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 pulls the mean towards it. It does not affect the median and only affects the mode if the mode is itself the outlier.
That would be outlier.
The outlier skews the mean towards it.
The outlier could affect the mean by making it drastically larger or smaller.
On the standard deviation. It has no effect on the IQR.
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.
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.
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 pulls the mean towards it. It does not affect the median and only affects the mode if the mode is itself the outlier.
That would be outlier.
An outlier can increase or decrease the mean and median It usually doesn't affect the mode
The outlier skews the mean towards it.
An outlier can significantly impact the median by pulling it towards the extreme value of the outlier, especially when the dataset is small. This can distort the central tendency measure that the median represents and provide a misleading representation of the typical value in the dataset.
Yes.
The outlier could affect the mean by making it drastically larger or smaller.
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.