Geoff ]]>

The only other thing I can say from my own experience is that the R shiny library is easy to get things running in, but can feel clunky to use, and hosting applications is potentially a problem. One potentially interesting use of shiny would be to offer a package in which some of the functions open small shiny apps to interact with.

]]>Thanks, that’s great. We appreciate the issues with Excel, including the lack of perfect compatability with Mac. Bob has some prototypes of various different approaches to building the simulations. The software decisions are critical, not least because any major change would be a very large task.

BTW, it’s good to see prediction intervals, and essential to have the definition at the bottom. My first inclination is to assume ‘prediction interval’ refers to an interval with a stated probability of including the mean of a replication, rather than a single data value. But, as you know, the term does not have a single agreed usage.

Geoff ]]>

For example, I made this Javascript version of the “Dance of the CIs”:

http://logarithmic.net/2017/dance/

Geoff ]]>

Yes, not the typical way to think about a CI. Many folks persist in thinking only of IN or OUT, which severely underplays the rich info a CI is giving us. The cat’s eye certainly deserves much more attention and discussion.

Observing that the 50% CI is roughly one-third the length of the 95% CI tells us that, yes, close to the point estimate (centre of the CI on a mean) is our best bet for where the true population value lies. About a 50% chance our CI has landed so that mu (population mean) is within the middle third of the 95% CI.

If we see the 95% CI, then implicitly we know any other CI, such as 99%, 90%, 50%, etc.

Enjoy the richness of the CI!

Geoff

]]>