泊松回归
过度分散
负二项分布
计数数据
统计
计量经济学
回归分析
回归诊断
准似然
广义线性模型
泊松分布
数学
二项回归
回归
零膨胀模型
线性回归
横截面线性回归法
真线性模型
多项式回归
人口
社会学
人口学
作者
William Gardner,Edward P. Mulvey,Esther Shaw
标识
DOI:10.1037/0033-2909.118.3.392
摘要
The regression models appropriate for counted data have seen little use in psychology. This article describes problems that occur when ordinary linear regression is used to analyze count data and presents 3 alternative regression models. The simplest, the Poisson regression model, is likely to be misleading unless restrictive assumptions are met because individual counts are usually more variable ("overdispersed") than is implied by the model. This model can be modified in 2 ways to accomodate this problem. In the overdispersed model, a factor can be estimated that corrects the regression model's inferential statistics. In the second alternative, the negative binomial regression model, a random term reflecting unexplained between-subject differences is included in the regression model. The authors compare the advantages of these approaches.
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