Discover Exam Mark Percentiles & Standard Deviation with Normal Curve

In summary, the distribution of marks on an exam is closely described by a normal curve with a mean of 65. The 84th percentile is 75, the 16th percentile is 55, and the approximate value of the standard deviation is 10. The z score associated with an exam mark of 50 is -1.5 and the 98th percentile corresponds to an exam mark of 85. Based on the data, it can be concluded that there were not many marks below 35, as 99.7% of the distribution falls between 35 and 95.
  • #1
BrownianMan
134
0
Suppose that the distribution of marks on an exam is closely described by a normal curve with a mean of 65. The 84th percentile of this distribution is 75.
(a) What is the 16th percentile?
(b) What is the approximate value of the standard deviation of exam
marks?
(c) What z score is associated with an exam mark of 50?
(d) What percentile corresponds to an exam mark of 85?
(e) Do you think there were many marks below 35? Explain.

These are some exercises the prof gave us. That's all of the information given.

I'm having some difficulty getting started on a and b particularly.
 
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  • #2
How does the 16th percentile compare to the 84th percentile? There is some symmetry you should be able to use.

Approximately how much of a normal distribution will be within 1 standard deviation of the mean?
 
  • #3
Ok, I attempted this again, and started by trying to find the standard deviation of the distribution. I got 10 for the st dev.

For the 16th percentile, I got 55%.

Is this correct?
 
  • #5
This is what I have:

a) 55

b) 10

c) -1.5

d) 98th percentile

e) No, not many are below 35, because 99.7% fall between 35 and 95.

Can anyone let me know if this is correct, or if I have made any errors?

Thanks.
 
  • #6
anyone?
 
  • #7
I won't guarantee that they're all correct, but they look fine. If you're off, you're not off by much. What would be helpful so that we don't also have to do this work, would be to include your supporting work.
 

What is the "Normal Curve"?

The "Normal Curve" is a bell-shaped curve that represents a frequency distribution of a continuous variable. It is also known as the Gaussian distribution or the bell curve.

What is the significance of the Normal Curve?

The Normal Curve is significant because it is a commonly occurring pattern in nature and can be used to describe many real-world phenomena. It is also important in statistics as it allows for the calculation of probabilities and making statistical inferences.

How is the Normal Curve calculated?

The Normal Curve is calculated using a mathematical formula that takes into account the mean, standard deviation, and the value of the variable being measured. This formula is known as the Gaussian function.

What are the characteristics of a Normal Curve?

The Normal Curve has several characteristics, including being symmetrical around the mean, having a single peak, and being asymptotic to the x-axis. It also follows the 68-95-99.7 rule, where approximately 68% of the data falls within one standard deviation of the mean, 95% falls within two standard deviations, and 99.7% falls within three standard deviations.

What is the purpose of using the Normal Curve in research?

The Normal Curve is often used in research because it allows for the comparison of data and the calculation of probabilities. It also allows for the identification of outliers and can help determine if a sample is representative of a larger population.

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