What is “Normal” data? How do you know if your data are Normal? What do you do if your data are not Normal? Why would you care?
What a crazy introduction! Lots of data collected has the famous “bell-shaped” curve, more precisely known as Normal distribution. We are talking about a statistical property, but do not be discouraged by thoughts of complex and puzzling procedures. While many practitioners in industry do not want to be bothered by such considerations, this is a vital topic. The goal of the webinar is to motivate the topic with very sad mistakes made regarding product or process quality and provide simple procedures to remedy the situation.
Given widely available computer software, we can make statistically valid conclusions based on the properties of Normal distributions. Better, yet, with the same software we can detect that data are not Normally distributed and also make statistically valid conclusions. This webinar will demonstrate all of this.
This topic has great importance to medical device and pharmaceutical manufactures. The FDA’s Quality System Regulation requires that, when used, statistical techniques will verify “the acceptability of process capability and product characteristics.” The underlying distribution of the data, whether Normal or not, is key to verifying the quality of the product or process.
This fast-paced webinar will cover the details of application without the burden of statistical complexity. Analysis results from popular statistical software programs will be illustrated. The presenter’s “keep-it-simple” approach will avoid statistical complexity – participants do not need to have a background in statistics.
Over the course of two hours, the program will:
This material will appeal to quality professionals and managers in large and small companies. Instruction is targeted for professionals involved in meeting FDA-regulatory requirements such as: members of new product development teams, members of research and development teams, design engineers, process and manufacturing engineers, quality engineers, regulatory affairs specialists, and members of the leadership team. It will also be of interest to FDA staff concerned with the application of valid statistical techniques in this area. No knowledge of statistics is required to understand and apply the material presented.
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