Statistical methods in practice : for scientists and technologists

This is a practical book on how to apply statistical methods successfully. The Authors have deliberately kept formulae to a minimum to enable the reader to concentrate on how to use the methods and to understand what the methods are for. Each method is introduced and used in a real situation from industry or research. Each chapter features situations based on the authors’ experience and looks at statistical methods for analysing data and, where appropriate, discusses the assumptions of these methods. Key features: Provides a practical hands-on manual for workplace applications. Introduces a broad range of statistical methods from confidence intervals to trend analysis. Combines realistic case studies and examples with a practical approach to statistical analysis. Features examples drawn from a wide range of industries including chemicals, petrochemicals, nuclear power, food and pharmaceuticals. Includes a supporting website, providing software to aid tutorials. Scientists and technologists of all levels who are required to design, conduct and analyse experiments will find this book to be essential reading. Content: Chapter 1 Samples and Populations (pages 1–4): Chapter 2 What is the True Mean? (pages 5–18): Chapter 3 Exploratory Data Analysis (pages 19–28): Chapter 4 Significance Testing (pages 29–39): Chapter 5 The Normal Distribution (pages 41–52): Chapter 6 Tolerance Intervals (pages 53–54): Chapter 7 Outliers (pages 55–58): Chapter 8 Significance Tests for Comparing Two Means (pages 59–69): Chapter 9 Significance Tests for Comparing Paired Measurements (pages 71–81): Chapter 10 Regression and Correlation (pages 83–100): Chapter 11 The Binomial Distribution (pages 101–109): Chapter 12 The Poisson Distribution (pages 111–119): Chapter 13 The chi?Squared Test for Contingency Tables (pages 121–127): Chapter 14 Non?Parametric Statistics (pages 129–138): Chapter 15 Analysis of Variance: Components of Variability (pages 139–153): Chapter 16 Cusum Analysis for Detecting Process Changes (pages 155–166): Chapter 17 Rounding of Results (pages 167–171): This Book Presents A Broad Range Of Methods Which Form The Core Of A Statistical Toolkit Required By Scientists And Technologists, Including Descriptive Statistics (e.g. Mean, Median, Standard Deviation), Inference (confidence Intervals), Significance Testing For One Mean, Two Means, Two Standard Deviations, Distributions (normal, Binomial, Poisson), Exploratory Data Analysis (box Plots), Nonparametric Methods, Regression And Correlation, Cusum Charts For Plotting Data Over Time And Detecting Changesa Hands-on, Practical Approach To Statistics For Those With Little Background In The Subject Introduces A Broad Range Of Methods Which Form The Core Of A Statistical Toolkit Required By Scientists And Technologists, Presented From Descriptive Statistics To Nonparametric Methods Includes A Supporting Website, Providing Software To Aid Tutorials Based On Practical Courses Given To Scientists And Technologists Of All Levels Combines Realistic Case Studies And Examples With A Practical Approach To Statistical Analysis--provided By Publisher. 1. Samples And Populations -- 2. What Is The True Mean? -- 3. Exploratory Data Analysis -- 4. Significance Testing -- 5. Normal Distribution -- 6. Tolerance Intervals -- 7. Outliers -- 8. Significance Tests For Comparing Two Means -- 9. Significance Tests For Comparing Paired Measurements -- 10. Regression And Correlation -- 11. Binomial Distribution -- 12. Poisson Distribution -- 13. Chi-squared Test For Contingency Tables -- 14. Non-parametric Statistics -- 15. Analysis Of Variance: Components Of Variability -- 16. Cusum Analysis For Detecting Process Changes -- 17. Rounding Of Results -- Statistical Tables. Richard Boddy, Gordon Smith. Includes Bibliographical References And Index. "This book presents a broad range of methods which form the core of a statistical toolkit required by scientists and technologists, including descriptive statistics (e.g. mean, median, standard deviation), inference (confidence intervals), significance testing for one mean, two means, two standard deviations, distributions (Normal, Binomial, Poisson), exploratory data analysis (box plots), nonparametric methods, regression and correlation, cusum charts for plotting data over time and detecting changesA hands-on, practical approach to statistics for those with little background in the subject Introduces a broad range of methods which form the core of a statistical toolkit required by scientists and technologists, presented from descriptive statistics to nonparametric methods Includes a supporting website, providing software to aid tutorials Based on practical courses given to scientists and technologists of all levels Combines realistic case studies and examples with a practical approach to statistical analysis"--[Résumé de l'éditeur]