<em><u>888 + 88 + 8 + 8 + 8 = 1000</u></em>
<em><u>have</u></em><em><u> </u></em><em><u>a</u></em><em><u> </u></em><em><u>nice</u></em><em><u> </u></em><em><u>day</u></em><em><u>!</u></em>
The question is incomplete! Complete question along with answer and step by step explanation is provided below.
Question:
The lifetime (in hours) of a 60-watt light bulb is a random variable that has a Normal distribution with σ = 30 hours. A random sample of 25 bulbs put on test produced a sample mean lifetime of = 1038 hours.
If in the study of the lifetime of 60-watt light bulbs it was desired to have a margin of error no larger than 6 hours with 99% confidence, how many randomly selected 60-watt light bulbs should be tested to achieve this result?
Given Information:
standard deviation = σ = 30 hours
confidence level = 99%
Margin of error = 6 hours
Required Information:
sample size = n = ?
Answer:
sample size = n ≈ 165
Step-by-step explanation:
We know that margin of error is given by
Margin of error = z*(σ/√n)
Where z is the corresponding confidence level score, σ is the standard deviation and n is the sample size
√n = z*σ/Margin of error
squaring both sides
n = (z*σ/Margin of error)²
For 99% confidence level the z-score is 2.576
n = (2.576*30/6)²
n = 164.73
since number of bulbs cannot be in fraction so rounding off yields
n ≈ 165
Therefore, a sample size of 165 bulbs is needed to ensure a margin of error not greater than 6 hours.
Answer: B, D, and E
Step-by-step explanation: 20% of 45 equals 9 D, D, and E all equal 9
Answer:
The meal was $16.00.
Step-by-step explanation:
since the equation for finding the tip is: decimal of percent times total bill, what you want to do is isolate the bill. you do this by dividing both sides by percent and your equation is total cost = tip/percent
let's say our total equation is x.
x=3.20/0.2
x=16
Now check your work.
Answer: True
Step-by-step explanation:
The sum of the residuals is always 0 so the plot will always be centered around the x-axis. An outlier is a value that is well separated from the rest of the data set. An outlier will have a large absolute residual value.
