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More sample proportions List all 4possible SRSs of size n=3, calculate the proportion of red cars in the sample, and display the sampling distribution of the sample proportion on a dot plot with the same scale as the dot plot in Exercise 19. How does the variability of this sampling distribution compare with the variability of the sampling distribution from Exercise 19? What does this indicate about increasing the sample size?

From exercise19:

Car NumberColorAge
1
Red
1
2
White
5
3
Silver
8
4
Red
20

Short Answer

Expert verified

Required dot plot is

Dot plots with sample sizes of n=3have less variability than dot plots with sample sizes of n=2.

As the sample size grows, the sampling variability reduces.

Step by step solution

01

Given Information

We are given following data:

Car NumberColorAge
1
Red1
2
White5
3
Silver8
4
Red 20

We need to calculate the minimum age for each sample, and draw it's dot plots.

We need to explain how variability of this sampling distribution compare with the variability of the sampling distribution from Exercise19

02

Explanation

All possible samples of size 3then contain any three cars all different of population of 4cars.

Car NumberColorAge
1
Red1
2
White5
3
Silver8
4
Red20

The sample proportion of red cars is calculated by dividing the number of red cars by the sample size.

Sample of size3Numbers of red carsProportion of red cars
1,2,3
1
p=13
1,2,4
2
p=23
1,3,4
2
p=23
2,3,4
1
p=13

From above data our Dot plot will be:


In exercise 19dot plots varies in range of 0to 1, whereas in this problem dot plot ranges from 0.33to 0.667

As a result, dot plots with sample sizes of n= 3have less variability than dot plots with sample sizes of n= 2.

This also means that as the sample size grows, the sampling variability reduces.

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Most popular questions from this chapter

Fillings How much do prices vary for filling a cavity? To find out, an insurance company randomly selects 10dental practices in California and asks for the cash (non-insurance) price for this procedure at each practice. The interquartile range is $74.

An insurance company claims that in the entire population of homeowners, the mean annual loss from fire is and the standard deviation of the loss is σ=\(5000.The distribution of losses is strongly right-skewed: many policies have \)0loss, but a few have large losses. The company hopes to sell 1000 of these policies for \(300each.

a. Assuming that the company’s claim is true, what is the probability that the mean loss from fire is greater than \)300for an SRS of 1000 homeowners?

b. If the company wants to be 90% certain that the mean loss from fire in an SRS of 1000 homeowners is less than the amount it charges for the policy, how much should the company charge?

Bottling cola A bottling company uses a filling machine to fill plastic bottles with cola. The bottles are supposed to contain 300 milliliters (ml). In fact, the contents vary according to a Normal distribution with mean μ=298mland standard deviation σ=3ml.

a. What is the probability that a randomly selected bottle contains less than 295ml?

b. What is the probability that the mean contents of six randomly selected bottles is less than 295ml?

According to government data, 22% of American children under the age of 6 live in households with incomes less than the official poverty level. A study of learning in early childhood chooses an SRS of 300 children from one state and finds that pp^=0.29.

a. Find the probability that at least 29% of the sample are from poverty-level households, assuming that 22% of all children under the age of 6 in this state live in poverty-level households.

b. Based on your answer to part (a), is there convincing evidence that the percentage of children under the age of 6 living in households with incomes less than the official poverty level in this state is greater than the national value of 22%? Explain your reasoning.

The number of hours a lightbulb burns before failing varies from bulb to bulb. The population distribution of burnout times is strongly skewed to the right. The central limit theorem says that

a. as we look at more and more bulbs, their average burnout time gets ever closer to the mean μ for all bulbs of this type.

b. the average burnout time of a large number of bulbs has a sampling distribution with the same shape (strongly skewed) as the population distribution.

c. the average burnout time of a large number of bulbs has a sampling distribution with a similar shape but not as extreme (skewed, but not as strongly) as the population distribution.

d. the average burnout time of a large number of bulbs has a sampling distribution that is close to Normal.

e. the average burnout time of a large number of bulbs has a sampling distribution that is exactly Normal.

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