BIOS601 AGENDA: Thursday September 05, 2013
[updated August 19, 2013]
Agenda for Thursday Sept 05, 2013
- Discussion of computing and statistical inference issues
in the
assignment on sampling of locations on Earth's surface
answers to be handed in for Q1, Q2, Q3, Q4, Q5
The first (general) computing issue is (if need be) to get up to speed
in the use of R. See the R links on the main course page.
If you run into problems, let JH know asap.
A statistical/computing issue might be how to come up
with a way to
randomly sample locations on the surface of a sphere, using
latitude and longitude co-ordinates. See the notes at the bottom of the
file containing the
2 R functions inside the Oceanography
link (on the height of the land and the depth of the ocean) inside the resources for surveys. JH thinks of the problem
by visualizing the segments of a peeled orange!
Remarks:
The statistical issues raised by this assignment include the
distinction between standard deviation and
standard error; the concept of a margin of error;
when it is appropriate to use the Normal (Gaussian) approximation to the binomial distribution;
the (often under-appreciated) centrality of the
Central Limit Theorem (CLT) in
applied statistical work, not just for the sampling distribution of a
sample proportion, but also for that of a
sample mean.
- Discussion of issues in the
Assignment on measurement
Q1 and Q2 (measuring 'Readability'): answers need not be handed in; just think about the issues;
If there is time, we might discuss and do some 'measuring' in class.
Q3, Q4, Q5, Q6, Q7, Q8: Answers to be handed in.
Q9, Q10: from last year; answers need not be handed in. If there's time,
we will think about what the answers might have looked like.
Remarks: this topic of measurement is probably new for you, as it was for JH
when he began in cancer clinical trials in 1973, and oncologists (cancer doctors)
were judging responses of advanced cancer to chemotherapy
by measuring tumours by
'palpation'.
Just because (random) measurement errors tend to cancel out in
averages doesn't mean that errors in measurement can be ignored. For example,
how comfortable would you be in measuring how much physical activity JH does
by having him wear a 'step-counter' for a randomly selected week of the
year, and using that 1-week
measurement as an 'x' in a multiple or logistic or Cox
regression? See slides 7 and 8 from part of JH's
"Scientific reasoning, statistical thinking, measurement issues, and use of
graphics: examples from research on children"
at Royal Children's Hospital in Melbourne, earlier this year.
pdf
Some of the the terminology will be new to you, and so (as you will discover
in your simulations of how well you can estimate the conversion factors between
degrees F and degrees C) will some of the consequences of measurement error.
The "animation (in R) of effects of errors in X on slope of Y on X" might be of interest,
as might the java applet accompanying "Random measurement error
and regression dilution bias".
These consequences are rarely touched on, yet alone emphasized, in theoretical courses on regression, where all
'x' values are assumed to be measured without error! Welcome to the REAL world.
For this exercise, and the topics it addresses, the most relevant portions of
the 'surveys' resources are
Measurement: Reliability and Validity and
Effects of Measurement Error