62 courses with the subject STA, 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.
The focus of this course will be on improving the problem solving and computational skills necessary to succeed in Elementary Introduction to Probability and Statistics.
Survey of statistics intended for undergraduates in any discipline. Graphical displays, numerical measures, relationships between variables, elements of good data collection. Basic probability, introduction to inferential techniques including confidence intervals and significance testing. Emphasis on statistical literacy.
Introductory course in statistics that emphasizes quantitative data analysis and interpretation. Topics covered include graphical display of data, descriptive measures, methods of data collection, probability, linear regression, correlation, interval estimates, hypothesis tests, and the use of statistical software. Prepares students for advanced courses in data analysis and research design.
Introduction to probability models and statistical inference. Descriptive statistics, basic probability laws, discrete and continuous probability models, sampling distributions, central limit theorem, estimation, hypothesis testing, simple regression, and correlation.
Two-group comparisons, simple and multiple regression, one and two factor ANOVA, categorical data analysis, nonparametric methods.
Descriptive and inferential statistics. Emphasis on sampling distributions; theory of estimation and tests of hypotheses, linear hypothesis theory, regression, correlation and analysis of variance.
Designing survey instruments; estimation of population mean, total, and proportion using simple random, stratified, systematic, and cluster sampling; other sampling techniques such as pps sampling and randomized response methods.
This number reserved for experimental courses. Refer to the Course Schedule for current offerings.
Methods for modeling and understanding complex data. Topics include linear regression models for sparse and high dimensional data sets, nonlinear models, tree-based methods, and clustering methods.
Events and probabilities (sample spaces), dependent and independent events, random variables and probability distribution, expectation, moment generating functions, multivariate normal distribution, sampling distributions.
Point estimation, hypothesis testing, confidence intervals, correlation and regression, small sample distributions.
Linear regression, least squares, inference, hypothesis testing, matrix approach to multiple regression. Estimation, Gauss-Markov Theorem, confidence bounds, model testing, analysis of residuals, polynomial regression, indicator variables.
Creating, importing, and working with SAS data sets. Using SAS procedures for elementary statistical analysis, graphical displays, and report generation.
Statistical methods requiring significant computing or specialized software. Simulation, randomization, bootstrap, Monte Carlo techniques; numerical optimization. Extensive computer programming involved. This course does not cover the use of statistical software packages.
Introduction to nonparametric statistical methods for the analysis of qualitative and rank data. Binomial test, sign test, tests based on ranks, nonparametric analysis of variance, nonparametric correlation and measures of association.
Methods for comparing time-to-event data, including parametric and nonparametric procedures for censored or truncated data, regression model diagnostics, group comparisons, and the use of relevant statistical computing packages.
Methods for analyzing dichotomous, multinomial and ordinal responses. Measures of association; inference for proportions and contingency tables; generalized linear models including logistic regression.
Planning and analysis of experimental and observational studies. Completely randomized, blocked, split-plot, and repeated measures designs. Factorial arrangements and interaction. Power and sample size calculation.
Estimation/removal of trend and seasonality, introduction to stationary stochastic processes, fitting ARMA/ARIMA models, forecasting techniques, miscellaneous topics, and introduction to a time series modeling software package.
Topics vary according to interest and demand. Intended to help prepare for the P/1, FM/2, or MLC exam.
Supervised research activity on contemporary research questions in the fields of statistics and its applications.
Introduction to statistical concepts. Basic probability, random variables, the binomial, normal and student's t distributions, hypothesis tests, confidence intervals, chi-square tests, introduction to regression, and analysis of variance.
Statistical methodology in research and use of statistical software. Regression, confidence intervals, hypothesis testing, design and analysis of experiments, one-and two-factor analysis of variance, multiple comparisons, hypothesis tests.
Introduction to fundamental statistical techniques for data analytics such as hypothesis testing, data transformation, estimation, confidence intervals, regressions models, ANOVA, multivariate analysis, non-parametric methods, and design of experiments.
How data analytics is used to solve applied problems in varied contexts. Students will learn how to choose appropriate methodologies, manage data, conduct analyses and report results.
Methods for modeling and understanding complex data. Topics include linear regression models for sparse and high dimensional data sets, nonlinear models, tree-based methods, and clustering methods.
Events and probabilities (sample spaces), dependent and independent events, random variables and probability distribution, expectation, moment generating functions, multivariate normal distribution, sampling distributions.
