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The degenerative disease osteoarthritis most frequently affects weight-bearing joints such as the knee. The article "Evidence of Mechanical Load Redistribution at the Knee Joint in the Elderly When Ascending Stairs and Ramps" (Annals of Biomed. Engr., \(2008: 467 - 476\)) presented the following summary data on stance duration (ms) for samples of both older and younger adults.

\(\begin{array}{*{20}{l}}{Age\;\;\;\;\;\;\;Sample Size\;\;Sample Mean\;\;Sample SD}\\{\;Older\;\;\;\;\;28\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;801\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;117}\\{Younger\;16\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;780\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;72}\end{array}\)

Assume that both stance duration distributions are normal.

a. Calculate and interpret a\(99\% \)CI for true average stance duration among elderly individuals.

b. Carry out a test of hypotheses at significance level\(.05\)to decide whether true average stance duration is larger among elderly individuals than among younger individuals.

Short Answer

Expert verified

(a) \(( - 56.0945,98.0945)\)

(b) There is not sufficient evidence to support the claim that the true average stance duration is larger among elderly individuals than among younger individuals.

Step by step solution

01

a)Step 1: Find the end point of confidence interval

\(\begin{array}{l}{{\bar x}_1} = 801\\{{\bar x}_2} = 780\\{n_1} = 28\\{n_2} = 16\\{s_1} = 117\\{s_2} = 72\\c = 99\% = 0.99\\\alpha = 0.05\end{array}\)

Determine the degrees of freedom (rounded down to the nearest integer):

\(\Delta = \frac{{{{\left( {\frac{{s_1^2}}{{{n_1}}} + \frac{{s_2^2}}{{{n_2}}}} \right)}^2}}}{{\frac{{{{\left( {s_1^2/{n_1}} \right)}^2}}}{{{n_1} - 1}} + \frac{{{{\left( {s_2^2/{n_2}} \right)}^2}}}{{{n_2} - 1}}}} = \frac{{{{\left( {\frac{{{{117}^2}}}{{28}} + \frac{{{{72}^2}}}{{16}}} \right)}^2}}}{{\frac{{{{\left( {{{117}^2}/28} \right)}^2}}}{{28 - 1}} + \frac{{{{\left( {{{72}^2}/16} \right)}^2}}}{{16 - 1}}}} \approx 41 > 40\)

Determine the t-value by looking in the row starting with degrees of freedom \(df = 40\) and in the column with\(1 - c/2 = 0.005\) in the Student's distribution table in the appendix:

\({t_{\alpha /2}} = 2.704\)

The margin of error is then:

\(E = {t_{\alpha /2}} \cdot \sqrt {\frac{{s_1^2}}{{{n_1}}} + \frac{{s_2^2}}{{{n_2}}}} = 2.704 \cdot \sqrt {\frac{{{{117}^2}}}{{28}} + \frac{{{{72}^2}}}{{16}}} \approx 77.0945\)

The endpoints of the confidence interval for\({\mu _1} - {\mu _2}\)are:

\(\begin{array}{l}\left( {{{\bar x}_1} - {{\bar x}_2}} \right) - E = (801 - 780) - 77.0945 = 21 - 77.0945 = - 56.0945\\\left( {{{\bar x}_1} - {{\bar x}_2}} \right) + E = (801 - 780) + 77.0945 = 21 + 77.0945 = 98.0945\end{array}\)

02

b)Step 2: Determine the test statistic

(b) Given claim: larger

The claim is either the null hypothesis or the alternative hypothesis. The null hypothesis and the alternative hypothesis state the opposite of each other. The null hypothesis needs to contain the value mentioned in the claim.

