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The Resource Basic Experimental Strategies and Data Analysis for Science and Engineering

Basic Experimental Strategies and Data Analysis for Science and Engineering

Label
Basic Experimental Strategies and Data Analysis for Science and Engineering
Title
Basic Experimental Strategies and Data Analysis for Science and Engineering
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Subject
Language
eng
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MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Basic Experimental Strategies and Data Analysis for Science and Engineering
Label
Basic Experimental Strategies and Data Analysis for Science and Engineering
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4732203
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Copyright
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online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- List of Figures -- List of Tables -- Preface -- Author Bios -- 1: Strategies for Experimentation with Multiple Factors -- 1.1 Introduction -- 1.2 Some Definitions -- 1.3 Classical Versus Statistical Approaches to Experimentation -- 1.4 Why Experiment? (Analysis of Historical Data) -- 1.5 Diagnosing the Experimental Environment -- 1.6 Example of a Complete Experimental Program -- 1.6.1 Chemical Process System -- 1.6.2 Step One: Screening -- 1.6.3 Step Two: Crude Optimization -- 1.6.4 Step Three: Final Optimization -- 1.7 Good Design Requirements -- 1.8 Summary -- 1.8.1 Important Terms and Concepts -- 2: Statistics and Probability -- 2.1 Introduction -- 2.2 Graphical and Numerical Summaries of a Single Response-Variable -- 2.2.1 Dot Diagrams -- 2.2.2 Sample Mean, Variance, and Standard Deviation -- 2.2.3 Histograms -- 2.2.4 Five-Number Summaries and Boxplots -- 2.3 Graphical and Numerical Summaries of the Relation Between Variables -- 2.4 What to Expect from Theory -- 2.4.1 Probability Density Functions and Probability -- 2.4.2 Expected Values, Variance, and Standard Deviation -- 2.4.3 Normal Distribution -- 2.4.4 Central Limit Theorem and Distribution of Sample Means -- 2.5 Using Theory to Help Interpret Experimental Data -- 2.5.1 Comparing the Mean of Experimental Results to a Standard -- 2.5.2 Comparing the Means of Two Experimental Conditions -- 2.5.3 Comparing the Means of Several Experimental Conditions -- 2.5.4 Comparing Observed Data to the Normal Distribution -- 2.6 Summary -- 2.6.1 Important Equations -- 2.6.2 Important Terms and Concepts -- 2.7 Exercise -- 3: Basic Two-Level Factorial Experiments -- 3.1 Introduction -- 3.2 Two-Level Factorial Design Geometry -- 3.3 Main Effect Estimation -- 3.4 Interactions
  • 3.5 General 2k Factorial Designs -- 3.6 Randomization -- 3.7 Example of a 23 Factorial Experiment -- 3.7.1 Background and Design -- 3.7.2 Calculation of Effects and Interactions -- 3.8 Significance of Effects and Interactions -- 3.8.1 Statistical Significance of Results -- 3.8.2 Pooled Variance -- 3.8.3 Statistical Significance of Results for Fly Ash -- 3.8.4 Interpretation of Results for Fly Ash Example -- 3.8.5 Example of Computer Analysis of Data -- 3.9 Example of an Unreplicated 24 Design (Stack Gas Treatment) -- 3.9.1 Background and Design -- 3.9.2 Graphical Analysis -- 3.9.3 Numerical Analysis -- 3.9.4 Interpretation of Results -- 