SCILAB IDENTIFY OUTLIER SERIES

The Median (Q2)В is the middle value of the data set. Let n be the number of data values in the data set.

SCILAB IDENTIFY OUTLIER SERIES

The very purpose of this diagram is to identify outliers and discard it from the data series before making any further observation so that the conclusion made from the study gives more accurate results not influenced by any extremes or abnormal values. Box Plot DiagramД«ox plot diagram also termed as WhiskerвЂ™s plot is a graphical method typically depicted by quartiles and inter quartiles that helps in defining the upper limit and lower limit beyond which any data lying will be considered as outliers. When performingВ least squares fittingВ to data, it is often best to discard outliers before computing the line of best fit since these points may greatly influence the result. Outliers is often regarded as the cause of an error in measurement due to presence of extreme values which may underestimate or overestimate a study because it lies at an abnormal distance from other values in a random sample from a population.ДЄs per the basic standards followed by all statisticians a convenient definition of an outlier is a point which falls more than 1.5 times theВ interquartile rangeВ above the thirdВ quartileВ or below the firstВ quartile.

Outliers are usually treated as abnormal values that can affect the overall observation due to its very high or low extreme values and hence should be discarded from the data series.ДЄn outlier can also be stated as a value that lies outside the overall pattern of a distribution and thus can affect the overall data series. Outliers are also termed as extremes because they lie on the either end of a data series. Outlier is a value that lies in a data series on its extremes, which is either very small or large and thus can affect the overall observation made from the data series.

Scilab identify outlier