Rationale
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.
A) Skewed right
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.
B) Bimodal
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.
C) Uniform
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.
D) Bell-shaped
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.
Conclusion
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.
