The Central Limit Theorem tells us that for population distribution(s), if we repeatedly take new random samples from this distr
ibution and calculate the average each time, then: a. If you take a really large sample size you would expect the sample average to be b. If your sample size is small, the sample averages will be spread out. c. If your sample size is small, the distribution of the sample averages will look more like distribution. d. If your sample size is very large, the distribution of the sample averages will look more like distribution.
d. If your sample size is very large, the distribution of the sample averages will look more like distribution.
Step-by-step explanation:
The central limit Theorem states that for population distribution if you repeatedly take samples from the distribution, then the normal thing for it to happen would be that the distribution means of the samples will be normally distributed, this is what it states, the option that comes closer to that statement would be d. If your sample size is very large, the distribution of the sample averages will look more like distribution, because they large sample will create for a normally distributed means distribution.
Assuming you meant "a person takes 8.4 x 10^6 breaths
per year" we would multiply the number of people 4.46 x 10° by the number of breaths each person takes 8.4 x 10^6 and we get 37464000 and in scientific notation it would be 3.75 x 10^7
