Logistic growth model, differential equation

In summary, the conversation is about finding an explicit solution for y in the equation dY/dt = y(c - yb), with c and b as constants. The attempt at a solution involves using partial fraction decompositions and combining natural logs, but there is an error in the last expression on the left-hand side. The coefficient of b should cancel out, but it is currently not possible to solve for y without knowing the value of b.
  • #1
Wiz14
20
0

Homework Statement


dY/dt = y(c - yb)

C and B are constants.
Im supposed to find and explicit solution for y, but I am having trouble.

Homework Equations





The Attempt at a Solution



dY/y(c - yb) = dt
∫(1/c)dy/y + ∫(b/c)dY/c - yb = ∫dt (i used partial fraction decompositions)
(1/c)ln|y| - (b/c)ln|c - yb| = t + K (K stands for an arbitrary constant)
ln|[(c-yb)^b]/y| = -ct + K (Multiplied by negative c and then combine the natural logs on the left)

What I am having trouble with is the last expression on the LHS. I have (c-yb)^b so how am I supposed to solve for y, if i don't know b?
 
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  • #2
Wiz14 said:

Homework Statement


dY/dt = y(c - yb)

C and B are constants.
Im supposed to find and explicit solution for y, but I am having trouble.

Homework Equations





The Attempt at a Solution



dY/y(c - yb) = dt
∫(1/c)dy/y + ∫(b/c)dY/c - yb = ∫dt (i used partial fraction decompositions)
(1/c)ln|y| - (b/c)ln|c - yb| = t + K (K stands for an arbitrary constant)
ln|[(c-yb)^b]/y| = -ct + K (Multiplied by negative c and then combine the natural logs on the left)

What I am having trouble with is the last expression on the LHS. I have (c-yb)^b so how am I supposed to solve for y, if i don't know b?

It's very difficult to read what you wrote (use LaTex!), but there's an error here:

(1/c)ln|y| - (b/c)ln|c - yb| = t + K

That should be: ##\displaystyle \frac{1}{c}\ln|y| - \frac{1}{b}.\frac{b}{c}\ln|c-yb| = t + K## so that coefficient of b should cancel out.
 

1. What is a logistic growth model?

A logistic growth model is a mathematical equation used to model the growth of a population over time. It takes into account the population's initial size, growth rate, and carrying capacity, which is the maximum number of individuals the environment can support.

2. How is the logistic growth model represented?

The logistic growth model is represented by a differential equation, specifically a first-order ordinary differential equation. It is written in the form of dP/dt = rP(K-P)/K, where P is the population, t is time, r is the growth rate, and K is the carrying capacity.

3. What is the significance of the carrying capacity in the logistic growth model?

The carrying capacity is an important parameter in the logistic growth model because it represents the maximum population size that can be sustained by the available resources in the environment. As the population approaches the carrying capacity, the growth rate decreases and eventually levels off.

4. What are some real-life applications of the logistic growth model?

The logistic growth model is commonly used in biology to model the growth of populations of organisms, such as bacteria, plants, and animals. It is also used in economics to model the growth of markets and in epidemiology to model the spread of infectious diseases.

5. How accurate is the logistic growth model in predicting real-world populations?

The accuracy of the logistic growth model depends on the quality and quantity of data used to estimate the model parameters. In some cases, it may provide a good approximation of population growth, but in others, it may not accurately reflect the complex dynamics of real-world populations. It is important to note that the model is a simplification of reality and should be used with caution.

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