Variable life insurance return rates. Refer to the International Journal of Statistical Distributions (Vol. 1, 2015) study of a variable life insurance policy, Exercise 4.97 (p. 262). Recall that a ratio (x) of the rates of return on the investment for two consecutive years was shown to have a normal distribution, with μ=1.5, σ=0.2. Consider a random sample of 100 variable life insurance policies and letx¯represent the mean ratio for the sample.

a. Find E(x) and interpret its value.

b. Find Var(x).

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

d. Find the z-score for the value x¯=1.52.

e. Find Px¯>1.52

f. Would your answers to parts a–e change if the rates (x) of return on the investment for two consecutive years was not normally distributed? Explain.

Short Answer

Expert verified

a. The mean ratio of the rates of return on the investment for two consecutive years for the sampling distribution of x¯ is 1.5.

b. Variance ofsampling distribution of x¯is 0.0004.

c. The shape of the sampling distribution of x¯ is normal.

d.The required z-score is 1.

e.Probability of x¯ greater than 1.52 is 0.1587.

f. The answers in the parts a-e would not change if the rates were not normally distributed

Step by step solution

01

Given information

From the given problem, the distribution of X follows normal with meanμ=1.5 and a standard deviationσ=0.2

02

Calculating the mean of x

a.

From the central theorem, as the sample size is large the mean of the sample follows normal distribution with mean μx¯=μ and variance σ2n.

The mean of the sampling distribution of x¯ is the population mean μ. That is,

μx¯=μ

Ex¯=μ

Thus,

Ex¯=1.5

The mean ratio of the rates of return on the investment for two consecutive years for the sampling distribution of x¯ is 1.5.

03

Calculating the variance of x 

b. From the given problem the sample size is n=100

Since,

Varx¯=σ2n

=0.22100=0.04100=0.0004

Thus,

Varx¯=0.0004

04

Describing the shape of sampling distribution

c.

From the central limit theorem as the sample size is large the mean of the sample follows normal distribution. The shape of the sampling distribution of x¯ is normal.

05

Calculating the z-score

d.

Consider x¯=1.52

μ=1.5 and Varx¯=0.0004

The z-score is,

z=x¯-μVarx¯

=1.52-1.50.0004=0.020.02=1

Thus, the required z-score is 1.

06

Calculating the probability 

e. Let,

Px¯>1.52=Px¯-μVarx¯>1.52-1.50.0004=Pz>1=1-Pz<1=1-0.5+P0<z<1=1-0.5-0.3413=0.1587

Therefore, Probability of x¯ greater than 1.52 is 0.1587.

07

Interpretation

f. The given sample size is 100, which is greater than 30. Thus, the distribution of sample mean x¯ is approximately normal without considering the population distribution. Therefore, the Central limit theorem is relevant for the given data.

Thus, the answers in the parts a-e would not change if the rates were not normally distributed.

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Most popular questions from this chapter

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?
  2. If the population of diameters has an extremely skewed distribution, how will your approximation in part a be affected?

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?

Surface roughness of pipe. Refer to the Anti-CorrosionMethods and Materials(Vol. 50, 2003) study of the surface roughness of oil field pipes, Exercise 2.46 (p. 96). Recall that a scanning probe instrument was used to measure thesurface roughness x(in micrometers) of 20 sampled sectionsof coated interior pipe. Consider the sample mean,X¯.

  1. Assume that the surface roughness distribution has a mean of = 1.8 micrometers and a standard deviation of = .5 micrometer. Use this information to find theprobability thatexceeds 1.85 micrometers.
  2. The sample data are reproduced in the following table.Compute.
  3. Based on the result, part b, comment on the validity ofthe assumptions made in part a.

1.72

2.50

2.16

2.13

1.06

2.24

2.31

2.03

1.09

1.40

2.57

2.64

1.26

2.05

1.19

2.13

1.27

1.51

2.41

1.95

Length of job tenure. Researchers at the Terry College ofBusiness at the University of Georgia sampled 344 business students and asked them this question: “Over the course of your lifetime, what is the maximum number of years you expect to work for any one employer?” The sample resulted in x= 19.1 years. Assume that the sample of students was randomly selected from the 6,000 undergraduate students atthe Terry College and that = 6 years.

  1. Describe the sampling distribution of X¯.
  2. If the mean for the 6,000 undergraduate students isμ= 18.5 years, findPx¯>19.1.
  3. If the mean for the 6,000 undergraduate students isμ= 19.5 years, findPx¯>19.1.
  4. If,P(x¯>19.1)=0.5 what isμ?
  5. If,Px¯>19.1=0.2 isμgreater than or less than 19.1years? Explain.

Refer to Exercise 5.18. Find the probability that

  1. x¯is less than 16.
  2. x¯is greater than 23.
  3. x¯is greater than 25.
  4. x¯falls between 16 and 22.
  5. x¯ is less than 14.
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