156 courses with the subject ST, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
or ST 311 or Introduction to Statistics Restricted Elective (p. ) GEP Social Sciences (https://catalog.ncsu.edu/undergraduate/ gep-category-requirements/gep-social-sciences/)
credit hours) Students will learn the basic fundamentals of coding, which will then evolve into learning methods for discovering patterns in data through the use of effective data visualization tools, exploratory data analysis, and modeling techniques. They will gain experience working with real-world messy data, and use data tidying and data wrangling techniques to make the data suitable for exploration. Students will be required to reformulate discipline-specific research questions into data science questions. An emphasis will be placed on creating reproducible and shareable work. The course will highlight the importance of and uses of version control software (GitHub) for individual and collaborative work. The course will focus on the R statistical computing language. Typically offered in Fall and Spring
This non-calculus course will provide an introduction to elementary probability and an introductory survey of the standard toolkit of statistical methods. Probability topics will include: basic rules of probability; random variables; expected values; sampling distributions; central limit theorem. Statistical methods covered will include: confidence intervals and hypothesis tests for means and proportions in both the one- and two- sample settings; introduction to simple linear and multiple regression and correlation, including inference procedures for key parameters; one-way and two-way ANOVA; analysis of two-way tables for count data. Point- and-click statistical software will be used for implementation. Restriction: Students with prior credit for ST 312 or 372 may not receive credit for ST 120. Typically offered in Fall and Spring Statistics (ST) 1
credit hours) Calculus-based treatment of univariate probability and mathematical statistics. The use of simulation to illustrate and supplement topics will be emphasized, and students will be expected to write basic computer code. Probability topics will include: survey of common discrete and continuous probability distributions; fundamental rules of probability; conditional probability; sampling distributions; expected values and variances; linear combinations of random variables; central limit theorem. Mathematical statistics topics will include methods of point and interval estimation, including method of moments and maximum likelihood; properties of estimators, including bias and variance; methods of hypothesis testing, including likelihood ratio tests; properties of tests, including Type I and Type II error rates. MA 241. Students with prior credit for ST 372 or ST 422 may not receive credit for ST 121. Typically offered in Fall and Spring
credit hours) This course is an extension of the data visualization and exploratory data analysis tools from ST 115. Realistic data sets and scenarios are used to motivate data manipulation and cleaning, data analysis, and presentation of results via tables and graphs. Good programming practices are emphasized both for writing programs and producing results. Topics include material from the Base SAS Certification Exam as well as practical skills beyond the base exam. This course uses the SAS statistical computing language and GitHub. No prior experience with SAS is expected, but prior experience with GitHub is required. Typically offered in Fall and Spring
This course uses python to build on prior training in statistical computing. Reproducible workflows and connections to the R and SAS languages are emphasized. Students learn to read, summarize, and analyze data with python, write advanced functions, and create reproducible reports using Quarto while using version control using Git. By comparing tools and paradigms across Python, R, and SAS, students develop flexible skills for modern statistical analysis and computing. Typically offered in Fall and Spring
Coverage of common methods of statistical analysis not typically included in standard two-semester introductory series. Principles of nonparametric statistics and a survey of such methods for standard statistical settings, including inferences about population means, proportions, correlations, and linear models. Methods for analyzing categorical data, including analyses of two-way tables, goodness of fit, and linear models for binary responses. Additional advanced topics at instructor's discretion. Typically offered in Fall and Spring
This course will compare and contrast observational vs experimental studies -- including scope of inference, their use cases, and an introduction into their designs. It will provide an overview of fundamental experimental designs and sampling methods, their proper application, and the effects of the collection method on potential analyses. This course will emphasize critical thinking through analysis and critique of design applications, including ethical implications, under various conditions. Software may be used to support the design of surveys and experiments, but no prior statistical software use is required. Typically offered in Fall and Spring
Presentation of material not normally available in regular course offerings or offering of new courses on a trial basis. Credits and content determined by faculty member in consultation with the Department Head. Typically offered in Fall, Spring, and Summer
hours) An introduction to using the SAS statistical programming environment. The course will combine lecture and a virtual computing laboratory to teach students how to use the SAS sytem for: basic data input and manipulation; graphical displays of univariate and bivariate data; one- and two-sample analyses of means; simple linear regression; one-way ANOVA. Documentation of code and writing of statistical reports will be included. BUS 350 Typically offered in Fall, Spring, and Summer
Introduction to the statistical programming language R. The course will cover: reading and manipulating data; use of common data structures (vectors, matrices, arrays, lists); basic graphical representations. Typically offered in Fall and Spring
Examining relationships between two variables using graphical techniques, simple linear regression and correlation methods. Producing data using experiment design and sampling. Elementary probability and the basic notions of statistical inference including confidence interval estimation and tests of hypothesis. One and two sample t-tests, one-way analysis of variance, inference for count data and regression. Credit not allowed if student has prior credit for another ST course or BUS 350
A further examination of statistics and data analysis. Inference for comparing multiple samples, experimental design, analysis of variance and post-hoc tests. Inference for correlation, simple regression, multiple regression, and curvilinear regression. Analysis of contingency tables and categorical data.
