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Q: In general the median of a data set is more resistant to outliers than the mean.?

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The median in a set of data, would be the middle item of the data string... such as: 1,2,3,4,5,6,7 the Median of this set of data would be: 4

the median is the middle number in a set of data.

If the outlier is excluded the median of the data will be higher.

no it can not because if you try to do it then i cannot be the same answer

the upper quartile is the median of the upper half of a set of data. ;p

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It is not.

I think it means that our data includes outliers.

Mean- If there are no outliers. A really low number or really high number will mess up the mean. Median- If there are outliers. The outliers will not mess up the median. Mode- If the most of one number is centrally located in the data. :)

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.

to organize your data set and figure out mean, median, mode, range, and outliers.

Helps you accurately measure the results of a population. It's simply the middle number in a data set, so half of the population is above and half of it is below. It is better than the mean since it is resistant to outliers.

the median is perferred when the data is strongly skewed or has outliers. =)

MEDIANUse the median to describe the middle of a set of data that does have an outlier.Advantages:• Extreme values (outliers) do not affect the median as strongly as they do the mean.• Useful when comparing sets of data.• It is unique - there is only one answer.Disadvantages:• Not as popular as mean.

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

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.

You will notice a difference in the data if you have outliers. The mean of a set is going to be heavily influenced by outliers due to the mean being dependant on the quantity of each unit (i.e. 2 cats, 7 cats, 300 cats, etc.) The median, however, is not influenced by outliers because it accounts for the number of units rather than the quantity associated with the units.

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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