Event

Introduction to Statistical Analysis of Laboratory Data

March 1, 2012, March 2, 2012 iCal

Location: King of Prussia, PA

The course curriculum will benefit R&D managers, analytical laboratory supervisors and staff, manufacturing and production professionals, scientists, technicians and others who wish to comprehend and interpret methods of data analysis relevant to laboratory experimentation. Where applicable, topics are presented with relevant regulatory requirements.

Course Description:

Basic Methods (Day One). This section of the course will detail the basic and intermediate statistical concepts that are essential for professionals in the field. The first day emphasizes the principles of descriptive and inferential statistical applications and focuses on actual study examples, problem solving and interpretation of results. Throughout the course the participants are encouraged to ask questions and discuss examples relevant to their own work. Topic areas to be discussed include, but are not limited to:

Basic statistical terminology including simple statistics as well as geometric ( e.g. means, standard deviations) transformations needed to effectively communicate and understand your data results
The statistical testing (one sided, two sided, non parametric, sample size, and power considerations) essentials required to initiate a research investigation (i.e., research questions in statistical terms)
Concepts of accuracy and precision in measurement analysis to ensure appropriate conclusions in experimental results including between and within laboratory variationresults
Discussion of statistical techniques to compare experimental approaches with respect to specificity, sensitivity and linearity

Advanced Topics (Day Two). This section of the course will go beyond the basics and cover more complex issues in laboratory investigations with examples. Topics will include:

• Association studies including correlation and regression analysis with laboratory applications
• Analysis of robustness and ruggedness
• Method comparison using more accurate alternatives to correlate analysis and other pair-wise comparisons

For more information please visit: http://www.cfpie.com/showitem.aspx?productid=046&source=lsam

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