This course includes a matrix-based treatment of both general and generalized linear models. General linear models containing continuous and/or categorical variables are explored in terms of model fitting, assessment, selection, and inference. Remediation techniques such as transformations are also covered. The framework for generalized linear models is introduced in enough detail to support both non- normal distributions via link functions and random effects. A review, and augmentation, of key concepts from MA 305/405 is included. Topics may include eigensystems, matrix decomposition, generalized inverses, and quadratic forms. Statistical software will be used extensively to carry out matrix operations, analyze data, and support the preparation of professional results. Typically offered in Fall and Spring ST 350/BUS 350 Economics and Business Statistics (3 credit hours) Introduction to statistics applied to management, accounting, and economic problems. Emphasis on statistical estimation, inference, simple and multiple regression, and analysis of variance. Use of computers to apply statistical methods to problems encountered in management and economics. Typically offered in Fall, Spring, and Summer
Natural & Physical Sciences
The class is a calculus-based introduction to probability and statistics, with a focus on collection and summary of data, along with making formal inferences and practical conclusions on the basis of data. Topics may include sampling, descriptive statistics, designed experiments, simple and multiple regression, basic probability, discrete and continuous distributions, sampling distributions, hypothesis testing, confidence intervals, one and two-way ANOVA. Typically offered in Fall, Spring, and Summer
1,3 Regression
This course will allow students to see many practical aspects of data analysis. Each section of this course will expose students to the process of data analysis in a themed area such as biostatistics or environmental statistics. Students will see problems of data collection and analysis through a combination of classroom demonstrations, hands on computer activities and visits to local industries. Typically offered in Summer only ST 404/BIO 404 Epidemiology and Statistics in Global Public Health (3 credit hours) This course will provide a general introduction to the quantitative methods used in global health, combining elements of epidemiology and biostatistics. The course will focus on linear and logistic regression, survival analysis, traditional study designs, and modern study designs. Students will learn fundamental principles in epidemiology, including statistical approaches, and apply them to topics in global public health. and either GPH 201 or GOH 201 Typically offered in Fall only ST 405/ST 505 Applied Nonparametric Statistics (3 credit hours) Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered. Typically offered in Fall only ST 412/MA 412 Long-Term Actuarial Models (3 credit hours) Long-term probability models for risk management systems. Theory and applications of compound interest, probability distributions of failure time random variables, present value models of future contingent cash flows, applications to insurance, health care, credit risk, environmental risk, consumer behavior and warranties. 301, ST 305, ST 311, ST 361, ST 370, ST 371, ST 380 or equivalent Typically offered in Fall only ST 413/MA 413 Short-Term Actuarial Models (3 credit hours) Short-term probability models for risk management systems. Frequency distributions, loss distributions, the individual risk model, the collective risk model, stochastic process models of solvency requirements, applications to insurance and businessdecisions. ST 370, ST 371, ST 380, ST 421. Typically offered in Summer only
ST/GPH 404 Epidemiology and Statistics in Global Public
a
First of a two-semester sequence of mathematical statistics, primarily for undergraduate majors in Statistics. Introduction to probability, univariate and multivariate probability distributions and their properties, distributions of functions of random variables, random samples and sampling distributions. Credit is not allowed for both ST 421 and MA 421. Typically offered in Fall and Spring
Second of a two-semester sequence of mathematical statistics, primarily for undergraduate majors in Statistics. Random samples, point and interval estimators and their properties, methods of moments, maximum likelihood, tests of hypotheses, elements of nonparametric statistics and elements of general linear model theory. Typically offered in Fall and Spring
Regression analysis as a flexible statistical problem solving methodology. Matrix review; variable selection; prediction; multicolinearity; model diagnostics; dummy variables; logistic and non-linear regression. Emphasizes use of computer. MA 305 or MA 405) Typically offered in Fall and Spring
