Hai. :3
An outlier will pull the mean and median towards itself. The extent to which the mean is affected will depend on the number of observations as well as the magnitude of the outlier. The median will change by a half-step.
The median.
It is the outlier.
The median is least affected by an extreme outlier. Mean and standard deviation ARE affected by extreme outliers.
50
If the outlier is excluded the median of the data will be higher.
it messes up the mean and sometimes the median. * * * * * An outlier cannot mess up the median.
An outlier will pull the mean and median towards itself. The extent to which the mean is affected will depend on the number of observations as well as the magnitude of the outlier. The median will change by a half-step.
need more info
The median.
It is the outlier.
There would be a difference to the median. The old number wouldn't be the median but the mode wouldn't change. If the outlier is a high value, it will cause the mean value to shift to the higher side, while a low valued outlier will drop the mean value to a lower number.
The median is least affected by an extreme outlier. Mean and standard deviation ARE affected by extreme outliers.
The mean is changed.
It will go down; with outlier, mean is 42 without outlier, mean is 32.5
The mean and median become smaller, the mode does not change.
An outlier, in a distribution of data points, is a value which does not fall within three standard deviations of the mean. You cannot, at least directly, remove an outlier without biasing the data itself, but you can choose different measures to try and soften their effects. For example, instead of using mean as a measurement for the central value of the data set, one can use median.