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Collaboration for pharmaceutical engineering and science
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Tablet Statistical Analysis Lab - Student and Instructor's Version
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16 Apr 2014 | Contributor(s):: Alexander Vincent Jannini, David J Krause, Heather Malino, Kevin Sweeney, C.S. Slater PhD, M.J. Savelski
This is one of a set of experiments designed for lower level undergraduate courses. The focus of the experimental set is to introduce pharmaceutical concepts to undergraduates while also incorporating general engineering educational objectives. This contains both a student version and instructor...
Introduction to Model Building
08 May 2008 | | Contributor(s):: Gary Blau
Course Background:Initial ideas developed for Pharmaceutical Scientists at the Dow Chemical Plant in Brindisi, Italy (1975)Subsequently evolved into a global course on Process Optimization presented to Dow Scientists and engineers in Europe and North America.Morphed into two courses in the...
Problems for Statistical Model Building
05 May 2008 | | Contributor(s):: Gary Blau
Scenarios, problem statements, JMP data files, and solutions for the course on Statistical Model Building and Design of Experiments. Scenario 1 is based on a batch reactor system for small molecule API production. Scenario 2 is based on a test-bed to study the feasibility of continuous operation...
Response Surface Methodology
This lecture describes response surface models:Models are simple polynomialsInclude terms for interaction and curvatureCoefficients are usually established by regression analysis with a computer programInsignificant terms are discarded
The objectives of this lecture are to:Show how to screen or select the most important main effects with fewer experiments.Show how to construct fractional factorial experiments by sacrificing interactionsUnderstand the concept of confounding / aliasesLearn how to write the mathematical model for...
A full factorial experiment is a set of experimental runs such that all levels of a given factor are combined with all levels of every other factor.
Single Factor Experiments
The purpose of single factor experiments is to:Quantify relationship between a single factor and a single measured or response variableCompare the relative effectiveness of two or more treatments (levels of the factor).Estimate the level of the factor that optimizes the response variable
Review of Statistics
This lecture covers the following topicsDifference between statistics and probabilityStatistical InferenceSamples and populationsIntro to JMP software packageCentral limit theoremConfidence intervalsHypothesis testingRegression and modeling fundamentalsIntroduction to Model BuildingSimple linear...
Probability Theory Review
For any real system or phenomenon, there will be a certain amount of variability associated with data generated by the system.Probability is the language used to characterize and interpret the variability of this data.Probability is the most widely used formalism for quantifying uncertainty.
Statistical Model Building and Design of Experiments
Topics covered in this module includeQuantification of Uncertainty in Experimental data and impact on model analysis using Probability TheoryReview of Statistics for building Statistical Models (Multilinear Regression analysis)Design of Experiments for Building Statistical models Single factor...