try again using this information: A correlation coefficient r is the strength of the linear relationship between two variables. The correlation between classroom seating and exam score using our example was .88. Regression equations are calculations used to predict a person's score on one variable when that person's score on another variable (or set of variables) is already known. They are essentially "prediction equations" that are based on known information about the relationship between the two variables. For example, after discovering that seating pattern and exam score are related, a regression equation may be calculated that predicts anyone's exam score based only on information about where the person sits in the class. The general form of a regression equation is: Y = a + bX where Y is the score we wish to predict, X is the known score, a is a constant, and b is a weighting adjustment factor that is multiplied by X (it is the slope of the line created with this equation). In our seating-exam score example, the following regression equation is calculated from the data: Y = 99 + (-8)X Thus, if we know a person's score on X (seating), we can insert that into the equation and predict what that person's exam score (Y) will be. If the person's X score is 2 (by sitting in the second row), we can predict that Y = 99 + (-16), or that the person's exam score will be 83. The regression equation can be used to produce a graph showing the straight line describing the linear