Experimental design as a method for organizing analysis procedures. Completely randomized, randomized block, factorial, nested, latin squares, split-plot and incomplete block designs. Response surface and covariance adjustment procedures. Stresses use of computer. Typically offered in Fall and Spring
Design principles pertaining to planning and execution of a sample survey. Simple random, stratified random, systematic and one- and two- stage cluster sampling designs. Emphasis on statistical considerations in analysis of sample survey data. Class project on design and execution of an actual sample survey. Typically offered in Fall and Spring ST 433/ST 533 Applied Spatial Statistics (3 credit hours) Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects. P: ST 422 and ST 430 Typically offered in Spring only ST 434/ST 534 Applied Time Series (3 credit hours) Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. An introduction and review of necessary statistical concepts will be given, and a statistical computing package will be introduced. Analyses of real data sets using statistical software will be emphasized. Typically offered in Fall only Graduate Catalog 1493 ST 435/ST 535 Statistical Methods for Quality and Productivity Improvement (3 credit hours) Use of statistics for quality control and productivity improvement. Control chart calculations and graphing, process control and specification; sampling plans; and reliability. Computer use will be stressed for performing calculations and graphing. Typically offered in Spring only ST 437/ST 537 Applied Multivariate and Longitudinal Data Analysis (3 credit hours) An introduction to use of statistical methods for analyzing multivariate and longitudinal data collected in experiments and surveys. Topics covered include multivariate analysis of variance, discriminant analysis, principal components analysis, factor analysis, covariance modeling, and mixed effects models such as growth curves and random coefficient models. Emphasis is on use of a computer to perform statistical analysis of multivariate and longitudinal data. Typically offered in Spring only ST 440/ST 540 Applied Bayesian Analysis (3 credit hours) Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics. Typically offered in Spring only ST 442/CSC 442 Introduction to Data Science (3 credit hours) Overview of data structures, data lifecycle, statistical inference. Data management, queries, data cleaning, data wrangling. Classification and prediction methods to include linear regression, logistic regression, k-nearest neighbors, classification and regression trees. Association analysis. Clustering methods. Emphasis on analyzing data, use and development of software tools, and comparing methods. ST 372 or ST 380) and (CSC 111 or CSC 112 or CSC 113 or CSC 114 or CSC 116 or ST 114 or ST 445) Typically offered in Fall only
s 3
3 ST 442 Introduction to Data Science 3 3 ST 445 Introduction to Statistical Computing and Data 3 3 Management
3 ST 546 Probability and Stochastic Processes I 3 3 Group A - Physical Sciences 3 BME 201 Computer Methods in Biomedical Engineering 3 3 CSC 442 Introduction to Data Science 3 1-6 EC 351 Econometrics I 3 1 ECE 489 Solid State Solar and Thermal Energy Harvesting 3
Management (3 credit hours) Detailed discussion of the program data vector and data handling techniques that are required to apply statistical methods. Topics are based on the current content of the Base SAS Certification Exam and typically include: importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; basic detail and summary reporting; and code debugging. Additional topics with practical applications, such as graphics and advanced reporting, may also be introduced. Statistical methods for analyzing data are not covered in this course. Regular access to a computer for homework and class exercises is required. Previous exposure to SAS is expected. Typically offered in Fall and Spring
hours) This course covers a wide range of SAS skills that build on the topics introduced in ST 445: Introduction to Statistical Computing and Data Management. In particular, many topics related to the Advanced SAS Certifi cation Exam are covered in order to help students prepare for that exam. However, an additional goal of equal importance is to synthesize statistical content such as regression, distributional assumptions for inference, and power from multiple courses through simulation- and graphics-based investigations. Typically offered in Spring only
Statistical reasoning in sports. Quantitative methods for analyzing observational data collected in professional and amateur sports. Probability concepts, such as joint probability, conditional probability, and Bayes Theorem to model uncertainty in sports. Quantifying variation in estimation for comparative purposes including resampling techniques; statistical significance. Linear and generalized linear regression models. Focus on baseball, basketball, North American football, hockey. Other areas of application include tennis, soccer, and golf. P: (ST 307 or ST 308 or CSC 111 or CSC 116) and (ST 312 or ST 370 or ST 372) Typically offered in Fall only
