Hi all
I am teaching statistics to Students in Physical Activity. They do not
care that much about the math formula, they are focused... well, they
are not that much focused, but the few that are focused want to know how
to do the stats. One of the main problem with this kind of student
(student that do not know computing at all) are the "R inconsistency" :
since there is "as.numeric" and "as.factor", they want to use
"as.ordered(variable,levels=c(...))". But it does not work that way...
Or sometime, we should use "x,y", sometime "x~y"... quantile works for
numeric, but not for ordered (whereas I teach them that quartiles are
computable for ordered variables...)
It is very hard to deal with all the "rules but many exception". So I
started to write a package, some kind of "simplified R" in which all the
syntax will be simplified. But it takes time...
Christophe
My teaching situation is a bit different from the others who have responded so far. I do
teach an occasional class at the university, but my main job is with a group of hospitals
and doctors doing their statistics. As part of that I teach some classes within the
hospital system to doctors/nurses/whoever. These tend to be very different students from
the undergrads at the university (anyone else ever had a student use the excuse "I
was performing surgery" for missing class?).
These classes are less focused on how to do the stats (they will have me do
them for them), but more on the concepts so that they can understand the
literature that they read, work with me better in designing studies, and do
better at writing/presenting the results.
The university where I teach occasionally uses some web based stats program for
the intro class (even simpler than minitab in what it can do). I keep trying
to talk them into using R (through the Excel interface or RCmdr), but so far
have made little progress.
In both situations I am not teaching the students how to use R, but I still use
it to demonstrate various concepts. I mostly use simple examples (plots, quick
numbers) or those from the TeachingDemos package (that is what it was written
for (disclosure of possible conflict of interest in promoting the package: I
believe that the package author/maintainer raids my fridge more often than he
should)). Even though the students are not learning R itself, they appreciate
the concepts illustrated. The mainly GUI based illustrations could be written
in any language (but using R means that I can customize them and more advanced
students can look at the code themselves and learn more).
The R-based code usually does not phase the students even though they have not
leaned R themselves yet. Those that do not understand it just look at the
results, but many do understand the main ideas. For example, at the
university, the standard course materials use the 1970 draft lottery as an
example for showing scatterplots, correlation, relationships and other
concepts. As a bit of a teaser there is a slide that asks if the relationship
could be due to chance (this is before we get into the inference part of the
course). We don't come back and answer that teaser until the last week of
class (when we have done hypothesis testing an regression). But I like to show
the students a simple simulation at the time of the teaser with simple code
such as:
out <- replicate(10000, cor( sample(1:366), 1:366 ) )
hist(out)
sum( abs(out) > 0.18 )
[1] 5
sum( abs(out) > 0.1 )
[1] 533
quantile( out, c(0.025, 0.975) )
2.5% 97.5%
-0.1021715 0.1003527
It takes less than a minute to explain that 1:366 is the numbers from 1 to 366
and that the sample function randomizes the order. The replicate function and
the rest of the code is fairly self explanatory even for people who don't know
any programming, but this example clearly shows that the amount of correlation
in the 1970 draft lottery was unlikely due to random chance as well as what
values we would expect to see by chance. Some of the students will stay after
class and ask about R, I explain that it is not part of the official class, but
that if they are interested here is the website and additional info ...
So, I think that R is a great tool for teaching statistical concepts even when
R is not the statistical package being used for the course itself.
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