Miller & Freund’s Probability and Statistics for Engineers, Global Edition
This book introduces probability and statistics to students of engineering and the physical sciences. It is primarily applications focused but it contains optional enrichment material. Each chapter begins with an introductory statement and concludes with a set of statistical guidelines for correctly applying statistical procedures and avoiding common pitfalls. These Doâ€™s and Donâ€™ts are then followed by a checklist of key terms. Important formulas, theorems, and rules are set out from the text in boxes.
The exposition of the concepts and statistical methods is especially clear. It includes a careful introduction to probability and some basic distributions. It continues by placing emphasis on understanding the meaning of confidence intervals and the logic of testing statistical hypotheses. Confidence intervals are stressed as the major procedure for making inferences. Their properties are carefully described and their interpretation is reviewed in the examples. The steps for hypothesis testing are clearly and consistently delineated in each application. The interpretation and calculation of the P-value is reinforced with many examples.
In this ninth edition, we have continued to build on the strengths of the previous editions by adding several more data sets and examples showing application of statistics in scientific investigations. The new data sets, like many of those already in the text, arose in the authorâ€™s consulting activities or in discussions with scientists and engineers about their statistical problems. Data from some companies have been disguised, but they still retain all of the features necessary to illustrate the statistical methods and the reasoning required to make generalizations from data collected in an experiment.
The time has arrived when software computations have replaced table lookups for percentiles and probabilities as well as performing the calculations for a statistical analysis. Todayâ€™s widespread availability of statistical software packages makes it imperative that students now become acquainted with at least one of them.We suggest using software for performing some analysis with larger samples and for performing regression analysis. Besides having several existing exercises describing the use of MINITAB, we now give the R commands within many of the examples. This new material augments the basics of the freeware R that are already in Appendix C.
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