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  1. 1-125-T-DiceyPopulations

    04 Mar 2019 | Modeling Scenarios

    We offer students an opportunity to generate unique data for their team on a death and immigration model using 12 and 20 sided dice and then pass on the data to another student team for analysis with a model they built. The key is to recover the parameters and try to explain how the simulation...

  2. 1-042-Kool-Aid Modeling Video

    24 Feb 2019 | General Resources

    This is a video which can be used with students after they have worked on the Modeling Scenario 1-042-KoolAid. Dina Yagodich, Frederick Community College, Frederick MD USA produced the video in support of her students who thoroughly enjoyed doing this Modeling Scenario.This video is only...

  3. 1-143-S-PopulationModelVariationsMATLAB

    17 Feb 2019 | Modeling Scenarios

    Students will walk through a detailed derivation and review of basic population models (exponential and logistic) to create and understand variations of those models while learning some basic MATLAB functions for working with differential equations.  They will also work with other utilities...

  4. 1-003-T-Text-IntroNumericalMethods

    10 Jan 2019 | Technique Narratives

    We develop elementary approaches to numerically solving first order differential equations with Euler's Method and Improved Euler's Method. We ask students to develop these geometrically and to compute numeric solutions and compare them to the analytic solutions for a number of different...

  5. 1-003-S-Text-IntroNumericalMethods

    10 Jan 2019 | Technique Narratives

    We ask students to develop two numerical methods for solving first order differential equations  geometrically and to compute numeric solutions and compare them to the analytic solutions for a number of different step sizes. 

  6. 3-009-T-BallDropInWater

    16 Dec 2018 | Modeling Scenarios

    We conduct an analysis of a falling ball in liquid to determine its terminal velocity and to ascertain just what radius ball for a given mass density is necessary to attain a designated terminal velocity.

  7. 1-063-T-ThreeHoleColumn

    15 Dec 2018 | Modeling Scenarios

    We consider a column of water with three holes or spigots through which water can exit and ask students to model the height of the column of water over time.

  8. 2008-Will-Hansen-ExamplesOfODEsInPhysics

    09 Dec 2018 | Potential Scenario Ideas

    Witt-Hansen, Ole. 2008. Examples of the Differential equations of Physics. www.olewitthansen.dk. Contents1. The dependence of pressure with altitude.............................................................................. 12. Radioaktive chains of decay...

  9. 2004-PhoebusReily-TheParachuteProblem

    09 Dec 2018 | Potential Scenario Ideas

    Phoebus, Ronald and Cole Reilly. 2004, Differential Equations and the Parachute Problem. Presentation 10 May 2004.  https://mse.redwoods.edu/darnold/math55/DEproj/sp04/coleron/presentation.pdf .Abstract: The parachute problem is a classical first semester differential equations problem...

  10. 1987-Rodin-Murthy-ComparativeEvalOfMathModelingBooks

    09 Dec 2018 | Potential Scenario Ideas

    Murthy, D. N. P. and E. Y. Rodin. 1987. A comparative evaluation of books on mathematical modeling. Mathematical Modeling. 9(1): 17-28.Abstract: In this paper we present a comparative evaluation of books on mathematical modelling that have appeared in the last 1.5 years.While dated this gives...

  11. 1986-Cooke-Linear And Logistic Harvesting Models-Math Modeling Journal

    09 Dec 2018 | Potential Scenario Ideas

    Cook, K. L. and M. Witten. 1986. One-dimensional linear and logistic harvesting models. Mathematical Modeling. 7: 301-340.Abstract: Some of the results in the literature on simple one-dimensional, density dependent, discrete and continuous models-with and without harvesting-are reviewed. Both...

  12. 6-019-T-EnablingEpidemicExploration

    17 Sep 2018 | Modeling Scenarios

    We offer several strategies for estimating parameters in models of epidemics, one using a Michaelis-Menten saturation infected rate.

  13. 6-019-S-EnablingEpidemicExploration

    17 Sep 2018 | Modeling Scenarios

    We offer several strategies for estimating parameters in models of epidemics, one using a Michaelis-Menten saturation infected rate.

  14. 1-062-T-BacterialGrowth

    15 Sep 2018 | Modeling Scenarios

    We offer students a simulation experience or data from a simulation and ask them to model the simulation using several approaches, to include exponential growth fit, difference equation, differential equation, and parameter estimation using EXCEL spreadsheet. In this particular modeling...

  15. 1-053-T-SlimeSpread

    30 Aug 2018 | Modeling Scenarios

    We offer a video showing real time spread of a cylinder of slime and challenge students to build a mathematical model for this phenomenon.

  16. 1-053-S-SlimeSpread

    30 Aug 2018 | Modeling Scenarios

    We offer a video showing real time spread of a cylinder of slime and challenge students to build a mathematical model for this phenomenon.

  17. 6-003-T-SchoolFluEpidemic

    28 Aug 2018 | Modeling Scenarios

    We offer a model of the spread of flu in a school dormitory and are asked to find when the flu levels reach their peak and explain long term behavior of the spread of the flu.

  18. 6-003-S-SchoolFluEpidemic

    28 Aug 2018 | Modeling Scenarios

    We offer a model of the spread of flu in a school dormitory and are asked to find when the flu levels reach their peak and explain long term behavior of the spread of the flu.

  19. 1-108-S-PoissonProcess

    27 Aug 2018 | Modeling Scenarios

    In this project students learn to derive the probability density function (pdf) of the Poisson distribution and the cumulative distribution (cdf) of the waiting time. They will use them to solve problems in stochastic processes.

  20. 1-108-T-PoissonProcess

    27 Aug 2018 | Modeling Scenarios

    In this project students learn to derive the probability density function (pdf) of the Poisson distribution and the cumulative distribution (cdf) of the waiting time. They will use them to solve problems in stochastic processes.