BIOMOLECULES AND ENZYMES

Detailed explanation-1: -We call a data point an outlier if it doesn’t fit the pattern.

Detailed explanation-2: -Calculate the interquartile range The general rule for using it to calculate outliers is that a data point is an outlier if it is over 1.5 times the IQR below the first quartile or 1.5 times the IQR above the third quartile. To calculate the IQR, you need to know the percentile of the first and third quartile.

Detailed explanation-3: -Outliers are observations that are distant from the mean or location of a distribution. However, they don’t necessarily represent abnormal behavior or behavior generated by a different process. On the other hand, anomalies are data patterns that are generated by different processes.

Detailed explanation-4: -In simple terms, an outlier is an extremely high or extremely low data point relative to the nearest data point and the rest of the neighboring co-existing values in a data graph or dataset you’re working with. Outliers are extreme values that stand out greatly from the overall pattern of values in a dataset or graph.

Detailed explanation-5: -A value that “lies outside” (is much smaller or larger than) most of the other values in a set of data. For example in the scores 25, 29, 3, 32, 85, 33, 27, 28 both 3 and 85 are “outliers".