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Ambulance response time. Ambulance response time is measured as the time (in minutes) between the initial call to emergency medical services (EMS) and when the patient is reached by ambulance. Geographical Analysis (Vol. 41, 2009) investigated the characteristics of ambulance response time for EMS calls in Edmonton, Alberta. For a particular EMS station (call it Station A), ambulance response time is known to be normally distributed with m = 7.5 minutes and s = 2.5 minutes.

a. Regulations require that 90% of all emergency calls be reached in 9 minutes or less. Are the regulations met at EMS Station A? Explain.

b. A randomly selected EMS call in Edmonton has an ambulance response time of 2 minutes. Is it likely that this call was serviced by Station A? Explain

Short Answer

Expert verified

⦁ 90% of the emergency calls can be reached in less than or equal to 9 minutes.

⦁ A randomly selected EMS call in Edmonton has an ambulance response time of 2 minutes.

Step by step solution

01

Given information

Ambulance response time is measured as the time (in minutes) between the initial call to emergency medical services (EMS) and when the patient is reached by ambulance.

For a particular EMS station ambulance response time is known to be normally distributed with m = 7.5 minutes and s = 2.5 minutes.

02

Verifying 90% of emergency calls can reach in less than or equal to 9 minutes.

Let X be the random variable which represents the ambulance response time with mean μand variance σ2. That is μ=7.5minσ2=2.5min

Calculating Z-value ofX1

Z1=X1-μσ=9-7.52.5=1.52.5=0.6

If area A is 90% or more than 90%, then regulations are met at EMS station A.

03

Verifying a randomly selected eMS call in  Edmonton has an ambulance response time of 2 minutes.

Let t=2 minutes.

If probability of getting X2=2 is less than 0.05, then it is unlikely that call was service by station A. That is

If area A2<0.05

Therefore t is unlikely that call was serviced by station A.

Hence,

Z1=X2-μσ=2-7.52.5Z2=-2.2

From Z-table Z2=-2.2, therefore A2=0.014

Since A20.014<0.05, it is unlikely that the call was serviced by Station A.

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Most popular questions from this chapter

Checkout lanes at a supermarket. A team of consultants working for a large national supermarket chain based in the New York metropolitan area developed a statistical model for predicting the annual sales of potential new store locations. Part of their analysis involved identifying variables that influence store sales, such as the size of the store (in square feet), the size of the surrounding population, and the number of checkout lanes. They surveyed 52 supermarkets in a particular region of the country and constructed the relative frequency distribution shown below to describe the number of checkout lanes per store, x.

a. Why do the relative frequencies in the table represent the approximate probabilities of a randomly selected supermarket having x number of checkout lanes?

b. FindE(x) and interpret its value in the context of the problem.

c. Find the standard deviation of x.

d. According to Chebyshev’s Rule (Chapter 2, p. 106), what percentage of supermarkets would be expected to fall withinμ±σ? withinμ±2σ?

e. What is the actual number of supermarkets that fall within? ? Compare your answers with those of part d. Are the answers consistent?

The binomial probability distribution is a family of probability distributions with every single distribution depending on the values of n and p. Assume that x is a binomial random variable with n = 4.

  1. Determine a value of p such that the probability distribution of x is symmetric.
  2. Determine a value of p such that the probability distribution of x is skewed to the right.
  3. Determine a value of p such that the probability distribution of x is skewed to the left.
  4. Graph each of the binomial distributions you obtained in parts a, b, and c. Locate the mean for each distribution on its graph.\
  5. In general, for what values of p will a binomial distribution be symmetric? Skewed to the right? Skewed to the left?

If x is a binomial random variable, compute for each of the following cases:

  1. n = 4, x = 2, p = .2
  2. n = 3, x = 0, p = .7
  3. n = 5, x = 3, p = .1
  4. n = 3, x = 1, p = .9
  5. n = 3, x = 1, p = .3
  6. n = 4, x = 2, p = .6

The random variable x has a normal distribution with μ=1000 and σ=10.

a. Find the probability that x assumes a value more than 2 standard deviations from its mean. More than 3 standard deviations from .μ

b. Find the probability that x assumes a value within 1 standard deviation of its mean. Within 2 standard deviations of μ.

c. Find the value of x that represents the 80th percentile of this distribution. The 10th percentile.

Identify the type of continuous random variable—uniform,normal, or exponential—described by each of the following probability density functions:

a.f(x)=e-x77;x>o

b.f(x)=120;5<x<25

c.f(x)=e-.5[x-10/5]252π

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