3.10 Judging Significance of Effects When There Are No Replicates -- 3.11 Summary -- 3.11.1 Analysis of 2k Experiments -- 3.11.2 Important Equations -- 3.11.3 Important Terms and Concepts -- 3.12 Exercise -- 4: Additional Tools for Two-Level Factorials -- 4.1 Introduction -- 4.2 Number of Replicates Needed for Desired Precision -- 4.3 Results in Equation Form -- 4.4 Testing for Curvature -- 4.5 Blocking Factorial Experiments -- 4.5.1 Calculating the Error of an Effect (sE) in Blocked 2k Experiments -- 4.5.2 Two Block Example -- 4.5.3 Designs for Blocked Factorials -- 4.6 Split-Plot Designs -- 4.6.1 Split-Plot Example -- 4.6.2 Designs for Split-Plot 2k Experiments -- 4.7 Summary -- 4.7.1 Important Equations -- 4.7.2 Important Terms and Concepts -- 4.8 Exercises -- 5: General Factorial Experiments and ANOVA -- 5.1 Introduction -- 5.2 Multiple Level Factorial Designs -- 5.3 Mathematical Model for Multiple Level Factorials -- 5.4 Testing the Significance of Main Effects and Interaction Effects -- 5.5 Example of a Multilevel Two-Factor Factorial -- 5.6 Comparison of Means after the ANOVA -- 5.6.1 Least Significant Difference (LSD) Method
  • 5.6.2 Tukey's Method of Comparing Means after the ANOVA -- 5.6.3 Orthogonal Contrasts -- 5.6.4 Other Applications of Orthogonal Contrasts -- 5.6.4.1 Orthogonal Polynomial Contrasts -- 5.6.4.2 Analysis of Unreplicated Multilevel Factorials -- 5.7 Analysis of Blocked and Split-Plot Factorial Experiments with Multilevel Factors -- 5.7.1 Analysis of a Blocked Factorial by ANOVA -- 5.7.2 Analysis of a Split-Plot Factorial by ANOVA -- 5.8 Summary -- 5.8.1 Important Equations -- 5.8.2 Important Terms -- 5.9 Exercises -- 6: Variance Component Studies -- 6.1 Introduction -- 6.2 Additivity of Variances -- 6.3 Simple Experiments for Estimating Two Sources of Variability -- 6.4 Estimation of Variance Components Using ANOVA -- 6.5 Graphical Representation of Data from Simple Experiments for Estimating Two Sources of Variability -- 6.6 Components of Variance-Multiple Sources -- 6.6.1 Introduction -- 6.6.2 Nested Designs for Estimating Multiple Components of Variance -- 6.7 Staggered Nested Designs -- 6.7.1 Design and Analysis with Staggered Nested Designs -- 6.7.2 Staggered Nested Design Example with Four Sources -- 6.8 Sequential Experimentation Starting With Components of Variation-Case Study -- 6.8.1 Introduction -- 6.8.2 Staggered Nested Experiment -- 6.8.3 Follow-up Split-Plot Experiment -- 6.8.4 Conclusions -- 6.9 Summary -- 6.9.1 Important Equations -- 6.9.2 Important Terms -- 6.10 Exercises -- 7: Screening Designs -- 7.1 Introduction -- 7.2 Cause-and-Effect Diagrams -- 7.3 Fractionating Factorial Designs -- 7.4 Fractional Factorial Designs -- 7.4.1 Constructing Half Fractions -- 7.4.2 Confounding in Half Fractions -- 7.4.3 Simple Example of a Half Fraction -- 7.4.4 One-Quarter and Higher Fractional Factorials -- 7.4.5 Fractional Factorial Design Tables
