Understanding Regression Analysis: An Introductory Guide presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style. It illustrates how regression coefficients are estimated, interpreted, and used in a variety of settings within the social sciences, business, law, and public policy. Packed with applied examples and using few equations, the book walks readers through elementary material using a verbal, intuitive interpretation of regression coefficients, associated statistics, and hypothesis tests. The Second Edition features updated examples and new references to modern software output.
In the food expenditure example, the hypothesis was advanced that family food consumption increases as income increases. Since the estimated coefficient was found to be a positive number, one might want to immediately conclude that we have proven our case. Unfortunately, drawing such inferences is not so easy, since our hypothesis concerns the population of all families, not just the 50 families in our sample. By way of example, although the coefficient on income is greater than zero for our food example, how confident are you that β, the population coefficient, is really greater than zero? Or, how confident would you be if, rather than basing the estimate on 50 households, the coefficient had been ...