Skip to main content
added 7 characters in body
Source Link

I was analysing a distribution. I have attached the link for the list.

https://drive.google.com/file/d/1o1Zr9bwy_wzrDAIdsxRVXxAPzRrblQOg/view?usp=sharing

This histogram of this distribution looks like this,

enter image description here

Now I was evaluating the skewness of the distribution. First I used the basic formula of skewness.

enter image description here

[ I don't know name of the formula. If someone can enlighten me that's a plus. :) ]

Code:

meanY = np.mean(yArr)
stdY = np.std(yArr)
s = 0
for yd in yArr:
    s += (yd-meanY)**3
print((s/(stdY**3))/len(yArr))

Output:

-0.6510082464944021

Then I used Pearson's formula for skewness ie.

enter image description here

Code:

meanY = np.mean(yArr)
medianY = np.median(yArr)
stdY = np.std(yArr)
print(3*(meanY-medianY)/stdY)

Output:

0.34088557298815947

Now the first formula is saying the graph is right-skewed but the second formula is saying the graph is left-skewed. Why there is a conflict between the results of the two formulas?

Overall, I want to know why there is a difference in the report of both the formulas and the general conditions where Pearson's skewness formula will come at conflict with the traditional formula.

I was analysing a distribution. I have attached the link for the list.

https://drive.google.com/file/d/1o1Zr9bwy_wzrDAIdsxRVXxAPzRrblQOg/view?usp=sharing

This histogram of this distribution looks like this,

enter image description here

Now I was evaluating the skewness of the distribution. First I used the basic formula of skewness.

enter image description here

[ I don't know name of the formula. If someone can enlighten me that's a plus. :) ]

Code:

meanY = np.mean(yArr)
stdY = np.std(yArr)
s = 0
for yd in yArr:
    s += (yd-meanY)**3
print((s/(stdY**3))/len(yArr))

Output:

-0.6510082464944021

Then I used Pearson's formula for skewness ie.

enter image description here

Code:

meanY = np.mean(yArr)
medianY = np.median(yArr)
stdY = np.std(yArr)
3*(meanY-medianY)/stdY

Output:

0.34088557298815947

Now the first formula is saying the graph is right-skewed but the second formula is saying the graph is left-skewed. Why there is a conflict between the results of the two formulas?

Overall, I want to know why there is a difference in the report of both the formulas and the general conditions where Pearson's skewness formula will come at conflict with the traditional formula.

I was analysing a distribution. I have attached the link for the list.

https://drive.google.com/file/d/1o1Zr9bwy_wzrDAIdsxRVXxAPzRrblQOg/view?usp=sharing

This histogram of this distribution looks like this,

enter image description here

Now I was evaluating the skewness of the distribution. First I used the basic formula of skewness.

enter image description here

[ I don't know name of the formula. If someone can enlighten me that's a plus. :) ]

Code:

meanY = np.mean(yArr)
stdY = np.std(yArr)
s = 0
for yd in yArr:
    s += (yd-meanY)**3
print((s/(stdY**3))/len(yArr))

Output:

-0.6510082464944021

Then I used Pearson's formula for skewness ie.

enter image description here

Code:

meanY = np.mean(yArr)
medianY = np.median(yArr)
stdY = np.std(yArr)
print(3*(meanY-medianY)/stdY)

Output:

0.34088557298815947

Now the first formula is saying the graph is right-skewed but the second formula is saying the graph is left-skewed. Why there is a conflict between the results of the two formulas?

Overall, I want to know why there is a difference in the report of both the formulas and the general conditions where Pearson's skewness formula will come at conflict with the traditional formula.

Source Link

Different skewness formulas are giving different conclusions!

I was analysing a distribution. I have attached the link for the list.

https://drive.google.com/file/d/1o1Zr9bwy_wzrDAIdsxRVXxAPzRrblQOg/view?usp=sharing

This histogram of this distribution looks like this,

enter image description here

Now I was evaluating the skewness of the distribution. First I used the basic formula of skewness.

enter image description here

[ I don't know name of the formula. If someone can enlighten me that's a plus. :) ]

Code:

meanY = np.mean(yArr)
stdY = np.std(yArr)
s = 0
for yd in yArr:
    s += (yd-meanY)**3
print((s/(stdY**3))/len(yArr))

Output:

-0.6510082464944021

Then I used Pearson's formula for skewness ie.

enter image description here

Code:

meanY = np.mean(yArr)
medianY = np.median(yArr)
stdY = np.std(yArr)
3*(meanY-medianY)/stdY

Output:

0.34088557298815947

Now the first formula is saying the graph is right-skewed but the second formula is saying the graph is left-skewed. Why there is a conflict between the results of the two formulas?

Overall, I want to know why there is a difference in the report of both the formulas and the general conditions where Pearson's skewness formula will come at conflict with the traditional formula.