Appreciate for your answer. Thank you Question 3The logistic model for population growth developed by P.F. Verhulst, is given bydp= ap – bp,dtp(0) = Po >0where p = p(t) is the population at time t and a, b are positive constants.a(a)Show that p(t) =ap.and explain why Po # 0,--atbp, + (a – bp,)eat

Name: Appreciate for your answer. Thank you Question 3The logistic model for
Uploaded: 2024-03-20T07:07:37.000Z
Channel: Bartleby
Description: Appreciate for your answer. Thank you Question 3The logistic model for population growth developed by P.F. Verhulst, is given bydp= ap – bp,dtp(0) = Po >0where p = p(t) is the population at time t and a, b are positive constants.a(a)Show that p(t) =a

Given logistic Growth model is dpdt=ap-bp2, p(0)=p0>0 p=p(t) is the population at time t and…

Math

Advanced Math

Question 3 The logistic model for population growth developed by P.F. Verhulst, is given by - = ap – bp², p(0) = Po > 0 dt where p = p(t) is the population at time t and a, b are positive constants. ap, bp, + (a -bp.)е a and explain why p, # 0,- . b. (a) Show that p(t):

Question 3 The logistic model for population growth developed by P.F. Verhulst, is given by - = ap – bp², p(0) = Po > 0 dt where p = p(t) is the population at time t and a, b are positive constants. ap, bp, + (a -bp.)е a and explain why p, # 0,- . b. (a) Show that p(t):

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 56SE: Recall that the general form of a logistic equation for a population is given by P(t)=c1+aebt , such...

See similar textbooks

Similar questions

Recall that the general form of a logistic equation for a population is given by P(t)=c1+aebt , such that the initial population at time t=0 is P(0)=P0. Show algebraically that cP(t)P(t)=cP0P0ebt .
What is the carrying capacity for a population modeled by the logistic equation P(t)=250,0001+499e0.45t ? initial population for the model?
More on the Pacific Sardine This is a continuation of Example 5.1. In this exercise, we explore the Pacific sardine population further, using the model in Example 5.1. a. If the current level of the Pacific sardine population is 50,000 tons, how long will it take for the population to recover to the optimum growth level of 1.2milliontons? Suggestion: One way to solve this is to make a new logistic formula using K2.4, r0.338, and N(0)0.05. b. The value of r used in Example 5.1 ignores the effects of fishing. If fishing mortality is taken into account, then r drops to 0.215 per year with the carrying capacity still at 2.4milliontons. Answer the question in part a using this lower value of r. Note: The population estimate of 50,000 tons and the adjusted value of r are given in the paper by Murphy see footnote 3 on page 347. Murphy points out that factoring in the growth of the competing anchovy population makes the recovery times even longer, and he adds. "It is disconcerting to realize how slowly the population will recover to its level of maximum productivity ... even if fishing stops." Studies to fit a logistic model to the Pacific sardine population have yielded. N=241+239e0.338t where t is measured in years and N is measured in millions of tons of fish. Part 1 What is r for the Pacific sardine? Part 2 According to the logistic model, in the absence of limiting factors, what would be the annual percentage growth rate for the Pacific sardine? Part 3 What is the environmental carrying capacity K? Part 4 What is the optimum yield level? Part 5 Make a graph of N versus t. Part 6 At what time t should the population he harvested? Part 7 What portion of the graph is concave up? What portion is concave down?
Sales of a video game released in the year 2000 took off at first, but then steadily slowed as time moved on. Table 4 shows the number of games sold, in thousands, from the years 20002010. a. Let x represent time in years starting with x=1 for the year 2000. Let y represent the number of games sold in thousands. Use logarithmic regression to fit a model to these data. b. If games continue to sell at this rate, how many games will sell in 2015? Round to the nearest thousand.