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Variation of Heights Use the sample data given in Exercise 3 “Heights” and test the claim that women and men have heights with the same variation. Use a 0.05 significance level.

Short Answer

Expert verified

There is enough evidence to conclude that women and men do not have heights with the same variation.

Step by step solution

01

Given information

Two samples of heights of women and men (in cm) are tabulated.

02

Hypotheses

It is claimed that women and men have heights with the same variation.

Corresponding to the given claim, the following hypotheses are set up:

Null Hypothesis: The standard deviation of the heights of women is equal to the standard deviation of the heights of men.

\({H_0}:{\sigma _1} = {\sigma _2}\)

Alternative Hypothesis: The standard deviation of the heights of women is not equal to the standard deviation of the heights of men.

\({H_1}:{\sigma _1} \ne {\sigma _2}\)

The test is two-tailed.

If the test statistic does not lie between the two critical values, the null hypothesis is rejected.

03

Sample variances

The sample size for the heights of women is equal to\({n_1} = 10\).

The sample size for the heights of men is equal to\({n_2} = 10\).

The sample mean height of women is computed below:

\(\begin{aligned} {{\bar x}_1} &= \frac{{160.3 + 167.7 + ...... + 171.1}}{{10}}\\ &= 162.35\end{aligned}\)

The sample mean height of men is computed below:

\(\begin{aligned} {{\bar x}_2} &= \frac{{190.3 + 169.8 + ...... + 181.3}}{{10}}\\ &= 178.77\end{aligned}\)

The sample variance of heights of women is computed below:

\(\begin{aligned} s_1^2 &= \frac{{\sum\limits_{i = 1}^n {{{({x_i} - \bar x)}^2}} }}{{n - 1}}\\ &= \frac{{{{\left( {160.3 - 162.35} \right)}^2} + {{\left( {167.7 - 162.35} \right)}^2} + ....... + {{\left( {166.9 - 162.35} \right)}^2}}}{{10 - 1}}\\ &= 140.35\end{aligned}\)

The sample variance of heights of men is computed below:

\(\begin{aligned} s_2^2 &= \frac{{\sum\limits_{i = 1}^n {{{({x_i} - \bar x)}^2}} }}{{n - 1}}\\ &= \frac{{{{\left( {190.3 - 178.77} \right)}^2} + {{\left( {169.8 - 178.77} \right)}^2} + ....... + {{\left( {181.3 - 178.77} \right)}^2}}}{{10 - 1}}\\ &= 28.11\end{aligned}\)

04

Test statistic

The value of the test statistic is computed as shown:

\(\begin{aligned} F &= \frac{{s_1^2}}{{s_2^2}}\\ &= \frac{{140.35}}{{28.11}}\\ &= 4.99\end{aligned}\)

The degrees of freedom are computed below:

\(\begin{aligned} df &= \left( {{n_1} - 1,{n_2} - 1} \right)\\ &= \left( {10 - 1,10 - 1} \right)\\ &= \left( {9,9} \right)\end{aligned}\)

The critical values of F at\(\alpha = 0.05\)and degrees of freedom equal to (9,9) (for numerator and denominator)are 0.2484 and 4.026.

The corresponding p-value is equal to 0.0252.

Since the absolute value of the test statistic (4.99) does not lie between the critical values and the p-value is less than 0.05, the null hypothesis is rejected.

05

Conclusion

There is enough evidence to conclude that women and men do not have the same variation in their heights. In other words, there is enough evidence to warrant rejection of the claim that women and men have heights with the same variation.

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Most popular questions from this chapter

In Exercises 5–20, assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. (Note: Answers in Appendix D include technology answers based on Formula 9-1 along with “Table” answers based on Table A-3 with df equal to the smaller of\({n_1} - 1\)and\({n_2} - 1\).)

Are Male Professors and Female Professors Rated Differently?

a. Use a 0.05 significance level to test the claim that two samples of course evaluation scores are from populations with the same mean. Use these summary statistics: Female professors:

n = 40, \(\bar x\)= 3.79, s = 0.51; male professors: n = 53, \(\bar x\) = 4.01, s = 0.53. (Using the raw data in Data Set 17 “Course Evaluations” will yield different results.)

b. Using the summary statistics given in part (a), construct a 95% confidence interval estimate of the difference between the mean course evaluations score for female professors and male professors.

c. Example 1 used similar sample data with samples of size 12 and 15, and Example 1 led to the conclusion that there is not sufficient evidence to warrant rejection of the null hypothesis.

Do the larger samples in this exercise affect the results much?

Family Heights. In Exercises 1–5, use the following heights (in.) The data are matched so that each column consists of heights from the same family.

Father

68.0

68.0

65.5

66.0

67.5

70.0

68.0

71.0

Mother

64.0

60.0

63.0

59.0

62.0

69.0

65.5

66.0

Son

71.0

64.0

71.0

68.0

70.0

71.0

71.7

71.0

Scatterplot Construct a scatterplot of the father/son heights, then interpret it.

Testing Claims About Proportions. In Exercises 7–22, test the given claim. Identify the null hypothesis, alternative hypothesis, test statistic, P-value or critical value(s), then state the conclusion about the null hypothesis, as well as the final conclusion that addresses the original claim.

Smoking Cessation Programs Among 198 smokers who underwent a “sustained care” program, 51 were no longer smoking after six months. Among 199 smokers who underwent a “standard care” program, 30 were no longer smoking after six months (based on data from “Sustained Care Intervention and Postdischarge Smoking Cessation Among Hospitalized Adults,” by Rigotti et al., Journal of the American Medical Association, Vol. 312, No. 7). We want to use a 0.01 significance level to test the claim that the rate of success for smoking cessation is greater with the sustained care program.

a. Test the claim using a hypothesis test.

b. Test the claim by constructing an appropriate confidence interval.

c. Does the difference between the two programs have practical significance?

Using Confidence Intervals

a. Assume that we want to use a 0.05 significance level to test the claim that p1 < p2. Which is better: A hypothesis test or a confidence interval?

b. In general, when dealing with inferences for two population proportions, which two of the following are equivalent: confidence interval method; P-value method; critical value method?

c. If we want to use a 0.05 significance level to test the claim that p1 < p2, what confidence level should we use?

d. If we test the claim in part (c) using the sample data in Exercise 1, we get this confidence interval: -0.000508 < p1 - p2 < - 0.000309. What does this confidence interval suggest about the claim?

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