Question

Students at a major university are complaining of a serious housing crunch. Many of the university's students, they complain, have to commute too far to school because there is not enough housing near campus. The university officials respond with the following information:
The mean distance commuted to school by students is 17.1 miles, and the standard deviation of the distance commuted is 3.7 miles. Assuming that the university officials' information is correct, complete the following statements about the distribution of commute distances for students at this university.
1) According to Chebyshev's theorem, at least 36% of the commute distances lie between miles and miles. (Round your answer to 1 decimal place.)
2) According to Chebyshev's theorem, at least of the commute distances lie between 9.7 miles and 24.5 miles.
a) 56%
b) 75%
c) 84%
d) 89%
3) Suppose that the distribution is bell-shaped. According to the empirical rule, approximately of the commute distances lie between 9.7 miles and 24.5 miles.
a) 68%
b) 75%
c) 95%
d) 99.7%
4) Suppose that the distribution is bell-shaped. According to the empirical rule, approximately 68% of the commute distances lie between miles and miles.

Answer 1

Answer:

1) Between 12.5 miles and 21.7 miles.

2) b) 75%

3) c) 95%

4) Between 13.7 miles and 20.5 miles.

Step-by-step explanation:

Empirical Rule:

The Empirical Rule states that, for a normally distributed random variable:

68% of the measures are within 1 standard deviation of the mean.

95% of the measures are within 2 standard deviations of the mean.

99.7% of the measures are within 3 standard deviations of the mean.

Chebyshev Theorem:

The Chebyshev Theorem can also be applied to non-normal distribution. It states that:

At least 75% of the measures are within 2 standard deviations of the mean.

At least 89% of the measures are within 3 standard deviations of the mean.

An in general terms, the percentage of measures within k standard deviations of the mean is given by $P = 100(1 - \frac{1}{k^{2}})$.

1) According to Chebyshev's theorem, at least 36% of the commute distances lie between miles and miles.

Within k standard deviations of the mean, and k is found when $P = 36$. So

$P = 100(1 - \frac{1}{k^{2}})$

$36 = 100 - \frac{100}{k^2}$

$\frac{100}{k^2} = 64$

$64k^2 = 100$

$k^2 = \frac{100}{64}$

$k = \sqrt{\frac{100}{64}}$

$k = \frac{10}{8}$

$k = 1.25$

Within 1.25 standard deviations of the mean.

1.25*3.7 = 4.6 miles

17.1 - 4.6 = 12.5 miles

17.1 + 4.6 = 21.7 miles

Between 12.5 miles and 21.7 miles.

2) According to Chebyshev's theorem, at least of the commute distances lie between 9.7 miles and 24.5 miles.

17.1 - 9.7 = 24.5 - 17.1 = 7.4 miles, so within 2 standard deviations of the mean, which is 75%, option B.

3) Suppose that the distribution is bell-shaped. According to the empirical rule, approximately of the commute distances lie between 9.7 miles and 24.5 miles.

Within 2 standard deviations of the mean, by the Empirical Rule, which is 95%, option c.

4) Suppose that the distribution is bell-shaped. According to the empirical rule, approximately 68% of the commute distances lie between miles and miles.

Within 1 standard deviation of the mean.

17.1 - 3.4 = 13.7

17.1 + 3.4 = 20.5

Between 13.7 miles and 20.5 miles.