*801. Statistical Methods in Research (4 cr) Lec 3, lab 2.

Prereq: Introductory course in statistics.

Statistical concepts and statistical methodology useful in descriptive, experimental, and analytical study of biological and other natural phenomena. Practical application of statistics rather than on statistical theory.

*802. Experimental Design (4 cr) Lec 3, lab 2.

Prereq: STAT *801.

Suitability and efficiency of various designs in conducting experimental investigations in related areas and the statistical analysis of the data.

*803. Ecological Statistics (NRES *803) (4 cr I) Lec 3, lab 1.

Prereq: STAT *801 or equivalent.

Model-based inference for ecological data, generalized linear and additive models, mixed models, survival analysis, multi-model inference and information theoretic model selection, and study design.

*804. Survey Sampling (3 cr)

Prereq: STAT 880 or IMSE 321 or permission

Sampling techniques: simple random sampling, sampling proportions, estimation of sample size, stratified random sampling, ratio and regression estimates.

830. Sensory Evaluation (FDST 830) (3 cr I) Lec 2, lab 3.

Prereq: Introductory course in statistics

Offered fall semester of odd-numbered calendar years. Food evaluation using sensory techniques and statistical analysis.

*831. Spatial Statistics (3 cr)

Prereq: STAT *802

Offered odd-numbered calendar years. Statistical methods for modeling and analyzing correlated data, with emphasis on spatial correlation. Descriptive statistics, time series, correlograms, semivariograms, kriging and designing experiments in the presence of spatial correlation.

*832. Statistics in Sports (2 cr) Lec 2.

Offered even-numbered calendar years.

Statistical methods useful for analyzing sports-related data. Descriptive statistics, graphical representations, experimental design, discriminant analysis and optimization.

*841. Statistical methods for Micro-array and Related Technologies (3 cr) Lec 3.

Prereq: STAT *801 or equivalent.

Basic biological concepts. Image analysis for two-color and oligonucleotide micro-arrays. Normalization, experimental design and mixed linear models for micro-array data. Empirical Bayes methods and false discovery rate. Clustering and gene category based methods. Tiling micro-arrays, massively parallel signature sequencing, and other related technologies.

*842. Computational Biology (BIOC 842) (3 cr) Lec 1, lab 2.

Prereq: Any introductory course in biology, genetics, or statistics.

Databases, high-throughput biology, literature mining, gene expression, next-generation sequencing, and proteomics, metabolics, systems biology, and biological networks.

*870. Multiple Regression Analysis (3 cr)

Prereq: STAT *801, *802

Linear regression and related analysis of variance and covariance methods for models with two or more independent variables. Techniques for selecting and fitting models, interpreting parameter estimates, and checking for consistency with underlying assumptions. Partial and multiple correlation, dummy variables, covariance models, stepwise procedures, response surfaces estimation, and evaluation of residuals.

*873. Applied Multivariate Statistical Analysis (3 cr) Lec 3.

Prereq: STAT *801

Multivariate techniques used in research. Reduction of dimensionality and multivariate dependencies, principle components, factor analysis, canonical correlation, classification procedures, discriminant analysis, cluster analysis, multidimensional scaling, multivariate extensions to the analysis of variance, and the general linear model.

*874. Nonparametric Statistics (3 cr)

Prereq: STAT *801 or 880

Statistical methods useful when data does not adhere to classical distributional assumptions. Analysis of interval/ordinal/categorical data for one, two and k sample problems, correlation and regression, goodness-of-fit methods and related topics.

*875. Categorical Data Analysis (3 cr)

Prereq: STAT *801, *802 or *870 recommended

Measures of associating contingency tables analysis, chi-squared tests, log-linear and logistic models, generalized estimating equations, planning studies involving categorical data.

880. Introduction to Mathematical Statistics (3 cr) Lec 3.

Prereq: MATH 208 or 107H; STAT 218 or equivalent

STAT 880 is not open to students earning a MA or MS degree in mathematics or statistics. Introductory mathematical statistics. Probability calculus; random variables, their probability distributions and expected values; sampling distributions; point estimation, confidence intervals and hypothesis testing theory and applications.

*882. Mathematical Statistics I-Distribution Theory (3 cr)

Prereq: MATH 208 or MATH 107H.

Sample space, random variable, expectation, conditional probability and independence, moment generating functions, special distributions, sampling distributions, order statistics, limiting distributions and central limit theorem.

*883. Mathematical Statistics II-Statistical Inference (3 cr)

Prereq: STAT 882

Interval estimation; point estimation, sufficiency and completeness; Bayesian procedures; uniformly most powerful tests, sequential probability ratio test, likelihood ratio test, goodness of fit tests; elements of analysis of variance and nonparametric tests.

*884. Applied Stochastic Models (3 cr)

Prereq: STAT 880 or IMSE 321 or equivalent

Introduction to stochastic modeling in operations research. Includes the exponential distribution and the Poisson process, discrete-time and continuous-time Markov chains, renewal processes, queueing models, stochastic inventory models, stochastic models in reliability theory.

