Let me suggest the interactive graphic NTplot in the HH package.
## install.packages("HH") ## if you don't have it yet.
library(HH)
NTplot(shiny=TRUE)
[to exit a shiny app, use C-c C-c in the R window in Macintosh, and
<esc> in the R window in Windows]
This single display covers about 60% of the introductory course.
See ?NTplot for additional examples.
Be sure to read the Details section and the examples.
On Thu, Apr 20, 2017 at 11:07 AM, David Monterde <[email protected]> wrote:
> First of all I have to apologize because my english is horrible.
>
> I am working in a basic and practical statistic course.
>
> I will speak about confidence intervals, of course.
>
> But when I was preparing this chapter I had a doubt: I wonder if it has sence
> today to explain the confidence intervals for a mean in terms of the standard
> normal distribution.
>
> If we show that the mean follows a normal distribution with variance/n then
> it is easy to explain (and to understand) that the confidence interval is due
> to two percentiles, the percentile alpha/2 and the percentile 1-alpha/2 from
> a normal distribution with the sample mean and the sample variance/n.
>
> I think that the standard gaussian distribution was usefull when we had to
> calculate CI in the past.
> That is, it was a simple way to have all possibilities in a single table (in
> paper).
>
> But now, we have R functions that let us to obtain the exact values for any
> normal distribution.
>
> I think that is not necessary to explain how to work with transformations if
> we can explain that a CI is simply to identify percentiles.
>
> What do you think?
>
> Thanks
>
> PD I know that the normal distribution for CI need some hipothesis. But I
> have focused the question
>
> _________________________
>
> David Monterde
>
> Lo difícil se hace.
> Lo imposible se intenta.
> _________________________
> [[alternative HTML version deleted]]
>
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