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Finding Critical Values An alternative to using Table A–9 to find critical values for rank correlation is to compute them using this approximation:

\({r_s} = \pm \sqrt {\frac{{{t^2}}}{{{t^2} + n - 2}}} \)

Here, t is the critical t value from Table A-3 corresponding to the desired significance level and n - 2 degrees of freedom. Use this approximation to find critical values of\({r_s}\)for Exercise 15 “Blood Pressure.” How do the resulting critical values compare to the critical values that would be found by using Formula 13-1 on page 633?

Short Answer

Expert verified

The critical values of\({r_s}\)using the given formula are equal to -0.159 and 0.159.

The critical values of\({r_s}\)using Formula 13-1 are equal to -0.159 and 0.159.

The two critical values are the same.

Step by step solution

01

Given information

Data are given onthe blood pressures of males.

02

Critical values using the given formula

Consider\(\alpha = 0.05\).

The number of observations (n) = 153.

The degrees of freedom are computed as follows:

\(\begin{array}{c}df = n - 1\\ = 153 - 1\\ = 152\end{array}\)

The value oft corresponding to the degrees of freedom equal to 152 and\(\alpha = 0.05\)is 1.975.

Thus, the critical values using the formula are obtained as:

\(\begin{array}{c}{r_s} = \pm \sqrt {\frac{{{t^2}}}{{{t^2} + n - 2}}} \\ = \pm \sqrt {\frac{{{{1.975}^2}}}{{{{1.975}^2} + 153 - 2}}} \\ = \left( { - 0.159,0.159} \right)\end{array}\)

Thus, the critical values using the given formula are -0.159 and 0.159.

03

Critical values using the original formula

The value of z corresponding to\(\alpha = 0.05\)for a two-tailed test is equal to 1.96.

The critical values of\({r_s}\)using the following formula are:

\(\begin{array}{c}{r_s} = \pm \frac{z}{{\sqrt {n - 1} }}\\ = \pm \frac{{1.96}}{{\sqrt {153 - 1} }}\\ = \left( { - 0.159,0.159} \right)\end{array}\)

Thus, the critical values using Formula 13-1 are -0.159 and 0.159.

04

Comparison

The critical values calculated using the two formulas are the same.

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Most popular questions from this chapter

Efficiency of the Sign Test Refer to Table 13-2 on page 600 and identify the efficiency of the sign test. What does that value tell us about the sign test?

Sign Test vs. Wilcoxon Signed-Ranks Test Using the data in Exercise 1, we can test for no difference between body temperatures at 8 AM and 12 AM by using the sign test or the Wilcoxon signed-ranks test. In what sense does the Wilcoxon signed-ranks test incorporate and use more information than the sign test?

Using Nonparametric Tests. In Exercises 1–10, use a 0.05 significance level with the indicated test. If no particular test is specified, use the appropriate nonparametric test from this chapter.

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Finding Critical Values Assume that we have two treatments (A and B) that produce quantitative results, and we have only two observations for treatment A and two observations for treatment B. We cannot use the Wilcoxon signed ranks test given in this section because both sample sizes do not exceed 10.

Rank

Rank Sum of Treatment A

1

2

3

4


A

A

B

B

3

a. Complete the accompanying table by listing the five rows corresponding to the other five possible outcomes, and enter the corresponding rank sums for treatment A.

b. List the possible values of R and their corresponding probabilities. (Assume that the rows of the table from part (a) are equally likely.)

c. Is it possible, at the 0.10 significance level, to reject the null hypothesis that there is no difference between treatments A and B? Explain.

Sign Test and Wilcoxon Signed-Ranks Test What is a major advantage of the Wilcoxon signed-ranks test over the sign test when analyzing data consisting of matched pairs?

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