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Who should attend Applied Statistics for Scientists and Engineers
Process Scientist/Engineers, Design Engineers, Manufacturing Engineers, R&D Scientists, Product Development Engineers, Regulatory/Compliance Professionals, Design Controls Engineers, Six Sigma Green Belt, Six Sigma Black Belt, Continuous Improvement Managers
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Applied Statistics for Scientists and Engineers  

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Seminar Summary:

This course provides instruction on how to apply the appropriate statistical approaches: descriptive statistics, data intervals, hypothesis testing, ANOVA, regression, ANCOVA, and model building.  (see full course description)

 
 

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Training Course Syllabus:


Course Description

Throughout 21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries, the application of statistical methods are specified for: performing measurement systems analysis (MSA), using design of experiment (DOE) for process development and validation, developing process control charts, and determining process capability indices.
Different statistical methods are required for each of these particular applications. Data and tolerance intervals are common tools used for setting acceptance criteria and specifications. Simple linear regression and analysis-of-covariance (ANCOVA) are used for setting expiries and conducting stability analysis studies. Two-sample hypothesis tests, analysis-of-variance (ANOVA), regression, and ANCOVA are methods used for analyzing designed experiment for process development and validation studies. Descriptive statistics (distribution, summary statistics), run charts, and probability (distributions) are used for developing process control charts and developing process capability indices.
This course provides instruction on how to apply the appropriate statistical approaches: descriptive statistics, data intervals, hypothesis testing, ANOVA, regression, ANCOVA, and model building. Once competence in each of these areas is established, select industry-specific applications are presented for the participants: MSA, DOE, SPC, and process capability.

Learning Objectives:

Upon completing this course participants should be able to:
describe and analyze a distribution of data
develop summary statistics
generate and analyze statistical intervals and hypothesis tests to make data-driven decisions
describe the relationship between and among two or more factors or responses
understand issues related to sampling and calculate appropriate sample sizes
use statistical intervals: confidence, prediction, and tolerance
understand how Measurement Systems Analysis (MSA) can be to estimate variance associated with: repeatability, intermediate precision, and reproducibility
understand how design of experiments (DOE) is used in process development
understand what it means for a process to be in (statistical) control and capable

DAY 01(8:30 AM - 4:30 PM)

08.30 AM - 09.00 AM: Registration
09.00 AM: Session Start
Basic Statistics
sample versus population
descriptive statistics
describing a distribution of values
Intervals
confidence intervals
prediction intervals
tolerance intervals
Hypothesis Testing
introducing hypothesis testing
performing means tests
performing normality tests
ANOVA
defining analysis of variance and other terminology
discussing assumptions and interpretation
interpreting hypothesis statements for ANOVA
performing one-way ANOVA

DAY 02(8:30 AM - 4:30 PM)

Regression
producing scatterplots and performing correlation
performing simple linear regression
Model Building
introduction
performing N-way ANOVA
performing multiple linear regression
performing ANCOVA
Applied Statistics
introduction to Measurement Systems Analysis (MSA)
introduction to design of experiments (DOE)
introduction to process control and capability

Seminar Summary:

This course provides instruction on how to apply the appropriate statistical approaches: descriptive statistics, data intervals, hypothesis testing, ANOVA, regression, ANCOVA, and model building.  (see full course description)

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