Statistical concepts for critically evaluation quantitative information. Descriptive statistics, probability, estimation, hypothesis testing, correlation and regression. Students may not receive credit if they have received or are concurrently receiving credit for a higher numbered course offered by the Statistics Department. Math Reasoning Proficiency Course.

Credit Hours: 3
Prerequisites: grade of C- or better in MATH 1050 or MATH 1100 or MATH 1120 or MATH 1160 or MATH 1180 or exemption from College Algebra by examination

Designed to upgrade the curriculum of STAT 1200 or STAT 1300 or STAT 1400 to the level of STAT 2500. Students may not receive credit for STAT 2200 if they have completed a course from the Department of Statistics numbered 2500 or higher. Math Reasoning Proficiency Course.

Credit Hour: 1
Prerequisites: grade in C- or higher in STAT 1200 or STAT 1300 or STAT 1400

Designed primarily for students in College of Business. Descriptive statistics, probability, random variables, sampling distributions, estimation, confidence intervals, hypothesis tests. Math Reasoning Proficiency course.

Credit Hours: 3
Prerequisites: grade of C- or better in MATH 1300 or MATH 1400 or MATH 1500

Statistical methods, with emphasis on applications to natural resources and including computer exercises. Math Reasoning Proficiency Course.

Credit Hours: 3
Prerequisites: a college-level computing course and a grade in the C range or better in MATH 1100, MATH 1120, MATH 1160, or MATH 1180

Continuation of STAT 2500. Coverage of additional topics including: Regression; model building; ANOVA; nonparametic methods; use of a statistical computer package.

Credit Hours: 3
Prerequisites: grade in the C - or higher in STAT 2200 or STAT 2500 or STAT 2530, or STAT 4710 or concurrent enrollment in STAT 2200

Organized study of selected topics. Subjects and earnable credit may vary from semester to semester. Repeatable with departmental consent.

Credit Hour: 1-99
Prerequisites: Consent of instructor required

Primarily for middle and secondary mathematics education majors. Uses standards-based curricular materials to demonstrate connections between college-level statistics and content taught in middle and secondary schools. No credit toward a graduate degree in statistics.

Credit Hours: 3
Prerequisites: STAT 1200 or STAT 1300 or STAT 1400 or STAT 2500 or STAT 4710 or ESC_PS 4170 or MATH 2320

Independent investigations. Reports on approved topics.

Credit Hour: 1-3
Prerequisites: instructor's consent

Programming with major statistical packages emphasizing data management techniques and statistical analysis for regression, analysis of variance, categorical data, descriptive statistics, non-parametric analyses, and other selected topics.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

The study of statistical models and methods used in analyzing categorical data. The use of computing is emphasized and calculus is not required. No credit for students who have previously completed STAT 4830. No credit toward a graduate degree in statistics.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Statistical methods when the functional form of the population is unknown. Applications emphasized. Comparisons with parametric procedures. Goodness of-fit, chi-square, comparison of several populations, measures of correlation.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Theory of probability sampling designs. Unrestricted random sampling. Stratified sampling. Cluster sampling. Multistage or subsampling. Ratio estimates. Regression estimates. Double sampling.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Introductory course on collecting, processing, visualizing, and analyzing data in sports. Technologies used in data collection and processing will be explored, along with methods for measuring and comparing individual and team performance.

Credit Hours: 3
Prerequisites: Any one of STAT 3500, STAT 7020, STAT 7070, STAT 4710/7710, STAT 4760/7760, or instructor's consent

 

 Advanced course in methods for analyzing individual and team based performance in sports and the use of data to drive strategy and tactics. Emphasis will be put on analytical methods to improve skills and optimize the performance of athletes.

Credit Hours: 3
Prerequisites: Both STAT 4330/7330 and STAT 4510/7510

(cross-leveled with STAT 7410). Study of statistical techniques for the design and analysis of clinical trials, laboratory studies and epidemiology. Topics include randomization, power and sample size calculation, sequential monitoring, carcinogenicity bioassay and case-cohort designs. Prerequisites: any of the following: STAT 3500, STAT 7070, STAT 4710, STAT 7710, STAT 4760, STAT 7760, or instructor's consent.

Credit Hours: 3

Parametric models; Kaplan-Meier estimator; nonparametric estimation of survival and cumulative hazard functions; log-rank test; Cox model; Stratified Cox model; additive hazards model partial likelihood; regression diagnostics; multivariate survival data.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Repeated measurements; event history studies; linear and nonlinear mixed effects models; growth models; marginal mean and rate models; pattern-mixture models; selection models; non-informative and informative drop-out; joint analysis of longitudinal and survival data.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Random variables; Point estimation; Multiple t-test; Likelihood principle; Analysis of variance; Probabilistic methods for sequence modeling; Gene expression analysis; Protein structure prediction; Genome analysis; Hierarchical clustering and Gene classification.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Introduction to applied statistical models including regression and ANOVA, logistic regression, discriminant analysis, tree-based methods, semi-parametric re- gression, support vector machines, and unsupervised learning through principal component analysis and clustering. No credit toward a graduate degree in statistics.

Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070, STAT 4710 or STAT 7710, STAT 4760 or STAT 7760

Advanced course in applied statistical modeling focusing on extensions of the linear model. Topics include generalized linear models, such as logistic and Poisson regression. Random effects models will also be introduced, with emphasis on linear and generalized linear mixed models, repeated measures, and longitudinal data. These methods will extend to general models for dependent data, such as spatially-referenced data and time series. Lastly, nonlinear models through neural networks and deep learning will also be discussed.  

Credit Hours: 3
Prerequisites: STAT 4510 or STAT 7510 or instructor's consent

Study of analysis of variance and related modeling techniques for cases with fixed, random, and mixed effects. Exposure to designs other than completely randomized designs including factorial arrangements, repeated measures, nested, and unequal sample size designs.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 7070 or STAT 4710 or STAT 7710, STAT 4760 or STAT 7760

Examination and analysis of modern statistical techniques applicable to experimentation in social, physical or biological sciences.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 4510 or STAT 7510 or STAT 4530 or STAT 7530

Testing mean vectors; Discriminant analysis; Principal components; Factor analysis; Cluster analysis; Structural equation modeling; Graphics.

Credit Hours: 3
Prerequisites: STAT 3500, STAT 7070 STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760 or instructor's consent. No credit towards a graduate degree in statistics

(cross-leveled with STAT 7580). Introduction to basic concepts of and statistical methods used in customized pricing. Focuses on applying statistical methods to real customized pricing problems. Students will gain an understanding of customized pricing and some hands on experience with SAS Enterprise Miner.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 4510 or STAT 7510 or instructor's consent

Introduction to spatial random processes, spatial point patterns, kriging, simultaneous and conditional autoregression, and spatial data analysis.

Credit Hours: 3
Prerequisites: STAT 4510 or instructor's consent
Recommended: basic knowledge of calculus and matrices

Bayes formulas, choices of prior, empirical Bayesian methods, hierarchal Bayesian methods, statistical computation, Bayesian estimation, model selection, predictive analysis, applications, Bayesian software.

Credit Hours: 3
Prerequisites: STAT 3500 or STAT 4510 or STAT 7510

(same as MATH 4315). Introduction to theory of probability and statistics using concepts and methods of calculus. No credit for MATH 4315.

Credit Hours: 3
Prerequisites: MATH 2300

(same as MATH 4320). Probability spaces; random variables and their distributions; repeated trials; probability limit theorems.

Credit Hours: 3
Prerequisites: MATH 2300

(same as MATH 4520). Sampling; point estimation; sampling distribution; tests of hypotheses; regression and linear hypotheses.

Credit Hours: 3
Prerequisites: STAT 4750 or STAT 7750

A first course in Non-parametric statistical methods based on ranks. Both theory and application are emphasized. Two-sample problems. K-sample problems. Tests for independence. Contingency tables. Goodness-of-fit tests.

Credit Hours: 3
Prerequisites: STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Discrete distributions, frequency data, multinomial data, chi-square and likelihood ratio tests, logistic regression, log linear models, rates, relative risks, random effects, case studies.

Credit Hours: 3
Prerequisites: STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

Study of random processes selected from: Markov chains, birth and death processes, random walks, Poisson processes, renewal theory, Brownian motion, Gaussian processes, white noise, spectral analysis, applications such as queuing theory, sequential tests.

Credit Hours: 3
Prerequisites: STAT 4750 or STAT 7750

A study of univariate and multivariate time series models and techniques for their analyses. Emphasis is on methodology rather than theory. Examples are drawn from a variety of areas including business, economics and soil science.

Credit Hours: 3
Prerequisites: STAT 4710 or STAT 7710 or STAT 4760 or STAT 7760

A capstone course required of and open only to junior or senior statistics majors. Students will participate in statistical consulting, attend colloquia, and review articles in professional journals. Writing of reports will be emphasized.

Credit Hours: 3
Prerequisites: Statistics major with Junior or Senior class standing or instructor's consent
Recommended: 12 completed hours of statistics courses with grade of C or better; STAT 4110

Special work for Honors candidates in statistics. May be repeated for credit.
Credit Hour: 1-3

Contact Info

Prof. Larry Ries
Associate Chair and Director of Undergraduate Studies
RiesLD@missouri.edu

General Inquiries:
umcasstat@missouri.edu