Statistics Courses

Courses of Instruction (STAT)

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Course Formats
ACE Outcomes
Prereqs:
Removal of all entrance deficiencies in mathematics.
Credit toward the degree may be earned in only one of: CRIM 300 or ECON 215 or EDPS 459 or SOCI 206.
The practical application of statistical thinking to contemporary issues; collection and organization of data; probability distributions; statistical inference; estimation; and hypothesis testing.
Credit Hours: 3
Course Format: Lecture 3
Course Delivery: Classroom
ACE Outcomes: 3
Prereqs:
STAT 218 or equivalent.
Tests for means/proportions of two independent groups, analysis of variance for completely randomized design, contingency table analysis, correlation, single and multiple linear regression, nonparametric procedures, design of experiments.
Credit Hours: 3
Course Format: Lecture 3
Course Delivery: Classroom
STAT 380
Statistics and ApplicationsCrosslisted as MATH 380
Prereqs:
Credit toward the degree can not be earned in STAT 218 if taken after or taken in parallel with STAT/MATH 380.
Probability calculus; random variables, their probability distributions and expected values; t, F and chi-square sampling distributions; estimation; testing of hypothesis; and regression analysis with applications.
Credit Hours: 3
Course Format: Lecture 3
Course Delivery: Classroom
ACE Outcomes: 3
Prereqs:
Survey of elementary experimental designs and their analyses completely randomized, randomized block, factorial, and split-plot designs.
Credit Hours: 3
Course Delivery: Classroom
Prereqs:
STAT/MATH 380 or IMSE 321 or permission.
Sampling Techniques: simple random sampling, sampling proportions, estimation of sample size, stratified random sampling, ratio and regression estimates.
Credit Hours: 3
Course Delivery: Classroom
STAT 430/830
Sensory EvaluationCrosslisted as FDST 430/830
Prereqs:
Introductory course in statistics.
Offered fall semester of odd-numbered calendar years.
Food evaluation using sensory techniques and statistical analysis.
Credit Hours: 3
Course Format: Lab 3, Lecture 2
Course Delivery: Classroom
Prereqs:
STAT 218 or equivalent.
Spatial point patterns, test of randomness, Morans I statistic and similar measures, checking assumptions for independence of observations, variography, estimation (point and global), Kriging, nearest neighbor techniques, cokriging, mixed models and their role in designed spatial experiments.
Credit Hours: 3
Course Format: Lab, Lecture
Course Delivery: Classroom
STAT 442/842
Computational BiologyCrosslisted as BIOC 442/842
Prereqs:
Any introductory course in biology, or genetics, or statistics.
Databases, high-throughput biology, literature mining, gene expression, next-generation sequencing, proteomics, metabolomics, systems biology, and biological networks.
Credit Hours: 3
Course Format: Lab 2, Lecture 1
Course Delivery: Classroom
Prereqs:
STAT/MATH 380 or IMSE 321, and knowledge of matrix algebra.
General linear models for estimation and testing problems, analysis and interpretation for various experimental designs.
Credit Hours: 3
Course Delivery: Classroom
Prereqs:
STAT 380 or equivalent is strongly recommended.
Sample space, random variable, expectation, conditional probability and independence, moment generating function, special distributions, sampling distributions, order statistics, limiting distributions, and central limit theorem.
This course is a prerequisite for: ACTS 401, ACTS 470, ACTS 471, ACTS 473, ACTS 474, STAT 463
Credit Hours: 4
Course Format: Lecture 3, Recitation 1
Course Delivery: Classroom
Prereqs:
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.
This course is a prerequisite for: ACTS 410, ACTS 425, ACTS 430, ACTS 450
Credit Hours: 4
Course Format: Lecture 3, Recitation 1
Course Delivery: Classroom
Prereqs:
Permission.
Special topics in either statistics or the theory of probability.
Credit Hours: 1-5
Max credits per degree: 24
Course Delivery: Classroom
Prereqs:
Prior arrangement with a faculty member and submission of proposed study plan to department office.
This course has no description.
Credit Hours: 1-5
Max credits per degree: 5
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 4
Course Format: Lab 2, Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
Essential statistical characteristics of a research study intended to assess the impact of treatment, environmental or population conditions on response. Focus is on both designed experiments and on studies for which controlled experiments are not feasible but characteristics of controlled experiment must be mimicked to the extent possible. Methods to assess power and compare efficiency of alternative designs are considered. Course covers major design structures, including blocking, nesting, multilevel models, split-plot and repeated measures, and statistical analysis associated with these structures.
Credit Hours: 4
Course Format: Lab 2, Lecture 3
Campus:
Course Delivery: Classroom
STAT 803
Ecological StatisticsCrosslisted as NRES 803
Prereqs:
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.
Credit Hours: 4
Course Format: Lab 1, Lecture 3
Campus:
Course Delivery: Classroom
STAT 804
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
A valid teaching certificate or permission.  An undergraduate coruse in introductory statistics is desirable, but not essential.
Not open to MA or MS students in mathematics or statistics.
