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Course Criteria
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3.00 Credits
Prerequisite: ACSC 370 or MATH 370 This course introduces the use of mathematical modeling applied to decision making in business and industry. Topics include linear programming, dynamic programming, scheduling, decision making under uncertainty, queueing models, network analysis and stochastic simulation. Practical applications are emphasized and computers will be employed to illustrate the underlying mathematical principles.
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3.00 Credits
Prerequisite: MATH 152 This course introduces discrete math modeling using linear methods. Topics include matrices and linear transformations, systems of linear equations, applications to linear programming, network analysis and game theory. Practical applications are emphasized and computers will be employed to illustrate the underlying mathematical principles.
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4.00 Credits
Prerequisite: MATH 251; Minimum grade C- This course introduces the use of mathematical modeling based on calculus and differential equations. Topics include non-linear constrained optimization, linear and non-linear differential equations, stability of solutions, transforms, numerical methods and systems of equations. Practical applications are emphasized and computers will be employed to illustrate the underlying mathematical principles.
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3.00 Credits
Prerequisite: MATH 152; Minimum grade C- The course deals with Euclidean, projective, hyperbolic and other non-Euclidean geometries.
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3.00 Credits
“The following courses were not found in the supplied content but, were listed in program requirements. Please review and provide us, if possible, with the correct information.”
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3.00 Credits
Prerequisite: MATH 152 Corequisite: MATH 152 This is the first in a sequence of two one-semester courses on mathematical statistics. Topics include distribution of random variables; conditional probability and marginal distributions; stochastic independence; distributions of functions of random variables; and sampling theory. Note: The course is calculus-based. Cross-listed: See ACSC 370
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3.00 Credits
Prerequisite: MATH 370; Minimum grade C- This course should be taken in sequence with MATH 370. Topics include order statistics and maximum likelihood estimators; sampling distributions of estimators; point and interval estimation of parameters; statistical hypotheses; and statistical tests, including uniformly most powerful tests. See ACSC 371 Cross-listed: See ACSC 371
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3.00 Credits
Prerequisite: MATH 371 Topics include classical basic concepts of inference, inference for single samples, inference for two samples, inferences for proportion, simple linear regression and advance estimation methods including Moment, Maximum Likelihood, and Bayesian Estimation. This course is calculus - based. See ACSC 372 Cross-listed: See ACSC 372
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3.00 Credits
Prerequisite: ACSC 372 or MATH 372 This course covers the following topics, Evaluation data for estimation and goodness-0f-fit to models, least square estimates of parameters, Single linear regression, multiple linear regressions, Hypothesis testing, confidence intervals in linear regression models. Topics also include Testing of models, data analysis and appropriateness of models, Linear time series models, Moving average, autoregressive and/or ARIMA models, Estimation, data analysis and forecasting with time series models, Forecast errors and confidence intervals. Note: This course has been approved by VEE Administration Committee of Society of Actuaries to fulfill the requirements of topics in Applied Statistical Methods. To receive credit for the subject from the Society of Actuaries, students will need a grade of B- or better. Cross-listed: See ACSC 405
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3.00 Credits
Prerequisite: MATH 405 This course covers the materials on the professional actuarial exam C. Topic includes Construction of Empirical Models, Estimate failure time and loss distributions using Kaplan-Meier estimator, including approximations for large data sets, Nelson-Aalen estimator. Kernel density estimators, Estimate the variance of estimators and confidence intervals for failure time and loss distributions, Estimate failure time and loss distributions with the Cox proportional hazards model and other basic models with covariates. The course will also cover the topics, Unbiasedness, Consistency, Mean squared error, Estimate the parameters of failure time and loss distributions using, Maximum likelihood, Method of moments, Percentile matching, Bayesian procedures, Estimate the parameters of failure time and loss distributions with censored and/or truncated data. Cross-listed: See ACSC 406
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