Continuous joint random variable

In summary, the conversation discusses the integration of a given function and the derivation of values for constants. It also touches on the marginal probability density functions and the joint cumulative density function of two variables. The conversation ends with a calculation involving conditional probability.
  • #1
docnet
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(a)

$$\int_0^1\int_0^1x+cy^2 dxdy=\int_0^1 [\frac{x^2}{2}+cxy^2]_0^1dy= \int_0^1\frac{1}{2}+cy^2 dy=[\frac{y}{2}+\frac{cy^3}{3}]_0^1=\frac{1}{2}+\frac{c}{3}=1$$
$$\Rightarrow c=\frac{3}{2}$$

(b) The marginal pdf of X is

$$f_X(a)=\int_0^1 f_{X,Y}(a,b)db=\int_0^1 x+\frac{3}{2}y^2 dy=[xy+\frac{3y^3}{6}]_0^1=x+\frac{1}{2}$$The marginal pdf of Y is

$$f_Y(b)=\int_0^1 f_{X,Y}(a,b)da=\int_0^1 x+\frac{3}{2}y^2 dx=[\frac{x^2}{2}+\frac{3xy^2}{2}]_0^1=\frac{1}{2}+\frac{3y^2}{2}$$(c) The joint CDF of (X,Y) is

$$\int_0^a\int_0^b(x+cy^2)dxdy=\int_0^y[\frac{x^2}{2}+cxy^2=]_0^ady=\int_0^y\frac{a^2}{2}+cay^2dy=$$
$$\frac{a^2y}{2}+\frac{3ab^3}{6}\Rightarrow F_{X,Y}(a,b)= \frac{a^2b}{2}+\frac{3ab^3}{6}$$

(d)
$$P(X\geq 0.5|Y\leq 0.5)=\frac{3b}{16}+\frac{3b^3}{12\cdot 8}$$
 
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  • #2
Hi @docnet -- I don't think this thread belongs in precalculus math. It clearly involves integration, and anything related to Probability Theory would probably go in Calculus & Beyond, I'd imagine.

You seem to have arrived at a full answer for every sub-part of your question. I think part of the homework template calls for describing what's tripping you up. How can we be of help here?
 
  • #3
LastScattered1090 said:
Hi @docnet -- I don't think this thread belongs in precalculus math. It clearly involves integration, and anything related to Probability Theory would probably go in Calculus & Beyond, I'd imagine.

You seem to have arrived at a full answer for every sub-part of your question. I think part of the homework template calls for describing what's tripping you up. How can we be of help here?
I'm not sure.. I was hoping that I made a mistake somewhere so someone could correct me.
 

FAQ: Continuous joint random variable

What is a continuous joint random variable?

A continuous joint random variable is a mathematical concept used in probability and statistics to describe the relationship between two or more continuous random variables. It represents the probability distribution of two or more variables occurring simultaneously.

How is a continuous joint random variable different from a discrete joint random variable?

A continuous joint random variable is different from a discrete joint random variable in that it deals with continuous variables, while a discrete joint random variable deals with discrete variables. This means that the values of a continuous joint random variable can take on any real number within a given range, while the values of a discrete joint random variable are limited to a specific set of values.

What is the joint probability density function of a continuous joint random variable?

The joint probability density function of a continuous joint random variable is a function that describes the probability of two or more continuous random variables occurring simultaneously. It is represented by a multi-dimensional function that maps the values of the variables to a probability value.

How is the joint probability density function used in finding probabilities?

The joint probability density function is used in finding probabilities by integrating over a specific region of the function. This region represents the set of values for which the probability is desired. The integral of the joint probability density function over this region gives the probability of the variables falling within that region.

Can a continuous joint random variable have a negative probability?

No, a continuous joint random variable cannot have a negative probability. The probability of a continuous random variable is always greater than or equal to zero, and the total probability of all possible values must equal one. Therefore, any negative values in the joint probability density function would violate this rule and are not possible.

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