Buch, Englisch, 1040 Seiten, Format (B × H): 183 mm x 257 mm, Gewicht: 1701 g
ISBN: 978-1-119-51663-7
Verlag: Wiley
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.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
Weitere Infos & Material
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 the Binomial 148
4.9 The Negative Binomial Distribution 153
4.10 Some Derivations and Proofs (Optional) 156
4.11 A Case Study 156
4.12 Using JMP 157
Review Practice Problems 157
5 Continuous Random Variables and Some Important Continuous Probability Distributions 164
5.1 Continuous Random Variables 165
5.2 Mean and Variance of Continuous Random Variables 168
5.2.1 Expected Value of Continuous Random Variables and Their Functions 168
5.2.2 The Moment-Generating Function and Expected Value of a Special Function of X 171
5.3 Chebyshev’s Inequality 173
5.4 The Uniform Distribution 175
5.4.1 Definition and Properties 175
5.4.2 Mean and Standard Deviation of the Uniform Distribution 178
5.5 The Normal Distribution 180
5.5.1 Definition and Properties 180
5.5.2 The Standard Normal Distribution 182
5.5.3 The Moment-Generating Function of the Normal Distribution 187
5.6 Distribution of Linear Combination of Independent Normal Variables 189
5.7 Approximation of the Binomial and Poisson Distributions by the Normal Distribution 193
5.7.1 Approximation of the Binomial Distribution by the Normal Distribution 193
5.7.2 Approximation of the Poisson Distribution by the Normal Distribution 196
5.8 A Test of Normality 196
5.9 Probability Models Commonly used in Reliability Theory 201
5.9.1 The Lognormal Distribution 202
5.9.2 The Exponential Distribution 206
5.9.3 The Gamma Distribution 211
5.9.4 The Weibull Distribution 214
5.10 A Case Study 218
5.11 Using JMP 219
Review Practice Problems 220
6 Distribution of Functions Of Random Variables 228
6.1 Introduction 229
6.2 Distribution Functions of Two Random Variables 229
6.2.1 Case of Two Discrete Random Variables 229
6.2.2 Case of Two Continuous Random Variables 232
6.2.3 The Mean Value and Variance of Functions of Two Random Variables 233
6.2.4 Conditional Distributions 235
6.2.5 Correlation between Two Random Variables 238
6.2.6 Bivariate Normal Distribution 241
6.3 Extension to Several Random Variables 244
6.4 The Moment-Generating Function Revisited 245
Review Practice Problems 249
7 Sampling Distributions 253
7.1 Random Sampling 253
7.1.1 Random Sampling from an Infinite Population 254
7.1.2 Random Sampling from a Finite Population 256
7.2 The Sampling Distribution of the Sample Mean 258
7.2.1 Normal Sampled Population 258
7.2.2 Nonnormal Sampled Population 258
7.2.3 The Central Limit Theorem 259
7.3 Sampling from a Normal Population 264
7.3.1 The Chi-Square Distribution 264
7.3.2 The Student t-Distribution 271
7.3.3 Snedecor’s F-Distribution 276
7.4 Order Statistics 279
7.4.1 Distribution of the Largest Element in a Sample 280
7.4.2 Distribution of the Smallest Element in a Sample 281
7.4.3 Distribution of the Median of a Sample and of the kth Order Statistic 282
7.4.4 Other Uses of Order Statistics 284
7.5 Using JMP 286
Review Practice Problems 286
8 Estimation of Population Parameters 289
8.1 Introduction 290
8.2 Point Estimators for the Population Mean and Variance 290
8.2.1 Properties of Point Estimators 292
8.2.2 Methods of Finding Point Estimators 295
8.3 Interval Estimators for the Mean µ of a Normal Population 301
8.3.1 s2 Known 301
8.3.2 s2 Unknown 304
8.3.3 Sample Size is Large 306
8.4 Interval Estimators for The Difference of Means of Two Normal Populations 313
8.4.1 Variances are Known 313
8.4.2 Variances are Unknown 314
8.5 Interval Estimators for the Variance of a Normal Population 322
8.6 Interval Estimator for the Ratio of Variances of Two Normal Populations 327
