2. (a) the P(X ≤ 4), P(X ≥ 5) and P(X=3). and also find moment generating function M_X=3(t = 3) for same given problem.

(b)X: -2, -1, 0, 1, 2, 3       Here : The base of M is 3, M_X=3                                                 

p(x): 0.1, k, 0.2, 2k, 0.3, k

1) Find the value of k, mean and variance. (2). Construct CDF and drar the graph.

3.(a)X: -0, -1, 2, 3 p(x): 0.1, 0.3, 0.5, 0.1 Find probability function of Y=X^2+2X . Also find P(X ≤ 3), P(1 < X ≤ 3) and P(X = 3). (b) f(x)=cx^2(1 − x), 0 < x < 1 then find the constant c, P(−1 < X < 0.5), P(0.2 < X < 0.8).

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2) In the absence of other information I'm assuming these probabilities are associated with rolling a fair die. So dataset=1, 2, 3, 4, 5, 6.

Therefore P(X≤4)=4/6=⅔; P(X≥5)=2/6=⅓; P(X=3)=⅙.

MGF is MX(t)=∑etXP(X)=⅙∑etX, because P(X)=⅙ for all X between 1 and 6 (rectangular or uniform distribution).

MX(t) = ⅙(et+e2t+e3t+e4t+e5t+e6t). From this if we put X=3 and t=3 we get: MX=3(t=3)=⅙e9 (not sure if this what is meant in your question).

2b) p(-2)=0.1, p(-1)=k, p(0)=0.2, p(1)=2k, p(2)=0.3, p(3)=k.

If the given values of X and p(X=x) represent the entire population of discrete probabilities, then ∑p(x)=1.

Therefore, 0.1+k+0.2+2k+0.3+k=1, 0.6+4k=1, 4k=0.4, k=0.1.

2b1) If MX(t)=∑etXp(X) for X=-2, -1, 0, 1, 2, 3.

So MX(t)=∑etXp(X)=0.1e-2t+ke-t+0.2+2ket+0.3e2t+ke3t, and, substituting for k:

MX(t)=0.1e-2t+0.1e-t+0.2+0.2et+0.3e2t+0.1e3t.

To find the mean μ=E(X), we need to differentiate MX(t) wrt t to get the first moment:

-0.2e-2t-0.1e-t+0.2et+0.6e2t+0.3e3t, then set t=0: -0.2-0.1+0.2+0.6+0.3=0.8, so mean, μ=0.8.

Variance, σ2, is calculated from the second moment. Differentiate again wrt t:

0.4e-2t+0.1e-t+0.2et+1.2e2t+0.9e3t then set t=0: 0.4+0.1+0.2+1.2+0.9=2.8.

Variance is 2.8.

2b2) To construct the CDF we need to accumulate probabilities:

X: -2 -1 0 1 2 3
p(X): 0.1 0.1 0.2 0.2 0.3 0.1
CDF: 0.1 0.2 0.4 0.6 0.9 1.0

The CDF graph is represented by the points A to E.

More to follow in due course...

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