Chris Jones - End-to-end Data Analytics for Product Development

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An interactive guide to the statistical tools used to solve problems during product and process innovation End to End Data Analytics for Product Development The book reviews information on feasibility screening, formulation and packaging development, sensory tests, and more. The authors – noted experts in the field – explore relevant techniques for data analytics and present the guidelines for data interpretation. In addition, the book contains information on process development and product validation that can be optimized through data understanding, analysis and validation. The authors present an accessible, hands-on approach that uses MINITAB and JMP software. The book:
• Presents a guide to innovation feasibility and formulation and process development
• Contains the statistical tools used to solve challenges faced during product innovation and feasibility
• Offers information on stability studies which are common especially in chemical or pharmaceutical fields
• Includes a companion website which contains videos summarizing main concepts
Written for undergraduate students and practitioners in industry, 
offers resources for the planning, conducting, analyzing and interpreting of controlled tests in order to develop effective products and processes.

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Table of Contents

1 Cover

2 Biographies

3 Preface

4 About the Companion Website

5 1 Basic Statistical Background1.1 Introduction

6 2 The Screening Phase2.1 Introduction 2.2 Case Study: Air Freshener Project

7 3 Product Development and Optimization3.1 Introduction 3.2 Case Study for Single Sample Experiments: Throat Care Project 3.3 Case Study for Two‐Sample Experiments: Condom Project 3.4 Case Study for Paired Data: Fragrance Project 3.5 Case Study: Stain Removal Project

8 4 Other Topics in Product Development and Optimization: Response Surface and Mixture Designs4.1 Introduction 4.2 Case Study for Response Surface Designs: Polymer Project 4.3 Case Study for Mixture Designs: Mix‐Up Project

9 5 Product Validation5.1 Introduction 5.2 Case Study: GERD Project 5.3 Case Study: Shelf Life Project (Fixed Batch Factor) 5.4 Case Study: Shelf Life Project (Random Batch Factor)

10 6 Consumer Voice6.1 Introduction 6.2 Case Study: “Top‐Two Box” Project 6.3 Case Study: DOE – Top Score Project 6.4 Final Remarks

11 References

12 Index

13 End User License Agreement

List of Tables

1 Chapter 5Table 5.1 Raw data.Table 5.2 Raw data.

2 Chapter 6Table 6.1 Cross‐tabulation.Table 6.2 Cross‐tabulation of purchase intent by gender.Table 6.3 Counts and conditional percentages.Table 6.4 Cross‐tabulation of purchase intent by gender and chi‐square test re...Table 6.5 Odds ratios.

List of Illustrations

1 Chapter 1Figure 1.1 Population, samples, sampling units.Figure 1.2 Shapes of distributions.Figure 1.3 Shapes of distributions (symmetric and skewed distributions).Figure 1.4 Other shapes of distributions.Figure 1.5 Mean and median in symmetric distributions.Figure 1.6 Mean and median in skewed distributions.Figure 1.7 Quartiles.Figure 1.8 Frequency distributions and variability.Figure 1.9 Dotplot.Figure 1.10 Histograms and boxplots.

2 Chapter 2Figure 2.1 Process variables and conditions.Figure 2.2 Response, factors, and levels.Figure 2.3 Factors, levels, and treatments.Figure 2.4 Full and fractional designs and treatments.Figure 2.5 Advantages and disadvantages of full and fractional designs treat...Figure 2.6 Randomized design for plastic tensile strength (Example 2.1).Figure 2.7 Advantages of randomization.Figure 2.8 Advantages of blocking.Figure 2.9 RCBD for plastic tensile strength.Figure 2.10 Presence of replications for plastic tensile strength (Example 2...Figure 2.11 Advantages of replication.Figure 2.12 Model assumptions for ANOVA (Example 2.4).Figure 2.13 Residuals versus order plots.Figure 2.14 Normal probability plots.Figure 2.15 Normal distribution.Figure 2.16 Residuals versus fitted values plots.

3 Chapter 3Figure 3.1 Desirability plots with different goals.

4 Chapter 4Figure 4.1 Response surface plot for a first‐order (linear) model (graph A) ...Figure 4.2 Central composite designs for k = 2 and k = 3.Figure 4.3 Spherical CCD for k = 2 with α = картинка 1.Figure 4.4 Face‐centered central composite designs for k = 2 and k = 3....Figure 4.5 The Box‐Behnken design for k = 3.Figure 4.6 The simplex for (a) p = 2, (b) p = 3, and (c) p = 4 mixture compo...Figure 4.7 The simplex for p = 3 mixture components.Figure 4.8 Axial axis for component x 1.Figure 4.9 Locating design points.Figure 4.10 Edge midpoints.Figure 4.11 Edge trisectors.Figure 4.12 Simplex centroid design with p = 3 components.Figure 4.13 Simplex centroid design with p = 4 components.Figure 4.14 Augmented simplex centroid design with p = 3 components.Figure 4.15 Simplex lattice design of degree 3 with p = 3 components.Figure 4.16 Augmented simplex lattice design of degree 3 with p = 3 componen...Figure 4.17 Constrained mixture design.Figure 4.18 Constrained mixture design.Figure 4.19 Response surface plots for a mixture experiment with three compo...

5 Chapter 5Figure 5.1 Scatterplot of thickness vs. CEW; each point representing one obs...Figure 5.2 Scatterplot of thickness vs. CEW showing a moderate linear relati...Figure 5.3 Strength and direction of correlations.Figure 5.4 Scatterplot of thickness vs. CEW showing a strong, positive relat...Figure 5.5 Scatterplot of thickness vs. CEW showing a very strong relationsh...Figure 5.6 Scatterplot of thickness vs. CEW with regression line.Figure 5.7 Scatterplot of thickness vs. CEW with regression line, CEW = 6.25...Figure 5.8 Scatterplot of thickness vs. CEW showing a statistically signific...Figure 5.9 Residuals.Figure 5.10 Residuals versus order plots.Figure 5.11 Normal distribution.Figure 5.12 Normal probability plots.Figure 5.13 Residuals versus fitted values plots.

6 Chapter 6Figure 6.1 Bar chart of purchase intent by gender (%).Figure 6.2 Scatterplots.Figure 6.3 Different categorical response variables.

Guide

1 Cover

2 Table of Contents

3 Begin Reading

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