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STAT 350: Statistics With Applications in the Biological Sciences
4.00 Credits
University of Maryland-Baltimore County
GEP/GFR: Meets Mathematics. Organization and presentation of data, summary of descriptive measures, probability, binomial and normal distributions, sampling natural populations and the estimation of population parameters, hypothesis testing, chi-square analysis experimental designs and the analysis of variance, linear regression and correlation, and non-parametric statistics. Students will be introduced to statistical computing. All the statistical procedures will be illustrated using data from biology and the health sciences. Prerequisites: MATH 150 or placement into 151. Notes: Not open to students who have passed or are concurrently taking STAT351, 355, 356, 453, or CMPE 320. This course does not satisfy the statistics requirement for CMSC majors and does not qualify as part of the MATH major or minor.
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STAT 351: Applied Statistics for Business and Economics
4.00 Credits
University of Maryland-Baltimore County
GEP/GFR: Meets Mathematics. Organization and presentation of data, summary of descriptive measures, probability, binomial, normal distributions and Pareto distribution, estimation of population parameters, sampling distributions, hypothesis testing, chi-square analysis, analysis of variance, linear regression and correlation, index numbers, time seried analysis and forecasting. Students will be introduced to statistical computing. All the statistical procedures will be illustrated using data from management, business and economics. Prerequisites: MATH 150 or 155. Notes: Not open to students who have passed or are concurrently taking STAT 350, 355, 356, 453, or CMPE 320. This course does not satisfy the statistics requirement for CMSC majors and does not qualify as part of the MATH major or minor.
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STAT 351 - Applied Statistics for Business and Economics
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STAT 356: Probability, Statistics and Random Processes
3.00 Credits
University of Maryland-Baltimore County
This course presents the fundamental concepts of probability, statistics and random processes from a computer and electrical engineering prospective, emphasizing applications in communications and signal processing. Students will learn the basics to analyze and model the probabilistic behavior of engineering systems and to analyze experimental data associated with such systems. Prerequisites: MATH 225, 251 and ENEE 206. Notes: Credit will not be given after or concurrent with STAT 453. Credit will be given for only one of STAT 350, 351, 355, 356 or CMPE 320.
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STAT 356 - Probability, Statistics and Random Processes
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STAT 405: Survey Sampling
3.00 Credits
University of Maryland-Baltimore County
Sampling versus total enumeration, planning of sample surveys, simple random sampling, stratified sampling, systematic sampling, cluster sampling, double and multistage sampling, variance estimation, problem of non-response and practical case studies. Prerequisites: STAT 453.
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STAT 405 - Survey Sampling
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STAT 414: Environmental Statistics
3.00 Credits
University of Maryland-Baltimore County
The purpose of this course is to study statistical methods used in environmental applications. This course includes a brief review of the basic methods of inference for percentiles, means and differences in mean with special emphasis on non-parametric methods, which are used widely in environmental applications. The course will cover the following topics: analysis of variance, estimation of components of variance and regression methodology using environmental data. Methods of analyzing data with observations that are below detection limits. Parametric and non-parametric methods of estimating trends in seasonal and auto-correlated data. Sampling methods that are particularly useful in environmental applications will be discussed. Prerequisites: STAT 350, 351, 355 451 or permission of the instructor.
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STAT 414 - Environmental Statistics
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STAT 417: Introduction to Time Series Data Analysis
3.00 Credits
University of Maryland-Baltimore County
Concepts in time series analysis, such as stationarity; some commonly used time series models, such as autoregressive moving average models, are introduced using examples. Time series data analysis tools, namely, autocorrelation function (ACF), partial autocorrelation function (PACF), detrending, differencing and forecasting will be discussed using real data sets. Some selected topics from time series modeling, such as transfer function models and intervention models, will be discussed. Data analysis using statistical software such as SPLUS will be an integral part of the course. Prerequisites: STAT 355 or 453. Notes: Approved by the Society of Actuaries to satisfy its Validation by Educational Experience (VEE) in Applied Statistical Methods.
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STAT 417 - Introduction to Time Series Data Analysis
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STAT 418: Applied Multivariate Methods
3.00 Credits
University of Maryland-Baltimore County
Topics include multiple regression, partial and multiple correlations, the multivariate normal distribution, statistical inference for mean vector and covariance matrix, multivariate analysis of variance, principal components, canonical correlations, discriminant analysis, factor analysis and cluster analysis use of statistical packages. Prerequisites: STAT 453 and MATH 221.
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STAT 418 - Applied Multivariate Methods
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STAT 419: Introduction to Biostatistics
3.00 Credits
University of Maryland-Baltimore County
Topics include an introduction to statistics used in epidemiologic studies and clinical trials, measures of association, logistic regression, co-variate adjustment, introductory life table analysis, Cox regression, randomization techniques, ethics in human experimentation and statistical analysis using SAS. Prerequisites: STAT 454.
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STAT 419 - Introduction to Biostatistics
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STAT 420: Statistics for Bioinformatics
3.00 Credits
University of Maryland-Baltimore County
This course surveys the statistical methodology underlying current bioinformatics techniques. Topics to be covered include: dynamic programming, including the Needleman-Wunsch algorithm and Smith-Waterman algorithm; methods of inference, including maximum likelihood and Bayesian approach; Markov models, including Markov chains, hidden Markov models and inferences for these models; Monte-Carlo Markov chain methods, including Gibbs sampling and Metropolis-Hastings algorithm; extreme-value theory, including Gumbel distribution and significance of alignments; cluster analysis, including hierarchical methods, Kmeans method and determination of number of clusters; classification methods, including CART algorithm and QUEST algorithm; generalized linear models, including model types, inference and statistics for model fit; model validation, cross-validation and predictive assessment. Prerequisites: MATH 152 and either STAT 350 or STAT 355 or permission of instructor.
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STAT 420 - Statistics for Bioinformatics
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STAT 432: Statistical Computer Packages and Their Applications
1.00 Credits
University of Maryland-Baltimore County
The student will become familiar with the commercial statistical package, SAS, its use for various statistical applications. Prerequisites: STAT 350, 351, 355 or 453, or permission of instructor.
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STAT 432 - Statistical Computer Packages and Their Applications
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