Lecture 10. Variable Selection and Model Building

Variable Selection and Model Building Dr. Kalanka Jayalath Dept. of Mathematics & Statistics
Lecture Highlights Chapter 10 Model Building Problem. Computational Techniques Strategy for Variable Selection and Model Building 2
3 In most practical problems, the analyst has a rather large pool of possible candidate regressors, of which only a few are likely to be important. Finding an appropriate subset of regressors for the model is often called the variable selection problem . Building a regression model that includes only a subset of the available regressors involves two conflicting objectives. 1. We would like the model to include as many regressors as possible so that the information content in these factors ____________________ value of y . 2. We want the model to include as few regressors as possible because the ________________________________ as the number of regressors increases. Also the more regressors there are in a model, the greater the costs of data collection and model maintenance. The process of finding a model that is a compromise between these two objectives is called selecting the " best " regression equation. Consequences of Model Misspecification
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