A linear correlation relationship between two variables that shows up on a scatter diagram as dots roughly approximating a straight line Linear Correlation
Curvilinear Correlation Curvilinear correlation any association between two variables other than a linear correlation relationship between two variables that shows up on a scatter diagram as dots following a systematic pattern that is not a straight line
The Correlation Coefficient ( r ) The sign of r (Pearson correlation coefficient) tells the general trend of a relationship between two variables. + sign means the correlation is positive. - sign means the correlation is negative. The value of r ranges from -1 to 1. A correlation of 1 or -1 means that the variables are perfectly correlated. 0 = no correlation
Strength of Correlation Coefficients The value of a correlation defines the strength of the correlation regardless of the sign. e.g., -.99 is a stronger correlation than .75
The Statistical Significance of a Correlation Coefficient A correlation is statistically significant if it is unlikely that you could have gotten a correlation as big as you did if in fact there was no relationship between variables. If the probability (p) is less than some small degree of probability (e.g., 5% or 1%), the correlation is considered statistically significant.
Proportion of variance accounted for (r2) To compare correlations with each other, you have to square each correlation. This number represents the proportion of the total variance in one variable that can be explained by the other variable. If you have an r= .2, your r2= .04 Where, a r= .4, you have an r2= .16 So, relationship with r = .4 is 4x stronger than r=.2 The Correlation Coefficient and the Proportion of Variance Accounted for