Today:
- Hand back tests
- Quiz next time over basics of the t-distribution
- Section 8.2: Estimating
when
is unknown
- The t-distribution
- similar in shape to the normal
- we estimate
using the sample standard deviation, s:
whereas
- Properties of the t-distribution above:
- symmetric, like the normal
- centered at zero, like the normal
- had n-1 degrees of freedom
- the standard deviation of the distribution is
- Since the standard deviation of the distribution is
greater than 1, it is a little broader than the normal,
a little more variable.
- The major difference between the z- and t-distributions is that
for the normal we have one single table; for the t-distribution, we need to
know the value of n, and then use the table for that value of
df=n-1. (Rats!) Of course, with technology, this is not really much of a
problem.
- How does this affect our strategy? It's basically the same: see
page 290....
- Let's try an example:
Links
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Comments appreciated.