A strong positive correlation does not prove causation.
People only get sunburned during daylight hours. Sundials only work during daylight hours. Therefore sundials cause sunburns.
The above sentences show how absurd such predicate thinking could be.
Simply because two events usually occur at the same time does not mean they are related.
One man found a perfect correlation between the price of whiskey and Chicago school teachers' salaries. No possible relationship could possibly exist except the rate of prosperity and inflation.
Causation is difficult to prove.
does not prove
You cannot say it because it is not true.First of all, correlation simple states that two variables change so in such a way that a change in one leads to a change in the other. Changes of the same magnitude in the first variable brings about the consistent changes in the second variable. There is no way to determine whetherthe first causes the second,the second causes the first,they cause one another, orthey are both caused by an unknown third variable.A simplistic example from economics will illustrate the first three. Capital investment (spending on machinery, for example) by a company and the company's profits are positively correlated. But the direction of the causal relationship is not simple to establish. A company needs to be profitable before it can raise the money to invest. On the other hand, by investing well, it becomes more competitive and so is more profitable.As an example of the fourth type, in the UK there is a significant correlation between the sales of ice cream and swimming accidents. This is not because ice cream causes swimming accidents nor that ice cream is caused (?) by swimming accidents. The hidden variable is hot weather. People are more likely to eat ice cream. They are also more likely to go to beaches.The converse of the statement in the question is also untrue: the absence of correlation does not prove that there is no causation. Suppose you have one variable X which is defined on a the interval (-p, p) for some positive number a. And then let Y = X^2. There is clearly a perfect relationship between the two variables. However, if the X-values are symmetric, then the symmetry of the relationship ensures that the correlation coefficient is 0! No correlation but a perfect relationship.
1 is the best, 0 is the worst. So the closer you are to 1, the better. Beyond that, I can't tell you a specific cutoff. It depends on what you're trying to prove. Sometimes, you won't settle for anything less than 0.99. Other times, you'll be tickled pink to get a 0.3. But the whole point of an R-squared is to give a numerical representation of how close the correlation is without resorting to vague terms like "good correlation". Publish the value of R-squared and let the readers make their own decisions about whether it's "good" or "bad".
To prove the hypothesis. To disprove the hypothesis.
you dont silly :)
No! Correlation by itself is not sufficient to infer or prove causation.
does not prove
Correlation is a statistical relationship between two variables, while causation implies that one variable directly influences the other. Correlation does not prove causation, as there may be other factors at play. It is important to consider other evidence before concluding a causal relationship.
In order to prove causation, researchers need to establish correlation and time order and rule out alternative explanations.
Correlation in research studies shows a relationship between two variables, but it does not prove that one variable causes the other. Causation, on the other hand, indicates that changes in one variable directly result in changes in another variable.
Causation in statistical analysis refers to a direct cause-and-effect relationship between two variables, where changes in one variable directly cause changes in the other. Correlation, on the other hand, simply indicates a relationship between two variables without implying causation. In other words, correlation shows that two variables tend to change together, but it does not prove that one variable causes the other to change.
Prove causation
correlation does not imply causation, meaning that a negative correlation between two variables does not prove that one causes the other; it could be due to other factors influencing both variables. It is important to consider other variables and conduct more research to establish a causal relationship between self-esteem and anxiety levels in students.
The advantage of the correlational research method is the ability to prove a positive or negative correlation between two subjects . The disadvantage of this is the unclear interpreation of cause and affect. moletsane
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.
A outcome / result to positive conflict is stronger friendship. The conflict that you had with your friend may prove how strong your friendship is.
An experiment can show: Cause and effect (Apex).