General end-of term info: Additions&
changes in this color
Final Exam: open book takehome, due Thursday, Dec. 15, noon.
Into my hands or under my door, please!
It is available in class today, in the pink folder in the 251 box outside
my door if you weren't here..
Data file (Chess) are:
--on a floppy in the pocket on my door--please copy and return
--on the web here, as SPSS chess.sav
and
as a text file (should import OK--check your
results)
chess.txt
--on several computers in Mac 101, as I put it into Class Materials\Math251\ForFinal.
Save it under your own name for your own use; don't save back to the Lab
computer as Chess! As of Wed afternoon,
(from east to west)
Sugar; Sage, Pepper, Olive; Clove and its
unnamed neighbor; Basil; Rosemary, Paprika, Lemon; Oregano, Parsley.
I'll be on campus: this afternoon till 3:45 , and after that,
roughly
Friday 10-2 , Tuesday 9:30-2, Wednesday 11-5, Thursday
(exam due)10:45-12. (I may leave to eat, help other students, etc.--email
for an appointment to be sure.) Changes will appear here
.
Math 300 will be mostly probability models, using
Carol Ash, Probability Tutoring Book, an Intuitive Course for Engineers
and Scientists and everyone else! IPS will be helpful to
supplement this, especially to finish chapter 4 (especially Sec.
4.5), and review Binomial (Ch. 5)
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Questions?
A glimpse of the wider world of statistics, in your textbook:
3 or more independent samples: comparing proportions--
use
two-way tables and "Chi-square" statistics (Ch. 9) to test if proportions
different; Extension: two "dimensions" of table (color of medicine
package, willingness to buy it) are ?? independent. (Research methods of
Sociology)
"Chi-square
goodness-of fit" (9.4)--Biology models.
3 or more independent samples: comparing means--
Analysis of variance (Ch. 12&13) (Quantitative Research Methods
of Psychology)
Inference for regression: (Ch. 10) Assuming a population
where:
for each possible x, corresponding y's are normal, same s.d.
for all x's ("homoscedasticity"), and means of y's from each x lie on a
straight line.
Is the slope significantly different from
0?
Confidence intervals: for slope and intercept
for a specific x: 1) interval for what the predicted y-hat (line)
value would be. (mean)
2) interval for where 95% of individual y's would be (more scatter)"Prediction
interval"
Example 10-1, miles per gallon on miles per
hour (data from a single car). Logmph makes it straighter.
SPSS file
In graph, Insert>fit line>Regression,
Edit >Regression Parameters, "Prediction Interval", mean, individual.
Multiple regression: (Ch 11) Instead of one x-variable, 2
or more all predicting y. (Econometrics)
Miles per gallon as a linear
function of logmph and miles traveled (age of car).
Chapters on the CD (or downloadable)
Logistic regression (Ch 16) gives an introduction to a way to make predictions where the y is a yes/no, true/false variable (the prediction is a version of the probability of yes). And where one (or more) of the predictor (x) variables is also a yes/no variable. (Econometrics?)
"Bootstrap" methods (Ch. 14) -- (nice, fairly new, need powerful software) "Resampling" methods. Make no assumptions about underlying population distributions; take a zillion subsamples of your sample to get a handle on variability of your sample, therefore of the population it "represents."
Nonparametric tests (Ch. 15) The most commonly used tests for non-normal populations. Based mostly on "order statistics" (percentiles), but usually presented as easily computed results. (Biology, other fields)
Quality control (Ch 17) for improving the processes involved in producing a product or service, by collecting and monitoring data. Some nice ideas, not hard mathematically, but a lot of jargon. American ideas (Mr. Deming), taken up in Japan and very instrumental in Japanese manufacturing success around the world; ideas returned to U.S. corporations. Fad level "Total quality management" but settling into fundamental use now, I think.
And much much more out there; variations and embroideries and
refinements and different names for the same things --- but "Everything
" is about
Exploratory (descriptive)
data analysis
or Confirmatory (inference) on
data assumed to be collected in some way as free of biases as possible,
and as much like a random sample as possible (Design
of samples, experiments).
Analysis of variance nod, if time:
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Thank you for a very pleasant and interesting semester!
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