Basic principles and techniques of data analytics, data wrangling, and visualization. Statistical learning techniques, including (a) supervised learning topics like regression and classification, (b) unsupervised learning techniques like clustering and principal component analysis, (c) an introduction to deep learning, and (d) an Introduction to text mining and natural language processing. The concepts will be implemented with R, and the focus will be on practical case studies with real-world datasets. ISE 135) Typically offered in Fall only
hours) This course is designed to survey topics and tools needed for an undergraduate statistics major to begin to develop a broad and thorough working knowledge of modern computational techniques for statistical inferences. Statistical methods and the algorithms used to facilitate their computations are motivated by building logical foundations for statistical reasoning. Algorithms surveyed can broadly be categorized as either optimization based or sampling based. Rather than focusing on learning standard software packages for implementing common statistical routines, all codes will be written from scratch using the R programming language (or any other high-level language of the students' choosing). Emphasis is placed on developing a practical understanding of how and why existing methods work, and when to apply a particular method. Some programming proficiency is assumed. and ST 308 Typically offered in Spring only
Mentored experience in applied statistical analysis. Students will work in small groups in collaboration with local scientists to answer real questions about real data. The experience involves mentoring by both the project scientist and the instructor. P: ST 430 Typically offered in Spring only
Offered as needed to present material not normally available in regular departmental course offerings, or for offering new courses on a trial basis. Typically offered in Fall, Spring, and Summer
Mentored professional experience in statistics. A minimum of 45 hours must be completed for each credit hour earned. The experience must d be arranged in advance by the student and approved by the Department of Statistics prior to enrollment. Approval requires completion of the Statistics Department's Experiential Learning Contract, which must be signed by the student, their professional mentor, and their academic advisor. Professional mentors are encouraged to require a research paper or poster presentation as part of the work expectations when appropriate. Students should refer to their curriculum requirements for possible restrictions on the total number of ST 497 credit hours that may be applied to their degree. identifying their own internship mentor and experience. Typically offered in Fall, Spring, and Summer
Detailed investigation of topics of particular interest to advanced undergraduates under faculty direction. Individualized/Independent Study and Research courses require a "Course Agreement for Students Enrolled in Non-Standard Courses" be completed by the student and faculty member prior to registration by the department. Typically offered in Fall, Spring, and Summer
Mentored research experience in statistics. A minimum of 45 hours must be completed for each credit hour earned. The experience must be arranged in advance by the student and approved by the Department of Statistics prior to enrollment. Approval requires completion of the Statistics Department's Experiential Learning Contract, which must be signed by the student, their research mentor, and their academic advisor. Research mentors are encouraged to require a research paper or poster presentation as part of the work expectations when appropriate. Students should refer to their curriculum requirements for possible restrictions on the total number of ST 499 credit hours that may be applied to their degree. identifying their own research mentor and experience. Typically offered in Fall, Spring, and Summer
First of a two-semester sequence in probability and statistics taught at a calculus-based level. Probability: discrete and continuous distributions, expected values, transformations of random variables, sampling distributions. Credit not given for both ST 701 and ST 501. Note: this course will be offered in person (Fall) and online (Summer). Typically offered in Fall and Summer
hours) Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss- Markov theorem. Estimability, analysis of variance and co variance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on use of the computer to apply methods with data sets. Credit not given for both ST 705 and ST 503. Note: this course will be offered in person (Spring) and online (Summer). P: ST 501 and MA 405 or equivalent (Linear Algebra); C: ST 502 Typically offered in Spring and Summer ST 505/ST 405 Applied Nonparametric Statistics (3 credit hours) Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered. Typically offered in Fall only
