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Statistical Methods In Quality Control

This course examines the philosophy, tools, and techniques used to effectively monitor and continually improve the quality of an organisation's product or service. The course commences with the basic concepts of statistics including population versus sample, descriptive and inferential statistics, uncertainty, and data types. Methods of visualizing patterns of variation using pictures or graphs, such as stem-and leaf displays, run charts, pie charts, bar graphs, histograms, and box & whisker plots are studied. When graphical representations do not lend themselves to inference making, numerical descriptive measures must be used to indicate the location, shape, and spread of data sets. Numerical analysis is accomplished using mean, median, midrange, range, percentiles, variance, interqurtile range (IQR), and standard deviation. Basic probability concepts are introduced since analysis, in Statistics, is based on probability. Discrete probability distributions that play an important role in technical applications include the hypergeometric distribution, the binomial distribution, and the Poisson distribution. Continuous distributions are handled using the normal distribution (bell curve). To help make statistical inferences about populations, sampling distributions, interval estimates, and hypothesis tests are studied. Statistical quality control is introduced and includes Deming's quality management steps as well as ISO-9000 certification. The tools of quality control are also introduced including cause-and-effect diagrams, Pareto charts, histograms, and control charts. Finally, students learn how to apply statistics to process control (SPC) including how to use and interpret various control charts for variables and attributes.