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First, a correlation is an indicator of the linear relationship between two events or manifestations. As such, it does not indicate that A causes B or B causes A, but rather that A and B coexists together. A correlation will vary between -1 and +1. A correlation of 0 will mean that there is no relationship between A and B. The closer the correlation is to the extreme, the stronger the relationship is. It is important to note that the sign only indicates whether the relationship is positive or negative. More specific to this question, a positive correlation will mean that as A increases, so does B. For example, perfectionism has been found to be positively correlated to depression. In other words, as the person presents more severe form of perfectionism, he or she will also show more symptoms of depression. This relationship could be represented in a graph as a diagonal line starting low and gradually moving higher as it moves towards the right.
"or" is used in the context of sets [of events] rather than probability (and certainly not probibility!),An event described as A or B means either event A or event B or both events."or" is used in the context of sets [of events] rather than probability (and certainly not probibility!),An event described as A or B means either event A or event B or both events."or" is used in the context of sets [of events] rather than probability (and certainly not probibility!),An event described as A or B means either event A or event B or both events."or" is used in the context of sets [of events] rather than probability (and certainly not probibility!),An event described as A or B means either event A or event B or both events.
He measures times by events his experiences rather than by societal measures time.
This is a rather confused question.The first issue is the assumption that there is an independent variable and a dependent variable. If your data comprise measurements of the height and mass (weight) of school children, which one is the independent variable? The answer is: neither. It is most likely to be age.A second issue is the very serious danger of confusing correlation with causality. Yes, statistics may show high correlation but that does not imply causality. A simplistic example from economics: correlation between companies with large profits and large investment in machinery. Profitability is required to enable the company to finance investment. Proper investment helps the company become more competitive and so generate more profits.Finally, consider the two variables X and Y. X is uniform on the interval [-p, p]; Y = X^2. The regression coefficient between X and Y is 0 but the relationship is far from non-existent. You need some educated guesses to find the correct statistics to make educated guesses!
Not answerable. You need to quote speed as well, then you can calculate it yourself (as I suspect you are meant to do rather than asking us to do it for you!) from the relationship Distance = Speed X Time.
occurred at the same time but did not influence each other.
If the events happened around the same time but one did not cause the other
The correlation coefficient gives a measure of the degree to which changes in the variables are related. However, the relationship need not be causal.
One example of events that are correlated but do not have a causal relationship is the rise in ice cream sales and drownings. While both events may peak during summer months, there is no direct link between them causing one another. Another example is the correlation between the amount of TVs sold and the number of births in a population, which are linked to economic and societal factors rather than a direct causal relationship.
First, a correlation is an indicator of the linear relationship between two events or manifestations. As such, it does not indicate that A causes B or B causes A, but rather that A and B coexists together. A correlation will vary between -1 and +1. A correlation of 0 will mean that there is no relationship between A and B. The closer the correlation is to the extreme, the stronger the relationship is. It is important to note that the sign only indicates whether the relationship is positive or negative. More specific to this question, a positive correlation will mean that as A increases, so does B. For example, perfectionism has been found to be positively correlated to depression. In other words, as the person presents more severe form of perfectionism, he or she will also show more symptoms of depression. This relationship could be represented in a graph as a diagonal line starting low and gradually moving higher as it moves towards the right.
Correlation means that when one quantity increases, the other tends to increase as well. Causation means that the increase in one quantity CAUSES an increase in another quantity. It is a common error to assume that correlation implies causation; sometimes correlation is caused by causation, but not always. For example: let's say that the price of sugar gradually went up over the last 10 years; so did the price of cooking oil. Neither one is caused by the increase of the other; rather, they are both part of a larger tendency, namely, inflation. As another example, during the same 10-year period, the population of your country gradually increased. This is independent of the inflation; both prices and population simply tend to increase over time.
There is some evidence to suggest that individuals with lower IQs may be at a higher risk for alcoholism. However, the relationship between IQ and alcoholism is complex and influenced by various factors such as genetic predisposition, environmental influences, and social factors. It is not a direct causation, but rather a correlation.
Economincs based on observation or experience rather than theory or pure logic. Empirical Economists estimate elasticities and try to navigate the difficult path of distinguishing causation from correlation. For example, given that those who are breastfed for a longer time in Africa tend to be unhealthier than those who aren't, does brestfeeding in Africa cause illness for children or are the children who are breastfed longer those who deal with constant healt problem? Is breastfeeding making them unhealthy, causation. Or are unhealthy children breastfed further into their life because it is good for them, correlation.
Hume questioned the notion of cause and effect as a necessary connection between events. He argued that our understanding of causation is based on our past experiences of one event following another, rather than any inherent connection between them. He suggested that we cannot know for certain that one event causes another, but rather we infer causation based on our observed regularities in experience.
You cannot. Or rather, you should not. You do not know if the relationship is linear or something else. A scatter graph is the best way to establish the nature of the relationship. For example, the correlation between x and y, when y = x2 between, say, -4 and +4 is zero (because of symmetry). That would lead you to conclude that there was no relationship. You could not be more incorrect!
A correlation with an absolute value near one (ie, either near -1 or near 1) indicates that two variables are in a particularly simple relationship that is uncluttered if you will with the effects of error or other variables. That simple relationship is linear rather than curvilinear or something else which would be more challenging to deal with. Moreover, correlation is a single number that is fairly easy to compute.
The phrase "at the time" typically indicates a specific moment or period in which events occur, rather than a cause-and-effect relationship. It is often used to provide context for when something happened or will happen.