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How do you choose the best linear regression model in R?

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Open access•Posted Content•DOI Significant non-linearity in nitrous oxide chamber data and its effect on calculated annual emissions P.C. Stolk, Cor Jacobs, Eddy Moors, A. Hensen, G. L. Velthof, Pavel Kabat - Show less +5 more 06 Jan 2009-Biogeosciences Discussions 21 Citations	The non-linear regression model should be used if the fit is significantly better.
Journal Article•DOI An Investigation of the Fit of Linear Regression Models to Data from an SAT® Validity Study Jennifer L. Kobrin, Sandip Sinharay, Shelby J. Haberman, Michael Chajewski - Show less +3 more 01 Jun 2011-ETS Research Report Series 10 Citations	The results suggest that the linear regression model mostly provides an adequate fit to the data and that more complicated models do not significantly improve the prediction of FYGPA from SAT scores and HSGPA.
Proceedings Article•DOI Application of Improved Multiple Linear Regression Method in Oilfield Output Forecasting Liang Guo, Xianghui Deng - Show less +1 more 26 Dec 2009 14 Citations	The improved multiple linear regression model is better than the multiple linear regression model in the forecast accuracy of the oilfield output.
Journal Article•DOI Determination of kinetic parameters by direct non-linear regression from TG curves Václav Slovák 10 May 2001-Thermochimica Acta 42 Citations	The method of non-linear regression appears to be the best of the used methods from this point of view.
Proceedings Article•DOI Heuristic prediction of rainfall using machine learning techniques Chandrasegar Thirumalai, K Sri Harsha, M Lakshmi Deepak, K Chaitanya Krishna - Show less +3 more 11 May 2017 28 Citations	Linear regression helps to find
Open access•Journal Article•DOI OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis. Kianoush Fathi Vajargah, Homayoun Sadeghi-Bazargani, Robab Mehdizadeh-Esfanjani, Daryoush Savadi-Oskouei, Mehdi Farhoudi - Show less +4 more 30 Aug 2012-Neuropsychiatric Disease and Treatment 15 Citations	In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.
Open access•Book Chapter•DOI Comparison of shape regression methods under landmark position uncertainty Nora Baka, Coert Metz, Michiel Schaap, Boudewijn P. F. Lelieveldt, Wiro J. Niessen, Marleen de Bruijne - Show less +5 more 18 Sep 2011 9 Citations	Results show that in the first scenario linear regression methods tend to perform similar.

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What is linear regression model?5 answersLinear regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It assumes that the dependent variable is linearly correlated with the independent variables. The goal of linear regression is to find the optimal parameters of the linear regression model by minimizing the sum of squares of the residuals. It is widely used in prediction tasks due to its intuitiveness and interpretability. Linear regression is considered the foundation of statistical learning theory and is often used in various fields such as computer science, big data analysis, engineering, and science.

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