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Review
. 2010 Nov;107(44):776-82.
doi: 10.3238/arztebl.2010.0776. Epub 2010 Nov 5.

Linear regression analysis: part 14 of a series on evaluation of scientific publications

Astrid Schneider  1 , Gerhard Hommel, Maria Blettner
Affiliations
Review

Linear regression analysis: part 14 of a series on evaluation of scientific publications

Astrid Schneider et al. Dtsch Arztebl Int. 2010 Nov.

Abstract

Background: Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication.

Methods: This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience.

Results: After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately.

Conclusion: The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

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Figures

Figure 1
Figure 1
A scatter plot showing a linear relationship
Figure 2
Figure 2
A scatter plot showing an exponential relationship. In this case, it would not be appropriate to compute a coefficient of determination or a regression line
Figure 3
Figure 3
A scatter plot and the corresponding regression line and regression equation for the relationship between the dependent variable body weight (kg) and the independent variable height (m). r = Pearsons’s correlation coefficient R-squared linear = coefficient of determination
None
See this image and copyright information in PMC

References

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