Math 151 , Spring 2004, Wednesday Day 23, March 31 After class Hit reload ...

Exam 2 Friday, next class (Day 24, April 2)  Covers Chapters 2 and 3.  Sample exam problems handed out last week/today.  Solutions outside my door + on reserve.   Not Stratified, Multistage, Systematic samples. Ch.2 Tech detail, Day 21
New Reading: 4.1, 4.2 through 226, 228.  Next 4.3.  Skip 4.4 and Skip Ch. 5.
Day 23 Nothing to Hand in Monday: 
Probability: Sec.  4.1 

 4.9  3 of a kind
 4.10 numbers-->words
 4.12 world series prob?
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Sec. 4.2 Probability models: 
p. 221  4.14 sample spaces
p. 224  4.16 social mobility in Denmark
 4.17 cause of death
 4.18 husbands' share

Finite sample spaces 
p. 226  4.19 legitimate dice?
  4.21 
p. 232  4.31 SRS size 2
4.32 farm size

Read, to discuss 

Probability: Sec.  4.1 
 p. 218 4.6 random digits
 

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Optional 
 
 

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(more of same) 
4.15sample spaces
 
 

4.28 land in Canada
4.29 m&m

Record your coin flips---
Questions for HW?  exam?  Placebo effect!   Good questions-- took whole class
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Start here Monday:
Ch. 4, Probability and Sampling Distributions.
Toward Inference: Table p. 210, Exploratory Data Analysis vs. Statistical Inference
  Sec. 4.1: Sample/Population see day 22

' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' '
Chance  behavior (a random phenomenon): Unpredictable in the short run,  predictable regular pattern in the long run.
    Random numbers:  equally likely in the long run.
   "Random" here is more general--pattern is not necessarily equally likely

"Probability" of particular something happening: proportion of times it would happen in a very long series of independent repetitions of the phenomenon.
    (independence:  outcome of one trial (repetition) must not influence the outcome of any other.)

Sec. 4.2 Probability Models
Random phenomenon,
    Sample space S:  set of all possible outcomes (no overlap of descriptions)
    Event:  any outcome or set of outcomes
    Probability model: S, and a way of assigning a probability to each event.
Sample space depends on what you want to know:
Phenomenon: Flip coin twice.
    S1 = {HH, HT, TH, TT}     S2 = {0, 1, 2} number of heads   S3 = {Y, N} both are heads?

Probability rules:  pp. 222-3, in words, then in notation.
A an event in sample space S, P(A) is "the probability that  A occurs"
    These rules are all true for proportions in long run (Probabilities), prop.of counts, proportions of areas.
    1.  0 < P(A) < 1
    2. P(S) = 1
    3. For any event A, P(A does not occur) = 1 - P(A)
    4.  A and B are  disjoint if they have no outcomes in common (can't happen simultaneously.)
        If A and B are disjoint, their probabilities add:  P(A or B) = P(A) + P(B)

Pick one person from U.S. Pop. (Age 25 +)
Sample space:
No HS degree
       HS only     .
1-3 yrs College
 4 + yrs College
Proportion in pop.
18.3%
33.9%
24.8%
23.0%
Probability 
.183
.339
.248
.230
P(not finished college or didn't start) = ?
P( HS or less) = ?

Finite sample spaces:
Assign a probability to each outcome (>0) so they add to 1.   (Sometimes equal values make sense.)
    Prob. of an event is sum of prob's of its outcomes.

Phenomenon: Flip coin twice.
    S1 = {HH, HT, TH, TT}     S2 = {0, 1, 2} number of heads   S3 = {Y, N} both are heads?
Sample space  | HH | HT | TH | TT |
       Prob's | .25| .25| .25| .25|  P(tail followed by head)=?
Sample space  | 2  |    1    |  0 P(at least 1 tail)=?   P(1 of each) = ?
       Prob's | .25|   .50   | .25|  P(at least 1 Head)= ?  P(2Heads) = ?
Sample space  | Y  |       N      |
       Prob's | .25|     .75      |


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