<u>Answer:
</u>
The U.S. National Longitudinal Survey of Youth showed a small but significant positive correlation between youths' intelligence test scores and their subsequent income levels.
Option: (B)
<u>Explanation:
</u>
- The potential and the eligibility to work in the industries of specific knowledge fields are mostly determined by the employers by testing how intelligent the candidate is.
- The more intelligent the candidate the more would be his chances of getting employed for a better pay rate.
- Thus, there is an observable positive correlation between the youths' intelligence test scores and their subsequent income levels.
Answer:
4) They had a rate of cognitive impairment several times higher than the children adopted at less than 6 months of age.
Explanation:
This experiment shows how important the first months of development are in childhood. The effects of deprived nutrion, afection, and cognitve stimulation can cause serious damages. When adopting, all these conditions can improve, so the earlier a child is adopted, the best it would do to their development.
Cognitive development depends very much on emotional facts as well as on nutrional facts. A child needs the most optimal conditions to fully developed, and the earlier that is corrected, the ealier it can improve.
A bureaucracy
ie) the civil service exams in China during period 2 or 3
Answer:
Correlation does not prove causation
Explanation:
Correlation and causation are the two terms that are mostly confused and used interchangeably used. These terms are often misunderstood by people. It is very important to know that these terms are used for the conclusion. So that person has to make understand what is the correct meaning of these two terms. Correlation does not imply causation. It is very important to understand this term. Correlation is basically about the two variables. It tells how the two variable is linearly related to each other and change together. It does not tell about how it is related but it tells about the relationship between the variables. Causation is a little bit more than a correlation. It tells about change in one variable that will cause a change in another variable.
