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What else can I help you with?
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 is the statistics are used as a way to make an educated guess about the relationship between the independent variable and the dependent variable?
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!
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 the relationship between correlation and causation?
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.
How can we distinguish between correlation and causation in research studies?
Correlation means two things are related, but causation means one thing directly causes another. To distinguish between them in research studies, we need to consider factors like the timing of events, the presence of a plausible mechanism, and the possibility of other variables influencing the relationship. Conducting controlled experiments and using statistical analysis can help determine if there is a causal relationship or just a correlation between variables.
Mr brown gathered evidence that the self-esteem of students is negatively correlated with their typical levels of anxiety Mr brown should first be reminded that?
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.
What is correlation analysis?
We consider correlation as a several independent variables.
What does it mean when large amounts of data support a hypothesis?
When large amounts of data support a hypothesis, it suggests that there is a strong correlation between the variables involved, indicating that the hypothesis may be valid. This accumulation of evidence can enhance the reliability and credibility of the hypothesis, leading researchers to consider it a potential explanation for the observed phenomena. However, it's essential to remain cautious, as correlation does not imply causation, and further investigation is often required to establish a definitive relationship.
Is marijuana considered a gateway drug?
Research on the topic of marijuana as a gateway drug is mixed. Some studies suggest that using marijuana may lead to trying other, more harmful substances, while others argue that this correlation is not necessarily causation. It is important to consider individual factors and circumstances when discussing the potential for marijuana to be a gateway drug.
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.
Does the study prove that getting more hours of sleep will make people hapier?
The study may suggest a correlation between more hours of sleep and increased happiness, but it does not necessarily prove causation. Other factors could also contribute to a person's happiness, so it's important to consider the study's limitations and the potential influence of variables beyond just hours of sleep.
