Basic inference procedures and linear regression, model adequacy checking, transformations and weighted least squares, handling with influential observations and outliers, polynomial regression modeling, use of indicator variables, multicollinearity, variable selection, introduction of logistic regression, conventional and less common uses of linear regression in today's cutting-edge scientific research. Understanding of the basic principles for applied regression model-building techniques in various fields of study, including engineering, management and the health sciences.