Abinary logistic regression was fitted using a sample of employees of a large corporation to investigate if the employee’s monthly salary (salary) predicts whether (1) or not (0) the employee has a 401(k) retirement plan (401k). p(401k) = exp(−6.13 + .000881 salary) 1 + exp(−6.13 + .000881 salary)

use the fitted equation to predict the probability of a 401(k) plan for an employee whose monthly salary is

(a) $3,000. (round your answer to 6 decimal places.) p(401k) 0.059491 0.059491 incorrect
(b) $5,000. (round your answer to 6 decimal places.) p(401k) 0.262193 0.262193 incorrect
(c) $8,000. (round your answer to 6 decimal places.) p(401k) 0.770157 0.770157 incorrect

85.560%

step-by-step explanation:

conditional probability is defined as:

p(a|b) = p(a∩b) / p(b)

in other words, the probability that a occurs, given that b has occurred = the probability that both a and b occur / the probability that b occurs.

in this example:

p(turns 64 | turns 44) = p(turns 64 & turns 44) / p(turns 44).

if someone turns 64, they must have already turned 44, so:

p(turns 64 | turns 44) = p(turns 64) / p(turns 44)

on page 42, we see that of the original 100,000, 94944 turn 44, and 81234 turn 64.   therefore:

p(turns 64 | turns 44) = 81234 / 94944

p(turns 64 | turns 44) = 0.85560

i wish i could you, i have no idea how to answer this

step-by-step explanation:

no because mercury has a density of 13.6 grams per centimeter squared while gold has 19.32 grams centimeter squared

step-by-step explanation: