Abstract
The multiple linear regression model is the most commonly applied statistical technique for relating a set of two or more variables. In Chapter 3 the concept of a regression model was introduced to study the relationship between two quantitative variables X and Y. In the latter part of Chapter 3, the impact of another explanatory variable Z on the regression relationship between X and Y was also studied. It was shown that by extending the regression to include the explanatory variable Z, the relationship between Y and X can be studied while controlling or taking into account Z. In a multivariate setting, the regression model can be extended so that Y can be related to a set of p explanatory variables X 1, X 2, …, X p . In this chapter, an extensive outline of the multiple linear regression model and its applications will be presented. A data set to be used as a multiple regression example is described next.
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Jobson, J.D. (1991). Multiple Linear Regression. In: Applied Multivariate Data Analysis. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0955-3_4
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DOI: https://doi.org/10.1007/978-1-4612-0955-3_4
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