Statistical Data Treatment and Evaluation - Quantitative Analysis - Lecture Slides, Slides for Analytical Chemistry

Analytical Chemistry

Description: This course is for chemistry students. Many methods for Quantitative Analysis are explained in this course. This lecture is about: Statistical Data Treatment and Evaluation, Statistical Data Treatment, Evaluation, Confidence Interval, Confinence Limits, Standard Deviation, Detecting Gross Errors, Using the Q Test, Least-Squares Method, Assumptions of the Least-Squares Method
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Statistical Data Treatment
and Evaluation
Experimentalist use statistical calculations to sharpen
their judgments concerning the quality of experimental
measurements. These applications include:
Defining a numerical interval around the mean of a set
of replicate analytical results within which the
population mean can be expected to lie with a certain
probability. This interval is called the confidence
interval (CI).
Determining the number of replicate
measurements required to ensure at a given
probability that an experimental mean falls
within a certain confidence interval.
Estimating the probability that (a) an
experimental mean and a true value or (b) two
experimental means are different.
Deciding whether what appears to be an outlier
in a set of replicate measurements is the result of
a gross error or it is a legitimate result.
Using the least-squares method for constructing
calibration curves.
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