if coefficient of skewness is zero then distribution is symmetric or zero skewed.
Skewness is a measure of the asymmetry in a distribution. In a non-symmetrical distribution, skewness can be calculated using a formula that considers the deviation of each data point from the mean. A positive skewness indicates a longer tail on the right side of the distribution, while a negative skewness indicates a longer tail on the left side.
If a random variable X has mean value m, and standard deviation s, then Skewness = E{[(x - m)/s]3} which can be simplified to skewness = [E(X3) - 3ms2 - m3]/s3 and for discrete X, E(X3) = sum of x3*Prob(X = x) where the summation is over all possible values of x. While for continuous X, E(X3) = integral of x3*f(x) where the integral is over the domain of X.
If the skewness is different, then the data sets are different.Incidentally, there is one [largely obsolete] definition of skewness which is in terms of the mean and median. Under that definition, it would be impossible for two data sets to have equal means and equal medians but opposite skewness.
-5a4 The coefficient would be -5. The variable is a and the power is 4.
The slope would be -2 (moving 2 units down and one across). When you have a linear equation, the slope is always the variable's coefficient.
Skewness is a measure of the asymmetry in a distribution. In a non-symmetrical distribution, skewness can be calculated using a formula that considers the deviation of each data point from the mean. A positive skewness indicates a longer tail on the right side of the distribution, while a negative skewness indicates a longer tail on the left side.
While skewness is the measure of symmetry, or if one would like to be more precise, the lack of symmetry, kurtosis is a measure of data that is either peaked or flat relative to a normal distribution of a data set. * Skewness: A distribution is symmetric if both the left and right sides are the same relative to the center point. * Kurtosis: A data set that tends to have a distant peak near the mean value, have heavy tails, or decline rapidly is a measure of high kurtosis. Data sets with low Kurtosis would obviously be opposite with a flat mean at the top, and a distribution that is uniform.
If a random variable X has mean value m, and standard deviation s, then Skewness = E{[(x - m)/s]3} which can be simplified to skewness = [E(X3) - 3ms2 - m3]/s3 and for discrete X, E(X3) = sum of x3*Prob(X = x) where the summation is over all possible values of x. While for continuous X, E(X3) = integral of x3*f(x) where the integral is over the domain of X.
If the mean is greater than mode the distribution is positively skewed.if the mean is less than mode the distribution is negatively skewed.if the mean is greater than median the distribution is positively skewed.if the mean is less than median the distribution is negatively skewed. 18-226
If the skewness is different, then the data sets are different.Incidentally, there is one [largely obsolete] definition of skewness which is in terms of the mean and median. Under that definition, it would be impossible for two data sets to have equal means and equal medians but opposite skewness.
If the sample is small or not randomly chosen, it may not have much meaning at all. If the random sample is large, it would generally be inferred that the distribution is symmetrical. The skewness of the data can be calculated.
A coefficient is a number before a variable. For example, in 2x, the 2 would be the coefficient
It called the coefficient of a variable. As an example 16x. 16 would be the coefficient and x would be the variable or term.
The coefficient in an expression is the multiplier of the variable in the equation. Here, the coefficient would be 6.
The coefficient of cubical expansivity would normally be the cube of the coefficient of linear expansivity unless that coefficient is different in different directions for a material. In that case it would be the product of the linear coefficients in the different directions.
In this case you would divide each side by negative one. That makes the coefficient of y, 1. so now you have y=12 which is your final answer.
-5a4 The coefficient would be -5. The variable is a and the power is 4.