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What is coefficient of skewness in a variable concentration?
A measure of skewness is Pearson's Coefficient of Skew. It is defined as: Pearson's Coefficient = 3(mean - median)/ standard deviation The coefficient is positive when the median is less than the mean and in that case the tail of the distribution is skewed to the right (notionally the positive section of a cartesian frame). When the median is more than the mean, the cofficient is negative and the tail of the distribution is skewed in the left direction i.e. it is longer on the left side than on the right.
How do one determine when a data is normally distributed?
Use the Kolmogorov Smirnoff goodness-of-fit test. A normal distribution is a bell shaped curve, which is nearly symmetrica. It looks like an upside down bell. It can be squished low (platykurtic) or pulled high and skinny (leptokurtic) but it is still bell shaped and symmetrical. A mathematical test is to use the pearson's skew. If the pearson's skew is between 0 and 0.49, then the data is a non-problematic or normally distributed. If it is greater than 0.50, then it is not a normal distribution so one cannot treat it as such. The pearson's skew equation is skew p= (3 (mean - median)) / (SD(x) SD(y))
Is it possible to normalize the data when the population shape has a known skew?
Sometimes.
How do you calculate the skew?
Skew applies to the average difference between two timing states of a single signal as it transit from low to high and high to low. Skew is frequently refereed to as the Pulse Width Distortion (tpHL-tpLH). When multiple independent and equal device types are used for parallel data transmission, the skew of each device is important since the max and mix Skew can establish the maximum data rate.
What is the definition of skewed distribution?
Probability distribution in which an unequal number of observations lie below (negative skew) or above (positive skew) the mean.
