How does skew and range affect standard deviation

AI Thread Summary
Skewness and range both influence standard deviation, but their effects differ. An increase in range typically leads to a higher standard deviation, as it reflects greater dispersion from the mean. Skewness, however, affects the distribution shape and can alter the mean and standard deviation in less intuitive ways. Understanding how skewness interacts with mean and range is essential for grasping its impact on standard deviation. Clarifying these concepts can enhance comprehension of statistical measures.
Mr Davis 97
Messages
1,461
Reaction score
44

Homework Statement



How does skew and range affect the standard deviation; does one affect it more than the other?

Homework Equations



None

The Attempt at a Solution



It seems as though if the range increases, the standard deviation increases because the SD is a measure of how spread apart from the mean each observation is. However, I have no idea how skew for a data set would affect the SD of that data set. Also, I am not really sure what the question is asking.
 
Physics news on Phys.org
Mr Davis 97 said:

Homework Statement



How does skew and range affect the standard deviation; does one affect it more than the other?

Homework Equations



None

The Attempt at a Solution



It seems as though if the range increases, the standard deviation increases because the SD is a measure of how spread apart from the mean each observation is. However, I have no idea how skew for a data set would affect the SD of that data set. Also, I am not really sure what the question is asking.
How is skew defined in your textbook? Range, standard deviation, and mean are pretty well-known terms, but skew is not so well known.
 
The effects of skewness on mean, range, and standard deviation are clearly demonstrated in the accompanying graphic. Additionally, if you understand how mean, range, and standard deviation are calculated, the differences in the mean, range, and standard deviation between a Normal distribution with skewness parameter 0 and a skewed Normal distribution with skewness parameter 2 should be intuitively obvious and with an appropriate amount of thought, these differences should be able to be explained conceptually and mathematically.

Note: While the two pairs of arrows were meant to demonstrate visually the phenomena discussed above, I am afraid they serve no value without some explanation. Therefore, the point of the two pairs of arrows (pun intended) is to demonstrate by the lengths of corresponding arrows that the skewed data is less dispersed in both directions than the Normal data at a randomly selected level. I suspect the vertical lines "hide" the differences in the lengths of the arrows, but if you measure them, the differences in lengths become obvious.

I will be delighted to clarify any points or answer any questions and would appreciate any comments, suggestions, or criticisms.

Jim
 

Attachments

  • Skewness.png
    Skewness.png
    70.4 KB · Views: 3,324
I tried to combine those 2 formulas but it didn't work. I tried using another case where there are 2 red balls and 2 blue balls only so when combining the formula I got ##\frac{(4-1)!}{2!2!}=\frac{3}{2}## which does not make sense. Is there any formula to calculate cyclic permutation of identical objects or I have to do it by listing all the possibilities? Thanks
Since ##px^9+q## is the factor, then ##x^9=\frac{-q}{p}## will be one of the roots. Let ##f(x)=27x^{18}+bx^9+70##, then: $$27\left(\frac{-q}{p}\right)^2+b\left(\frac{-q}{p}\right)+70=0$$ $$b=27 \frac{q}{p}+70 \frac{p}{q}$$ $$b=\frac{27q^2+70p^2}{pq}$$ From this expression, it looks like there is no greatest value of ##b## because increasing the value of ##p## and ##q## will also increase the value of ##b##. How to find the greatest value of ##b##? Thanks

Similar threads

Back
Top