6th Edition

ISBN: 9780135901137

Author: Triola

Publisher: PEARSON

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Chapter 11.3, Problem 2BSC

In Exercises 1-4, use the following listed arrival delay times (minutes) for American Airline flights from New York to Los Angeles. Negative values correspond to flights that arrived early. Also shown are the SPSS results for analysis of variance. Assume that we plan to use a 0.05 significance level to test the claim that the different flights have the same mean arrival delay time.

Chapter 11.3, Problem 2BSC, In Exercises 1-4, use the following listed arrival delay times (minutes) for American Airline , example 1

Chapter 11.3, Problem 2BSC, In Exercises 1-4, use the following listed arrival delay times (minutes) for American Airline , example 2

2. Why Not Test Two at a Time? Refer to the sample data given in Exercise 1. If we want to test for equality of the three means, why don’t we use three separate hypothesis tests for μ₁ = μ₂, μ₁ = μ₃, and μ₂ = μ₃?

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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Chapter 11.3, Problem 1BSC

Chapter 11.3, Problem 3BSC

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Chapter 11 Solutions

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Solution Summary: The author explains that when the number of individual tests of significance is increased, the risk of Type I error increases.

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