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“Finding the Outliers that Matter.” Journal of the Royal Statistical Society, Series B 40, 85–93.Ītkinson, A.C. This process is experimental and the keywords may be updated as the learning algorithm improves.Īndrews, D.F. These keywords were added by machine and not by the authors. A data set to be used as a multiple regression example is described next. In this chapter, an extensive outline of the multiple linear regression model and its applications will be presented. 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. 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 the latter part of Chapter 3, the impact of another explanatory variable Z on the regression relationship between X and Y was also studied. In Chapter 3 the concept of a regression model was introduced to study the relationship between two quantitative variables X and Y.

The multiple linear regression model is the most commonly applied statistical technique for relating a set of two or more variables.

The model’s error term (also known as the residuals) Slope coefficients for each explanatory variable

MLR is used extensively in econometrics and financial inference.įormula and Calculation of Multiple Linear Regression.

Multiple regression is an extension of linear (OLS) regression that uses just one explanatory variable.

Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.