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Linear and Logistic Regression

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All of Statistics

Part of the book series: Springer Texts in Statistics ((STS))

Abstract

Regression is a method for studying the relationship between a response variable Y and a covariateX. The covariate is also called a predictor variable or a feature.

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Bibliographic Remarks

  • Weisberg, S. (1985). Applied Linear Regression. Wiley.

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  • Hardle, W. (1990). Applied nonparametric regression. Cambridge University Press.

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  • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Second International Symposium on Information Theory 267–281.

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  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics6 461–464.

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  • Agresti, A. (1990). Categorical Data Analysis. Wiley.

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  • Dobson, A. J. (2001). An introduction to generalized linear models. Chapman & Hall.

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© 2004 Springer Science+Business Media New York

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Wasserman, L. (2004). Linear and Logistic Regression. In: All of Statistics. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21736-9_13

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  • DOI: https://doi.org/10.1007/978-0-387-21736-9_13

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-2322-6

  • Online ISBN: 978-0-387-21736-9

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