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In each of the given Exercises, we have given the number of possible values for two variables of a population. For each exercise, determine the maximum number of expected frequencies that can be less than 5 in order that Assumption 2 of Procedure 12.2 on page 506 to be satisfied. Note: The number of cells for a contingency table with m rows and n columns is m⋅n.

12.74 six and seven

Short Answer

Expert verified

Ans: The maximum number of expected frequencies that can be less than 5 by using Assumption 2 is 8.

Step by step solution

01

Step 1. Given information.

given,

six and seven

02

Step 2. First, determine the maximum number of expected frequencies that can be less than 5 by using Assumption 2.  

Assumption:

  1. All expected frequencies are at least 1.
  2. At most 20% of the expected frequencies are less than 5.
  3. All the selected samples should be a simple random samples.
03

Step 3. Now,  

According to the given information, the number of possible values for the first variable is 6 and the number of possible values for the second variable is 7. Therefore, there are 42(=6×7)expected frequencies.

The maximum number of expected frequencies that can be less than 5, by using Assumption 2 is,

20%of42=20100×42=8.48

Hence, the maximum number of expected frequencies that can be less than 5 by using Assumption 2 is 8.

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Most popular questions from this chapter

To decide whether two variables of a population are associated, we usually need to resort to inferential methods such as the chi-square independence test. Why?

We have presented a contingency table that gives a cross-classification of a random sample of values for two variables x and y, of a population.

Perform the following tasks

a. Find the expected frequencies Note: You will first need to compute the row totals, column totals, and grand total.

b. Determine the value of the chi-square statistic

c. Decide at the 5% significance level whether the data provide sufficient evidence to conclude that the two variables are associated.

Table 12.4 on page 486 showed the calculated sums of the observed frequencies, the expected frequencies, and their differences. Strictly speaking, those sums are not needed. However, they serve as a check for computational errors.

a) In general, what common value should the sum of the observer frequencies and the sum of the expected frequencies equal? Ex plain your answer.

b) Fill in the blank. The sum of the differences between each observed and expected frequency should equal

c) Suppose that you are conducting a chi-square goodness-of-fit test. If the sum of the expected frequencies does not equal the sample size, what do you conclude?

d) Suppose that you are conducting a chi-square goodness-of-fit test. If the sum of the expected frequencies equals the sample size, can you conclude that you made no error in calculating the expected frequencies? Explain your answer.

12.53 AIDS Cases. Refer to Exercise 12.47. For AIDS cases in the United States in 2011, solve the following problems:

RegionWhiteBlackOtherTotal
Northeast1,1002,493
5,177
Northwest1,137
5043221
South2,7617,848
12,867
West
76417664,230
Total

605225,435

a. Find and interpret the conditional distributions of region by race.

b. Find and interpret the marginal distribution of region.

c. Are the variables "region" and "race" associated? Explain your answer.

d. What percentage of AIDS cases were in the South?

e. What percentage of AIDS cases among whites were in the South?

f. Without doing further calculations, respond true or false to the distributions of race by region are not identical.

g. Find and interpret the marginal distribution of race and the conditional distributions of race by region.

In given exercise use either the critical-value approach or the P-value approach to perform a chi square independence lest. provided the conditions for using the test are met.

Diabetes in Native Americans. Preventable chronic diseases are increasing rapidly in Native American populations, particularly diabetes. F. Gilliland et al. examined the diabetes issue in the paper "Preventative Health Care among Rural American Indians in New Mexico" (Preventative Medicine, Vol. 28, pp. 194-202). Following is a contingency table showing cross-classification of educational attainment and diabetic state for a sample of 1273 Native Americans (HS is high school).

At the 1% significance level, do the data provide sufficient evidence to conclude that an association exists between education level and diabetic state for native Americans?

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