the mode is 8 more than the outlier.
An outlier is a number that is noticeably larger or smaller than the other numbers. Example- {3,4,5,6,7,8,9,50,3,2,5,6,7} the number 50 is the outlier. It is basically the one that does not belong.
Outlier: an observation that is very different from the rest of the data.How does this affect the data: outliers affect data because it means that your calculations might be off which makes it a possibility that more than the outlier is off.
Not sure about an outlair, but an outlier in a set of values is one that is significantly smaller or greater than the others. There is no formally agreed definition for an outlier.
an outlier can be found with this formula... Q3-Q1= IQR( inner quartile range) IQR*1.5=x x+Q3= anything higher than this # is an outlier Q1-x= anything smaller than this # is an outlier
Yes there can be more then one outlier
Yes.
Yes, any data point outside thestandard deviation its an outlier
An outlier does affect the mean of the data. How it's affected depends on how many data points there are, how far from the data the outlier is, whether it is greater than the mean (increases mean) or less than the mean (decreases the mean).
An outlier.
An outlier can be very large or small. its usally 1.5 times the mean. they can be seen with a cat and whisker box * * * * * The answer to the question is YES. "Its usually 1.5 times the mean" is utter rubbish - apart from the typo. If a distribution had a mean of zero, such as the standard Normal distribution, then almost every observation would be greater than 1.5 times the mean = 0 and so almost every observation would be an outlier! No. There is no universally agreed definition for an outlier but one contender is values that are more than 1.5 times the interquartile range away from the median.
No, median is not an outlier.
The one that does not belong
0s are not the outlier values
An* outlier is a number that is much, much greater or much, much less than all/most of the other points. Basically the one that messes up the average, so usually outliers are counted out when finding the mean of a set.
No. A single observation can never be 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.