<ul><li>Using advanced data analysis techniques like crPlots, influence.measures, splines, gam, glm, lmer, rlm, bootstrapping, mice, selection, and cross-validation can lead to unbiased and reliable results.</li><li>Ignoring influential points or nonlinearity can result in biased estimates and overconfidence in results.</li><li>Assuming linearity can obscure true effects, leading to incorrect policy or business decisions.</li><li>Using OLS on counts or proportions violates distributional assumptions, producing nonsensical predictions.</li></ul>

Advanced Data Analysis: Elevating Your Skills with Modern Techniques

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