An interactive guide to the statistical tools used to solve problems during product and process innovation
End to End Data Analytics for Product Development is an accessible guide designed for practitioners in the industrial field. It offers an introduction to data analytics and the design of experiments (DoE) whilst covering the basic statistical concepts useful to an understanding of DoE. The text supports product innovation and development across a range of consumer goods and pharmaceutical organizations in order to improve the quality and speed of implementation through data analytics, statistical design and data prediction.
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, End to End Data Analytics for Product Development offers resources for the planning, conducting, analyzing and interpreting of controlled tests in order to develop effective products and processes.
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Yes, you can access End-to-end Data Analytics for Product Development by Rosa Arboretti Giancristofaro,Mattia De Dominicis,Chris Jones,Luigi Salmaso in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Statistics and data analytics play a central role in improving processes and systems and in decision‐making for strategic planning and manufacturing (Roberts, H. V., 1987). During experimental research, statistical tools can allow the experimenter to better organize observations, to specify working hypotheses and possible alternative hypotheses, to collect data efficiently, and to analyze the results and come to some conclusions about the hypotheses made.
This is an introductory chapter where readers can review several basic statistical concepts before moving on to the next chapters. Sixteen sections titled Stat Tools will introduce some key terms and procedures that will be further elaborated and referred to throughout the text.
Specifically, this chapter deals with the following:
Topics
Stat tools
Statistical variables and types of data
1.1
Statistical Units, populations, samples
1.2
Introduction to descriptive and inferential analyses
1.3, 1.12, 1.13
Data distributions
1.4, 1.5
Mean values
1.6, 1.7
Measures of variability
1.8, 1.9, 1.10
Boxplots
1.11
Introduction to confidence intervals
1.14
Introduction to hypothesis testing procedures, including the p‐value approach
1.15, 1.16
Learning Objectives and Outcomes
Upon completion of the review of these basic statistical concepts, you should be able to do the following:
Recognize and distinguish between different types of variables.
Distinguish between a population and a sample and know the meaning of random sampling.
Detect the shape of data distributions.
Calculate and interpret descriptive measures (means, measures of variability).
Understand the basic concept and interpretation of a confidence interval.
Understand the general idea of hypothesis testing.
Understand the p‐value approach to hypothesis testing.
Stat Tool 1.1 Statistical Variables and Types of Data
In statistical studies, several characteristics are observed or measured to obtain information on a phenomenon of interest. The observed or measured characteristics are called statistical variables. Statistical variables differ according to the type of values they store.
Qualitative or categorical variables can assume values that are qualitative categories and can be either ordinal or nominal.
Quantitative or numeric variables can assume numeric values and can be discrete or continuous. Discrete data (or count data) are numerical values only measura...
Table of contents
Cover
Table of Contents
Biographies
Preface
About the Companion Website
1 Basic Statistical Background
2 The Screening Phase
3 Product Development and Optimization
4 Other Topics in Product Development and Optimization: Response Surface and Mixture Designs
