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- W1829609079 abstract "This chapter considers the analysis of nutritional data. It includes summarising data, producing estimates of effects and differences and testing them. The chapter also talks about techniques such as principal component analysis that are exploratory, looking for patterns in data. This essentially is statistics, which may be defined as the study of variation. It covers a number of topics in the handling and interpretation of nutritional data. Regression modelling is the most commonly used technique for linking one variable to others. Analysis of variance, often shortened to ANOVA, is, along with regression, one of the standard ways of analysing and interpreting nutritional (or any other scientific) data. Morbidity and mortality can be regarded as outcomes in an experiment or observational study that have the characteristic of being binary: for each individual they occur or they do not. The chapter explain the basics of principal component analysis (PCA) and Bayesian statistics." @default.
- W1829609079 created "2016-06-24" @default.
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- W1829609079 date "2015-05-29" @default.
- W1829609079 modified "2023-09-26" @default.
- W1829609079 title "Methods of Data Analysis" @default.
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- W1829609079 doi "https://doi.org/10.1002/9781119180425.ch7" @default.
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