Statistics and probability with applications for engineers and scientists using Minitab, R and JMP /
Gupta, Bhisham C., 1942-
Statistics and probability with applications for engineers and scientists using Minitab, R and JMP / Bhisham C. Gupta, Irwin Guttman, Kalanka P. Jayalath. - Second edition. Hoboken, NJ : John Wiley & Sons, Inc., 2020. ©2020 - 1 online resource
Revision of: Statistics and probability with applications for engineers and scientists. 2013.
Cover -- Title Page -- Copyright -- Contents -- Chapter 1 Introduction -- 1.1 Designed Experiment -- 1.1.1 Motivation for the Study -- 1.1.2 Investigation -- 1.1.3 Changing Criteria -- 1.1.4 A Summary of the Various Phases of the Investigation -- 1.2 A Survey -- 1.3 An Observational Study -- 1.4 A Set of Historical Data -- 1.5 A Brief Description of What is Covered in this Book -- Chapter 2 Describing Data Graphically and Numerically -- 2.1 Getting Started with Statistics -- 2.1.1 What Is Statistics? -- 2.1.2 Population and Sample in a Statistical Study 2.2 Classification of Various Types of Data -- 2.2.1 Nominal Data -- 2.2.2 Ordinal Data -- 2.2.3 Interval Data -- 2.2.4 Ratio Data -- 2.3 Frequency Distribution Tables for Qualitative and Quantitative Data -- 2.3.1 Qualitative Data -- 2.3.2 Quantitative Data -- 2.4 Graphical Description of Qualitative and Quantitative Data -- 2.4.1 Dot Plot -- 2.4.2 Pie Chart -- 2.4.3 Bar Chart -- 2.4.4 Histograms -- 2.4.5 Line Graph -- 2.4.6 Stem-and-Leaf Plot -- 2.5 Numerical Measures of Quantitative Data -- 2.5.1 Measures of Centrality -- 2.5.2 Measures of Dispersion -- 2.6 Numerical Measures of Grouped Data 2.6.1 Mean of a Grouped Data -- 2.6.2 Median of a Grouped Data -- 2.6.3 Mode of a Grouped Data -- 2.6.4 Variance of a Grouped Data -- 2.7 Measures of Relative Position -- 2.7.1 Percentiles -- 2.7.2 Quartiles -- 2.7.3 Interquartile Range (IQR) -- 2.7.4 Coefficient of Variation -- 2.8 Box-Whisker Plot -- 2.8.1 Construction of a Box Plot -- 2.8.2 How to Use the Box Plot -- 2.9 Measures of Association -- 2.10 Case Studies -- 2.10.1 About St. Luke's Hospital -- 2.11 Using JMP -- 2.11 Review Practice Problems -- Chapter 3 Elements of Probability -- 3.1 Introduction 3.2 Random Experiments, Sample Spaces, and Events -- 3.2.1 Random Experiments and Sample Spaces -- 3.2.2 Events -- 3.3 Concepts of Probability -- 3.4 Techniques of Counting Sample Points -- 3.4.1 Tree Diagram -- 3.4.2 Permutations -- 3.4.3 Combinations -- 3.4.4 Arrangements of n Objects Involving Several Kinds of Objects -- 3.5 Conditional Probability -- 3.6 Bayes's Theorem -- 3.7 Introducing Random Variables -- 3.7 Review Practice Problems -- Chapter 4 Discrete Random Variables and Some Important Discrete Probability Distributions -- 4.1 Graphical Descriptions of Discrete Distributions 4.2 Mean and Variance of a Discrete Random Variable -- 4.2.1 Expected Value of Discrete Random Variables and Their Functions -- 4.2.2 The Moment-Generating Function-Expected Value of a Special Function of X -- 4.3 The Discrete Uniform Distribution -- 4.4 The Hypergeometric Distribution -- 4.5 The Bernoulli Distribution -- 4.6 The Binomial Distribution -- 4.7 The Multinomial Distribution -- 4.8 The Poisson Distribution -- 4.8.1 Definition and Properties of the Poisson Distribution -- 4.8.2 Poisson Process -- 4.8.3 Poisson Distribution as a Limiting Form of the Binomial
"This new edition shows how real world problems can be solved using statistical concepts, now with many timely updates. The authors have included R software and removed the Excel exhibits throughout the book. The new Chapter 20 discusses data mining including topics in big data, classification, machine learning, and visualization. The new Chapter 21 covers cluster analysis methodologies including in hierarchical, nonhierarchical, and model based clustering. In addition, the authors have included a chapter on Response Surveys within the book, which was previously only available on the book's companion website. This book is broken into two parts. Part I covers topics such as: elements of probability, discrete random variables and some important discrete probability distributions, distribution functions of random variables, and estimation of population parameters. Part II covers: elements of reliability theory, statistical quality control, analysis of categorical data, and analysis of variance. The appendices contain statistical tables and charts and answers to selected problems"--
9781119516620 1119516625 9781119516644 1119516641 9781119516651 111951665X
Probabilities.
