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Chapter: Biostatistics for the Health Sciences: Estimating Population Means

Biostatistics for the Health Sciences: Estimating Population Means - Exercises questions answers


8.1 In your own words define the following terms:

a.     Descriptive statistics

b.     Inferential statistics

c.      Point estimate of a population parameter

d.     Interval (confidence interval) estimate of a population parameter

e.      Type I error

f.       Biased estimator of a population parameter

g.     Mean square error

8.2 What are the desirable properties of an estimator of a population parameter?

8.3 What are the advantages and disadvantages of using point estimates for sta-tistical inference?

8.4 What are the desirable properties of a confidence interval? How do sample size and the level of confidence (e.g., 90%, 95%, 99%) affect the width of a confidence interval?

8.5 State the advantages and disadvantages of using confidence intervals for sta-tistical inference.

8.6 Two situations affect the choice of a calculation of a confidence interval: (1) the population is known; (2) the population variance is unknown. How would you calculate a confidence interval given these two different circumstances?

8.7 Explain the bootstrap principle. How can it be used to make statistical infer-ences?

8.8 How can bootstrap confidence intervals be generated? Name the simplest form of a bootstrap confidence interval. Are bootstrap confidence intervals exact?

8.9 Suppose we randomly select 20 students enrolled in an introductory course in biostatistics and measure their resting heart rates. We obtain a mean of 66.9 (S = 9.02). Calculate a 95% confidence interval for the population mean and give an interpretation of the interval you obtain.

8.10 Suppose that a sample of pulse rates gives a mean of 71.3, as in Exercise 8.9, with a standard deviation that can be assumed to be 9.4 (close to the estimate observed in exercise 8.9). How many patients should be sampled to obtain a 95 % confidence interval for the mean that has half-width 1.2 beats per minute?

8.11 In a sample of 125 experimental subjects, the mean score on a postexperi-mental measure of aggression was 55 with a standard deviation of 5. Con-struct a 95% confidence interval for the population mean.

8.12 Suppose the sample size in exercise 8.11 is 169 and the mean score is 55 with a standard deviation of 5. Construct a 99% confidence interval for the popu-lation mean.

8.13 Suppose you want to construct a 95% confidence interval for mean aggres-sion scores as in Exercise 8.11, and you can assume that the standard devia-tion of the estimate is 5. How many experimental subjects do you need for the half-width of the interval to be no larger than 0.4?

8.14 What would the number of experimental subjects have to be under the as-sumptions in Exercise 8.13 if you want to construct a 99% confidence inter-val with half-width no greater then 0.4? Under the same criteria we decide that n should be large enough so that a 95% confidence interval would have this half-width of 0.4. Which confidence interval requires the larger sample size and why? What is n for the 95% interval?

8.15 The mean weight of 100 men in a particular heart study is 61 kg with a stan-dard deviation of 7.9 kg. Construct a 95% confidence interval for the mean.

8.16 The standard hemoglobin reading for normal males of adult age is 15 g/100 ml. The standard deviation is about 2.5 g/100 ml. For a group of 36 male con-struction workers, the sample mean was 16 g/100 ml.

a.     Construct a 95% confidence interval for the male construction workers. What is your interpretation of this interval relative to the normal adult male population?

b.     What would the confidence interval have been if the above results were obtained based on 49 construction workers?

c.      Repeat b for 64 construction workers.

d.     Do fixed-level confidence intervals shrink or widen as the sample size in-creases (all other factors remaining the same)? Explain your answer.

e.      What is the half-width of the confidence interval that you would obtain for 64 workers?

8.17 Repeat Exercise 8.16 for 99% confidence intervals.

8.18 The mean diastolic blood pressure for 225 randomly selected individuals is 75 mmHg with a standard deviation of 12.0 mmHg. Construct a 95% confi-dence interval for the mean.

8.19 Change exercise 8.18 to assume there are 400 randomly selected individuals with a mean of 75 and standard deviation of 12. Construct a 99% confidence interval for the mean.

