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A correlation reflects the strength of the relationship between two variables. A correlation doesn't reflect causation, but merely that two phenomena are present at the same time. The closer the value is to 1, the stronger the relationship between two variables is. This value can be positive or negative. A negative value merely indicates that, as the values on one variable increase, the values on the second variable decrease. A positive correlation indicates that both values will increase or decrease together.

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0the degree of correlation between two sets of data

correlation measure the strength of association between to variables.but some times both variables are not in same units.so we cannot measure it with the help of correlation. in this case we use its coefficent which mean unit free. that,s why we use it.

A positive value for a correlation indicates a positive correlation; e.g. it has a positive slope.

The product-moment correlation coefficient or PMCC should have a value between -1 and 1. A positive value shows a positive linear correlation, and a negative value shows a negative linear correlation. At zero, there is no linear correlation, and the correlation becomes stronger as the value moves further from 0.

No. The units of the two variables in a correlation will not change the value of the correlation coefficient.

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The correct spelling is "coefficient" (a co-value, such as an integer used with a variable).

The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).

A correlation exists in a scatter plot if there is a general trend in the outputs as inputs increase. If the outputs generally increase in value, then there is a positive correlation. If the outputs generally decrease in value, then there is a negative correlation.

A serious error. The maximum magnitude for a correlation coefficient is 1.The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them. Here they are given less than -1 value so it is not a value of correlation coefficient.

Why the value of correlation coefficient is always between -1 and 1?

It will be invaluable if (when) you need to calculate sample correlation coefficient, but otherwise, it has pretty much no value.

a strong negative correlation* * * * *No it is not. It is a very weak positive correlation.

-1 to 1

the value of a number that dose NOT match the value of the other.

The further the correlation coefficient is from 0 (ie the closer to ±1) the stronger the correlation.Therefore -0.75 is a stronger correlation than 0.25The strength of the correlation is dependant on the absolute value of the correlation coefficient; the sign of the correlation coefficient gives the "relative" slope of correlation line:+ve (0 to +1) means that as one variable increases the other also increases;-ve (0 to -1) means that as one variable increases the other decreases.

It is 9

The number in the front of the equation. Example 2x-3k..so the 2 nd the 3 are the coefficent.

Strong positive correlation

It is not possible to use correlation when the two variables are not related at all. the corelation coefficient value that will be obtained will have no significance.

Yellow is the color that reflects the most amount of light, resulting in a light value.

A negative correlation highlights the similarity of opposites. In a negative correlation, as the value of variable a increases, the value of variable b decreases. It can work the other way too, as the value of a decreases, the value of b increases. A negative correlation is also routinely referred to as an inverse. For instance, every time a person goes to jail, there is one less person in the work force. Therefore, incarceration rates, and work force numbers have an inverse relationship.

Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.

The observed relationship indicates that the a one-to-one correspondence exists between the variables of interest. In effect, the value of the obtained r-value is -1 or 1.

A positive correlation coefficient means that as the value of one variable increases, the value of the other variable increases; as one decreases the other decreases. A negative correlation coefficient indicates that as one variable increases, the other decreases, and vice-versa.

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