Mathematical statistics.
Mathematical statistics.
Probabilities.
Statistics and probability with applications for engineers and scientists using Minitab, R and JMP / Bhisham C. Gupta, Irwin Guttman, Kalanka P. Jayalath. - Second edition. Hoboken, NJ : John Wiley & Sons, Inc., 2020. ©2020 - 1 online resource
Revision of: Statistics and probability with applications for engineers and scientists. 2013.
Cover -- Title Page -- Copyright -- Contents -- Chapter 1 Introduction -- 1.1 Designed Experiment -- 1.1.1 Motivation for the Study -- 1.1.2 Investigation -- 1.1.3 Changing Criteria -- 1.1.4 A Summary of the Various Phases of the Investigation -- 1.2 A Survey -- 1.3 An Observational Study -- 1.4 A Set of Historical Data -- 1.5 A Brief Description of What is Covered in this Book -- Chapter 2 Describing Data Graphically and Numerically -- 2.1 Getting Started with Statistics -- 2.1.1 What Is Statistics? -- 2.1.2 Population and Sample in a Statistical Study 2.2 Classification of Various Types of Data -- 2.2.1 Nominal Data -- 2.2.2 Ordinal Data -- 2.2.3 Interval Data -- 2.2.4 Ratio Data -- 2.3 Frequency Distribution Tables for Qualitative and Quantitative Data -- 2.3.1 Qualitative Data -- 2.3.2 Quantitative Data -- 2.4 Graphical Description of Qualitative and Quantitative Data -- 2.4.1 Dot Plot -- 2.4.2 Pie Chart -- 2.4.3 Bar Chart -- 2.4.4 Histograms -- 2.4.5 Line Graph -- 2.4.6 Stem-and-Leaf Plot -- 2.5 Numerical Measures of Quantitative Data -- 2.5.1 Measures of Centrality -- 2.5.2 Measures of Dispersion -- 2.6 Numerical Measures of Grouped Data 2.6.1 Mean of a Grouped Data -- 2.6.2 Median of a Grouped Data -- 2.6.3 Mode of a Grouped Data -- 2.6.4 Variance of a Grouped Data -- 2.7 Measures of Relative Position -- 2.7.1 Percentiles -- 2.7.2 Quartiles -- 2.7.3 Interquartile Range (IQR) -- 2.7.4 Coefficient of Variation -- 2.8 Box-Whisker Plot -- 2.8.1 Construction of a Box Plot -- 2.8.2 How to Use the Box Plot -- 2.9 Measures of Association -- 2.10 Case Studies -- 2.10.1 About St. Luke's Hospital -- 2.11 Using JMP -- 2.11 Review Practice Problems -- Chapter 3 Elements of Probability -- 3.1 Introduction 3.2 Random Experiments, Sample Spaces, and Events -- 3.2.1 Random Experiments and Sample Spaces -- 3.2.2 Events -- 3.3 Concepts of Probability -- 3.4 Techniques of Counting Sample Points -- 3.4.1 Tree Diagram -- 3.4.2 Permutations -- 3.4.3 Combinations -- 3.4.4 Arrangements of n Objects Involving Several Kinds of Objects -- 3.5 Conditional Probability -- 3.6 Bayes's Theorem -- 3.7 Introducing Random Variables -- 3.7 Review Practice Problems -- Chapter 4 Discrete Random Variables and Some Important Discrete Probability Distributions -- 4.1 Graphical Descriptions of Discrete Distributions 4.2 Mean and Variance of a Discrete Random Variable -- 4.2.1 Expected Value of Discrete Random Variables and Their Functions -- 4.2.2 The Moment-Generating Function-Expected Value of a Special Function of X -- 4.3 The Discrete Uniform Distribution -- 4.4 The Hypergeometric Distribution -- 4.5 The Bernoulli Distribution -- 4.6 The Binomial Distribution -- 4.7 The Multinomial Distribution -- 4.8 The Poisson Distribution -- 4.8.1 Definition and Properties of the Poisson Distribution -- 4.8.2 Poisson Process -- 4.8.3 Poisson Distribution as a Limiting Form of the Binomial
"This new edition shows how real world problems can be solved using statistical concepts, now with many timely updates. The authors have included R software and removed the Excel exhibits throughout the book. The new Chapter 20 discusses data mining including topics in big data, classification, machine learning, and visualization. The new Chapter 21 covers cluster analysis methodologies including in hierarchical, nonhierarchical, and model based clustering. In addition, the authors have included a chapter on Response Surveys within the book, which was previously only available on the book's companion website. This book is broken into two parts. Part I covers topics such as: elements of probability, discrete random variables and some important discrete probability distributions, distribution functions of random variables, and estimation of population parameters. Part II covers: elements of reliability theory, statistical quality control, analysis of categorical data, and analysis of variance. The appendices contain statistical tables and charts and answers to selected problems"--
9781119516620 1119516625 9781119516644 1119516641 9781119516651 111951665X
Probabilities.
Mathematical statistics.
Mathematical statistics.
Probabilities.