8.7 Point and Interval Estimators for the Parameters of Binomial Populations 331
8.7.1 One Binomial Population 331
8.7.2 Two Binomial Populations 334
8.8 Determination of Sample Size 338
8.8.1 One Population Mean 339
8.8.2 Difference of Two Population Means 339
8.8.3 One Population Proportion 340
8.8.4 Difference of Two Population Proportions 341
8.9 Some Supplemental Information 343
8.10 A Case Study 343
8.11 Using JMP 343
Review Practice Problems 344
9 Hypothesis Testing 352
9.1 Introduction 353
9.2 Basic Concepts of Testing a Statistical Hypothesis 353
9.2.1 Hypothesis Formulation 353
9.2.2 Risk Assessment 355
9.3 Tests Concerning the Mean of a Normal Population Having Known Variance 358
9.3.1 Case of a One-Tail (Left-Sided) Test 358
9.3.2 Case of a One-Tail (Right-Sided) Test 362
9.3.3 Case of a Two-Tail Test 363
9.4 Tests Concerning the Mean of a Normal Population Having Unknown Variance 372
9.4.1 Case of a Left-Tail Test 372
9.4.2 Case of a Right-Tail Test 373
9.4.3 The Two-Tail Case 374
9.5 Large Sample Theory 378
9.6 Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances 380
9.6.1 The Left-Tail Test 380
9.6.2 The Right-Tail Test 381
9.6.3 The Two-Tail Test 383
9.7 Tests Concerning the Difference of Means of Two Populations Having Normal Distributions with Unknown Variances 388
9.7.1 Two Population Variances are Equal 388
9.7.2 Two Population Variances are Unequal 392
9.7.3 The Paired t-Test 395
9.8 Testing Population Proportions 401
9.8.1 Test Concerning One Population Proportion 401
9.8.2 Test Concerning the Difference Between Two Population Proportions 405
9.9 Tests Concerning the Variance of a Normal Population 410
9.10 Tests Concerning the Ratio of Variances of Two Normal Populations 414
9.11 Testing of Statistical Hypotheses using Confidence Intervals 418
9.12 Sequential Tests of Hypotheses 422
9.12.1 A One-Tail Sequential Testing Procedure 422
9.12.2 A Two-Tail Sequential Testing Procedure 427
9.13 Case Studies 430
9.14 Using JMP 431
Review Practice Problems 431
Part II Statistics in Actions
10 Elements of Reliability Theory 445
10.1 The Reliability Function 446
10.1.1 The Hazard Rate Function 446
10.1.2 Employing the Hazard Function 455
10.2 Estimation: Exponential Distribution 457
10.3 Hypothesis Testing: Exponential Distribution 465
10.4 Estimation: Weibull Distribution 467
10.5 Case Studies 472
10.6 Using JMP 474
Review Practice Problems 474
11 On Data Mining 476
11.1 Introduction 476
11.2 What is Data Mining? 477
11.2.1 Big Data 477
11.3 Data Reduction 478
11.4 Data Visualization 481
11.5 Data Preparation 490
11.5.1 Missing Data 490
11.5.2 Outlier Detection and Remedial Measures 491
11.6 Classification 492
11.6.1 Evaluating a Classification Model 493
11.7 Decision Trees 499
11.7.1 Classification and Regression Trees (CART) 500
11.7.2 Further Reading 511
11.8 Case Studies 511
11.9 Using JMP 512
Review Practice Problems 512
12 Cluster Analysis 518
12.1 Introduction 518
12.2 Similarity Measures 519
12.2.1 Common Similarity Coefficients 524
12.3 Hierarchical Clustering Methods 525
12.3.1 Single Linkage 526
12.3.2 Complete Linkage 531
12.3.3 Average Linkage 534
12.3.4 Ward’s Hierarchical Clustering 536
12.4 Nonhierarchical Clustering Methods 538
12.4.1 K-Means Method 538
12.5 Density-Based Clustering 544
12.6 Model-Based Clustering 547
12.7 A Case Study 552
12.8 Using JMP 553
Review Practice Problems 553
13 Analysis of Categorical Data 558
13.1 Introduction 558
13.2 The Chi-Square Goodness-of-Fit Test 559
13.3 Contingency Tables 568
13.3.1 The 2 × 2 Case with Known Parameters 568
13.3.2 The 2 × 2 Case with Unknown Parameters 570
13.3.3 The r × s Contingency Table 572
13.4 Chi-Square Test for Homogeneity 577
13.5 Comments on the Distribution of the Lack-of-Fit Statistics 581
13.6 Case Studies 583
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