Stat 505 - Spring 2005 - Homework Guidelines and Suggestions
Statistics is best learnt by doing. The homework problems are
intended to help you understand the methods we discuss in class, and
apply them to solve problems. I also try to set homework problems
that make interesting points.
There will be four homework sets, one for each of the first four
sections of the course. They will be distributed near the end of the
appropriate set of lectures and due approximately two weeks later.
Each homework set will consist of four problems. Some problems may
have multiple parts. You are to turn in
answers to three of the four problems. The first two problems are
intended to be simpler than the last two, so I presume most of you
will answer the first two problems and choose one of the second two.
However, the choice is yours. In general, the four problems will be:
An environmental question (or questions) and a set of data. You are to
analyze the data set and answer the question(s).
Some theoretical question(s) about the methods.
A more complicated question and data set.
More complicated theoretical question(s) about the methods.
You are encouraged to work together on the problems. I encourage
groups to include both statisticians and non-statisticians. However, you
must each write your own answers. Copying will not be tolerated.
Answers to data questions should be in the following form:
'Executive summary' or abstract: short statement of question,
approach, and answer. Should be no more than one paragraph. No formulae
or technical language here. This should be written for the busy
plant manager or state regulator.
Approach: Describe your model or approach. Briefly explain why
you chose it. Describe computational approach. If you considered
multiple models, do not explain all
of them in detail. Just say what you considered and why you
rejected it.
Answer: answer the ecological question(s) using the 'final' model.
Make sure you provide
the context for the answer. For example, saying 'Estimated Phi
equals 0.2349887' is not a sufficient answer because it does not
provide context (what is Phi? How precise is the estimate? How big a
value really matters?). You must interpret the answer. Estimates of
precision (or confidence intervals) are (almost) always useful.
Critique: use model diagnostics (if appropriate and available)
or other information to critique your answer. What, if any, are the
weaknesses or problematic assumptions? Another way to critique your
answer is to discuss what are better models/methods to use if you
had more time. You should discuss (briefly) why each model or
method would be more appropriate. I
do not want a laundry list of all possible analyses.
I expect total length to be no more than three pages for each data
problem.
Answers to theoretical questions are more straightforward.
Answer the question, showing your work.
Please ask me if you have any questions about the problems. I am
very willing to give advice, especially with computing.
I give
you two weeks to work on the assignment because I don't think the
problems can be answered overnight. Please do not wait until the
day before to start working.