Correlation is a statistical measure of the linear association between two variables. It is important to remember that correlation does not mean causation and also that the absence of correlation does not mean the two variables are unrelated.
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.
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 correlation between two variables, say X and Y, means that if one increases, the other will too. No correlation means that they are not related. A negative correlation means that as one increases, the other decreases. Normally you will see this in studies as "Recent studies demonstrated a positive correlation between eating too much and obesity." Or, "recent studies demonstrate a negative correlation between a healthy, balanced diet and obesity".
The time period may not affect the correlation coefficient at all. If looking at the correlation between the mass and volume of steel objects, time is totally irrelevant. The effect of the number of variables depends on whether or not the extra variables are related to ANY of the variables in the equation.
It mean that there is no correlation between the two variables. The variables are the same.
The three different types of correlation are positive correlation (both variables move in the same direction), negative correlation (variables move in opposite directions), and no correlation (variables show no relationship).
Correlation is a statistical measure of the linear association between two variables. It is important to remember that correlation does not mean causation and also that the absence of correlation does not mean the two variables are unrelated.
A positive correlation between two variables means that there is a direct correlation between the variables. As one variable increases, the other variable will also increase.
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
correlation
Correlation analysis is the relationship of two values. When two items are similar, they will have a high correlation. Should they differ, they will be much lower in variables.
Pearson's Product Moment Correlation Coefficient indicates how strong the relationship between variables is. A PMCC of zero or very close would mean a very weak correlation. A PMCC of around 1 means a strong correlation.
partial correlation is the relation between two variable after controlling for other variables and multiple correlation is correlation between dependent and group of independent variables.
We consider correlation as a several independent variables.
If the correlation is positive, as one increases so does the other.
No. The units of the two variables in a correlation will not change the value of the correlation coefficient.