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    1-001d-HotelPopulationDecay-ModelingScenario
    You will be modeling the following situation: 100 people are in a hotel. Each day, each person has a random chance of 50% of leaving the hotel. No new people enter the hotel. Before you run the...

    Keywords: simulationlogisticexponential decayfitting

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    1-011-Kinetics-ModelingScenario
    We make the connection between chemistry course and differential equations coursework. We do this through modeling kinetics, or rates of chemical reaction. We study zeroth, first, and second order...

    Keywords: kineticschemistryLaw of Mass Actionchemical kineticsorder of reactionchemical reaction

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    1-055-WaterFallingInCone-ModelingScenario
    We offer an opportunity to model the height of a falling body of water in a right circular cone (funnel) and to estimate an appropriate parameter based on data collected from a video of the...

    Keywords: Torricelli's Lawdischarge coefficientconecontraction coefficient

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    1-081-TumorGrowth-ModelingScenario
    Students will transform, solve, and interpret a tumor growth scenario using non-linear differential equation models. Two population growth models (Gompertz and logistic) are applied to model tumor...

    Keywords: logisticpopulationtumorGompertz

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    1-086-MedicinalPill-ModelingScenario
    Administration of a medicinal pill in single and multiple doses is modeled.

    Keywords: medicinespillovercompartmentbloodstreamGIT-Track

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    1-098-NeuronDetection-ModelingScenario
    Students study a linear, first order, one-dimensional ordinary differential equation (ODE) and learn how it can help understand basics of neural dynamics. The modeling framework is known in ...

    Keywords: biologyneuroscienceneuron physiologyvoltageintegrate and firethrefholscoincidence detectionRC circuit

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    1-102C-CancerGrowth-ModelingScenario
    This module guides students in the use of differential equation models to predict cancer growth and study treatment outcomes. Several classical models for cancer growth are presented including...

    Keywords: optimizationcancerlogisticexponentialpredictiontumorgrowthdata fittingpower lawBertanffyGompertz

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    1-110-TidePoolSnails-ModelingScenario
    Students use linear differential equations to model temperature change, in a sand tide pool and inside the shell of a snail in the tide pool. We offer data on temperature in a tide pool as the...

    Keywords: datasnailstemperatureNewton's Law of CoolingcoolingNewtonwarmingtital pooltide pool

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    1-137-SheepGraze-ModelingScenario
    In this activity, students will apply graphical analysis (such as phase lines) to determine the long-term predictions of a differential equation model for pasture grass using two different formulas...

    Keywords: herbivoreplantphase linesbifurcationoverexploitationsheepgraze

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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...

    Keywords: Probabilitystochastic processesBayesian methodsprobability distribution

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    1-142-WaterBottles-ModelingScenario
    This project involves the application of Newton's law of cooling to the study of insulated water bottles. Students have the option to conduct experiments with their own bottles outside of class or...

    Keywords: data collectionexperimenthalf-lifeNewton's Law of Coolingwater bottle

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    1-001a-MMDeathImmigration-Variation-ModelingScenario
    We model exponential death with m&m's as well as death with immigration.

    Keywords: simulationdatam&mimmigrationdeath

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    1-170-CensusModeling-ModelingScenario
    Students who have studied models for population are likely to be familiar with the exponential and the logistic population models. The goal here is to explore the role of modeling assumptions in...

    Keywords: logistic growthexponentialpopulationU.S. Census DataassumptionGoogle SheetsUnites StatesGuatemala

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    3-002-ModelsMotivatingSecondOrder-ModelingScenario
    Ordinary differential equations involve second derivatives and second derivatives appear in many contexts, chief among them are the study of forces and resulting motion. This is principally because...

    Keywords: resistancespring massoscillationHooke's Lawdampening

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    3-006-Buoyancy-ModelingScenario
    We offer data from a physical experiment in which the depth of a container in water is measured and ask students to build a model of buoyancy based on Newton's Second Law of Motion and a Free Body...

    Keywords: data collectionexperimentbuoyancyNewton's Second Law of Motion

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    3-011-EulerBallThrowing-ModelingScenario
    If a tennis ball is thrown through the air it will hit the ground due to gravity. Using Euler's method, write a short script (Python, Matlab, R, etc.) to find the trajectory of the ball which will...

    Keywords: codingdragonVectorsEuler's methodball

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    3-016-FallingCoffeeFilters-ModelingScenario
    We are given data on the time and position of a stack of coffee filters as it falls to the ground. We attempt to model the falling mass and we confront the different resistance terms and models.

    Keywords: resistanceFree Body Diagramcoffee filterforcegraviityfalling object

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    3-029-FerrisWheelCatch-ModelingScenario
    We offer the opportunity to model the throw of an object to a person on a moving Ferris wheel.

    Keywords: parametric equationsprojectile motioninitial velocityferris wheelcatchcollision

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    3-060-DataToDifferentialEquation-ModelingScenario
    Students use knowledge of second-order linear differential equations in conjunction with physical intuition of spring-mass systems to estimate the damping coefficient and spring constant from data.

    Keywords: Association & Data Fittingmassspring-mass systemspring\total distancenumerical differentiation

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    3-091-SpringModeling-ModelingScenario
    In this lab students will collect data on their spring mass systems and compare their empirical models to their theoretical ones—giving them an opportunity to actually test a model against data.

    Keywords: datalaboratoryLoggerProVerniermass-spring systemmodel validationempirical modeltheoretical model

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