Build logistic differential equation for given data

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Discussion Overview

The discussion revolves around constructing a logistic differential equation to model US oil production based on provided data. Participants explore the appropriateness of the logistic model and discuss fitting techniques, including least squares fitting, while addressing the complexities of the data involved.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks assistance in building a logistic differential equation for US oil production data, suggesting the form dP/dt = k(1-P/N)P.
  • Some participants question the necessity of using a logistic model, with one noting that the project requirements dictate its use.
  • Another participant proposes using least squares fitting to model the function with the data, providing a specific equation for P(t) and discussing the potential non-linearity of the data.
  • A later reply emphasizes the need for careful interpretation of the data, particularly noting that US oil production appears to be declining, which may challenge the logistic model's applicability.
  • One participant mentions the need for code to perform least squares fitting and provides specific parameter estimates for US and world oil production, while cautioning that the suitability of the logistic curve for the data is uncertain.

Areas of Agreement / Disagreement

Participants express differing views on the appropriateness of the logistic model for oil production, with some supporting its use due to project requirements, while others remain skeptical about its fit to the data. The discussion does not reach a consensus on whether the logistic model is the best choice.

Contextual Notes

Participants highlight the potential non-linearity of the data and the need for careful interpretation, particularly regarding the declining trend in US oil production. There are unresolved questions about the assumptions underlying the logistic model and the fitting process.

killer2008
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http://img258.imageshack.us/img258/3518/01ck5.png

I would like to build a logistic differential equation for US oil production from the given data above.
dP/dt = k(1-P/N)P

I tried many different models but nothing came close to the given data.

Any help and suggestion?
 
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Why should it be logistic?
 
John Creighto said:
Why should it be logistic?

This is a project from the textbook, there are many requirements (answering about 12 questions) and one of them is the equation must be logistic.
 
I think you will have to model the resulting function with the data using a least squares fitting. This equation is:

[tex]P(t)=\frac{N\cdot P_0 \cdot e^{kt}}{N+P_0\cdot \left(e^{kt}-1\right)}[/tex]

It's very late over here, I will have a look at it tomorrow, it might be that it is non-linear and then I have to adapt my program to do this.
 
coomast said:
I think you will have to model the resulting function with the data using a least squares fitting. This equation is:

[tex]P(t)=\frac{N\cdot P_0 \cdot e^{kt}}{N+P_0\cdot \left(e^{kt}-1\right)}[/tex]

It's very late over here, I will have a look at it tomorrow, it might be that it is non-linear and then I have to adapt my program to do this.

If the above equation is correct, linear least squares while suffice. Just cross multiply:[tex]P(t)(N+P_0\cdot \left(e^{kt}-1\right))=N\cdot P_0 \cdot e^{kt}[/tex]

Then rearrange:

[tex]P(t)P_0\cdot \left(e^{kt}-1\right)=N (P_0 \cdot e^{kt} - P(t))[/tex]

[tex]P_0[/tex] is the initial production level and use least squares to solve for N. I'm still not convinced oil production should be logistic. The only reason it should be logistic is because the production is driven by the demand which is driven by the population.
 
Last edited:
You need some code to do this least squares fitting in case one wants to calculate N, P0 and k at the same time. When this is done you will end up with N=14.9395; P0=0.23349 and k=0.102758 for the US and N=116.435; P0=0.04219 and k=0.12401 for the world production. This in case the time is considered the center of the intervals as is usually done in statistics. I used my own code for this, however I assume that this must be possible in some other mathematical package available. I can't help you in this though. One final remark is the following, I'm not giving any advice on whether this is a good curve for describing the data, this is something you need to do yourself. Other curves or equations might be more suitable. If you look at the one for the US, it is strange data, it's going down. Interpret this very carefully.
 

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