Prediction interval, which brackets?

In summary, the conversation discusses the use of brackets in a prediction interval for a statistics coursework. The question is whether to use open or closed brackets, and it is mentioned that it likely does not make a difference. The individual in the conversation will use closed brackets based on information found on a website.
  • #1
likearollings
27
0
Prediction interval, which brackets!?

Hi, this is a quick question for a piece of statistics coursework I am doing.

I have made a prediction interval with an upper bound of let's say 30 and lower of 20.

I just don't know which brackets to use open or close e.g.

(20,30)

[20,30]

(20,30]

[20,30)

I know its a bit of a stupid question :redface: and is maybe not worthy to be in this forum but I am pretty pedantic when it comes to coursework

Ok any help is appreciated.
 
Physics news on Phys.org
  • #2


likearollings said:
Hi, this is a quick question for a piece of statistics coursework I am doing.

I have made a prediction interval with an upper bound of let's say 30 and lower of 20.

I just don't know which brackets to use open or close e.g.

(20,30)

[20,30]

(20,30]

[20,30)

I know its a bit of a stupid question :redface: and is maybe not worthy to be in this forum but I am pretty pedantic when it comes to coursework

Ok any help is appreciated.

If we're talking about a continuous distribution as opposed to a discrete distribution, I don't think it makes any difference. Pr(a < X < b) = Pr(a <= X < b) = Pr(a < X <= b) = Pr(a <= X <= b). IOW, the probability that a variable is a single value is zero, so including an endpoint of an interval or omitting it makes no difference.

The only thing I can think of where including/omitting an interval endpoint makes a difference is when your null hypothesis includes the endpoint.

Hope that helps.
 
  • #3

FAQ: Prediction interval, which brackets?

What is a prediction interval?

A prediction interval is a range of values that is likely to contain the true value of a future observation. It takes into account both the uncertainty in the data and the uncertainty in the model used to make the prediction.

How is a prediction interval different from a confidence interval?

A prediction interval is used to estimate the value of a future observation, while a confidence interval is used to estimate the true value of a population parameter. Additionally, prediction intervals take into account the variability of both the data and the model, while confidence intervals only consider the variability of the data.

How is a prediction interval calculated?

A prediction interval is calculated using the standard error of the prediction, the t-statistic, and the degrees of freedom. The formula for a prediction interval is: prediction ± (t-statistic * standard error of prediction).

Why is it important to consider prediction intervals in data analysis?

Prediction intervals provide a more accurate estimate of the value of a future observation, taking into account both the data and the model used. They give a range of values that is likely to contain the true value, rather than just a point estimate, which may be affected by outliers or other sources of error.

Can prediction intervals be used for any type of data?

Yes, prediction intervals can be used for any type of data, as long as the assumptions of the model used to make the prediction are met. However, it is important to note that prediction intervals may not be as reliable for data that is highly non-linear or has extreme outliers.

Similar threads

Replies
21
Views
2K
Replies
11
Views
3K
Replies
22
Views
3K
Replies
4
Views
4K
Replies
1
Views
2K
Replies
12
Views
3K
Replies
4
Views
2K
Back
Top