MEAN SQUARE DEVIATION Meaning and
Definition
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Mean square deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data points. It is calculated by finding the average of the squared differences between each data point and the mean of the data set. The squared differences help to capture both positive and negative deviations from the mean.
To calculate the mean square deviation, one needs to follow a few steps. Firstly, the mean (average) of the data set is determined. Then, the difference between each data point and the mean is computed. These differences are then squared to eliminate the negative signs. The squared differences are added together and divided by the total number of data points to find the average. The resulting value is the mean square deviation.
The mean square deviation is a useful tool in various fields, including statistics, finance, and engineering. It provides a measure of how spread out the data points are from the mean value and serves as an indicator of the consistency or volatility of the data. A larger mean square deviation suggests a higher degree of variability in the data set, whereas a smaller value indicates greater uniformity or consistency. Comparison of mean square deviations can be employed to determine which data set has a higher level of dispersion or variability.
Common Misspellings for MEAN SQUARE DEVIATION
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