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Solutions to various problems related to expected values and probability distributions in a university-level mathematics course. Topics covered include calculating expected values using different functions, series convergence, and binomial distributions. Students can use this document as a reference for understanding these concepts and for checking their own work.
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MATH 511, Meade HW Solutions 3.2 –3 (omit c), 5, 14, 18, **3c, **8 2/13/
a. f(x) = x/10, x = 1,2,3, E(x) = (1) (1/10) + (2) (2/10) + (3) (3/10) + (4) (4/10) = (1 + 4 + 9 + 16 )/10 = 30/10 = 3 b. f(x) = x/55 , x = 1, 2, 3, 4 ,5 , 6, 7, 8, 9, 10 E(x) = (^) x=1∑^10 x^2 /55 = [(10)(11)(21)/(6)]/55 = 385/55 = 7 **c. f(x) = 3(1/4)x^ , x = 1, 2, 3, … E(x) = (^) x=1∑
∞ 3x (1/4) x^ = (^) x=1∑
∞ 3x/4x^ ≤ (^) x=1∑
∞ 3 x/4x^ = 3 This assures that the series does indeed converge. The series, (^) x=1∑ ∞ (^) x /4x^ , can now be approximated. After about ten approximations it is clear that the series converges to 0.44444… which is 4/9. So the E(X) = 3(4/9) = (4/3) d. f(x) = (1/30)(x+1)^2 , x = 0, 1, 2, 3 E(x) = (1/30) [ 0 + 4 + 18 + 48] = 70/30 = 7/ e. f(x) = 2/n(n+1)x , x = 1, 2, 3, …, n E(x) =2/n(n+1) (^) i=1∑n^ x^2 = [2/n(n+1)] [n(n+1)(2n+1)/6)] = (2n+1)/
**8. f(x) = 6/( π^2 x^2 ), x = 1, 2, 3, …
E(x) = (6/ π^2 ) (^) x=1∑ ∞ x/x^2 = (6/ π^2 ) (^) x=1∑ ∞ 1/x ; this is the harmonic series, which diverges. Therefore E(x) does not exist. One way to show this is the integral test for infinite series.
a. [16(25) + 3(100) + 1(300)]/20 = 1,000/20 = 50. Note that this makes sense: 1000 students divided into 20 classes; the average class size must be 1000/20=50. b. f(x) = 0.4, x = 25 = 0.3, x = 100 = 0.3, x = 300. Note that here you are selecting a random student, not a random class. c. E(x) = 25(0.4) + 100(0.3) + 300(0.3) = 130
a. E[(X - μ) / σ] = (1/σ) E(X - μ) = (1/σ) [E(X) – E(μ)] =(1/σ) [μ – μ] = 0 b. E{[(X – μ) / σ] 2 } = (1/σ^2 ) (E[(X – μ) 2 ]) = (1/σ^2 ) (E(X^2 ) – 2μE(X) + μ^2 ) = (1/σ^2 ) (E(X^2 ) – 2μ^2 + μ^2 ) = (1/σ^2 ) (E(X^2 ) – μ^2 ) = (1/σ^2 ) σ^2 = 1
3.3 – 3, 5, 7(abc), 14, **7(de)
a. P(X ≥ 13) = P(Y ≤ 12) = 0. b. P(X ≤ 11) = P(Y ≥ 14) = 1 – P(Y < 14) = 1 – P(Y ≤ 13) = 1 – 0.9940 = 0. c. P(X = 12) = 25 C 12 (0.7) 12 (0.3)^13 = 0. d. μ(x) = np = (25)(0.7) = 17. σ^2 (x) = np(1 – p) = (25)(0.7)(0.3) = 5. σ(x) = √σ^2 = 2.
a. W has a binomial distribution b(2000, π/4). The probabilty comes from that the fact that exactly one fourth of the unit circle sits inside the unit square. The area of the unit circle is Acircle (r = 1) = π(1) 2 = π. b. μ(w) = np = (2000)( π/4) = 1,570. σ^2 (w) = np(1 – p) = (2000)( π/4)(1 - π/4)= 337. σ(w)= √σ^2 = 18. c. E(W/500) = μ(w)/500 = π **d. Click on the Excel link on the solutions column for a spreadsheet concerning parts d and e of this problem. **e. Notice that exactly one eighth of the unit sphere is contained in the unit cube The volume of the unit sphere is (4/3) π. So the probability that a point on The unit cube is also on the unit sphere is (1/8) (4/3) π = π/ 6
a. X = b(8, 0.90) , Let Y = number of mints weighing less than 20.7g = b(8, 0.10) b. i. P(X = 8) = 8 C 8 (0.90) 8 (0.10) 0 = 0. ii. P(X ≤ 6) = P(Y ≥ 2) = 1 – P(Y < 2) = 1 – P(Y ≤ 1) = 1 – 0.8131 = 0. iii. P(X ≥ 6) = P(Y ≤ 2 = 0.