ABSTRACT
In this chapter you will:
Learn how to compute the slope and Y intercept for the best-fit line.
Learn how to compute and interpret the correlation coefficient.
Learn how to account for variance with a single predictor, either quantitative or binary.
Learn how to graph the regression line for both a quantitative and a binary predictor variable.
In the preceding chapter, as a way of describing the relation between two variables, you determined the best-fit line by trial and error. You also learned that the slope of that line (the regression coefficient) indicates the exact nature of the linear relation between the independent and dependent variable. Topics introduced in the last chapter are discussed further in this chapter. The focus remains on descriptive statistics, and the major question remains: How can the strength of the relation between a quantitative dependent variable and an independent variable (either quantitative or qualitative) be assessed? Other ways to state this question are: How can we describe the effect of one variable on another, and how much variability in one variable can be accounted for by another?