### Math 151 , Spring '12 Monday Day 21, March 12 Hit reload.....After class...

HW:   (Re) Read p. 198 (explore vs infer).  Now:  Chapter 8.  (Read p. 210-11 (Other designs) last (it's optional)).  Check: p. 217, 8.16-18, 23 at first, then 8.19-21 with Table B. 8.22 optional. In 8.24, there are 2 populations: All adults, and parents of children.  Which pct. is more accurate for its population?.
Read Chapter 9, first to p. 235.  Check p. 240: 9.19, 20, 21, 23 (obs/expt, factors)  : then 24 (choosing groups), 27 (cautions). Then read p. 236 on (other designs) Check 9.19, 22, 25, 26. You can do any time now:  Read Data Ethics, pp. 249-260. Next: Ch. 10 (Normal dist. is back)

Exam 2 handed back.  Link to statistics, comments, solutions.  I may make a few more comments on content on Wednesday
Midterm grades will be posted by Friday afternoon; will be slanted pessimistically at the high end.
Because it's so important to have control of the Normal distribution:
Makeup work to get 90% of the 42 Normal distribution points.  To me by 3:30 pm Day 27 (Monday, April 2--2 school weeks).  START NOW!  (Print the sheet if you need it.)
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On the sign-in clipboard. Next to your name: Put the number of East Asian surname individuals in your SRS, (p. 209 8.7)  Be sure it's in your HW also, with the names of your sample!  If you did it with another person, put that on the clipboard!

recap: Ch. 8&9:  Producing Data:  Aim:  create data sets that will allow us to make inferences to a larger world than just the data we have.

Recap: Sampling  (see day 20 for notes )
>>Population: Entire group  that we want information about.
>>Sample: The part of the population we actually examine.
Hope:  Sample will be representative of the population.
>> Sampling design:  Describes exactly how sample is to be chosen from population.

(SAMPLING) BIAS:  The design of a study is biased if it systematically favors certain outcomes.
Non-probability samples (sampling badly): Voluntary response sample , Convenience sample

Our main sampling design:
Simple Random Sample
(
SRS) of size n n individuals
chosen in such a way that every possible set of n individuals has an equal chance of being chosen.
Homework Questions Day 20
New today:
Using Random Number Table. See Day 20 for details.
(Simple Random Sample Applet, done last time.  Enter population size, sample size, hit Reset, then Sample.)
See Day 20 for rest of details on Ch. 8:
Some more sources of bias:
**Undercoverage:     One possible source of undercoverage: your Sampling frame excludes some individuals.
** Nonresponse
**Response bias
**Wording of questions
A random sample (p.210) is from a design where impersonal chance is used to pick the individuals.  SRS (p. 205) is the most straightforward.  More sophisticated methods are often used, but they're optional this term. (More info)
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Larger (RANDOM) samples give more accurate results than smaller random samples.
(but Not because you have more of the population.)
More discussion of terms used in sampling

.Start Ch. 9 Wednesday.....
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Chapter 9,
intro

Observational Study:  Observes
individuals, measures variables, does not influence the responses. (ch.8)

Sometimes observe individuals who are (more or less) conveniently at hand, or, better,
Take Sample from a population, examine it.... (ch.8)
Experiment: Imposes treatment  on individuals, to see how the treatment influences  the response. (ch.9)   Confounding:  Two variables (explanatory or lurking) are confounded when you can't sort out their effects on a response variable.  (Rats:  Mothers' grooming causes sociability, or inherited sociability from mothers who like to groom? Health: Coffee and cigarettes (till recently)).

Designing Experiments

Do something to:
"Experimental Units" = "Subjects" = individuals.
Treatment:  Specific experimental condition we impose on one or more subjects.
Factor:  Explanatory Variable we manipulate.
There will be Specific values of a factor that we set. (Sometimes called "levels")
Response variable(s)  Results that we measure.
E.g. Corn planting (HW day ??, p. 116, 4.32)  1 factor = planting rate.  5 different values (levels). 16 individuals (plots of ground). Response:  yield per acre.

E.g. 2 headache medications, in combination?
A two-factor experiment, each with 3 values (levels). 9 possible treatments.
Factor A: Aspirin: values: None, 500 mg, 1000 mg
Factor B: Caffeine: values: None, 50 mg, 100 mg
Response variable: reported pain relief
 Aspirin None 500 mg 1000 mg None Treatment 1 Treatment 2 Treatment 3 Caffeine 50 mg Treatment 4 Treatment 5 Treatment 6 100 mg Treatment 7 Treatment 8 Treatment 9

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