B In Problems 17-26, evaluate each iterated integral. (See the indicated problem for the evaluation of the inner integral.) 18. ∫ 0 1 ∫ − 1 2 12 x 2 y 3 d x d y (See Problem 8.) 8. (A) ∫ 12 x 2 y 3 d x (B) ∫ − 1 2 12 x 2 y 3 d x
B In Problems 17-26, evaluate each iterated integral. (See the indicated problem for the evaluation of the inner integral.) 18. ∫ 0 1 ∫ − 1 2 12 x 2 y 3 d x d y (See Problem 8.) 8. (A) ∫ 12 x 2 y 3 d x (B) ∫ − 1 2 12 x 2 y 3 d x
Solution Summary: The author calculates the iterated integral with respect to x.
BIn Problems 17-26, evaluate each iterated integral. (See the indicated problem for the evaluation of the inner integral.)
18.
∫
0
1
∫
−
1
2
12
x
2
y
3
d
x
d
y
(See Problem 8.)
8. (A)
∫
12
x
2
y
3
d
x
(B)
∫
−
1
2
12
x
2
y
3
d
x
With differentiation, one of the major concepts of calculus. Integration involves the calculation of an integral, which is useful to find many quantities such as areas, volumes, and displacement.
7. In a 2011 article, M. Radelet and G. Pierce reported a logistic prediction equation
for the death penalty verdicts in North Carolina. Let Y denote whether a subject
convicted of murder received the death penalty (1=yes), for the defendant's race
h (h1, black; h = 2, white), victim's race i (i = 1, black; i = 2, white), and
number of additional factors j (j = 0, 1, 2). For the model
logit[P(Y = 1)] = a + ß₁₂ + By + B²²,
they reported = -5.26, D
â
BD
=
0, BD
=
0.17, BY = 0, BY
=
0.91, B = 0,
B = 2.02, B = 3.98.
(a) Estimate the probability of receiving the death penalty for the group most
likely to receive it. [4 pts]
(b) If, instead, parameters used constraints 3D = BY = 35 = 0, report the esti-
mates. [3 pts]
h
(c) If, instead, parameters used constraints Σ₁ = Σ₁ BY = Σ; B = 0, report
the estimates. [3 pts]
Hint the probabilities, odds and odds ratios do not change with constraints.
f(x)
1/3 f(2(x-2))+4
(2,4)
(-3,6)
Please explain in step by step simple terms how to answer this question
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