tailieunhanh - Báo cáo khoa học: "Statistics review 7: Correlation and regression"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Statistics review 7: Correlation and regression. | Available online http content 7 6 451 Review Statistics review 7 Correlation and regression Viv Bewick1 Liz Cheek1 and Jonathan Ball2 Senior Lecturer School of Computing Mathematical and Information Sciences University of Brighton Brighton UK 2Lecturer in Intensive Care Medicine St George s Hospital Medical School London UK Correspondence Viv Bewick Published online 5 November 2003 Critical Care 2003 7 451-459 DOI cc2401 This article is online at http content 7 6 451 2003 BioMed Central Ltd Print ISSN 1364-8535 Online ISSN 1466-609X Abstract The present review introduces methods of analyzing the relationship between two quantitative variables. The calculation and interpretation of the sample product moment correlation coefficient and the linear regression equation are discussed and illustrated. Common misuses of the techniques are considered. Tests and confidence intervals for the population parameters are described and failures of the underlying assumptions are highlighted. Keywords coefficient of determination correlation coefficient least squares regression line Introduction The most commonly used techniques for investigating the relationship between two quantitative variables are correlation and linear regression. Correlation quantifies the strength of the linear relationship between a pair of variables whereas regression expresses the relationship in the form of an equation. For example in patients attending an accident and emergency unit A E we could use correlation and regression to determine whether there is a relationship between age and urea level and whether the level of urea can be predicted for a given age. Scatter diagram When investigating a relationship between two variables the first step is to show the data values graphically on a scatter diagram. Consider the data given in Table 1. These are the ages years and the logarithmically transformed admission serum urea natural logarithm ln urea for