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The U.S. Census Bureau complies data on the U.S. population by region and race and publishes its finding in Current Population Reports. Independent simple random samples of residents in the four U.S. regions gave the following data on race.

At the 1%significance level, do the data provide sufficient evidence to conclude that a difference exists in race distributions among the four U.S. regions?

Short Answer

Expert verified

In H0, no difference exists in race distributions among the four U.S. regions.

In Ha, a difference exists in race distributions among the four U.S. regions.

The value of αis 0.01,localid="1651935588360" x2=26.897and critical value is16.812whereP<0.005.

Step by step solution

01

Step 1. Given information.

Consider the given question,

02

Step 2. Consider the null and alternative hypotheses.

According to the null and alternative hypotheses,

H0denotes race distributions among the four U.S. regions are homogeneous.

Hadenotes race distributions among the four U.S. regions are non-homogeneous.

Specific level of significance is α=0.01

The given table contains the expected frequencies corresponding to the observed frequencies.

All of the expected frequencies are greater than 1, we can verify using the table.

At most 20%of the expected frequencies are less than 5.

Hence, we can say all the assumptions are satisfied.

03

Step 3. Calculate the chi-square.

The test statistics,x2=26.897.

The row variable has 4values and the column variable has 3values. Therefore, we can say r=4,c=2.

According to the chi-square, the critical value is 16.812.

We see that the value of the test statistics falls in the rejection region. Thus, we reject. The test results are significant.

At significance level of 1%, the data provide sufficient evidence, concluding a difference in race distributions among the four U.S. regions.

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Most popular questions from this chapter

Find the type of table that is used to group bivariate data.

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.

In each of exeercises 12.57-12.59, use the technology of your choice to solve the specified problems.

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