BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -For example, a z-score of 2.5 indicates that the data point is 2.5 standard deviation away from the mean. Usually z-score =3 is considered as a cut-off value to set the limit. Therefore, any z-score greater than +3 or less than-3 is considered as outlier which is pretty much similar to standard deviation method.

Detailed explanation-2: -A positive z-score says the data point is above average. A negative z-score says the data point is below average. A z-score close to 0 says the data point is close to average. A data point can be considered unusual if its z-score is above 3 or below −3 .

Detailed explanation-3: -z-score is a measure of how close the given data point is to the mean of the values given with the standard deviation. If the z-score is less than-2 or greater than 2, then the data is unusual. Therefore, a data value is considered unusual if its z-score is less than minus 2 or greater than 2.

Detailed explanation-4: -Using Z-scores to Detect Outliers Z-scores can quantify the unusualness of an observation when your data follow the normal distribution. Z-scores are the number of standard deviations above and below the mean that each value falls.

Detailed explanation-5: -Z score shows how far away a single data point is from the mean relatively. Lower z-score means closer to the meanwhile higher means more far away. Positive means to the right of the mean or greater while negative means lower or smaller than the mean.