Project
Due Dec 1, 2011
This is an individual project, you may ask me questions but do not consult your
fellow students.
You may consult YOUR BOOK (Devore and Berk) for this
project, but DO NOT CONSULT OTHER BOOKS or look on the internet for
answers. You may well find them there, but it will ruin your learning experience.
During the second half of this course, we’re discussing two primary forms
of inference: frequentist and Bayes. Both of these forms of inference begin the
same way, by specifying a probability model for the observed data. For instance,
if we want to estimate the mean SBP of the population of UMN undergrads we
might say
X
i
∼
Normal
(
μ, τ
2
). We view the SBP as random and collect some
sample of size
n
. Maximum likelihood theory tells us we can get an unbiased
estimate of
μ
with ˆ
μ
=
∑
x
i
n
.
If we don’t specify a probability model for the
observed data we can’t make any progress with either method and because of
this, these two forms of inference are called “model based estimation.”
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
This is the end of the preview.
Sign up
to
access the rest of the document.
 Spring '11
 RichMacLehose
 Statistics, zi

Click to edit the document details