6/29/2023 0 Comments Multiple regression statplus![]() ![]() The well-known phrase “correlation does not imply causation” should be taken seriously. Because the number of hate groups was not randomly assigned to states, there are possible confounding factors that could account for the result. This relation was found with an observational study, not an experimental one. In the previous chapter, we found evidence for a relation between the number of hate groups and votes for Trump. 17.6.2 Example of reporting a factorial ANOVAĥ.1 Trump, votes, and hate groups (again).17.6.1 Example of reporting a multiple regression analysis.17.5 Aim for openness and reproducibility.17.4 Distinguish between confirmatory and exploratory analyses.17.3 Evaluate the assumptions underlying your analyses.17.1 Consider analysis before data collection.16.6 To Bayes or not to Bayes? A pragmatic view.16.5.3 Results of a NHST are often misinterpreted.16.5.2 The \(p\)-value depends on researcher intentions. ![]() 16.5.1 The \(p\)-value is not a proper measure of evidential support.16.5 Some objections to null-hypothesis significance testing.16.4 Bayes factors for General Linear Models.16.2 Parameter estimates and credible intervals.16.1 Hypothesis testing, relative evidence, and the Bayes factor.16 Introduction to Bayesian hypothesis testing.15.1.4 The marginal likelihood and prior predictive distribution.15.1 Fundamentals of Bayesian inference.13.9 Principles in constructing path models.13.7 Evaluation and selection of the mediation path models.13.4.2 Assumptions: Exogenous vs endogenous variables.13.4.1 The multivariate Normal distribution.13.1.1 Exogenous and endogenous variables.12.9 Generalized linear mixed-effects models.12.8.4 Alternative logit models for ordinal categories.12.8.3 Reconstructing probabilities of responses.12.7.2 A three-way table example: Rock-Paper-Scissors.12.6.1 Example: Gestures in different social contexts.12.5.3 Using a different link function: Probit regression.12.5.2 Example: Metacognition in visual perception.12.3 Inference in generalized linear models.11.8 Choosing the random effects structure.11.7 Crossed random effects: dating partners in the speed dating experiment.11.6.1 Correlation between random effects. ![]()
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