ST(ZO) 506 Sampling Animal Populations
A general introduction to the use of descriptive and inferential statistics in behavioral science research. Methods for describing and summarizing data presented, followed by procedures for estimating population parameters and testing hypotheses concerning summarized data. Typically offered in Fall and Spring
hours) This course provides an in-depth study of building, validating, and predicting using regression models. Topics include multiple linear regression models with both continuous and categorical predictors, model selection techniques, and residual diagnostics. Bayesian regression models are also explored. Categorical data analysis is covered including contingency table analysis and logistic regression models. Students will gain considerable experience working with data. Software is used throughout the course with the expectation of students being able to produce their own analyses. P: ST 511, ST 513, ST 517, or equivalent Typically offered in Spring only
An introduction to the foundations of probability theory and mathematical statistics useful for research in engineering. Topics include descriptive statistics, probability, discrete and continuous random variables and probability distributions, joint probability distributions and random samples, point estimation, confidence intervals, hypothesis testing, and analysis of variance. Typically offered in Fall and Spring
This course is intended to give students a background in the methods of statistical analysis and design of experiments that will assist them in conducting research and analyzing data in engineering. Concentration in this course will be on principles of the design of experiments and analysis of variance and regression including post-hoc tests, inference for simple regression, multiple regression, and curvilinear regression. Typically offered in Fall and Spring
Course covers basic methods for summarizing and describing data, accounting for variability in data, and techniques for inference. Topics include basic exploratory data analysis, probability distributions, confidence intervals, hypothesis testing, and regression analysis. This is a calculus-based course. Statistical software is used; however, there is no lab associated with the course. Credit not given for this course and ST 511 or ST 513 or ST 515. This course does NOT count as an elective towards a degree or a minor in Statistics. Note: the course will be offered in person (Fall) and online (Fall and Summer). equivalent (Linear Algebra) Typically offered in Fall and Summer
This second course in statistics for graduate students is intended to further expand students' background in the statistical methods that will assist them in the analysis of data. Course covers many fundamental analysis methods currently used to analyze a wide array of data, mostly arising from designed experiments. Topics include multiple regression models, factorial effects models, general linear models, mixed effect models, logistic regression analysis, and basic repeated measures analysis. This is a calculus-based course. Statistical software is used, however, there is no lab associated with the course. Credit not given for this course and ST 512 or ST 514 or ST 516. Note: this course will be offered in person (Spring) and online (Fall and Spring). Typically offered in Fall and Spring ST 519/EMS 519 Teaching and Learning of Statistical Thinking (3 credit hours) This course is designed to bridge theory and practice on how students develop understandings of key concepts in data analysis, statistics, and probability. Discussion of students' understandings, teaching strategies and the use of manipulatives and technology tools. Topics include distribution, measures of center and spread, sampling, sampling distribution, randomness, and law of large numbers. Restriction: Graduate standing in STEM Education or Statistics Typically offered in Spring only This course is offered alternate even years
