Introduction to Statistics Through Resampling Methods and R, 2nd Edition

Chapter 9

Developing Models

A recent help wanted ad touted openings with a sportsbook; they were looking for statisticians who could help them develop real-time software for forecasting the outcomes of cricket matches. In this chapter, you will learn valuable techniques with which to develop forecasts and classification schemes. These techniques have been used to forecast parts s4ales by the Honda Motors Company and epidemics at naval training centers, to develop criteria for retention of marine recruits, optimal tariffs for Federal Express, and multitiered pricing plans for Delta Airlines. And these are just examples in which I’ve been personally involved!

9.1 MODELS

A model in statistics is simply a way of expressing a quantitative relationship between one variable, usually referred to as the dependent variable, and one or more other variables, often referred to as the predictors. We began our text with a reference to Boyle’s law for the behavior of perfect gases, V = KT/P. In this version of Boyle’s law, V (the volume of the gas) is the dependent variable; T (the temperature of the gas) and P (the pressure exerted on and by the gas) are the predictors; and K (known as Boyle’s constant) is the coefficient of the ratio T/P.

An even more familiar relationship is that between the distance S traveled in t hours and the velocity V of the vehicle in which we are traveling: S = Vt. Here, S is the dependent variable, and V and t are predictors. If we travel at a velocity of 60 mph for ...