Math 151 , Fall 2002, Monday Day 17, October 7 Hit reload to get most currentAfter class

SPSS: The newer version 11.5 is on the Westmost 10 machines in 101.  I hope this will solve some of our problems.  Let me know if you have a problem, either way.
Exam comments:  1) All but 1 got the skewness "right"!  2) draw pictures for normal--picture first then put labels on the axes.  (Trouble with normal?  get extra practice sheet, see me or Math Clinician)
- - - - - - - - - - - - - - - - - - - - - - -  -
Homework:  Reading:  Sec. 2.4.  Skip 2.5. Ch. 3 Intro.   Ahead in Ch. 3.
Hand in  Wednesday
Sec. 2.4, all Moore:
p. 131, 2.53 farm population (SPSS)
   Also connect the dots, or plot the residuals--is there any curve to the relationship?
p. 132  2.54 Dow average/stocks
p. 138 2.63 math&verbal r, states/individuals
B.  Look again at p. 122, 2.37.   These values are averaged values, over a bunch of people's guesses.  What would the graph look like if all the individuals' separate guesses had been graphed?  Add points to your graph to give the idea.

p. 133  2.55 tv watching & grades 
 2.56 economists&pay 
 2.64 herbal tea
* * * * * * * * * * * * * *
Postpone Ch.3 Intro: 
p. 167, 3.1, 3.2, 3.3 exp, obs

 Read, to discuss (all Moore)
Sec. 2.4
p.136 2.57 firefighters, 
   2.58 self-esteem
p. 138 2.61 shoe size/reading
2.66 Education/income
* * * * * * * * * * * *

postponeCh.3 Intro: 
p. 170, 3.5 pop, samp...
p.182, 3.17 obsn/exp
    3.18 novel--pop, samp.

 Optional 
Sec. 2.4
p. 137 2.59 size of hospital
* * * * * * * * * 
 
 
 
 
 
 

 
Activstats, for Moore Ch. 3, Sample surveys, 10-1  Know Sample/Population, Simple Random Sample.  (Don't get bogged down in taking your own potato sample in 3rd activity.  If it's confusing, skip it) Do the last activity p. 10-1, pop. size doesn't matter.  10-2 Know Bias, Voluntary Response bias, Nonresponse, Undercoverage. 10-3 optional, but 1st activity is amusing, 2nd is a good check on above ideas.
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
Homework questions?
Mopping up 2-variable
The Line formula yhat = a + bx tells us our best prediction or estimate of a response (y) value for a particular value of the explanatory (x) value.  It says NOTHING about how good that "best" is--that is, it says nothing about how tight or scattered the data is around the line.  R-squared does that job.

Cautions  Sec. 2.4
Plot the data: Summary formulas and numbers don't tell the whole story.  (Anscombe's quartet, Moore p.127, 2.46-7)

Extrapolation-- extra (outside) polation (putting a point): Using the line to predict outside the range of x's you have data for.  Unavoidable if x is time; but inevitably dangerous--nothing says the mechanism you see will persist in a wider range.  (Many relationships are curved or bent; but over short intervals "pass for" straight.  Straight may be a good approximation, but only in the short run.)

Averaged data will produce a stronger relationship (higher correlation, R2) than the merged raw data from individuals (the averaging hides much variability) You did a problem on height vs. age--they were averaged values.

"Lurking" variable:  has an important effect, but not one of the variables studied.
    Meatloaf shrinkage vs. placement in oven?  (cooking thermometer/not had greatest influence)
    Time sequence of observations a common one.  (Learning, tiring, aging)
    The trouble with lurking variables is that by definition you don't know they're there.  Look behind every tree.

Association does not imply causation
    Manatees:                                           Year
             boat registrations            kills

            If you didn't know boat registrations, would you believe that "year" was the cause of "kills"?
                (Are all boats actually registered?  Possible lurking variable= unregistered boats.)
Direction?  Rooster causes sun to rise by crowing?
Both variables "caused" by a lurking variable?

Baby rats whose mothers licked and groomed them more   grew up to be more exploratory, social, less timid.
            Cause? Effect?  How to tell?
Establishing that x "causes" y:  difficult:
    Best: Do an experiment in which we change x, keep lurking variables under control. (Sec. 3.2)  Rats.
    Otherwise: Strong association. Consistent over many studies. Higher x-->stronger y.  X precedes y in time.  A plausible mechanism exists (parallel studies?)
                Generalize rat grooming to humans?

    E.g. hydrogenated oils --> heart disease?  Homocysteines --> heart disease?

= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
Start here WEDNESDAY
Chapters 1 and 2 have covered analyzing data that was given to us--what it said about itself.
    Informally, develop guesses, suspicions, hypotheses about the world the data came from.
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.
       Observational Study:  Observes individuals, measures variables, does not influence the responses. (3.1)
                    Take Sample from a population, examine it,
                           hope it's representative so we can infer population is like sample.
                            (Not very useful for cause-and-effect--see above)
        Experiment: Imposes treatment  on individuals, to see how the treatment influences  the response. (3.2)
                            Best for cause-and-effect.

Confounding:  Two variables (explanatory or lurking) are confounded when you can't sort out their effects on a response variable.
--Used to be: coffee drinking and smoking--most people did both, or neither...
______________________
Ch. 3.1 Designing Samples

>>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) BIAS:  The design of a study is biased if it systematically favors certain outcomes.

Sievers home  Math151-Fall02/Day17.htm  9pm 10/6/02
This page belongs to Sally Sievers who is solely responsible for its content. Please see our statement of responsibility.