To determine the level of measurement for the lengths of 22 Ford automobiles (in centimeters), we need to understand the different levels of measurement and apply them to this dataset. Here are the levels of measurement:
1. Nominal: This level involves categorizing data without any order. For example, labeling Ford automobile models by name or category.
2. Ordinal: This level involves ordering or ranking data. The order matters, but the differences between data points are not meaningful. An example might be ranking cars as small, medium, and large.
3. Interval: This level allows for ordering data and calculating meaningful differences between data points. However, an interval level does not have a true zero point. An example would be temperature in Celsius or Fahrenheit.
4. Ratio: This is the highest level of measurement, which includes ordering, calculating meaningful differences, and having a true zero point that indicates the absence of the quantity being measured. Examples include weight, height, length, and duration.
Now, let's apply these to the data set of Ford automobile lengths:
- Can the data be categorized? Yes, each measurement is a length of a Ford automobile.
- Can the data be ordered or ranked meaningfully? Yes, 404.3 cm is less than 406.7 cm, which is less than 409.9 cm, and so on.
- Are differences between data points meaningful? Yes, the difference between 406.7 cm and 404.3 cm is meaningful, as is the difference between 504.1 cm and 479.7 cm.
- Is there a true zero point indicating absence of length? Yes, 0 cm would mean the complete absence of length.
Given the properties above, the data for lengths of Ford automobiles:
- Can be ordered
- Differences between measurements are meaningful
- Has a true zero point (0 cm)
Thus, the measurement level for this data set is:
C. Ratio. The data can be ordered and differences between data entries are meaningful, and the zero entry is an inherent zero.
