The dataframe students contains information on 1000 students, with the columns marks, age, country of origin, study style, ATAR and gender.

Which of the following could NOT be represented by a scatterplot?

Note:

Marks are measured 0-100 (from mid semester exam)
Age is number of days from birth
Country of Origin is Australia, China etc
Study Style is: independent or dependent
ATAR is Australia Tertiary Admission Rank (a number between 0 and 99.95)
Gender is Male, Female, NotSpecified etc

Question

The dataframe students contains information on 1000 students, with the columns marks, age, country of origin, study style, ATAR and gender.

Which of the following could NOT be represented by a scatterplot?

Note:

Marks are measured 0-100 (from mid semester exam)
Age is number of days from birth
Country of Origin is Australia, China etc
Study Style is: independent or dependent
ATAR is Australia Tertiary Admission Rank (a number between 0 and 99.95)
Gender is Male, Female, NotSpecified etc

BlackTom AI · Accepted Answer

Let's break down what a scatterplot is best used for and what kinds of data each option represents.
Option 1: Marks vs Age. Here both variables are numerical (Marks ranges 0-100, Age is a numeric count of days). A scatterplot is well-suited to show the relationship, correlation, or spread between two quantitative measures, so this can be represented by a scatterplot.
Option 2: ATAR vs Study Style. ATAR is a numeric measure (0 to 99.95), but Study Style is a categorical variable (e.g., independent or dependent). Scatterplots are designed to plot two quantitative axes; when one axis is categorical, the plot becomes a different type (like a box plot or violin plot grouped by category) rather than a true scatterplot. This combination does not fit a standard scatterplot.
Option 3: Marks vs ATAR. Both variables are numeric (Marks 0-100, ATAR 0-99.95). A scatterplot can display how marks relate to ATAR values, revealing patterns or correlations between these two quantitative measures.
Option 4: Age vs ATAR. Again, both are numeric quantities (Age in days, ATAR in the 0–100 range). A scatterplot can illustrate any potential relationship between age and ATAR, including clusters or trends.
Overall, the only pairing that involves a categorical variable on one axis and a numeric variable on the other, which conflicts with the typical numeric-to-numeric scatterplot design, is ATAR vs Study Style. The other pairings are all valid for a scatterplot.

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