Normally distributed probability problem

In summary, for a normally distributed exam with a mean of 75 and a variance of 64, students with a mark of 90 or above receive an A+. Using the z-score formula and the normal distribution chart, we can determine that approximately 3.01% of students will receive an A+. When 12 students write the exam, the probability of 3 students receiving an A+ is 0.0301, calculated using the binomial theorem.
  • #1
subopolois
86
0

Homework Statement


an exam is normally distributed with a mean of 75 and a variance of 64. students with a mark of 90 or above receive an A+
a) what percentage of students receive an A+?
b) if 12 students write the exam what is the probability 3 get A+?


Homework Equations


z-score- z=mark-mean/standard deviation
my teacher gave us a chart that gives probabilities as area under a curve for normal distributions

The Attempt at a Solution



a) since the standard deviation is the square root of the variance:
SD= 8
z-score= 90-75/8= 1.88
now using the area under the curve chart, for a z-score of 1.88 it reads 0.9699. from this i subtract 1 because i want the amount above 90 and this value is below 90, so i get:
= 1-0.9699
= 0.0301 or 3.01%

b) if 12 students write and 3 get an A+ then 9 students must get something else:
p(x=3)= 3(0.0301)/9(0.9699)= 0.0103
since the probability of getting over A+ is 0.0301 and anything else is one subtract 0.0301.



do my answers seen correct? if not can someone guide me in the right direction?
 
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  • #2
The answer to part a) is correct, however, I don't really understand what calculation you've done for part b).

Personally I would just use the Choose function i.e. 12C3 * 0.0301^3 * 0.9699^9
 
  • #3
vikkisut88 said:
The answer to part a) is correct, however, I don't really understand what calculation you've done for part b).

Personally I would just use the Choose function i.e. 12C3 * 0.0301^3 * 0.9699^9

Ok, would I also put in 12C9 when multlying0.9699?. Also why areyou putting 0.0301 with an exponent also for the other0.9699?
 
  • #4
nope you just have one 12C3 - it's how you use the binomial theorem:

P(X=r) = nCr * p^r * q^(n-r) where q = 1-p :)
 
  • #5
oh and n = total so in this case 12
 
  • #6
vikkisut88 said:
oh and n = total so in this case 12

Alright! Thank you
 

Related to Normally distributed probability problem

1. What is a normally distributed probability problem?

A normally distributed probability problem is a type of statistical problem that involves using the normal distribution to calculate probabilities. The normal distribution is a bell-shaped curve that is commonly used to represent the distribution of data in many real-world situations.

2. How is the normal distribution calculated?

The normal distribution is calculated using a mathematical formula that takes into account the mean and standard deviation of the data. The formula is called the probability density function (PDF) and it gives the probability of a value falling within a certain range of the mean.

3. What is the 68-95-99.7 rule in a normally distributed probability problem?

The 68-95-99.7 rule, also known as the empirical rule, states that in a normally distributed data set, 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. This rule is often used to estimate probabilities in normal distribution problems.

4. How can I determine if a data set is normally distributed?

There are a few methods for determining if a data set is normally distributed. One way is to create a histogram of the data and see if it resembles a bell-shaped curve. Another way is to use statistical tests, such as the Shapiro-Wilk or Kolmogorov-Smirnov tests, to assess the normality of the data.

5. How are normally distributed probability problems used in real-world applications?

Normally distributed probability problems are used in a variety of fields, including finance, economics, and psychology. They are used to model and analyze data in order to make predictions and inform decision making. Some examples of real-world applications include predicting stock prices, analyzing consumer behavior, and studying the effects of medication on patients.

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