Left
Right
Symmetric
Answer this question...similarities and differences between normal curve and skewness
No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.
describe the properties of the standard deviation.
skewness=(mean-mode)/standard deviation
When the data are skewed to the right the measure of skewness will be positive.
Answer this question...similarities and differences between normal curve and skewness
No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.No. Skewness is 0, but kurtosis is -3, not 3.
A positive skewness is when the value of mean is greater than the mode. that is, the curve is more skewed at the right hand side or the right tail is longer than the left tail. The negative skewness is when the mean is smaller than the mode, and in this case the curve is more skewed on the left hand side.
Skewness is measured as the third standardised moment of the random variable. Skewness is the expected value of {[X - E(X)]/sd(X)}3 where sd(X) = sqrt(Variance of X)
if coefficient of skewness is zero then distribution is symmetric or zero skewed.
The skewness of a random variable X is the third standardised moment of the distribution. If the mean of the distribution is m and the standard deviation is s, then the skewness, g1 = E[{(X - m)/s}3] where E is the expected value. Skewness is a measure of the degree to which data tend to be on one side of the mean or the other. A skewness of zero indicates symmetry. Positive skewness indicates there are more values that are below the mean but the the ones that are above the mean, although fewer, are substantially bigger. Negative skewness is defined analogously.
distinguish between dispersion and skewness
Ah, the Pearson Coefficient of Skewness, fancy term for measuring the asymmetry of a probability distribution. It tells you if your data is skewed to the left, right, or if it's all hunky-dory symmetrical. Just plug in your numbers, crunch some math, and voila, you'll know how wonky your data is. Just remember, skewness doesn't lie, so embrace those skewed curves!
The answer is: 1. Straight ball 2. Curve ball 3. Hook ball.
describe the properties of the standard deviation.
skewness=(mean-mode)/standard deviation
When the data are skewed to the right the measure of skewness will be positive.