Let "X" be a normal random variable with mean "m" and variance "\\sigma^2" such that
"P(X<35)=15\\%=0.15" and "P(X>65)=10\\%=0.1", then
"P(X<65)=1-0.1=0.9."
Since "X" is a normal random variable, then
"Z=\\frac{X-m}{\\sigma}" is the standard normal random variable.
"P(X<35)=P(\\frac{X-m}{\\sigma}<\\frac{35-m}{\\sigma})=\\\\\nP(Z<\\frac{35-m}{\\sigma})=0.15,"
"P(X<65)=P(\\frac{X-m}{\\sigma}<\\frac{65-m}{\\sigma})=\\\\\nP(Z<\\frac{65-m}{\\sigma})=0.9."
Using quantile function "Q" for the standard normal random variable,
"\\frac{35-m}{\\sigma}=Q(0.15)=-1.04",
"\\frac{65-m}{\\sigma}=Q(0.9)=1.28",
hence
"35-m=-1.04\\sigma",
"65-m=1.28\\sigma",
"65-m-(35-m)=1.28\\sigma+1.04\\sigma,"
"2.32\\sigma=30, \\sigma=\\frac{30}{2.32}\\approx12.931,"
"65-m=1.28\\cdot12.931\\approx 16.552, \\\\\nm=65-16.552=48.448."
Answer: "48.448."
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