In a particular illness , a false-positive result is obtained about 1 in 125 times the test is administered. if the test is administered to 15,000 people, estimate the probabilty of there being more than 135 false-positive results.
(hint: use the normal approximation to the binomial distribution)

0.0778

Step-by-step explanation:

Probability of false positive result, p = = 0.008

Sample size, n = 15,000

mean, μ = np = 15000 × 0.008 = 120

Now,

Standard deviation, σ =

or

=  

= 10.91

Now,

Probability of there being more than 135 false-positive results

= P(X > 135) ≈

or

= P(z > 1.42)

or

= 1 - P(z ≤ 1.42)

= 1 - 0.9222                 [P(z ≤ 1.42) = 0.9222 from standard z table]

= 0.0778

Hence,

P(X > 135) = 0.0778

function 2 has a greater rate of change than function 1

function 2 has a greater y-intercept than function 1

step-by-step explanation:

i got 10.28 but that rounds up to 10.3