Question #1
You have always been told that your car insurance rate will decrease when you
become older. Let us check this claim out using the data on 15 persons' ages and their
quarterly insurance premiums in dollars.
Download the data from
here
and then run the appropriate regression to test the linear
dependence of insurance rate for cars on ages at the 1% level of significance.
Hint: there are 5 correct answers
•
We want to test H
0
: β = 0 against alternative H
1
: β ≠‚ 0
•
We want to test H
0
: β = 0 against alternative H
1
: β < 0
•
We want to test H
0
: β = 0 against alternative H
1
: β > 0
•
We want to test H
0
: ρ = 0 against alternative H
1
: ρ ≠‚ 0
•
We want to test H
0
: ρ = 0 against alternative H
1
: ρ < 0
•
We want to test H
0
: ρ = 0 against alternative H
1
: ρ > 0
•
The pvalue for the test is 0.009
•
The pvalue for the test is 0.016
•
The pvalue for the test is 0.086
•
The pvalue for the test is 0.170
•
The pvalue for the test is 0.287
•
We will reject the null hypothesis.
•
We will fail to reject the null hypothesis.
•
We can conclude that insurance premium does depend negatively and
linearly on age.
•
There is insufficient evidence to conclude that insurance premium
depends negatively and linearly on age.
Question #2
Look at the example on page 75 (slide 41) of the course packet. They want you to do
a test on the correlation coefficient (that is, a test to see if any linear relationship
exists between two variables) between student population size and pizza sales, based
on the Armani Pizza example. The data is provided
here
. Answer the following
questions based on your findings.
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1.
The sample correlation coefficient between sales in thousands of dollars and
the number of students in thousands is given by
0.963366334
. If sales was
measured in millions of dollars instead of thousands of dollars then the
correlation coefficient
o
cannot be computed
o
would stay the same
o
would go up
o
would go down
.
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 Spring '09
 PETRY
 Linear Regression, Regression Analysis

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