Loose Leaf Elementary Statistics with Formula Card https://www.mheducation.com/cover-images/Jpeg_400-high/1259345300.jpeg 2 9781259345302 Navidi/Monk, Elementary Statistics was developed around three central themes – Clarity, Quality, and Accuracy. These central themes were born out of extensive market research and feedback from statistics instructors across the country. The authors paid close attention to how material is presented to students, ensuring that the content in the text is very clear, concise, and digestible. High quality exercises, examples and integration of technology are important aspects of an Introductory Statistics text. The authors have provided robust exercise sets that range in difficulty. They have also focused keen attention to ensure that examples provide clear instruction to students. Technology is integrated throughout the text, providing students examples of how to use the TI-83 Plus and TI-84 Plus Graphing Calculators, Microsoft Excel and Minitab. The accuracy of Elementary Statistics was a foundational principle always on the minds of the authors. While this certainly pertains to all aspects of the text, the authors also exhausted energy in ensuring the supplements have been developed to fit cohesively with the text.
Loose Leaf Elementary Statistics with Formula Card

Loose Leaf Elementary Statistics with Formula Card

2nd Edition
By William Navidi and Barry Monk
ISBN10: 1259345300
ISBN13: 9781259345302
Copyright: 2016
09781259345302

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ISBN10: 1259345300 | ISBN13: 9781259345302

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Program Details

h1> Table of Contents—Navidi/Monk Elementary Statistics

Chapter 1: Basic Ideas

1.1 Sampling

1.2 Types of Data

1.3 Design of Experiments

1.4 Bias in Study

Chapter 1 Summary

Chapter Quiz

Chapter 1 Review Exercises

Case Study

Chapter 2: Graphical Summaries of Data

2.1 Graphical Summaries for Qualitative Data

2.2 Frequency Distributions and Their Graphs

2.3 More Graphs for Quantitative Data

2.4 Graphs Can Be Misleading

Chapter 2 Summary

Chapter Quiz

Chapter 2 Review Exercises

Case Study

Chapter 3: Numerical Summaries of Data

3.1 Measures of Center Data

3.2 Measures of Spread

3.3 Measures of Position

Chapter 3 Summary

Chapter Quiz

Chapter 3 Review Exercises

Case Study

Chapter 4: Summarizing Bivariate Data

4.1 Correlation

4.2 The Least-Squares Regression Line

4.3 Features and Limitations of the Least-Squares Regression Line

Chapter 4 Summary

Chapter Quiz

Chapter 4 Review Exercises

Case Study

Chapter 5: Probability

5.1 Basic Ideas

5.2 The Addition Rule and the Rule of Compliments

5.3 Conditional Probability and the Multiplication Rule

5.4 Counting

Chapter 5 Summary

Chapter Quiz

Chapter 5 Review Exercises

Case Study

Chapter 6: Discrete Probability Distributions

6.1 Random Variables

6.2 The Binomial Distribution

6.3 The Poisson Distribution

Chapter 6 Summary

Chapter Quiz

Chapter 6 Review Exercises

Case Study

Chapter 7: The Normal Distribution

7.1 The Standard Normal Curve

7.2 Applications of the Normal Distribution

7.3 Sampling Distributions and the Central Limit Theorem

7.4 The Central Limit Theorem for Proportions

7.5 The Normal Approximation to the Binomial Distribution

7.6 Assessing Normality

Chapter 7 Summary

Chapter Quiz

Chapter 7 Review Exercises

Case Study

Chapter 8: Confidence Intervals

8.1 Confidence Intervals for a Population Mean, Standard Deviation Known

8.2 Confidence Intervals for a Population Mean, Standard Deviation Unknown

8.3 Confidence Intervals for a Population Proportion

8.4 The Central Limit Theorem for Proportions

8.5 Determining Which Method to Use

Chapter 8 Summary

Chapter Quiz

Chapter 8 Review Exercises

Case Study

Chapter 9: Hypothesis Testing

9.1 Basic Principles of Hypothesis Testing

9.2 Hypothesis Tests for a Population Mean, Standard Deviation Known

9.3 Hypothesis Tests for a Population Mean, Standard Deviation Unknown

9.4 Hypothesis Tests for Proportions

9.5 Hypothesis Tests for a Standard Deviation

9.6 Determining Which Method to Use

9.7 Power

Chapter 9 Summary

Chapter Quiz

Chapter 9 Review Exercises

Case Study

Chapter 10: Two-Sample Confidence Intervals

10.1 Confidence Intervals for the Difference Between Two Means: Independent Samples

10.2 Confidence Intervals for the Difference Between Two Proportions

10.3 Confidence Intervals for the Difference Between Two Means: Paired Samples

Chapter 10 Summary

Chapter Quiz

Chapter 10 Review Exercises

Case Study

Chapter 11: Two-Sample Hypothesis Tests

11.1 Hypothesis Tests for the Difference Between Two Means: Independent Samples

11.2 Hypothesis Tests for the Difference Between Two Proportions

11.3 Hypothesis Tests for the Difference Between Two Means: Paired Samples

11.4 Hypothesis Tests for Two Population Standard Deviations

11.5 The Multiple Testing Problem

Chapter 11 Summary

Chapter Quiz

Chapter 11 Review Exercises

Case Study

Chapter 12: Tests with Qualitative Data

12.1 Testing Goodness-of-Fit

12.2 Tests for Independence and Homogeneity

Chapter 12 Summary

Chapter Quiz

Chapter 12 Review Exercises

Case Study

Chapter 13: Inference in Linear Models

13.1 Inference on the Slope of the Regression Line

13.2 Inference About the Response

Chapter 13 Summary

Chapter Quiz

Chapter 13 Review Exercises

Case Study

Chapter 14: Analysis of Variance

14.1 One-way Analysis of Variance

14.2 Two-way Analysis of Variance

Chapter 14 Summary

Chapter Quiz

Chapter 14 Review Exercises

Case Study

About the Author

William Navidi

William Navidi received a B.A. in mathematics from New College, an M.A in mathematics from Michigan State University, and a Ph.D. in statistics from the University of California at Berkeley. Dr. Navidi is a professor of applied mathematics and statistics at the Colorado School of Mines in Golden, Colorado. He began his teaching career at the County College of Morris in Dover, New Jersey. He has taught mathematics and statistics at all levels, from developmental through the graduate level. Dr. Navidi has written two engineering statistics textbooks for McGraw-Hill and has authored more than 50 research papers, both in statistical theory and in a wide variety of applications, including computer networks, epidemiology, molecular biology, chemical engineering, and geophysics.

Barry Monk

Barry Monk received a B.S. in mathematical statistics, an M.A. in mathematics specializing in optimization and statistics, and a Ph.D. in applied mathematics, all from the University of Alabama. Dr. Monk is a professor of mathematics at Middle Georgia State University in Macon, Georgia, where he has been employed since 2001. Dr. Monk was asked to serve as program coordinator of mathematics after only two years at Macon State College; he led the development of the bachelor's degree in mathematics program and the organization of the Southeastern Scholarship Conference on E-Learning for more than 10 years. He has been teaching introductory statistics since 1992 in the classroom and online.

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