Jejek | Course Paper

Unlock access to all the studying documents.

View Full Document

Solutions to Homework 8

Statistics 302 Professor Larget

Textbook Exercises

6.12 Impact of the Population Proportion on SE Compute the standard error for sam-

ple proportions from a population with proportions p= 0.8, p = 0.5, p = 0.3,and p= 0.1 using a

sample size of n= 100. Comment on what you see. For which proportion is the standard error the

greatest? For which is it the smallest?

Solution

We compute the standard errors using the formula:

p= 0.8 : SE =rp(1 −p)

n=r0.8(0.2)

100 = 0.040

p= 0.5 : SE =rp(1 −p)

n=r0.5(0.5)

100 = 0.050

p= 0.3 : SE =rp(1 −p)

n=r0.3(0.7)

100 = 0.046

p= 0.1 : SE =rp(1 −p)

n=r0.1(0.9)

100 = 0.030

The largest standard error is at a population proportion of 0.5 (which represents a population split

50-50 between being in the category we are interested in and not begin in). The farther we get from

this 50-50 proportion, the smaller the standard error is. Of the four we computed, the smallest

standard error is at a population proportion of 0.1.

Standard Error from a Formula and a Bootstrap Distribution In exercise 6.20, use Statkey

or other technology to generate a bootstrap distribution of sample proportions and find the stan-

dard error for that distribution. Compare the result to the standard error given by the Central

Limit Theorem, using the sample proportion as an estimate of the population proportion.

6.20 Proportion of home team wins in soccer, with n= 120 and ˆp= 0.583.

Solution

Using StatKey or other technology to create a bootstrap distribution, we see for one set of 1000

simulations that SE = 0.045. (Answers may vary slightly with other simulations.) Using the

formula from the Central Limit Theorem, and using ˆp= 0.583 as an estimate for p, we have

SE =rp(1 −p)

n≈r0.583(1 −.583)

120 = 0.045

We see that the bootstrap standard error and the formula match very closely.

6.38 Home Field Advantage in Baseball There were 2430 Major League Baseball (MLB)

games played in 2009, and the home team won in 54.9% of the games. If we consider the games

played in 2009 as a sample of all MLB games, find and interpret a 90% confidence interval for the

proportion of games the home team wins in Major League Baseball.

Solution

To find a 90% confidence interval for p, the proportion of MLB games won by the home team, we

use z∗= 1.645 and ˆp= 0.549 from the sample of n= 2430 games. The confidence interval is

Sample statistic ±z∗·SE

ˆp±z∗rˆp(1 −ˆp)

0.549 ±1.645r0.549(0.451)

2430

0.549 ±0.017

0.532 to 0.566

We are 90% confident that the proportion of MLB games that are won by the home team is between

0.532 and 0.566. This statement assumes that the 2009 season is representative of all Major League

Baseball games. If there is reason to assume that that season introduces bias, then we cannot be

confident in our statement.

6.50 What Proportion Favor a Gun Control Law? A survey is planned to estimate the

proportion of voters who support a proposed gun control law. The estimate should be within a

margin of error of ±2% with 95% confidence, and we do not have any prior knowledge about the

proportion who might support the law. How many people need to be included in the sample?

Solution

The margin of error we desire is ME = 0.02, and for 95% confidence we use z∗= 1.96. Since we

have no prior knowledge about the proportion in support p, we use the conservative estimate of

˜p= 0.5. We have:

n=z∗

ME 2

˜p(1 −˜p)

=1.96

0.022

0.5(1 −0.5)

= 2401

We need to include 2, 401 people in the survey in order to get the margin of error down to within

±2%.

6.64 Home Field Advantage in Baseball There were 2430 Major League Baseball (MLB)

games played in 2009, and the home team won the game in 54.9% of the games. If we consider

the games played in 2009 as a sample of all MLB games, test to see if there is evidence, at the 1%

level, that the home team wins more than half the games. Show all details of the test.

Solution

We are conducting a hypothesis test for a proportion p, where pis the proportion of all MLB games

won by the home team. We are testing to see if there is evidence that p > 0.5, so we have

H0:p= 0.5

Ha:p > 0.5

This is a one-tail test since we are specifically testing to see if the proportion is greater than 0.5.

The test statistic is:

z=Sample Statistic −Null parameter

SE =ˆp−p0

qp0(1−p0)

=0.549 −0.5

q0.5(0.5)

2430

= 4.83.

Using the normal distribution, we find a p-value of (to five decimal places) zero. This provides

very strong evidence to reject H0and conclude that the home team wins more than half the games

played. The home field advantage is real!

6.70 Percent of Smokers The data in Nutrition Study, introduced in Exercise 1.13 on page 13,

include information on nutrition and health habits of a sample of 315 people. One of the variables

is Smoke, indicating whether a person smokes or not (yes or no). Use technology to test whether

the data provide evidence that the proportion of smokers is different from 20%.

