Answer: x-axis
Step-by-step explanation:
If you visualize the line of reflection, it must be vertical. Option A is the only option that has a vertical line.
The wording of the question is a little strange. The percentage of dog owners is already estimated at 52%, so no simulation seems useful for that. However, if you want to simulate dog ownership within any given household, you want to apply some algorithm to the given numbers so that about 52% of the time you will see the equivalent of "owns at least one dog."
We assume the numbers are uniformly distributed on 00000 .. 99999. You could, for example, take 4 of the 5-digit numbers (20 digits total), divide them into pairs of digits, and declare "owns at least one dog" if the pair of digits is 51 or less.
For example, the first set of 4 numbers so divided will be ...
95 91 15 52 41 74 05 34 10 02
and "owns at least one dog" would then be ...
no no yes no yes no yes yes yes yes . . . 6 of the 10 simulated households
_____
This sort of approach can work well if you're simulating something described by a percentage. If there is some other ratio involved, say 3 out of 248, then you could throw out any number that is 99944 or higher (403*248) and look at the remainder when dividing by 248. If it is 2 or less, your condition is satisfied.
Making use of random number tables is a bit of an art. The idea is to choose the algorithm for processing the numbers so that the desired distribution is obtained. If the desired distribution is non-uniform, then there are ways to apply functions to the numbers or simply put them in bins of different width so that you get the desired simulated result.
Answer:
6,000 total items
Step-by-step explanation:
3*600=1800 (total juice)
2*600=1200 (total fruit)
2*600=1200 (total burritos)
2*600=1200 (total doritos)
1*600=600 (total milk)
1800+1200+1200+1200+600=6000
Answer:
a) n= 1045 computers
b) n= 442 computers
c) A. Yes, using the additional survey information from part (b) dramatically reduces the sample size.
Step-by-step explanation:
Hello!
The variable of interest is
X: Number of computers that use the new operating system.
You need to find the best sample size to take so that the proportion of computers that use the new operating system can be estimated with a 99% CI and a margin of error no greater than 4%.
The confidence interval for the population proportion is:
p' ± 
* 
a) In this item there is no known value for the sample proportion (p') when something like this happens, you have to assume the "worst-case scenario" that is, that the proportion of success and failure of the trial are the same, i.e. p'=q'=0.5
The margin of error of the interval is:
d= *
![n= [p'(1-p')]*(\frac{Z_{1-\alpha /2}}{d} )^2](https://tex.z-dn.net/?f=n%3D%20%5Bp%27%281-p%27%29%5D%2A%28%5Cfrac%7BZ_%7B1-%5Calpha%20%2F2%7D%7D%7Bd%7D%20%29%5E2)
![n=[0.5(1-0.5)]*(\frac{2.586}{0.04} )^2= 1044.9056](https://tex.z-dn.net/?f=n%3D%5B0.5%281-0.5%29%5D%2A%28%5Cfrac%7B2.586%7D%7B0.04%7D%20%29%5E2%3D%201044.9056)
n= 1045 computers
b) This time there is a known value for the sample proportion: p'= 0.88, using the same confidence level and required margin of error:
![n= [0.88*0.12]*(\frac{{2.586}}{0.04})^2= 441.3681](https://tex.z-dn.net/?f=n%3D%20%5B0.88%2A0.12%5D%2A%28%5Cfrac%7B%7B2.586%7D%7D%7B0.04%7D%29%5E2%3D%20441.3681)
n= 442 computers
c) The additional information in part b affected the required sample size, it was drastically decreased in comparison with the sample size calculated in a).
I hope it helps!
