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Question:Quality control. Refer to Exercise 5.68. The mean diameter of the bearings produced by the machine is supposed to be .5 inch. The company decides to use the sample mean from Exercise 5.68 to decide whether the process is in control (i.e., whether it is producing bearings with a mean diameter of .5 inch). The machine will be considered out of control if the mean of the sample of n = 25 diameters is less than .4994 inch or larger than .5006 inch. If the true mean diameter of the bearings produced by the machine is .501 inch, what is the approximate probability that the test will imply that the process is out of control?

Short Answer

Expert verified

The probability that the test will imply that the process is out of control is 0.97725.

Step by step solution

01

Given Information

The sample size is 25

The mean is 0.501.inch

The standard deviation is 0.001.inch

The standard deviation of x¯is calculated as

σX¯=σn=0.00125=0.0002

02

Explanation

The probability is computed as

px¯<0.4994=px¯-μσn<0.4994-0.5010.0002=pz<-0.00160.0002=pz<-8=0

And

px¯>0.5006=px¯-μσn>0.5006-0.5010.0002=pz>-0.00040.0002=pz>-2=0.97725

Here we used equation 1 & 2 to get required probability

px¯<0.4994+px¯>0.5006=0+0.97725=0.97725

Hence, the probability is 0.97725.

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Most popular questions from this chapter

Question: Refer to Exercise 5.3.

a. Find the sampling distribution of s2.

b. Find the population variance σ2.

c. Show that s2is an unbiased estimator of σ2.

d. Find the sampling distribution of the sample standard deviation s.

e. Show that s is a biased estimator of σ.

Question: The standard deviation (or, as it is usually called, the standard error) of the sampling distribution for the sample mean, x¯ , is equal to the standard deviation of the population from which the sample was selected, divided by the square root of the sample size. That is

σX¯=σn

  1. As the sample size is increased, what happens to the standard error of? Why is this property considered important?
  2. Suppose a sample statistic has a standard error that is not a function of the sample size. In other words, the standard error remains constant as n changes. What would this imply about the statistic as an estimator of a population parameter?
  3. Suppose another unbiased estimator (call it A) of the population mean is a sample statistic with a standard error equal to

σA=σn3

Which of the sample statistics,x¯or A, is preferable as an estimator of the population mean? Why?

  1. Suppose that the population standard deviation σis equal to 10 and that the sample size is 64. Calculate the standard errors of x¯and A. Assuming that the sampling distribution of A is approximately normal, interpret the standard errors. Why is the assumption of (approximate) normality unnecessary for the sampling distribution ofx¯?

The probability distribution shown here describes a population of measurements that can assume values of 0, 2, 4, and 6, each of which occurs with the same relative frequency:

  1. List all the different samples of n = 2 measurements that can be selected from this population. For example, (0, 6) is one possible pair of measurements; (2, 2) is another possible pair.
  2. Calculate the mean of each different sample listed in part a.
  3. If a sample of n = 2 measurements is randomly selected from the population, what is the probability that a specific sample will be selected.
  4. Assume that a random sample of n = 2 measurements is selected from the population. List the different values of x found in part b and find the probability of each. Then give the sampling distribution of the sample mean x in tabular form.
  5. Construct a probability histogram for the sampling distribution ofx.

Consider the following probability distribution:

  1. Findandσ2.
  2. Find the sampling distribution of the sample mean x for a random sample of n = 2 measurements from this distribution
  3. Show that xis an unbiased estimator of μ. [Hint: Show that.]x=xpx=μ.]
  4. Find the sampling distribution of the sample variances2for a random sample of n = 2 measurements from this distribution.

Suppose xequals the number of heads observed when asingle coin is tossed; that is, x= 0 or x= 1. The population corresponding to xis the set of 0s and 1s generated when thecoin is tossed repeatedly a large number of times. Supposewe select n= 2 observations from this population. (That is,we toss the coin twice and observe two values of x.)

  1. List the three different samples (combinations of 0s and1s) that could be obtained.
  2. Calculate the value of X¯ffor each of the samples.
  3. Show that the sample proportion of 1s, p^, is equal to X¯.
  4. List the values thatp^can assume, and find the probabilitiesof observing these values.
  5. Construct a graph of the sampling distribution ofp^.
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