Solution

We use technology to determine that the number of smokers in the sample is 43, so the sample

proportion of smokers is ˆp= 43/315 = 0.1365. The hypotheses are:

H0:p= 0.20

Ha:p6= 0.20

The test statistic is:

z=Sample Statistic −Null Parameter

SE =ˆp−p0

qp0(1−p0)

=0.1365 −0.20

q0.2(0.8)

325

=−2.82

This is a two-tail test, so the p-value is twice the area below -2.82 in a standard normal distribution.

We see that the p-value is 2(0.0024) = 0.0048. This small p-value leads us to reject H0. We find

strong evidence that the proportion of smokers is not 20%.

6.84 How Old is the US Population? From the US Census, we learn that the average age of all

US residents is 36.78 years with a standard deviation of 22.58 years. Find the mean and standard

deviation of the distribution of sample means for age if we take random samples of US residents of

size:

(a) n= 10

(b) n= 100

Solution

(a) The mean of the distribution is 36.78 years old. The standard deviation of the distribution of

sample means is the standard error:

SE =σ

√n=22.58

√10 = 7.14

(b) The mean of the distribution is 36.78 years old. The standard deviation of the distribution of

sample means is the standard error:

SE =σ

√n=22.58

√100 = 2.258

sample means is the standard error:

SE =σ

√n=22.58

√1000 = 0.714

Notice that as the sample size goes up, the standard error of the sample means goes down.

Standard Error from a Formula and a Bootstrap Distribution In Exercises 6.96 to 6.99,

use StatKey or other technology to generate a bootstrap distribution of sample means and find the

standard error for that distribution. Compare the result to the standard error given by the Central

Limit Theorem, using the sample standard deviation as an estimate of the population standard

deviation.

6.97 Mean commute time in Atlanta, in minutes, using the data in CommuteAtlanta with n=

500,¯x= 29.11,and s= 20.72.

Solution

Using StatKey or other technology to create a bootstrap distribution, we see for one set of 1000

simulations that SE ≈0.92. (Answers may vary slightly with other simulations.) Using the formula

from the Central Limit Theorem, and using s= 20.72 as an estimate for σ, we have

SE =s

√n=11.11

√25 = 2.22.

We see that the bootstrap standard error and the formula match very closely.

6.120 Bright Light at Night Makes Even Fatter Mice Data A.1 on page 136 introduces

a study in which mice that had a light on at night (rather than complete darkness) ate most of

their calories when they should have been resting. These mice gained a significant amount of

weight, despite eating the same number of calories as mice kept in total darkness. The time of

eating seemed to have a significant effect. Exercise 6.119 examines the mice with dim light at night.

A second group of mice had bright light on all the time (day and night). There were nine mice in

the group with bright light at night and they gained an average of 11.0g with a standard deviation

of 2.6. The data are shown in the figure in the book. Is it appropriate to use a t-distribution in this

situation? Why or why not? If not, how else might we construct a confidence interval for mean

weight gain of mice with a bright light on all the time?

Solution

The sample size of n= 9 is quite small, so we require a condition of approximate normality for the

underlying population in order to use the t-distribution. In the dotplot of the data, it appears that

the data might be right skewed and there is quite a large outlier. It is probably more reasonable

to use other methods, such as a bootstrap distribution, to compute a confidence interval using this

data.

6.130 Find the sample size needed to give, with 95% confidence, a margin of error within ±10.

Within ±5. Within ±1. Assume that we use ˜σ= 30 as our estimate of the standard deviation in

each case. Comment on the relationship between the sample size and the margin of error.

Solution

We use z∗= 1.96 for 95% confidence, and we use ˜σ= 30. For a desired margin of error of ME = 10,

we have:

n=z∗·˜σ

ME 2

=1.96 ·30

10 2

= 34.6

We round up to n= 35.

For a desired margin of error of M E = 5, we have:

n=z∗·˜σ

ME 2

=1.96 ·30

52

= 138.3

We round up to n= 139.

For a desired margin of error of M E = 1, we have:

n=z∗·˜σ

ME 2

=1.96 ·30

12

= 3457.4

We round up to n= 3,458.

We see that the sample size goes up as we require more accuracy. Or, put another way, a larger

sample size gives greater accuracy.

6.145 The Chips Ahoy! Challenge in the mid-1990s a Nabisco marketing campaign claimed

that there were at least 1000 chips in every bag of Chips Ahoy! cookies. A group of Air Force

cadets collected a sample of 42 bags of Chips Ahoy! cookies, bought from locations all across the

country to verify this claim. The cookies were dissolved in water and the number of chips (any