Distribution function is "P(X\\le x)=F(x)=\\frac{1}{250\\sqrt{2\\pi}}\\int\\limits_{-\\infty}^x e^{-\\frac{(t-3500)^2}{2\\cdot 250^2}}dt"
If we take "u=\\frac{t-3500}{250}", then "\\frac{(t-3500)^2}{2\\cdot 250^2}=\\frac{u^2}{2}" and "dt=250du", so "F(x)=\\frac{1}{250\\sqrt{2\\pi}}\\int\\limits_{-\\infty}^\\frac{x-3500}{250} e^{-\\frac{u^2}{2}}\\cdot 250du=\\frac{1}{\\sqrt{2\\pi}}\\int\\limits_{-\\infty}^\\frac{x-3500}{250} e^{-\\frac{u^2}{2}}du="
"=\\Phi(\\frac{x-3500}{250})", where "\\Phi(y)=\\frac{1}{\\sqrt{2\\pi}}\\int\\limits_{-\\infty}^y e^{-\\frac{u^2}{2}}du"
So the required probability is "P(3300\\le X\\le 3800)=F(3800)-F(3300)="
"=\\Phi(1.2)-\\Phi(-0.8)"
From the table of standard normal distribution https://www.math.arizona.edu/~rsims/ma464/standardnormaltable.pdf we can see that "\\Phi(1.2)\\approx 0.88493" and "\\Phi(-0.8)\\approx 0.21186", so "P(3300\\le X\\le 3800)\\approx0.88493-0.21186=0.67307"
Answer: "0.67307"
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