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.
Hardle, W. (1990). Applied nonparametric regression. Cambridge University Press.
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Second International Symposium on Information Theory 267–281.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics6 461–464.
Agresti, A. (1990). Categorical Data Analysis. Wiley.
Dobson, A. J. (2001). An introduction to generalized linear models. Chapman & Hall.
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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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