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Data on household vehicle miles of travel (VMT) are compiled annually by the Federal Highway Administration and are published in the National Household Travel Survey, Summary of Travel Trends. Independent random samples of 15midwestern households and 14southern households provided the following data on last year's VMT, in thousands of miles.

At the 5%significance level, does there appear to be a difference in last year's mean VMT for midwestern and southern households? (Note: x¯1=16.23,s1=4.06,x¯2=17.69, and s2=4.42.)

Short Answer

Expert verified

The presented data do not provide adequate evidence to infer that there is a difference in last year's mean VMT between Midwestern and Southern households at the significance level of 5%.

Step by step solution

01

Given Information

Vehicle miles traveled (VMT) data for two populations of Midwestern and Southern families is presented as a sample.

x¯1=16.23,s1=4.06

x¯2=17.69,s2=4.42

The significance level is5%.

02

Explanation

Population 1: Midwestern households, x¯1=16.23,s1=4.06, and n1=15.

Population 2: Southern households, x¯2=17.69,s2=4.42, and n2=14.

The major goal is to determine whether there is a difference in mean VMT between Midwestern and Southern homes from the previous year.

Define null and alternate hypotheses.

Null hypotheses:H0:μ1=μ2

Alternate hypotheses:Ha:μ1≠μ2

Hypotheses is two-tailed.

We decided significance level as5%

03

Calculation

Pooled standard deviation, sp=n1-1s12+n2-1s22n1+n2-2

⇒sp=(15-1)(4.06)2+(14-1)(4.42)215+14-2

⇒sp=14(16.4836)+13(19.5364)27

⇒sp=4.237

Test statistic,t0=x¯1-x¯2sp1n1+1n2

⇒t0=16.23-17.694.237115+114

⇒t0=-0.927

We determine the critical values

Here,localid="1651300882093" role="math" df=n1+n2-2=15+14-2=27

⇒df=27

Using table IV

localid="1651300891246" role="math" Critical value,±tα/2=±t0.05/2=±t0.025=±2.052

Since t0=-0.927, the test statistic does not fall into the two-tailed hypotheses test rejection zone. As a result, null hypotheses are not ruled out.

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Most popular questions from this chapter

Tukey's Quick Test.

In this exercise, we examine an alternative method, conceived by the late Professor John Tukey, for performing a two-tailed hypothesis test for two population means based on independent random samples. To apply this procedure, one of the samples must contain the largest observation (high group) and the other sample must contain the smallest observation (low group). Here are the steps for performing Tukey's quick test.
Step I Count the number of observations in the high group that are greater than or equal to the largest observation in the low group. Count ties as 12.

Step 2 Count the number of observations in the low group that are less than or equal to the smallest observation in the high group. Count ties as 12.

Step 3 Add the two counts obtained in Steps 1 and 2, and denote the sum c.

Step 4 Reject the null hypothesis at the 5% significance level if and only if ³¦â‰¥7; reject it at the 1% significance level if and only if ³¦â‰¥10; and reject it at the0.1% significance level if and only
if³¦â‰¥13.
a. Can Tukey's quick test be applied to Exercise 10.48 on page 416? Explain your answer.
b. If your answer to part (a) was yes, apply Tukey's quick test and compare your result to that found in Exercise 10.48, where a t-test was used.
c. Can Tukey's quick test be applied to Exercise 10.74? Explain your answer.
d. If your answer to part (c) was yes, apply Tukey's quick test and compare your result to that found in Exercise 10.74, where a t-test was used.

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a. identify the variable.

b. identify the two populations,

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d. classify the hypothesis test as two-tailed, left-tailed, or right-tailed.

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