Help? I'll be in my office Monday 11:30-12:30 & 1:30-2:30, Tuesday after 1:15 and till exam time. LaReina will be in the Clinic Sunday 7-9. Also--study with one another!
Homework: you may hand in late homework
up to the time you begin the exam. After now, to me directly,
or under my door. (Will get registered in but not carefully read.)
NO
CAMPUS MAIL! Returned HW will be in usual yellow folder outside
my door.
~~~~~~~~~~~~~~~
Please fill out an evaluation,
return it to the ENVELOPE circulating
or on the projection cart.
Day 42 (Friday. May13): For final... Review, making your notes for your sheet, list questions and get answers.
Homework questions? Day 41
Group exercise --Particulates in air. Handout
Data
Regroup, look at results. (Not here last time?
Make a group with others who weren't) Hand in your group's work.
Some questions/analyses suggested
in class: (Others? There are
definitely others to look at.)
Compare means: confidence interval for differences.
Test for alternative: rural is less polluted (Method: paired samples?
[why?]
A. Rural is "significantly"
less polluted (one sided P is .02-.03) but the difference in means is only
1.
Is city/rural more variable (spread out)?
Look at side by side somethings. A. Forgot to look in class.
From stemplots, not noticeably different.
Are distributions nearly normal? A. Pretty much.
no outliers.
Do the pollution levels "travel together"--scatterplot,
correlation, regression--can one be used to predict another (which way
makes sense?) A. Rural predicts City, since the wind blows
that way. Very strong relationship. r = .95
The data were taken over 7 months. Do levels
change appreciably during that period? (Seasonal?) Not strongly enough
to see in scatterplots--Day vs. Rural, Day vs. City.
Some good questions people came up with that
are not answerable from the data; that we'd like to know in understanding
this situation: What units are these?
Where is this (dry/dusty Southwest? damp leafy Northeast?)
(Those I caught; others??)
-------
ON the EXAM?
Computing standard deviation by hand
YES. (4 values, simple computations.)
Doing a two-sample t procedure by hand (chapter
24) NO.
Figuring out SPSS output: how to read,
which output is appropriate (including two-sample) YES,
telling which
menu commands, NO.
What we studied: (Overall: questioning source,
context of data)
>>Data Analysis: description and exploration<<
>>Data Production: Sampling,
Designing Experiments<<
>>Statistical Inference:
formal Estimating and Testing--
quantifying our uncertainty and satisfying the skeptic<<
Anything you'll meet will fall into one of those categories--
--Fancy ways of torturing a data set to make it give up
its secrets--"data mining," subtle and complex summary methods
--Sophisticated experimental designs
--Estimations (usually intervals) , tests (P-values, "significant
at") based on other parameters
"If your only tool is a hammer, every problem looks like a nail."
Studies are often set up so that they can be analyzed using certain techniques.
Conversely--if you want to do statistical inference, you'd better
know what statistical processes you want to use, and design your study
so those processes are appropriate. Don't expect to just gather
data and then figure out how to do statistics on it (not that this
isn't done--all too often!) If you've got nails, you need a hammer,
if you have screws, you need a screwdriver. It's not too hard to
create data sets for which good inferential techniques don't exist!
What haven't we done?
--Chapter 22, comparing two proportions from independent samples.
Like comparing means, with niggling details in the SE computations.
--Chapter 26, testing whether categorical variables in two-way
tables are dependent (the departures from equal proportions in all
the columns are too much to attribute to sampling ("natural") variation,
given independence) "Chi-Square" (Quantitative Res. methods in Sociology)
--Chapter 27, testing if a correlation coefficient is really
different from 0, making confidence interval-type fudge factors around
our regression line. Chapter 29 on CD, Multiple correlation--relationships
when there are more than 2 variables (Econometrics)
--Experiments with more than 2 treatments, and quantitative results
("Analysis of Variance" Ch. 28 on CD--take Quantitative Research Methods
in Psychology)
--Methods that work when our normality assumptions aren't met.
("Nonparametric" methods--"Mann-WhitneyU")
Example (Optional): Tukey's Quick
test (p. 465) for two independent samples. Doesn't need Normal!!
(Not well known; but worth knowing!) Put data in
order (back to back Stemplot?). One group must have the highest value
and the other group the lowest to use this. How much do they not
overlap?
Count the number of items in the "Higher" set that are bigger
than all the items in the "Lower" set. Plus all the items in the "Lower"
set that are smaller than all the items in the "Higher" set. (A
tie at the edge = 1/2.)
"7, 10, 13" 7 or more? (2-sided) Sig. at .05. 10
or more? (2-sided)Sig. at .01. 13 or more? (2-sided)Sig. at .001.
Unfortunately, text doesn't seem to have any problems this will
work well on.
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