The prices of a certain model of new homes are normally distributed with a mean of $150,000 and a standard deviation of $900

The prices of a certain model of new homes are normally distributed with a mean of $150,000 and a standard deviation of $900. What percentage of homes exceed $152,700?

The correct answer and explanation is :

To determine what percentage of homes exceed \$152,700, we need to first standardize the value and then use the standard normal distribution to find the corresponding percentage.

Step 1: Standardize the Value

We can standardize the value of \$152,700 using the formula for the z-score:

$$
Z = \frac{X – \mu}{\sigma}
$$

Where:

  • $X = 152,700$ (the value we are interested in),
  • $\mu = 150,000$ (the mean price of homes),
  • $\sigma = 900$ (the standard deviation of home prices).

Plugging the values into the formula:

$$
Z = \frac{152,700 – 150,000}{900} = \frac{2,700}{900} = 3
$$

So, the z-score for \$152,700 is 3.

Step 2: Find the Percentage from the Standard Normal Distribution

Now that we have the z-score, we can use a standard normal distribution table or a calculator to determine the cumulative probability for $Z = 3$. The cumulative probability for a z-score of 3 is approximately 0.99865, which means that about 99.865% of the home prices fall below \$152,700.

Step 3: Calculate the Percentage Above \$152,700

Since the cumulative probability tells us the percentage of homes that cost less than \$152,700, the percentage of homes that cost more than \$152,700 is the complement of this value:

$$
1 – 0.99865 = 0.00135
$$

Thus, the percentage of homes that exceed \$152,700 is approximately 0.135%.

Final Answer:

0.135% of homes exceed \$152,700.

Explanation:

This result shows that the distribution of home prices is tightly clustered around the mean, with most homes priced close to \$150,000. Only a very small percentage of homes have prices significantly higher than this value, such as those exceeding \$152,700. This illustrates the properties of a normal distribution, where extreme values (far from the mean) are rare.

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