This means that the data points lie perfectly on a line with negative slope.
For example, the points (0,4), (1,3), (2,2), (4,0) are perfectly correlated since they lie on the line y = -x + 4.
It is a negative correlation since the slope of the line is -1, a negative number, or alternatively because as x rises, y falls.
Negative
If the two variables increase together and decrease together AND in a linear fashion, the correlation is positive. If one increases when the other decreases, again, in a linear fashion, the correlation is negative.
Since y=14x is a perfect linear relation, the correlation would be 1.
If the data have a positive or negative correlation, it means the data have a linear relationship in the form of an equation of a line; or Y = mX + b.
It is a measure of the extent to which a linear change in one quantity is accompanied by a linear change in the other quantity. Note that only linear changes are measured and that there is no causality.
A negative correlation is a measure of the linear component of a relationship where one variable increase as the other decrease.
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.
Negative
If the two variables increase together and decrease together AND in a linear fashion, the correlation is positive. If one increases when the other decreases, again, in a linear fashion, the correlation is negative.
men and penises
Since y=14x is a perfect linear relation, the correlation would be 1.
If the data have a positive or negative correlation, it means the data have a linear relationship in the form of an equation of a line; or Y = mX + b.
False.
A negative correlation
It means that here is no linear relationship between the two variables. There may be a perfect non-linear relationship, though.
The correlation coefficient takes on values ranging between +1 and -1. The following points are the accepted guidelines for interpreting the correlation coefficient:0 indicates no linear relationship.+1 indicates a perfect positive linear relationship: as one variable increases in its values, the other variable also increases in its values via an exact linear rule.-1 indicates a perfect negative linear relationship: as one variable increases in its values, the other variable decreases in its values via an exact linear rule.Values between 0 and 0.3 (0 and -0.3) indicate a weak positive (negative) linear relationship via a shaky linear rule.Values between 0.3 and 0.7 (0.3 and -0.7) indicate a moderate positive (negative) linear relationship via a fuzzy-firm linear rule.Values between 0.7 and 1.0 (-0.7 and -1.0) indicate a strong positive (negative) linear relationship via a firm linear rule.The value of r squared is typically taken as "the percent of variation in one variable explained by the other variable," or "the percent of variation shared between the two variables."Linearity Assumption. The correlation coefficient requires that the underlying relationship between the two variables under consideration is linear. If the relationship is known to be linear, or the observed pattern between the two variables appears to be linear, then the correlation coefficient provides a reliable measure of the strength of the linear relationship. If the relationship is known to be nonlinear, or the observed pattern appears to be nonlinear, then the correlation coefficient is not useful, or at least questionable.
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.