Welcome to Westonci.ca, your go-to destination for finding answers to all your questions. Join our expert community today! Experience the convenience of getting reliable answers to your questions from a vast network of knowledgeable experts. Experience the convenience of finding accurate answers to your questions from knowledgeable experts on our platform.
Sagot :
To address the problem, we need to fit the data to the model of a logistic growth curve given by the equation:
[tex]\[ N(t) = \frac{c}{1 + a e^{-k t}} \][/tex]
Here, [tex]\( t \)[/tex] is the time in days, and [tex]\( N(t) \)[/tex] is the number of people who have heard the rumor by time [tex]\( t \)[/tex]. The logistic function parameters are [tex]\( c \)[/tex], [tex]\( a \)[/tex], and [tex]\( k \)[/tex], which need to be determined using regression analysis. Below is a detailed step-by-step explanation of how these parameters can be obtained and what they signify:
1. Collect the Data:
- Time, [tex]\( t \)[/tex] (in days): [tex]\[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 \][/tex]
- Number of people, [tex]\( N \)[/tex], who have heard the rumor: [tex]\[ 1, 2, 4, 7, 13, 19, 24, 26, 28, 28, 29, 30 \][/tex]
2. Logistic Function:
- The logistic function is given by:
[tex]\[ N(t) = \frac{c}{1 + a e^{-k t}} \][/tex]
Where:
- [tex]\( c \)[/tex] is the carrying capacity, which is the maximum number of people who can hear the rumor.
- [tex]\( a \)[/tex] is a parameter related to the initial amount of individuals who heard the rumor.
- [tex]\( k \)[/tex] is the growth rate, which represents how quickly the number of people hearing the rumor grows.
3. Fit the Logistic Function to the Data:
- Use regression techniques to find the best-fit parameters [tex]\( c \)[/tex], [tex]\( a \)[/tex], and [tex]\( k \)[/tex].
4. Interpreting the Parameters:
- Based on the regression analysis, the fitted parameters are:
[tex]\[ c \approx 29.29777054499323, \quad a \approx 79.16289409130772, \quad k \approx 0.8269580729967296 \][/tex]
5. Construct the Fitted Logistic Equation:
- Substitute these values back into the logistic equation:
[tex]\[ N(t) = \frac{29.29777054499323}{1 + 79.16289409130772 e^{-0.8269580729967296 t}} \][/tex]
Therefore, the logistic equation that fits this data is:
[tex]\[ N(t) = \frac{29.29777054499323}{1 + 79.16289409130772 e^{-0.8269580729967296 t}} \][/tex]
This fitted equation can be used to predict the number of people who have heard the rumor at any given time [tex]\( t \)[/tex].
[tex]\[ N(t) = \frac{c}{1 + a e^{-k t}} \][/tex]
Here, [tex]\( t \)[/tex] is the time in days, and [tex]\( N(t) \)[/tex] is the number of people who have heard the rumor by time [tex]\( t \)[/tex]. The logistic function parameters are [tex]\( c \)[/tex], [tex]\( a \)[/tex], and [tex]\( k \)[/tex], which need to be determined using regression analysis. Below is a detailed step-by-step explanation of how these parameters can be obtained and what they signify:
1. Collect the Data:
- Time, [tex]\( t \)[/tex] (in days): [tex]\[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 \][/tex]
- Number of people, [tex]\( N \)[/tex], who have heard the rumor: [tex]\[ 1, 2, 4, 7, 13, 19, 24, 26, 28, 28, 29, 30 \][/tex]
2. Logistic Function:
- The logistic function is given by:
[tex]\[ N(t) = \frac{c}{1 + a e^{-k t}} \][/tex]
Where:
- [tex]\( c \)[/tex] is the carrying capacity, which is the maximum number of people who can hear the rumor.
- [tex]\( a \)[/tex] is a parameter related to the initial amount of individuals who heard the rumor.
- [tex]\( k \)[/tex] is the growth rate, which represents how quickly the number of people hearing the rumor grows.
3. Fit the Logistic Function to the Data:
- Use regression techniques to find the best-fit parameters [tex]\( c \)[/tex], [tex]\( a \)[/tex], and [tex]\( k \)[/tex].
4. Interpreting the Parameters:
- Based on the regression analysis, the fitted parameters are:
[tex]\[ c \approx 29.29777054499323, \quad a \approx 79.16289409130772, \quad k \approx 0.8269580729967296 \][/tex]
5. Construct the Fitted Logistic Equation:
- Substitute these values back into the logistic equation:
[tex]\[ N(t) = \frac{29.29777054499323}{1 + 79.16289409130772 e^{-0.8269580729967296 t}} \][/tex]
Therefore, the logistic equation that fits this data is:
[tex]\[ N(t) = \frac{29.29777054499323}{1 + 79.16289409130772 e^{-0.8269580729967296 t}} \][/tex]
This fitted equation can be used to predict the number of people who have heard the rumor at any given time [tex]\( t \)[/tex].
Thank you for visiting. Our goal is to provide the most accurate answers for all your informational needs. Come back soon. We appreciate your visit. Our platform is always here to offer accurate and reliable answers. Return anytime. Keep exploring Westonci.ca for more insightful answers to your questions. We're here to help.