Browse Questions

# Find the expected value of the number on a die when thrown.

Toolbox:
• If S is a sample space with a probability measure and X is a real valued function defined over the elements of S, then X is called a random variable.
• Types of Random variables :
• (1) Discrete Random variable (2) Continuous Random variable
• Discrete Random Variable :If a random variable takes only a finite or a countable number of values, it is called a discrete random variable.
• Continuous Random Variable :A Random Variable X is said to be continuous if it can take all possible values between certain given limits. i.e., X is said to be continuous if its values cannot be put in 1 − 1 correspondence with N, the set of Natural numbers.
• The probability mass function (a discrete probability function) $P(x)$ is a function that satisfies the following properties :
• (1) $P(X=x)=P(x)=P_x$
• (2) $P(x)\geq 0$ for all real $x$
• (3) $\sum P_i=1$
• Moments of a discrete random variable :
• (i) About the origin : $\mu_r'=E(X^r)=\sum P_ix_i^{\Large r}$
• First moment : $\mu_1'=E(X)=\sum P_ix_i$
• Second moment : $\mu_2'=E(X^2)=\sum P_ix_i^2$
• (ii) About the mean : $\mu_n=E(X-\bar{X})^n=\sum (x_i-\bar{x})^nP_i$
• First moment : $\mu_1=0$
• Second moment : $\mu_2=E(X-\bar{X})^2=E(X^2)-[E(X)]^2=\mu_2'-(\mu_1')^2$
• $\mu_2=Var(X)$
Step 1:
Let $X$ be the random variable denoting the number that turns up when a die is thrown.
$X$ takes the values 1,2,3,4,5,6
Step 2:
Now each of these values is equally likely $P(x_i)=\large\frac{1}{6}$$(i=1,2,......6) Step 3: The probability of X is given by Step 4: E(X)=\sum P_i x_i=1\times \large\frac{1}{6}$$+2\times \large\frac{1}{6}$$+3\times \large\frac{1}{6}$$+4\times \large\frac{1}{6}$$+5\times \large\frac{1}{6}$$+6\times \large\frac{1}{6}$
$\Rightarrow \large\frac{1}{6}$$\times 21=\large\frac{7}{2}$$=3.5$