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An outlier is a value in a data set that is numerically distant from the other values. This definition is not rigorous, as I have not clearly identified a test to determine a value which is "numerically distant." However, in many situations, the definition is sufficient to identify values outside of an observable trend or grouping. The importance of the outlier depends on the statistics being calculated. If we are calculating the median price of a home, and we include a one huge mansion, the median will only be slightly effected. However, if we are calculating the cost of the most expensive houses, say in the top 10% percentile, the outlier will have a significant effect. See related link on outliers.
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How can you tell whether a data set has an outlier explain?
It is important to remember that there is no formal definition of an outlier. An outlier is an observation (or a small number of observations) which is (area) out of line with the rest of the observations.
Would a mean be smaller or larger if you leave out an outlier?
Depends on whether the outlier was too small or too large. If the outlier was too small, the mean without the outlier would be larger. Conversely, if the outlier was too large, the mean without the outlier would be smaller.
How do you determine how the outlier affects the mean median mode and range?
Calculate the mean, median, and range with the outlier, and then again without the outlier. Then find the difference. Mode will be unaffected by an outlier.
What is an example of a outlier range?
1,2,3,4,20 20 is the outlier range
If you take off the outlier does it make the mean change?
Yes, it will. An outlier is a data point that lies outside the normal range of data. This means that if it is factored in the mean will move in the direction the outlier is, really high if the outlier was high, and really low if the outlier was low.
