Measures of dispersion
Two basketball players both average points a game. One scores night after night; the other scores . Same mean, completely different players: one is steady, one is a gamble. The mean alone hides this. To describe data honestly we also need a number for how spread out it is.
Range and interquartile range
The range is easy but depends only on two values, so one extreme value ruins it. The interquartile range ignores the extremes.
Variance and standard deviation
Both measures above ignore most of the data. The variance uses every value: it is the average of the squared distances from the mean.
The standard deviation has the same unit as the data, which makes it easier to interpret. A small means the values hug the mean; a large means they are scattered.
The box plot
A box plot draws five numbers: the minimum, , the median, and the maximum. The box covers the middle half of the data and the "whiskers" reach out to the extremes.
Outliers
A value is an outlier if it lies far from the bulk of the data. A common rule: is an outlier if
Key terms
Summary
- and .
- and .
- A smaller (or ) means more consistent data.
- is an outlier if or .
Practice
Show solutions
- . , , so .
- . Squared deviations: , sum . and .
- and every deviation is , so . All values are equal: there is no spread.
- and . and .
- For A, ; for B, . B has the smaller standard deviation, so B is more consistent.
- , (median left out), , . Upper fence . Since , it is an outlier.
- The new data is : the mean becomes (it increases by ), while does not change. Shifting all values does not change the spread.
- The new data is with mean . and . The variance is multiplied by and by .
- Mean gives , so . Deviations , squares sum to . .
- . .