0
What would lead a historian to consider two events to have a relationaship of correlation rather than causation?
Wiki User
∙ 6y agoWant this question answered?
Be notified when an answer is posted
Add your answer:
Earn +20 pts
Does the strength of correlation depend on the direction?
Absolutely not. The simplest way to demonstrate this is to consider a measure of agreement - disagreement. If we scored it so that "strongly agree" is 5 and "strongly disagree" is 1, we would get one value of the correlation. If we reverse-scored it, we would get exactly the same value, but with the opposite sign. The strength of the correlation is the same, but the direction of the relation has switched. Another consideration is the fact that the actual strength of the correlation is based on the square of its value. 0.20 squared is 0.04; 0.40 squared is 0.16. A correlation of 0.40 is four times as strong as a correlation of 0.20. But when you square something, you automatically lose the sign. The square of a negative number is positive. So by definition, correlations of the same size but different signs are equal in strength.
Does correlation prove causality?
No. If Factor X is correlated to Factor Y then you can use one as a predictor of the other, but you should never assume that one causes the other (it may, but correlation alone doesn't prove it).Consider the correlation between proximity to a swampland and chances of contracting malaria. Do swamplands cause malaria? No. Malaria is propagated via mosquitoes which of course love to live in swamplands. So your proximity to a swampland is a useful predictor of your chances of contracting malaria, but doesn't cause it.
What is correlation. What are the different types of correlation. Why is it important to determine correlation. What does it mean when it is said that two variables have no correlation?
A correlation is the relationship between two variables.Correlations are described as either weak or strong, and positive or negative, however there can be a perfect correlation between variables, or no correlation between variables.It is important to determine the correlation between variables in order to know if and how strongly one variable affects another variable (if one variable changes, how will the other variable react). This is done by determining the coefficient of correlation (r), which describes the strength of the relationship between variables and the direction.-1 is less than or equal to r, r is less than or equal to +1if r= +1 or -1, there is a perfect relationshipif r= 0 there is no relationship between the variables, meaning that one variable does not affect the other variable and one variable could change without any change to the other variable.a value closer to + or - 1 demonstrates a strong relationship, while a value closer to 0 demonstrates a weak relationship.a + value demonstrates that when one variable increases the other variable increases, while a - value demonstrates that when one variable increases the other variable decreases.* * * * *Mostly a very good answer but ...It is very important to understand that correlation is not the same as relationship. Consider the two variables, x and y such that y = x2 where x lies between -a and +a. There is a clear and well-defined relationship between x and y, but the correlation coefficient r is 0. This is true of any pair of variables whose graph is symmetric about one axis.Conversely, a high correlation coefficient does not mean a strong relationship - at least, not a strong causal relationship. There is pretty strong correlation between my age and [the log of] the number of television sets in the world. That is not because TV makes me grow old nor that my ageing produces TVs. The reason is that both variables are related to the passage of time.
Is the probability always expressed as a ratio?
Yes, provided you consider fractions and percentages as ratios.Yes, provided you consider fractions and percentages as ratios.Yes, provided you consider fractions and percentages as ratios.Yes, provided you consider fractions and percentages as ratios.
What factors did you consider in numbers?
I would consider both prime and composite factors - depending on what was required.
What would lead a historian to consider two events to have a relationship of correlation rather than causation?
If the events happened around the same time but one did not cause the other
What is correlation analysis?
We consider correlation as a several independent variables.
How do you prove causation?
Proving causation requires establishing a direct relationship between a specific factor (cause) and a particular outcome. This is typically done through empirical evidence, such as controlled experiments or observational studies, that show a consistent association between the cause and effect. It is important to consider alternative explanations and potential confounding variables when attempting to prove causation.
What is considered a strong correlation?
"Strong" is very much a subjective term. Not only that, but it depends on expectations. In economics I would consider 70% to be a strong correlation, but for physics I would want more than 95% before I called the correlation strong!
