Ø INTRODUCTION

   In the laboratory environment, massive data is generated as a result of the continuing measurements, testing and calibration processes. In this training program participants will discover how to derive technically and managerially meaningful facts from the information gathered through their measurements and data collection programs. A considerable variety of the quantitative and qualitative techniques will be presented to enable the laboratory professionals target and collect the right data types and volumes, validate data collected, aggregate and summarize masses of data, characterize the operations, derive inferences and optimize the parameters of their processes.

Emphasis is placed on the practicality and applicability of the techniques presented. This training will concentrate on the philosophy and understanding of the statistical analytical principles required for conducting sound scientific investigations and validation of the

laboratory processes. Participants will have the opportunity to apply the principles and techniques learned to actual laboratory problems through the use of illustrative case studies under the guidance of the instructor.

Ø Objectives

At this program's conclusion, participants should be able to:

l  Define the data categories relevant to laboratory functions.

l  Design and implement measurement and data collection programs.

l  Check data adequacy, validity and integrity.

l  Detect and eliminate outliers to assure data correctness and consistency.

l  Summarize data for effective reporting and communication.

l  Define what statistical test to use and carry out significance tests for different purposes.

l  Perform repeatability and reproducibility studies and other measurement system

l  analysis procedures.

l  Validate measurement processes.

l  Apply Statistical Process Control (SPC) and capability studies to the measurement processes.

l  Manage inter-laboratory studies and proficiency tests and interpret the results

Ø TRAINING METHODOLOGY

This training course will combine presentations with instructor-guided interactive discussions between participants relating to their individual workplace. Practical exercises, video material and case studies aiming at stimulating these discussions and providing maximum benefit to the participants will support the training.

This interactive training course includes the following training methodologies as a percentage of the total tuition hours:

l  30% Lectures, Concepts, Role Play

l  30% Workshops & Work Presentations, Techniques

l  20% Based on Case Studies & Practical Exercises

l  20% Videos, Software & General Discussions

Pre and Post Test

Ø WHO SHOULD ATTEND?

l  Laboratory managers, supervisors, engineers, and chemists, in addition to other quality assurance and quality control professionals who are interested in managing their functions in accordance with a data-based approach.

l  The training program is designed to reply the needs of all laboratory professionals in oil and gas plants, pharmaceutical companies, petrochemical industries, food manufacturing companies and other enterprises involved in mass and continuous production

                                                                               Outline

Day 1

l  Laboratory functions, systems and culture

l  Laboratory’s role in enterprise performance

l  Fundamentals, dimensions and determinants of quality in laboratory functions

l  Quality planning, control and improvement

l  The major quality initiatives

l  Laboratory quality assurance procedures in the international standards

l  Types of data in laboratory environment

l  Laboratory information management

l  Procedures for assuring validity and quality in laboratory data

Day  2

l  Categories of data and purposes of data collection and analysis

l  Data collection techniques and tools

l  Data adequacy, quality, stability and integrity

l  Detection and handling of outliers

l  Statistical terminology

l  Populations and samples

l  Descriptive and inferential statistics

l  Descriptive statistics (centralization and dispersion)

l  Concept, sources and measurement of variation

l  Graphical data displaying (histograms and charts)

l  Estimation of parameters, proportions and statistics

Day 3

l  Modeling massive data as probability distributions

l  Measuring association between the qualitative variables

l  X-Y covariance and correlation analysis

l  Computing, validating and applying regression models

l  Setting and testing of hypothesis

l  Computing confidence intervals to express uncertainty

l   Robust statistical analysis of laboratory data

l   Statistical analysis of calibration data

Day 4

 l  Measurement system analysis (accuracy and precision)

l   Assessing the bias, stability and linearity

l   Analysis of gage repeatability and reproducibility

l   Statistical process control (SPC) and control charts

l   Measuring process capability (performance and potential indicators)

l   Estimating the process yield

l   Measurement method validation

Day 5

l  Measurement uncertainty: concepts and sources

l   Errors and uncertainty: understanding the differences

l   Type A and Type B uncertainty

l   Combined and expanded uncertainty

l   Computing and reporting measurement uncertainty

l   Inter-laboratory studies and proficiency testing (PT)

l   Scoring and analysis of proficiency test results

l   Understanding and acting on Z-Scores in proficiency testing