Math and Arithmetic
Statistics

# How does the outlier affect the interquartile range of the data?

789

###### 2012-09-18 15:23:02

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.

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## Related Questions

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.

Range subtracts the lowest value from the value in your data set. If you have an outlier, meaning a number either obviously outside the data, your range will be incorrect because one of the values will not represent the average pattern of the data. For example: if your data values include 1,2,3,4,and 17, 17 would be the outlier. The range would be 16 which is not truly representative of the rest of the data.

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

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.

Range is the largest minus the smallest value in the data set. An outlier is a value that is far away from the majority of the data.

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).

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.

It gives a better picture of data collected because the data is not so spread out.

An outlier can make the range go up.Example:say you have the numbers. 2,5,4,1,7,and 18.in order from least to greatest: 1,2,4,5,7,18range is the greatest number - the littlest number.so in this case it is 18-1 which equals 17say if there was no outlier and the highest number was 7it would be 7-1 which is 6 and it makes the outlier smaller....

Yes, it will. An outlier is a data point that lies outside the normal range of data. This means that if it is factored in the mean will move in the direction the outlier is, really high if the outlier was high, and really low if the outlier was low.

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.

The Inter-quartile range is the range of the middle half of the data. It is the difference between the upper and lower quartile.Example: 35,80,100 110,120,120,170,180.The Inter-quartile range would be 145-90 or 55To find the interquartile range, you:1) Arrange the data in numerical order.2) Then find the median of the data sets.3) Find the median of the top half and bottom half. (of the set of numbers)4) The groups you now have are "quartiles"5) Find the interquartile range. (subtract the smaller range from the range)

it messes up the mean and sometimes the median. * * * * * An outlier cannot mess up the median.

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) ^

The standard deviation is the value most used. Others are variance, interquartile range, or range.

No. The data set will remain the data set: they are the observations that are recorded.

If your data range is a1:a10 then the interquantile range equation is =percentile(a1:a10,0.75)-percentile(a1:a10,0.25)

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).

It tells you that middle half the observations lie within the IQR.

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.

there is no outlier because there isn't a data set to go along with it. so theres no outlier

The exact definition of which points are considered to be outliers is up to the experimenters. A simple way to define an outlier is by using the lower (LQ) and upper (UQ) quartiles and the interquartile range (IQR); for example: Define two boundaries b1 and b2 at each end of the data: b1 = LQ - 1.5 &times; IQR and UQ + 1.5 &times; IQR b2 = LQ - 3 &times; IQR and UQ + 3 &times; IQR If a data point occurs between b1 and b2 it can be defined as a mild outlier If a data point occurs beyond b2 it can be defined as an extreme outlier. The multipliers of the IQR for the boundaries, and the number of boundaries, can be adjusted depending upon what definitions are required/make sense.

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