No
An outlier is an extreme point of data on the plot. It is significantly greater or less than the rest of the data. Example: *50% of the class got 80/100 points on the test *49% of the class got 78/100 points on the test. *However, only 1% of the class got 99/100 points on the test. The small percentage that got the 99 would be the extreme 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.
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
No
An outlier is an extreme point of data on the plot. It is significantly greater or less than the rest of the data. Example: *50% of the class got 80/100 points on the test *49% of the class got 78/100 points on the test. *However, only 1% of the class got 99/100 points on the test. The small percentage that got the 99 would be the extreme outlier.
There is no outlier, using the (Q3-Q1)1.5 test. Other tests for outliers can yield different results, but this is the one generally accepted by statisticians. If you are told that "no outlier" is not an option, then it's definitely going to be 7.0Cm.
No, median is not an outlier.
0s are not the outlier values
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.
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.
An example of an outlier might be an exceptionally high or low value in a data set that does not fit the overall trend of the data. For instance, if a group of students' test scores mostly range from 60-90, but one student scores a 20 or a 100, that student's score would be considered 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.
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 outlier is 558286.