Issuu on Google+

Examples On Continuous Variables Expected Value Examples On Continuous Variables Expected Value We have already looked at Variance and Standard deviation as measures of dispersion under the section on Averages. We can also measure the dispersion of Random variables across a given distribution using Variance and Standard deviation. This allows us to better understand whatever the distribution represents. The Variance of a random variable X is also denoted by σ;2 but when sometimes can be written as Var(X). Variance of a random variable can be defined as the expected value of the square of the difference between the random variable and the mean. Given that the random variable X has a mean of μ, then the variance is expressed as: In the previous section on Expected value of a random variable, we saw that the method/formula for calculating the expected value varied depending on whether the random variable was discrete or continuous.

Know More About Applications Of Integration

Tutorcircle.com

Page No. : ­ 1/4


As a consequence, we have two different methods for calculating the variance of a random variable depending on whether the random variable is discrete or continuous. For a Discrete random variable, the variance σ2 is calculated as: For a Continuous random variable, the variance σ2 is calculated as: In both cases f(x) is the probability density function. The Standard Deviation σ in both cases can be found by taking the square root of the variance. Example 1 A software engineering company tested a new product of theirs and found that the number of errors per 100 CDs of the new software had the following probability distribution: x f(x) 2 0.01 3 0.25 4 0.4 5 0.3 6 0.04 Find the Variance of X Solution The probability distribution given is discrete and so we can find the variance from the following: We need to find the mean μ first:

Read  More About Antiderivative Trig

Tutorcircle.com

Page No. : ­ 2/4


The possible outcomes for one coin toss can be described by the state space \Omega = {heads, tails}. We can introduce a real-valued random variable Y as follows: Y(\omega) = \begin{cases} 1, & \text{if} \ \ \omega = \text{heads} ,\\ 0, & \text{if} \ \ \omega = \text{tails} . \end{cases} If the coin is equally likely to land on either side then it has a probability mass function given by: \rho_Y(y) = \begin{cases}\frac{1}{2},& \text{if }y=1,\\ \frac{1}{2},& \text{if }y=0.\end{cases}

Tutorcircle.com

Page No. : ­ 2/3 Page No. : ­ 3/4


Thank You

TutorCircle.com


Examples On Continuous Variables Expected Value