\(\begin{array}{l}{H_0}:{\mu _1} = {\mu _2}\\{H_a}:{\mu _1} > {\mu _2}\end{array}\)

Determine the test statistic:

\(t = \frac{{{{\bar x}_1} - {{\bar x}_2}}}{{\sqrt {\frac{{s_1^2}}{{{n_1}}} + \frac{{s_2^2}}{{{n_2}}}} }} = \frac{{801 - 780}}{{\sqrt {\frac{{{{117}^2}}}{{28}} + \frac{{{{72}^2}}}{{16}}} }} \approx 0.737\)

The\({\rm{P}}\)-value is the probability of obtaining the value of the test statistic, or a value more extreme. The\({\rm{P}}\)-value is the number (or interval) in the column title of Student's T distribution in the appendix containing the t-value in the row\(df = 40\):

\(P > 0.10\)

If the P-value is less than or equal to the significance level, then the null hypothesis is rejected:

\(P > 0.05 \Rightarrow {\rm{ Fail to reject }}{H_0}\)

There is not sufficient evidence to support the claim that the true average stance duration is larger among elderly individuals than among younger individuals.

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Most popular questions from this chapter

1. An article in the November \(1983\) Consumer Reports compared various types of batteries. The average lifetimes of Duracell Alkaline \(AA\)batteries and Eveready Energizer Alkaline \(AA\) batteries were given as \(4.1\) hours and \(4.5\) hours, respectively. Suppose these are the population average lifetimes.

a. Let \(\bar X\) be the sample average lifetime of \(100\) Duracell batteries and \(\bar Y\) be the sample average lifetime of \(100\) Eveready batteries. What is the mean value of \(\bar X - \bar Y\) (i.e., where is the distribution of \({\bf{\bar X - \bar Y}}\) centered)? How does your answer depend on the specified sample sizes?

b. Suppose the population standard deviations of lifetime are \(1.8\) hours for Duracell batteries and \(2.0\) hours for Eveready batteries. With the sample sizes given in part (a), what is the variance of the statistic \(\bar X - \bar Y\), and what is its standard deviation?

c. For the sample sizes given in part (a), draw a picture of the approximate distribution curve of \(\bar X - \bar Y\) (include a measurement scale on the horizontal axis). Would the shape of the curve necessarily be the same for sample sizes of \(10\) batteries of each type? Explain

Refer back to the previous exercise.

a. By far the most frequently tested null hypothesis when data is paired is\({H_0}:{\mu _D} = 0\). Is that a sensible hypothesis in this context? Explain.

b. Carry out a test of hypotheses to decide whether there is compelling evidence for concluding that on average diagnosis occurs more than\(\;25\)months after the onset of symptoms.

Example\(7.11\)gave data on the modulus of elasticity obtained\(1\)minute after loading in a certain configuration. The cited article also gave the values of modulus of elasticity obtained\(4\)weeks after loading for the same lumber specimens. The data is presented here. \(\begin{array}{l} Type\\ \begin{array}{*{20}{c}}{}&1&2&3&4&5&6\\{ M: }&{82.6}&{87.1}&{89.5}&{88.8}&{94.3}&{80.0}\\{ LD: }&{86.9}&{87.3}&{92.0}&{89.3}&{91.4}&{85.9}\\{}&7&8&9&{10}&{11}&{12}\\{ M: }&{86.7}&{92.5}&{97.8}&{90.4}&{94.6}&{91.6}\\{ LD: }&{89.4}&{91.8}&{94.3}&{92.0}&{93.1}&{91.3}\\{}&{}&{}&{}&{}&{}&{}\end{array}\end{array}\)

a. Estimate the difference in true average strength under the two drying conditions in a way that conveys information about reliability and precision, and interpret the estimate. What does the estimate suggest about how true average strength under moist drying conditions compares to that under laboratory drying conditions?

b. Check the plausibility of any assumptions that underlie your analysis of (a).

Persons having Reynaud’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 \({\mu _1} - {\mu _2} = - 1.2?\)

Teen Court is a juvenile diversion program designed to circumvent the formal processing of first-time juvenile offenders within the juvenile justice system. The article "An Experimental Evaluation of Teen Courts" (J. of Experimental Criminology, 2008: 137-163) reported on a study in which offenders were randomly assigned either to Teen Court or to the traditional Department of Juvenile Services method of processing. Of the \(56TC\) individuals, 18 subsequently recidivated (look it up!) during the 18 -month follow-up period, whereas 12 of the 51 DJS individuals did so. Does the data suggest that the true proportion of TC individuals who recidivate during the specified follow-up period differs from the proportion of DJS individuals who do so? State and test the relevant hypotheses using a significance level of 0.10.

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