In each of the following variables, decide which type of data grouping (single-value, limit, or cutpoint) is probably the best. 4 a. Number of Bedrooms. The number of bedrooms per single family dwelling b. Ages of Householders. The ages of householders, given as a whole number c. Sleep Aids. The additional sleep, to the nearest tenth of an hour, obtained by a sample of 100 patients by using a particular brand of sleeping pill. d. Number of Smartphones. The number of smartphones per person. e. Gas Mileage. The gas mileages, rounded to the nearest number of miles per gallon, of all new car models. f. Grammostola. The weight, in grams, of a sample of 50 Grammostola, a variety of tarantulas
In each of the following variables, decide which type of data grouping (single-value, limit, or cutpoint) is
probably the best.
4
a. Number of Bedrooms. The number of bedrooms per single family dwelling
b. Ages of Householders. The ages of householders, given as a whole number
c. Sleep Aids. The additional sleep, to the nearest tenth of an hour, obtained by a sample of 100 patients
by using a particular brand of sleeping pill.
d. Number of Smartphones. The number of smartphones per person.
e. Gas Mileage. The gas mileages, rounded to the nearest number of miles per gallon, of all new car
models.
f. Grammostola. The weight, in grams, of a sample of 50 Grammostola, a variety of tarantulas
The Correct Answer and Explanation is:
Answers:
a. Number of Bedrooms – Single-value grouping
b. Ages of Householders – Cutpoint grouping
c. Sleep Aids (Additional sleep in tenths of an hour) – Cutpoint grouping
d. Number of Smartphones – Single-value grouping
e. Gas Mileage (rounded to nearest mpg) – Limit grouping
f. Grammostola (weights in grams) – Limit grouping
Explanation
In statistics, the method of data grouping depends on the nature and precision of the data being analyzed. The three common grouping types are:
- Single-value grouping: Used when the data values are discrete and have relatively few possible outcomes (often integers).
- Limit grouping: Used for continuous or rounded data where class intervals have clearly defined lower and upper boundaries (e.g., 20–29).
- Cutpoint grouping: Applied to continuous data where intervals are defined using precise boundaries (e.g., 20–<30), often when dealing with decimal values.
Let’s evaluate each variable:
a. Number of Bedrooms – These are countable whole numbers (e.g., 1, 2, 3). Since the range is limited and values are discrete, single-value grouping is appropriate.
b. Ages of Householders – Though given as whole numbers, age spans a wide range (0–100+), and we typically want to analyze it in meaningful intervals (e.g., 20–29, 30–39). Cutpoint grouping better reflects age categories and allows for cleaner interpretation, especially in demographic studies.
c. Sleep Aids – The data is recorded to the nearest tenth of an hour, making it continuous and precise. Cutpoint grouping is ideal here, as it accommodates decimals and helps show trends across intervals (e.g., 0.0–<0.5, 0.5–<1.0).
d. Number of Smartphones – Like bedrooms, this is a count variable with limited values (usually 0–3). Single-value grouping best preserves detail.
e. Gas Mileage – Values are rounded to whole numbers but originate from continuous measurements. Limit grouping (e.g., 20–29 mpg) appropriately accommodates such rounded data.
f. Grammostola Weights – Weights are continuous and measured precisely. Limit grouping is preferred for representing ranges (e.g., 15–19 g, 20–24 g).
Choosing the right grouping method ensures clarity and analytical accuracy in data visualization and interpretation.
