Understanding Skewness in Excel

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The discussion focuses on the differences in skewness calculations between a user's formula and Excel's built-in function. The user calculates skewness using a specific formula but receives a different result from Excel for the same data set. It is noted that Excel employs a different formula that includes a correction factor for sample size. The conversation highlights that there are multiple methods to estimate skewness, and no single method is universally accepted as the best. Ultimately, the discrepancy arises from the choice of formulas used in different contexts.
FrostScYthe
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Hi everyone,

I'm using the following formula to calculate skewness

g_1 = \frac{m_3}{m_2^{3/2}} <br /> = \frac{\tfrac{1}{n} \sum_{i=1}^n (x_i-\overline{x})^3}{\left(\tfrac{1}{n} \sum_{i=1}^n (x_i-\overline{x})^2\right)^{3/2}}\ ,

However, when I try excel on to calculate skewness I get a different results. For example this set:

1
1
1
1
1
1
1
9
9

I get: 1.33630621
Excel gets: 1.619847741

Is excel using a different formula, or am I doing something wrong? O_o

Ted.
 
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I believe Excel uses this:

<br /> \frac n {(n-1)(n-2)} \sum{\left(\frac{x - \bar x}{s}\right)^2}<br />
 
Any particular reason why
excel uses a different formula?


Ted.
 
FrostScYthe said:
Any particular reason why
excel uses a different formula?


Ted.

I can't really answer why their choice was made. Note that unlike the mean (for example) there are several quantities proposed to estimate skewness, and none is really preferred as "the correct way" to do it.
 
I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...
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