A hospital keeps records of its emergency-room traffic. Those records indicate that, beginning at 6:00 P.M. on any given day, the elapsed time until the first patient arrives has an exponential distribution with parameter λ = 6.9, where time is measured in hours. Determine the probability that, beginning at 6:00 P.M. on any given day, the first patient arrives
a. between 6:15 P.M. and 6:30 P.M.
b. before 7:00 P.M.
c. given that the first patient doesn’t arrive by 6:15 P.M., determine the probability that she arrives by 6:45 P.M.
Denote by "T" a random variable that has an exponential distribution. Its probability density function is: "f(x)=\\lambda e^{-\\lambda x}, x\\geq0". "f(x)=0" for "x<0." "\\lambda=6.9" is a parameter of the distribution. a. The aim is to find the probability: "P(\\frac{1}{4}<T<\\frac12)." We get: "P(\\frac{1}{4}<T<\\frac12)=\\int_{\\frac14}^{\\frac12}6.9 e^{-6.9 x}dx=-e^{-6.9 x}|_{\\frac14}^{\\frac12}=e^{-6.9\\cdot\\frac14}-e^{-6.9\\cdot\\frac12}\\approx0.1464".
b. The aim is to find "P(T<1)." We get: "P(T<1)=\\int_{0}^{1}6.9 e^{-6.9 x}dx=-e^{-6.9x}|_0^1=1-e^{-6.9}\\approx0.9990"
c. The aim is to find "P(T<\\frac34|T>\\frac14)". Using the definition of the conditional probability, we have: "P(T<\\frac34|T>\\frac14)=\\frac{P(\\frac14<T<\\frac34)}{P(T>\\frac14)}". Compute both probabilities in the latter formula: "P(\\frac14<T<\\frac34)=\\int_{\\frac14}^{\\frac34}6.9e^{-6.9x}dx=-e^{-6.9x}|_{\\frac14}^{\\frac34}=e^{-6.9\\cdot\\frac14}-e^{-6.9\\cdot\\frac34}\\approx0.1725".
"P(T>\\frac14)=\\int_{\\frac14}^{+\\infty}6.9e^{-6.9x}dx=-e^{-6.9x}|_{\\frac14}^{+\\infty}\\approx0.1782."
We get: "\\frac{P(\\frac14<T<\\frac34)}{P(T>\\frac14)}=\\frac{0.1725}{0.1782}\\approx0.9680".
Answer: a. "P(\\frac{1}{4}<T<\\frac12)\\approx0.1464", b. "P(T<1)\\approx0.9990", c. "P(T<\\frac34|T>\\frac14)\\approx0.9680". All values are rounded to "4" decimal places.
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