3)
Extensions of exploratory data analysis and modeling to multivariate response observations and to non-Gaussian data. Singular value decomposition and projection, principal components, factor analysis and latent structure analysis, discriminant analysis and clustering techniques, cross-validation, E-M algorithm, cart. Introduction to generalized linear modeling. Case studies of complex data sets with multiple objectives for analysis. Prerequisite: STAT 325.

STAT 332. Statistics for Signal Processing (3)
For advanced undergraduate students in engineering, physical sciences, life sciences. Introduction to probability models and statistical methods. Emphasis on probability as relative frequencies. Derivation of conditional probabilities and memoryless channels. Joint distributions of random variables, transformations, autocorrelation, series of irregular observations, stationarity. Random harmonic signals with noise, random phase and/or random amplitude. Gaussian and Poisson signals. Modulation and averaging properties. Transmission through linear filters. Power spectra, bandwidth, white and colored noise, ARMA processes and forecasting. Optimal linear systems, signal-to-noise ratio, Wrener filters. Prerequisite: MATH 122

STAT 333. Uncertainty in Engineering and Science (3)
Phenomena of uncertainty appear in engineering and science for various reasons and can be modeled in different ways. The course integrates the mainstream ideas in statistical data analysis with models of uncertain phenomena stemming from three distinct viewpoints: algorithmic/computational complexity; classical probability theory; and chaotic behavior of nonlinear systems. Descriptive statistics, estimation procedures and hypothesis testing (including design of experiments). Random number generators and their testing. Monte Carlo methods. Mathematica notebooks and simulations will be used. Prerequisite: MATH 122 Note: Credit given for only one (1) of STAT 312, 313, 333, 433

STAT 345. Theoretical Statistics I (3)
Topics provide the background for statistical inference. Random variables; distribution and density functions; transformations, expectation. Common univariate distributions. Multiple random variables; joint, marginal and conditional distributions; hierarchical models, covariance. Distributions of sample quantities, distributions of sums of random variables, distributions of order statistics. Methods of statistical inference. Prerequisite: MATH 122 or MATH 223

STAT 346. Theoretical Statistics II (3)
Point estimation: maximum likelihood and moment estimators. Methods of evaluating estimators including mean squared error, consistency, "best" unbiased and sufficiency. Hypothesis testing: likelihood ratio, and union-intersection tests. Properties of tests including power function, bias. Interval estimation by inversion of test statistics, use of pivotal quantities. Application to regression. Prerequisite: STAT 345.

STAT 391. Statistics Student Seminar (1-3)
Seminar run collaboratively by students to investigate an area of current research, the topic chosen each semester. All students participate in presentation of material each semester. Recommended for all students majoring in statistics in their senior year. Emphasis on written and oral presentation of statistical summaries, reports and projects. Prerequisite: Statistics major or minor and 9 credits approved statistics courses numbered 240 or above.

STAT 395. Senior Project in Statistics (3)
An individual project done under faculty supervision involving the investigation and statistical analysis of a real problem encountered in university research or an industrial setting. Written report. Prerequisite: permission of instructor.