1. Predicted values, , of the dependent, or endogenous, variable as a func- tion of the independent or exogenous variable 2. Estimated marginal change in the endogenous variable, by, that results from a one-unit change in the independent, or exogenous, variable Early mathematicians struggled with the problem of developing a procedure for estimating the coefficients for the linear equation. Simply minimizing the deviations was not useful because the deviations have both positive and negative signs. Various proce- dures using absolute values have also been developed, but none has proven as useful or as popular as least squares regression. We will learn later that the coefficients devel- oped using this procedure also have very useful statistical properties. One important caution for least squares is that extreme outlier points can have such a strong influence on the regression line that the line is shifted toward this point. Thus, you should always examine scatter plots to be sure that the regression relationship is not based on just a few extreme points. Our discussion continues with an example that indicates a typical application of re- gression analysis and the kind of results that can be obtained. X25 Example 11.2 Sales Prediction for Northern Household Goods (Regression Model Estimation) The president of Northern Household Goods has asked you to develop a model that will predict total sales for proposed new retail store locations, Northern is a rapidly expanding general retailer, and it needs a rational strategy for determining where new stores should be located. As part of the project you need to estimate a linear equation that predicts retail sales per household as a function of household disposable income for their customers. The company has obtained data from a sampling survey of house- holds in each of the target sales areas for their existing stores, and the variables retail sales (Y) and income (X) per household will be used to develop the model