Simple Linear Regression

Rationale Frequently decision-making situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related to its advertising expenditures for marketing, the health of the economy as measured by the stock market performance, and the number of sales people etc? The regression analysis provides tools for modeling a "response" variable with the help of a number of "predictor" variables for predicting and managing risks. The simple linear regression accounts for only one predictor in modeling a response variable. Though of limited practical use, it provides an understanding of the basic tools of the methodology, which can be easily extended to realistic models involving more than one predictor. • Scatter plot and the simple linear regression model • Estimation and significance testing of the slope and intercept parameters • Coefficient of determination and coefficient of correlation • Confidence and prediction intervals for the mean and individual responses • Error analysis for testing normality and homogeneity assumptions