Question #180930

Question 2

In a study of hypertension and optimal treatment conducted by the National Heart Institute, 10,000 patients had a mean systolic blood pressure (BP), 𝝁 = 𝟏𝟔𝟏 mm Hg and standard deviation, 𝝈 = 𝟐𝟓 mm Hg. Assume the systolic blood pressure is normally distributed.

a. What is the probability of patients with a systolic blood pressure of more than 180 mm Hg?

b. How many patients will have a systolic blood pressure of more than 180 mm Hg?

c. What is the probability of patients with a systolic blood pressure between 145 and 160 mm Hg?

d. If 60 random samples each of size 30 are drawn from this population, determine:

  i. the sampling distribution of the mean systolic blood pressure.

  ii. the probability that the of mean systolic blood pressure between 140 and 165 mm Hg.


1
Expert's answer
2021-04-15T06:50:58-0400

Let X denotes the systolic blood pressure. The X follows normal with μ=161,σ=25\mu = 161, \sigma = 25

Let Z=XμσZ = \frac{X - \mu}{\sigma} ~N(0,1)

a. P(X>180)

P(X>x)=P(Xμσ>xμσ)=P(Z>xμσ)=1P(Zxμσ)P(X>x) = P(\frac{X-\mu}{\sigma}>\frac{x-\mu}{\sigma}) = P(Z>\frac{x-\mu}{\sigma}) = 1 - P(Z\le \frac{x-\mu}{\sigma})

Therefore, when x = 180,

xμσ=18016125=0.76P(X>180)=1P(Z0.76)=10.7764=0.2236\frac{x-\mu}{\sigma}=\frac{180-161}{25}=0.76 \\ \therefore P(X>180) = 1 - P(Z\le 0.76) = 1 - 0.7764 = 0.2236

b. Number of patients will have systolic blood pressure more than 180=0.2236×10000=2236180 = 0.2236 \times 10000 = 2236

c. P(145X160)P(145 \le X \le 160)

Now,

P(x1Xx2)=P(x1μσXμσx2μσ)P(x1Xx2)=P(x1μσZx2μσ)P(x1Xx2)=P(Zx2μσ)P(Zx1μσ)P(x_1\le X \le x_2) = P(\frac{x_1-\mu}{\sigma}\le \frac{X-\mu}{\sigma} \le \frac{x_2-\mu}{\sigma}) \\ P(x_1\le X \le x_2) = P(\frac{x_1-\mu}{\sigma}\le Z \le \frac{x_2-\mu}{\sigma}) \\ P(x_1\le X \le x_2) = P(Z \le \frac{x_2-\mu}{\sigma}) - P(Z \le \frac{x_1-\mu}{\sigma})

when x = 145,

xμσ=14516125=0.64\frac{x-\mu}{\sigma}=\frac{145-161}{25}=-0.64

when x = 160,

xμσ=16016125=0.04\frac{x-\mu}{\sigma}=\frac{160-161}{25}=-0.04

P(145X160)=P(Z0.04)P(Z0.64)=0.2229P(145 \le X \le 160) = P(Z\le-0.04) - P(Z\le -0.64) = 0.2229

d.

i. The sampling distribution for mean with sample size =60×30=1800= 60 \times 30 = 1800 is

N(161,1800)=N(161,42.4264)N(161,\sqrt{1800}) = N(161,42.4264)

ii. P(X165)P(X\le 165)

P(Xx)=P(Xμσxμσ)=P(Zxμσ)P(X\le x) = P(\frac{X-\mu}{\sigma}\le \frac{x-\mu}{\sigma}) = P(Z\le \frac{x-\mu}{\sigma})

When x = 165

xμσ=x16142.4264=0.4478P(Z0.4478)=0.5376\frac{x-\mu}{\sigma}=\frac{x-161}{42.4264}=0.4478 \\ \therefore P(Z\le 0.4478) = 0.5376


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