Solving Normal Random Variable Equations for P(X(X-1) > 2) and P(|X| > a)

In summary, for a normal random variable X with mean 1 and variance 4, we need to find the probability of X(X-1) being greater than 2 and a value 'a' for which the probability of |X| being greater than a is equal to 0.25. For the first problem, we can approach it by finding the complement of P(-1 < X < 2) and for the second problem, we can use the formula P(X > |a|) = P(X ≤ -a) + P(X ≥ a) to solve it. It's important to draw a picture first before attempting any calculations.
  • #1
twoski
181
2

Homework Statement



X is a normal random variable with mean 1, variance 4.

1. Find P( X(X-1) > 2 )

2. Find a value 'a' for which P(|X| > a ) = .25


The Attempt at a Solution



I had no idea how to start 1.

For 2, i got this far then got stuck:

P(|X| > a) = 1 - P((X-1)/2 <= (a-1)/2) = 1 - Ф((a-1)/2)
 
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  • #2
twoski said:

Homework Statement



X is a normal random variable with mean 1, variance 4.

1. Find P( X(X-1) > 2 )

2. Find a value 'a' for which P(|X| > a ) = .25


The Attempt at a Solution



I had no idea how to start 1.

##X^2-X>2## is the same as ##X^2-X-2>0## or ##(X-2)(X+1)>0##. What values of ##X## satisfy that?

For 2, i got this far then got stuck:

P(|X| > a) = 1 - P((X-1)/2 <= (a-1)/2) = 1 - Ф((a-1)/2)

##P(X>|a|)=P(X\le -a)+P(X\ge a)##

Is that enough to get you going?
 
  • #3
For the 1st bit it's the complement of P(-1<X<2) I think.
 
  • #4
twoski said:

Homework Statement



X is a normal random variable with mean 1, variance 4.

1. Find P( X(X-1) > 2 )

2. Find a value 'a' for which P(|X| > a ) = .25


The Attempt at a Solution



I had no idea how to start 1.

For 2, i got this far then got stuck:

P(|X| > a) = 1 - P((X-1)/2 <= (a-1)/2) = 1 - Ф((a-1)/2)

This is incorrect; start over, and be more careful. Draw a picture first, before trying to compute anything!
 

What is a normal random variable?

A normal random variable is a type of continuous random variable that follows a normal distribution. This means that the values of the variable are distributed symmetrically around the mean and the probability of a particular value occurring is determined by the standard deviation.

How is a normal random variable different from other types of random variables?

Normal random variables are different from other types of random variables because they have a symmetric distribution and the probability of a particular value occurring is determined by the standard deviation. Other types of random variables, such as binomial or exponential, have different distributions and probabilities.

What is the central limit theorem and how does it relate to normal random variables?

The central limit theorem states that the sum of a large number of independent and identically distributed random variables will tend towards a normal distribution. This means that even if the individual variables do not follow a normal distribution, their sum will approach a normal distribution. Normal random variables are often used to model this behavior.

How is the mean and standard deviation of a normal random variable calculated?

The mean and standard deviation of a normal random variable can be calculated using the formula: mean = μ and standard deviation = σ. These values can also be calculated using a table or a statistical software program.

What are some common applications of normal random variables in science?

Normal random variables are commonly used in science to model natural phenomena, such as human height, IQ scores, or blood pressure. They are also used in statistical analysis and hypothesis testing, as many statistical tests assume a normal distribution. Additionally, normal random variables are used in risk analysis and modeling in fields like finance and engineering.

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