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  • 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.
  • 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 Problem 1

In Chapter 8 we examined the study by Jackson and colleagues (2002) who evaluated the relationship between BMI and percent body fat. Please refer to that chapter for more details on the study. We found a significant relationship between these two measures and calculated a correlation coefficient of r = 0.73. These investigators knew, however, that variables other than BMI may also ...

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