M's question at Yahoo Answers regarding normally distributed data

In summary, the question asks for the approximate number of people in the United States with an IQ higher than 125, assuming a total population of 280,000,000. Using the normal distribution formula, we find that there are approximately 4,900,000 people in the United States with an IQ higher than 125.
  • #1
MarkFL
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Here is the question:

Help with Z Score Problem?

Help with this one problem! Please show work if possible

IQ scores (as measured by the Stanford-Binet intelligence test) are normally distributed with a mean of 85 and a standard deviation of 19. Find the approximate number of people in the United States (assuming a total population of 280,000,000) with an IQ higher than 125. (Round your answer to the nearest hundred thousand.)

Here is a link to the question:

Help with Z Score Problem? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
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  • #2
Hello m,

We know that in a normal distribution, half of the data is to the left of the mean $\mu$. So, we want to find the area under the normal curve to the right of the mean, and to the left of the datum value 125. To do this, we need to convert this datum to a $z$-score using:

\(\displaystyle z=\frac{x-\mu}{\sigma}=\frac{125-85}{19}\approx2.11\)

Now, consulting a table, we find the area associated with this $z$-score is:

$0.4826$

Hence, the area $A_L$ under the curve to the left of $x=125$ is:

\(\displaystyle A_L=0.5+0.4826=0.9826\)

Since the total area under the curve is $1$, we know the area $A_R$ to the right of $x=125$ is:

\(\displaystyle A_R=1-A_L=0.0174\)

Multiplying this number with the given population, we may state the number $N$ in that population with an IQ greater than 125 is:

\(\displaystyle N=0.0174\,\times\,2.8\,\times\,10^8\approx4,900,000\)

To m and any other guests viewing this topic, I invite and encourage you to post other pre-calculus statistics questions here in our http://www.mathhelpboards.com/f23/ forum.

Best Regards,

Mark.
 

FAQ: M's question at Yahoo Answers regarding normally distributed data

What is normally distributed data?

Normally distributed data, also known as Gaussian distribution, is a type of probability distribution where the data is spread out evenly and symmetrically around the mean. This means that most of the data points are close to the mean, and the further away from the mean a data point is, the less likely it is to occur. It is often represented by a bell-shaped curve.

How do I know if my data is normally distributed?

To determine if your data is normally distributed, you can visually inspect the data using a histogram or a normal probability plot. You can also use statistical tests such as the Shapiro-Wilk test or the Kolmogorov-Smirnov test. These tests compare your data to the expected normal distribution and provide a p-value, which can help determine the level of normality.

Why is it important for data to be normally distributed?

Having normally distributed data is important because it allows for the use of many statistical methods and tests. These methods assume that the data is normally distributed, and using them on non-normal data can lead to inaccurate results. Additionally, normal distribution is often seen in natural phenomena and can help in understanding and analyzing the data.

Can I transform my data to make it normally distributed?

Yes, it is possible to transform non-normal data to make it more normally distributed. This can be done through various methods such as taking the logarithm or square root of the data, or using a Box-Cox transformation. However, it is important to note that transforming data may also change the interpretation of the data and should be used with caution.

What should I do if my data is not normally distributed?

If your data is not normally distributed, there are a few options you can consider. You can try transforming the data, as mentioned before, or you can use non-parametric statistical tests that do not assume normality. You can also consult with a statistician for further advice on how to analyze your non-normal data.

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