Statistics for Engineers and Scientists, 5th Edition
- Length: 944 pages
- Edition: 5
- Language: English
- Publisher: McGraw Hill
- Publication Date: 2019-01-25
- ISBN-10: 1259717607
- ISBN-13: 9781259717604
- Sales Rank: #850363 (See Top 100 Books)
Statistics for Engineers and Scientists stands out for its crystal clear presentation of applied statistics. The book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work. This edition features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets, to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.
McGraw-Hill Education’s Connect is also available. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, how they need it, so that class time is more effective. Connect allows the professor to assign homework, quizzes, and tests easily and automatically grades and records the scores of the student’s work. Problems are randomized to prevent sharing of answers an may also have a “multi-step solution” which helps move the students’ learning along if they experience difficulty.
Cover Page Title Page Copyright Information Dedication About the Author Brief Contents Contents Preface Key Features Connect Chapter 1 Sampling and Descriptive Statistics Introduction 1.1 Sampling 1.2 Summary Statistics 1.3 Graphical Summaries Chapter 2 Probability Introduction 2.1 Basic Ideas 2.2 Counting Methods 2.3 Conditional Probability and Independence 2.4 Random Variables 2.5 Linear Functions of Random Variables 2.6 Jointly Distributed Random Variables Chapter 3 Propagation of Error Introduction 3.1 Measurement Error 3.2 Linear Combinations of Measurements 3.3 Uncertainties for Functions of One Measurement 3.4 Uncertainties for Functions of Several Measurements Chapter 4 Commonly Used Distributions Introduction 4.1 The Bernoulli Distribution 4.2 The Binomial Distribution 4.3 The Poisson Distribution 4.4 Some Other Discrete Distributions 4.5 The Normal Distribution 4.6 The Lognormal Distribution 4.7 The Exponential Distribution 4.8 Some Other Continuous Distributions 4.9 Some Principles of Point Estimation 4.10 Probability Plots 4.11 The Central Limit Theorem 4.12 Simulation Chapter 5 Confidence Intervals Introduction 5.1 Large-Sample Confidence Intervals for a Population Mean 5.2 Confidence Intervals for Proportions 5.3 Small-Sample Confidence Intervals for a Population Mean 5.4 Confidence Intervals for the Difference Between Two Means 5.5 Confidence Intervals for the Difference Between Two Proportions 5.6 Small-Sample Confidence Intervals for the Difference Between Two Means 5.7 Confidence Intervals with Paired Data 5.8 Confidence Intervals for the Variance and Standard Deviation of a Normal Population 5.9 Prediction Intervals and Tolerance Intervals 5.10 Using Simulation to Construct Confidence Intervals Chapter 6 Hypothesis Testing Introduction 6.1 Large-Sample Tests for a Population Mean 6.2 Drawing Conclusions from the Results of Hypothesis Tests 6.3 Tests for a Population Proportion 6.4 Small-Sample Tests for a Population Mean 6.5 Large-Sample Tests for the Difference Between Two Means 6.6 Tests for the Difference Between Two Proportions 6.7 Small-Sample Tests for the Difference Between Two Means 6.8 Tests with Paired Data 6.9 Distribution-Free Tests 6.10 Tests with Categorical Data 6.11 Tests for Variances of Normal Populations 6.12 Fixed-Level Testing 6.13 Power 6.14 Multiple Tests 6.15 Using Simulation to Perform Hypothesis Tests Chapter 7 Correlation and Simple Linear Regression Introduction 7.1 Correlation 7.2 The Least-Squares Line 7.3 Uncertainties in the Least-Squares Coefficients 7.4 Checking Assumptions and Transforming Data Chapter 8 Multiple Regression Introduction 8.1 The Multiple Regression Model 8.2 Confounding and Collinearity 8.3 Model Selection Chapter 9 Factorial Experiments Introduction 9.1 One-Factor Experiments 9.2 Pairwise Comparisons in One-Factor Experiments 9.3 Two-Factor Experiments 9.4 Randomized Complete Block Designs 9.5 2P Factorial Experiments Chapter 10 Statistical Quality Control Introduction 10.1 Basic Ideas 10.2 Control Charts for Variables 10.3 Control Charts for Attributes 10.4 The CUSUM Chart 10.5 Process Capability Appendix A: Tables Appendix B: Partial Derivatives Appendix C: Bibliography Answers to Odd-Numbered Exercises Index
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