:: DEFINE | Management Consultancy & Training Provider In UAE, Abu Dhabi,Dubai,Saudi Arabia ::

Best Practice Of Statistics Method & Measurement Uncertainty Of Chemical Analysis

Location

Amsterdam, Netherlands

Date

From Date: 17-Nov-2025
To Date: 21-Nov-2025

Duration

5 days

Language

English

Discipline

Laboratory Management

Introduction

Majority of modern instruments in the laboratory is computerized and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program or excels spreadsheet. The quantitative chemical measurement has linked with it an uncertainty in the result, which is determined by the performance characteristics of the analytical method used. Measurement uncertainty has often been evaluated on the basis of repeatability and reproducibility data, but the measurement uncertainty, as expressed in the "Guide to the Expression of Uncertainty in Measurement" published by ISO in 2005 goes further and gives general rules for the evaluation of measurement uncertainty based on both statistical and non-statistical uncertainties.

This is an extremely practical course for laboratory employees who want to know how to use data analysis and interpretive methods from statistical theory. The most commonly used mathematical and statistical methods used to analyze chemical data are described and explained using a wide range of examples.

In addition, the Software program Minitab 15 (Free trial) is available for the participants in the course for statistical training. Examination will be conducted at the end of the course.

Objective

To understand how the analysis of data derives from the statement of an analysis problem or hypothesis and the availability of experiential data.
To understand how to conduct a variety of statistical analyses, including testing of statistical assumptions, data transformations, and validation of statistical findings.
Ability to design a data analysis strategy that answers a research or routine work question, including specifications for data elements, requirements of the statistic, and limitations to the interpretation.
To understand how to present and interpret the results of statistical analyses.
To calculate measurement uncertainty in a practical and pragmatic manner.
To identify sources of measurement error.
To use different methods to evaluate measurement uncertainty.
To run statistics comparison between two or three analyses methods.
How to use statistics software program.

Audience

Chemists, analysts, lab technicians and whoever tasked to estimate the uncertainties of measurements in their respective laboratories.

Engineers interested in acquiring an understanding of uncertainty measurements fundamentals and advance knowledge

Lab manager, supervisor or middle laboratory personnel in the management, establishment of laboratory quality system or those involved in testing or calibration activities.

Content

COURSE OUTLINE

INSTRUMENT ANALYSIS DATA
PEAK EVALUATION
INTERPOLATED GRAPH CALIBRATION USING EXTERNAL/ INTERNAL STANDARDS
STANDARD ADDITION METHOD EXTRAPOLATED GRAPH
ERRORS IN QUANTITATIVE ANALYSIS
RANDOM AND SYSTEMATIC ERRORS IN TITRATION ANALYSIS
STANDARD DEVIATION OF REPEATED MEASUREMENTS
DISTRIBUTION OF ERRORS
CONFIDENCE LIMIT OF THE MEAN OF REPLICATE MEASUREMENTS
MEASUREMENT UNCERTAINTY
ERRORS IN INSTRUMENTAL ANALYSIS REGRESSION AND CORRELATION
USE OF REGRESSION LINES FOR COMPARING ANALYTICAL METHODS
CONFIDENCE LIMIT FOR X-VALUE
OUTLIERS IN REGRESSION
LIMIT OF DETECTION
SIGNIFICANCE TESTS FOR EVALUATION OF EXPERIMENTAL RESULTS
(T-TEST) COMPARISON OF A MEAN WITH A KNOWN VALUE
(T-TEST) COMPARISON OF THE MEANS OF TWO SAMPLES WITH S1»S2
(T-TEST) COMPARISON OF THE MEANS OF TWO SAMPLES WITH S1 ¹ S2
PAIRED T-TEST
ONE-TAILED AND TWO-TAILED TESTS
(F-TEST) FOR THE COMPARISON OF STANDARD DEVIATIONS
ANOVA-TEST ANALYSIS OF SEVERAL MEANS AND VARIANCES
TESTING FOR NORMALITY OF DISTRIBUTION
OUTLIERS TEST
NON-PARAMETRIC OR DISTRIBUTION-FREE METHODS
BOX AND WHISKER PLOTS
COMPARISON OF A MEDIAN WITH A KNOWN VALUE (THE SIGN TEST)
CONFIDENCE INTERVAL FOR NON-PARAMETRIC METHODS
COMPARISON OF THE MEDIANS OF TWO METHODS (THE SIGN TEST)
COMPARISON MEDIAN OF TWO UNDEPENDED SAMPLES (WILCOXON RANK-SUM TEST)
COMPARISON SPREAD OF TWO SETS OF NON-PARAMETRIC RESULTS (SIEGEL-TUKEY TEST)
RANK CORRELATION FOR NOT QUANTIFIED RESULTS (SPEARMAN METHOD)
NON-PARAMETRIC METHOD ON MORE THAN TWO SAMPLES (FRIEDMAN´S TEST)
NON-PARAMETRIC REGRESSION METHODS (THEIL´S TEST) QUALITY CONTROL CHARTS
QUALITY CONTROL CHARTS
SHEWHART CHART
CUSUM CHART
EXPERIMENTAL DESIGN AND OPTIMIZATION METHODS
FACTORIAL DESIGNS
ESTIMATION OF FACTORS INTERACTION BY TWO-WAY ANOVA TEST
OPTIMIZATION METHOD
THREE FACTORS DESIGN

NOTE:

Pre & Post Tests will be conducted

Case Studies, Group Exercises, Group Discussions, Last Day Review & Assessments will be carried out.

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Certificate

Define Management Consultancy & Training Certificate of course completion will be issued to all attendees.

Methodology

A highly interactive combination of lectures and discussion sessions will be managed to maximize the amount and quality of information and knowledge transfer. The sessions will start by raising the most relevant questions, and motivate everybody find the right answers. You will also be encouraged to raise your own questions and to share in the development of the right answers using your own analysis and experiences. Tests of multiple-choice type will be made available on daily basis to examine the effectiveness of delivering the course.

All presentations are made in excellent colorful power point. Very useful Course Materials will be given.

30% Lectures
30% Workshops and work presentation
20% Group Work& Practical Exercises
20% Videos& General Discussions

Course Details