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:: Course SummaryThis one-day training covers Statistical Monitoring & Control of Measurement Processes, which comprises an array of statistical tools to reveal sources of variability and make necessary adjustments in laboratory measurement processes.
The goals are:
To master each step of the measurement process in order to increase its performance
To increase the quality of the process by minimizing the impact of assignable sources of variability and by reducing the sources of random error that introduce noise in the data
To ensure that the quality of the process is within technical specifications and that it is reproducible:: Learning ObjectivesUpon completion of this course, participants will have learned:
The importance of statistical monitoring and control of measurement processes
The sources of variability in a measurement process
To identify the type of variability: assignable or random
To master the statistical tools to quantify, understand and interpret process variability
How to improve settings, predict and minimize drifts, increase process performance and stability and lower costs due to poor quality processes
How to calculate, report and interpret results
How to make a decision on the acceptability of the process:: Target AudienceThis applied training session in statistics is intended for all scientific staff who collect quality assurance data and who need to make decisions based on the measurement process based on the results. This course is particularly pertinent for researchers, managers and technicians working in development, quality control and production.:: PrerequisiteParticipants should know the essential tools in statistics - descriptive statistics, both numerical (mean, standard deviation, standard error, etc.) and graphical (histogram, box-plot, scatter plot, etc.), hypothesis testing and confidence intervals - either by having attended the training session Fundamental Tools in Statistics or by possessing a similar background. | | |
:: Topics Covered
- Introduction to Statistical Monitoring and Process Control
- Statistical Tools for Process Control
- Sources of Variation : The 5 « M »'s and Cause & Effect Diagram
- Types of Variability: Assignable and Random Variation
- Overview of Descriptive Statistics to Explore Process Variability: Types of variables, distributions, graphical tools, characterizing distributions, central tendency measures(mean), dispersion measures (standard deviation, range)
- Statistical Inference and the Notion of Sample vs. Population
- Fundamental Distinction between a Standard Deviation and Standard Error
- Properties of the Normal Distribution : Empirical Rule of 68%, 95% and 99.7%
- Confidence and Tolerance Intervals: principle, construction, utilization in SPC and interpretation
- Principle and Utilization of Control Charts
- Basic Control Chart : Case of an observation at regular intervals
- Elements of a Control Chart : Characteristic measured, target, limits
- Construction of a Control Chart and Graphical Representation
- Assumptions Underlying Control Chart
- Interpretation of a Control Chart: Identification of natural or intrinsic process variability and assignable variability (extreme data, drifts, shifts, ...)
- Recommended Actions
- More Elaborate Control Charts : Mean, range, moving average, etc.
- Control Charts for Attributes (counts, proportions)
- Conforming to Specifications
- Proportion of Non-Conforming Units (p-Chart)
- Sampling Methods
- Definition of the Target Population
- Principles in Determining Sample Size and Testing Frequency
- Random and Systematic Sampling
- Other Tools : CUSUM Charts, Process Capability
:: Course ContentThe training covers the fundamentals of Statistical Process Control (SPC).
This course first addresses the different causes of process variation, responsable for the degree of quality of in the data, and to quantify them. These concepts will form the basis to understand and control the deviations from the target values.
Statistical tools for quality assurance are studied next, with the emphasis put on concepts, examples, discussions and case studies rather
than statistical theory.
Finally, control charts, the most commonly used tool in quality control, are explained in detail. With control charts, process attributes can be studied over time to determine their quality and precision.
The training reviews the principles underlying control charts, the different types of charts available, their construction and interpretation.
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