  • 7.4.6 Example of Fractional Factorial in Process Improvement -- 7.4.7 Advantages of Fractional Factorial Designs -- 7.4.8 Resolution of Fractional Factorial Designs -- 7.5 Plackett-Burman Screening Designs -- 7.5.1 Tables of Plackett-Burman Designs -- 7.5.2 Using the Tables of Plackett-Burman Designs -- 7.5.3 An Example of Using a Plackett-Burman Design -- 7.6 Other Applications of Fractional Factorials -- 7.6.1 Fractional Factorial Designs for Estimating Some Interactions -- 7.6.2 Designs for Blocked Factorials in Fractional Arrangements -- 7.7 Screening Designs with Multiple Level Factors -- 7.7.1 Combination of Factors Method (Pseudofactors) -- 7.7.2 Collapsing Levels (Dummy Levels) -- 7.7.3 L18 Orthogonal Array -- 7.8 Summary -- 7.8.1 Important Terms and Concepts -- 7.8.2 Important Formulas -- 7.9 Exercises -- 8: Regression Analysis -- 8.1 Introduction -- 8.2 Method of Least Squares -- 8.2.1 Estimating the Slope of a Straight Line Through the Origin -- 8.3 Linear Regression -- 8.3.1 Estimating the Slope and Intercept of a Straight Line -- 8.3.2 Statistical Significance of Coefficients -- 8.3.3 Confidence Intervals for Coefficients -- 8.3.4 Precision of Predictions -- 8.4 Multiple Regression -- 8.4.1 Introduction -- 8.4.2 Estimation of Coefficients -- 8.4.3 Statistical Significance of Coefficients -- 8.4.4 Confidence Intervals for Coefficients -- 8.4.5 Precision of Predictions -- 8.5 Quantifying Model Closeness (R2) -- 8.6 Checking Model Assumptions (Residual Plots) -- 8.6.1 Checking for Normal Distribution of Errors -- 8.6.2 Checking for Constant Variance -- 8.6.3 Checking for Independence of Errors -- 8.6.4 Checking for a Mean of Zero for the Errors -- 8.7 Data Transformation for Linearity -- 8.8 Summary -- 8.8.1 Important Equations -- 8.8.2 Important Terms and Concepts -- 8.9 Exercises -- 9: Response Surface Designs
  • 9.1 Response Surface Concepts and Methods -- 9.2 Empirical Quadratic Model -- 9.3 Design Considerations -- 9.4 Central Composite Designs -- 9.5 Central Composite Design Example -- 9.6 Graphical Interpretation of Response Surfaces -- 9.7 Other Response Surface Designs -- 9.7.1 Box-Behnken Designs -- 9.7.2 Small Composite Designs -- 9.7.3 Example-Coal Gasification Modeling -- 9.8 Summary -- 9.8.1 Procedure for Design of Experiments for RSM -- 9.8.2 Important Terms and Concepts -- 9.9 Exercises -- 10: Response Surface Model Fitting -- 10.1 Introduction -- 10.2 Estimation of Coefficients in a Quadratic Model -- 10.3 Checking Model Assumptions (Residual Plots) -- 10.4 Statistical Check of Model Adequacy - Lack of Fit (LoF) -- 10.5 Trimming Insignificant Terms from a Model -- 10.5.1 Justification -- 10.5.2 Deleting Statistically Non-Significant Coefficients from Model -- 10.6 Exploring the Response Surface -- 10.6.1 Analytical Interpretation of Response Surfaces -- 10.6.2 Numerical Methods for Interpreting Response Surfaces -- 10.7 Precision of Predictions -- 10.8 Summary -- 10.8.1 General Procedure for Analysis of Data from RSM Design -- 10.8.2 Important Equations -- 10.8.3 Important Terms and Concepts -- 10.9 Exercises -- 11: Sequential Experimentation -- 11.1 Introduction -- 11.2 Augmenting Screening Designs to Resolve Confounding -- 11.3 Application of Sequential Experimentation in a Process Start-up -- 11.4 Sequential Analysis without Follow-up Experiments -- 11.4.1 Sequential Analysis of Data from a Plackett-Burman Design -- 11.4.2 Sequential Analysis of Screening Designs to Find Quadratic Optima -- 11.5 Summary -- 11.5.1 Important Terms and Concepts -- 11.6 Exercises -- 12: Mixture Experiments -- 12.1 Introduction -- 12.2 Models for Mixture Problems -- 12.2.1 Overview of Modeling -- 12.2.2 Slack Variable Models
  • 12.2.3 Scheffé Models
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