Prediction interval - what did I do wrong?

In summary, the book says that an automobile is driven on average 23,500 kilometers per year with a standard deviation of 3900 kilometers. However, the value of z-value is closer to 2.576 than to your 2.575, so the computed interval should be very slightly larger than yours (13,404 → 33,596). Furthermore, in this problem the t and the z are noticeably different (##t_{\alpha/2}(99) = 2.626##), so that gives a still wider interval, but still not as wide as that in the book.
  • #1
mnphys
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Homework Statement


A random sample of 100 automobile owners in the state of Virginia shows that an automobile is driven on average 23,500 kilometers per year with a standard deviation of 3900 kilometers. Assume the distribution of the measurements to be approximately normal. Construct a 99% prediction interval for the kilometers traveled annually by an automobile owner in Virginia.

Homework Equations



qkjwMyn.png

x-bar is the mean, z-value is self-explanatory, σ is the standard deviation and n is the sample size.

The Attempt at a Solution


Here is my attempt at a solution:

rvUuiLN.png


However, the book has the bounds as (13,075, 33,925).

I also considered trying a t-value instead of z-value, but...
a) the book uses z-values for problems like this
b) the book never, ever asks for a t-value with n > 30
c) the t-value I do get if I try it that way doesn't work anyway.

I did this backwards based on the book's answer. With an interval of 10425, standard deviation of 3900, and n=100, the z/t value you would need is 2.66 - this doesn't work as a z-value, and as a t-value it would only work with a sample size of 60 or so. Please help, I'm lost!
 

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  • #2
mnphys said:

Homework Statement


A random sample of 100 automobile owners in the state of Virginia shows that an automobile is driven on average 23,500 kilometers per year with a standard deviation of 3900 kilometers. Assume the distribution of the measurements to be approximately normal. Construct a 99% prediction interval for the kilometers traveled annually by an automobile owner in Virginia.

Homework Equations



View attachment 215551
x-bar is the mean, z-value is self-explanatory, σ is the standard deviation and n is the sample size.

The Attempt at a Solution


Here is my attempt at a solution:

View attachment 215552

However, the book has the bounds as (13,075, 33,925).

I also considered trying a t-value instead of z-value, but...
a) the book uses z-values for problems like this
b) the book never, ever asks for a t-value with n > 30
c) the t-value I do get if I try it that way doesn't work anyway.

I did this backwards based on the book's answer. With an interval of 10425, standard deviation of 3900, and n=100, the z/t value you would need is 2.66 - this doesn't work as a z-value, and as a t-value it would only work with a sample size of 60 or so. Please help, I'm lost!

The value of ##z_{\alpha/2} ## is closer to 2.576 than to your 2.575, so the computed interval should be very slightly larger than yours (13,404 → 33,596). Furthermore, in this problem the t and the z are noticeably different (##t_{\alpha/2}(99) = 2.626##), so that gives a still wider interval, but still not as wide as that in the book.

I don't think you made a substantial error, so I would say that the book's answer is wrong. That type of issue is unfortunate, but is not uncommon.
 
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  • #3
Ok. I was kinda wondering if maybe the book was wrong in this case. Thanks for your reply!
 

1. What is a prediction interval?

A prediction interval is a range of values that is likely to include the true value of a future observation or outcome. It takes into account both the variability of the data and the uncertainty of the prediction model.

2. How is a prediction interval different from a confidence interval?

A confidence interval is used to estimate the true value of a population parameter, while a prediction interval is used to estimate the value of a future observation or outcome. A confidence interval only takes into account the variability of the data, while a prediction interval takes into account both the variability of the data and the uncertainty of the prediction model.

3. Why is my prediction interval wider than my confidence interval?

This is because a prediction interval takes into account both the variability of the data and the uncertainty of the prediction model, while a confidence interval only takes into account the variability of the data. This means that the prediction interval is wider because it includes the uncertainty of the prediction model in addition to the variability of the data.

4. Why is my prediction interval so large?

There are several possible reasons for a large prediction interval. It could be due to a large amount of variability in the data, a small sample size, or a weak prediction model. It is important to carefully consider these factors when interpreting a prediction interval.

5. How can I improve the accuracy of my prediction interval?

To improve the accuracy of a prediction interval, you can try increasing the sample size, using a more precise prediction model, or reducing the variability in the data. It is also important to carefully evaluate the assumptions and limitations of your prediction model.

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