#rnorm(n, mu,sigma) generates n random values from the normal distribution with mean mu and standard deviation sigma.
#If omitted, the parameters default to mu=0 and sigma=1


#pnorm(x,mu,sigma) is the CDF.
pnorm(12:16,14,.8)

#qnorm(p,mu,sigma) is the inverse CDF.It returns the value x such that pnorm(x,mu,sigma)=p
qnorm(c(.25,.50,.75),14,.8)

#dnorm(x,mu,sigma) is the PDF.In R, it's generally only used to draw bell curves.

#Example. Flipper lengthsof a certain kind of penguin are normally distributed with mean 192.9mm and standard deviation 7.1mm
#What is the probability that a random selected has a flipper less than 200mm long? More than 200mm?
pnorm(200,192.9,7.1)
1-pnorm(200,192.9,7.1)

#2.what is the 90th percentile for flippers lengthin these penguins?
qnorm(.9,192.9,7.1)