Michael's question at Yahoo Answers concerning normally distributed data

In summary, the dividing line for sending letters to the top 29% of SAT math scores would be 575, and for the top 15% it would be 635.
  • #1
MarkFL
Gold Member
MHB
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Here is the question:

Help With A Statistics Problem! 10 Points!?

SAT math scores are scaled so that they are approximately normal, with a mean of 506 and a standard deviation of 124.
A college wants to send letter to students scoring in the top 29% on the math portion of the exam. What SAT math score should they use as the dividing line between those who get letters and those who do not?

What score would they use as the dividing line if they choose to send letters to only the top 15% instead?

Here is a link to the question:

Help With A Statistics Problem! 10 Points!? - Yahoo! Answers

I have posted a link there to this topic so that the OP may find my response.
 
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  • #2
Re: Michael's question at Yahoo! Answers concerning normally distrubted data

Hello Michael,

For the first part of the question, to find the score which divides the top 29% from the remainder, we need to consult our chart for areas under the standard normal curve. We are looking for the z-score with an area of 0.21, since 50% will be to the left of the mean, 0.21 will be between the mean and the z-score we want, and the remaining 0.29 will be to the right of this z-score.

Consulting the chart, we find this z-score is closest to 0.55. If we use numeric integration, we find it is closer to 0.553384719556.

I used my TI-89 Titanium and the command:

solve((1/√(2π))∫(e^(-t^2/2),t,0,z)=0.21,z)

Now we wish to convert this z-score to a raw data score, via the relation:

$\displaystyle z=\frac{x-\mu}{\sigma}$

Solve for $x$ to get:

$\displaystyle x=z\sigma+\mu$

We are given:

$\displaystyle \sigma=124,\,\mu=506$

and so:

$\displaystyle x\approx0.553384719556\cdot124+506=574.619705224944\approx575$

So, scores which are at least 575 would be in the top 29%.

Using the same method to determine the bottom score for the top 15%, we may use the command:

solve((1/√(2π))∫(e^(-t^2/2),t,0,z)=0.35,z)

to get:

$z\approx1.03643338949$

and so:

$\displaystyle x\approx1.03643338949\cdot124+506=634.5177402967599\approx635$

So, scores which are at least 635 would be in the top 15%.
 

Related to Michael's question at Yahoo Answers concerning normally distributed data

1. What is normally distributed data?

Normally distributed data, also known as Gaussian distribution, is a type of probability distribution where the data follows a bell-shaped curve, with the majority of the data falling in the middle and fewer data points falling towards the extremes.

2. How is normally distributed data different from other types of distributions?

Unlike other distributions, normally distributed data has a symmetrical shape, meaning that the data is equally likely to be above or below the mean. It also has certain properties, such as the mean, median, and mode being equal.

3. How do you determine if a dataset is normally distributed?

There are several methods to determine if a dataset is normally distributed, including visual inspection of a histogram or a Q-Q plot, conducting a statistical test such as the Kolmogorov-Smirnov test, or calculating the skewness and kurtosis of the data.

4. What is the importance of having normally distributed data?

Having normally distributed data is important because many statistical methods and tests assume that the data follows a normal distribution. This allows for better understanding and interpretation of the data, as well as more accurate predictions and conclusions.

5. Can data be transformed to become normally distributed?

Yes, there are certain transformations that can be applied to data to make it more normally distributed. This includes logarithmic, square root, and Box-Cox transformations. However, it is important to assess the effectiveness of these transformations and ensure that they do not introduce any biases or distortions in the data.

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