twoski said:So the idea is, if i do a double integral on this function i should end up with c * some value, and i need to find a value for c such that c * some value = 1?
twoski said:So i did it and i ended up with 3/4 * c... So the idea now is that i want C to equal 1?
And how would i start computing the density function of Y?
twoski said:Okay I've managed to figure out everything except for the last question. I have formulas for P( X ≤ x ) but it's asking me to find P( X < Y ).
A Joint Probability Density Function (JPDF) is a mathematical function that describes the probability of two or more random variables occurring simultaneously. It is used to model the relationship between multiple variables in a probability distribution.
The Joint Probability Density Function differs from the Probability Density Function in that it describes the probability of multiple variables occurring together, while the Probability Density Function describes the probability of a single variable occurring.
The shape of a Joint Probability Density Function gives us information about the relationship between the variables. For example, if the function is symmetric, it indicates a strong correlation between the variables. If the function is skewed, it indicates a weaker correlation.
The Joint Probability Density Function is used in statistics to analyze and model the relationship between multiple variables. It is often used in regression analysis, hypothesis testing, and other statistical techniques to understand the probability of certain events occurring.
A Joint Probability Density Function gives the probability of multiple variables occurring together, while a Joint Cumulative Density Function gives the probability of the variables falling below a certain value. In other words, the JPDF is a function of multiple variables, while the JCDF is a function of a single variable.