Point estimation, hypothesis testing, confidence intervals, correlation and regression, small sample distributions.
Linear regression, least squares, inference, hypothesis testing, matrix approach to multiple regression. Estimation, Gauss-Markov Theorem, confidence bounds, model testing, analysis of residuals, polynomial regression, indicator variables.
Creating, importing, and working with SAS data sets. Using SAS procedures for elementary statistical analysis, graphical displays, and report generation.
Statistical methods requiring significant computing or specialized software. Simulation, randomization, bootstrap, Monte Carlo techniques; numerical optimization. Extensive computer programming involved. This course does not cover the use of statistical software packages.
Introduction to nonparametric statistical methods for the analysis of qualitative and rank data. Binomial test, sign test, tests based on ranks, nonparametric analysis of variance, nonparametric correlation and measures of association.
This course covers persistent homology and topological data analysis. Topics include persistence modules, bar codes, stability, and the use of statistics and machine learning in topological data analysis.
Requisite mathematics; distribution and integration with respect to a distribution. Theory of random variable and probability distributions. Sampling distributions, statistical estimation, and tests of significance. Random processes. Numerical examples.
Requisite mathematics; distribution and integration with respect to a distribution. Theory of random variable and probability distributions. Sampling distributions, statistical estimation, and tests of significance. Random processes. Numerical examples.
Based on the specialty of the instructor, selected well-known machine learning algorithms will be studied in great mathematical detail. General topics may include Regression, Dimension Reduction, Density Estimations, and Classification. Advanced topics may include Google PageRank algorithm, Manifold Learning, and the Mapper algorithm.
An introduction to Markov chains, Poisson processes, renewal processes, Brownian motion, and survival models. Examples drawn from applied field such as engineering, management, finance, and sciences.
Statistical techniques and design considerations for controlled experiments and observational studies. Exploratory data analysis, elementary probability theory, principles of statistical inference, contingency tables, one-way ANOVA, bivariate regression and correlation.
Continuation of STA 661. Multiple regression and correlation, analysis of covariance, factorial ANOVAs, randomized block designs, multiple comparisons, split-plot designs, repeated measures.
Methods for comparing time-to-event data, including parametric and nonparametric procedures for censored or truncated data, regression model diagnostics, group comparisons, and the use of relevant statistical computing packages.
Statistical consultation on a doctoral or master's research. Access to the Statistical Consulting Center. Students are required to attend the initial class meeting during the beginning of the semester.
Development of consulting skills through reading and discussion of literature on statistical consulting and participation in statistical consulting sessions.
Methods for analyzing dichotomous, multinomial and ordinal responses. Measures of association; inference for proportions and contingency tables; generalized linear models including logistic regression and loglinear models.
Multivariate normal distribution. Cluster analysis, discriminant analysis, canonical correlation, principal component analysis, factor analysis, multivariate analysis of variance. Use and interpretation of relevant statistical software.
Abstract vector spaces, inner product spaces, projections, the Spectral Theorem, least squares, multiple regression, ANOVA, multiple comparisons, data analysis.
Abstract vector spaces, inner product spaces, projections, the Spectral Theorem, least squares, multiple regression, ANOVA, multiple comparisons, data analysis.
Topics include factorials and fractional factorials, incomplete block designs, split-plot and repeated measures, random and mixed effects models, crossover designs, response surface designs, power analysis.
Survey methods for students from any discipline. Random, stratified, cluster, multi-stage and other sampling schemes. Estimation of population means, variances, and proportions. Questionnaire design and analysis.
Topics vary according to interest and demand. Quantitative methods not normally covered in detail in other statistics courses. Topics may be selected from psychometrics, econometrics, biometrics, sociometrics, quantitative epidemiology.
Examples of time series, objectives in time series modeling, theory and applications of linear and non-linear time series models, ARMA/ARIMA/ARCH/GARCH models, and time series modeling using computer packages.
Development of presentation skills through reading, discussions, and presentation of current research topics in applied statistics.
Topics vary according to interest and demand. Intended to help prepare for the P/1, FM/2, or MLC exam.
Directed research project in statistics.
Individual guidance in the development of a specific research problem.
Readings from the literature in Computational Statistics.
Advanced study in special topics in statistical data analysis with large scale data sets. The course may be repeated up to 9 hours as topics vary.
Advanced study in special topics in Computational Statistics.
Thesis Extension.
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Source: University of North Carolina at Greensboro's catalog, linked per course · table learning_unit · CourseShelf publish 61
