How Do You Calculate Probabilities for Normally Distributed Scores?

In summary, the percentage of the population with scores between 100 and 125 is between a) Between 100 and 125%b) Between 82 and 106%c) Between 110 and 132%d) Above 132%
  • #1
apoechma
14
0
Continous Random Variable HELP PLEASE!

Scores on a particular test are normally distributed in the population, with a mean of 100 and a standard deviation of 15. What percentage of the population have scores ...

a) Between 100 and 125

b) Between 82 and 106

c) Between 110 and 132

d) Above 132

e) Equal to 132
 
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  • #2


This seems like a homework question so maybe someone can move it there.

Apochema, have you tried anything? If so, show us what and we can better help you. If not, you certainly don't expect us to do your homework for you.
 
  • #3


Here is a hint. Look at the definition of a mean = E(X) and standard deviation sqrt(Var(X))

which both happen to be integrals. Once you get your p(x) from those 2 equations, you simply take the integral for every problem a) integral(p(x)dx, 100, 125), and so on

mean = E(X) = http://upload.wikimedia.org/math/5/2/b/52bc687e1475806a8abb8b8252f220cf.png = 100
standard deviation = http://upload.wikimedia.org/math/f/4/c/f4c7ea85a64ca1819288007e6994e349.png = 15
 
  • #4


cronxeh said:
Here is a hint. Look at the definition of a mean = E(X) and standard deviation sqrt(Var(X))

which both happen to be integrals. Once you get your p(x) from those 2 equations, you simply take the integral for every problem a) integral(p(x)dx, 100, 125), and so on

mean = E(X) = http://upload.wikimedia.org/math/5/2/b/52bc687e1475806a8abb8b8252f220cf.png = 100
standard deviation = http://upload.wikimedia.org/math/f/4/c/f4c7ea85a64ca1819288007e6994e349.png = 15

This is certainly not the way to approach the problem. The biggest hint is basically given to you in the problem that the scores are distributed normally.
 
  • #5


Right. And you should know what the probability density function for normally distributed random variable (with given mean and variance) looks like.

Then you will have to integrate this density over the appropriate intervals or better look up the corresponding values in a table of the cumulative distribution function.

Maybe you first have to apply a linear transformation to make the distribution standard normal if you only have access to cdf values for this special case.
 

Related to How Do You Calculate Probabilities for Normally Distributed Scores?

1. What is a continuous random variable?

A continuous random variable is a type of random variable in which the possible values are uncountable and can take on any numerical value within a certain range. It is often represented by a curve or line on a graph and is used to model real-world phenomena such as height, weight, or time.

2. How is a continuous random variable different from a discrete random variable?

A continuous random variable can take on any value within a given range, while a discrete random variable can only take on specific, distinct values. For example, a continuous random variable may represent the height of a person, while a discrete random variable may represent the number of people in a room.

3. What is the probability density function (PDF) of a continuous random variable?

The probability density function (PDF) of a continuous random variable is a function that describes the probability of a given value occurring within a certain range. It is represented by a curve on a graph, and the area under the curve represents the probability of the variable taking on a certain value or range of values.

4. How is the cumulative distribution function (CDF) related to a continuous random variable?

The cumulative distribution function (CDF) of a continuous random variable is a function that describes the probability of the variable being less than or equal to a specific value. It is the integral of the probability density function (PDF) and can be used to calculate the probability of a variable falling within a certain range.

5. How can a continuous random variable be used in real-world applications?

Continuous random variables can be used to model and analyze various real-world phenomena, such as stock prices, weather patterns, and population growth. They can also be used in statistical analysis to make predictions and inform decision-making processes in various fields such as finance, economics, and engineering.

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