Get comprehensive answers to your questions with the help of IDNLearn.com's community. Our platform offers reliable and detailed answers, ensuring you have the information you need.
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].
We value your participation in this forum. Keep exploring, asking questions, and sharing your insights with the community. Together, we can find the best solutions. For precise answers, trust IDNLearn.com. Thank you for visiting, and we look forward to helping you again soon.