Tags: prediction

All Categories (1-4 of 4)

  1. 1-102C-S-CancerGrowth

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

    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 exponential, power law, Bertalanffy, logistic, and Gompertz. Students solve first-order differential...

  2. 6-067-S-LotkaVolterra

    07 Aug 2021 | | Contributor(s):: Iordanka Panayotova, Maila Hallare

    This modeling scenario guides students through the process of fitting the Lotka-Volterra model of two differential equations to a real time series observational data. Students use the capabilities of R and R studio, an integrated development environment for R, and the gauseR package, a collection...

  3. 1-124-S-WorldPopulation

    30 May 2020 | | Contributor(s):: Lenka Pribylova, Jan Sevcik, Pavel Morcinek, Brian Winkel

    We build models of world population using data to estimate growth rate.CZECH LANGUAGE VERSION  We have placed in Supporting Docs a Czech version of this Student Modeling Scenario. Name will be x-y-S-Title-StudentVersion-Czech.

  4. 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...