Calculating the marginal density function

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  • #1
stgermaine
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Homework Statement


Let f(x,y) = 24xy where x=[0,1], y=[0,1], x+y=[0,1]
Find E[X] and E[Y]


Homework Equations


E[X] = the integral from neg. infinity to positive infinity of x * f_X(x) dx where f_X is the marginal density function of X.

The Attempt at a Solution


f_X is found by integrating f(x,y) in terms of dy over the span of neg. infinity to positive infinity.

For the integral, I used the boundaries 0 and 1. Solution guides online suggest that the marginal density function f_X is equal to 24x.
 
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  • #2
stgermaine said:

Homework Statement


Let f(x,y) = 24xy where x=[0,1], y=[0,1], x+y=[0,1]
Find E[X] and E[Y]


Homework Equations


E[X] = the integral from neg. infinity to positive infinity of x * f_X(x) dx where f_X is the marginal density function of X.

The Attempt at a Solution


f_X is found by integrating f(x,y) in terms of dy over the span of neg. infinity to positive infinity.

For the integral, I used the boundaries 0 and 1. Solution guides online suggest that the marginal density function f_X is equal to 24x.

So, what did YOU get for f_X(x)? Show all your work.

RGV
 

FAQ: Calculating the marginal density function

What is the marginal density function?

The marginal density function is a mathematical concept used in statistics to calculate the probability of a specific value occurring in a subset of a larger set of data. It represents the total distribution of a variable in a multivariate dataset by considering only one variable at a time.

How is the marginal density function calculated?

The marginal density function is calculated by integrating the joint density function over all possible values of the other variables in the dataset. This means summing up the probabilities of all possible values of the variable of interest, while holding all other variables constant.

What is the difference between marginal density function and joint density function?

The marginal density function represents the probability distribution of a single variable in a multivariate dataset, while the joint density function represents the probability distribution of multiple variables together. The marginal density function is calculated by integrating the joint density function over all other variables.

Why is the marginal density function important?

The marginal density function is important in statistics because it allows us to understand the distribution of a variable in a multivariate dataset without being influenced by other variables. This can help in making accurate predictions and inferences about the variable of interest.

How is the marginal density function used in real life?

The marginal density function is used in various fields such as economics, finance, and biology to analyze and model data. For example, in economics, it can be used to predict the demand for a product based on factors such as income, price, and consumer preferences. In biology, it can be used to study the effects of different variables on a particular trait in a population.

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