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

step-by-step explanation:

example 2 – simplify: step 1: completely factor both the numerators and denominators of all fractions. step 2: change the division sign to a multiplication sign and flip (or reciprocate) the fraction after the division sign; essential you need to multiply by the reciprocal. step 3 : cancel or reduce the fractions.

whole numbers.

step-by-step explanation:

we are given the average monthly rainfall (in millimeters) model r(m) for the years 1900-2009 as a function of the month number m.

as we can see m is taken for the number of months for years 1900-2009.

we know that number of months can't be a negative number or decimal number or a fraction.

so, the number of months could be only positive whole numbers.

therefore, whole numbers is more appropriate for the domain of r.

the clb she uses is a driver, so its a 2/5 chance she hits a 180-189 yard shot

step-by-step explanation: