ABSTRACT
The correlation coefficient measures the extent to which data cluster around a line. In linear regression analysis, we determine that “best-fitting” line and use it to better understand the relationship between the variables under consideration. The results of a linear regression analysis include an equation that relates one variable to another. This chapter applies Simple linear regression, when the data consist of two variables, commonly denoted by X and Y, and goal is to model Y, called the response variable, in terms of X, called the predictor variable. With a larger sample size, the margin of error of an estimate is smaller; hence, more estimates are significant, generally speaking. A regression model is often used as a way to model the relationship between the variables. Another use for a regression model is to better understand how one variable is related to the other.
