%0 Journal Article %1 Guyatt1995a %A Guyatt, G %A Walter, S %A Shannon, H %A Cook, D %A Jaeschke, R %A Heddle, N %D 1995 %J CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne %K DataInterpretation Female Humans LogisticModels Male OddsRatio RegressionAnalysis Statistical StatisticsasTopic %N 4 %P 497-504 %T Basic statistics for clinicians: 4. Correlation and regression. %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1337703&tool=pmcentrez&rendertype=abstract %V 152 %X Correlation and regression help us to understand the relation between variables and to predict patients' status in regard to a particular variable of interest. Correlation examines the strength of the relation between two variables, neither of which is considered the variable one is trying to predict (the target variable). Regression analysis examines the ability of one or more factors, called independent variables, to predict a patient's status in regard to the target or dependent variable. Independent and dependent variables may be continuous (taking a wide range of values) or binary (dichotomous, yielding yes-or-no results). Regression models can be used to construct clinical prediction rules that help to guide clinical decisions. In considering regression and correlation, clinicians should pay more attention to the magnitude of the correlation or the predictive power of the regression than to whether the relation is statistically significant.

@article{Guyatt1995a, abstract = {Correlation and regression help us to understand the relation between variables and to predict patients' status in regard to a particular variable of interest. Correlation examines the strength of the relation between two variables, neither of which is considered the variable one is trying to predict (the target variable). Regression analysis examines the ability of one or more factors, called independent variables, to predict a patient's status in regard to the target or dependent variable. Independent and dependent variables may be continuous (taking a wide range of values) or binary (dichotomous, yielding yes-or-no results). Regression models can be used to construct clinical prediction rules that help to guide clinical decisions. In considering regression and correlation, clinicians should pay more attention to the magnitude of the correlation or the predictive power of the regression than to whether the relation is statistically significant.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Guyatt, G and Walter, S and Shannon, H and Cook, D and Jaeschke, R and Heddle, N}, biburl = {https://www.bibsonomy.org/bibtex/25f896b18a3e1e0a52a632ac89bb71392/jepcastel}, interhash = {fe8b64e43d443fab34319013baa24853}, intrahash = {5f896b18a3e1e0a52a632ac89bb71392}, issn = {0820-3946}, journal = {CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne}, keywords = {DataInterpretation Female Humans LogisticModels Male OddsRatio RegressionAnalysis Statistical StatisticsasTopic}, month = {3}, note = {2829<m:linebreak></m:linebreak>Anàlisi de dades; Introductori}, number = 4, pages = {497-504}, pmid = {7859197}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Basic statistics for clinicians: 4. Correlation and regression.}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1337703&tool=pmcentrez&rendertype=abstract}, volume = 152, year = 1995 }