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May 11, 2015 3:14 AM
(5770 views)

Hello dear community,

I am working with climate data in order to predict emissions of gasses from plant leaves. I created estimates for the amount of gasses (in gram) that was released over a season.

I performed several measurements in order to predict values for periods that were not measured.

I created two multiple regression models to predict for every hour the emissions (y2):

1) x1, x2, x3 to predict **y1**

2) **y1**, x4, x5, to predict **y2**

I predicted y1 and y2 values according to the estimates from the model. Then I summed up all the predicted values for y2 to get the estimate I needed.

What I lack is an error term for this estimate.

I though originally to get from jmp to predict the values of the error for each value by : fit model > Save columns > Std for predicted.

and then sum then up according to the formula:

n = 2500

Mutual SE = square root(se1^2+se2^2+......+se2500^2)

But the error barely grows as I create my cumulative estimate. so it does not seem as the right way

Any Ideas on how to approach this problem?

Thanks

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Re: Error for empirical model estimates

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Re: Error for empirical model estimates

Thanks Peter for you answer,

I do not have experience (yet) with Monte Carlo.

I did not mention that I did this process several times, creating estimates for different plant species p1, p2... p6.

I would like to put error bars on these cumulative estimates in a graph, so I could show the reliability of the estimates and perform a contrast test between categories.

For these needs do you think this is the right method?

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Re: Error for empirical model estimates

Only one last question,

How would you work when you have estimates that are the result of two model as above, where I first predict y1 and then use it to predict y2?

Since the prediction of error for y2 relies on two models.

Thanks

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Re: Error for empirical model estimates