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show how the answer below looks in Matlab. this is from chapter 3 of this book answer from answer key please show what it looks

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show how the answer below looks in Matlab.

this is from chapter 3 of this book

image text in transcribedanswer from answer key please show what it looks like in matlab

image text in transcribed

23. Two models of population growth are the exponential growth model p(t)=p(0)ert and the logistic growth model p(t)=p(0)+[Kp(0)]ertKp(0) where p(t) is the population size as a function of time t, and p(0) is the initial population size at t=0. The constant r is the growth rate, and the constant K is called the carrying capacity of the environment. As t the exponential predicts that p(t) but the logistic model predicts that p(t)K. Both models have been used extensively to model a number of different populations, including bacteria, animals, fish, and human populations. If p(0) and r are the same for both models, it is easy to see that the exponential model will predict a larger population for all t>0. But suppose that p(0) is the same for both models but the r values are different. In particular, let r=0.1 for the exponential model, r=1 and K=10 for the logistic model, and p(0)=10 for both models. Then the two models will predict the same population at time t if 10+40et50=e0.1t This equation cannot be solved analytically, so we must use a numerical method. Use the fzero function to solve this equation for t, and calculate the population at that time. Product: MATLAB for Engineering Applications Edition: 5th Author: William Palm ISBN10: 1264926804 3.23 Substituting the given parameter values and subtracting the exponential from the logistic function, we obtain y(t)=10+40et50et So we look for a value of t that makes y(t) zero. Create the following function. function y=f23(t) \% Logistic minus exponential. y=(50./(10+40exp(t))exp(0.1t)); end Plotting y shows that a zero occurs between 0 and 50 , so the session is: > fzero (@f23,50) ans = 16.0944 >10exp(0.1 ans ) ans = 2.0000 The population size predicted by both models is 2 at t=16.0944

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