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Statistics for Engineers and Scientists 3rd edition [Kietas viršelis]

3.56/5 (78 ratings by Goodreads)
  • Formatas: Hardback, 928 pages, aukštis x plotis x storis: 239x193x41 mm, weight: 1490 g, Illustrations
  • Išleidimo metai: 16-Apr-2010
  • Leidėjas: McGraw-Hill Professional
  • ISBN-10: 0073376337
  • ISBN-13: 9780073376332
Kitos knygos pagal šią temą:
Statistics for Engineers and Scientists 3rd editionDidesnis paveikslėlis
  • Formatas: Hardback, 928 pages, aukštis x plotis x storis: 239x193x41 mm, weight: 1490 g, Illustrations
  • Išleidimo metai: 16-Apr-2010
  • Leidėjas: McGraw-Hill Professional
  • ISBN-10: 0073376337
  • ISBN-13: 9780073376332
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780073376332.html
Raktažodžiai:
Statistics for Engineers and Scientists stands out for its crystal clear presentation of applied statistics. Suitable for a one or two semester course, the book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work.

Statistics for Engineers and Scientists 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.

Preface xiii
Acknowledgments of Reviewers and Contributors xvii
Key Features xix
Supplements for Students and Instructors xx
Sampling and Descriptive Statistics
1(47)
Introduction
1(2)
Sampling
3(10)
Summary Statistics
13(12)
Graphical Summaries
25(23)
Probability
48(116)
Introduction
48(1)
Basic Ideas
48(14)
Counting Methods
62(7)
Conditional Probability and Independence
69(21)
Random Variables
90(26)
Linear Functions of Random Variables
116(11)
Jointly Distributed Random Variables
127(37)
Propagation of Error
164(36)
Introduction
164(1)
Measurement Error
164(6)
Linear Combinations of Measurements
170(10)
Uncertainties for Functions of One Measurement
180(6)
Uncertainties for Functions of Several Measurements
186(14)
Commonly Used Distributions
200(122)
Introduction
200(1)
The Bernoulli Distribution
200(3)
The Binomial Distribution
203(12)
The Poisson Distribution
215(15)
Some Other Discrete Distributions
230(11)
The Normal Distribution
241(15)
The Lognormal Distribution
256(6)
The Exponential Distribution
262(9)
Some Other Continuous Distributions
271(9)
Some Principles of Point Estimation
280(5)
Probability Plots
285(5)
The Central Limit Theorem
290(12)
Simulation
302(20)
Confidence Intervals
322(74)
Introduction
322(1)
Large-Sample Confidence Intervals for a Population Mean
323(15)
Confidence Intervals for Proportions
338(6)
Small-Sample Confidence Intervals for a Population Mean
344(10)
Confidence Intervals for the Difference Between Two Means
354(4)
Confidence Intervals for the Difference Between Two Proportions
358(5)
Small-Sample Confidence Intervals for the Difference Between Two Means
363(7)
Confidence Intervals with Paired Data
370(4)
Prediction Intervals and Tolerance Intervals
374(5)
Using Simulation to Construct Confidence Intervals
379(17)
Hypothesis Testing
396(109)
Introduction
396(1)
Large-Sample Tests for a Population Mean
396(9)
Drawing Conclusions from the Results of Hypothesis Tests
405(8)
Tests for a Population Proportion
413(5)
Small-Sample Tests for a Population Mean
418(5)
Large-Sample Tests for the Difference Between Two Means
423(7)
Tests for the Difference Between Two Proportions
430(5)
Small-Sample Tests for the Difference Between Two Means
435(9)
Tests with Paired Data
444(6)
Distribution-Free Tests
450(9)
The Chi-Square Test
459(10)
The F Test for Equality of Variance
469(4)
Fixed-Level Testing
473(6)
Power
479(9)
Multiple Tests
488(4)
Using Simulation to Perform Hypothesis Tests
492(13)
Correlation and Simple Linear Regression
505(87)
Introduction
505(1)
Correlation
505(18)
The Least-Squares Line
523(16)
Uncertainties in the Least-Squares Coefficients
539(21)
Checking Assumptions and Transforming Data
560(32)
Multiple Regression
592(66)
Introduction
592(1)
The Multiple Regression Model
592(18)
Confounding and Collinearity
610(9)
Model Selection
619(39)
Factorial Experiments
658(103)
Introduction
658(1)
One-Factor Experiments
658(25)
Pairwise Comparisons in One-Factor Experiments
683(13)
Two-Factor Experiments
696(25)
Randomized Complete Block Designs
721(10)
2P Factorial Experiments
731(30)
Statistical Quality Control
761(39)
Introduction
761(1)
Basic Ideas
761(3)
Control Charts for Variables
764(20)
Control Charts for Attributes
784(5)
The CUSUM Chart
789(4)
Process Capability
793(7)
Appendix A: Tables 800(25)
Appendix B: Partial Derivatives 825(2)
Appendix C: Bibliography 827(3)
Answers to Odd-Numbered Exercises 830(68)
Index 898
William Navidi received a B.A. in mathematics from New College, an M.A in mathematics from Michigan State University, and a Ph.D. in statistics from the University of California at Berkeley. Dr. Navidi is a professor of applied mathematics and statistics at the Colorado School of Mines in Golden, Colorado. He began his teaching career at the County College of Morris in Dover, New Jersey. He has taught mathematics and statistics at all levels, from developmental through the graduate level. Dr. Navidi has written two engineering statistics textbooks for McGraw-Hill and has authored more than 50 research papers, both in statistical theory and in a wide variety of applications, including computer networks, epidemiology, molecular biology, chemical engineering, and geophysics.