Coverpage by Douglas C. Montgomery, George C. Runger

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This chapter generalizes the simple linear regression to a situation where there is more than one predictor or regressor variable. This situation occurs frequently in science and engineering; for example, in Chapter 1 we provided data on the pull strength of a wire bond on a semiconductor package and illustrated its relationship to the wire length and the die height. Understanding the relationship between strength and the other two variables may provide important insight to the engineer when the package is designed, or to the manufacturing personnel who assemble the die into the package. We used a multiple linear regression model to relate strength to wire length and die height. There are many examples of such relationships: The life of a cutting tool is related to the cutting speed and the tool angle; patient satisfaction in a hospital is related to patient age, type of procedure performed, and length of stay; and the fuel economy of a vehicle is related to the type of vehicle (car versus truck), engine displacement, horsepower, type of transmission, and vehicle weight. Multiple regression models give insight into the relationships between these variables that can have important practical implications.

This chapter shows how to fit multiple linear regression models, perform the statistical tests and confidence procedures that are analogous to those for simple linear regression, ...

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