8.20 In Exercise 8.18, how many individuals must you select to obtain the half-width of a 99% confidence interval no larger than 0.5 mmHg?


8.2 A point estimate is a single value intended to approximate a population para-meter. An unbiased estimate is an estimate or a function of observed random vari-ables that has the property that the average of its sampling distribution is equal to the population parameter, whatever that value might be. Unbiasedness is a desirable property but the key for an estimator is accuracy. Unbiased estimators with small variance are desirable but an unbiased estimator with a large variance is not if other estimates can be found that are more accurate. The mean square error is a measure of accuracy. It penalizes an estimate for both bias and variance. An estimate with small mean square error tends to be close to the true parameter value.

8.7 The bootstrap principle states that we can approximate the sampling distribu-tion of a point estimate by mimicking the random sample we observe to compute the estimate. The bootstrap estimates are obtained by sampling with replacement from the observed data. Bootstrap sampling mimics the random sampling of the original data. The original sample replaces the population and the bootstrap sample replaces the original sample. The bootstrap estimates are obtained by applying the function of the observations to the bootstrap sample. The distribution of these bootstrap esti-mates is used as an approximation to the sampling distribution for the estimate.

8.8 The bootstrap confidence intervals are obtained by generating bootstrap samples by the Monte Carlo approximation. The histogram of values of the bootstrap estimates can then be used to generate confidence intervals. One of the simplest of bootstrap confidence intervals is called Efron’s percentile method. It constructs a 100(1 – a)% confidence interval by taking the lower endpoint to be the 100(a/2) percentile and the upper endpoint to be the 100(1 – a/2) percentile.

8.10 We need to find C, the 97.5 percentage point from the t distribution with n 1 degrees of freedom such that Cσ/√n d. Here d = 1.2 and S = 9.4. So we need to find the smallest n such that n C2S2/d2 = C2(61.36). From the table of Student’s t distribution, we see the results in the following table:

From the table, we see that n > 235, since for n = 235, C > 1.96 and (1.96)2(61.36) = 235.72. Also, C < 1.9719 for n = 235, so for n = 235, 235.72 < C2(61.36) < 238.59. Now 239 is clearly large enough.

8.14 Since the mean score is 55 and the standard deviation is 5, we want to find n so that the half-width of a 99% confidence interval for the population mean has a half-width d no greater than 0.4. Again, n must satisfy n C2S2/d2 = C2(156.25), where C is the 99.5 percentile of a t distribution with n – 1 degrees of freedom. We use the following table:

After df = 200, the value of C is close enough to the limiting normal value that we use the limiting value of 2.5758. We see that we need df = 1036 or n = 1037 to meet our requirement. For a 95% confidence interval with the same mean and standard deviation, we would require a smaller n for the same d = 0.4 since the constant C is smaller—1.96 compared to 2.5758. We reduce the sample size by lowering the lev-el of confidence. We still require n C2S2/d2 = C2(156.25) but now since C = 1.96, we have n > 600.25 or n = 601.

8.16 a. We have assumed that the standard deviation is known to be 2.5. A 95% confidence interval for 36 construction workers would then be [16 – (1.96) (2.5)/36, 16 + (1.96)(2.5)/36] = [15.1833, 16.8167].

b. Had n been 49, we just replace 36 = 6 by 49 = 7. This gives [16 – (1.96) (2.5)/7, 16 + (1.96)(2.5)/7] = [15.3, 16.7].

c. Now if n = 64 we replace 7 by 8 = 64 to get [16 – (1.96)(2.5)/8, 16 + (1.96)(2.5)/8] = [15.3875, 16.6125].

d. As we see from a through c we kept the level the same and we found that the width continued to decrease as the sample size increased. With each new interval being contained in the previous one (since the mean and standard deviation did not change). This just illustrates that the width of the interval, which is a constant divid-ed by the square root of the sample size, decreases because the square root of the sample size increases as the sample size increases.

e. The halfwidth of the interval in c is 0.6125 = (1.96)(2.5)/8.

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