Q: Does an outlier have a greater effect on the standard deviation or interquartile range?

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Yes.

Yes, any data point outside thestandard deviation its an outlier

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.

cuz when it does it gon mess it up in a way where u cant use it no more * * * * * That is a rubbish answer. By definition, all outliers lie outside the interquartile range and therefore cannot affect it.

Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.

Related questions

The mean is "pushed" in the direction of the outlier. The standard deviation increases.

Yes.

The median is least affected by an extreme outlier. Mean and standard deviation ARE affected by extreme outliers.

No. The IQR is a resistant measurement.

The Interquartile Range because it affects how much space is left between the median on either side....So there you go! I hope that I helped You... : D

Yes, any data point outside thestandard deviation its 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.

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.

Not necessarily. If the data are not ordered by size, it could be anywhere in the data set. If the data are ordered, it could be the last. But equally, it could be the first. Also, it could be the last two, three etc, or one from each end. Essentially, an outlier is a value that is an "abnormal" distance from the "middle". The middle may be the median or the mean of the data set (usually not the mode). The "abnormal" distance is generally defined in terms of a multiple of the interquartile range (when median is used) or standard deviation (when the mean is used).

Common method is to find the mean and the standard deviation of the data set and then call anything that falls more than three standard deviations away from the mean an outlier. That is, x is an outlier if abs(x - mean) --------------- > 3 std dev This is usually called a z-test in statistics books, and the ratio abs(x-mean)/(std dev) is abbreviated z. Source: http://mathforum.org/library/drmath/view/52720.html

cuz when it does it gon mess it up in a way where u cant use it no more * * * * * That is a rubbish answer. By definition, all outliers lie outside the interquartile range and therefore cannot affect it.

Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.