University Catalog

STAT 193. Statistical Thinking (GED CORE)

Credits: 3 View Details
Description: Statistical background to critically read results reported in today's media regarding social, environmental and medical choices; how to collect good data; describe data graphically and numerically; uses and abuses of statistics; understanding variation and statistical significance; modeling chance; statistics in the courtroom, lotteries, opinion pools and other case studies; emphasis on understanding concepts rather than on computations; use of software packages and the internet.
Prerequisites: MATH 070 or high school advanced algebra with satisfactory math placement score
Semester Offered: Fall| Spring| Summer
Goal Area: GOAL AREA 4: MATHEMATICAL THINKING & QUANTITATIVE REASONING


STAT 219. Statistics for the Social Sciences

Credits: 3 View Details
Description: Descriptive statistics, graphical displays, random sampling, and normal distribution; introduction to confidence intervals and hypothesis tests for means and proportions; paired t-test, pooled t-test, chi-square test in contingency tables, brief introduction to correlation and simple linear regression; social science applications; use of statistical software package.
Prerequisites: STAT 193 or MATH 193 or satisfactory math placement score
Semester Offered: Fall| Spring| Summer
Goal Area: GOAL AREA 4: MATHEMATICAL THINKING & QUANTITATIVE REASONING


STAT 239. Statistics for the Biological and Physical Sciences

Credits: 3 View Details
Description: Descriptive statistics, design and sampling methods, normal, binomial and poisson distributions, basic probability rules, one and two sample inferences for means, proportions, variances, introduction to correlation and regression, introduction to chi-square tests, one and two-way ANOVA, use of statistical software package.
Prerequisites: STAT 193 or MATH 112 or satisfactory math placement score
Semester Offered: Fall| Spring| Summer
Goal Area: GOAL AREA 4: MATHEMATICAL THINKING & QUANTITATIVE REASONING


STAT 242. Business Statistics

Credits: 4 View Details
Description: Business problem solving: Data collection, summarizing and describing data, estimation and hypotheses testing, analysis of variance, regression analysis, time series, quality control, decision analysis. Statistical software. Tutorial.
Prerequisites: MATH 196 or equivalent
Semester Offered: Fall| Spring| Summer


STAT 304. SAS Programming

Credits: 3 View Details
Description: SAS statistical package; basic data manipulations and procedures; formatting, if-then-else, merge, arrays, do-loops, macros, functions, table look-up, custom reports.
Corequisites: A statistics course or consent of the instructor
Semester Offered: Fall


STAT 321. Statistical Methods II

Credits: 3 View Details
Description: Statistical methods for analyzing data beyond Statistical Methods I. Modeling data using simple and multiple regression, and one- and two-way analysis of variance. Transformations, model selection, multiple comparisons, randomized block design, and interactions.
Prerequisites: One of: STAT 219, STAT 239, STAT 242, or STAT 353
Semester Offered: Fall| Spring


STAT 325. Statistical Modeling with R

Credits: 3 View Details
Description: An introduction to R, RStudio, and R Markdown. R programming basics; R packages; descriptive statistics and graphics; statistical inference; statistical modeling; simulation and resampling methods.
Prerequisites: STAT 321
Semester Offered: Spring


STAT 332. Survey Planning and Contingency Tables

Credits: 3 View Details
Description: Important aspects of survey sampling from initial planning phases through collection and storage of the data; chi-square contingency table analyses for two and three way tables; handling of small expected frequencies; matched pairs; measures of association; use of statistical software on large survey data.
Prerequisites: STAT 219, STAT 229, STAT 319, STAT 353 or equivalent
Semester Offered: Fall


STAT 353. Statistics for Engineers

Credits: 3 View Details
Description: Probability distributions; introduction to statistical methods, including hypothesis testing and confidence intervals, one-way anova, simple linear regression, quality control basics; applications, and the use of statistical software.
Prerequisites: MATH 222
Semester Offered: Fall| Spring


STAT 360. Introduction to Data Visualization

Credits: 3 View Details
Description: Graphically explore a wide variety of data sets. Visual techniques to improve the understanding and communication of complex data. Hands-on implementation of these techniques with real data sets. Methods for visualizing large data sets, including high dimensional data. Dynamic data visualizations. Good design practices for visualization and presentation of results.
Prerequisites: One of: STAT 219, STAT 239, STAT 242, or STAT 353
Semester Offered: Fall


STAT 380. Statistical Consulting and Data Analysis I

Credits: 3 View Details
Description: Introduction to statistical consulting. Principles of good consulting practice. Effective communication skills for understanding the client's problem and available data, and choosing an appropriate procedure. Understanding client expectations, dealing with difficult clients, and working effectively with people individually and in teams.
Prerequisites: STAT 321
Semester Offered: Fall


