They are called outliers
It i the smallest value in the data set and corresponds to the value of the left-most end of the whisker. Unless there were outliers, in which case it will be an "X" to the left of the left-whisker.
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.
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.
It is a set that you derive from some initial data or formula depnds on the question. I assume you know what a set is?
There is no limit to the number of outliers there can be in a set of data.
They are called extreme values or outliers.
They are called outliers
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.
In chemistry, outliers are data points that deviate significantly from the rest of the data set. Outliers can result from measurement errors, experimental uncertainties, or unexpected reactions. It is important to identify and address outliers in data analysis to ensure accurate and reliable results.
Anomalous Data
Yes, it is.
Grubbs test is used to detect outliers in a univariate data set.
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. :)
to organize your data set and figure out mean, median, mode, range, and outliers.
Values that are either extremely high or low in a data set are called 'outliers'. They are typically 3 standard deviations or more from the mean.
Coefficient of Determination