STAT 400 Applied Probability and Statistics I (3 credits) - UMCP
Prerequisite: minimum grade of C- in MATH 131 or MATH 141 or permission of department
Random variables, standard distributions, moments, law of large numbers and central limit theorem. Sampling methods, estimation of parameters, testing of hypotheses.
STAT 401 Applied Probability and Statistics II (3 credits) - UMCP
rerequisite: STAT 400
oint estimation - unbiased and consistent estimators. Interval estimation. Minimum variance and maximum likelihood estimators. Testing of hypotheses. Regression, correlation and analysis of variance. Sampling distributions. Elements of non-parametric methods.
TAT 420 Introduction to Statistics (3 credits) - UMCP
rerequisite: SURV 410 or STAT 401
oint estimation, sufficiency, completeness. Cramer-Rao inequality, maximum likelihood. Confidence intervals for parameters of normal distribution. Hypothesis testing, most powerful tests, likelihood ration tests. Chi-square tests, analysis of variance, regression, correlation. Nonparametric methods.
STAT 440 Sampling Theory (3 credits) - UMCP
rerequisite: STAT 401 or STAT 420
imple random sampling. Sampling for proportions. Estimation of sample size. Sampling with varying probabilities. Sampling: stratified, systematic, cluster, double, sequential, incomplete.
STAT 464 Introduction to Biostatistics (3 credits) - UMCP
Prerequisite: one semester of calculus
Probabilistic models. Sampling. Some applications of probability in genetics. Experimental designs. Estimation of effects of treatments. Comparative experiments. Fisher-Irwin test. Wilcoxon tests for paired comparisons.
STAT 600 Probability Theory I (3credits) - UMCP
rerequisite: STAT 410
robability space; distribution functions and densities; Poisson limit theorem; de Moivre-Laplace theorem; measure-theoretic definition of expectation; classification of measures on R; convergence of random variables; Radon-Nikodym theorem; LP spaces; conditional probabilities; independence of events, sigma-algebras and random variables; Bayes; theorem; pi-systems and Dynkin systems; discrete Markov chains; random walks; gambler's ruin problem; Markov chains on a general phase space; Borel-cantelli lemmas; Kolmogorov inequality; three series theorem; laws of large number.
STAT 601 Probability Theory II (3 credits) - UMCP
Prerequisite: STAT 600
Weak convergence of measure; characteristic functions: Central limit theorem and local limit theorem; stable laws; Kolmogorov consistency theorem (without proof); conditional expectation and martingales; Brownian motion; Markov processes and families; stochastic integral and ito formula.
STAT 601 Applied Statistics I (3 credits) - UMBC
Prerequisite: STAT 453 or consent of instructor
Theory and application of the linear regression model, least squares estimation, model building, influence diagnostics, multi-collinearity and graphical analysis of residuals, nonlinear regression, logistic regression. Data analysis using statistical packages and other topics as time permits.
STAT 602 Applied Statistics II (3 credits) - UMBC
Prerequisite: STAT 453 or consent of instructor
Principles of experimental design, the analysis of variance and covariance, randomized designs, Latin share designs, incomplete block designs, factorial designs, confounding and fractional replication, split-plot designs and use of statistical packages.
STAT 605 Survey Sampling (3 credits) - UMBC
Prerequisite: STAT 453 or consent of instructor
Sampling versus total enumeration, planning of a survey sampling, statistical sampling methods and their analysis, simple random sampling, stratified sampling, systematic sampling, cluster sampling, and double and multi-stage sampling, problem of non-response and variance estimation, and practical case study.
STAT 614 Environmental Statistics - UMBC
Prerequisite STAT453/653 or consent of instructor
Graduate-level introduction to statistical methods used in environmental applications. The following will be emphasized throughout the course: non-parametric methods using environmental data,; methods of analyzing data that are below the limit of detection; sampling designs, including stratified sampling, composite sampling and ranked set sampling; sampling to determine hot spots; trend estimation methods for uncorrelated, correlated and seasonal data; discussion of some basic ideas from spatial statistics; and environmental data analysis using statistical software.