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# L17handout - PUBH 7430 Midterm review(Lecture 17 J Wolfson...

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Unformatted text preview: PUBH 7430 Midterm review (Lecture 17) J. Wolfson Division of Biostatistics University of Minnesota School of Public Health November 1, 2011 Preliminaries: bivariate dependence The correlation coefficient • Know the formula: ρ ( X , Y ) = Cov ( X , Y ) p Var ( X ) p Var ( Y ) • Intuition: Covariances depend on scale of random variables, correlation gives scaled version of covariance • Key properties: [L5, p.6] • Indicates strength of linear relationship • May be affected by outliers • Alternatives: Spearman’s ρ , Kendall’s τ Preliminaries: multivariate dependence Notation • Outcome vector Y i and Y • Mean vectors μ i and μ • Variance-covariance matrices Σ i and Σ • Predictor/covariate vector x ij and matrices X i and X • The linear predictor X β Exploratory data analysis • Wide format and long format for longitudinal data [L3, p. 5] • Cluster-invariant vs. cluster-varying covariates [L6, p. 14] • Paired data: Two-sample, one-sample, and paired t-test [L3, p. 16] Exploratory data analysis Graphical summaries • Spaghetti plots and ways to make them more readable [L4, p. 17] • Within-person residuals : Compare longitudinal patterns (trajectories) by eliminating differences in individual variability [L4, p. 20] • Within-time residuals : Make cross-sectional comparisons by eliminating time trends [L4, p. 22] Exploratory data analysis Smoothers • Basic goal: Estimate the mean response curve non-parametrically • Kernel smoothers: [L4, p. 27] • Kernel determines how to average observations • Bandwidth determines how much influence nearby vs....
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L17handout - PUBH 7430 Midterm review(Lecture 17 J Wolfson...

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