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Journal ArticleDOI

Categorical Data Analysis

Alan Agresti
- 01 May 1991 - 
- Vol. 33, Iss: 1, pp 241-241
TLDR
In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
Abstract
categorical data analysis , categorical data analysis , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

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Journal ArticleDOI

Bias in meta-analysis detected by a simple, graphical test

TL;DR: Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials.
Journal ArticleDOI

PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Journal ArticleDOI

Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study

TL;DR: In patients with type 2 diabetes the risk of diabetic complications was strongly associated with previous hyperglycaemia, with the lowest risk being in those with HbA1c values in the normal range (<6.0%).
Journal ArticleDOI

Generalized linear mixed models: a practical guide for ecology and evolution

TL;DR: The use (and misuse) of GLMMs in ecology and evolution are reviewed, estimation and inference are discussed, and 'best-practice' data analysis procedures for scientists facing this challenge are summarized.
Journal ArticleDOI

Predictive habitat distribution models in ecology

TL;DR: A review of predictive habitat distribution modeling is presented, which shows that a wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management.