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Consider the following probability distribution:

a. Findμ.

b. For a random sample of n = 3 observations from this distribution, find the sampling distribution of the sample mean.

c. Find the sampling distribution of the median of a sample of n = 3 observations from this population.

d. Refer to parts b and c, and show that both the mean and median are unbiased estimators ofμfor this population.

e. Find the variances of the sampling distributions of the sample mean and the sample median.

f. Which estimator would you use to estimateμ? Why?

Short Answer

Expert verified

a)μ=1

b) 1

c) 1

d) Both the mean and median are unbiased

e) Variance of mean=0.214

Variance of median=0.67

f) Mean is a better estimator

Step by step solution

01

Calculation of the meanμ

a.

The calculation of the meanμin the case of the three values of x is shown below:

μ=xp(x)=013+113+213=0+13+23=33=1

Therefore the value ofμis 1.

02

Computation of the sample distribution of the mean

b.

The calculation of the probabilities of the means considering the three values of x is shown below:

Therefore, the sample distribution of the means on adding the probabilities will give 1.

03

Computation of the sample distribution of the median

b.

The list of medians along with the associated probabilities is shown below:

Therefore, the sample distribution of the medians on adding the probabilities will give 1.

04

Determining the unbiasedness of the mean and the median

c.

To check unbiasness of mean and median, following tables are required.

On adding the above probabilities, the final value will be 1 and so the mean will be an unbiased estimator of the μ.

On adding the above probabilities, the final value will be 1 and so the median will be an unbiased estimator of the .

05

Computation of the variances of means and medians

d.

The average of the means can be found and it is subtracted from the respective value of the means. It is then squared and the last column shows the respective probabilities.

The variance is then calculated by multiplying the respective values of by the respective probabilities.

variance=1×0+0.44×0.3327+0.11×0.6727+0.44×0.3327+0.11×0.6727+0.11×0.6727+0.11×1.3327+0××1.3327+0.11×0.6727+0.11×1.3327+0.11×0.6727+0.11×0.3327+0.44×0+0.44×1.3327+1×227+0.11×1.3327+0.44×1.6727+0.11×1.3327+0.44×1.6727+0.11×1.6727+0.11×1.3327+0×127+0×127+0×127+0×127+0×127+0×127=1270.1452+0.0737+0.1452+0.0737+0.0737+0.1463+0+0.0737+0.1463+0.0737+0.1463+0+0.5852+2+0.1463+0.7348+0.1463+0.7348+0.1837+0.1463+0+0+0+0+0+0=5.775227=0.214

The average of the medians has been found and it has been subtracted from the respective value of the medians. It has then squared and the third column shows the respective probabilities.

The variance is then calculated by multiplying the respective values of mean-meanofmedian2by the respective probabilities.

variance=1×0+1×127+1×127+1×127+1×127+0×027+1×127+0×027+0×027+0×027+0×027+0×027+1×227+1×227+1×227+1×227+1×227+1×227+1+×0+1×127+0×127+0×127+0×127+0×127+0×127+0×127+0×127=1270+1+1+1+1+0+1+0+0+0+0+0+2+2+2+2+2+2+0+1+0+0+0+0+0+0+0=1827=0.67

06

Determining the better estimator

e.

It has been observed from the above calculations that the variance of the means is greater than that of the medians.Therefore, it can be envisaged that the median is the better estimator.

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Producing machine bearings. To determine whether a metal lathe that produces machine bearings is properly adjusted, a random sample of 25 bearings is collected and the diameter of each is measured.

  1. If the standard deviation of the diameters of the bearings measured over a long period of time is .001 inch, what is the approximate probability that the mean diameter xof the sample of 25 bearings will lie within.0001 inch of the population mean diameter of the bearings?
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The random variable x is observed twice. If these observations are independent, verify that the different samples of size 2 and their probabilities are as shown below.

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Question:Consider a sample statistic A. As with all sample statistics, A is computed by utilizing a specified function (formula) of the sample measurements. (For example, if A were the sample mean, the specified formula would sum the measurements and divide by the number of measurements.

a. Describe what we mean by the phrase "the sampling distribution of the sample statistic A."

b. Suppose A is to be used to estimate a population parameterθ. What is meant by the assertion that A is an unbiased estimator of θ?

c. Consider another sample statistic, B. Assume that B is also an unbiased estimator of the population parameterα. How can we use the sampling distributions of A and B to decide which is the better estimator of θ?

d. If the sample sizes on which A and B are based are large, can we apply the Central Limit Theorem and assert that the sampling distributions of A and B are approximately normal? Why or why not?

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