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The article "Quantitative MRI and Electrophysiology of Preoperative Carpal Tunnel Syndrome in a Female Population" (Ergonomics, 1997: 642-649) reported that (-473.13,1691.9) was a large-sample 95 % confidence interval for the difference between true average thenar muscle volume \(\left( {m{m^3}} \right.)\) for sufferers of carpal tunnel syndrome and true average volume for nonsufferers. Calculate and interpret a 90 % confidence interval for this difference.

Short Answer

Expert verified

\(( - 299.3,1517.9)\)

Step by step solution

01

To Calculate and interpret a 90 % confidence interval for this difference.

For m, n large enough, confidence interval for\({\mu _1} - {\mu _2}\) with confidence level of approximately\(100(1 - \alpha )\% \) is

\(\bar x - \bar y \pm {z_{\alpha /2}} \times \sqrt {\frac{{s_1^2}}{m} + \frac{{s_2^2}}{n}} \)

where + and - give the adequate upper/lower limit of the interval. By replacing\({z_{\alpha /2}} by {z_\alpha }\) and pm with only + and - an upper/lower bound is obtained.

Value\(\bar x - \bar y\) is in the middle of the interval (the center), thus

\(\bar x - \bar y = \frac{{ - 473.3 + 1691.9}}{2} = 609.3\)

Value for

\(\sqrt {\frac{{s_1^2}}{m} + \frac{{s_2^2}}{n}} \)

can be obtained from

\({z_{\alpha /2}} \times \sqrt {\frac{{s_1^2}}{m} + \frac{{s_2^2}}{n}} = \frac{w}{2}\)

where\(w\)is the width of interval

\(w = 1691.9 - ( - 473.3) = 2165.2\)

02

Step 2: Calculate and interpret a 90 % confidence interval for this difference.

Also, \({z_{\alpha /2}} for 95\% is {z_{\alpha /2}} = {z_{0.025}} = 1.96,\) which was obtained using the table in the appendix. Therefore,

\(\begin{array}{l}{z_{\alpha /2}} \times \sqrt {\frac{{s_1^2}}{m} + \frac{{s_2^2}}{n}} = \frac{w}{2}\\1.96 \times \sqrt {\frac{{s_1^2}}{m} + \frac{{s_2^2}}{n}} = 1082.6 \\\sqrt {\frac{{s_1^2}}{m} + \frac{{s_2^2}}{n}} = 552.35\end{array}\)

The only missing value to compute 90 % confidence interval is \({z_{\alpha /2}} = {z_{0.1/2}} = 1.645\) which was obtained using the table in the appendix. Thus, the 90% \(Cl\ ) for this difference is

\(\bar x - \bar y \pm {z_{\alpha /2}} \times \sqrt {\frac{{s_1^2}}{m} + \frac{{s_2^2}}{n}} = 609.3 \pm 1.645 \times 552.35\ )

\( = ( - 299.3,1517.9)\)

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Most popular questions from this chapter

Arsenic is a known carcinogen and poison. The standard laboratory procedures for measuring arsenic \(concentration (\mu g/L)\) in water are expensive. Consider the accompanying summary data and Minitab output for comparing a laboratory method to a new relatively quick and inexpensive field method (from the article "Evaluation of a New Field Measurement Method for Arsenic in Drinking Water Samples," J. of Emvir. Engr., 2008: 382-388).

Two-Sample T-Test and CI

Sample N Mean StDev SE Mean

1 3 19.70 1.10 0.65

2 3 10.90 0.60 0.35

Estimate for difference: 8.800

95% CI for difference: 6.498,11.102

T -Test of difference =0 (vs not = ):

T -Value =12.1 P -Value =0.001 DF=3

What conclusion do you draw about the two methods, and why? Interpret the given confidence interval. (Note: One of the article's authors indicated in private communication that they were unsure why the two methods disagreed.)

The article "Fatigue Testing of Condoms" cited in Exercise 7.32 reported that for a sample of 20 natural latex condoms of a certain type, the sample mean and sample standard deviation of the number of cycles to break were 4358 and 2218 , respectively, whereas a sample of 20 polyisoprene condoms gave a sample mean and sample standard deviation of 5805 and 3990 , respectively. Is there strong evidence for concluding that true average number of cycles to break for the polyisoprene condom exceeds that for the natural latex condom by more than 1000 cycles? Carry out a test using a significance level of 01 . (Note: The cited paper reported P-values of t tests for comparing means of the various types considered.)

According to the article "Modeling and Predicting the Effects of Submerged Arc Weldment Process Parameters on Weldment Characteristics and Shape Profiles" (J. of Engr. Manuf., \(2012: 1230 - 1240\)), the submerged arc welding (SAW) process is commonly used for joining thick plates and pipes. The heat affected zone (HAZ), a band created within the base metal during welding, was of particular interest to the investigators. Here are observations on depth\((mm)\)of the HAZ both when the current setting was high and when it was lower.

\(\begin{array}{*{20}{l}}{ Non - high }&{1.04}&{1.15}&{1.23}&{1.69}&{1.92}\\{}&{1.98}&{2.36}&{2.49}&{2.72}&{}\\{}&{1.37}&{1.43}&{1.57}&{1.71}&{1.94}\\{ High }&{2.06}&{2.55}&{2.64}&{2.82}&{}\\{}&{1.55}&{2.02}&{2.02}&{2.05}&{2.35}\\{}&{2.57}&{2.93}&{2.94}&{2.97}&{}\end{array}\)

a. Construct a comparative boxplot and comment on interesting features.

b. Is it reasonable to use the two-sample \(t\) test to test hypotheses about the difference between true average HAZ depths for the two conditions?

c. Does it appear that true average HAZ depth is larger for the higher current condition than for the lower condition? Carry out a test of appropriate hypotheses using a significance level of . \(01\).

Persons having Reynaudโ€™s syndrome are apt to suffer a sudden impairment of blood circulation in fingers and toes. In an experiment to study the extent of this impairment, each subject immersed a forefinger in water and the resulting heat output \((cal/c{m^2}/min)\) was measured. For \(m = 10\) subjects with the syndrome, the average heat output was \(\bar x = .64\), and for \(n = 10\) non-sufferers, the average output was \(2.05\). Let \({\mu _1}\) and \({\mu _2}\) denote the true average heat outputs for the two types of subjects. Assume that the two distributions of heat output are normal with \({\sigma _1} = .2\) and \({\sigma _2} = .4\).

a. Consider testing \({H_0}:{\mu _1} - {\mu _2} = - 1.0\) versus \({H_2}:{\mu _1} - {\mu _2} < - 1.0\)at level . \(01\). Describe in words what \({H_a}\) says, and then carry out the test.

b. What is the probability of a type II error when the actual difference between \({\mu _1}\) and \({\mu _2}\) is \({\mu _1} - {\mu _2} = - 1.2?\)

c. Assuming that \(m = n\), what sample sizes are required to ensure that \(\beta = .1\) when \({\mu _1} - {\mu _2} = - 1.2?\)

Researchers sent 5000 resumes in response to job ads that appeared in the Boston Globe and Chicago Tribune. The resumes were identical except that 2500 of them had "white sounding" first names, such as Brett and Emily, whereas the other 2500 had "black sounding" names such as Tamika and Rasheed. The resumes of the first type elicited 250 responses and the resumes of the second type only 167 responses (these numbers are very consistent with information that appeared in a Jan. 15. 2003, report by the Associated Press). Does this data strongly suggest that a resume with a "black" name is less likely to result in a response than is a resume with a "white" name?

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