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Applied Statistics for Engineers and Scientists (with CD-ROM), by Jay L. Devore, Nicholas R. Farnum
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This concise book for engineering and sciences students emphasizes modern statistical methodology and data analysis. APPLIED STATISTICS FOR ENGINEERS AND SCIENTISTS is ideal for one-term courses that cover probability only to the extent that it is needed for inference. The authors emphasize application of methods to real problems, with real examples throughout. The text is designed to meet ABET standards and has been updated to reflect the most current methodology and practice.
- Sales Rank: #782631 in Books
- Brand: Cengage Learning
- Published on: 2004-02-20
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 1.10" h x 7.60" w x 9.32" l, 2.55 pounds
- Binding: Hardcover
- 624 pages
- Example Bullet Point 1
- Example Bullet Point 2
Review
1. DATA AND DISTRIBUTIONS. Populations, Samples and Processes. Visual Displays for Univariate Data. Describing Distributions. The Normal Distribution. Other Continuous Distributions. Several Useful Discrete Distributions. Supplementary Exercises. Bibliography. 2. NUMERICAL SUMMARY MEASURES. Measures of Center. Measures of Variability. More Detailed Summary Quantities. Quantile Plots. Supplementary Exercises. Bibliography. 3. BIVARIATE AND MULTIVARIATE DATA AND DISTRIBUTIONS. Scatter Plots. Correlation. Fitting a Line to Bivariate Data. Nonlinear Relationships. Using More Than One Predictor. Joint Distributions. Supplementary Exercises. Bibliography. 4. OBTAINING DATA. Operational Definitions. Data from Sampling. Data from Experiments. Measurement Systems. Supplementary Exercises. Bibliography. 5. PROBABILITY AND SAMPLING DISTRIBUTIONS. Chance Experiments. Probability Concepts. Conditional Probability and Independence. Random Variables. Sampling Distributions. Describing Sampling Distributions. Supplementary Exercises. Bibliography. 6. QUALITY CONTROL. Terminology. How Control Charts Work. Control Charts for Mean and Variance. Process Capability Analysis. Control Charts for Attribute Data. Reliability. Supplementary Exercises. Bibliography. 7. ESTIMATION AND STATISTICAL INTERVALS. Point Estimation. Large-Sample Confidence Intervals for a Population Mean. More Large-Sample Confidence Intervals. Small-Sample Intervals Based on a Normal Population Distribution. Intervals for 1- 2 Based on a Normal Population Distributions. Other Topics in Estimation (Optional). Supplementary Exercises. Bibliography. 8. TESTING STATISTICAL HYPOTHESES. Hypotheses and Test Procedures. Tests Concerning Hypotheses About Means. Tests Concerning Hypotheses About a Categorical Population. Testing the Form of a Distribution. Further Aspects of Hypothesis Testing. Supplementary Exercises. Bibliography. 9. THE ANALYSIS OF VARIANCE. Terminology and Concepts. Single-Factor ANOVA. Interpreting ANOVA Results. Randomized Block Experiments. Supplementary Exercises. Bibliography. 10. EXPERIMENTAL DESIGN. Terminology and Concepts. Two-Factor Designs. Multifactor Designs. 2k Designs. Fractional Factorial Designs. Supplementary Exercises. Bibliography. 11. INFERENTIAL METHODS IN REGRESSION AND CORRELATION. Regression and Models Involving a Single Independent Variable. Inferences About the Slope Coefficient ss. Inferences Based on the Estimated Regression Line. Multiple Regression Models. Inferences in Multiple Regression. Further Aspects of Regression Analysis. Supplementary Exercises. Bibliography. APPENDIX TABLES. ANSWERS TO ODD-NUMBERED EXERCISES. INDEX.
About the Author
Jay Devore is Professor Emeritus of Statistics at California Polytechnic State University. He earned his undergraduate degree in Engineering Science from the University of California at Berkeley, spent a year at the University of Sheffield in England, and finished his Ph.D. in statistics at Stanford University. Jay previously taught at the University of Florida and at Oberlin College and has had visiting appointments at Stanford, Harvard, the University of Washington, New York University, and Columbia University. From 1998 to 2006, he served as Chair of the Statistics Department. In addition to this book, Jay has written several widely used engineering statistics texts and a book in applied mathematical statistics. He recently coauthored a text in probability and stochastic processes. He is the recipient of a distinguished teaching award from Cal Poly, is a Fellow of the American Statistical Association , and has served several terms as an Associate Editor of the "Journal of the American Statistical Association." In his spare time, he enjoys reading, cooking and eating good food, tennis, and travel to faraway places. He is especially proud of his wife, Carol, a retired elementary school teacher, his daughter Allison, who has held several high-level positions in nonprofit organizations in Boston and New York City, and his daughter Teresa, an ESL teacher in New York City.
Nicholas Farnum received his B.S. and Ph.D. in Mathematics from University of California at Irvine. He is currently a professor in the Information Systems and Decision Sciences Department at California State University, Fullerton. Professor Farnum has published several papers in applied and theoretical statistics and has also written texts in Quality Control and Forecasting. He is a member of the American Statistical Association and the Mathematical Association of America. In his spare time Professor Farnum enjoys cooking, playing music, and traveling.
Most helpful customer reviews
0 of 0 people found the following review helpful.
statistics for support rather than illumination
By Amazon Customer
This book did not explain the mathematical theory behind the calculation and use of the various statistical methods it presented. It merely showed how to obtain the desired results. Engineers and scientists of all people need to understand these things in order to avoid the misuse of statistical methods which is so prevalent.
How can outliers be reasonably identified? What does "degrees of freedom" really mean? Is a sample arbitrarily chosen from a group of random subsamples of a larger random sample still random? Sadly with this textbook students will learn to use statistics as a drunkard uses a lamppost: for support rather than illumination.
A second but less important omission: the homework questions make odd use of example data from real research papers. The data sets chosen are very small, even though all data sets are available for download in multiple file formats. Each question introduces a new study with a brief paragraph that somehow fails to reproducibly define the experiments actually performed. This makes for a lot of irrelevant reading and students quickly learn to skip this part of each question. Given that scientists and engineers will presumably be designing experiments, why not include some related details? Merely describing the data makes no logical contribution, except perhaps to suggest an "expected" outcome.
I have a degree in math and am seeking a degree in mechanical engineering.
0 of 0 people found the following review helpful.
Four Stars
By K. Baumann
had to order for class
0 of 0 people found the following review helpful.
Learned a lot from this book
By Michael Fiebig
I learned a lot from this book, but it could be written a little better. More examples can be given, and rather than assuming prior knowledge, it should re-explain the abbreviations they use and some of the more technical concepts, and show clearer step by step instructions.
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