Solving Forearm Length Stats Problem

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The discussion revolves around solving a statistics problem related to forearm length and height in men. The average height is 68 inches with a standard deviation of 2.7 inches, while the average forearm length is 18 inches with a standard deviation of 1 inch and a correlation coefficient of 0.80. The initial calculations suggested that 38% of men have forearms measuring 18 inches, but the correct percentage for men who are 68 inches tall is 60%. The correlation between height and forearm length is crucial for determining this percentage. The original poster successfully resolved the problem after receiving guidance on using multivariate normal distributions and understanding the correlation coefficient.
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Hi Everyone,

I am new to the forum and really need so help before I have a test tomorrow. I have been going over my book and working through the problems but I am stuck on this problem. Would someone please help me? thank you very much for your time

From a study
average height of men =68in sd=2.7inch
average forearm length=18in sd 1inch r=0.80

a) what percentage of men have forearms which are 18 inches long to the nearest inch? for this problem I figured out 38%

b) of the men who are 68inches tall, what percentage have forearms which are 18 inches long to the nearest inch?

I know that the nearest inch refers to 17.5 and 18.5 and the answer in the back of the book is 60% however, I do not know how they solve the problem to get 60%
 
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Since I don't believe that "forearm length" and "height" are independent, I don't see any way to do this with knowing the correlation.
 
I wonder whether "r=0.80" in the OP stands for corr.

For indep. normal distributions, since sd = 1 for the forearm length, the question is, what is the probability around the mean plus/minus half a sd? You can look it up from a standard normal table (e.g. http://www.math.unb.ca/~knight/utility/NormTbl0.htm ). I believe 38.3%.

For correlated distributions you need to think about multivariate normal.
 
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found the answer

Thank you for the replies, I already found a way to solve the problem.

r stands for coefficient correlation
and the answer as I included is 60%
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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