Multiple predictors

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  • When sitting in statistics classes or when trying to read and understand statistical material, too many otherwise intelligent and capable students and researchers feel dumb. This book is intended as an antidote. It is designed to make you feel smart and competent. Its approach is conservative in that it attempts to identify and present the essentials of data analysis as developed by statisticians over the last two or three centuries.

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  • In a classification problem, you typically have historical data (labeled examples) and unlabeled examples. Each labeled example consists of multiple predictor attributes and one target attribute (dependent variable). The value of the target attribute is a class label. The unlabeled examples consist of the predictor attributes only. The goal of classification is to construct a model using the historical data that accurately predicts the label (class) of the unlabeled examples.

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  • The number one predictor of successful salespeople is their ability to connect with their customers. In the years leading up to the present, the best sales executives have accomplished this by having multiple, face-to-face meetings. However, the demands on our clients’ time, coupled with the increasing expense and frustration of travel, have minimized our ability to actually meet with clients.

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  • After studying this chapter you will be able to understand: How to classify and select multivariate techniques, that multiple regression predicts a metric dependent variable from a set of metric independent variables, that discriminant analysis classifies people or objects into categorical groups using several metric predictors.

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