Bhisham C. Gupta, Irwin Guttman, Kalanka P. Jayalath

Statistics and Probability with Applications for Engineers and Scientists Using Minitab, R and Jmp

• Gebundenes Buch

Produktbeschreibung

Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientists

Aimed at undergraduate/graduate-level engineering and natural science students, this timely, fully updated edition of a popular book on statistics and probability shows how real-world problems can be solved using statistical concepts. It removes Excel exhibits and replaces them with R software throughout, and updates both MINITAB and JMP software instructions and content. A new chapter discussing data mining--including big data, classification, machine learning, and visualization--is featured. Another new chapter covers cluster analysis methodologies in hierarchical, nonhierarchical, and model based clustering. The book also offers a chapter on Response Surfaces that previously appeared on the book's companion website.

Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP, Second Edition is broken into two parts. Part I covers topics such as: describing data graphically and numerically, elements of probability, discrete and continuous random variables and their probability distributions, distribution functions of random variables, sampling distributions, estimation of population parameters and hypothesis testing. Part II covers: elements of reliability theory, data mining, cluster analysis, analysis of categorical data, nonparametric tests, simple and multiple linear regression analysis, analysis of variance, factorial designs, response surfaces, and statistical quality control (SQC) including phase I and phase II control charts. The appendices contain statistical tables and charts and answers to selected problems.
_ Features two new chapters--one on Data Mining and another on Cluster Analysis
_ Now contains R exhibits including code, graphical display, and some results
_ MINITAB and JMP have been updated to their latest versions
_ Emphasizes the p-value approach and includes related practical interpretations
_ Offers a more applied statistical focus, and features modified examples to better exhibit statistical concepts
_ Supplemented with an Instructor's-only solutions manual on a book's companion website

Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP is an excellent text for graduate level data science students, and engineers and scientists. It is also an ideal introduction to applied statistics and probability for undergraduate students in engineering and the natural sciences.

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Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119516630
• 2. Aufl.
• Seitenzahl: 1040
• Erscheinungstermin: 5. Februar 2020
• Englisch
• Abmessung: 257mm x 183mm x 43mm
• Gewicht: 1701g
• ISBN-13: 9781119516637
• ISBN-10: 1119516633
• Artikelnr.: 56711248

Autorenporträt

BHISHAM C. GUPTA, PHD, is Professor Emeritus of Statistics in the Department of Mathematics and Statistics at the University of Southern Maine, and the co-author of Statistics and Probability with Applications for Engineers and Scientists. IRWIN GUTTMAN, PHD, is Professor Emeritus of Statistics in the Department of Mathematics at the State University of New York at Buffalo and Department of Statistics at the University of Toronto, Canada. He is the co-author of Statistics and Probability with Applications for Engineers and Scientists. KALANKA P. JAYALATH, PHD, is Assistant Professor in the Department of Mathematics and Statistics at the University of Houston.

Inhaltsangabe

Preface xvii

Acknowledgments xxi

About The Companion Site xxiii

1 Introduction 1

1.1 Designed Experiment 2

1.1.1 Motivation for the Study 2

1.1.2 Investigation 3

1.1.3 Changing Criteria 3

1.1.4 A Summary of the Various Phases of the Investigation 5

1.2 A Survey 6

1.3 An Observational Study 6

1.4 A Set of Historical Data 7

1.5 A Brief Description of What is Covered in this Book 7

Part I Fundamentals of Probability and Statistics

2 Describing Data Graphically and Numerically 13

2.1 Getting Started with Statistics 14

2.1.1 What is Statistics? 14

2.1.2 Population and Sample in a Statistical Study 14

2.2 Classification of Various Types of Data 18

2.2.1 Nominal Data 18

2.2.2 Ordinal Data 19

2.2.3 Interval Data 19

2.2.4 Ratio Data 19

2.3 Frequency Distribution Tables for Qualitative and Quantitative Data 20

2.3.1 Qualitative Data 21

2.3.2 Quantitative Data 24

2.4 Graphical Description of Qualitative and Quantitative Data 30

2.4.1 Dot Plot 30

2.4.2 Pie Chart 31

2.4.3 Bar Chart 33

2.4.4 Histograms 37

2.4.5 Line Graph 44

2.4.6 Stem-and-Leaf Plot 45

2.5 Numerical Measures of Quantitative Data 50

2.5.1 Measures of Centrality 51

2.5.2 Measures of Dispersion 56

2.6 Numerical Measures of Grouped Data 67

2.6.1 Mean of a Grouped Data 67

2.6.2 Median of a Grouped Data 68

2.6.3 Mode of a Grouped Data 69

2.6.4 Variance of a Grouped Data 69

2.7 Measures of Relative Position 70

2.7.1 Percentiles 71

2.7.2 Quartiles 72

2.7.3 Interquartile Range (IQR) 72

2.7.4 Coefficient of Variation 73

2.8 Box-Whisker Plot 75

2.8.1 Construction of a Box Plot 75

2.8.2 How to Use the Box Plot 76

2.9 Measures of Association 80

2.10 Case Studies 84

2.10.1 About St. Luke's Hospital 85

2.11 Using JMP 86

Review Practice Problems 87

3 Elements of Probability 97

3.1 Introduction 97

3.2 Random Experiments, Sample Spaces, and Events 98

3.2.1 Random Experiments and Sample Spaces 98

3.2.2 Events 99

3.3 Concepts of Probability 103

3.4 Techniques of Counting Sample Points 108

3.4.1 Tree Diagram 108

3.4.2 Permutations 110

3.4.3 Combinations 110

3.4.4 Arrangements of n Objects Involving Several Kinds of Objects 111

3.5 Conditional Probability 113

3.6 Bayes's Theorem 116

3.7 Introducing Random Variables 120

Review Practice Problems 122

4 Discrete Random Variables and Some Important Discrete Probability Distributions 128

4.1 Graphical Descriptions of Discrete Distributions 129

4.2 Mean and Variance of a Discrete Random Variable 130

4.2.1 Expected Value of Discrete Random Variables and Their Functions 130

4.2.2 The Moment-Generating Function-Expected Value of a Special Function of X 133

4.3 The Discrete Uniform Distribution 136

4.4 The Hypergeometric Distribution 137

4.5 The Bernoulli Distribution 141

4.6 The Binomial Distribution 142

4.7 The Multinomial Distribution 146

4.8 The Poisson Distribution 147

4.8.1 Definition and Properties of the Poisson Distribution 147

4.8.2 Poisson Process 148

4.8.3 Poisson Distribution as a Limiting Form of