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Denomination Effect In the article “The Denomination Effect” by Priya Raghubir and Joydeep Srivastava, Journal of Consumer Research, Vol. 36, researchers reported results from studies conducted to determine whether people have different spending characteristics when they have larger bills, such as a \(20 bill, instead of smaller bills, such as twenty \)1 bills. In one trial, 89 undergraduate business students from two different colleges were randomly assigned to two different groups. In the “dollar bill” group, 46 subjects were given dollar bills; the “quarter” group consisted of 43 subjects given quarters. All subjects from both groups were given a choice of keeping the money or buying gum or mints. The article includes the claim that “money in a large denomination is less likely to be spent relative to an equivalent amount in smaller denominations.” Test that claim using a 0.05 significance level with the following sample data from the study.

Short Answer

Expert verified

There is enough evidence to support the claim that money in a large denomination is less likely to be spent relative to the same amount of money in smaller denominations.

Step by step solution

01

Given information

The number of people who were given a $1 bill/4 quarters and who spent the money is tabulated along with the individual sample sizes.

02

Hypotheses

It is claimed that money in a large denomination is less likely to be spent relative to the same amount of money in smaller denominations.

Since the given claim does not have an equality sign, the following hypotheses are set up:

Null Hypothesis: The proportion of people who spend the money is equal and does not depend on its denomination.

\({H_0}:{p_1} = {p_2}\)

Alternative Hypothesis: The proportion of people who spend the money when they receive a large denomination ($1 bill) is less than the proportion of people who spend the money when they receive smaller denominations (4 quarters).

\({H_1}:{p_1} < {p_2}\)

The test is left-tailed.

If the absolute value of the test statistic is greater than the critical value, the null hypothesis is rejected.

03

Important values

Let\({\hat p_1}\)denote the sampleproportion of people who received a $1 bill and spent it.

\(\begin{aligned}{c}{{\hat p}_1} &= \frac{{{\rm{Number}}\;{\rm{of}}\;{\rm{people}}}}{{{\rm{Sample}}\;{\rm{size}}}}\\ &= \frac{{{x_1}}}{{{n_1}}}\\ &= \frac{{12}}{{46}}\\ &= 0.261\end{aligned}\)

Let\({\hat p_2}\)denote the sampleproportion of people who received 4 quarters and spent the money.

\(\begin{aligned}{c}{{\hat p}_2} &= \frac{{{\rm{Number}}\;{\rm{of}}\;{\rm{people}}}}{{{\rm{Sample}}\;{\rm{size}}}}\\ &= \frac{{{x_2}}}{{{n_2}}}\\ &= \frac{{27}}{{43}}\\ &= 0.628\end{aligned}\)

\({n_1}\)is equal to 46 and\({n_2}\)is equal to 43.

The value of the pooled sample proportion is computed as follows:

\(\begin{aligned}{c}\bar p &= \frac{{{x_1} + {x_2}}}{{{n_1} + {n_2}}}\\ &= \frac{{12 + 27}}{{46 + 43}}\\ &= 0.438\end{aligned}\)

\(\begin{aligned}{c}\bar q &= 1 - \bar p\\ &= 1 - 0.438\\ &= 0.562\end{aligned}\)

04

Compute the test statistic, critical value and p-value

The value of the test statistic is computed below:

\(\begin{aligned} z &= \frac{{\left( {{{\hat p}_1} - {{\hat p}_2}} \right) - \left( {{p_1} - {p_2}} \right)}}{{\sqrt {\frac{{\bar p\bar q}}{{{n_1}}} + \frac{{\bar p\bar q}}{{{n_2}}}} }}\;\;\;\;{\rm{where}}\left( {{p_1} - {p_2}} \right) &= 0\\ &= \frac{{\left( {0.261 - 0.628} \right) - 0}}{{\sqrt {\frac{{\left( {0.438} \right)\left( {0.562} \right)}}{{46}} + \frac{{\left( {0.438} \right)\left( {0.562} \right)}}{{43}}} }}\\ &= - 3.49\end{aligned}\)

Thus, z= –3.49.

Using the standard normal table, the critical value of z corresponding to\(\alpha = 0.05\)for a left-tailed test is equal to –1.645.

The corresponding p-value is equal to 0.0002 which is obtained from the standard normal table using the test statistic (–3.49).

Since the absolute z-score value is greater than the critical value and the p-value is less than 0.05, the null hypothesis is rejected.

05

Conclusion

There is enough evidence to support the claim thatmoney in a large denomination is less likely to be spent relative to the same amount of money in smaller denominations.

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Most popular questions from this chapter

Testing Claims About Proportions. In Exercises 7–22, test the given claim. Identify the null hypothesis, alternative hypothesis, test statistic, P-value or critical value(s), then state the conclusion about the null hypothesis, as well as the final conclusion that addresses the original claim.

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a. Use the methods of this section to construct a 95% confidence interval estimate of the difference p1-p2. What does the result suggest about the equality of p1andp2?

b. Use the methods of Section 7-1 to construct individual 95% confidence interval estimates for each of the two population proportions. After comparing the overlap between the two confidence intervals, what do you conclude about the equality ofp1andp2?

c. Use a 0.05 significance level to test the claim that the two population proportions are equal. What do you conclude?

d. Based on the preceding results, what should you conclude about the equality ofp1andp2? Which of the three preceding methods is least effective in testing for the equality ofp1andp2?

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a. Test the claim using a hypothesis test.

b. Test the claim by constructing an appropriate confidence interval.

c. An explanation given for the results is that those over the age of 55 grew up exposed to media that was mostly displayed in black and white. Can the results from parts (a) and (b) be used to verify that explanation?

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Are Male Professors and Female Professors Rated Differently?

a. Use a 0.05 significance level to test the claim that two samples of course evaluation scores are from populations with the same mean. Use these summary statistics: Female professors:

n = 40, \(\bar x\)= 3.79, s = 0.51; male professors: n = 53, \(\bar x\) = 4.01, s = 0.53. (Using the raw data in Data Set 17 “Course Evaluations” will yield different results.)

b. Using the summary statistics given in part (a), construct a 95% confidence interval estimate of the difference between the mean course evaluations score for female professors and male professors.

c. Example 1 used similar sample data with samples of size 12 and 15, and Example 1 led to the conclusion that there is not sufficient evidence to warrant rejection of the null hypothesis.

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a. Use a 0.05 significance level to test the claim that nonsmokers exposed to tobacco smoke have a higher mean cotinine level than nonsmokers not exposed to tobacco smoke.

b. Construct the confidence interval appropriate for the hypothesis test in part (a).

c. What do you conclude about the effects of second-hand smoke?

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b. Construct the confidence interval appropriate for the hypothesis test in part (a).

c. Can you explain why cans of Diet Coke would weigh less than cans of regular Coke?

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