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Dead battery? A car company claims that the lifetime of its batteries varies from car to car according to a Normal distribution with mean μ=48months and standard deviation σ=8.2months. A consumer organization installs this type of battery in an SRS of 8 cars and calculates x¯=42.2months.

a. Find the probability that the sample mean lifetime is 42.2 months or less if the company's claim is true.

b. Based on your answer to part (a), is there convincing evidence that the company is overstating the average lifetime of its batteries?

Short Answer

Expert verified

(a). The resultant probability is 2.28\%

(b). Yes, there is compelling evidence that the firm is exaggerating the average battery life.

Step by step solution

01

Part(a) step 1: Given information 

μ=48σ=8.2n=8x=42.2

The following formula was used:

z=x−μx¯σx¯

02

Part(a) step 2: Calculation 

The sampling distribution of the sample mean is normal because the population distribution is normal x¯is also typical.

z-score is

z=x−μx¯σx¯=x¯−μσ/n=42.2−488.2B¯=−2.00

The normal probability is used to calculate the associating probability

P(Z<-2.00)is presented in the standard normal probability table in the row beginning with 2.0 and the column beginning with .00.

P(X¯<42.2)=P(Z<-2.00)=0.0228=2.28%

03

Part(b) step 1: Given information 

μ=48σ=8.2n=8x=42.2

The following formula was used:

z=x−μx¯σx¯

04

Part(b) Step 2: Calculation

The sampling distribution of the sample mean is normal because the population distribution is normal x¯ is also typical.

The sample mean's sampling distribution x¯ has mean μas well as standard deviationσn

The z-score is the difference between the mean and the standard deviation:

z=x−μx¯σx¯=x¯−μσ/n=42.2−488.2/8=−2.00

The normal probability is used to calculate the associating probabilit

P(Z<-2.00)is given in the first row, beginning with -2.0 in the column that begins with .00 in the appendix to the standard normal probability table

P(X¯<42.2)=P(Z<−2.00)=0.0228=2.28%

When the chance is less than 0.05, it is considered less.

The possibility of a sample mean of at most 42.2 months occurring by accident is negligible, therefore the event is unlikely to happen by random, and there is compelling proof that the corporation is overstating the average lifetime in its batteries.

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Most popular questions from this chapter

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.

More sample minimums List all 4possible SRSs of size n=3, calculate the minimum age for each sample, and display the sampling distribution of the sample minimum on a dot plot with the same scale as the dot plot in Exercise 20. How does the variability of this sampling distribution compare with the variability of the sampling distribution from Exercise 20? What does this indicate about increasing the sample size?

From exercise20:

Car NumberColorAge
1
Red1
2
White5
3
Silver8
4
Red20

The number of undergraduates at Johns Hopkins University is approximately 2000 , while the number at Ohio State University is approximately 60,000. At both schools, a simple random sample of about 3%of the undergraduates is taken. Each sample is used to estimate the proportion p of all students at that university who own an iPod. Suppose that, in fact, p=0.80 at both schools. Which of the following is the best conclusion?

a. We expect that the estimate from Johns Hopkins will be closer to the truth than the estimate from Ohio State because it comes from a smaller population.

b. We expect that the estimate from Johns Hopkins will be closer to the truth than the estimate from Ohio State because it is based on a smaller sample size.

c. We expect that the estimate from Ohio State will be closer to the truth than the estimate from Johns Hopkins because it comes from a larger population.

d. We expect that the estimate from Ohio State will be closer to the truth than the estimate from Johns Hopkins because it is based on a larger sample size.

e. We expect that the estimate from Johns Hopkins will be about the same distance from the truth as the estimate from Ohio State because both samples are 3 % of their populations.

The math department at a small school has 5teachers. The ages of these teachers are 23,34,37,42,58. Suppose you select a random sample of 4teachers and calculate the sample minimum age. Which of the following shows the sampling distribution of the sample minimum age?

a.

b.

c.

d.

e. None of these

The central limit theorem is important in statistics because it allows us to use a Normal distribution to find probabilities involving the sample mean if the

a. sample size is reasonably large (for any population).

b. population is Normally distributed (for any sample size).

c. population is Normally distributed and the sample size is reasonably large.

d. population is Normally distributed and the population standard deviation is known (for any sample size).

e. population size is reasonably large (whether the population distribution is known or not).

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