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Name: 8. Formulating directional and nondirectional hypothesesThe body mass
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Description: 8. Formulating directional and nondirectional hypothesesThe body mass index (BMI) of a person is the person’s weight divided by the square of his or her height. It is an indirect measure of the person’s body fat and an indicator of obesity. Results f

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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8. Formulating directional and nondirectional hypotheses

The body mass index (BMI) of a person is the person’s weight divided by the square of his or her height. It is an indirect measure of the person’s body fat and an indicator of obesity. Results from surveys conducted by the Centers for Disease Control and Prevention (CDC) showed that the estimated mean BMI for US adults increased from 25.0 in the 1960–1962 period to 28.1 in the 1999–2002 period. [Source: Ogden, C., et al. (2004). Mean body weight, height, and body mass index, United States 1960–2002. Advance Data from Vital and Health Statistics, No. 347.]

Suppose you are a health researcher. You conduct a hypothesis test to determine whether the mean BMI of US adults in the current year is lower than the mean BMI of US adults in 2000. Assume that the mean BMI of US adults in 2000 was 28.1 (the population mean). You obtain a sample of BMI measurements of 1,034 US adults, which yields a sample mean of M = 28.9.

Let μ denote the mean BMI of US adults in the current year. Formulate your null and alternative hypotheses by selecting the appropriate values in the blue dropdown menus that follow.

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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