Logistic Growth Rate Analysis
Создано: 20 ноября 2024 г.
Отвечено с использованием OpenAI 01 Preview от OpenAI01
Создано: 20 ноября 2024 г.
Отвечено с использованием OpenAI 01 Preview от OpenAI01
Extracting details: The user provides extracted text from an image, detailing a problem involving multiple parts of logistic growth.
Formulating steps: The user requests a solution with at least two steps, detailing given information, what needs to be found, and a step-by-step approach.
Presenting findings: The solution should be clearly organized, with headings for each step and a concise final answer.
Crafting the solution
I’m working through a detailed solution, breaking it into two key steps, with each step clearly defined and articulated carefully to avoid disallowed reasoning.
Understanding the problem
I’m gathering information to solve the problem. The population size is 40,000 people, and the news is spreading through word of mouth.
Tracking news diffusion
I'm examining the spread of news over time with . After 8 days, about 5.621 thousand people have heard it. Calculating reveals a rate of 1.0208 thousand people per day. Comparing to the logistic growth models is next.
Formulating the differential equation
I’m working out the differential equation for the news spread, expressed as .
Determining the spread
I’m calculating the number of people reached when the news spreads at 3.99 thousand people per day, and the times at which this rate occurs.
Tracing the spread
I’m identifying the fastest rate of news spread, noting it as 160 thousand people per day, and distinguishing between two distinct values for verification.
Piecing together the scenario
I’m thinking through part (a) of the problem, which involves determining how many thousands of people have heard the news after 8 days using the given function .
Calculating population
OK, let's plug in t = 8 into the logistic growth equation . After simplifying, it turns out approximately 5.621 thousand people have heard the news after 8 days.
Mapping out the rate
I’m working through the derivative using the chain rule, aiming to simplify and determine its form. Evaluating it at will pinpoint the spreading rate.
Step 1: Given Information and Objective
Step 2: Substitute into the Equation
Calculate the exponent:
Compute :
Calculate the denominator:
Compute :
Final Answer:
Approximately 5.621 thousand people have heard the news after 8 days.
Step 1: Differentiate the Given Function
Step 2: Apply Differentiation Techniques
Let , so .
Differentiate with respect to :
Use the chain rule for :
Substitute and :
Simplify:
Final Answer:
Step 1: Given Information and Objective
Step 2: Substitute into
Calculate the exponent:
Compute :
Calculate the numerator:
Compute the denominator:
Calculate :
Final Answer:
The news is spreading at a rate of 1.0208 thousand people per day after 6 days.
Step 1: Identify the Logistic Growth Model
Step 2: Recognize the Standard Logistic Equation
The standard logistic differential equation is: where:
Step 3: Match Parameters to the Given Function
Final Answer:
Step 1: Set and Solve for
Step 2: Relate to
From the derivative and the logistic equation:
Set up the equation:
Simplify:
Write the quadratic equation:
Step 3: Solve the Quadratic Equation
Use the quadratic formula:
Calculate the discriminant:
Find the two values:
Final Answer:
Approximately 250 people and 39,750 people have heard the news when its spread rate is 3.99 thousand people per day.
Step 1: Use the Relationship Between and
Step 2: Solve for When
Substitute :
Solve for :
Step 3: Solve for When
Substitute :
Solve for :
Final Answer:
The news is spreading at a rate of 3.99 thousand people per day at approximately 12.263 days and 12.788 days.
Step 1: Identify the Maximum of
Step 2: Recognize That Maximum Occurs at
In logistic growth, the maximum rate occurs when , where .
So, thousand.
Step 3: Calculate the Maximum Rate
Use the differential equation:
Substitute :
Final Answer:
The fastest rate at which the news spreads is 160 thousand people per day.
Understanding constraints
I’m analyzing the context of the problem, which involves optimizing under the constraint .
Taking a closer look
I’m pulling together the problem by clearly defining what's given and what's needed. This involves understanding the concepts and equations to use and methodically solving each step to ensure accuracy throughout.
Outlining the approach
I’m working through the optimization problem by setting up equations with the gradients of the function and the constraint, using the method of Lagrange multipliers.
Pulling together gradients
First, I determined the gradients and to set up the Lagrange multiplier equations for optimization. This step involved solving straightforward algebraic expressions.
Linking equations
First, I’m connecting the derivatives, then substituting, and solving for . This leads to:
Pinpointing solutions
OK, narrows down and gives us two points. Plugging these into the function, I find the maximum value is 52.
Understanding values
I’m summarizing the maximum and minimum values of a function, noting that the maximum is 52 and the minimum is -52.
Given Function to Optimize:
Constraint Equation:
Objective: Use Lagrange multipliers to find the maximum and minimum values of subject to the given constraint.
Explanation:
We will use the method of Lagrange multipliers, which involves setting the gradient of the function equal to a scalar multiple of the gradient of the constraint:
where is the Lagrange multiplier.
Calculate the gradients:
Gradient of :
Gradient of :
Set up the equations:
:
:
Constraint equation:
From equation (1):
From equation (2):
Set Equations A and B equal to each other:
Cross-multiply:
Therefore:
Substitute into :
Simplify:
Solve for :
When :
When :
First Point :
Second Point :
Maximum Value:
Minimum Value: