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Outliers are observations that are unusually large or unusually small. There is no universally agreed definition but values smaller than Q1 - 1.5*IQR or larger than Q3 + 1.5IQR are normally considered outliers. Q1 and Q3 are the lower and upper quartiles and Q3-Q1 is the inter quartile range, IQR.

Outliers distort the mean but cannot affect the median. If it distorts the median, then most of the data are rubbish and the data set should be examined thoroughly.

Outliers will distort measures of dispersion, and higher moments, such as the variance, standard deviation, skewness, kurtosis etc but again, will not affect the IQR except in very extreme conditions.

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Q: What are outliers and how do they affect data?

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an outliers can affect the symmetry of the data because u can still move around it

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.

Outliers

Go into your data to determine which values are outliers and if they're significant and random (not an apparent group), eliminate them. This will take them out of your boxplot.

They are called outliers

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No. Outliers are part of the data and do not affect them. They will, however, affect statistics based on the data and inferences based on the data.

an outliers can affect the symmetry of the data because u can still move around it

There is no limit to the number of outliers there can be in a set of data.

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.

It is not.

Outliers

Of course. In a large sampling of data, a relatively small group of outliers is possible.

If the data numbers are all really close together than no. But if the data has numbers; for example: 12,43,45,51,57,62,90 (12 and 90 are the outliers) which are really far aprt, than yes.

Go into your data to determine which values are outliers and if they're significant and random (not an apparent group), eliminate them. This will take them out of your boxplot.

They are called extreme values or outliers.

I think it means that our data includes outliers.

Mode: Data are qualitative or categoric. Median: Quantitative data with outliers - particularly if the distribution is skew. Mean: Quantitative data without outliers, or else approx symmetrical.

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