Bivariate Plots

A bivariate plot graphs the relationship between two variables that have been measured on a single sample of subjects. Such a plot permits you to see at a glance the degree and pattern of relation between the two variables. On a bivariate plot, the abscissa (X-axis) represents the potential scores of the predictor variable and the ordinate (Y-axis) represents the potential scores of the predicted or outcome variable. Each point on the plot shows the X and Y scores for a single subject. This is what we mean by "bivariate" plot -- each point represents two variables. A bivariate plot of two scores (self-esteem and Interpersonal Avoidance) from our class dataset is shown below. The red line on the graph shows a perfect linear relationship between the two variables.

As can be seen, the points on this graph do not follow a perfect straight line. The distance of the points to the line is called "scatter". A large amount of scatter around the line indicates a weak relationship. Little scatter represents a strong relationship. If all points fall directly on a straight line, we have a perfect linear relationship between our two variables.

We also look at the graph to determine the direction of the linear relationship. A line that begins in the upper left corner of the plot and ends in the lower right corner (like the relationship shown above) is called a negative relationship. In a negative linear relationship, high scores on the X variable predict low scores on the Y variable. In the example above, high levels of self-esteem are associated with low levels of interpersonal avoidance. A line that begins in the lower left corner of the plot and ends in the upper right corner is called a positive relationship. In a positive linear relationship, high scores on the X variable predict high scores on the Y variable. Scores scattered randomly around a straight line in the middle of the graph indicate no relationship between variables. Sometimes a scatter plot will show a curvilinear relationship between two variables. If this happens, we need to use special statistics developed for curvilinear relationships. Each type of relationship is presented below.

Click here to review correlation coefficient:
Click here to review effect size:
Click here to review regression line:
Click here to begin your scatter plots: