Abstract
Another widely used analysis method originating from statistics is linear regression. Since many application problems require the prediction of a numerical output variable, such as for forecasting stock prices, temperatures, or sales, such models are often used in economics, climate science, and marketing. In this chapter, we introduce ordinary least squares (OLS) linear regression models, including methods for diagnosing such models. Furthermore, we discuss extended models that allow interaction terms, nonlinearities, or categorical predictors. Finally, we introduce generalized linear models (GLMs), which allow the response variable to have a distribution other than a normal distribution, thus enabling a flexible modeling of the response.
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References
R. Bender, Introduction to the use of regression models in epidemiology, in Cancer Epidemiology (Springer, Berlin, 2009), pp. 179–195.
F. Emmert-Streib, S. Moutari, M. Dehmer, Mathematical foundations of data science using R. (Walter de Gruyter GmbH & Co KG, Berlin, 2020).
R.A. Gordon, Regression analysis for the social sciences (Routledge, Milton Park, 2015).
F.E. Harrell, Regression modeling strategies (Springer, New York, 2001).
T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning: data mining, inference and prediction (Springer, New York, 2009).
R.L. Kaufman, Heteroskedasticity in regression: detection and correction, vol. 172 (Sage Publications, Thousand Oaks, 2013).
D.G. Kleinbaum, L.L. Kupper, Applied regression analysis and other multivariable methods. (Duxbury Press, London, 1978).
J.S. Long, The origins of sex differences in science. Soc. Forces 68, 1297–1315 (1990).
S. Sheather, A modern approach to regression with R (Springer Science & Business Media, Berlin, 2009).
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Emmert-Streib, F., Moutari, S., Dehmer, M. (2023). Linear Regression Models. In: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R. Springer, Cham. https://doi.org/10.1007/978-3-031-13339-8_11
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DOI: https://doi.org/10.1007/978-3-031-13339-8_11
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Online ISBN: 978-3-031-13339-8
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