Answers:
- a) Stratified random sampling, or simply stratified sampling. Each group individually is known as a stratum. The plural is strata. The key here is that each stratum is sampled, though we don't pick everyone from every stratum. We randomly select from each unit to have them represent their unit. Think of it like house of representative members that go to congress. We have members from every state, but Be sure not to mix this up with cluster sampling. Cluster sampling is where we break the population into groups or clusters, then we randomly select a few clusters in which every individual from those clusters is part of the sample.
- b) Simple random sampling (SRS). This is exactly what it sounds like. We're randomly generating numbers to help determine who gets selected. Think of it like a lottery. A computer is useful to make sure this process is quick, efficient and unbiased as possible. Though numbers in a box or a hat work just as well.
For each of the methods mentioned, they aren't biased since they have randomness built into their processes.
34/100
= 0.34
3 tenths, 4 hundreths
The probability of tossing a sum of 7:
6/36
the probability of tossing a sum greater than 7 :
not 100% sure
but I think it is 16/36
the probability of tossing a sum less than 13
36/36
good luck
She originally started with 23.
We can tell this by working the equation backwards. Since she has 5 left and she had just given half to her brother, we know she had 10 a moment ago.
Before that, Mary had eaten a cookie. So we add 1 to the total and now have 11.
Before eating that cookie, she has given half to her sister, which would give her 22.
And before giving half to her sister, she had eaten the first one, which would give us 23 to start with.
