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1.9 Practice – Age Problems (Normal Distribution and z-Scores)

In a normal age model with mean 38 years and standard deviation 12 years, find P(25 ≤ X ≤ 50), P(X > 60), and the 90th percentile age.

Subject: Statistics Chapter: Continuous Random Variables and the Normal Distribution Topic: Standardizing a Normal Distribution Answer included
1.9 practice - age problems age problems statistics normal distribution z-score standardizing a normal distribution standard normal distribution probability between two values percentile
Accepted answer Answer included

1.9 Practice – Age Problems

Ages (in years) of licensed drivers in a region are modeled as approximately normal with mean \(\mu=38\) and standard deviation \(\sigma=12\). Denote age by \(X\), so \(X \sim N(38,12^2)\).

Compute the following:
1) \(P(25 \le X \le 50)\)
2) \(P(X > 60)\)
3) The 90th percentile age (the value \(x\) such that \(P(X \le x)=0.90\))
4) In a random sample of \(n=200\) drivers, the expected number older than 60

Key idea: standardize to a z-score

Standardizing converts an \(x\)-value from the normal model into a standard normal value:

\[ z=\frac{x-\mu}{\sigma}. \]

After standardizing, probabilities are read from the standard normal cumulative distribution function \(\Phi(z)\), where \(\Phi(z)=P(Z \le z)\) and \(Z \sim N(0,1)\).

1) Probability between two ages: \(P(25 \le X \le 50)\)

  1. Standardize the endpoints: \[ z_{25}=\frac{25-38}{12}=\frac{-13}{12}\approx -1.0833, \qquad z_{50}=\frac{50-38}{12}=\frac{12}{12}=1.0000. \]
  2. Convert to a standard normal probability: \[ P(25 \le X \le 50)=P(z_{25}\le Z \le z_{50})=\Phi(1.0000)-\Phi(-1.0833). \]
  3. Use typical table/CDF values (rounded): \(\Phi(1.00)\approx 0.8413\) and \(\Phi(-1.0833)\approx 0.1391\). Then \[ P(25 \le X \le 50)\approx 0.8413-0.1391=0.7022. \]

Result: \(P(25 \le X \le 50)\approx 0.702\).

2) Probability above an age: \(P(X > 60)\)

  1. Standardize 60: \[ z_{60}=\frac{60-38}{12}=\frac{22}{12}\approx 1.8333. \]
  2. Right-tail probability: \[ P(X > 60)=P(Z > 1.8333)=1-\Phi(1.8333). \]
  3. Using a typical table/CDF value \(\Phi(1.8333)\approx 0.9666\): \[ P(X > 60)\approx 1-0.9666=0.0334. \]

Result: \(P(X > 60)\approx 0.033\).

3) 90th percentile age

The 90th percentile is the value \(x\) for which \(P(X \le x)=0.90\). On the standard normal scale, this corresponds to a z-value \(z_{0.90}\) such that \(\Phi(z_{0.90})=0.90\).

  1. Use the inverse-CDF (or a standard table): \(z_{0.90}\approx 1.2816\).
  2. Convert back to the original age scale: \[ x=\mu+z_{0.90}\cdot\sigma=38+1.2816\cdot 12. \]
  3. Compute: \[ x=38+15.3792=53.3792 \approx 53.4. \]

Result: the 90th percentile age is approximately \(53.4\) years.

4) Expected number older than 60 in \(n=200\)

If each selected driver is older than 60 with probability \(p=P(X > 60)\approx 0.0334\), then the expected count is

\[ E=\;n\cdot p=200\cdot 0.0334=6.68 \approx 7. \]

Quantity Key computation Approximate value
\(P(25 \le X \le 50)\) \(\Phi(1.0000)-\Phi(-1.0833)\) \(0.702\)
\(P(X > 60)\) \(1-\Phi(1.8333)\) \(0.033\)
90th percentile \(38+1.2816\cdot 12\) \(53.4\) years
Expected count in \(n=200\) \(200\cdot 0.0334\) \(\approx 7\)

Visualization: normal curve with the “between” region

0 10 20 30 40 50 60 70 80 Age (years) 25 μ = 38 50 60 P(25 ≤ X ≤ 50)
The shaded region under the normal curve represents the probability that an age falls between 25 and 50. The solid line marks the mean age 38, and the dashed marker at 60 corresponds to the right-tail probability P(X > 60).
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