Question
Each week Fred records the miles per gallon for his car, to the nearest whole number. Over a period of 10 weeks, the data he collected
was as follows (in miles per gallon):
Fred decides that it would be a good idea to calculate various measures of central tendency and variation to help describe his
findings. Your job will be to helpl Answer the questions below related to Fred’s car.
What were these measures of variation for Fred’s data?
- The range of Fred’s data is 13 miles per gallon.
- The interquartile range (IQR) of Fred’s data is 8 miles per gallon.
- The standard deviation of Fred’s data is 4.7 miles per gallon.
was as follows (in miles per gallon):
Fred decides that it would be a good idea to calculate various measures of central tendency and variation to help describe his
findings. Your job will be to helpl Answer the questions below related to Fred’s car.
What were these measures of variation for Fred’s data?
Ask by Simpson Ortega. in the United States
Mar 28,2025
Upstudy AI Solution
Tutor-Verified Answer
Answer
Fred’s data measures are:
- Range: 13 miles per gallon
- Interquartile Range (IQR): 8 miles per gallon
- Standard Deviation: 4.7 miles per gallon
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Mind Expander
Did you know that calculating measures of variation allows you to understand the spread and consistency of your data? Fred’s range, which is the difference between the highest and lowest values, tells us how much variability he experienced in his car’s mileage—13 mpg between his most and least efficient weeks! The interquartile range (IQR) enhances this by focusing on the middle 50% of his data. Since this can give a better idea of where most of his MPG values lie, it’s great for identifying how consistent his performance is without being skewed by outliers.
If Fred wants to dig deeper into understanding his car’s fuel efficiency, he should take a look at the standard deviation. It shows how much the individual MPG readings deviate from the mean. In this case, a standard deviation of 4.7 mpg indicates that while most of his readings hover around the average, there can be a significant divergence at times. This insight could help him identify patterns or make adjustments for better efficiency in the future!