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    Modeling Scenario
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    1-001-MM-DeathAndImmigration-ModelingScenario
    We conduct a simulation of death and immigration, using a small set of "individuals", m&m candies or any two sided object (coin, chips), in which upon tossing a set of individuals we cause some to die and others then to immigrate. Modeling ensues!
    simulationimmigrationdeathSpanishdatsWinkelBrian Winkel
    Modeling Scenario
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    49

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    1-141-MMGameRevisited-ModelingScenario
    It is assumed that the probability of an M&M chocolate, when tossed, falling on the M side is 0.5 The goal is to find a probability distribution of the probability q which is Pr(randomly chosen M&M falling M up when tossed).
    Probabilitystochastic processesBayesian methodsprobability distribution
    Modeling Scenario
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    1-001s-StochasticMDeathImmigration-ModelingScenario
    We develop a mathematical model of a death and immigration process using m&ms as a stochastic process with the help of probability generating functions (pgf). We start with 50 m&ms in a bag.
    simulationdatalogisticimmigrationstochasticdeathfitting
    Modeling Scenario
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    299

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    1-001a-MMDeathImmigration-Variation-ModelingScenario
    We model exponential death with m&m's as well as death with immigration.
    simulationdatam&mimmigrationdeath
    Article or Presentation
    235

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    70

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    2009-Brian_Winkel-Population_modelling_with_MandMs
    Several activities in which population dynamics can be modeled by tossing M&M’s® candy are presented.
    simulationparameter estimationimmigrationdeathdifference equation
    Modeling Scenario
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    420

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    1-001B-DeathImmigrationMystery-ModelingScenario
    We describe a classroom activity in which students use M\&M candies to simulate death and immigration. Each student conducts an experiment with an immigration rate unique to that student - of that student's choice
    m&mMystery Novelsimmigrationexperimentdeath
    Potential Scenario
    162

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    48

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    1969-Carl_Metzler-A mathematical model for the pharmacokinetics of LSD effect
    Contains data on performance scores vs. plasma levels of LSD in human tissues.
    LSDtestingpharmacokineticsperformanceplasma level
    Potential Scenario
    217

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    46

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    1980-Peastrel-Lynch-Armenti-Terminal velocity of a shuttlecock in vertical fall
    We have performed a straightforward vertical fall experiment for a case where the effects of air resistance are important and directly measurable.
    fallingterminal velocityshuttlecockvertical fall
    Modeling Scenario
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    5-077-MandMAttritionWarfare-ModelingScenario
    Students model attrition between two opposing forces using M&M candies and discover a system of linear differential equations of order one, often called the Lanchester equations.
    attritionLanchester modelpredator-predatorN-square Law
    Article or Presentation
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    53

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    1980-Peastrel-Lynch-Armenti-Terminal_velocity_of_a_shuttlecock_in_vertical_fall
    This paper offers a set of data on a falling shuttlecock (the moving object in badminton) in air. How the authors collected the data is described fully and can be replicated easily by modern equipment.
    parameter estimationdata collectionresistancefallingterminal velocityNewton's Second Law of Motionshuttlecock
    Modeling Scenario
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    169

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    4-035-ParEstSteadyState-ModelingScenario
    Students estimate parameters in a second order, linear, ordinary differential equations through analysis of the steady state solution. By applying a driver we can collect data in terms of the parameters and estimate these parameters
    driversteady statesum of square errors
    Potential Scenario
    210

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    89

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    1971-Carl_Metzler-Usefulness Of Two Compartment Open Model In Pharmacokinetics
    Compartment models are widely used in pharmaceutical research to quantitate the kinetics of absorption, distribution, metabolism and excretion of a drug. In this article two applications of the two compartment open model are discussed.
    LSDcompartmentpharmacokinetics
    Modeling Scenario
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    501

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    1-017-DiseaseSpread-ModelingScenario
    Using a grid and m&m candies, we simulate the spread of disease. Students conduct the simulation and collect data to estimate parameters (in several ways) in a differential equation model for the spread of the disease.
    Potential Scenario
    119

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    53

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    2008-Brewer-EtAl-Fitting ordinary differential equations to short time course data
    In this paper, we present a survey of existing algorithms and describe the main approaches. We also introduce and evaluate a new efficient technique for estimating ODEs linear in parameters particularly suited to situations where noise levels are...
    time seriesEvaluation SPLINEgeneleast squaresbifurcation
    Potential Scenario
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    57

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    2007-Choisy-Guégan-Rohani-Mathematical Modeling of Infectious Diseases Dynamics
    After presenting general notions of mathematical modeling (Section 22.2) and the nature of epidemiological data available to the modeler (Section 22.3), we detail the very basic SIR epidemiological model (Section 22.5).
    stochasticSIR modeldeterministicincubationinfectives
    Potential Scenario
    285

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    79

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    2014-Blomhøj-EtAl-Compartment models
    An overview of compartment models with many rich illustrations and much attention to various numerical methods in solutions of the resulting differential equations using Matlab. Also included is a section on the limitations of compartment models.
    concentrationkineticsphysiologycompartmentsalttankbalancereactionsminkbathtub
    Potential Scenario
    155

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    97

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    2014-John_Cimbala-Dynamic System Response
    In this learning module, we discuss the dynamic system response of sensors and their associated electronic circuits.
    sensor networksoscillationssystem responseelectronic circuit
    Potential Scenario
    222

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    75

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    2008-Lawson-Marion-An Introduction to Mathematical Modelling
    This is a broad based set of notes with sections: Building Models, Studying Models, and Using Models. No depth on any model, no solution strategies, but good references to models, and general approaches that apply in most situations are offered.
    time serieslinear regressionAkaike Information Criterionmcomparision models
    Potential Scenario
    137

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    50

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    1987-MS_Greenwood-Using videotapes to study underdamped motion of a pendulum
    Using a video camera to study the effects of air resistance acting on pendulum bob, ping-pong ball, styrofoam spheres, and brass spheres.
    Videoresistancependulumping-pong ballstyrofoam ballbrass ball
    Modeling Scenario
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    3-065-UpDown-ModelingScenario
    We model the height of a launched object which is subject to resistance proportional to velocity during its flight. We ask questions about the motion as well, e.g., highest point or apex and terminal velocity.
    terminal velocityNewton's Second Law of Motionvelocitymotionapex