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Refer to Exercise 5.5, in which we found the sampling distribution of the sample median. Is the median an unbiased estimator of the population mean m?

Short Answer

Expert verified

Yes, the median is an unbiased estimator of the population mean “m”.

Step by step solution

01

List of probabilities

The list of the probabilities found in Exercise 5.5 corresponding to the respective mean is shown below.

Mean

Probability

1

0.04

1.5

0.12

2

0.17

2.5

0.20

3

0.20

3.5

0.14

4

0.08

4.5

0.04

5

0.01

02

Determination of the biasedness of the median

The calculation of the mean and is shown below.

μX=xpx=10.2+20.3+30.2+40.2+50.1=2.7Em=Empm=1×0.04+1.5×0.12+2×0.17+2.5×0.20+3×0.20+0.04×0.18+4×0.08+4.5×0.04+5×0.01=0.04+0.18+0.34+0.5+0.6+0.49+0.32+0.18+0.05=2.7

As the value of μandEmis 2.7 each, somis an unbiased estimator of.

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Most popular questions from this chapter

A random sample ofn=100observations is selected from a population withμ=30and σ=16. Approximate the following probabilities:

a.P=(x28)

b.localid="1658061042663" P=(22.1x26.8)

c.localid="1658061423518" P=(x28.2)

d.P=(x27.0)

Dentists’ use of laughing gas. According to the American Dental Association, 60% of all dentists use nitrous oxide (laughing gas) in their practice. In a random sample of 75 dentists, let p^represent the proportion who use laughing gas in practice.

a. Find Ep^.

b. Find σp^.

c. Describe the shape of the sampling distribution of p^.

d. Find Pp^>0.70.

Question: Consider the following probability distribution:

a. Calculate μfor this distribution.

b. Find the sampling distribution of the sample meanxfor a random sample of n = 3 measurements from this distribution, and show thatxis an unbiased estimator of μ.

c. Find the sampling distribution of the sample median x for a random sample of n = 3 measurements from this distribution, and show that the median is a biased estimator of μ.

d. If you wanted to use a sample of three measurements from this population to estimate μ, which estimator would you use? Why?

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¯?

Voltage sags and swells. Refer to the Electrical Engineering (Vol. 95, 2013) study of the power quality (sags and swells) of a transformer, Exercise 2.76 (p. 110). For transformers built for heavy industry, the distribution of the number of sags per week has a mean of 353 with a standard deviation of 30. Of interest is , that the sample means the number of sags per week for a random sample of 45 transformers.

a. FindEχ¯ and interpret its value.

b. FindVarχ¯.

c. Describe the shape of the sampling distribution ofχ¯.

d. How likely is it to observe a sample mean a number of sags per week that exceeds 400?

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