Modern industrial statistics : with applications in R, MINITAB and JMP / created by Ron S. Kenett, Shelemyahu Zacks with contributions from Daniele Amberti

Includes bibliographical references and index.

Preface to the third edition Preface to the second edition (abbreviated) Preface to the first edition (abbreviated) List of abbreviations Part A: Modern Statistics: A Computer Based Approach 1 Statistics and Analytics in Modern Industry 2 Analyzing Variability: Descriptive Statistics 3 Probability Models and Distribution Functions 4 Statistical Inference and Bootstrapping 5 Variability in Several Dimensions and Regression Models 6 Sampling for Estimation of Finite Population Quantities 7. Time Series Analysis and Prediction 8 Modern analytic methods Part B: Modern Industrial Statistics: Design and Control of Quality and Reliability 9 The Role of Industrial Analytics in Modern Industry 10 Basic Tools and Principles of Process Control 11 Advanced Methods of Statistical Process Control 12 Multivariate Statistical Process Control 13 Classical Design and Analysis of Experiments 14 Quality by Design 15 Computer Experiments 16 Reliability Analysis 17 Bayesian Reliability Estimation and Prediction 18 Sampling Plans for Batch and Sequential Inspection List of R packages References Author index Subject index Solution manual Appendices (available on book?s website) Appendix I Intro to R Appendix II Intro to MINITAB and Matrix Algebra Appendix III R scripts Appendix IV mistat Appendix V csv Files Appendix VI MINITAB macros Appendix VII JMP scripts

"Industrial Statistics is concerned with maintaining and improving the quality of goods and services. It involves a broad range of statistical tools but maintaining and improving quality is its main concern. Variability is inherent in all processes, whether they be manufacturing processes or service processes. This variability must be controlled to create high quality goods and services and must be reduced to improve quality. Industrial Statistics focuses on the use of statistical thinking, i.e., the appreciation of the inherent variability of all processes in order that all possible outcomes can be assessed. It also focuses on developing skills for modeling data and designing experiments that can lead to improvements in performance and reductions in variablity"--