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Stats & Probability for Science & Medicine Textbooks

by Jay Devore and Nicholas Farnum

Edition: 99Copyright: 1999

Publisher: Brooks/Cole Publishing Co.

Published: 1999

International: No

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This concise book for engineering and sciences students emphasizes modern statistical methodology and data analysis. Coverage of probability is reduced to that which 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 is appropriate for one-term courses.

- Examples use real data from industry reports and articles to introduce students to real-world situations while learning statistical concepts.
- 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.
- Incorporates use of Minitab and SPSS.
- Numerous relevant, current exercises and examples appear throughout.

**PART I. DATA AND DISTRIBUTIONS. **

Populations, Samples, and Processes.

Visual Displays for Univariate Data.

Describing Distributions.

The Normal Distribution.

Other Continuous Distributions.

Several Useful Discrete Distributions.

**PART II. NUMERICAL SUMMARY MEASURES.**

Measures of Center.

Measures of Variability.

More Detailed Summary Quantities.

Quartile Plots.

**PART III. BIVARIATE AND MULTIVARIATE DATA.**

Scatter Plots.

Correlation.

Fitting a Straight Line.

Nonlinear Relationships.

Using More than One Predictor.

Joint Distributions.

**PART IV. PRODUCING DATA. **

Operational Definitions.

Data from Samples.

Data from Experiments.

Measurement Systems.

**PART V. PROBABILITY AND SAMPLING DISTRIBUTIONS. **

Chance Experiments.

Probability Concepts.

Conditional Probability and Independence.

Random Variables.

Sampling Distributions.

Describing Sampling Distributions.

**PART VI. QUALITY CONTROL.**

Terminology.

How Control Charts Work.

Control Charts for Process Mean and Variation.

Process Capability Analysis.

Control Charts for Attribute Data.

**PART VII. ESTIMATION AND STATISTICAL INTERVALS. **

Point Estimation.

Large-Sample Confidence Intervals for a Population Mean.

More Large-Sample Intervals.

Small-Sample Intervals Based on a Normal Population Distribution.

Intervals for u1 - u2 Based on a Normal Population Distribution.

Further Aspects of Estimation.

**PART VIII. TESTING STATISTICAL HYPOTHESES.**

Hypotheses and Test Procedures.

Tests Concerning Hypotheses about Means.

Testing Concerning Hypotheses about a Categorical Population.

Testing the Form of a Distribution.

Further Aspects of Hypothesis Testing.

**PART IX. THE ANALYSIS OF VARIANCE. **

Terminology and Concepts.

Single-Factor ANOVA.

Interpreting ANOVA Results.

Randomized Block Experiments.

**PART X. EXPERIMENTAL DESIGN. **

Terminology and Concepts.

Two-Factor Designs.

Multi-Factor Designs.

2k Designs.

Fractional Factorial Designs.

**PART XI. INFERENCES IN REGRESSION ANALYSIS. **

Regression Models Involving a Single Independent Variable.

Inferences about the Slope Coefficient b.

Inferences Based on the Estimated Regression Line.

Multiple Regression Models.

Inferences in Multiple Regression.

Further Aspects of Regression Analysis.

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Summary

This concise book for engineering and sciences students emphasizes modern statistical methodology and data analysis. Coverage of probability is reduced to that which 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 is appropriate for one-term courses.

- Examples use real data from industry reports and articles to introduce students to real-world situations while learning statistical concepts.
- 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.
- Incorporates use of Minitab and SPSS.
- Numerous relevant, current exercises and examples appear throughout.

Table of Contents

**PART I. DATA AND DISTRIBUTIONS. **

Populations, Samples, and Processes.

Visual Displays for Univariate Data.

Describing Distributions.

The Normal Distribution.

Other Continuous Distributions.

Several Useful Discrete Distributions.

**PART II. NUMERICAL SUMMARY MEASURES.**

Measures of Center.

Measures of Variability.

More Detailed Summary Quantities.

Quartile Plots.

**PART III. BIVARIATE AND MULTIVARIATE DATA.**

Scatter Plots.

Correlation.

Fitting a Straight Line.

Nonlinear Relationships.

Using More than One Predictor.

Joint Distributions.

**PART IV. PRODUCING DATA. **

Operational Definitions.

Data from Samples.

Data from Experiments.

Measurement Systems.

**PART V. PROBABILITY AND SAMPLING DISTRIBUTIONS. **

Chance Experiments.

Probability Concepts.

Conditional Probability and Independence.

Random Variables.

Sampling Distributions.

Describing Sampling Distributions.

