Math 151 , Spring 2004, Monday Day 19, March 15 After class Hit reload ...

HW   Reading:  Ch. 3 thru 3.1.  Ahead in  3.2
Hand in 
Ch.3 Intro: 
p. 167, 3.1, 3.2, 3.3 exp, obs
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Sampling
 p. 170, 3.4employed women Also: What is the sampling frame? (Def. p. 179, #3.13)
 3.6 letters to Congress
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POSTPONE THE REST
p. 173 3.7 SRS
p. 207, 3.65 SRS
p. 184, 3.26 Random digits
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p. 185 3.30 survey questions
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p. 181  3.16 bigger sample size
p.185 3.31 sampling error for men		 Read, to discuss 
Ch.3 Intro: 
p. 170, 3.5 pop, samp...
p.182, 3.17 obsn/exp
    3.18 novel--pop, samp.
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Sampling 
p. 183, 3.22 president
3.23black police
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POSTPONE THE REST
p.180 3.14 ring-no-answer
3.15 2 campaign questions		 Optional 
 

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p. 3.24SRS
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Exam 2, the Friday after Break!
Pick a digit (from 0,1,2,3,4,5,6,7,8,9).  Write it down.
    HW questions?  (C, D) from Day 13
A. Income depends on height?!
    What is "$789", and what kind of analysis did they do? Footnote adds what?
Cars.sav ,cars.spo relevant to econ graduates problem (2.56).  (X=weight, Y=time to accelerate to 60.  Heavier car should be slower? Oops. Panel with #of cylinders, or color with horsepower.)
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Association--->> Causation? Day 17
Ch. 3:  Producing Data:  Aim:  create data sets that will allow us to make inferences to a larger world than just the data we have. Day17
       Observational Study:  vs.  Experiment:  Day 17)
Confounding:
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Ch. 3.1 Designing SamplesDay 17
  Population/Sample, Sampling Bias (Sampling Frame, Nonresponse) , Nonprobability Samples, Probability Sample
Start here Wednesday
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.
HOW?  A chance mechanism: Cards, dice, computer program, or
Table of random digits (Simulates rolling a die with 0,1,....9, over and over...) (Table B, back flyleaf)
    Every digit, every sequence of digits, is equally likely to be "next" in any direction.
To use:  label everyone in the population with a number.
    Important:  Every labeling number needs the same number of digits.
    To label 9 people, use the labels 1,2,3,....9 (1-digit chunks)
    To label 15 people, use the labels 01, 02, ...10, 11, ...15 (2-digit chunks)
    To label 125 people, use the labels 001, 002, ... 124, 125 (3-digit chunks)
Pick a place (at random) in the table, start reading across in that size chunk.  Get n eligible numbers (discard repeats)
                    Read Row 150:   07511   88915   41267   16853   84569   79367 ..
From 9 people, a sample n = 5:   0,7, 5, 1, 1, 8, 8, 9, 1, 5, 4,     (sample is individuals 7, 5, 1, 8, 9)
From 15 people, a sample   07, 51, 18, 89, 15, 41, 26, 71, 68, 53, 84, 56, 97, 93, 67.... keep reading,
    go to next line (or back to top line) if you need more.  Individuals 7, 15,...are chosen using this line.
From 125 people, a sample 075, 118, 891, 541, 267, 168, 538, 456, 979, 367...keep reading.  Individuals 75, 118, ...

    Why the same number of digits in each label?  Each individual 3-digit chunk is as likely as any other 3-digit chunk.  But a 1- or 2-digit chunk is more likely than any 3-digit chunk. So 2 will come up more often than 12, but 02 will come up just as often as 12.

    Why across?  For consistency on HW, go the way they say (so you get the answer in the book).  In practice, you can read up, down, backwards, as long as you decide beforehand, and don't change in the middle of choosing the sample.

Sources of bias, even in probability samples:

Inference to the population: Sample results will vary.
   Different samples will represent the population with differing accuracy.
   Well-designed Random (probability) sampling will avoid systematic bias.
   In general,  A larger random sample will give more accurate information about the population than a smaller random sample.
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