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.
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.
Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
1st Edition
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:HOUGHTON MIFFLIN HARCOURT
Chapter4: Writing Linear Equations
Section: Chapter Questions
Problem 14CR
Related questions
Question
![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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F18b0a4f9-8c29-4cd5-9848-9d5c204aef83%2F62999f95-9009-4c65-a5dd-e54e777f8195%2Fe8zgo4t_processed.jpeg&w=3840&q=75)
Transcribed Image Text: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.
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