Answer:
The type of sample is Stratified sampling.
Step-by-step explanation:
Consider the provided information.
Types of sampling.
- Random sampling is similar to placing the name of everyone in a hat and pulling out a few names.
- In Systematic sampling, we list of elements is counted off.
- Convenience sampling: data which is readily available is used. That is, the first people are running into by the surveyor.
- In Cluster sampling, we divide the population into groups, usually geographically.
- In Stratified sampling we divide population into groups called strata. but this time population might be separated into males and females.
Here the population is divided into groups of males and females therefore it is stratified sampling.
Hence, the type of sample is Stratified sampling.
Male Female Total
Income over $50,000 485 385 870
Income below $50,000 65 65 130
<span>Total 550 450 1,000
Probability of being male: 550/1000 = 0.55
Probability of earning over $50,000: 870/1000 = 0.87
0.55 x 0.87 = 0.4785
Probability of being male and earning over $50,000: 485/550 = 0.8818
</span><span>C) No, P(being male | the person earns over $50,000) ≠ P(being male)</span><span>
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Answer:
It does
Step-by-step explanation:
It does have a proportional relationship because if you try to find the relationship between the first two inputs and outputs, you can find that it is 21 (1 to 21, 21 divided by 1 would be 21) then if you use that relationship with the other numbers (times 21) you would get the same answer. For example in the second one, the two numbers are 2 and 42, 2 times 21 would equal 42. The next one would be 3 times 21 to equal 63 and etc.
Hope this Helps!!
