Resources: Modeling Scenarios

The Modeling Scenarios are organized to roughly follow the topics found in a traditional differential equations course. Hence, the numbering system reflects chapter sequencing in a standard differential equations text.

You may wish to visit our Starter Kit to see some groupings of Modeling Scenarios which have proven to be successful.

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  1. 1-001-S-MandMDeathAndImmigration

    4.0 out of 5 stars 

    30 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

    We describe a classroom activity in which students use M&M candies to simulate death and immigration. Students build a mathematical model, collect data, estimate parameters, and compare their model prediction with their actual data. There is a video of one run of the main simulation in...

  2. 1-002-S-Tossing

    0.0 out of 5 stars 

    11 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

    We offer students simulation experience or data from a simulation and ask them to model the simulation using several approaches, to include exponential decay fit, difference equation, and differential equation.We add a Hand Out Working Version which can be used in class authored by Rachel ...

  3. 1-003-S-CollegeSavings

    0.0 out of 5 stars 

    12 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

    We present a modeling opportunity for students in which they have to plan and model for saving for a child's complete college education.

  4. 3-020-S-ChordPathTime

    0.0 out of 5 stars 

    30 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

  5. 6-010-S-SocialCampaigns

    0.0 out of 5 stars 

    23 Sep 2018 | Modeling Scenarios | Contributor(s): Hyunsun Lee

    Mathematical epidemic models are crucial tools to understand, analyze, predict, and control infectious diseases. The Susceptible-Infected-Recovered (SIR) model is a basic compartment model, describing how an infectious disease propagates through a population. The problem is formulated as a system...

  6. 3-013-S-WhiffleBallFall

    0.0 out of 5 stars 

    30 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

    We are given data on the time and position of a whiffle ball as it falls to the ground. We attempt to model the falling ball and we confront the different resistance terms and models. Finally, we introduce a new way of comparing models, the Akaike Information Criterion, and apply it to our models.

  7. 3-016-S-FallingCoffeeFilters

    0.0 out of 5 stars 

    30 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

  8. 3-019-S-ShuttlecockFall

    0.0 out of 5 stars 

    30 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

    We are given data on the time and position of a shuttlecock as it falls to the ground from a set height. We attempt to model the falling object and we confront the different resistance terms and models. Finally, we introduce a new way of comparing models, the Akaike Information Criterion, and...

  9. 1-004-S-MicroorganismImmigration

    0.0 out of 5 stars 

    30 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

    We present a modeling opportunity for population death with non-constant immigration and suggest the use of both discrete and continuous models with a comparison of results.

  10. 1-005-S-OilSlick

    0.0 out of 5 stars 

    30 May 2015 | Modeling Scenarios | Contributor(s): Brian Winkel

    We describe a modeling activity for students in  which modeling with difference and differential equations is appropriate. We have used this model in our coursework for years and have found that it enlightens students as to the model building process and parameter estimation for a ...