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The Linear Regression Model

  • Helge Toutenburg
  • Shalabh
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

The main focus of this chapter will be the linear regression model and its basic principle of estimation.We introduce the fundamental method of least squares by looking at the least squares geometry and discussing some of its algebraic properties.

In empirical work, it is quite often appropriate to specify the relationship between two sets of data by a simple linear function. For example, we model the influence of advertising time on the number of positive reactions from the public. From the scatterplot in Figure 3.1 one could suspect a linear function between advertising time (x{axis) and the number of positive reactions (y{axis). The study was done on 66 people in order to investigate the impact and cognition of advertising on TV.

Keywords

Mean Square Error Ordinary Little Square Linear Regression Model Leverage Point Ordinary Little Square Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Institut für StatistikLudwig-Maximilians-UniversitätMünchenGermany
  2. 2.Department of Mathematics & StatisticsIndian Institute of TechnologyKanpurIndia

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