Regression Models

Wolfgang Karl Härdle and Leopold Simar
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Wolfgang Karl Härdle: Humboldt-Universität zu Berlin, C.A.S.E. Centre f. Appl. Stat. & Econ. School of Business and Economics

Chapter Chapter 8 in Applied Multivariate Statistical Analysis, 2015, pp 253-280 from Springer

Abstract: Abstract The aim of regression models is to model the variation of a quantitative response variable y in terms of the variation of one or several explanatory variables (x 1, …, x p )⊤. We have already introduced such models in Chaps. 3 and 7 where linear models were written in ( 3.50 ) as $$\displaystyle{y = \mathcal{X}\beta +\varepsilon,}$$ where y(n × 1) is the vector of observation for the response variable, $$\mathcal{X}(n \times p)$$ is the data matrix of the p explanatory variables and $$\varepsilon$$ are the errors.

Keywords: Logit Model; Contingency Table; Design Matrix; ANOVA Model; Free Cell (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-662-45171-7_8

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