When the weights of newborn babies are graphed, the distribution of weights is symmetric with the majority of weights centered around a single peak. Which of the following describes the shape of this distribution?
The distribution of the weights of newborn babies is bell-shaped.
This means that the data is symmetrically distributed around the mean, median, and mode, which are all equal in a bell-shaped distribution. The majority of the values are concentrated around the center, with fewer values occurring as we move farther away from the center in either direction.
In a right-skewed distribution, most of the data points fall to the left of the graph, with a long tail extending to the right. This means that a small number of very large values pull the mean to the right of the median. However, the newborn weights are symmetrically distributed, not skewed to the right.
A bimodal distribution has two peaks, indicating two different modes, or most common values, in the data set. The distribution of newborn weights is described as having a majority of weights centered around a single peak, not two, so it cannot be bimodal.
A uniform distribution has roughly the same frequency of occurrence for all values in the data set, meaning that the graph would appear as a flat line. This does not describe the distribution of newborn weights, which is centered around a single peak.
A bell-shaped distribution, also known as a normal or Gaussian distribution, is symmetric and has a peak at the mean, median, and mode. This accurately describes the distribution of newborn weights, which is symmetric and centered around a single peak.
The weights of newborn babies follow a bell-shaped distribution, meaning that the weights are symmetrically distributed around a central peak. This distribution has a distinct shape with the majority of weights clustered around the mean, median, and mode, and fewer weights as you move away from the center in either direction. The other options - right-skewed, bimodal, and uniform - do not accurately describe this pattern of distribution.
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