Designed primarily to develop and equip middle-level teachers with the statistical knowledge they need for teaching.  The course follows an inquiry/discovery design, dedicating much of class time to activities, discussion and group work.  The course emphasizes topics in statistics that are part of the middle-school mathematics curriculum, as well as their application in other disciplines.  The course also includes statistics that are used in education and school-based research.
Credit Hours: 3
Course Format: Lecture 3
Course Delivery: Classroom
Prereqs:
A valid secondary mathematics teaching certificate or permission.
Not open to MA or MS students in mathematics or statistics.
The statistical concepts typically taught in a high school statistics class, including linear regression, two-way tables, sampling distributions, statistical inference for means and proportions, chi-square tests, and inference for regression.  Some experience with basic statistical concepts (mean, standard deviation, elementary probability) is necessary.  The course is inquiry-based, and will emphasize applications and statistical thinking.
Credit Hours: 3
Course Format: Lecture 3
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Offered even-numbered calendar years.
Statistical methods useful for analyzing sports-related data. Descriptive statistics, graphical representations, experimental design, discriminant analysis and optimization.
Credit Hours: 2
Course Format: Lecture 2
Campus:
Course Delivery: Classroom
Prereqs:
STAT *801 or equivalent.
Basic biological concepts. Multiple testing and false discovery rate. Second generation sequencing and statistical issues. ChIP-seq. RNA-seq. Empirical Bayes methods and software. Normalization, experimental design and commonly used models for microarray data. Metabolomics.
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
Any introductory course in biology, statistics, computer science or mathematics.
Next-generation RNA and genome sequencing, systems biology. Regulatory networks of transcription, protein-protein interaction networks, theory and practice. Databases, data integration and visualization. Students present computational biology publications and projects.
Credit Hours: 3
Course Format: Lecture 3
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
STAT 876 is offered every other odd-numbered calendar year. Knowledge of at least one statistical package (SAS, R, Splus, SPS) is required.
Application, theory and computational aspects of survival analysis. Survival and hazard functions; parametric models for survival data; censoring and truncation mechanisms; nonparametric estimation (confidence bands for the survival function, interval estimation of the mean and median survival time); univariate estimation of the hazard function; hypothesis testing; regression models (with fixed covariates, with time dependent covariates); and model diagnostics.
Credit Hours: 3
Course Format: Lecture 3
Course Delivery: Classroom
Prereqs:
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.
This course is a prerequisite for: IMSE 406, MATH 489
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
Sample space, random variable, expectation, conditional probability and independence, moment generating functions, special distributions, sampling distributions, order statistics, limiting distributions and central limit theorem.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
Permission
This course has no description.
Credit Hours: 1
Campus:
Course Delivery: Classroom
Prereqs:
Permission
Special topics in either statistics or the theory of probability.
Credit Hours: 1-5
Max credits per degree: 24
Campus:
Course Delivery: Classroom
Prereqs:
Permission
This course has no description.
Credit Hours: 1-5
Max credits per degree: 5
Campus:
Course Delivery: Classroom
STAT 899
Prereqs:
Admission to the Masters Degree Program and permission of major adviser
This course has no description.
Credit Hours: 1-6
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Course Format: Lecture 2
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 2
Course Format: Lecture 2
Campus:
Course Delivery: Classroom
STAT 932
Prereqs:
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.
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 2
Course Format: Lecture 2
Campus:
Course Delivery: Classroom
STAT 970
Prereqs:
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.
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
STAT 802, STAT 882, STAT 883, STAT 970 and take Statistics MS Comprehensive Exam prior to start of STAT 971.
Statistical modeling beyond the “general linear model” normally-distributed data, fixed-effects-only case. Focus on, but not limited to, the theory and practice of generalized and mixed linear models. Issues include translation of study design to plausible models, inference space, data and model scale, conditional vs. marginal models, correlated data, zero-inflated data, likelihood-based estimation and inference.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
STAT 873  and STAT 970, or equivalent.
Statistical inference concerning parameters of multivariate normal distributions with applications to multiple decision problems.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
MATH *825
Probability spaces and random variables, expectations and fundamental inequalities, characteristic functions, four types of convergence, central limit theorem, introduction to stochastic processes.
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
MATH *825 and STAT *883
General decision problems, admissibility, mini-max and Bayes rules, invariance and unbiasedness, families of distributions problems in estimation theory.
Credit Hours: 3
Course Format: Lecture 3
Campus:
Course Delivery: Classroom
Prereqs:
UMP tests, likelihood ratio tests, confidence ellipsoid multiple decision and multiple comparisons, sequential decision problems.
Credit Hours: 3
Campus:
Course Delivery: Classroom
Prereqs:
Permission
Special topics in either statistics or probability.
Credit Hours: 1-5
Max credits per degree: 24
Campus:
Course Delivery: Classroom
Prereqs:
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.
Credit Hours: 4
Course Format: Field
Campus:
Course Delivery: Classroom
Prereqs:
Admission to Doctoral Degree Program and permission of supervisory committee
This course has no description.
Credit Hours: 1-24
Campus:
Course Delivery: Classroom