This question assesses interpreting dotplots for quantitative data like packages per route. The dotplot builds to a peak at 90 packages (7 routes), with symmetric decreases to both lower and higher values, showing a roughly symmetric, unimodal distribution centered around 90. The shape is bell-like without skew or gaps. Choice D could distract by suggesting bimodality, but there's only one peak, not two distinct ones. A mini-lesson: symmetric distributions often have mean ≈ median; always describe shape, center, spread (e.g., from 60 to 120), and any unusual features to understand the data's central tendency and variability.

This question tests describing distributions from dotplots for quantitative data like books checked out. The dotplot reveals a high frequency at 0 books (16 patrons), with frequencies decreasing as the number increases, but a long tail extending to 10 books, creating a strong right skew. Most data points are clustered at lower values, with fewer high values stretching the tail. Choice B could mislead by suggesting left skew, but the tail is toward higher numbers, not lower. When analyzing distributions, note the shape (e.g., right-skewed means tail to the right), center (pulled toward the tail in skewed data), spread, and any peaks or gaps to understand the variable's behavior.

This question tests your ability to describe the shape of a distribution from a dotplot. Looking at the dotplot of wait times, we can see that most customers waited between 2-6 minutes, with the frequency decreasing as wait times increase. There appears to be one or two customers who waited around 12 minutes, which stands out from the main cluster. This creates a distribution that has a longer tail extending to the right (toward higher values), which is the definition of right-skewed. The value around 12 minutes could be considered a high outlier since it's separated from the main body of data. When describing distributions, always look for the overall shape (symmetric, skewed, uniform), the number of peaks (unimodal, bimodal), and any unusual observations (outliers).

This question involves identifying features in dotplots of quantitative data like rainfall amounts. The dotplot shows a high stack at 0.0 inches (19 days), decreasing frequencies toward higher amounts, with a tail extending to 1.0 inches, indicating strong right skew. Most days have little to no rain, with infrequent higher rainfall days. Choice B distracts by suggesting left skew, but the long tail is to the right, toward higher values. Key lesson: right-skewed distributions often occur with non-negative variables like rainfall; describe shape, center (low due to skew), spread, and peaks to highlight common versus rare events.

This question focuses on describing dotplots for quantitative data such as items per order. The dotplot has the highest frequency at 1 item (26 orders), with frequencies steadily decreasing toward higher numbers, forming a long tail to the right and strong right skew. Most orders are small, with rare larger ones extending the distribution. Choice C might mislead by claiming left skew, but the tail is to higher values, not lower. A mini-lesson: for skewed distributions, the mean is pulled toward the tail compared to the median; always describe shape, center (e.g., median for skew), spread, and any clusters to interpret the data meaningfully.

This question tests your ability to recognize a bimodal distribution in a dotplot. Looking at the fish length data, we can see two distinct clusters of dots: one group centered around 15-20 cm and another group centered around 35-40 cm, with relatively few fish in between. This creates two clear peaks or modes in the distribution, making it bimodal. This pattern often suggests two different populations or groups within the data - perhaps two different species of fish or fish at different life stages. Option C incorrectly identifies this as right-skewed with an outlier, but the second cluster is too substantial to be considered outliers. When examining distributions, bimodal patterns indicate that the data may come from two distinct groups that should potentially be analyzed separately.

This question tests your understanding of skewness in the context of quiz scores. Looking at the dotplot, you can see that most students scored high on the quiz (dots clustered on the right side near scores of 8, 9, and 10), with fewer students receiving lower scores that trail off toward the left. This creates a distribution where the tail extends toward the lower values, which defines a left-skewed distribution. Choice B correctly identifies this as strongly skewed left because most scores are high. The key insight is that skewness is named for the direction of the tail, not where most of the data is located - when most values are high and the tail points toward low values, the distribution is skewed left. This pattern often occurs with relatively easy assessments where most students perform well.

This question asks you to identify the shape of a distribution from a dotplot of library checkouts. The dotplot shows that most patrons check out a small number of books (clustered on the left), with the frequency decreasing as the number of books increases, and a few patrons checking out many more books than typical. This creates a long tail extending to the right (toward higher book counts), which is the defining characteristic of a right-skewed distribution. Choice C (skewed left) would show the opposite pattern with most data on the right. Right-skewed distributions are common for count data like library checkouts because while most people have moderate usage, a few heavy users create the long right tail. Remember that skewness is always named for the direction of the tail, not where the bulk of the data sits.

This question tests your ability to identify the shape of a distribution from a dotplot. Looking at the dotplot of treadmill times, we can see that most students have times clustered on the left side (shorter times), with the frequency gradually decreasing as we move toward longer times on the right. The distribution has a long tail extending toward the higher values (longer treadmill times), which is the defining characteristic of a right-skewed distribution. Choice A (symmetric) is incorrect because the data clearly has more values on one side than the other. When describing distributions, remember that skewness is named for the direction of the tail: right-skewed means the tail points right toward larger values, while left-skewed means the tail points left toward smaller values.

This question asks you to identify the distribution shape from a dotplot of quiz scores. Looking at the dotplot, we can see that most scores are concentrated near the maximum score of 20, with the frequency decreasing as scores get lower, creating a tail that extends toward the left (lower scores). This pattern indicates a left-skewed distribution - the bulk of the data is on the right (high scores) with a tail pointing left toward lower values. Choice A (skewed right) would show the opposite pattern. Left-skewed distributions in test scores often occur when the assessment is relatively easy for most students, causing scores to bunch up near the maximum. When identifying skewness, always look for the direction of the tail: left-skewed means the tail points toward smaller values on the left.