This is an introductory course in industrial statistics that will equip the attendee to understand what he or she needs to know about basic descriptive statistics such as measurements of central tendency (average) and variation (range and standard deviation), and to use graphical methods such as the box and whisker plot to visualize these statistics for data sets.
The concepts of variation and accuracy, and their effects on outgoing quality, will be introduced at the beginning. The basic data visualization tools of the histogram and Pareto chart also will be presented.
The next major subject will be statistical hypothesis testing, the foundation of almost everything we do in industrial statistics. The material is applicable not only to statistical process control and acceptance sampling (both of which will be discussed in this course) but also to design of experiments.
The purpose of statistical process control (SPC) is to distinguish between random or common cause variation that is inherent in a process, and special or assignable cause variation that means there is a problem with the process. SPC begins with a discussion of the rational subgroup, or a sample that accounts for all the variation in a process. It is important to select it correctly if SPC is to work properly.
Attribute control charts include charts for the number nonconforming (np) and the number of defects (c). The np chart is based on the binomial distribution, and the c chart on the Poisson distribution. (The p chart for fraction nonconforming and u chart for defect density serve similar purposes.)
Charts for variables data are far more powerful, i.e. better able to detect process shifts, than attribute charts. The X chart is for individual measurements, and the x-bar/R (sample average and range) and x-bar/s (sample average and sample standard deviation) are for samples. Variables data also make it possible to calculate process capability and process performance indices. If these indices are substantially different, it means that the rational subgroup has not been selected properly.
- Statistical process control (SPC), which is covered in this course
- Design of Experiments (DOE), a powerful approach for testing assumptions during root cause analysis, process improvement, and generation of mathematical models for physical and chemical processes
- Acceptance sampling procedures such as ANSI/ASQ Z1.4 (formerly MIL-STD 105) and ANSI/ASQ Z1.9 (formerly MIL-STD 414), this course will show how some of the basic material applies to ANSI/ASQ Z1.4
- Measurement systems analysis (MSA), or gage reproducibility and repeatability
Who Should Attend:
- Manufacturing Engineers
- Managers, and Technicians
For more information about this conference visit http://www.researchandmarkets.com/research/wsfxqs/introduction_to