Data di Pubblicazione:
2013
Citazione:
Comparing regression relationships: application to chemically modified electrodes / S. Benedetti, B. Brunetti, M.S. Cosio, E. Desimoni. ((Intervento presentato al 24. convegno Congresso della Divisione di Chimica Analitica della Società Chimica Italiana tenutosi a Sestri Levante nel 2013.
Abstract:
When developing new chemically modified electrodes (CMEs), as currently
done in this laboratory, attention is always paid to carefully evaluate their
experimental performances [1-3]. This is especially needed after some
tentative modifications of experimental conditions. Comparing regression
relationships (RRs), usually obtained by ordinary least square regression,
may help in deciding the right direction towards the optimization of
electrode performances. This implies evaluating if significant differences
exist between slopes and/or intercepts of RRs, hence if experimental
differences arise only by inevitable random errors or not. In practice, the
necessary tests should allow verifying if the considered regression
relationships come from the same RRs population from which the actual
samples are drawn. It was suggested that such a kind of comparisons can be
performed by specific Student’s t-tests [4]. The flow chart for comparing
two RRs is presented in the figure.
H0: b1=b2
H0: not rejected H0: rejected
1 Compute common
slope
2 H0: a1=a2
END
H0: not rejected H0: rejected
1 Compute common END
regression relationship
2 END
(regression lines are significantly different)
(regression lines are parrallel)
Figure: Flow chart for the regression of two RRs; b: slopes; a: intercepts.
Comparing more that two RRs is also possible by similar tests [4]. The matter is presented with the help of specifically developed Mathcad worksheets. Some examples are described by using simulated results as well
as results relevant to CMEs developed in this laboratory.
[1] E. Desimoni, Analyst, 124 (1999) 1191-1196
[2] E. Desimoni, B. Brunetti, Anal. Chim. Acta, 655 (2009) 30-37
[3] E. Desimoni, B. Brunetti, Accred. Qual. Assur., 17 (2012) 635-637
[4] J.H. Zar, Biostatistical analysis, 5th Ed., Prentice Hall, Inc., Upper Saddle River, New Jersey, US, 2010
Tipologia IRIS:
14 - Intervento a convegno non pubblicato
Elenco autori:
S. Benedetti, B. Brunetti, M.S. Cosio, E. Desimoni
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