Final answer:
The dataset contains clusters around 70-80 and 80-90, with the most common score being 84. Clusters, gaps, and outliers can be identified using a stem plot or by calculating IQR or standard deviation. To describe the distribution, one would look at the frequency of scores in the stem plot.
Step-by-step explanation:
When analyzing the given dataset consisting of scores (98, 74, 84, 92, 84, 74, 80, 84, 98, 92, 82, 92, 76), we can identify any clusters, gaps, or outliers through various methods. A cluster is a range of data points that are grouped together, showing a concentration of scores in a particular range, while a gap represents an empty range or a significant difference between clusters. To identify an outlier, we can use interquartile range (IQR) calculations or examine if any score is more than two standard deviations away from the mean if the data is symmetric and mound-shaped. Upon observing the scores, clusters are apparent around the score ranges of 70-80 and 80-90, with the most common score being 84. To determine outliers, we would need additional steps such as calculating the IQR or standard deviation.
Constructing a stem plot for this data (in the range 0-100) would involve separating the tens and ones into stem and leaf, respectively, showing a clear picture of the score distribution. Stem plots are a useful way to visualize the frequency distribution of the data, allowing for an easier identification of clusters, gaps, and potential outliers. The distribution of these exam scores would then be described based on this visualization, indicating where the scores tend to concentrate and whether the higher, median, or lower scores are more prevalent.