STAT 381. Statistical Consulting and Data Analysis II

Credits: 3 View Details
Description: Data analysis for statistical consulting projects. Working with the client to understand the problem and available data. Carrying out and documenting an appropriate analysis. Preparing written and oral summaries. Communication of results to the client.
Prerequisites: STAT 380
Semester Offered: Spring


STAT 411. Statistics and Probability for Teachers

Credits: 3 View Details
Description: Descriptive statistics, exploratory data analysis, probability, sampling, simulation, random variables, sampling distributions, confidence intervals, hypothesis testing; use of statistical software.
Prerequisites: MATH 222
Semester Offered: DEMAND


STAT 415. Data Mining

Credits: 3 View Details
Description: Data mining principles and applications. Predictive modeling techniques for large data sets include classification and regression trees, logistic regression, neural networks, random forests and boosted trees. Handle missing values and outliers. Compare models and deploy best model to predict new data. Hands-on use of data mining software.
Prerequisites: STAT 321
Semester Offered: Spring


STAT 417. Applied Probability and Simulation

Credits: 3 View Details
Description: Probability distributions and random variables, simulation of random variates, probability modeling, applications to Markov chains, queueing models, reliability and survival; use of software.
Prerequisites: MATH 211 or MATH 221, and one programming course
Semester Offered: Spring


STAT 421. Applied Regression Methods

Credits: 3 View Details
Description: Advanced regression methods focused on complex real-world data. Model checking and diagnostics, model building, transformations, polynomial regression, logistic regression, general linear models, nonparametric regression methods.
Prerequisites: STAT 321
Semester Offered: Spring


STAT 424. Statistical Design for Process Improvement

Credits: 3 View Details
Description: A study of statistically designed experiments which have proven useful in product development and process improvement; topics include randomization, blocking, factorial treatment structures, fractional factorial designs, screening designs, Taguchi methods, response surface methods; use of statistical software.
Prerequisites: STAT 321
Semester Offered: DEMAND


STAT 427. Applied Time Series

Credits: 3 View Details
Description: A study of the most useful techniques of analysis and forecasting using time series data. Topics include an introduction to forecasting, time series regression, decomposition methods, smoothing, smoothing techniques, basic techniques of Box-Jenkins methodology; use of statistical software.
Prerequisites: STAT 321
Semester Offered: DEMAND


STAT 430. Multivariate Statistical Methods

Credits: 3 View Details
Description: Principal component analysis, factor analysis, discriminant analysis, cluster analysis, manova, profile analysis, repeated measures; applications and use of statistical software.
Prerequisites: STAT 321
Semester Offered: DEMAND


STAT 433. Nonparametric Statistics

Credits: 3 View Details
Description: Efficiency comparison of mean and median, one and two sample location problems, effect of alternative score functions, randomization and permutation tests, the independence problem, and selected problems in regression. Use of statistical software.
Prerequisites: STAT 321
Semester Offered: DEMAND


STAT 436. Applied Categorical Data Analysis

Credits: 3 View Details
Description: Introduction to the analysis of discrete data; log-linear models for two-way and multi-way tables; linear logistics regression models; association models and models of symmetry; applications, use of statistical software.
Prerequisites: STAT 321
Semester Offered: DEMAND


STAT 440. Topics in Statistics

Credits: 3 View Details
Description: Study of modern topics in theoretical or applied statistics.
Semester Offered: DEMAND


STAT 444. Internship

Credits: 3-12 View Details
Description: Participation in a full or part-time position with a cooperating business, governmental, or civic organization whose program has been approved in advanced by the department of statistics. Credits are provided upon completion of all requirements of the internship. Can substitute for STAT 480 if approved by the department. Any remaining credits apply to university electives for graduation.
Semester Offered: Fall| Spring| Summer


STAT 447. Basic Elements of Probability Theory

Credits: 3 View Details
Description: A more mathematical treatment of probability distributions than STAT 417. Probability concepts and laws; sample spaces, combinations and permutations, Bayes' theorem, discrete and continuous random variables, expected value, distribution of functions of random variables, two-demensional variates, central limit theorem; T, F, and chi-square distributions.
Prerequisites: MATH 320 or MATH 321
Semester Offered: Fall


STAT 448. Basic Elements of Statistical Theory

Credits: 3 View Details
Description: Theory of estimation and hypothesis testing; maximum likelihood, method of moments, likelihood ratio tests; elementary mathematical functions illustrate theory.
Prerequisites: STAT 447
Semester Offered: Spring


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