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Georgia [21]
3 years ago
13

In your own words, describe to someone who knows only a little statistics how to recognize when an observation is an outlier. Wh

at action(s) should be taken with an outlier?
(A) Outliers are observed values far from the main group of data. In a histogram they are separated from the others by space.
(B) Outliers must be looked at in closer context to know how to treat them. If they are​mistakes, they might be removed or corrected. If they are not​ mistakes, you might do the analysis​ twice, once with and once without the outliers.
(C) Outliers are observed values far from the main group of data. In a histogram they are separated from the others by space. Outliers should be ignored because they can distort any calculations done and are not representative of typical values.
(D) Outliers are the minimum and maximum values observed in a data set. They should be treated as any other value, as the term "outliers" is just another name for the minimum and maximum.
Mathematics
1 answer:
erik [133]3 years ago
3 0

Answer:

B)

Step-by-step explanation:

In statistics, an outlier is a data point that differs significantly from other observations.

This can happen because of different reasons: it may be due to variability in the measurement or it may indicate an actual experimental error. If it is an actual experimental error then it  can create problems when doing the statistical analysis.

A) We can say that this is true, the outliers are observed values far from the other data, however we can make a much deeper analysis of this.

B) This is the right answer, since we don't know what is really causing the outlier, we need to take a closer look to see if they are just mistakes (and therefore be removed). If they are not mistakes we need to do the analysis with and without them to reach correct conclusions.

C) this is wrong because although the part of the histogram is true. It is not true that they should be ignored.

d) The outliers differ significantly from the other data but this doesn't make them the minimum and maximum values in a data set and therefore they should not be treated as such.

Therefore, the correct answer is B)

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