Refer to the data set below (body mass index of men) and determine whether the requirement of a normal distribution is satisfied. Assume that this requirement is loose in the sense that the population distribution need not be exactly normal, but it must be a distribution that is basically symmetric with only one mode. 28.6 21.4 23.8 27.4 33.5 26.1 22.8 24.2 25.8 24.6 25.1 26.3 26.8 23.5 26.7 20.6 19.3 27.8 32.2 30.8 D Is the requirement of a normal distribution satisfied?
Refer to the data set below (body mass index of men) and determine whether the requirement of a normal distribution is satisfied. Assume that this requirement is loose in the sense that the population distribution need not be exactly normal, but it must be a distribution that is basically symmetric with only one mode. 28.6 21.4 23.8 27.4 33.5 26.1 22.8 24.2 25.8 24.6 25.1 26.3 26.8 23.5 26.7 20.6 19.3 27.8 32.2 30.8 D Is the requirement of a normal distribution satisfied?
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
Transcribed Image Text:### Understanding Normal Distribution: Body Mass Index (BMI) Analysis
#### Data Set:
The following data represents the body mass index (BMI) of a group of men. We aim to determine whether it follows a normal distribution. The requirement for this is relaxed to allow for a distribution that is essentially symmetric with only one mode, rather than strictly normal.
- Data Set: 28.6, 21.4, 23.8, 27.4, 33.5, 26.1, 22.8, 24.2, 25.8, 24.6, 25.1, 26.3, 26.8, 23.5, 26.7, 20.6, 19.3, 27.8, 32.2, 30.8
#### Question:
Is the requirement of a normal distribution satisfied for this data set?
#### Multiple Choice Answers:
1. **A.** Yes. The points in the normal quantile plot exhibit some systematic pattern that is not a straight-line pattern.
2. **B.** Yes. The points in the normal quantile plot lie reasonably close to a straight line.
3. **C.** No. The histogram of the data is not bell-shaped. There is also more than one outlier.
4. **D.** No. The points in the normal quantile plot do not lie close to a straight line.
### Explanation:
To assess if the given set of BMIs adheres to a normal distribution, we can utilize graphical tools such as histograms, normal quantile plots (Q-Q plots), and statistical metrics including skewness and kurtosis. A normal distribution typically appears as a bell-shaped curve on a histogram and points should lie close to a straight line in a Q-Q plot if the distribution is normal. In this educational illustration, the available choices focus on patterns observed in these graphical tools to determine normality.
Students are encouraged to plot the given data themselves to visualize and determine the most fitting option.
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