**PART VI. QUALITY CONTROL.**

Terminology.

How Control Charts Work.

Control Charts for Process Mean and Variation.

Process Capability Analysis.

Control Charts for Attribute Data.

**PART VII. ESTIMATION AND STATISTICAL INTERVALS. **

Point Estimation.

Large-Sample Confidence Intervals for a Population Mean.

More Large-Sample Intervals.

Small-Sample Intervals Based on a Normal Population Distribution.

Intervals for u1 - u2 Based on a Normal Population Distribution.

Further Aspects of Estimation.

**PART VIII. TESTING STATISTICAL HYPOTHESES.**

Hypotheses and Test Procedures.

Tests Concerning Hypotheses about Means.

Testing Concerning Hypotheses about a Categorical Population.

Testing the Form of a Distribution.

Further Aspects of Hypothesis Testing.

**PART IX. THE ANALYSIS OF VARIANCE. **

Terminology and Concepts.

Single-Factor ANOVA.

Interpreting ANOVA Results.

Randomized Block Experiments.

**PART X. EXPERIMENTAL DESIGN. **

Terminology and Concepts.

Two-Factor Designs.

Multi-Factor Designs.

2k Designs.

Fractional Factorial Designs.

**PART XI. INFERENCES IN REGRESSION ANALYSIS. **

Regression Models Involving a Single Independent Variable.

Inferences about the Slope Coefficient b.

Inferences Based on the Estimated Regression Line.

Multiple Regression Models.

Inferences in Multiple Regression.

Further Aspects of Regression Analysis.

Publisher Info

Publisher: Brooks/Cole Publishing Co.

Published: 1999

International: No

Published: 1999

International: No

This concise book for engineering and sciences students emphasizes modern statistical methodology and data analysis. Coverage of probability is reduced to that which 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 is appropriate for one-term courses.

- Examples use real data from industry reports and articles to introduce students to real-world situations while learning statistical concepts.
- 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.
- Incorporates use of Minitab and SPSS.
- Numerous relevant, current exercises and examples appear throughout.

**PART I. DATA AND DISTRIBUTIONS. **

Populations, Samples, and Processes.

Visual Displays for Univariate Data.

Describing Distributions.

The Normal Distribution.

Other Continuous Distributions.

Several Useful Discrete Distributions.

**PART II. NUMERICAL SUMMARY MEASURES.**

Measures of Center.

Measures of Variability.

More Detailed Summary Quantities.

Quartile Plots.

**PART III. BIVARIATE AND MULTIVARIATE DATA.**

Scatter Plots.

Correlation.

Fitting a Straight Line.

Nonlinear Relationships.

Using More than One Predictor.

Joint Distributions.

**PART IV. PRODUCING DATA. **

Operational Definitions.

Data from Samples.

Data from Experiments.

Measurement Systems.

**PART V. PROBABILITY AND SAMPLING DISTRIBUTIONS. **

Chance Experiments.

Probability Concepts.

Conditional Probability and Independence.

Random Variables.

Sampling Distributions.

Describing Sampling Distributions.

**PART VI. QUALITY CONTROL.**

Terminology.

How Control Charts Work.

Control Charts for Process Mean and Variation.

Process Capability Analysis.

Control Charts for Attribute Data.

**PART VII. ESTIMATION AND STATISTICAL INTERVALS. **

Point Estimation.

Large-Sample Confidence Intervals for a Population Mean.

More Large-Sample Intervals.

Small-Sample Intervals Based on a Normal Population Distribution.

Intervals for u1 - u2 Based on a Normal Population Distribution.

Further Aspects of Estimation.

**PART VIII. TESTING STATISTICAL HYPOTHESES.**

Hypotheses and Test Procedures.

Tests Concerning Hypotheses about Means.

Testing Concerning Hypotheses about a Categorical Population.

Testing the Form of a Distribution.

Further Aspects of Hypothesis Testing.

**PART IX. THE ANALYSIS OF VARIANCE. **

Terminology and Concepts.

Single-Factor ANOVA.

Interpreting ANOVA Results.

Randomized Block Experiments.

**PART X. EXPERIMENTAL DESIGN. **

Terminology and Concepts.

Two-Factor Designs.

Multi-Factor Designs.

2k Designs.

Fractional Factorial Designs.

**PART XI. INFERENCES IN REGRESSION ANALYSIS. **

Regression Models Involving a Single Independent Variable.

Inferences about the Slope Coefficient b.

Inferences Based on the Estimated Regression Line.

Multiple Regression Models.

Inferences in Multiple Regression.

Further Aspects of Regression Analysis.