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

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2016-05-02 08:40:09
2016-05-02 08:40:09

On the standard deviation. It has no effect on the IQR.

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2016-04-27 09:46:40
2016-04-27 09:46:40

An outlier has no effect on the interquartile range!

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Related Questions


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


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.


Yes, any data point outside thestandard deviation its an outlier


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


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.


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.


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.


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


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).


Standard deviation is basically how much your scores vary from the mean or average score. So if you have a mean of 5 and a standard deviation of 2 it indicates that most of your values are around 5, and if they are not they will usually be +/- 2 units different (between 3 and 7). If you have a large standard deviation it simply means that your data includes a wide range of values. In some cases it may mean that you have an outlier, or an error in your data, in other cases it is normal depending on what you are measuring.For example if you are taking a sample of peoples ages and you get a mean of 50 and a standard deviation of 20 that would be normal because you can expect ages to range from 0-100. But if you are measuring shoe size and you get a mean of 8 and a standard deviation of 6 you can expect that something is wrong with your data because not many people have size 2 or size 14 shoes.


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.


Providing that the number of outliers is small compared to sample size, their effect on the interquartile range should be limited since their effects are realised mainly in the extremes of the sample.


By definition a quarter of the observations are below the lower quartile and a quarter are above the upper quartile. In all, therefore, half the observations lie outside the interquartile range. Many of these will be more than the inter-quartile range (IQR) away from the median (or mean) and they cannot all be outliers. So you take a larger multiple (1.5 times) of the interquartile range as the boudary for outliers.


the number in your piece of data = n lower quartile, n+1 divided by 4 upper quartile, n+1 divded by 4 and times by three interquartile range(IQR) = upper quartile - lower quartile outliers(O) = interquartile range x 1.5 lower than IQR-O is an outlier (h) above IQR+O is an outlier (h) the outliers on your box plot are any numbers that are the value i have named (h) ^


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).


It is one of the informal definitions for an outlier.



An outlier is 1.5 times the mean, when you are taking an average it may give an inaccurate representation of the data. It usually does not affect the median.* * * * * The above definition of an outlier is total rubbish! It is necessary to have a measure of the central tendency (mean or median) AND spread (standard deviation or inter quartile range - IQR) to define an outlier.If Q1 and Q3 are the lower and upper quartiles, then outliers are normally defined as observations lying below Q1 - k*IQR or above Q3 + k*IQR. There is no universally agreed definition of outliers and hence no fixed value for k. But k = 1.5 is often used.


No, median is not an outlier.


Median is a good example of a resistant statistic. It "resists" the pull of outliers. The mean, on the other hand, can change drastically in the presence of an outlier.The interquartile range is a resistant measure of spread.


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.


Outlier does not affect the median.


No. A single observation can never be an outlier.



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