

FlashChat  Actuarial Discussion  Preliminary Exams  CAS/SOA Exams  Cyberchat  Around the World  Suggestions 
DW Simpson 
Actuarial Salary Surveys 
Actuarial Meeting Schedule 
Contact DW Simpson 

Thread Tools  Display Modes 
#1




Is 50th percentile of data, the mean? or median?
What I don't understand is if the median and mean are not the same (there's a skew).....in a sample
Then the amount by which your score is higher than half the sample, would be the median, NOT the mean. So then if you calculate percentile by taking x  mu/ std deviation Then use the NDistribution table to get percentile, then is that the wrong thing to do? Or does percentile mean something else here? Since this uses mean, not median, as the 50th percentile, where x = mu and Zscore is 0. 
#2




Or is there another distribution table for skewed distributions? Isn't this because I am using a normal distribution for a sample that is not normally distributed, and that's the problem?
This is embarassing, but what I'm referring to are Superbowl Square bets. Where you bet on say Packers to have score ending in 7, Steelers to end in 2. And since there are 100 squares, the mean percent chance of winning is 1% assuming one payout at the end of the game. BUT the median is much closer to .39%, this because the squares like 7,0 and 0,7 are biggies...they are as much as 10 times the percent likelihood to win as other squares..... so if I take the percentage of my 7,2 which comes out to lets say 0.51%, I am above the median but well below the mean. If I go 0.0051  0.01 / Std Dev I will get a percentile score below the 50% because I'm below the mean... is this logical? Or how SHOULD the percentile score be calculated mathematically? Last edited by UFActuary; 02052011 at 12:59 PM.. 
#3




Order all of the boxes in order of least likely to hit to most likely to hit, and then wherever your box comes in that order is the percentile it's in.
50th percentile is median. 
#4




Quote:
50% is always the median 
#5




From the distribution, create a table of the distribution function.
E.g. F(a) = 0 F(b) = 0.01 F(c) = 0.25 F(d) = 0.40 F(e) = 0.50 F(f) = 0.62 F(g) = 0.87 ... F(z) = 1.00 You may have a lot more values whose distribution function you calculate. For example if we took a group of adult males and ordered by weight in pounds, we might have values for F(120), F(121), F(122)...,F(250), and if our scale was sensitive we might have F(160.138) even. Going back to my original example, since F(e)=0.50, then e is the median. If I wanted to know what percentile f is, the answer would be .62 since F(f) = .62. If I wanted to know what is the 40th percentile, I would say d, since F(d) = .40. All that is true for any distribution, skewed or not. The mean could equal e, or be much bigger than e, or much less than e. Doesn't matter. All the percentiles are based just on the distribution function. If you don't know enough about the distribution to know what the cumulative distribution function is, you might have to estimate it based on whatever you do know about the distribution. The normal approximation might be something you would use in those estimates. The estimates might or might not be any good. 
#7




__________________
(\ (\ ( ^_^) (_(")(") It all comes full akchigs eventually. ~ FullOn Devi 
#8




What's the name for the 42nd percentile?

#9




VaR
 The normal distribution has some nice properties which create easy ways to think of the distribution. The thing to remember is that these properties are unique to the normal distribution. Trying to take extrapolate the normal distribution properties to nonnormal distributions is just going to cause problems  if not be flat out wrong. 
#10




__________________
(\ (\ ( ^_^) (_(")(") It all comes full akchigs eventually. ~ FullOn Devi 
Thread Tools  
Display Modes  

