Z-Score

A Z-Score, also known as a standard score, is a statistical measurement that describes a value's relationship to the mean of a group of values. It is expressed as the number of standard deviations a data point is from the mean. Z-Scores are useful in identifying how unusual or typical a particular data point is within a dataset. They are widely used in various fields such as finance, research, and quality control to standardize different datasets and make them comparable. The Z-Score is calculated using the formula: \[ Z = \frac{(X - \mu)}{\sigma} \] where \( X \) is the value being measured, \( \mu \) is the mean of the dataset, and \( \sigma \) is the standard deviation of the dataset. By converting data points into Z-Scores, one can easily determine how far and in what direction (above or below the mean) a particular value lies. # What is Z-Score? A Z-Score is a statistical measurement that indicates how many standard deviations a data point is from the mean of a dataset. It helps in understanding whether a data point is typical or atypical compared to the rest of the data. # How is Z-Score Calculated? The Z-Score is calculated using the formula: \[ Z = \frac{(X - \mu)}{\sigma} \] where: - \( X \) is the value being measured, - \( \mu \) is the mean of the dataset, - \( \sigma \) is the standard deviation of the dataset. This formula helps in standardizing different datasets, making them comparable by converting data points into a common scale based on their distance from the mean.