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Coefficient of variation is an important statistical measure used in data analysis to understand the relative variability or dispersion of data values in relation to their mean. It provides a standardized measure of variation and is particularly useful when comparing the degree of variability between two or more datasets that may have different units, scales, or average values. Unlike absolute measures of dispersion such as range, variance, and standard deviation, the coefficient of variation expresses variability as a percentage of the mean, making comparisons easier and more meaningful. In data analysis, understanding the spread of data is essential because measures of central tendency such as mean, median, and mode only describe the central position of observations. They do not provide information about how much the data values differ from one another. The coefficient of variation helps analysts examine the consistency, stability, and reliability of datasets by showing how large the standard deviation is compared with the average value. The coefficient of variation is calculated by dividing the standard deviation by the mean and multiplying the result by 100 to express it as a percentage. Coefficient of Variation (CV) = (Standard Deviation / Mean) × 100 Where standard deviation represents the amount of variation or dispersion in the dataset, and the mean represents the average value of the observations. For example, consider two datasets representing the performance of two different machines. Machine A produces items with an average weight of 100 grams and a standard deviation of 5 grams. Machine B produces items with an average ___ ___ __________ _________ ______ __________.
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