Introduction to Industrial Statistics 2017

Overview:
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.
Attribute data (the kind that must be counted as integers, such as defects and nonconformances) will then be addressed through the hypergeometric, binomial, and Poisson distributions. The binomial distribution will be applied to the ANSI/ASQ Z1.4 plan for sampling by attributes to illustrate its application. Discovery sampling also will be addressed here, along with awareness of sequential sampling and its advantages.
Variables data are continuous scale data, the kind that are measured with real numbers. Models include the normal (bell curve distribution) and, for comparison of sample properties, the t and chi square distributions. Confidence intervals can be determined for the mean of a population on the basis of the sample average and its known or estimated standard deviation. The Central Limit theorem, which says that the averages of large samples will behave like a normal distribution regardless of the normality of the underlying population, also will be addressed.
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.
The Anderson-Darling test for goodness of fit, along with the normal probability plot, can detect the presence of a non-normal distribution for the process data. If the data are not normally distributed, the traditional textbook methods will not work properly, but alternative methods are fortunately available.
Measurement systems analysis (MSA), or gage reproducibility and repeatability, allows the scientific estimation of gage precision, or the ability of the gage to get the same measurement consistently from the same specimen. Precision is not the same thing as accuracy, which is ensured by calibration, although both are important. Accuracy means that, on average, the measurement will equal the true measurement of the specimen, while precision reflects the ability to get the same measurement (right or wrong) consistently.

Why should you attend :
An understanding of descriptive statistics and hypothesis tests provides the foundation for everything we do with industrial statistics. This includes:
• 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. This also is covered in this course.
Most statistical courses rely on the assumption that process data follow the normal or bell curve distribution. The bell curve is far more common in textbooks than it is in real processes, and mistaken reliance on it can deliver very misleading and inaccurate results. As an example, the process pe