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    Modeling Scenario
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    1-160-HeartDeathRate-ModelingScenario
    Students simulate experience from a given data set which represents the heart death rate during the period 2000 - 2010 using several approaches to include exponential decay, difference equation, differential equation, and parameter estimation...
    dataheart death rate
    Modeling Scenario
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    1-071-NewtonWatson-ModelingScenario
    Sherlock Holmes determines the time of death for a body found on a street in London and we need to reproduce his astute analysis
    temperaturedeathNewton's Law of Coolingtime of deathSherlock Holmes
    Modeling Scenario
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    1-125-DiceyPopulation-ModelingScenario
    We offer students an opportunity to generate 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...
    simulationimmigrationdeathdice
    Modeling Scenario
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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
    Modeling Scenario
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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
    Modeling Scenario
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    1-004-MicroorganismImmigration-ModelingScenario
    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.
    continuousimmigrationpopulationdeathdiscrete
    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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    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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    1-101-ClassMMDeathImmigration-ModelingScenario
    We offer students an opportunity to generate unique data for their team on a death and immigration model using m&m's and then pass on the data to another student team for analysis with a model they built. The key is to recover the parameters.
    simulationimmigrationdata collectiondeath
    Modeling Scenario
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    1-013-SleuthingWithDifferentialEquations-ModelingScenario
    We present several situations in which differential equation models serve to aid in sleuthing and general investigations.
    projectile motionaccelerationNewton's Law of Coolingspeeding
    Modeling Scenario
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    6-003-SchoolFluEpidemic-ModelingScenario
    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.
    infectionepidemicSIR modelflupeakschool children
    Modeling Scenario
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    1-119-DairyFarming-ModelingScenario
    A simple first order population growth model is presented. The challenge is to produce a final differential equation which is the result of the difference or ratio of birth and death rates. This ratio is not immediately intuitive.
    population dynamicsdairyfarmers' marketsgrowthcow
    Modeling Scenario
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    1-027-StochasticProcesses-ModelingScenario
    We build the infinite set of first order differential equations for modeling a stochastic process, the so-called birth and death equations. We will only need to use integrating factor solution strategy or DSolve in Mathematica for success.
    VarianceMeanstochasticrandomoperations researchindustrial engineeringPoisson processtraffic