Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Applied Statistics on 12/02/2016, available online: http://www.tandfonline.com/10.1080/02664763.2015.1117584
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Analysis of means
T2 - a generalized approach using R
AU - Pallmann, Philip Steffen
AU - Hothorn, Ludwig A.
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Applied Statistics on 12/02/2016, available online: http://www.tandfonline.com/10.1080/02664763.2015.1117584
PY - 2016
Y1 - 2016
N2 - Papers on the analysis of means (ANOM) have been circulating in the quality control literature for decades, routinely describing it as a statistical stand-alone concept. Therefore we clarify that ANOM should rather be regarded as a special case of a much more universal approach known as multiple contrast tests (MCTs). Perceiving ANOM as a grand-mean-type MCT paves the way for implementing it in the opensource software R. We give a brief tutorial on how to exploit R's versatility and introduce R package ANOM for drawing the familiar decision charts. Beyond that, we illustrate two practical aspects of data analysis with ANOM: rstly, we compare merits and drawbacks of ANOM-type MCTs and ANOVA F-test and assess their respective statistical powers, and secondly, we show that the benet of using critical values from multivariate t-distributions for ANOM instead of simple Bonferroni quantiles is oftentimes negligible.
AB - Papers on the analysis of means (ANOM) have been circulating in the quality control literature for decades, routinely describing it as a statistical stand-alone concept. Therefore we clarify that ANOM should rather be regarded as a special case of a much more universal approach known as multiple contrast tests (MCTs). Perceiving ANOM as a grand-mean-type MCT paves the way for implementing it in the opensource software R. We give a brief tutorial on how to exploit R's versatility and introduce R package ANOM for drawing the familiar decision charts. Beyond that, we illustrate two practical aspects of data analysis with ANOM: rstly, we compare merits and drawbacks of ANOM-type MCTs and ANOVA F-test and assess their respective statistical powers, and secondly, we show that the benet of using critical values from multivariate t-distributions for ANOM instead of simple Bonferroni quantiles is oftentimes negligible.
KW - ANOVA F-test
KW - multiple contrast test
KW - multivariate t-distribution
KW - control chart
KW - industrial quality assessment
U2 - 10.1080/02664763.2015.1117584
DO - 10.1080/02664763.2015.1117584
M3 - Journal article
VL - 43
SP - 1541
EP - 1560
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
IS - 8
ER -