What Percentile is K in a Normal Distribution?

In summary, the question involves finding the relationship between the values of two percentiles of a normally distributed variable X, and the correct answer is that (B) is greater than (A) (i.e. 30 > K). Solutions involve using the 68-95-99.7% rule and the normal CDF to estimate the values of the mean and standard deviation of X, and using this information to compare the z-values of the two percentiles. It can be solved without a calculator and with high school level mathematics.
  • #1
FaNgS
91
0
This is a question from the GRE General Test, which I am not able to understand its solution. Maybe someone can help ^^

Question: The variable X is normally distributed. If the values of Xat the 45th,15th and kth percentile are 550, 350 and 450 respectively.

(A) K
(B) 30

Answer Choices:
(A) is greater than (B)
(B) is greater than (A)
(A) and (B) are equal
Can NOT determine the answer from the given infromation.

The answer given is that (B) is greater than (A) (i.e. 30>K)

I don't get it, help please!
 
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  • #2
I wonder if this is what is going on: Let s:=sigma, Mu:=mean

using the 1-2-3 , aka, 68-95-99.7% rule , 34.3% of data will be within 1 s of the mean

this means that around 15.7% , say 15% will be more than 1s below the mean.

There is another useful rule that 19% of data is within 1/2s of Mu


Use 15% instead of 15.7% . Then approximate:


(350-Mu)/s


To find a general form for s. Then use that to estimate the z-value for

450, and find a bound for it. Then use the fact that the mean is larger

than 550 to bound the z-value of 450, and compare it to 1/2.

Maybe not elegant, but I think it will work.
 
  • #3
thanks for the reply, but is there an easier way to solve it?
you are not allowed to use a calculator in the GRE Test and also the level of mathematics involved is supposedly that of high-school
 
  • #4
You don't need a calculator. All of the points in question are less than the mean. The normal CDF is convex in this interval.
 
  • #5
Another way to look at it, by drawing a diagram of the normal pdf it's easy to see that the halfway point between the 15th and 45th percentiles divides the corresponding areas into two unequal parts, the greater being nearer the 45th percentile.
 

Related to What Percentile is K in a Normal Distribution?

What is normally distributed data?

Normally distributed data, also known as Gaussian distribution, is a type of probability distribution where the data is symmetrically distributed around the mean, forming a bell-shaped curve. In this type of distribution, the majority of the data falls within one standard deviation of the mean, and the remaining data is spread out evenly on either side.

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

Normally distributed data is different from other types of distributions because it follows a specific pattern and has specific characteristics. It has a symmetrical shape, a bell-shaped curve, and the mean, median, and mode are all equal. It also follows the 68-95-99.7 rule, where 68% of the data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.

What are the benefits of having normally distributed data?

Having normally distributed data has several benefits. First, it allows for easier data analysis and interpretation as the data follows a specific pattern. Additionally, many statistical tests and models assume that the data is normally distributed, so having normally distributed data can improve the accuracy and reliability of these tests and models.

What are some common causes of non-normally distributed data?

There are several common causes of non-normally distributed data. These include small sample sizes, extreme outliers, and skewed data. In some cases, the data may also be transformed to better fit a normal distribution.

How can I determine if my data is normally distributed?

There are several ways to determine if your data is normally distributed. One way is to visually inspect the data using a histogram or a probability plot. Another way is to use statistical tests such as the Shapiro-Wilk test or the Kolmogorov-Smirnov test. If the p-value for these tests is greater than 0.05, the data can be considered normally distributed.

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