query: Negative values for AIC in General Mixed Model
positive_passages.docid: 28199
positive_passages.text: Negative values for AICc (corrected Akaike Information Criterion)
negative_passages.docid: 4085
negative_passages.text: Cronbach's alpha negative result
negative_passages.docid: 156953
negative_passages.text: Pearson VS Deviance Residuals in logistic regression
negative_passages.docid: 131097
negative_passages.text: Checking residuals for normality in generalised linear models
negative_passages.docid: 108527
negative_passages.text: Interpreting output from anova() when using lm() as input
negative_passages.docid: 24298
negative_passages.text: Justification for low/high or tertiary splits in ANOVA
negative_passages.docid: 55629
negative_passages.text: How to interpret a negative linear regression coefficient for a logged outcome variable?
negative_passages.docid: 123269
negative_passages.text: Are unequal groups a problem for one-way ANOVA?
negative_passages.docid: 19603
negative_passages.text: Conflicting approaches to variable selection: AIC, p-values or both?
negative_passages.docid: 107805
negative_passages.text: Statistical test for positive and negative predictive value
negative_passages.docid: 71537
negative_passages.text: Interpreting a generalised linear mixed model with binomial data
negative_passages.docid: 7113
negative_passages.text: Interpreting exp(B) in multinomial logistic regression
negative_passages.docid: 122420
negative_passages.text: AIC versus Likelihood Ratio Test in Model Variable Selection
negative_passages.docid: 91186
negative_passages.text: Interpretation of AIC value
negative_passages.docid: 43921
negative_passages.text: What does the Akaike Information Criterion (AIC) score of a model mean?
negative_passages.docid: 77184
negative_passages.text: Can I ignore the warning: "tests made without re-estimating 'theta'" from an analysis of deviance test of a negative binomial model?
negative_passages.docid: 137199
negative_passages.text: Comparing the fit of quasi-Poisson and negative binomial models