0
In some situationsX is continuous but Y is discrete. For example, in a logistic regression, one may wish to predict the probability of a binary outcome Y conditional on the value of a continuously-distributed X. In this case, (X, Y) has neither a probability density function nor a probability mass function in the sense of the terms given above. On the other hand, a "mixed joint density" can be defined in either of two ways:
Formally, fX,Y(x, y) is the probability density function of (X, Y) with respect to the product measure on the respective supports of X and Y. Either of these two decompositions can then be used to recover the joint cumulative distribution function:
The definition generalizes to a mixture of arbitrary numbers of discrete and continuous random variables.
Still curious? Ask our experts.
Chat with our AI personalities
Steve
Blake
ReneAdd your answer:
Earn +20 pts
What is the likelihood of the probability 0 to infinity?
The answer depends on the probability distribution function for the random variable.
What does the mean have to do with a normal distribution?
Almost all statistical distribution have a mean. It is the expected value of the random variable which is distributed according to that function.
Can the Poisson distribution be a continuous random variable or a discrete random variable?
True
Define a normal random variable?
I have included two links. A normal random variable is a random variable whose associated probability distribution is the normal probability distribution. By definition, a random variable has to have an associated distribution. The normal distribution (probability density function) is defined by a mathematical formula with a mean and standard deviation as parameters. The normal distribution is ofter called a bell-shaped curve, because of its symmetrical shape. It is not the only symmetrical distribution. The two links should provide more information beyond this simple definition.
How often the value of a random variable is at or below a certain value.?
The probability of a random variable being at or below a certain value is defined as the cumulative distribution function (CDF) of the variable. The CDF gives the probability that the variable takes on a value less than or equal to a given value.
