Skip to Main Content

KEY CONCEPTS

KEY CONCEPTS

  • Image not available.The choice of statistical methods depends on the research question, the scales on which the variables are measured, and the number of variables to be analyzed.

  • Image not available.Many of the advanced statistical procedures can be interpreted as an extension or modification of multiple regression analysis.

  • Image not available.Many of the statistical methods used for questions with one independent variable have direct analogies with methods for multiple independent variables.

  • Image not available.The term “multivariate” is used when more than one independent variable is analyzed.

  • Image not available.Multiple regression is a simple and ideal method to control for confounding variables.

  • Image not available.Multiple regression coefficients indicate whether the relationship between the independent and dependent variables is positive or negative.

  • Image not available.Dummy, or indicator, coding is used when nominal variables are used in multiple regression.

  • Image not available.Regression coefficients indicate the amount the change in the dependent variable for each one-unit change in the X variable, holding other independent variables constant.

  • Image not available.Multiple regression measures a linear relationship only.

  • Image not available.The Multiple R statistic is the best indicator of how well the model fits the data—how much variance is accounted for by the model.

  • Image not available.Several methods can be used to select variables in a multivariate regression.

  • Image not available.Polynomial regression can be used when the relationship is curvilinear.

  • Image not available.Cross-validation tell us how applicable the model will be if we used it in another sample of subjects.

  • Image not available.A good rule of thumb is to have ten times as many subjects as variables.

  • Image not available.Analysis of covariance controls for confounding variables; it can be used as part of analysis of variance or in multiple regression.

  • Image not available.Logistic regression predicts a nominal outcome; it is the most widely used regression method in medicine.

  • Image not available.The regression coefficients in logistic regression can be transformed to give odds ratios.

  • Image not available.The Cox model is the multivariate analogue of the Kaplan–Meier curve; it predicts time-dependent outcomes when there are censored observations.

  • Image not available.The Cox model is also called the proportional hazard model; it is one of the most important statistical methods in medicine.

  • Image not available.Meta-analysis provides a way to combine the results from several studies in a quantitative way and is especially useful when studies have come to opposite conclusions or are based on small samples.

  • Image not available.An effect size is a measure of the magnitude of differences between two groups; it is a useful concept in estimating sample sizes.

  • Image not available.The Cochrane Collection is a set of very well-designed meta-analyses and is available at libraries and online.

  • Image not available.Several methods are available when the goal is to classify subjects into groups.

  • Image not available.Multivariate analysis of variance, or MANOVA, is analogous to using ANOVA when there are several dependent variables.

PRESENTING PROBLEMS

Presenting Problem 1

In Chapter 8, we examined the study by Neidert and colleagues (2016), studying the relationship between body composition measurements: plasma DPP-IV activity, gynoid fat, BMI, and lean mass.

The full article may be found here:

Neidert LE, ...

Pop-up div Successfully Displayed

This div only appears when the trigger link is hovered over. Otherwise it is hidden from view.