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Skewness is when a distribution is asymmetrical or lacks symmetry The skewed portion is the long thin part of the curve Many researchers use skewed distribution to mean that the data are sparse at one of the distribution and piled up on the other end An example is like the grades on a particular test that a teacher gives The grade distribution is skewed meaning that few students scored at one end of the grading scale and many students scored at the other end Like the relationship of As to Cs and Bs It is more probable that you would have more Cs and Bs compared to As So the tail of the skewed graph would be more of a bell towards the Bs and Cs And the tapered off end would be the portion of the class that had gotten an A The concept of skewness helps us to understand the relationship of the mean median and the mode Now when graphing the skewness the mode is considered the high point or the apex The mean tends to be located toward the tail of the distribution because the mean is affected by all values Now with a bell shaped or normal distribution it has no skewness because the mode mean and the median are all at the center of the distribution In other words there all equal Now the coefficient of skewness measures the degree of skewness that exists in a distribution of numbers It compares the mean and the median in light of the magnitude of the standard of deviation Now when the distribution is symmetrical the mean and the median are the same value and hence the coefficient of skewness is equal to zero To determine if the skewness is positive or negative you would use the coefficient of skewness You would then multiply 3 times the mean minus the median Then divide the total by the standard deviation If the answer
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