885. Applied Statistics I (3 cr)

Prereq: STAT 880 or IMSE 321, and knowledge of matrix algebra

General linear models for estimation and testing problems analysis and interpretation for various experimental designs.

*889. Statistics Seminar (1 cr)

Prereq: Permission

*892. Topics in Statistics and Probability (1-5 cr, max 24)

Prereq: Permission

Special topics in either statistics or the theory of probability.

*898. Statistics Project (1-5 cr, max 5)

Prereq: Permission

*899. Masters Thesis (1-6 cr)

Prereq: Admission to the Masters Degree Program and permission of major adviser

902. Advanced Experimental Design (3 cr) Lec 2.

Prereq: STAT *802.

Advanced design concepts and methods used in research: construction, analysis and interpretation of incomplete block designs, split-plots, confounded and fractional factorials, response surface methods, and other topics.

904. Theory of Experimental Design (3 cr)

Prereq: Permission

Theory of underlying construction and analysis of designed experiments. Multifactor designs, fractional factorials, incomplete block designs, row and column designs, orthogonal arrays, and response to surface designs. Optimality criteria. Mathematical and computer-aided design theory.

930. Principles of Statistical Consulting (2 cr) Lec 2.

Prereq: Permission

STAT 930 is primarily for graduate students in statistics. Role and purpose of consulting. Statistical issues: understanding the client’s problem and choosing an appropriate procedure. Interpersonal issues: client expectations, difficult clients, working effectively with people and teamwork.

932. Biometrical Genetics and Plant Breeding (AGRO 932) (3 cr) Lec 3.

Prereq: AGRO 931

STAT *802 recommended. Offered odd-numbered calendar years. Theoretical concepts involved in planning breeding programs for the improvement of measurable morphological, physiological, and biochemical traits that are under polygenic control in crop plants of various types.

950. Bootstrap Methods and Their Application (3 cr) Lec 3.

Prereq: STAT *883; STAT *870 or 970; prior experience with “R” software

Application, theory, and computational aspects of the bootstrap. Parametric, nonparametric, and jackknife re-sampling; influence function and nonparametric delta method; bootstrap confidence intervals and hypothesis tests; permutation tests; applications to regression; implementation using statistical software.

960. Matrix Algebra Applications in Statistics (2 cr) Lec 2.

Prereq: STAT *801 and *802

Concepts and matrix operations useful to expanding determinants, computing matrix inverses, determining ranks and linear (in)dependence, and finding latent roots and latent vectors. Introduction to matrix algebra applications in regression analyses and linear models.

970. Linear Models (3 cr) Lec 3.

Prereq: MATH 314/814.

Methods and underlying theory for analyzing data based on linear statistical models. General linear model with specific models as special cases: including linear models applications.

971. Advanced Statistical Modelling (3 cr)

Prereq: STAT 970

Advanced theory and methods for statistical analysis. Systematic development of the needs and requirement of statistical modelling in research. Distribution and estimation theory for analysis of categorical data, survival data, data with correlated errors. Theory and practice of generalized linear models, mixed linear models. Introduction to non-linear models.

972. Variance Component Estimation (3 cr)

Prereq: STAT 970

Offered odd-numbered calendar years. Design and analysis of random effects and mixed models Basic theoretical background for models with fixed effects, distribution of quadratic forms, quadratic estimators including ANOVA methods, likelihood estimators including ML and REML, computing strategies, and optimal design for nested and cross classifications.

973. Theory of Multivariate Analysis (3 cr)

Prereq: STAT 873 or equivalent

Statistical inference concerning parameters of multivariate normal distributions with applications to multiple decision problems.

974. Nonlinear Regression Analysis (3 cr)

Prereq: STAT 870 and introductory calculus.

Basic concepts of nonlinear models and their associated applications. Estimating the parameters of these models under the classical assumptions as well as under relaxed assumptions. Major theoretical results and implementation using standard statistical software.

980. Advanced Probability Theory (3 cr) Lec 3.

Prereq: MATH *825

Probability spaces and random variables, expectations and fundamental inequalities, characteristic functions, four types of convergence, central limit theorem, introduction to stochastic processes.

982. Statistics Theory I (3 cr) Lec 3.

Prereq: MATH *825 and STAT *883

General decision problems, admissibility, mini-max and Bayes rules, invariance and unbiasedness, families of distributions problems in estimation theory.

983. Statistics Theory II (3 cr)

Prereq: STAT 982

UMP tests, likelihood ratio tests, confidence ellipsoid multiple decision and multiple comparisons, sequential decision problems.

992. Advanced Topics in Probability and Statistics (1-5 cr, max 24)

Prereq: Permission

Special topics in either statistics or probability.

997. Practicum in Statistical Consulting (4 cr) Fld.

Prereq: STAT 930

Participation in statistical consulting activities of the Statistics Department under faculty supervision. Prepare written reports to clients summarizing consultation results and to statistics supervisor summarizing statistical issues and findings.

999. Doctoral Dissertation (1-24 cr)

Prereq: Admission to Doctoral Degree Program and permission of supervisory committee