The distribution is a discrete probability distribution because each probability is between 0 and 1 and the sum of all probabilities equals 1. Thus, the correct answer is B. Both key conditions for a discrete probability distribution are satisfied in this case.
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Each probability must be between 0 and 1.
The sum of all probabilities must equal 1.
The sum of the given probabilities is 0.36 + 0.24 + 0.18 + 0.13 + 0.09 = 1.0 .
Therefore, the distribution is a discrete probability distribution because both conditions are met. The answer is B.
Explanation
Analyze the problem and data We are given a table that shows the probability P ( x ) for different values of x . To determine if this is a discrete probability distribution, we need to check two conditions:
Each probability P ( x ) must be between 0 and 1, inclusive (i.e., 0 ≤ P ( x ) ≤ 1 ).
The sum of all probabilities must equal 1.
Verify that each probability is between 0 and 1 First, let's check if each probability is between 0 and 1:
P ( 0 ) = 0.36 , which is between 0 and 1.
P ( 1 ) = 0.24 , which is between 0 and 1.
P ( 2 ) = 0.18 , which is between 0 and 1.
P ( 3 ) = 0.13 , which is between 0 and 1.
P ( 4 ) = 0.09 , which is between 0 and 1.
So, the first condition is satisfied.
Calculate the sum of the probabilities Next, let's calculate the sum of the probabilities:
S = 0.36 + 0.24 + 0.18 + 0.13 + 0.09
From the calculation tool, we know that the sum is:
S = 1.0
So, the second condition is also satisfied.
Conclusion Since both conditions are satisfied (each probability is between 0 and 1, and the sum of the probabilities is equal to 1), the given distribution is a discrete probability distribution. Therefore, the correct answer is B.
Examples
Discrete probability distributions are used in various real-life scenarios. For example, in quality control, we can use a discrete probability distribution to model the number of defective items in a batch. In finance, it can be used to model the number of successful trades in a given period. In sports, it can be used to model the number of goals scored by a team in a match. Understanding these distributions helps in making informed decisions based on probabilities.