Applied linear regression 3rd Edition Pdf written by Sanford Weisberg. Regression analysis answers questions about the dependence of a response variable on one or more predictors, including prediction of future values of a response, discovering which predictors are important, and estimating the impact of changing a predictor or a treatment on the value of the response. At the publication of the second edition of this book about 20 years ago, regression analysis using least squares was essentially the only methodology available to analysts interested in questions like these. Cheap, widely available high-speed computing has changed the rules for examining these questions. Modern competitors include nonparametric regression, neural networks, support vector machines, and tree-based methods, among others. A new field of computer science, called machine learning, adds diversity, and confusion, to the mix. With the availability of software, using a neural network or any of these other methods seems to be just as easy as using linear regression.
So, a reasonable question to ask is: Who needs a revised book on linear regression using ordinary least squares when all these other newer and, presumably, better methods exist? This question has several answers. First, most other modern regression modeling methods are really just elaborations or modifications of linear regression modeling. To understand, as opposed to use, neural networks or the support vector machine is nearly impossible without a good understanding of linear regression methodology. Second, linear regression methodology is relatively transparent, as will be seen throughout this book. We can draw graphs that will generally allow us to see relationships between variables and decide whether the models we are using make any sense. Many of the more modern methods are much like a black box in which data are stuffed in at one end and answers pop out at the other, without much hope for the non expert to understand what is going on inside the box
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