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|Title: ||Applied statistics, microcomputers and analytical chemistry|
|Authors: ||Killoran, Gerald N.|
|Issue Date: ||1984|
|Publisher: ||© Gerald N. Killoran|
|Abstract: ||An applied statistics software package, containing a
unique weighted linear regression (WLR) routine, has been
developed. Its features are demonstrated using real and
Monte Carlo simulated data. The WLR routine is particularly
useful for absolute and comparative calibrations.
In the absolute calibration of analytical systems the
statistical analysis of the linear calibration curve
produces an analysis of variance (ANOVA), the curve's
equation and confidence band,
confidence limits for the slope
the regression coefficient,
and intercept, and the
standard error of regression. The routine also computes an
unknown's concentration and its "confidence" limits.
80th simple (SLR) and WLR can be used for absolute
calibration. WLR can be used without knowing the error's
standard deviation (SO); assuming the analytical error is
normally distributed, the SO is a linear function of
concentration or response, and the concentration range of
interest is well above the detection limit. Under these
conditions the computed "standard error of regression" is
the "relative SO" when using WLR, or the "SO" for SLR.
Comparative calibration is used for method validation
and for determining the relative economic and technical
merits of analytical systems.
Ways of estimating a system's precision, as a function
of concentration. are discussed. Two new, simple approaches
are demonstrated. The comparison of a new analytical system
to one of known accuracy, using SLR and WLR, is reviewed.
A previously reported technique for determining the
merits of analytical systems, using only "raw" measurements,
is reviewed and demonstrated far systems having constant
SOs, RSOs. or bath.' The effect of transforming transmittance
measurements to absorbances on the computations is examined.
The software package is also used for descriptive
statistics, Significance tests, and ANOVA. Many additional
features, e.g., normality and outlier checks, residual
analysis, and simulated data generation are demonstrated.
The role of applied statistics and the microcomputer in
chemometrics and the analytical laboratory is discussed.|
|Description: ||A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Appears in Collections:||PhD Theses (Chemistry)|
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