Gary Davis, PhD

Professor

Mathematics

508-999-8739

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Liberal Arts 394E


Teaching

Programs

Teaching

Courses

Introduction to exploratory data analysis using R, including graphical techniques, confirmatory statistics, interval estimates, hypothesis tests, bootstrap estimates.

An intensive study of advanced algebra and trigonometry. Topics include: linear, quadratic, polynomial, rational, exponential, logarithmic and trigonometric functions, modeling and graphing these functions, and the effects of affine transformations on the graphs of functions. This course prepares students for the study of Calculus I (MTH 151 or MTH 153), which is required for majors in Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology. This course fulfills the general Calculus I prerequisites for Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.

Introduction to exploratory data analysis using R, including graphical techniques, confirmatory statistics, interval estimates, hypothesis tests, bootstrap estimates.

This course is a rigorous analysis of the concept of limits, continuity, the derivative and other selected areas.

Continuation of MTH 311 with emphasis on uniform convergence and related topics.

Continuation of MTH 311 with emphasis on uniform convergence and related topics.

Continuation of MTH 331. Classical estimation methods and hypothesis testing are studied. This course also covers Chi square tests for goodness-of-fit and independence, regression and correlation analysis, and one-way and two-way analysis of variance including factorial designs and tests for the separation of means.

Continuation of MTH 331. Classical estimation methods and hypothesis testing are studied. This course also covers Chi square tests for goodness-of-fit and independence, regression and correlation analysis, and one-way and two-way analysis of variance including factorial designs and tests for the separation of means.

Investigation in applied statistics. Topics for investigation chosen in consultation with course Instructor and other academic advisers. Individual projects will address: question or issue under investigation; significance; literature review; methods used including experimental design, data collection, forms of analysis; results; discussion; conclusions.

Selected topics from the areas of linear programming, dynamic programming, Markov chains and game theory. Mathematical model building will be developed through the use of numerous case studies from the natural and social sciences, e.g., ecological models, network models, scheduling models, urban structure, traffic flow, growth, etc.

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