Applied Statistics and Probability for EngineersJohn Wiley & Sons, 22 มี.ค. 2010 - 784 หน้า Montgomery and Runger's bestselling engineering statistics text provides a practical approach oriented to engineering as well as chemical and physical sciences. By providing unique problem sets that reflect realistic situations, students learn how the material will be relevant in their careers. With a focus on how statistical tools are integrated into the engineering problem-solving process, all major aspects of engineering statistics are covered. Developed with sponsorship from the National Science Foundation, this text incorporates many insights from the authors' teaching experience along with feedback from numerous adopters of previous editions. |
เนื้อหา
The Role of Statistics in Engineering | 1 |
Probability | 17 |
Discrete Random Variables | 66 |
Continuous Random Variables | 107 |
Joint Probability Distributions | 152 |
Descriptive Statistics | 191 |
xiv | 219 |
Sampling Distributions and Point | 223 |
Simple Linear Regression | 401 |
of Regression | 417 |
Multiple Linear Regression | 449 |
Design and Analysis of SingleFactor | 513 |
Design of Experiments with | 551 |
Statistical Quality Control | 637 |
APPENDICES | 702 |
Cumulative Standard Normal Distribution | 708 |
Statistical Intervals for a Single | 251 |
Tests of Hypotheses for a Single | 283 |
Testing for Goodness of Fit | 330 |
Nonparametric Procedures | 337 |
Statistical Inference for | 351 |
104 | 371 |
Operating Characteristic Curves | 717 |
Critical Values for the Sign Test | 726 |
| 749 | |
| 761 | |
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alternative analysis approximately Assume average blocks Calculate called CHAPTER Compare concentration conclusions conditional confidence interval Construct contains continuous control chart defective defined denote described Determine effects engineering equal Equation error estimate event example Exercise experiment factors failure Figure Find four function given hypothesis important increase independent indicate interaction interest Interpretation least length less likelihood linear regression manufacturing measurements method multiple normal distribution null observations obtained operating P-value parameter plot population prediction presented probability distribution problem procedure proportion random sample random variable range regression model regressor reject replicates residuals response runs sample mean selected Show shown significance specifications squares standard deviation statistic strength Suppose surface Table temperature test statistic tion treatment true variance versus