Binomial Random Variable With Non-Integer value

In summary, the problem gives a binomial random variable with parameters n=5 and p=0.25 and asks for the probability P(X=1.5). The binomial probability mass function is defined only for integers and should be approximated using the normal distribution or the poisson distribution. The first question asks for the probability P(X<=2.5) and the second question asks for the probability P(X=-2). It seems like the questions are trying to emphasize the fact that these distributions are discrete and defined only for certain values.
  • #1
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So the problem gives a binomial random variable X with parameters n=5 and p=0.25 and ask for the probability P(X=1.5). The binomial probability mass function is defined only for integers. Should i approximate using the normal distribution or the poisson?
 
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  • #2
I would say that the probability would be 0. I don't think an approximation is really warrented here since n is not very large and p is not very small. Besides, to get a probability with a continuous PDF you would have to pick a range of X, not just X=1.5.

-Dale
 
  • #3
Yeah, that's what i was about to say, since n isn't large there isn't any guarantee that the binomial distribution will behave normally. I hate trick questions. I'm guessing that, if the questin asked P(X<=1.5) for the same parameters, then it would be equivalent to asking P(X<=1) right?
 
  • #4
Yeah, it makes sense to ask about the CDF for non-discrete values, it just looks like a stair step type function as you described. It makes sense, but it still isn't very nice :)

-Dale
 
  • #5
Ok so, the next question gives a Poisson random variable X with lambda = 1/3 and asks for both P(X<=2.5) and P(X=-2). I'm guessing the same trick applies to the first one and is equivalent to asking P(X<2) since a poisson distribution function is discrete and defined only at integer values.
The second one though is what, 0 again? I can't see that a poisson random variable can have negative values because its probability mass function contains a factorial which isn't defined for a negative value. What's up with these questions?
 
  • #6
Seems like they really want to emphasize the "discreteness" and the domain of some of these functions. But it feels like they are trying to emphasize the fact through trick questions. In any case, you are correct, the Poisson is not only a discrete distribution it is also defined only for non-negative numbers.

-Dale
 

1. What is a binomial random variable with non-integer value?

A binomial random variable with non-integer value is a type of discrete random variable that follows a binomial distribution, but can take on non-integer values. This means that the outcomes of the variable are limited to two possible outcomes, and the probability of each outcome remains constant for each trial. However, unlike a traditional binomial random variable which can only have integer values, a binomial random variable with non-integer value can have decimal values.

2. How is a binomial random variable with non-integer value different from a traditional binomial random variable?

A binomial random variable with non-integer value is different from a traditional binomial random variable in that it allows for non-integer values. In a traditional binomial random variable, the number of trials and the probability of success remain constant, while the values of the variable can only be integers. However, a binomial random variable with non-integer value allows for the number of trials and probability of success to remain constant, while also allowing for non-integer values as outcomes.

3. What are some real-life examples of a binomial random variable with non-integer value?

Some real-life examples of a binomial random variable with non-integer value include the number of goals scored in a soccer match, the number of heads obtained when flipping a coin multiple times, and the number of defective products in a batch of items produced. In all these cases, the variable can take on non-integer values, such as 1.5 goals, 2.25 heads, or 3.75 defective products.

4. How is the probability of a binomial random variable with non-integer value calculated?

The probability of a binomial random variable with non-integer value is calculated using the binomial distribution formula, which takes into account the number of trials, probability of success, and the desired outcome. However, in this case, the desired outcome can be a non-integer value, so the formula may need to be modified to account for this. Alternatively, the probability can also be calculated by creating a probability distribution table and determining the probability of each possible non-integer outcome.

5. What are the applications of a binomial random variable with non-integer value in research and data analysis?

A binomial random variable with non-integer value is commonly used in research and data analysis to model outcomes that can take on non-integer values. This can be useful in various fields, such as economics, biology, and psychology, where outcomes may not always be whole numbers. By using a binomial random variable with non-integer value, researchers can accurately model and analyze data, make predictions, and draw conclusions based on the probability of non-integer outcomes.

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