Tags: logistic

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  1. 1-009-S-ICUSpread

    02 Jun 2015 | | Contributor(s):: Brian Winkel

    We offer students the opportunity to model the percentage of voluntary nonprofit hospitals in the United States with Intensive Care Units during the period of 1958-1974.

  2. 1-017-S-DiseaseSpread

    04 Jun 2015 | | Contributor(s):: Brian Winkel

    We offer a physical situation, using a grid and M and M candies, to simulate the spread of disease. Students conduct the simulation and collect the data which is used to estimate parameters (in several ways)  in a differential equation model for the spread of the disease. Students ...

  3. 1-018-S-LogisticPopGrowth

    04 Jun 2015 | | Contributor(s):: Brian Winkel

    We offer artificial (toy) and historical data on limited growth population situations in the study of protozoa and lead students through several approaches to estimating parameters and determining the validity of the logistic  model in these situations. 

  4. 1-019-S-RocksInTheHead

    04 Jun 2015 | | Contributor(s):: Brian Winkel

     We describe an experiment and offer data from a previously conducted experiment on the perception of the individual mass of a collection of rocks in comparison to a 100 g brass mass. We lead students to use the logistic differential equation as a reasonable model, estimate the parameters,...

  5. 1-022-S-SpreadOfTechnology

    27 Nov 2015 | | Contributor(s):: Brian Winkel

    We examine  plots on the  spread of technologies and ask students to estimate and extract data from the plots and then model several of these spread of technologies phenomena with a logistic differential equation model.

  6. 1-023-S-RumorSpread

    04 Jun 2015 | | Contributor(s):: Brian Winkel

    We use a newspaper report on the spread of a rumor based on shares of articles on the Internet over a 5 day period to demonstrate the value of modeling with the logistic differential equation. The data shows and the intrinsic growth rates confirm that the false rumor spread faster than true rumor.

  7. 1-024-S-MalariaControl

    06 May 2016 | | Contributor(s):: David Culver

    This project offers students a chance to make policy recommendations based on the analysis of models using both linear (exponential decay) and non-linear (logistic growth) differential equations. The scenario is based on the deployment of the United States Army's 62nd Engineer Battalion to...

  8. 1-033-S-SouthernBarbeque

    22 Jun 2016 | | Contributor(s):: Troy Henderson

    We offer raw data collected from two thermometers used in the smoking process of Southern barbecue.  One thermometer measures the temperature inside of the smoke chamber and the other measures the internal temperature of the meat.  This data can be used to model and predict the amount...

  9. 1-067-S-ModelingWithSigmoidCurves

    18 Aug 2018 | | Contributor(s):: Natali Hritonenko

    The assignment considers two well-known models of population growth, Verhulst-Pearl and Gompertz models, for which qualitative and quantitative analyses are provided. The graphs of the corresponding functions have a sigmoidal or S-shape. The assignment contains two opposite, but related tasks:...

  10. 1-081-S-TumorGrowth

    09 Jun 2018 | | Contributor(s):: Randy Boucher, Ryan Edmund Miller

    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 growth. Students use technology to solve the Gompertz model and answer a series of questions...

  11. 1-084-S-GoingViral

    31 Aug 2019 | | Contributor(s):: Bill Skerbitz

    Students employ randomization in order to create a simulation of the spread of a viral disease in a population (the classroom).  Students then use qualitative analysis of the expected behavior of the virus to devise a logistic differential equation.  Finally, students solve the...

  12. 1-100-S-EngineeringDemographics

    20 Aug 2021 | | Contributor(s):: Brody Dylan Johnson, Elodie Pozzi

    The goal of this activity is to show students how population models can be used to examine social issues. The students will examine three different population models and will use numerical methods to apply each model to demographic data for the percentage of engineering degrees awarded to women...

  13. 1-102-S-CancerGrowth

    29 Jul 2018 | | Contributor(s):: Jue Wang

    This scenario guides students in the use of differential equation models to predict cancer growth and optimize treatment outcomes. Several classical models for cancer growth are studied, including exponential, power law, Bertalanffy, logistic, and Gompertz. They examine the behaviors of the...

  14. 1-102C-S-CancerGrowth

    19 Jan 2022 | | Contributor(s):: Jennie D'Ambroise, Jue Wang

    This module is designed for Calculus I class and 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 exponential, power law, Bertalanffy, logistic, and Gompertz....

  15. 1-104-S-InfectionRisk

    26 Aug 2021 | | Contributor(s):: Qingxia Li

    This project is designed to examine differences between the exponential and logistic growth models in biology and how to apply these models in solving epidemic questions. This project was designed for an introductory section in Calculus II or a course involving ordinary differential equations,...

  16. 1-111-S-SpreadOfInformation

    02 Sep 2019 | | Contributor(s):: Jeff Pettit

    Students perform experiments to model spread of information within a population. Students collect data, determine essential components and parameters and build a mathematical model which can be compared to predicted data, measured data, and modeled data. Part 3 can require students to move from...

  17. 1-127-S-FishHarvesting-StudentVersion

    06 May 2020 | | Contributor(s):: Will Mitchell

    We offer students a harvesting model for operating a fishery over a 25 year horizon and ask them to write a report on optimal harvesting policy with their analyses for fishing industry experts (not necessarily mathematicians).

  18. 1-143-S-PopulationModelVariationsMATLAB

    17 Feb 2019 | | Contributor(s):: Bill Skerbitz

    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...

  19. 1-170-S-CensusModeling-StudentVersion

    22 Dec 2021 | | Contributor(s):: Jean Marie Linhart, Gary William Epp

    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 choosing which model to use. We will compare and contrast the United States census data and the...

  20. 1-190-S-IntroClass

    05 Mar 2022 | | Contributor(s):: Bill Skerbitz

    Students are led step by step through the development of introductory ideas in mathematical modeling with differential equations. They will encounter the fundamental ideas underlying unlimited population growth (exponential models), limited population growth (logistic models), and a coupled...