Keeper uses numeric codes to represent gender, with each code tied to a specific gender value. The code for this title is defined as male, so Keeper Title ID 1 corresponds to male. It isn’t female, unknown, or not used because those possibilities are associated with different codes in the system. So the gender linked to this ID is male.

To find the lightest car, you look for the smallest value in the weight column across the entries. Each option is a single car described by its make and year, so you compare their weights directly. The 2016 Toyota entry has the smallest weight among the options, making it the lightest single car in the data. The other options have greater weights, so they aren’t the lightest. If weights happened to be tied, you’d note that multiple entries could share the minimum.

To find the heaviest car, you look for the largest weight value in the data and see which car make sits with that row. In this dataset, the biggest weight belongs to a Vauxhall entry, so the heaviest single car is the Vauxhall. You can confirm by locating the maximum weight value and checking the make in that same row. If two rows shared the same top weight, there would be multiple makes, but here the maximum weight is linked to Vauxhall.

Range is about how spread out the values are by looking at the smallest and largest numbers in the column. To compute it, find the largest valid value and the smallest valid value, then subtract the smallest value from the largest value. Using only valid records ensures missing or invalid data don’t skew the result. Subtracting the reverse would give a negative number, which doesn’t represent spread, and adding or multiplying the two values doesn’t measure how wide the data range is.

The key idea is to differentiate data by whether it describes categories or measures quantities. Qualitative fields store labels that describe a quality or type, not a number. Think of fields like location, month, or product type—these are categories you group by, not numbers you can arithmetic with. Quantitative fields hold numeric values that can be measured and operated on, such as counts, distances, temperatures, or prices. These can be added, averaged, or otherwise analyzed statistically. So, a field like Location or Month is qualitative because it labels a category, while a field like Distance or Temperature is quantitative because it contains numerical measurements. This is why the correct characterization is that qualitative fields are category labels and quantitative fields are numeric measurements. The other statements mix up these ideas—numeric values aren’t qualitative, and category labels aren’t quantitative—so they don’t fit.