Want this question answered?

Q: What does correlation coefficient of 0.15 indicates which type of relationship between variable?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

The correlation coefficient, plus graphical methods to verify the validity of a linear relationship (which is what the correlation coefficient measures), and the appropriate tests of the statisitical significance of the correlation coefficient.

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.

Pearson's correlation coefficient, also known as the product moment correlation coefficient (PMCC), and denoted by r, is a measure of linear agreement between two random variable. It can take any value from -1 to +1. +1 indicates a perfect positive linear relationship between the two variables, a value of 0 implies no linear relationship whereas a value of -1 shows a perfect negative linear relationship. A low (or 0) correlation does not imply that the variables are unrelated: it simply means a there is no linear relationship: a symmetric relationship will give a very low or zero value for r.The browser which we are compelled to use is not suited for any serious mathematical answer and I suggest that you look up Wikipedia for the formula to calculate r.

Why Correlation?Because there is some relationship. One variable depends on another. Using correlation we can make inferences. Brahmajyothi

I think you're referring to Correlation. This means the relationship between two variables. There can be a positive correlation, where as one variable increases, so does the other. There can be a negative correlation, where as one variable increases, the other decreases. Lastly, there can be no correlation, where there is no relationship between the two variables.

Related questions

The correlation coefficient is zero when there is no linear relationship between two variables, meaning they are not related in a linear fashion. This indicates that changes in one variable do not predict or explain changes in the other variable.

The correlation coefficient, plus graphical methods to verify the validity of a linear relationship (which is what the correlation coefficient measures), and the appropriate tests of the statisitical significance of the correlation coefficient.

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.

The correlation coefficient is a statistical measure that quantifies the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, with -1 indicating a perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation.

The value of a correlation coefficient reflects the strength and direction of the relationship between two variables. A correlation coefficient ranges from -1 to 1, with 1 indicating a perfect positive relationship, 0 indicating no relationship, and -1 indicating a perfect negative relationship.

The dependent variable has an inverse linear relationship with the dependent variable. When the dependent increases, the independent decreases, and conversely.

It means that the two variables are likely dependent. The higher the number of the positive correlation the stronger the connection.

The correlation coefficient for two variables is a measure of the degree to which the variables change together. The correlation coefficient ranges between -1 and +1. At +1, the two variables are in perfect agreement in the sense that any increase in one is matched by an increase in the other. An increase of twice as much in the first is accompanied by double the increase in the second. A correlation coefficient of -1 indicates that the two variables are in perfect opposition. The changes in the two variables are similar to when the correlation coefficient is +1, but this time an increase in one variable is accompanied by a decrease in the other. A correlation coefficient near 0 indicates that the two variables do not move in harmony. An increase in one is as likely to be accompanied by an increase in the other variable as a decrease. It is very very important to remember that a correlation coefficient does not indicate causality.

When variables in a correlation change simultaneously in the same direction, this indicates a positive correlation. This means that as one variable increases, the other variable also tends to increase. Positive correlations are typically represented by a correlation coefficient that is greater than zero.

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.

The correlation coefficient is a statistical measure of the extent to which two variables change. A correlation coefficient of -0.80 indicated that, on average, an increase of 1 unit in variable X is accompanied by a decrease of 0.8 units in variable Y. Note that correlation does not imply causation.

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.