Applied statistics for scientists and engineers is necessary for a number of reasons. 21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries specify the application of statistical methods for these functions:
o Setting validation criteria and specifications
o Performing Measurement Systems Analysis (MSA)
o Conducting stability analysis
o Using Design of Experiment (DOE) for process development and validation
o Developing process control charts, and
o Determining process capability indices.
Since scientists and engineers are at the heart of these functions, they need to have a thorough knowledge of how to use applied statistics. Each of these particular applications requires different and specified statistical methods. The common tools used for setting acceptance criteria and specifications are data and tolerance intervals, while for setting expiries and conducting stability analysis studies; simple linear regression and analysis-of-covariance (ANCOVA) are used.
For analyzing designed experiment for process development and validation studies, two-sample hypothesis tests, analysis-of-variance (ANOVA), regression, and ANCOVA are methods used, while for developing process control charts and developing process capability indices; descriptive statistics (distribution, summary statistics), run charts, and probability (distributions) are used.
Explaining the importance of applied statistics for scientists and engineers
A seminar that is being organized by GlobalCompliancePanel, a leading provider of professional trainings for the areas of regulatory compliance, will explain the importance of applied statistics for scientists and engineers.
In the course of making the importance of applied statistics for scientists and engineers known; the Director at this seminar, Heath Rushing, who is the cofounder of Adsurgo and author of the book Design and Analysis of Experiments by Douglas Montgomery: A Supplement for using JMP, and has been an invited speaker on applicability of statistics for national and international conferences, will provide instruction on applied statistics for scientists and engineers and statistical methods for data analysis of applications related to the pharmaceutical, biopharmaceutical, and medical device industries.
To enroll for this highly valuable and practical course on applied statistics for scientists and engineers, just register by visiting http://www.globalcompliancepanel.com/control/globalseminars/~produc... .
The course “Applied Statistics for Scientists and Engineers” has been pre-approved by RAPS as eligible for up to 12 credits towards a participant's RAC recertification upon full completion.
The tools that help an understanding of applied statistics for scientists and engineers
This course on applied statistics for scientists and engineers will offer thorough instruction on how scientists and engineers need to apply the appropriate statistical approaches: descriptive statistics, data intervals, hypothesis testing, ANOVA, regression, ANCOVA, and model building. The Director will present the ways of establishing competence in each of these areas and industry-specific applications.
The application of statistical methods across the product quality lifecycle is specified in the 21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries. There are many statistical methods that may be applied to satisfy this portion of the QSR. Yet, some commonly accepted methods can and should be used by all companies to:
o Develop acceptance criteria
o Ensure accurate and precise measurement systems
o Fully characterize manufacturing processes
o Monitor and control process results and
o To select an appropriate number of samples.
At this seminar on applied statistics for scientists and engineers, Rushing will provide instruction on all these. He will cover the following areas over the two days of this seminar:
o Describe and analyze the distribution of data
o Develop summary statistics
o Generate and analyze statistical intervals and hypothesis tests to make data-driven decisions
o Describe the relationship between and among two or more factors or responses
o Understand issues related to sampling and calculate appropriate sample sizes
o Use statistical intervals to setting specifications/develop acceptance criteria
o Use Measurement Systems Analysis (MSA) to estimate variance associated with: repeatability, intermediate precision, and reproducibility
o Ensure your process is in (statistical) control and capable