Sum of binomial random variables

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The discussion focuses on finding the joint probability density function (pdf) of the random variables u and v, derived from independent and identically distributed (iid) binomial random variables y_1 and y_2. The user notes that the sum of two binomial distributions results in another binomial distribution, specifically indicating that v follows a binomial distribution with parameters (15, 1/4) and u with parameters (5, 1/4). They inquire about more efficient methods for determining the joint pdf beyond using a table. Additionally, they seek clarification on the relationship between the values of Y1 and Y2 when U and V are defined. The conversation highlights the complexities involved in calculating joint distributions of transformed random variables.
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Homework Statement



let y_1 and y_2 be iid bin(5,1/4) random variables

let v=y_1+2*y_2 and u = 3*y_1 -2y_2

find f_uv (u,v) and the cov(u,v)

Homework Equations



f_y (y) = (5 choose y) (1/4)^y (3/4)^5-x for x=0,1,2,3,4,5

covariance=E(uv)-E(u)E(v)

The Attempt at a Solution



By convolution the Sum of bin(n,p) and bin(m,p) = bin (n+m,p)

so pdf of v = bin(15,1/4) and pdf of u = bin (5,1/4)

The only way I know of to get the joint pdf is by a table. Is there a faster way?
 
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For clarity, it's better to use the standard typecase: uppercase for names of r.v.s and lower case for values they take.
When U = u and V = v, what can you say about the values of Y1 and Y2?
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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