Having only the mean is not sufficient to identify outliers. You need some measure of dispersion.
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.
To determine how much an outlier decreases the answer, you need to compare the statistical measure before and after including the outlier. For example, if the mean of a dataset is 50 without the outlier and drops to 40 with the outlier included, the outlier decreases the answer by 10. The specific impact of an outlier can vary significantly depending on its value relative to the rest of the data.
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.
1,2,3,4,20 20 is the outlier range
Deviation-based outlier detection does not use the statistical test or distance-based measures to identify exceptional objects. Instead, it identifies outliers by examining the main characteristics of objects in a group.
Having only the mean is not sufficient to identify outliers. You need some measure of dispersion.
No, median is not an outlier.
0s are not the outlier values
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.
No. A single observation can never be an outlier.
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.
The answer depends on the nature of the outlier. Removing a very small outlier will increase the mean while removing a large outlier will reduce the mean.
1,2,3,4,20 20 is the outlier range
The outlier is 558286.
there is no outlier because there isn't a data set to go along with it. so theres no outlier
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.