What do many historian consider as marking the end of the middle ages?
around 1500 A.D.
What is legal causation?
Factual causation is the starting point and consists of applying the 'but for' test. In most instances, where there exist no complicating factors, factual causation on its own will suffice to establish causation. However, in some circumstances it will also be necessary to consider legal causation. Under legal causation the result must be caused by a culpable act, there is no requirement that the act of the defendant was the only cause, there must be no novus actus interveniens and the defendant must take his victim as he finds him (thin skull rule).Added: Causation means causing or producing an event. Causation is the relationship of cause and effect of an act or omission and damages alleged in a tort or personal injury action. A plaintiff in a tort action must prove a 'duty' to do, or not do, an action and a breach of that duty. It must also be established that the loss was caused by the defendant's action or inaction.
A relationship between two variables or sets of data is called?
Correlation * * * * * That is simply not true. Consider the coordinates of a circle. There is obviously a very strong relationship between the x coordinate and the y coordinate. But the correlation is not just small, but 0. The correlation between two variables is a measure of the linear relationship between them. But there can be non-linear relationships which will not necessarily be reflected by any correlation.
Who was herodotus and what do western historians consider him to be?
He was a Greek historian - and is considered to be the 'father of history' for his methods of categorising and testing materials.
A relationship between two variables or sets of data is called what?
Some people will give the answer "correlation". But that is not correct for the following reason: Consider the coordinates of a circle. There is obviously a very strong relationship between the x coordinate and the y coordinate. The correlation between the two is not just small, but 0. The correlation between two variables is a measure of the linear relationship between them. But there can be non-linear relationships which will not necessarily be reflected by any correlation.
Do you need to take birth control if I am going to be sexually active?
No. There is no correlation whatsoever between me taking birth control and you doing whatever it is you consider doing.
What is correlation What are the different types of correlation Why is it important to determine correlation What does it mean when it is said that two variables have no correlation?
A correlation is the relationship between two or more variables. Correlations are described as either weak or strong, and positive or negative. There can be a perfect correlation between variables, or no correlation between variables. It is important to determine the correlation between variables in order to know if and how closely changes in one variable are reflected by changes in another variable. This is done by determining the coefficient of correlation (r), which describes the strength of the relationship between variables and the direction. -1 ≤ r ≤ +1 if r= +1 or -1, there is a perfect correlation if r= 0 there is no correlation between the variables. a value closer to + or - 1 demonstrates a strong correlation, while a value closer to 0 demonstrates a weak correlation. a + value demonstrates that when one variable increases the other variable increases, while a - value demonstrates that when one variable increases the other variable decreases. However, it is very important to understand that correlation is not the same as relationship. Consider the two variables, x and y such that y = x2 where x lies between -a and +a. There is a clear and well-defined relationship between x and y, but the correlation coefficient r is 0. This is true of any pair of variables whose graph is symmetric about one axis. Conversely, a high correlation coefficient does not mean a strong relationship - at least, not a strong causal relationship. There is pretty strong correlation between my age and [the log of] the number of television sets in the world. That is not because TV makes me grow old nor that my ageing produces TVs. The reason is that both variables are related to the passage of time.
Does the strength of correlation depend on the direction?
Absolutely not. The simplest way to demonstrate this is to consider a measure of agreement - disagreement. If we scored it so that "strongly agree" is 5 and "strongly disagree" is 1, we would get one value of the correlation. If we reverse-scored it, we would get exactly the same value, but with the opposite sign. The strength of the correlation is the same, but the direction of the relation has switched. Another consideration is the fact that the actual strength of the correlation is based on the square of its value. 0.20 squared is 0.04; 0.40 squared is 0.16. A correlation of 0.40 is four times as strong as a correlation of 0.20. But when you square something, you automatically lose the sign. The square of a negative number is positive. So by definition, correlations of the same size but different signs are equal in strength.
