Final answer:
To determine the minimum class width, one would typically round up to avoid ambiguity with data points falling on class boundaries. The general guideline is to take the square root of the number of data values, round to the nearest whole number for classes, and if rounding to the nearest 1,000, the minimum class width would be 1,000 passengers if the calculated width is less.
Step-by-step explanation:
To determine the minimum class width for a dataset, you may need to round up to ensure no value falls on a boundary, which could create ambiguity in the classification of that data point. While rounding usually follows standard rules, in this context, especially when creating histograms or frequency tables, it is common to round to the next whole number or a significant number like 1,000 to make the data more manageable and the intervals more practical.
In the given scenario, you would initially calculate the required class width based on the square root of the number of data values and then round up. If you had 150 data values, you would take the square root of 150, which is approximately 12.247, and then round this number to determine the number of bars or intervals, which in this case would be 12 when rounded to the nearest whole number.
If the class width needs to be rounded to the nearest 1,000, and assuming the calculated width is less than 1,000, the minimum class width would automatically be set to 1,000. This would ensure that none of the classes are too narrow and avoid any data points falling on the boundaries between classes.