Question
Question: If x is a continuous random variable then \[P(x \geqslant a) = \] A.\(P(x < a)\) B.\(1 - P(x > a...
If x is a continuous random variable then P(x⩾a)=
A.P(x<a)
B.1−P(x>a)
C.P(x>a)
D.1−P(x⩽a−1)
Solution
If we are given with continuous random variables such as X, we can only calculate the probability that X lies within a certain interval; like P(X⩽k)or P(a⩽X⩽b)
If the probability of X to be equal to a specific value then we can’t calculate the probability i.e.
⇒P(X=k)=0
This fact remains true. As, the total number of possible values for a continuous random variable X is infinite so the possibility of any one single outcome tends towards 0.
Complete step-by-step answer:
A random variable where the data can have infinite values then it is called continuous random variable. For example, the time taken for something to be done is measured by a random variable and is continuous since there are an infinite number of possible times that can be taken.
Here in this question we have given that x is continuous random variable. Now we have given that x is a continuous random variable and we have to find the value of P(x⩾a).
let us consider that there are n number of points in total since x is continuous, therefore
the total number of possible values is infinite i.e. n→∞and hence the possibility of any single outcome i.e. a tends towards zero.
⇒P(x=a)=n1
As, n→∞, therefore n1→0. Hence,
⇒P(x=a)=0 ………..(1)
Now, we have to find the value of P(x⩾a)as below:
⇒P(x⩾a)=P(x>a)+P(x=a)
Put the value of equation 1 in this we get,
⇒P(x⩾a)=P(x>a)+0
⇒P(x⩾a)=P(x>a)
Therefore, option C is the correct option.
Note: In this question students can make the mistake of taking the possibility of a single outcome as 1 and your answer will get wrong. As probability is the favourable outcome out of the total number of outcomes so here the outcome is 1 out of n points i.e. getting a when 1 is divided by infinity, we get 0.
If we want to calculate probability of continuous random variables i.e. probability the X lies between a certain range then we need two functions:
1.The probability density function (PDF)
2.The cumulative density function (CDF)