Confidence (Prediction) Interval after transformation

In summary, to get a 95% prediction confidence interval after performing a transformation, you need to first calculate the CI in the ln space and then raise the values to the e power to get the CI in the original space.
  • #1
mattmns
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Hi, I am a stuck on figuring out how to get a 95% prediction confidence interval after I do a transformation. Here are some details.

So we have some data on the brain weight and body weight of different mammals, and we are to perform linear regression on the data, predicting brain weight from body weight. One of the transformations we have to do is ln(Br Wt) vs ln(Bo Wt). Using this transformation we are to predict the brain weight of the cat and get a 95% CI for this cat.

Here is some more information.

I got the following equation from the above transformation: ln(Br Wt) = 2 + .849*ln(Bo Wt)

The Cat's body weight is 3.3 (kg, but this should not be relevant).

So the predicted brain weight of the cat under this transformation is e^(2 + .849*ln(3.3)) = 20.37 which is reasonable given our data (the actual brain weight of this cat is 25.6 according to our data).

So now I am to find a 95% CI for the cat and this is where I am stuck at.

We have the following formula:

CI: [tex]\hat{Y_p} \pm t_{crit}SE_{pred}(\hat{Y_p})[/tex]

Where our critical t is going to be 2.09302 (DF_res = 19)

And [tex]SE_{pred}(\hat{Y_p}) = s_{y|x}\sqrt{1 + \frac{1}{n} + \frac{(X_p - \bar{X})^2}{s_x^2(n-1)}}[/tex]

We have [itex]s_{y|x} = 0.590996, \bar{X} = 1.25, s_x = 0.94, n = 21, X_p = 3.3[/itex]

So [itex]SE_{pred}(\hat{Y_p}) = .6049550631[/itex]

Now we should have everything we need to find this CI.

----------

I am sure that a 95% CI for ln(Cat Br Wt) would be (1.74, 4.28) [The center of this CI being ~3.01 = 2 + .849*ln(3.3)]. However, if I then raise each of these to the e power, my CI is no longer centered around my predicted value of 20.365. Have any ideas about how to fix this problem? Am I missing something silly here? Thanks!
 
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  • #2
It looks like you are raising the lower and upper bounds of your CI to the e power before calculating the CI. The CI should be calculated in the ln space first, as that is the space you are performing the regression in. So the 95% CI for ln(Cat Br Wt) should be (1.74, 4.28), and then you raise each of those values to the e power to get the 95% CI for the Cat Brain Weight, which would be (5.73, 75.69).
 

Related to Confidence (Prediction) Interval after transformation

What is a confidence interval after transformation?

A confidence interval after transformation is a range of values that is used to estimate the true value of a population parameter after applying a mathematical transformation to the data. It represents the level of uncertainty in the estimate and is often used in statistical analysis to report the precision of a prediction.

Why is it important to use a confidence interval after transformation?

Using a confidence interval after transformation allows for a more accurate estimate of the population parameter by taking into account the variability in the data. It also provides a measure of uncertainty in the estimate, which is important in making decisions based on the data.

How is a confidence interval after transformation calculated?

The calculation of a confidence interval after transformation involves using mathematical formulas and statistical methods to determine the upper and lower bounds of the interval. This is often done using software or a statistical calculator.

What factors affect the width of a confidence interval after transformation?

The width of a confidence interval after transformation is affected by several factors, including the sample size, the level of confidence chosen, and the variability in the data. A larger sample size and a higher level of confidence will result in a narrower confidence interval, while a higher variability in the data will result in a wider interval.

Can a confidence interval after transformation be used to make predictions?

Yes, a confidence interval after transformation can be used to make predictions about the population parameter. However, it is important to note that the interval only provides an estimate with a certain level of uncertainty, and it should not be used as a definitive answer. Other factors and sources of error should also be considered when making predictions.

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