| Hand in Wednesday:
SPSS problems from last time, if you didn't - - - - - - - - More complicated problems: putting together everything... Read them over, do what you can, bring questions ? p. 400, 7.33 Math sublimina.(SPSS) This is a complicated design: matched pairs, then 2-sample on the differences! But notice chicks (7.35 ) was also matched pairs--weight gain =after-before--but they gave us the pre-subtracted numbers. p.410, 7.45 fitness Do b. Then Look in the back at the answers for a and b. p.422, 7.63 pasture fertilization (SPSS) p.423 7.67 London bus people p.425 7.72 reading biology |
Final exam Final
exam is scheduled for Tuesday, Dec.17, 9-12 Please contact me ASAP
if you have a problem/conflict.
Get handout of info, and review problems if you
haven't. See Day
40
~~~~~~~~~~~~~~~
Questions on HW. SPSS?
Sec. 7.2, Comparing two means See Day 40
Summary: Once we have (xbar1 - xbar2) ,
SEdiff
, and the df, our formulas pattern on the earlier ones.
SEdiff = sqrt[SE(xbar1)2
+ SE(xbar2)2 ] (The Not-equal-variances
version.)
For doing by hand: df
= smaller of (n1- 1) and (n2- 1).
From a computer: df
= complicated formula on p. 403. Produces non-integer degrees
of freedom. Very good approximation to the exact distribution, if
both sample sizes are at least 5. Unsuitable for doing by hand.
Third way of doing these; the
"pooled two-sample t-procedure ." (See
Moore p.406.)
"Equal variances assumed"--a
different formula for SEdiff , different df. If
n1= n2, the two SEdiff formulas
give the same answer. But the df's are still different). Safer
to use "Equal variances NOT assumed" as a rule. More...
"Pooled
two-sample t-procedure " == "Equal variances assumed" was
the only choice in many circumstances before the above good approximations
were developed, computing power increased, and robustness was explored.
Big problem: How do we know that we have
equal
variances? We don't. The usual test for equal variances
has these problems: (Read Moore pp. 413-14)
1) the Null hypothesis is that the variances
are equal, and we gather evidence only against the null hypothesis.
So we don't have a way of assessing evidence for the null
hypothesis. Best we can say is we don't have strong evidence against.
2) the usual test is highly NONRobust (highly
sensitive) to departures from normality in the populations.
So don't bother.
- - - - - - - - - - - -
Exam 3 solutions outside my door/on reserve now.
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