Logistic modeling - help integrating/solving for P

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In summary, the conversation is about solving for the value of P(90) given that P(0)=2 and using the equation dP/dt=1/900P(9-P). The solution involves using separable differential equations and partial fractions to ultimately get the equation P(9-P)=Ce^(1/100t), but the discussion ends with the person being stuck and not understanding how P ever stops growing.
  • #1
brusier
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Homework Statement


Sorry about the title; I accidentally hit enter instead of 'Shift'. It should read Logistic Modeling -- help integrating/solving for P

If P(0)=2, find P(90).

Homework Equations



dP/dt=1/900P(9-P)

The Attempt at a Solution



my solution looks like:

dP/(P(9-P))=1/900dt (seperable diff eq.)

1/9(ln(P)(9-P))=1/900 t+C (partial fractions and property ln M + ln N = ln MN)

lnP(9-P) = 1/100t+9C

P(9-P)= Ce^(1/100t) (exponentiated; 9C=C)

kinda stuck here (and I don't see how P ever stops growing.)
 
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  • #2


be careful with your brackets, its pretty hard to read what is in your log, divided etc.

but following through your method
[tex] \frac{1}{P(9-P)} = \frac{A}{P}+\frac{b}{9-P} = \frac{9A + (b-A)P}{P(9-P)}[/tex]
giving A = 1/9, B = 1/9

so evaulating the integral
[tex] \int \frac{dP}{P(9-P)} = \frac{1}{9}(\int\frac{dP}{P}+\int\frac{dP}{9-P}) =\frac{1}{9}(ln(P) - ln(9-P)) +C = \frac{1}{9}ln(\frac{P}{9-P})+C [/tex]

maybe you missed a negative...?
 

1. What is logistic modeling?

Logistic modeling is a statistical method used to predict the probability of a binary outcome (such as yes or no, success or failure) based on one or more independent variables. It is commonly used in fields such as medicine, economics, and social sciences.

2. How is logistic modeling different from linear regression?

Logistic modeling is specifically used for predicting binary outcomes, while linear regression can be used for predicting continuous outcomes. Logistic modeling also uses a different type of regression equation called the logistic function, which constrains the predicted values to be between 0 and 1.

3. How do I interpret the coefficients in a logistic regression model?

The coefficients in a logistic regression model represent the effect of each independent variable on the log odds of the outcome. To interpret them, you can exponentiate the coefficient to get the odds ratio, which represents the change in odds for a one unit increase in the independent variable while holding all other variables constant.

4. What is the process for solving for P in logistic modeling?

In logistic modeling, P represents the probability of the binary outcome. To solve for P, you can use the logistic function, which is P = 1 / (1 + e^(-z)), where z is the linear combination of the independent variables and their coefficients. You can also use software programs such as R or Python to solve for P automatically.

5. How can I assess the accuracy of a logistic regression model?

There are several metrics that can be used to assess the accuracy of a logistic regression model, including the overall accuracy, sensitivity, specificity, and area under the ROC curve. These metrics can be calculated using a confusion matrix, which compares the predicted values to the actual values. Additionally, cross-validation techniques can be used to assess the performance of the model on new data.

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