Transcript Chapter 4 Power Point Review

Expose marine bacteria to x-rays for time periods from 1 to 15 minutes.
Here are the number of surviving bacteria in hundreds on a culture plate
after each exposure time.
Time
1
2
3
4
5
6
7
8
count
355
211
197
166
142
106
104
60
Time
9
10
11
12
13
14
15
Count
56
38
36
32
21
19
15
a) What Is the shape of the growth.
b) Since it is a decay model, try to transform the data by taking the
logarithm of counts. What happens?
c) What is the least squares regression line of your transformed
data?
d) Use an inverse transformation and give the new predicted line
Mammal
Heart Weight
Length of cavity
Mouse
.13
.55
Rat
.64
1
Rabbit
5.8
2.2
Dog
102
4
Sheep
210
6.5
Ox
2030
12
Horse
3900
16
Use the power function (logs) to transform the data.
a) What is new least squares regression line?
b) Now do an inverse transformation so as to get in a∙bx form.
A 1969 study among Pima Indians of Arizona investigated the relationship
between a mother’s diabetic status and the appearance of birth defects in
her children. The results appear below:
Birth
Defects
Nondiabetic Prediabetic
Diabetic
None
754
362
38
1 or more
31
13
9
a) Find the marginal distributions in counts
b) Compute the conditional distributions (in %’s) of birth defects for
each diabetic status.
State whether the following is an example of causation, confounding
variables or common response.
a) Over the past 30 years in the US there has been a strong
correlation between cigarette sales and the number of high
school graduates.
b) A serious study once found that people with 2 cars live longer
than people with only 1 car. Owning 3 cars is even better and so
on. There is a substantial positive correlation between the # of
cars x and the length of life y. What is this an example of , and
what lurking variables, if any may exist