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Statistics

Which is affected more by the outlier the ranger or the interquartile range?

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2013-03-06 22:56:39
2013-03-06 22:56:39
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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.


what is the interquartile range of 16,17,19,22,23,25,27,36,38,40,40,45,46


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.


1,2,3,4,20 20 is the outlier range


The interquartile range is less likely to be distorted by outliers (extreme values).



The interquartile range of a set of data is the difference between the upper quartile and lower quartile.


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.


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.


An outlier will have a huge affect on the range as the range is the largest value minus the smallest value.


If presents you with the upper and lower quartile range, although you have to do calculations in order to find the interquartile range, so no, it does not,


The interquartile range is the upper quartile (75th percentile) minus (-) the lower percentile (75th percentile). The interquartile range uses 50% of the data. It is a measure of the "central tendency" just like the standard deviation. A small interquartile range means that most of the values lie close to each other.


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) ^


how do you find the interquartile range of this data


Both are measures of spread or dispersion.


Range = maximum - minimum Interquartile range = Value of 75th percentile - value of 25th percentile. The 75th percentile is the value such that 25% of the observations are bigger and 75% are smaller.


Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.


If you are talking about statisitics, in a box and whisker graph it is the interquartile range.



The semi interquartile range is a measure for spread or dispersion. To find it you have to subtract the first quartile from Q3 and divide that by 2, (Q3 - Q1)/2


The interquartile range is well known as a measure of statistical dispersion. It is equal to difference between upper and lower quartiles. The quartiles is a type of quantile.



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.


An interquartile range is a measurement of dispersion about the mean. The lower the IQR, the more the data is bunched up around the mean. It's calculated by subtracting Q1 from Q3.



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