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.
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© 2009 Springer Science+Business Media, LLC
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Toutenburg, H., Shalabh (2009). The Linear Regression Model. In: Statistical Analysis of Designed Experiments, Third Edition. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1148-3_3
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DOI: https://doi.org/10.1007/978-1-4419-1148-3_3
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-1147-6
Online ISBN: 978-1-4419-1148-3
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