Let the probability of getting a tail in the biased coin be x.
∴ P (T) = x
⇒ P (H) = 3x
For a biased coin, P (T) + P (H) = 1
x+3x=1
⇒ 4x=\(\frac{1}{4}\)
∴\(P(T)=\frac{1}{4} and P(H)=\frac{3}{4}\)
When the coin is tossed twice, the sample space is {HH, TT, HT, TH}.
Let X be the random variable representing the number of tails.
∴ P (X = 0) = P (no tail) = P (H) × P (H) =\(\frac{3}{4}X\frac{3}{4}=\frac{9}{16}\)
P (X = 1) = P (one tail) = P (HT) + P (TH)
=\(\frac{3}{4}.\frac{1}{4}+\frac{1}{4}.\frac{3}{4}\)
=\(\frac{3}{16}+\frac{3}{16}\)
=\(\frac{3}{8}\)
P (X = 2) = P (two tails) = P (TT) =\(\frac{1}{4}X\frac{1}{4}=\frac{1}{16}\)
Therefore, the required probability distribution is as follows.
X | 0 | 1 | 2 |
P(X) | \(\frac{9}{16}\) | \(\frac{3}{8}\) | \(\frac{1}{16}\) |
What is the Planning Process?
A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment's results. Random variables are often deputed by letters and can be classified as discrete, which are variables that have particular values, or continuous, which are variables that can have any values within a continuous range.
Random variables are often used in econometric or regression analysis to ascertain statistical relationships among one another.
There are two types of random variables, such as: