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by Jay L. Devore and Nicholas R. Farnum

Edition: 2ND 05Copyright: 2005

Publisher: Brooks/Cole Publishing Co.

Published: 2005

International: No

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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.

**Benefits: **

- Examples that use real data from industry reports and articles introduce students to real-world situations while they learn statistical concepts.
- NEW! Computer output has been updated to reflect the latest technology.
- NEW! The integration of the "Six Sigma Terminology" in Chapter 6 adds to the text's modern approach.
- NEW! Chapter Exercises, which come at the end of the chapters, replace the Supplementary Exercises, which were previously at the beginning of the chapters.
- The authors cover all the important topics concisely, giving students a solid understanding of both statistical methods and design with a problem-solving focus.
- The authors emphasize modern statistical methods including quality and design of experiments to give students exposure to practical applications.
- An emphasis on graphical data analysis methods is consistent with the authors' computer-integrated approach.
- Practical computer pedagogy is integrated throughout the book so that learning of concepts can focus on real applications.
- Numerous relevant, current exercises and examples appear throughout.
- NEW! New exercises and examples, based on real data and information from published sources reinforce the practical, realistic approach that helps students relate to and understand statistical concepts better.
- NEW! Discussions of stratified sampling and reliability have been added.
- NEW! An update of confidence intervals for proportions reflects recent developments in improving the estimates.

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. 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 µ1-µ2 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.

Summary

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.

**Benefits: **

- Examples that use real data from industry reports and articles introduce students to real-world situations while they learn statistical concepts.
- NEW! Computer output has been updated to reflect the latest technology.
- NEW! The integration of the "Six Sigma Terminology" in Chapter 6 adds to the text's modern approach.
- NEW! Chapter Exercises, which come at the end of the chapters, replace the Supplementary Exercises, which were previously at the beginning of the chapters.
- The authors cover all the important topics concisely, giving students a solid understanding of both statistical methods and design with a problem-solving focus.
- The authors emphasize modern statistical methods including quality and design of experiments to give students exposure to practical applications.
- An emphasis on graphical data analysis methods is consistent with the authors' computer-integrated approach.
- Practical computer pedagogy is integrated throughout the book so that learning of concepts can focus on real applications.
- Numerous relevant, current exercises and examples appear throughout.
- NEW! New exercises and examples, based on real data and information from published sources reinforce the practical, realistic approach that helps students relate to and understand statistical concepts better.
- NEW! Discussions of stratified sampling and reliability have been added.
- NEW! An update of confidence intervals for proportions reflects recent developments in improving the estimates.

Table of Contents

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. 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 µ1-µ2 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.

Publisher Info

Publisher: Brooks/Cole Publishing Co.

Published: 2005

International: No

Published: 2005

International: No

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.

**Benefits: **

- Examples that use real data from industry reports and articles introduce students to real-world situations while they learn statistical concepts.
- NEW! Computer output has been updated to reflect the latest technology.
- NEW! The integration of the "Six Sigma Terminology" in Chapter 6 adds to the text's modern approach.
- NEW! Chapter Exercises, which come at the end of the chapters, replace the Supplementary Exercises, which were previously at the beginning of the chapters.
- The authors cover all the important topics concisely, giving students a solid understanding of both statistical methods and design with a problem-solving focus.
- The authors emphasize modern statistical methods including quality and design of experiments to give students exposure to practical applications.
- An emphasis on graphical data analysis methods is consistent with the authors' computer-integrated approach.
- Practical computer pedagogy is integrated throughout the book so that learning of concepts can focus on real applications.
- Numerous relevant, current exercises and examples appear throughout.
- NEW! New exercises and examples, based on real data and information from published sources reinforce the practical, realistic approach that helps students relate to and understand statistical concepts better.
- NEW! Discussions of stratified sampling and reliability have been added.
- NEW! An update of confidence intervals for proportions reflects recent developments in improving the estimates.

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. 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 µ1-µ2 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.