Stat 512 by zhouwenjuan

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```									Stat 512

Day 2: Designing Experiments
Leftovers from Tuesday

   Questions on syllabus?
   Repeating the question
   Blackboard problems?
   Sooner turn in, sooner get feedback?
Figure 1: Comparison of Airway Obstruction
from Level of Exposure

Last Time                                            100%

80%

Percentage
60%                     43       airw ay not obstructed
52
40%                              airw ay obstructed

20%
15
6
   Microwave popcorn factory                         0%
low          high

 Two categorical variables
Level of Exposure

   level of exposure, whether airway obstruction
   Graphical summary: segmented bar graph
   Numerical summary: difference in conditional
proportions
   Not able to draw cause-and-effect conclusions
   Other differences between the two groups that
might explain the higher airway obstruction rates in
high exposure group?
   Not able to generalize to all microwave plant
workers
   “Healthy worker effect”
100%

Last Time
80%

Percentage
60%
Control
Lung cancer patient
40%

20%

0%
none   light     mod   heavy excess chain

Smoking and lung cancer
Level of Smoking


   Two categorical variables (amount of smoking and
with or without disease)
   Graphical summary: segmented bar graph
   Numerical summary: difference in conditional
proportions
   Can’t draw cause and effect conclusion
   Could be some other difference (diet) between the EV
groups that explains higher lung cancer rates with more
smoking
Last Time

   Generalizing from sample to population?
   Reasonable to conclude 605/(605+780), or 44%,
of all males of similar ages and economic status
have lung cancer?
   Measurement issues
   Relying on recall…
   Know they are sick…
Second famous smoking study

   Hammond and Horn (1958)
   Find 12,000 healthy men, complete a
questionnaire on smoking habits, had 22,000
American Cancer Society volunteers follow
them for 44 months to see whether they die
from lung cancer
Practice Problems

   Identifying variables
   Supreme Court Justices:
   Qualitative: gender, party
   Quantitative: age, number of yes votes
   Not OK:
   number of republicans
Practice Problems

   Victims of violence
   OU: people (not “number of victims”)
   EV: whether abused
   RV: whether commit crime
   Not ok in defining variables:
   Those who…
   Number abused…
   Whether abuse leads to violent crime…
   (c) Mostly to spur discussion…
   Which variable was “controlled” by the researchers…
More practice:

   The book Day Hikes in San Luis Obispo County by
Robert Stone gives information on 72 different hikes
that one can take in the county. For each of the 72
hikes, Stone reports the distance of the hike (in
miles), the anticipated hiking time (in minutes), the
elevation gain (in feet), and the region of the county
in which the hike can be found (North County, South
County, Morro Bay, and so on, for a total of eight
regions).
   Observational units?
   Types of variables
More Practice: Hiking in SLO

   Are these legitimate variables?
   Longest hike in the book
   Those hikes in the North County region
   Proportion of hikes with an elevation gain of more
than 500 feet
   Is hiking time related to elevation gain?
Example 1: Near-sightedness and Night
Lights
   “Myopia and ambient lighting at night,”
   Quinn, G.E., Shin, C.H., Maguire, M.G. and
Stone, R.A.
   Nature, 399:113-114, 1999.
Example 1: Myopia and Night Lights

Room light                Night light          Darkness
Far-sighted     12                        39                   40
Normal          22                        115                  114
Near-sighted    41                        78                   18

100%
90%
80%
70%
60%                                          Near-sighted
50%                                          Normal
40%                                          Far-sighted
30%
20%
10%
0%
Room light   Night light   Darkness
Example 1: Myopia and Night Lights

   Base rate: .286
   Conditional Proportions
   Room light: .55 myopia, .16 hyperopia
   Night light: .336 myopia, .168 hyperopia
   Darkness: .105 myopia, .232 hyperopia
   Convincing evidence that using more light in
the child’s room causes a higher rate of
myopia?
Terminology: Confounding variable

   Has an influence on the response, but its
effects cannot be separated from those of the
explanatory variable
room light
eye-sight
darkness
parents with good eyes
Example 2: Have a Nice Trip

   Can instruction in a recovery strategy
improve an older person’s ability to recover
from a loss of balance?
   12 subjects have agreed to participate in the study
   Assign 6 people to use the lowering strategy and
6 people to use the elevating strategy
   Similar amounts of men in both groups?
   Proportion of group 1 that are male – proportion of group
2 that are male
   What would we like to be true about these proportions?
Investigation 1-7: Have a Nice Trip

   Take 12 index cards
   Put one of the 12 names on each card
   Shuffle the cards and deal out 6 to learn the
lowering strategy and the other 6 to learn the
elevating strategy
   What is the number of males in each group?
   The male proportion in each (out of 6)?
   What is the difference in these 2 proportions?
Pool Results

   Quantitative variable
   Different type of graph…
   What are the observational units and variable in
this graph?
Long-term pattern?

   Use applet to repeat the process a large
number of times
   Open IE, double click on “Dr. Beth Chance” >
“Statistical Methods” > “Stat 512 Java
Applets” > “Randomizing Subjects
   The applet mimics exactly what you did with
the index cards.
Effect of randomization

   If you randomly assign subjects to the
groups, what is generally true about the
groups?
   If after imposing the treatment, you later
observe a difference between the groups, to
what can you attribute that difference?
Moral

   Randomization equalizes variables between
groups
   Should not have potentially confounding variables
   If later (after treatments) observe a difference
between groups, feel comfortable attributing that
difference to the explanatory variable
   Remaining question
   Will always be some difference, by chance
   How big does this difference have to be?
Example: Friendly Observers

   “The trouble with friendly faces: Skilled
performance with a supportive audience,”
   Butler, J. L., and Baumeister, R. F.
   Journal of Personality and Social Psychology, 75:
1213-1230, (1998).
To do

   By Friday, noon
   Turn in HW 1 (4 problems)
   By Tuesday
   Preview the Friendly Observers example
(complete questions (a)-(e))
   Submit PP 2 in Blackboard
   Pick and start reading one of the 3 articles

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