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Unformatted text preview: Q13
Q9 Q57
Q1113 CHAPTER 3
Section 3.1, pp. 168–170 Solutions to Odd Number Problems
1. Answers may vary.
a) strong positive correlation
b) moderate positive correlation
c) weak negative correlation
d) zero correlation
e) weak positive correlation
3. a) b) hours studied versus exam score
moderate positive hours watching TV versus exam score
moderate to strong negative c) The hours spent watching TV has a stronger correlation with exam scores since
there is less scatter among the data points.
d) r = 0.7548 r =  0.8784 These correlation coefficients support the answer in part c).
Top 5. a)
This appears to be a weak negative linear
correlation. b) r =–0.61
c) The computed rvalue agrees with the classification in part a).
d) The point (1, 2) is an outlier.
e) Answers may vary. Karrie may have worked only part of last year.
7. a) b) This appears to be a moderate to strong positive linear correlation.
c) r = 0.765
Top 9. a) Data with a perfect positive linear correlation have a correlation coefficient of
1. Data with a perfect negative linear correlation have a correlation coefficient
of –1. Answers may vary.
An example of a perfect positive linear correlation is the number of chocolate bars
purchased versus the price paid. An example of a perfect negative linear correlation is the amount of fuel remaining in
the fuel tank versus the number of kilometers driven at a constant speed on a level
highway. Top 11. Answers may vary.
a) To make the data look better for the Rogers
Method, Rogers can adjust the scales so that their
data look as if there is a strong, positive correlation,
while the Laing data appear as if there is little
correlation. b) Laing could adjust the vertical scale and horizontal scale so that the Rogers data
graph looks flat. c) A calculation of correlation coefficients and a plot of both data sets on the same
graph will reveal the true comparison of the two methods.
13. Answers will vary.
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 Spring '10
 Mr.M
 Correlation

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