Final answer:
To identify outliers in quantitative data, scatter plots are the most helpful graph as they clearly show the values of two different variables and make any outliers conspicuous. Histograms can also be beneficial for spotting outliers, whereas bar charts and pie charts are not typically used for this purpose.
Step-by-step explanation:
Identifying Outliers in Quantitative Data
When it comes to identifying outliers in quantitative data, the most helpful graphical representation is a scatter plot. A scatter plot is a type of data visualization that uses dots to represent the values of two different variables, allowing you to see patterns and possible outliers. Outliers are points that lie away from the cluster of other data points, and they can easily be identified in a scatter plot.
Other graphs such as histograms or box plots can also be useful for spotting outliers. A histogram shows the distribution of a dataset and outliers may appear as bars that are separate from the main cluster of data. However, scatter plots are especially helpful because they can show the relationship between two variables, making outliers more conspicuous.
Comparatively, while bar charts are used to display and compare the frequency or count of items in different categorical groups, they are not typically used for identifying outliers within a dataset. Pie charts are also not generally used to identify outliers because they are designed to show parts of a whole for categorical data.