The Value Of Central Tendency

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Central tendency is the extent to which data values conjoin around a specific value or central value (Levine, Stephan, Krehbiel, & Berenson, 2008). The mean is a balance point in a set of data (Levine, et al., 2008). In order to calculate the mean, you must add together all the values and then divide that sum by the amount of values present in the data set (Levine, et al., 2008). One extreme value can alter the mean greatly, when this happens the mean my not be the best measure of central tendency (Levine, et al., 2008). The median is another measure of central tendency that measures the half waypoint in a data set (Levine, et al., 2008). If the data set has an odd number, the median will be the middle ranked value (n+1 / 2) (Levine, et al., 2008). If the data set has an even number of values the median with be measured using the average of the two middle-ranked values (Levine, et al., 2008). The mode is the value in a set of data that appears more frequently than any other value (Levine, et al., 2008). There are times there are no modes and there are times that there is more than one mode (Levine, et al., 2008). The geometric mean is used to measure the rate of change of a variable over time (Levine, et al., 2008). The geometric mean is equivalent to the nth root of the product of n values (Levine, et al., 2008). The geometric mean rate of return measures the average percentage return on an investment over a period (Levine, et al., 2008). Data can also be characterized by its variation and shape (Levine, et al., 2008). Variation measures the spread, or dispersion, of the values in the data set (Levine, et al., 2008). The range is one measure of variation that determines the difference between the largest and smallest values ... ... middle of paper ... ... a right-skewed data set, this cluster occurs to the left of the mean, and left-skewed to the right of the mean (Levine, et al., 2008). Using the empirical rule will help to measure how the values distribute above and below the mean and can help identify outliers (Levine, et al., 2008). The Chebyshev rule states that for any data set, the percentage of values that are found within distances of K standard deviations from the mean must be at least (1- 1 / k2) X 100% (Levine, et al., 2008). The covariance measures the strength of the linear relationship between two numerical variables (Levine, et al., 2008). The coefficient of correlation measures the relative strength of a linear relationship between two numerical variables (Levine, et al., 2008). The coefficient of correlation indicates the linear relationship between two numerical variables (Levine, et al., 2008).
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