Since heights are normally distributed, the probability "P(x\\leq x_0)" is given by (see https://en.wikipedia.org/wiki/Normal_distribution#Standard_normal_distribution):
"P(x\\leq x_0)=\\frac{1}{\\sigma\\sqrt{2\\pi}}\\int_{-\\infty}^{x_0}e^{-\\frac12(\\frac{x-\\mu}{\\sigma})^2}dx"
with "\\mu=144.5" , "\\sigma=6" . In a similar way, we can express the probabilities "P(x_1\\leq x\\leq x_2)"
and "P(x\\geq x_3)" for real "x_0,x_1,x_2,x_3" by changing the boundaries in the integral. We obtained the following answers:
(i) "P(x\\leq 130)=\\frac{1}{6\\sqrt{2\\pi}}\\int_{-\\infty}^{130}e^{-\\frac12(\\frac{x-144.5}{6})^2}dx\\approx0.0078"
(ii) "P(151.5\\leq x\\leq 182)=\\frac{1}{6\\sqrt{2\\pi}}\\int_{151.5}^{182}e^{-\\frac12(\\frac{x-144.5}{6})^2}dx\\approx0.1217"
(iii)"P(x\\geq 150)=\\frac{1}{6\\sqrt{2\\pi}}\\int_{150}^{+\\infty}e^{-\\frac12(\\frac{x-144.5}{6})^2}dx\\approx0.1797"
(iv) The aim is to find "F^{-1}(0.6)" , where "F(y)=P(x> y)=\\frac{1}{6\\sqrt{2\\pi}}\\int_y^{+\\infty}e^{-\\frac12(\\frac{x-144.5}{6})^2}dx"
For "y\\approx143.0" (it was rounded to 1 decimal place) we get that "F(143.0)\\approx0.5987"
For the computations we used the following program written in Anaconda (https://www.anaconda.com/products/individual):
from scipy import integrate
import numpy as np
import math
func = lambda x:(1/(6*math.sqrt(2)*math.sqrt(math.pi)))*math.exp(-1/2*((x-144.5)/6)*((x-144.5)/6))
Pr1 = integrate.quad(func, -np.Infinity, 130)
Pr2 = integrate.quad(func, 151.5, 182)
Pr3 = integrate.quad(func, 150, +np.Infinity)
print(Pr1)
print(Pr2)
print(Pr3)
For the last task we used the fact that "F(y)" decreases and substituted different values of "y" until we got the values near 0.6.
Answers: (i) 0.0078 (ii) 0.1217 (iii) 0.1797 (iv) 143.0; the answers (i)-(iii) are rounded to 4 decimal places; (iv) is rounded to 1 decimal place.
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