Help? I'll
be on campus: this afternoon till 3:45 , and after that, roughly
Friday 10-2 , Tuesday 9:30-2, Wednesday 11-5. (I may leave
to eat, help other students, etc.--email for an appointment to be sure.)
Changes will appear here .
Fay's sessions: she writes:
Monday, 12 December, 1-3 and Tuesday, 13 December,
5-7. She writes:
"I will discuss when to use formulas and trigger words. Look
through the book, start your cheat sheet and the sessions will be more
helpful. I recommend both of the sessions to all so we can cover
more material."
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.
Spring textbook: We'll use D&V, 1st
ed. (yours) one more semester. The bookshop will be buying; the class
is full so you may be able to sell directly, or lend...
~~~~~~~~~~~~~~~
Please fill out an evaluation,
return it to the ENVELOPE circulating
or on the projection cart. The envelope will be with Erna in the
Dean of the Faculty's office, if you miss doing it today.
Day 42: For final... Review, making your notes for your sheet, list questions and get answers.
Homework questions? Day 41
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: always questioning
the source, context of data)
>>Data Analysis: description and exploration<<
Normal distributions and "abnormal"--graphs, summary systems
(mean/s.d., 5-number group)
Categorical
data, two way tables; marginal and conditional proportions
Two related
Quantitative variables; correlation, regression, how good (r, r-squared,
residuals), predicting y from x
>>Data Production: Sampling, Designing Experiments<<
Sample,
Observational study, Experiment
All the
ways it can go wrong (biases, placebo effect, etc.)
>>Statistical Inference: formal Estimating
and Testing--
quantifying our uncertainty and satisfying the skeptic<<
Single
proportion. Single mean. Paired Differences. Difference
of means for two independent samples.
Confidence intervals (For shoebox, on overhead)
Hypothesis tests: null and alternative, P-value, significance
and alpha
More about alphas, and testing as decision making (will not be on exam).
Anything you'll meet will fall into one of those big categories--
--Fancy ways of torturing a data set to make it give up
its secrets--"data mining," subtle and complex summary methods
--Sophisticated experimental and sample 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 Research 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.
Thank you for a very interesting semester!
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