How do you interpret kurtosis in Excel?

How do you interpret kurtosis in Excel?

When interpreting kurtosis, the normal distribution is used a reference. A positive kurtosis implies a distribution with more extreme possible data values (outliers) than a normal distribution thus fatter tails (Leptokurtic distributions).

How do you interpret high kurtosis?

Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers. A uniform distribution would be the extreme case.

What does kurtosis values indicate?

Kurtosis is a measure of the combined weight of a distribution’s tails relative to the center of the distribution. When a set of approximately normal data is graphed via a histogram, it shows a bell peak and most data within three standard deviations (plus or minus) of the mean.

Does Excel report kurtosis or excess kurtosis?

With Excel it is very straightforward to calculate kurtosis. Excel’s kurtosis function calculates excess kurtosis. Enter the data values into cells.

What is positive kurtosis?

What does it mean when kurtosis is positive? Positive excess values of kurtosis (>3) indicate that a distribution is peaked and possess thick tails. A leptokurtic distribution has a higher peak (thin bell) and taller (i.e. fatter and heavy) tails than a normal distribution.

Is negative kurtosis good?

A distribution with a negative kurtosis value indicates that the distribution has lighter tails than the normal distribution. The solid line shows the normal distribution and the dotted line shows a distribution with a negative kurtosis value.

Is high kurtosis good or bad?

Kurtosis is only useful when used in conjunction with standard deviation. It is possible that an investment might have a high kurtosis (bad), but the overall standard deviation is low (good). Conversely, one might see an investment with a low kurtosis (good), but the overall standard deviation is high (bad).

How to calculate kurtosis example?

Here are the calculations to derive the Kurtosis: x̅ = (2+7+15+4+8) / 5 = 7.2 Σ (xi – x̅) 4 = (2-7.2) 4 + (7-7.2) 4 + (15-7.2) 4 + (4-7.2) 4 + (8-7.2) 4 = 4537.936 n = 5 SD = [Σ (xi – x̅) 2 ] 0.5 / (n-1) 0.5 = [98.8] 0.5 / (5-1) 0.5 = 4.970

Are the skewness and kurtosis useful statistics?

Skewness and kurtosis are two commonly listed values when you run a software’s descriptive statistics function. Many books say that these two statistics give you insights into the shape of the distribution. Skewness is a measure of the symmetry in a distribution. A symmetrical dataset will have a skewness equal to 0.

What does the kurtosis value mean?

DEFINITION of ‘Kurtosis’. Like skewness, kurtosis is a statistical measure that is used to describe the distribution. Whereas skewness differentiates extreme values in one versus the other tail, kurtosis measures extreme values in either tail.

What is kurtosis in statistics?

In probability theory and statistics, kurtosis (from Greek: κυρτός, kyrtos or kurtos, meaning “curved, arching”) is a measure of the “tailedness” of the probability distribution of a real-valued random variable.