Answer:
a) The probability that the mean printing speed of the sample is greater than 17.55 ppm = 0.4247
b) The probability that more than 48.6% of the sampled printers operate at the advertised speed = 0.4197
Step-by-step explanation:
The central limit theorem explains that for an independent random sample, the mean of the sampling distribution is approximately equal to the population mean and the standard deviation of the distribution of sample is given as
σₓ = (σ/√n)
where σ = population standard deviation
n = sample size
So,
Mean of the distribution of samples = population mean
μₓ = μ = 17.35 ppm
σₓ = (σ/√n) = (3.25/√10) = 1.028 ppm
a) The probability that the mean printing speed of the sample is greater than 17.55 ppm.
P(x > 17 55)
We first normalize 17.55 ppm
The standardized score for any value is the value minus the mean then divided by the standard deviation.
z = (x - μ)/σ = (17.55 - 17.35)/1.028 = 0.19
To determine the required probability
P(x > 17.55) = P(z > 0.19)
We'll use data from the normal probability table for these probabilities
P(x > 17.55) = P(z > 0.19) = 1 - P(z ≤ 0.19)
= 1 - 0.57535 = 0.42465 = 0.4247
b) The probability that more than 48.6% of the sampled printers operate at the advertised speed
We first find the probability that one randomly selected printer operates at the advertised speed.
Mean = 17.35 ppm
Standard deviation = 3.25 ppm
Advertised speed = 18 ppm
Required probability = P(x ≥ 18)
We standardize 18 ppm
z = (x - μ)/σ = (18 - 17.35)/3.25 = 0.20
To determine the required probability
P(x ≥ 18) = P(z ≥ 0.20)
We'll use data from the normal probability table for these probabilities
P(x ≥ 18) = P(z ≥ 0.20) = 1 - P(z < 0.20)
= 1 - 0.57926 = 0.42074
48.6% of the sample = 48.6% × 10 = 4.86
Greater than 4.86 printers out of 10 includes 5 upwards.
Probability that one printer operates at advertised speed = 0.42074
Probability that one printer does not operate at advertised speed = 1 - 0.42074 = 0.57926
probability that more than 48.6% of the sampled printers operate at the advertised speed will be obtained using binomial distribution formula since a binomial experiment is one in which the probability of success doesn't change with every run or number of trials. It usually consists of a number of runs/trials with only two possible outcomes, a success or a failure. The outcome of each trial/run of a binomial experiment is independent of one another.
Binomial distribution function is represented by
P(X = x) = ⁿCₓ pˣ qⁿ⁻ˣ
n = total number of sample spaces = 10
x = Number of successes required = greater than 4.86, that is, 5, 6, 7, 8, 9 and 10
p = probability of success = 0.42074
q = probability of failure = 0.57926
P(X > 4.86) = P(X ≥ 5) = P(X=5) + P(X=6) + P(X=7) + P(X=8) + P(X=9) + P(X=10) = 0.4196798909 = 0.4197
Hope this Helps!!!