Overview and comparison of observational studies and designed experiments followed by a thorough discussion of design principles. Review of estimation and inference for regression and ANOVA models from an experimental design perspective. Review of design and analysis for completely randomized, randomized complete block, and Latin square designs. Designs and analysis methods for factorial experiments, general blocking structures, incomplete block designs, confounded factorials, split-plot experiments, and fractional factorial designs. Examples used to illustrate application and analysis of these designs. Typically offered in Fall only ST 533/ST 433 Applied Spatial Statistics (3 credit hours) Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects. P: ST 422 and ST 430 Typically offered in Spring only ST 534/ST 434 Applied Time Series (3 credit hours) Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. An introduction and review of necessary statistical concepts will be given, and a statistical computing package will be introduced. Analyses of real data sets using statistical software will be emphasized. Typically offered in Fall only ST 535/ST 435 Statistical Methods for Quality and Productivity Improvement (3 credit hours) n Use of statistics for quality control and productivity improvement. Control chart calculations and graphing, process control and specification; sampling plans; and reliability. Computer use will be stressed for performing calculations and graphing. Typically offered in Spring only
Hours ST 555 Statistical Programming I 21 ST 556 Statistical Programming II Total Hours 6 tion Science * Other courses not listed can be approved as an elective upon consultation with an advisor.
ST(MA) 546 Probability and Stochastic Processes I
Students must take at least one ST and one CSC course Credit cannot be given for both CSC 522 Automated Learning and Data Analysis and ST 563 Introduction to Statistical Learning
This course will provide a discussion-based introduction to statistical practice geared towards students in the final semester of their Master of Statistics degree. Note: the course will be offered in person (Fall) and online (Spring and Summer). 522 or ST 702) Typically offered in Fall, Spring, and Summer
This course focuses on the concepts, methods, and models used to analyze categorical data, particularly contingency tables, count data and binary/binomial type of data. The topics covered include Pearson Chi-squared independence test for contingency tables, measures of marginal and conditional associations, small-sample inference, logistic regression models for independent binary/binomial data and many extended models for correlated binary/binomial data including matched data and longitudinal data. The course emphasizes the implementation of methods/models using SAS and the interpretation of the results from the output. Typically offered in Fall only
Statistical methods for analysis of time-to-event data, with application to situations with data subject to right-censoring and staggered entry, including clinical trials. Survival distribution and hazard rate; Kaplan-Meier estimator for survival distribution and Greenwood's formula; log-rank and weighted long-rank tests; design issues in clinical trials. Regression models, including accelerated failure time and proportional hazards; partial likelihood; diagnostics. Typically offered in Spring only
ST(ECG) 561 Intermediate Econometrics
Course discusses current big data management practices and software along with statistical paradigms important for big data and predictive analytics. Literate programming and good programming practices are covered. Typically offered in Spring only
An introduction to programming and data management using SAS, the industry standard for statistical practice. Detailed discussion of the program data vector and data handling techniques that are required to apply statistical methods. Topics are based on the current content of the Base SAS Certification Exam and typically include: importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; basic detail and summary reporting; and code debugging. Additional topics with practical applications are also introduced, such as graphics and advanced reporting. Statistical methods for analyzing data are not covered in this course. Regular access to a computer for homework and class exercises is required. Previous exposure to SAS is not expected. Typically offered in Fall, Spring, and Summer
Statistical procedures for importing/managing complex data structures using SQL, automated analysis using macro programming, basic simulation methods and text parsing/analysis procedures. Students learn SAS, the industry standard for statistical practice. Regular access to a computer for homework and class exercises is required. P: ST 555 or Base SAS Certification Typically offered in Spring only
Methods for reading, manipulating, and combining data sources including databases. Custom functions, visualizations, and summaries. Common analyses done by data scientists. Methods for communicating results including dashboards. Regular access to a computer for homework and class exercises is required. moderate computer programming experience) Typically offered in Fall and Summer ST 561/ECG 561 Applied Econometrics I (3 credit hours) Introduction and application of econometrics methods for analyzing cross- sectional data in economics, and other social science disciplines, such as OLS, IV regressions, and simultaneous equations models. Students should have had a statistical methods course at the 300 level or above as well as Calculus I and II. Typically offered in Fall only
