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Let X and Y be two random variables.
Case (1) - Discrete Case
If P(X = x) denotes the probability that the random variable X takes the value x, then the joint probability of X and Y is P(X = x and Y = y).
Case (2) - Continuous Case
If P(a < X < b) is the probability of the random variable X taking a value in the real interval (a, b), then the joint probability of X and Y is P(a < X< b and c < Y < d).
Basically joint probability is the probability of two events happening (or not).
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Can a joint probability have a value greater than 1?
A joint probability can have a value greater than one. It can only have a value larger than 1 over a region that measures less than 1.
What is joint probability function?
The joint probability function for two variables is a probability function whose domain is a subset of two dimensional space. The joint probability function for discrete random variables X and Y is given aspr(x, y) = pr(X = x and Y = y). If X and Y are independent random variables then this will equal pr(X =x)*pr(Y = y).For continuous variables, the joint funtion is defined analogously:f(x, y) = pr(X < x and Y < y).
What is best graphical method for calculating joint and conditional probability?
Tree diagram
What is marginal probability function?
If f(x, y) is the joint probability distribution function of two random variables, X and Y, then the sum (or integral) of f(x, y) over all possible values of y is the marginal probability function of x. The definition can be extended analogously to joint and marginal distribution functions of more than 2 variables.
What does it mean to find the compliment of the probability of an event?
The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.
Example of joint probability?
Joint probability is the probability that two or more specific outcomes will occur in an event. An example of joint probability would be rolling a 2 and a 5 using two different dice.
Can a joint probability have a value greater than 1?
A joint probability can have a value greater than one. It can only have a value larger than 1 over a region that measures less than 1.
What type of event is found by multiplying the probability of the first event by the probability of the second event?
That's the probability that both events will happen, possibly even at the same time. I think it's called the 'joint' probability.
What is joint probability function?
The joint probability function for two variables is a probability function whose domain is a subset of two dimensional space. The joint probability function for discrete random variables X and Y is given aspr(x, y) = pr(X = x and Y = y). If X and Y are independent random variables then this will equal pr(X =x)*pr(Y = y).For continuous variables, the joint funtion is defined analogously:f(x, y) = pr(X < x and Y < y).
What is best graphical method for calculating joint and conditional probability?
Tree diagram
What is the joint probability of having a good economy and a neutral stock performance?
0.09
What is function joint?
The joint probability function for two variables is a probability function whose domain is a subset of two dimensional space. The joint probability function for discrete random variables X and Y is given aspr(x, y) = pr(X = x and Y = y). If X and Y are independent random variables then this will equal pr(X =x)*pr(Y = y).For continuous variables, the joint funtion is defined analogously:f(x, y) = pr(X < x and Y < y).
What is marginal probability function?
If f(x, y) is the joint probability distribution function of two random variables, X and Y, then the sum (or integral) of f(x, y) over all possible values of y is the marginal probability function of x. The definition can be extended analogously to joint and marginal distribution functions of more than 2 variables.
What is the Relationship between marginal and conditional and joint probabilities?
The joint probability of two discrete variables, X and Y isP(x, y) = Prob(X = x and Y = y) and it is defined for each ordered pair (x,y) in the event space.The conditional probability of X, given that Y is y is Prob[(X, Y) = (x, y)]/Prob(Y = y) or equivalently,Prob(X = x and Y = y)/Prob(Y = y)The marginal probability of X is simply the probability of X. It can be derived from the joint distribution by summing over all possible values of Y.
How do you calculation joint probability?
You have a function with two arguments (inputs). After that, the calculations depend on whether or not the two random variables are independent. If they are then the joint distribution is simple the product of the individual distribution. But if not, you have some serious mathematics ahead of you!
What is marginal probability?
Suppose you have two random variables, X and Y and their joint probability distribution function is f(x, y) over some appropriate domain. Then the marginal probability distribution of X, is the integral or sum of f(x, y) calculated over all possible values of Y.
What is the conditional expectation function?
It is the integral (or sum) of the joint probability distribution function of the two events, integrated over the domain in which the condition is met.
