The Essentials on Linear Regression, ANOVA, General Linear and Linear Mixed Models for the Chemist

Bernadette Govaerts (), Bernard G. Francq (), Rebecca Marion (), Manon Martin () and Michel Thiel ()
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Bernadette Govaerts: Université catholique de Louvain, LIDAM/ISBA, Belgium
Bernard G. Francq: Université catholique de Louvain, LIDAM/ISBA, Belgium
Rebecca Marion: Université catholique de Louvain, LIDAM/ISBA, Belgium
Manon Martin: Université catholique de Louvain, LIDAM/ISBA, Belgium
Michel Thiel: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2020011, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: This article provides a brief and accessible guide for implementing general, ANOVA and linear mixed models for the analysis of real world data from chemistry, industrial, bio or life-sciences experiments. The reader is introduced to the main concepts and vocabulary of the subject and to the main statistical methods available for formalizing, estimating and performing statistical inference on these models. Mathematical notations are not avoided but kept accessible, methods are illustrated on several detailed applications and the use of appropriate computer software is discussed.

Keywords: Analysis of Variance (ANOVA); Experimental design analysis; General linear model (GLM); Least squares estimators; Linear mixed model (LMM); Linear regression; Maximum likelihood estimation; R Software; Random effect; SAS; Statistical modeling (search for similar items in EconPapers)
Date: 2020-05-26
Note: In: 2nd Edition, Comprehensive Chemometrics, Chemical and Biochemical Data Analysis, Elsevier 2020, p. 431-463
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2020011

DOI: 10.1016/b978-0-12-409547-2.14579-2

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