Introduction to Probability and Statistics for Engineers and ScientistsThis updated text provides a superior introduction to applied probability and statistics for engineering or science majors. Ross emphasizes the manner in which probability yields insight into statistical problems; ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and this emphasis on data motivates the probability coverage. As with the previous editions, Ross' text has remendously clear exposition, plus real-data examples and exercises throughout the text. Numerous exercises, examples, and applications apply probability theory to everyday statistical problems and situations.
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Contents
| 1 | |
| 9 | |
| 55 | |
| 89 | |
Chapter 5 Special Random Variables | 141 |
Chapter 6 Distributions of Sampling Statistics | 203 |
Chapter 7 Parameter Estimation | 231 |
Chapter 8 Hypothesis Testing | 293 |
Chapter 10 Analysis of Variance | 441 |
Chapter 11 Goodness of Fit Tests and Categorical Data Analysis | 485 |
Chapter 12 Nonparametric Hypothesis Tests | 517 |
Chapter 13 Quality Control | 547 |
Chapter 14 Life Testing | 583 |
Chapter 15 Simulation Bootstrap Statistical Methods and Permutation Tests | 613 |
Appendix of Tables | 641 |
Index | 647 |
Other editions - View all
Introduction to Probability and Statistics for Engineers and Scientists Sheldon M. Ross No preview available - 2009 |
Common terms and phrases
95 percent confidence assumed binomial random variable central limit theorem chi-square distribution chi-square random variable compute confidence interval estimate control chart control limits data set data values degrees of freedom density function determine distributed with mean distribution function equal Equation event EXAMPLE exponential distribution FIGURE following data given H0 is true Hence instance interval estimate large number least squares estimators level of significance linear maximum likelihood estimator normal population normal random variable normally distributed null hypothesis number of defective obtain occurs outcome p-value percent confidence interval percent level permutation Poisson distribution Poisson random variable probability mass function Program random number random sample randomly chosen regression reject H0 result sample mean sample standard deviation sample variance scores Section significance level simulation Suppose Table test H0 test statistic Test the hypothesis Var(X variable with mean variable with parameters versus H1 yields