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5 Model Validation and Prediction 5.1 INTRODUCTION From a mathematical perspective, validation is the process of assessing whether or not the quantity of interest (QOI) for a physical system is within some tolerance—determined by the intended use of the model—of the model prediction.

The process of validation and prediction, explored in previous publications (e.g., Klein et al., 2006; NRC, 2007, Chapter 4), is described in this chapter from a more mathematical perspective.Aim of Course: This online course, "Categorical Data Analysis" will focus on a logistic regression approach for the analysis of contingency table data, where the cell entries represent counts that are cross-tabulated using categorical variables.Tests for (conditional) independence are discussed in the context of odds-ratios and relative risks, for both two-way and three-way data tables.For example, we can make a good guess about the number of points a basketball player will score in his next game based on the player's historical performance, the opponent's quality, and various other factors.But it's impossible to account for every variable that affects the number of points scored every game.I’m going to use a hypothetical example so that you can see how it works when we really know the correct model to use.