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Statistical Data Treatment and Evaluation - Quantitative Analysis - Lecture Slides

Slides, Analytical Chemistry

Post: September 26th, 2013
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
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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Contents
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. CONFINENCE LIMITS _ Confidence limits define a numerical interval..

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