[Slides] Statistical Model Building and Design of Experiments
https://pharmahub.org/resources/145
Fri, 19 Jul 2024 15:48:48 +0000HUBzero - The open source platform for scientific and educational collaborationTopics covered in this module include
Quantification of Uncertainty in Experimental data and impact on model analysis using Probability Theory
Review of Statistics for building Statistical Models (Multilinear Regression analysis)
Design of Experiments for Building Statistical models
Single factor Experiments
Multifactor Experiments
Factorial Experimentation
Fractional Factorial Experimentation
Response Surface Modeling
Process ...Pharmahubsupport@pharmahub.orgnoStatistical model buildingen-gbCopyright 2024 PharmahubResourcesProbability Theory Review
https://pharmahub.org/resources/147
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.https://pharmahub.org/site/resources/2008/05/00161/t1probability.pptFor 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.noStatistical model buildingGary BlauGary BlauTeaching MaterialsMon, 05 May 2008 14:41:04 +0000https://pharmahub.org/site/resources/2008/05/00161/t1probability.pptReview of Statistics
https://pharmahub.org/resources/148
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 regressionMultiple linear regressionModel Buildinghttps://pharmahub.org/site/resources/2008/05/00162/t2statistics.pptThis 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 regressionMultiple linear regressionModel BuildingnoStatistical model buildingGary BlauGary BlauTeaching MaterialsMon, 05 May 2008 14:46:25 +0000https://pharmahub.org/site/resources/2008/05/00162/t2statistics.pptSingle Factor Experiments
https://pharmahub.org/resources/150
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 variablehttps://pharmahub.org/site/resources/2008/05/00163/t3singlefactor.pptThe 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 variablenoStatistical model buildingGary BlauGary BlauTeaching MaterialsMon, 05 May 2008 15:09:20 +0000https://pharmahub.org/site/resources/2008/05/00163/t3singlefactor.pptFactorial Experimentation
https://pharmahub.org/resources/151
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.https://pharmahub.org/site/resources/2008/05/00164/t4factorial.pptA 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.noStatistical model buildingGary BlauGary BlauTeaching MaterialsMon, 05 May 2008 15:10:40 +0000https://pharmahub.org/site/resources/2008/05/00164/t4factorial.pptScreening
https://pharmahub.org/resources/152
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 each fractional factorial experimenthttps://pharmahub.org/site/resources/2008/05/00165/t5screening.pptThe 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 each fractional factorial experimentnoStatistical model buildingGary BlauGary BlauTeaching MaterialsMon, 05 May 2008 15:11:25 +0000https://pharmahub.org/site/resources/2008/05/00165/t5screening.pptResponse Surface Methodology
https://pharmahub.org/resources/153
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 discardedhttps://pharmahub.org/site/resources/2008/05/00166/t6rsm.pptThis 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 discardednoStatistical model buildingGary BlauGary BlauTeaching MaterialsMon, 05 May 2008 15:12:11 +0000https://pharmahub.org/site/resources/2008/05/00166/t6rsm.pptIntroduction to Model Building
https://pharmahub.org/resources/190
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 chemical engineering department, Statistical Model Building and Design of Experiments for undergraduates, and Mathematical Model Building for Process Optimization for Graduate studentshttps://pharmahub.org/site/resources/2008/05/00208/moduleoverviewlecture.pptCourse 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 chemical engineering department, Statistical Model Building and Design of Experiments for undergraduates, and Mathematical Model Building for Process Optimization for Graduate studentsnoStatistical model buildingGary BlauGary BlauOnline PresentationsThu, 08 May 2008 20:20:18 +0000https://pharmahub.org/site/resources/2008/05/00208/moduleoverviewlecture.ppt