Correlation and simple linear regression.

By Kelly H. Zou, Kemal Tuncali, Stuart G. Silverman. Radiology, 2003

1,102 citations recorded by the source corpus.

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Subfields: Epidemiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/df47f2b448375bb301e0fddf91cd9fc11a134f52

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Citations in the corpus

• Modeling the seed yield of Ajowan ( Trachyspermum ammi L.) using artificial neural network and multiple linear regression models (2018, background): “Multiple linear regression (MLR) refers to linear relationship of a dependent variable as function of the multiple independent variables (Quirk, 2016).”