3 ECD 524 Career Counseling and Development
This is a hands-on course using modeling techniques designed mostly for large observational studies. Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis. Clustering and association analysis are covered under the topic "unsupervised learning," and the use of training and validation data sets is emphasized. Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves. SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful. Typically offered in Spring only
This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines. Typically offered in Fall and Spring
Typically offered in Fall, Spring, and Summer
Typically offered in Fall, Spring, and Summer
Instruction in research and research under the mentorship of a member of the Graduate Faculty. Typically offered in Fall, Spring, and Summer
ST(MA,OR) 706 Nonlinear Programming
Probability tools for statistics: description of discrete and absolutely continuous distributions, expected values, moments, moment generating functions, transformation of random variables, marginal and conditional distributions, independence, orderstatistics, multivariate distributions, concept of random sample, derivation of many sampling distributions. Typically offered in Fall only
General framework for statistical inference. Point estimators: biased and unbiased, minimum variance unbiased, least mean square error, maximum likelihood and least squares, asymptotic properties. Interval estimators and tests of hypotheses: confidence intervals, power functions, Neyman-Pearson lemma, likelihood ratio tests, unbiasedness, efficiency and sufficiency. Typically offered in Spring only
Introduction of statistical methods. Examples include multiple linear regression, concepts of experimental design, factorial experiments, and random-effects modeling. A computing laboratory addresses computational issues and use of statistical software. This course is a prerequisite for most advanced courses in statistics. This section is restricted to statistics and closely related majors. R: 17STPHD Students Only Typically offered in Fall only
This course will introduce many methods that are commonly used in applications. Examples include: model generation, selection, assessment, and diagnostics in the context of multiple linear regression (including penalized regression); linear mixed models; generalized linear models; generalized linear mixed models; nonparametric regression and smoothing; and finite-population sampling basics. Coverage will include some theory, plus implementation using SAS and/or R. Typically offered in Spring only
Theory of estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markoff theorem. Estimability and properties of best linear unbiased estimators. General linear hypothesis. Application of dummy variable methods to elementary classification models for balanced and unbalanced data. Analysis of covariance. Variance components estimation for balanced data. Typically offered in Spring only ST 706/MA 706/OR 706 Nonlinear Programming (3 credit hours) An advanced mathematical treatment of analytical and algorithmic aspects of finite dimensional nonlinear programming. Including an examination of structure and effectiveness of computational methods for unconstrained and constrained minimization. Special attention directed toward current research and recent developments in the field. Typically offered in Spring only
ST(GN) 721 Genetic Data Analysis ST(BMA,OR) 722 Decision Analytic Modeling
ELECTIVES
Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the use of exising software(e.g., WinBUGS). Typically offered in Fall only Graduate Catalog 1499
ST(MA) 746 Introduction to Stochastic Processes ST(MA) 747 Probability and Stochastic Processes II ST(MA) 748 Stochastic Differential Equations
Markov chains and Markov processes, Poisson process, birth and death processes, queuing theory, renewal theory, stationary processes, Brownian motion. . Typically offered in Spring only ST 747/MA 747 Probability and Stochastic Processes II (3 credit hours) Advanced development of stochastic processes. Conditional expectation, filtrations of sigma-algebras, stopping times. Martingales, associated convergence theorems and inequalities, martingale decomposition, optional stopping. Markov chains including random walks, recurrence versus transience, asymptotic behavior. General Markov processes and the related semigroup operators. Construction and properties of Brownian motion, Donsker's invariance principle. Other potential topics include stationary processes, Birkhoff's ergodic theorem, branching processes, Poisson processes. Typically offered in Spring only ST 748/MA 748 Stochastic Differential Equations (3 credit hours) Theory of stochastic differential equations driven by Brownian motions. Current techniques in filtering and financial mathematics. Construction and properties of Brownian motion, wiener measure, Ito's integrals, martingale representation theorem, stochastic differential equations and diffusion processes, Girsanov's theorem, relation to partial differential equations, the Feynman-Kac formula. Typically offered in Fall only ST 750/ECG 750 Introduction to Econometric Methods (3 credit hours) Introduction to principles of estimation of linear regression models, such as ordinary least squares and generalized least squares. Extensions to time series and panel data. Consideration of endogeneity and instrumental variables estimation. Limited dependent variable and sample selection models. Attention to implementation of econometric methods using a statistical package and microeconomic and macroeconomic data sets. Typically offered in Spring only ST 751/ECG 751 Econometric Methods (3 credit hours) Introduction to important econometric methods of estimation such as Least Squares, instrumentatl Variables, Maximum Likelihood, and Generalized Method of Moments and their application to the estimation of linear models for cross-sectional ecomomic data. Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods. Typically offered in Fall only
ST(ECG) 751 Econometric Methods ST(ECG) 752 Time Series Econometrics ST(ECG) 753 Microeconometrics
ST(GN) 756 Computational Molecular Evolution ST(BI,GN) 757 Statistics for Molecular Quantitative Genetics
Computational tools for research in statistics, including applications of numerical linear algebra, optimization and random number generation, using the statistical language R. A project encompassing a simulation experiment will be required. Typically offered in Fall only
ST(GN) 770 Statistical Concepts in Genetics ST(BMA,MA) 771 Biomathematics I ST(BMA,MA) 772 Biomathematics II ST(BMA,MA,OR) 773 Stochastic Modeling ST(MA) 778, 779 Measure Theory and Advanced Probability I, II
hours) Sets and classes, sigma-fields and related structures, probability measures and extensions, random variables, expectation and integration, uniform integrability, inequalities, L_p-spaces, product spaces, independence, zero-one laws, convergence notions, characteristic functions, simplest limit theorems, absolute continuity, conditional expectation and conditional probabilities, martingales. Typically offered in Spring only
and provide additional insight into courses in their programs of study
Typically offered in Fall, Spring, and Summer
Typically offered in Fall and Spring
Typically offered in Fall and Spring
Perspectives, Skills, and Strategies (1 credit hours) This course will expose PhD students in statistics to a structured overview of the skills they are expected to learn, how they are expected to develop these skills, what type of career opportunities they are being trained for, and how to best align with a particular career path. The course will focus on the skill development necessary for research-focused academic, teaching-focused academic, and industry career paths. The course will mainly focus on the following aspects, in particular: beginning in research in statistics, statistics research landscape, professional development, scientific writing, research, teaching, and job searches. The course will involve guest speakers, individual presentations, and class participation. Restriction: Statistics PhD students only (intended for second year students) Typically offered in Fall only
Typically offered in Spring only
Participation in regularly scheduled supervised statistical consulting sessions with faculty member and client. Consultant's report written for each session. Regularly scheduled meetings with course instructor and other student consultants to present and discuss consulting experiences. Typically offered in Spring only
CVD Research (1 credit hours) This is an external internship component of an NHLBI-funded training grant of the Statistics Department jointly with Duke for which PhD students, who are appointed to the grant, work on research projects at Duke Clinical Research Institute. Typically offered in Fall only
Teaching experience under the mentorship of faculty who assist the student in planing for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment. Typically offered in Fall and Spring Graduate Catalog 1501
Instruction in research and research under the mentorship of a member of the Graduate Faculty. Typically offered in Fall, Spring, and Summer
For graduate students whose programs of work specify no formal course work during a summer session and who will be devoting full time to thesis research. Typically offered in Summer only
For students who have completed all credit hour requirements, full-time enrollment, preliminary examination, and residency requirements for the doctoral degree, and are writing and defending their dissertations. Typically offered in Fall, Spring, and Summer
Source: North Carolina State University at Raleigh's catalog, linked per course · table learning_unit · CourseShelf publish 61
