Statistics with Minitab
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Date: 09-03-2010 Duration: 2 Days Location: Waterford Cost: €180 Members, €200 Non Members To book this Event or make an enquiry click this button. |
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Many of the process improvement and inspection techniques used in industry are based on statistics. The capability of a process is determined by comparing the manufacturing tolerance to the process variation, which is calculated using statistical techniques. The control limits on process control charts, used to maintain stability in manufacturing processes, are calculated statistically. Decisions on the outcomes of designed experiments in R&D and product development will be based on a statistical analysis of experimental data. The sampling tables used for inspection are founded on statistics. Decisions to release product inspected in the laboratory, on the factory floor, or in the field, are made using soundly based statistical techniques. Sample sizes are determined statistically. The validation of processes and the reliability of manufactured products are determined using statistical methodology.
Understanding how process improvement and inspection techniques work, and how data-based decisions are made, requires a sound knowledge of statistics. This course is directed towards those people who use these techniques in their work. The course material is based on the analysis of real industrial data gathered by the presenter during his consultancy work. Participants will be provided with knowledge of the basics of the subject, as well as an understanding of advanced techniques such as the analysis of variance.
The usefulness of statistics in industry and business has been greatly enhanced by the application of statistical computer software packages. There are a number of general-purpose user-friendly packages available at reasonable prices. Delegates will be trained in the use of Minitab, a leading brand of statistical software, for the range of subjects covered on the course On completing this course the participant will:
• Understand basic statistical principles
• Know how to analyse the variation that arises in manufacturing processes and business
• Understand the concepts of hypothesis testing and how statistics is used in data-based decision making
• Understand how to establish confidence levels for the results of data analysis
• Know how to use Minitab software for statistical analysis
• Have completed Sections VI A, B, C, D, E of the ASQ Certified Quality Engineer curriculum
Types of data – continuous (variables) and discrete (attributes) data •Measurement scales – nominal, ordinal, interval, and ratio measurement scales
• Methods for collecting data – check sheets, coding data, automatic g
auging • Techniques for assuring data accuracy and integrity – random sampling, stratifie
homogeneity
• Basic probability concepts – independence, mutually exclusive, multiplication rules, complementary probability, jo
• Construction of a histogram and explanation of the meaning of frequency distributions, cumulative fredistributions, measures of dispersion an
• The normal distribution Central limit theorem and sampling distribution of the mean
• Interpreting the efficiency
• Other continuous distributions - the uniform, bivariate normal, exponential, lognormal, Weibull, Chi-square,
• Normal and Weibull probability plots Discrete distributions – binomia
• Graphical methods – stem and leaf plo
• Hypothesis testing – tests for means, variances and proportions – meaning of significance le
type II error – difference between statistical and practical significance
• Paired comparison tests • Goodness-of-fit tests
• Analysis of variance (
•
• Simple and multiple linear regression. Calculation ofregression statistics. Using the re
the estimate
• Simple linear correlation – calculating the correlation coefficient and its confidence limit. Hypothesis testhe correlation coefficient. Basic time-se
• and cyclical variation
Trainer: Albert Plant & Associates
Albert Plant
Albert Plant is a leading consultant and trainer in Quality Assurance, and he is a FAS registered tutor. A chartered mechanical engineer with a masters degree from Trinity College, Dublin, and a B.Sc. degree majoring in statistics and mathematics, he heads up a consultancy practice based in Ireland. His experience spans consultancy and training in quality control and quality assurance in a wide range of industries in Ireland and the USA. He undertakes statistical analysis on a consultancy basis for several multi-national organizations, and he has extensive experience in the use of specialist statistical computer software including Minitab, Statgraphics, and other brands. He was a member of the team invited by Minitab Inc. to test and approve Version 15 of their software prior to its launch on the world market. Albert has developed and presented training courses on SPC, Design of Experiments, Response Surface Methodology, FMEA, Reliability Engineering, Problem Solving, Statistical Sampling, and other quality topics, in addition to this training course on Statistics with Minitab.
Presentation Style
The tutor has more than twenty years experience undertaking training and consultancy work in Ireland and the USA, and he brings this practical know-how to all of his training courses. He has incorporated real data from Irish industry into the material, which he has written for this training course, and he provides real-life examples of the application of statistics in industry and business during his presentation.
Dick Delaney
Dick Delaney, a Fellow of the Academy of Biomedical Science, started his career in Clinical Chemistry working in a number of hospitals in Ireland before being seconded to the World Health Organization in Africa. In 1980 he moved to Galway Mayo Institute of Technology as lecturer and course co-ordinator in Medical Laboratory Science. In 1992 Dick completed an MSc in Applied Statistics and has since been involved in lecturing on the statistical aspects of Quality Management to Diploma and Degree students. Through links with the pharmaceutical and medical device industry he has been responsible for a number of projects in process control and improvement, uncertainty estimation and SPC training. One of Dick’s main strengths is his ability to explain complex statistical concepts in simple everyday language. He believes in keeping the theory to a minimum, the emphasis being on practical applications using statistical software.
